development of the energy transition model - introduction of the object oriented modeling method

8/8/2019 Development of the Energy Transition Model - Introduction of the Object Oriented Modeling Method

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Development of the

Energy Transition ModelIntroduction of the Object Oriented Modeling method

Utrecht, November 2010

Name Wouter van LelyveldE-mail [email protected]

Student number 1051202Course Master Thesis Project (SPM5910)Degree Master of Science (MSc)Program Systems engineering, Policy analysis and Management (SPM)Faculty Faculty of Technology, Policy and Management (TPM)University Delft University of Technology (DUT)Professor Prof. dr. ir. M.P.C. Weijnen (Energy & Industry)

1st supervisor Dr. ir. I. Bouwmans (Energy & Industry) 2nd supervisor Dr. ir. C. E. van Daalen (Policy Analysis)External organization Quintel Intelligence (QI)

mailto:[email protected]





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Preface

This report is about the development of the online version of Quintel’s Energy Transition Model. Formy thesis project I was asked to help transform the existing Excel model into an online application.The first months of the assignment I analyzed the existing model. The more I started to understandthe model, the more challenging I found developing an online application that would be available foreveryone.

In a small but devoted team, everyone had the drive to make a product that would exceedrequirements. In the rapid developments that followed, I found it hard to keep focus on theacademic part of my assignment. The practicalities of the development consumed my time to greatextent.

Eventually, an online application has been developed that works better than was originally hoped

for. I am grateful that I have been able to contribute to the model development with my project. Ithink the intensity of the cooperation has led to both a successful product and an interesting thesis.

Although the master project took longer than anticipated, I am happy with the final product and theopportunity to continue to work on the model at Quintel Intelligence. I thank Ivo, Els, Margot,Alexander, Willem, John and Dennis for their help and patience.

Wouter van Lelyveld,November 2010



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Contents

Preface ..................................................................................................................................................... 1

Contents .................................................................................................................................................. 3

List of Figures ........................................................................................................................................... 5

List of Abbreviations ................................................................................................................................ 6

Executive Summary ................................................................................................................................. 7

Chapter 1: Introduction ........................................................................................................................... 9

1.1 Background of the ETM ........................................................................................................... 9

1.2 Problem description .............................................................................................................. 10

1.3 Thesis approach ..................................................................................................................... 12

1.4 Research question ................................................................................................................. 13

Chapter 2: Development of the ETM .................................................................................................... 15

2.1 The origins of the ETM .......................................................................................................... 15

2.2 The ETM development process ............................................................................................. 17

2.3 The ETM product in Excel ...................................................................................................... 19

2.4 The new design process ........................................................................................................ 21

2.4.1 Approach 1: Defining the relationship between input and output ............................... 21

2.4.2 Approach 2: Copying Excel’s equations ......................................................................... 22

2.5 Conclusion ............................................................................................................................. 23

Chapter 3: Methodology ....................................................................................................................... 25

3.1 Underlying principles ............................................................................................................. 25

3.2 Existing methods ................................................................................................................... 26

3.2.1 Model development methods ....................................................................................... 26

3.2.2 Software development methods ................................................................................... 29

3.2.3 Overview of the development methods ....................................................................... 33

3.3 Lessons from other models ................................................................................................... 34

3.4 Combining the lessons........................................................................................................... 37

3.4.1 Initial attempts .............................................................................................................. 37

3.4.2 Development methods .................................................................................................. 37

3.4.3 Other models ................................................................................................................. 39

3.5 Description of the Object Oriented Modeling method ......................................................... 39

3.5.1 Model development iterations ...................................................................................... 41

3.5.2 Software development iterations ................................................................................. 43

3.5.3 Defining the network structure ..................................................................................... 44

3.6 Conclusion ............................................................................................................................. 45



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List of Figures

Figure 1: Types of research [translated from Verschuuren and Doorewaard, 2003] ........................... 13Figure 2: Goals for the ETM [Schoenmakers, 2010] .............................................................................. 16Figure 3: Model walkthrough [ETM, 2009] ........................................................................................... 20Figure 4: The Model Cycle [van Daalen et al., 1999] ............................................................................. 27Figure 5: The waterfall model [Royce, 1970] ........................................................................................ 30Figure 6: The V-model [FHWA, 2005] .................................................................................................... 30Figure 7: The Spiral model [Boehm, 1988] ............................................................................................ 31Figure 8: Visualization of iterative development [Hung, 2007] ............................................................ 32Figure 9: The PRIMES model in relation with other models [NTUA, 2010] ........................................... 36Figure 10: Basis of the OOM method visualization ............................................................................... 40Figure 11: Visualization of a model iteration in the OOM method ....................................................... 42Figure 12: Visualization of a software iteration in the OOM method ................................................... 44

Figure 13: A part of the visualization of the energy system .................................................................. 47Figure 14: One of the first Sankey diagrams showing objects .............................................................. 48Figure 15: A gas heater as represented in the model ........................................................................... 49Figure 16: Representation of the calculation engine ............................................................................ 50Figure 17: Difference between the calculations of the Excel and the online model ............................ 51Figure 18: Example of the usable household heat demand .................................................................. 52Figure 19: The first four iterations, alternating model and software development ............................. 53Figure 20: A software iteration in the OOM method ............................................................................ 54Figure 21: A model iteration of the OOM method ................................................................................ 57Figure 22: The design elements............................................................................................................. 59Figure 23: Visualization of the front-end and the back-end of the interface ....................................... 60Figure 24: Visualization of converters in relation with energetic use of gas in households ................. 61Figure 25: Chart specifying the electricity demand in households ....................................................... 62Figure 26: The main elements in the layout of the interface (in June 2010) ........................................ 64Figure 27: Steps in both the model and software iterations of the OOM method ............................... 69Figure 28: Application of the OOM method .......................................................................................... 72Figure 29: The life cycle of a simulation study [Balci, 1990] ................................................................. 81Figure 30: Iterative relationship between model building steps [Jakeman et al., 2006] ...................... 82Figure 31: Model development cycle with tools used [De Jong et al., 2002] ....................................... 82

Figure 32: Representation of the electricity network [De Vries et al., 2009] ....................................... 84Figure 33: Representation of the electricity market [De Vries et al., 2009] ......................................... 85Figure 34: Representation of the gas market [De Vries et al., 2009] .................................................... 85

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List of Abbreviations

CBS Statistics Netherlands (in Dutch: Centraal Bureau voor de Statistiek)CCS Carbon Capture and StorageCHP Combined Heat and PowerCoP Coefficient of PerformanceDUT Delft University of TechnologyETM Energy Transition ModelEUR EuroGJ GigajouleGQL Graph Query LanguageGW GigawattIID Iterative and Incremental DevelopmentkW kilowattkWh kilowatt hourMJ MegajouleMW MegawattMWh Megawatt hourNGO Non-Governmental OrganizationO&M Operation and MaintenanceOO Object OrientedOOP Object Oriented ProgrammingPJ Petajoule

ppm parts per millionQI Quintel IntelligenceQSC Quintel Strategy ConsultingRoR Ruby on RailsSPM Systems engineering, Policy analysis and ManagementTPM Technology, Policy and ManagementUML Unified Modeling LanguageUSD United States DollarVBA Visual Basic for Applications



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Executive Summary

Global turmoil concerning fossil fuels, questions relating to sustainable sources of energy anddifficulties with sharing knowledge led to the creation of the Energy Transition Model (ETM) in the

start of 2008. The ETM aims to be a transparent, comprehensive, fact-based, and independent modelabout energy related matters, ranging from CO 2 emission levels to sustainability targets.

The original ETM was developed in Microsoft Excel ® until the beginning of 2010. Because of thechoice for Excel as a platform for the model, it suffered some serious limitations such as versioning,limited availability, and lack of compatibility. Independent of the chosen software, the companyfaced issues with the high level of complexity and a lack of transparency.

Due to these limitations, the plan arose to develop software that relies on open standards, andindependent, open-source software, so the model could be used without depending on commercialsoftware packages.

In the first attempt to create a new application it was tried to translate the Excel calculations to thedesired programming language Ruby on Rails. Due to the model’s complexity this approach proved tobe fruitless quickly. The way of thinking of the software used did not match with t he model’s design.While attempting to clarify the design of the model, it became clear the new model had to bedeveloped in another fashion. This resulted in the research question of this thesis:

What method should be used for the further development of t he ETM to fulfill Quintel’s

requirements?

Combining model and software development methods into one resulted in the Object OrientedModeling (OOM) method. The modeling steps in the OOM method are based on the model cycle, andthe software steps are based o n a combination of Boehm’s spiral model and iterative andincremental development (IID).

The OOM method has resulted in the development of a network structure of converters in themodel, which the software can use in a standardized calculation. For the ETM, this converter conceptuses the thermodynamic law of conservation of energy and has become basis of the model. The basicstructure of the converter concept supplies the required transparency of the model, and provides theflexibility to adjust or extend the model.

In conclusion, the way of thinking for both the software and the model was combined in the OOMmethod which resulted in the converter concept. This has led to a model that fulfills Qui ntel’srequirements for the ETM.



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Chapter 1: Introduction

1.1 Background of the ETM

Problems with fossil fuels

The energy demand of the western world continues to grow [EIA, 2010]. Next to this, rapidlyemerging economies in countries such as India and China have an increasing standard of living thatcoincides with a growing hunger for energy. Although the energy used per person is still relativelylow, their large population will place them among the largest consumers of energy in the near future.According to the US Energy Information Administration, the total world consumption of energy isprojected to increase by 44 percent from 2006 to 2030, where the largest projected increase inenergy demand is for the non-OECD economies [EIA, 2009].

This growing energy demand results in the fact that the world consumes its natural resources at anincreasing rate. The supply of energy is on the other hand becoming increasingly more challenging.

Easily recoverable energy sources have production limitations that are said to be reached in the nearfuture, if not already [Hirsch, 2007]. Uncertainty about future supply already led to extreme energyprice fluctuations in 2008. A small decrease in the world’s oil production can have large economicimpacts [Hirsch, 2007]. This fear also has consequences for stability between countries, becausemost of the fossil fuel reservoirs are concentrated around the Middle East and Russia. As thesecountries are known to be unstable, there is a growing fear that this will eventually lead to conflicts[Luft, 2009].

Besides potential economic and stability problems, the burning of fossil fuels leads to CO 2 emissionsinto the atmosphere. The notion that the climate is changing due to these man-caused emission as

argued by the IPCC [IPCC, 2007] is becoming more widely accepted in recent years as globaltemperatures are rising [NASA, 2009]. International climate initiatives to reduce the emission of CO 2 have however not been able to reduce the total amount of CO 2 that is emitted globally, leading to alarger concentration of CO 2 in the atmosphere. Currently it holds 388 parts per million (ppm) CO 2,with a steady incline of 2 ppm per year [NASA, 2009]. Scientists fear that a level of 450 ppm couldresult in acidification of the oceans [McNeil and Matear, 2008] and large-scale melting of the arcticice [Washington et al., 2009].

Regardless of the previously stated problems, the use of fossil fuels already creates an unhealthyenvironment today because of the fine particles that are emitted in transportation fuels [EPA, 2009].

Especially in large cities this already is a serious problem.

Problems with sustainable energyA solution to most of the mentioned problems is a switch to sustainable energy. Unfortunately thesealternatives also carry problems.

First of all, sustainable energy is more expensive than energy from fossil fuels, and will remain so forsome time. Subsidies can help bridge this gap, but investors want to have more certainty on pricedevelopments than the elected government can provide them. Uncertainty about subsidy policy orthe economic situation in the future creates a barrier for companies to invest in sustainable

technologies [UNEP, 2009]. Although the fuel costs of sustainable technologies are usually lower, the



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initial investment is mostly higher. The financial crisis in 2009 aggravated this conundrum, becausethis made financing for projects that require a large investment even harder.

Another issue is that although the general consensus exists that people will eventually have to switchto a sustainable form of energy, there is no consensus to which form should be favored. Because so

many forms of sustainable energy exists and are under development, it is hard to create a policy thatcan help to quickly reach a mature competitive technology. Each form of sustainable energy has itsown advantages and disadvantages which makes it cumbersome for the investor to form a completepicture of the situation.

Knowledge problemsGood governmental policy can help solve some of the energy problems described above. Theproblem here is that few policy makers fully understand the existing problems as the current energysystem is complex and has many interdependencies. In addition, the energy system is becomingmore diverse with new sustainable energy alternatives.

Each of these technologies has its own specifications for which the future developments areuncertain and all have different effects on the infrastructure. For example, growing shares of windand solar power, heat pumps and electric vehicles make issues such as network stability, importcapacity and intermittence increasingly more important. A combination of all of these developmentscontributes to the complexity of the energy system.

While the government can take measures to reduce CO 2 emissions or increase the share of sustainable energy use, the increased complexity makes it hard to determine what measures aremost effective to achieve the policy goals. Also, many external variables exist that can have a large

impact on the effect of possible measures. Large changes in fossil fuel prices for example have agreat impact on the relative competitiveness of renewable alternatives. The extreme fluctuation of the oil price, which peaked above 140 USD/barrel in 2007 and returned to below 50 USD/barrel in2009, proved that predicting the price development of fuels is difficult.

Models can be used to create insight in the sensitivities of the energy system, which can lead to morerobust policies.

1.2 Problem description

From 2001, the consultancy firm Quintel has been advising energy companies in the Netherlands. Toprovide insight in the effect of possible strategies in future energy scenarios, the basis of what wouldlater become the Energy Transition Model (ETM) was created. This was done because Quintelrecognized that the companies’ strategy depend g reatly on assumptions about future developmentof energy prices and technologies.

This basis of the ETM developed into a tool that can calculate the effects of possible futuredevelopments in the energy system, with the possibility to make a scenario in which the user canprovide his assumptions about the future energy system. Based on these assumptions for demandand supply of energy, costs and policies, the model calculates the consequences for energy use, CO 2

emissions, import dependency, sustainability and other indicators. Also, the effects of individualvariables can be analyzed as the model instantly responds to changes in any of the input variables.



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The development of the model also aroused interest with other parties, such as GasTerra, whichenvisioned the model as an educational tool to increase the general understanding of the energysystem.

In the further development of the ETM, it grew more complex as more variables could be set, and

various performance indicators were added. Integration of the additional features in the existingstructure of relationships definitions led to a decreasing level of transparency and flexibility. This waspartly due to the choice of platform, and partly because of other choices made during the modeldevelopment.

Problems with the original modelThe development of the ETM started in Microsoft Excel. As most of the calculations by consultantsare made in Excel, this was a logical choice. Microsoft Excel made it possible to create an extensivemodel, but also has some downsides for a model with a large target audience:

Different versionsMicrosoft Excel has different versions which all work a little differently. The modelcurrently works best Microsoft Excel 2003, as Excel 2007 shows several glitches in thevisualization of graphs. This means the different versions do not give the exact sameoutput. This makes it hard to design a single version that works the same for all users,regardless of the software they are using.

Availability People that do not have Microsoft Excel installed cannot use the model. As the objectiveis to reach as many people as possible Quintel wants the ETM to be an application that

does not require the user to have specific software installed.

Compatibility The model has to be downloaded from the website. This forms a barrier for some usersas they are cautious about downloading things from the internet. Other users may nothave the proper rights to install the model on the computer, as it requires administratorrights to install a needed plug-in. This is of special concern when trying to reach studentswho are working on school computers, as these computers are often protected againstinstalling plug-ins. This means that there are limitations in reaching the audiencebecause of the software.

Next to the problems because of the choice of software, the model encountered problems that werenot directly software-related:

Level of complexity Quintel faced the problem that the model had grown too complex. The ETM started outas a model dealing mainly with electricity generation and consumption. Since then morefeatures and possibilities had been integrated into the existing structure. To keep themodel working properly, work-around solutions had to be built in. This increased thecomplexity of the model, making it hard to maintain and further develop.



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Transparency Another result of the increasing complexity is the decreasing transparency. Because theETM is primarily a tool to gain insight in the workings of the energy system, transparencyis one of its most important requirements. As a result of the constant addition of newfeatures in the model, the transparency has gone down with the increasing complexity.This not only makes further development harder, it also deteriorates the educationalfunction.

Looking at the problems, it can be noted that the first three problems directly have to do with thechosen software. Because of this, it was decided to develop software specifically for the modelwithout the use of commercial products. Instead the model would rely on open standards andindependent, open-source software, which everybody can use using a web browser.

This can take away many of the problems, but developing new software to replace Excel means a lothas to be done to match the functions of Excel, let alone surpass the limitations that accompany

them. Standard functions of Microsoft Excel would have to be programmed from scratch. Because of this, the development of the new model is for a large part dependent on the development of newsoftware, and will be the foundation of the new model.

1.3 Thesis approach

The goal of this thesis will be to investigate how the model software should be designed to come upwith a design that fulfills the requirements. In principle, there are two types of research that can beperformed to approach the problem;

1. Theory oriented research One approach is to recommend a certain method to come to a better design based ontheory. By researching model development methods using desk research,recommendations can be given on how the model should be designed. The advantage of this approach is that the new design does not have to be described in full detail, as onlythe method is described which would lead to the desired design. The disadvantage of this approach is that the outcome is merely a description of a design method, whichcannot be validated to see if the proposed design method actually leads to a design thatfulfills the requirements. To validate the proposed method it will have to be followed at

least partially.

2. Practice oriented research Another approach is to actually apply methods and come up with recommendations inwhat way the model should be designed in practice. Because the recommendationswould be the result of a method described in the process approach, this will result inactual design specifications for this specific model. However, without scientific supportas to why these methods are chosen the scientific value of the work would besignificantly lower, as the chosen solutions may only be useful in the specific case of theETM.



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Along this line, Verschuuren and Doorewaard [2003] have defined types of research for both theoryoriented research and practice oriented research. This is visualized in figure 1.

Which approach to use?The theoretical and the practical approach individually both have downsides. Without propervalidation of the theoretical design, it cannot be concluded that the proposed approach will actuallylead to better results. Without a theoretical foundation general recommendations for the practicaldesign will have little scientific value because the outcome cannot be generalized for other designs.

In combination however, the theory can be applied in the actual model design to validate thetheoretical findings. At the same time, the resulting design will be based on a structured scientificapproach. Therefore, this thesis will use the practice of the ETM development as a validation of thesuggested method in this thesis. By applying the method in practice, possible implementationproblems in the real-world model development can also be used in the evaluation of the suggestedmethod. When the method is validated, the development of the ETM can be used as a showcase infuture design processes.

1.4 Research question

With the approach defined, the objective of this thesis will be to validate that the suggestedtheoretical method has positively contributed in the development of the ETM. The research questioncan be formulated as:

What method should be used for the further development of the ETM to fulfill Quintel’srequirements?

The main type of research that is required to answer this question is theory-developing research.However, before the main research question can be answered, two sub-questions need to beanswered:

1. What are Quintel’s requirements for the ETM? 2. What methods are available for the further development of the ETM?

When the answers to these sub-questions are known, the method that should be used can bedetermined. To answer the first question diagnostic research is done into Quintel’s requirements for

Figure 1: Types of research [translated from Verschuuren and Doorewaard, 2003]



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the ETM in chapter 2, which is based on interviews and desk research. This chapter also describes thedevelopment of the ETM so far, which includes two unsuccessful attempts to redesign the ETM.

With the requirements for the ETM determined, chapter 3 uses desk research to discuss methods fordeveloping both models and software, answering the second sub-question. This chapter contains the

theory-developing research that will result in a suggested method for the ETM development. As thisdeveloped method is proposed for the further development of the ETM but has not been validated, anew question arises:

Can the suggested method for the further ETM development fulfill Quintel’s requirements?

To answer this question, chapter 4 will apply design oriented research using the suggested method inthe development of the ETM, leading to a design of the ETM. The main characteristics of the newdesign are described in this chapter as well.

In chapter 5 the findings are used to evaluate the value of the suggested method used for the ETMand conclude whether or not the method has resulted in a model that fulfills Quintel’s requirements.The chapter ends with a reflection in which the value of the development method for other projectsis discussed.



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Chapter 2: Development of the ETM

This chapter describes the development of the ETM. First some further background information onthe origin of the model will be provided and the initial purpose of the model is explained in section2.1. This is relevant to understand several choices that were made in the original process. In section2.2 the development process of the initial Excel model is described. In section 2.3, this chaptersupplies a closer look at the final Excel model as a result of this process. Section 2.4 describes thestart of the process to develop a new online model, and section 2.5 describes the conclusions of thischapter.

2.1 The origins of the ETM

The origins of the ETM can be traced back to Quintel Strategy Consulting (QSC). This company hassome of the large energy companies in the Netherlands as clients, and received various questionsabout the possible impact of emerging new technologies. To create a better understanding of theinterrelations in the energy system, the idea was born to create an energy balance of the Dutchelectricity production and consumption.

Soon it became clear that a model limited to electricity production and consumption was notexplicitly valuable for the energy clients of QSC. But because of the societal character of the theme,both the development team and the clients of QSC seemed convinced that the model could havegreat impact if expanded and used as informational tool. Already in December 2008, six partnerswere willing to allocate budget to facilitate a public launch of the model. Since then severalcompanies have shown interest in the development of the model. At this moment (September 2010)the initiative has more than 20 partners that are involved in some way, either by financing or co-developing the model, or both.

Since December 2008 the purpose of the model has been discussed within QSC. First the ETM wasdeveloped as a tool for business development and to support work for clients. But as the clients of QSC were in favor of a public launch, the creation of an energy model became a goal in itself. This ledto the start of new company, named Quintel Intelligence (QI), in October 2009. This company focuseson the development of the model. QI’s objective is to make the ETM a valua ble tool for educationalpurposes, policy making and companies where energy plays an important role.

To better understand the choices made in developing the Excel model, the objectives of Quintel atthe time of development are discussed here.

Quintel’s vi sionQuintel’s vision can be retrieved from the business plan that was created to start the QI. The visionoriginates from the QI business plan.It states 1:

“People have the desire to understand complex issues in society (climate, energy, nutrition, finance etc.) and want to make informed choices concerning the future. Interactive scenario

1 Translated from Quintel Intelligence Business Plan, January 2009



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planning, through the internet if possible, can satisfy this desire. If large groups of people aresufficiently informed on these issues, these can be dealt with better and more quickly. ”

Quintel’s mission The role of the ETM in this vision of Quintel can be determined through the mission statement. In the

same business plan, the mission statement is formulated as follows 2:

“Quintel Intelligence’s (QI) mission is to devel op and market models and consulting services for interactive scenario planning on looming global issues in the field of energy, climate,nutrition and finance.QI strives to provide a significant contribution to the solutions for these broad issues.QI wo rks for business policy makers, the government and NGO’s and strives for a wideavailability of the models for the educational system and interested civilians.QI supports its employees in their personal effort to a sustainable and resilient society. ”

Objective of the ETM The ETM is currently Quintel’s only project to fulfill this mission. Quintel believes many energy savingand renewable energy options are already economically viable, and the barriers to move away fromfossil sources are a lack of information and education on energy. To change this, the ETM providescivilians, companies , NGO’s and politicians with well-founded information on our energy system. Theobjective is to enable all target groups to make well informed decisions on energy related topics.According to Quintel, this will directly and indirectly result in more sustainable behavior, as isillustrated in figure 2. This figure is a result of an interview with the one of the founders of QuintelIntelligence.

Figure 2: Goals for the ETM [Schoenmakers, 2010]

2 Translated from Quintel Intelligence Business Plan, January 2009



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Given these considerations, the choice was made to continue the development in Excel. It was clearthat eventually a new application would have to be developed, but the circumstances did not allowin-house development with staff specialized in the technologies.

Excel developmentThe ETM developed into a useable model when sliders were added to give users the opportunity tochange variables and see the direct feedback of their changes. Realizing that the electricity usemakes up just 15% of the total energy use in the Netherlands [CBS, 2010], the other parts of theenergy market were added as well. Because of the versatility of the model other parties started toshow interest in further development of the model.

The Excel model was developed in three stages:1. The first stage was released in April 2009 had a complete energy balance, where the

electricity-mix was already detailed.

2. In the second stage the heat demand and production for the Netherlands was added in detailto the existing model over the summer and fall of 2009.

3. In the third stage more renewable options were included in the model, and the energydemand was specified for the provinces in the Netherlands. This development continueduntil the end of 2009.

During the further development of the Excel model some programming was needed to fulfillrequirements that could not be met by the standard features of Excel. For example, additionalsoftware (LockXLS) was used to shield confidential data in the model, and to make specific charts (XYChart Labels). Also, Visual Basic for Applications (VBA) was extensively used in macros to build

advanced functionalities, which enabled the application to switch between languages, countries, andscenario year. In the latest phases of the Excel development, the model was reaching the limits of what Excel has to offer.

Until October 2009 the Energy Transition Model was still a project within Quintel Strategy Consulting(QSC), where the ETM development was not part of the company’s core business. As furtherdevelopment of the model would lead further away from QSC’s core business, a separate companywas started dedicated to the further development of the model. This resulted in the formation of Quintel Intelligence as an independent company with four former QSC employees.

Compromised requirementsBecause of the organic growth of the model, the choices that were made sometimes compromisedthe original requirements. These choices usually were a result of path dependency but as the biggerplan was kept in mind, it was realized that a platform switch had to be made sooner or later.

Now as the separate company fully focused on the further development on the model, the optionsfor the model were further investigated. Here it was decided that a new model would have to bedeveloped online. In November 2009 the thesis project was started where the initial assignment wasto develop the foundation of an online version of the ETM using the Excel model as a baseline.



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Excel model a baselineThere are three main reasons why the Excel model was used as a baseline to the new model:

1. First of all, the Excel model already had a lot of data that was required to make arepresentation of the Dutch energy system. When a completely different approach would

be used, information may have been needed that was not available.2. Another advantage of using Excel as a baseline is that the results of the calculations of the

new model can be compared with Excel. Differences in the outcomes can point to errors.The Excel model could therefore be used as validation of the new model.

3. As the Excel model was already publically used from April 2009, it was important that theoutcomes of a new model would be consistent with the Excel model. Different outcomescan make existing users question which model is correct, or if they are correct at all. Manyof the possible differences can be explained by the use of different definitions orcalculation methods and are therefore not wrong, but to prevent loss of confidence theobjective was to produce similar outcomes as much as possible. To do so, the basis of thecalculations had to be done in a similar way as the Excel model.

Documenting the existing modelIn order to use the Excel model as a baseline the main workings had to be well documented, whichhad not been done before. This was the main focus the first month of this thesis project. As thecalculations in Excel had become increasingly more complex, the only person who fully understoodthe workings of the model was its creator, Jacobs. With his help, overviews were made of the build-up of the interface, and of the databases used by the Excel model. To explain the basic workings of the model, a brief description of the Excel model is provided in the next section.

2.3 The ETM product in Excel

When a user of the model has downloaded and installed the model, it can be run in Excel. When themodel is opened, the user of the model is led through a sequence of screens where he is informedabout the current energy situation and is asked to make choices about energy trends to come to anenergy scenario for a future year. The user can also make a choice by selecting language (Dutch orEnglish), level (beginner or advanced), end year (2030, 2040 or 2050), country (Netherlands,Germany, United Kingdom, Romania or Poland) and units to be used (PJ and/or TWh). After a shortintroduction, there are 6 steps in the model:

1. Future energy demand2. Development of energy costs3. Future governmental energy policies4. Future energy supply5. Results6. Data exchange

To help the user keep an overview of the input screens, a small flow diagram below is always shownin the bottom of the screen. When this diagram is clicked, the user is informed in what step he

currently is, and how the input is used in the succeeding steps. This diagram is a representation of the steps of the model, shown in figure 3.



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Figure 3: Model walkthrough [ETM, 2009]

Each of these steps will be briefly discussed here.

1. In the first step the user is asked about the energy demand growth and the application of energy saving technologies such as heat pumps, efficient lighting, etc. This is split in energydemand by sector: households, transport, industrial, agriculture and other. At the end of thissection the user is informed about the present energy demand for energy (divided intodifferent energy carriers) and how his expectations impact that in the future.

2. In the second step the user is asked to estimate the costs of energy production in the future.These costs are divided into fuel, investment, operation and maintenance (O&M) and CO 2

costs. Here too a visual overview is given of the total energy costs for the differenttechnologies in the future year.

3. The third step gives an overview of policy goals that the government can have. For example,the user can set targets on CO 2 emissions, sustainability and costs and set limitations to landuse for wind turbines. After that an overview is given of the maximum production per energysource, considering the chosen constraints.

4. In step four, the future electricity and fuel supply can be determined by the user. Using theuser’s earlier input, he is shown what the energy mix will be, and if all constraints are met.

5. In step five the results of the created scenario are given in a series of overviews, showingenergy dependence, bio-energy footprint, CO 2 emissions per sector and a breakdown of sustainable technologies. These results reflect the situation in the chosen future year basedon the assumptions the user has entered in the model.

6. In the final step the user can upload the created scenario and compare it with scenarios of others.

Throughout the first four steps, the user can set sliders to quantify his assumptions. Over these fourcategories, 113 sliders can be used to adjust variables that affect the results in step 5. An overview of

these sliders is given in appendix I.



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The creation of overviews such as this helped to understa nd the Excel model’s main workings. Thefollowing task was to convert the Excel model to an online application. This process will be describedin the following section.

2.4 The new design process

In hindsight, three approaches were used to develop the online version of the model. This sectionwill discuss the first two approaches. The third approach will be discussed in chapter 4. In reality, theapproaches slowly developed from one to the next, but for clarity they will be separately discussedhere.

2.4.1 Approach 1: Defining the relationship between input and output

Since all of the sliders of the model have an effect in one of the outcome charts, Quintel suggested tomake an overview of the direct influence of each of the sliders in the outcome charts. With completeinsight into all the relationships between the sliders and the outcome, this approach would result inequations that could be programmed in the online application. This approach could make a replica of the existing Excel model in the new application, but was criticized by Jacobs. He expected the level of complexity would be too high to capture in individual equations and pushed to think of a Greenfieldapproach. Although all employees recognized this risk, the approach was initiated anyway, simplybecause no alternative approaches were suggested. Also, any attempt would result in a higherunderstanding of the Excel model’s workings, independent of whether it would be successful or not.

RelationshipsBefore starting this approach, the largest obstacle seemed to be finding the relationships betweenthe individual slider and its effect in a certain chart. Most of the sliders do not have a direct effect inthe charts, but are only a factor in the total equation that is used to draw the chart. As therelationships between variables are all individually set, each of the individual steps in the relationshipcan be found in Excel. Because of the sheer amount of relationships this indeed proved to be a lot of work.

High complexityThe problems encountered with this approach can best be illustrated with an example. In tab 2.i of the Excel model 3 a chart is shown for the cost of electricity production plants. In appendix II a

screenshot can be found of this tab, where 20 sliders have an effect on 29 variables. As mentioned,the objective of the approach was to create one formula that describes the effect of the slider value(X) on the values of all variables in the chart.

To keep the equation simple, it was planned to display the effect of each of the sliders on one axisonly. As all of the variables can move in two axes (total generation cost per MWh and investmentcost per MWh), two equations need to be created for each slider and each chart. For 20 sliders intwo axes this comes down to 40 equations for this specific chart. As this chart is one of the morecomplex charts in the model, the number of equations seemed acceptable and the project was

3

As the original Excel model on the website has already been replaced by the online version, the Excel model isincluded digitally in appendix VIII.



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initiated. Soon it was found that some of the sliders had a complex relationship with the variables inthe chart. For example, the relationship between the slider for the Operational and Maintenancecost of Carbon Capture and Storage (CCS O&M cost) and the variables on the Y-axis (cost inEuro/MWh) can be described as follows:

cost_in_euro_per_mwh =X * (( overnight_investment_co2_capture + overnight_investment_co2_transport_and_storage + overnight_investment_now/ ccs_operation_and_maintainance_cost ) * typical_capacity_effictive) / technical_lifetime / (typical_electricity_production *1000 ) + ( (( overnight_investment_co2_capture + overnight_investment_co2_transport_and_storage +overnight_investment_now / ccs_operation_and_maintainance_cost ) * typical_capacity_effictive) / 2 * wacc *((typical_lifetime + construction_time ) / typical_liftetime)/typical_electricity_production) +((overnight_investment_per_mw_now /typical_lifetime) / typical_electricity_production))

As the original Excel model consists of 113 sliders and 54 charts, this approach would need a largenumber of equations like the one above to make the online model work correctly. Given thecomplexity of some of the equations, this would conflict with the objective to create a moretransparent model, which is needed to maintain the model and for users to understand it.

2.4.2 Approach 2: Copying Excel’s e quations

Considering the project had now officially been launched and additional staff had been hired, thepriority of the project had significantly increased. Also the choice for Ruby on Rails, an ObjectOriented Programming (OOP) language had now been made. This is because OOP is the foundationof modern programming languages [Weisfeld, 2009] and Ruby on Rails is relatively easy tounderstand by people without programming experience. This would help to obtain a moretransparent model that could still be understood by the existing employees.

In OOP all data needs to be described in the form of objects and classes, so the first task was toconvert all the available data in Excel into objects. For example, all fuel types can be seen as objectsin the class ‘Energy carriers’, which all have certain attributes assigned to them, such as cost per MJ.Another example is that all countries can be seen as objects in the ‘Areas’ class, with all theproperties such as size and number of inhabitants defined as attributes of the object.

With the experience from the initial approach, a Unified Modeling Language (UML) class diagram wascreated together with the new programmer, in which the relationship between the classes wasportrayed. The basics of this UML can be traced back to the Excel model, where different worksheetswere already used for different types of data. The creation of the UML diagram helped to get insight

into the aspects of the model and the relationships between them. It can be found in appendix III.

PortingWith help of the UML diagram, it was clearer which calculations had to be defined. As the proposedclasses had a lot in common with the worksheets in Excel, it was now just a matter of copying thecalculations as they are stated in Excel. This way it was certain that the outcome of the newcalculation would be exactly the same as the results from Excel. In programming terms this process iscalled ‘porting’. The calculations remain the same and only the programming language is changed, inthis case from Excel to Ruby on Rails. As Excel shows the individual calculation in every cell, theycould easily be converted into Ruby code. A lot of the object classes and the attributes were defined

together with the programmer. After a total of four days of defining objects and classed it wasconcluded that this approach would not be successful either. An important class in the UML became



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the EnergyConsumer class, which defined the objects that used energy in some way or another. Theproblem was that the relationship between the EnergyConsumer objects was still not defined, and itwas clear that more transparency was required to create a model that would satisfy therequirements.

2.5 Conclusion

The model requirements are high levels of flexibility, transparency and accessibility. The fact that themodel will be available online will provide sufficient options for its accessibility, whereas the othertwo requirements will have to result from the model design. The first two approaches proved thatsoftware development in OOP does not automatically result in a design that will meet therequirements, and software development needs to be compatible with the choices made in modeldevelopment. Because of this, the software development needs to be combined with the modeldevelopment.



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Chapter 3: Methodology

Two elements of the research methodology are described in this chapter; a way of thinking, whichcan be described in underlying principles, and a way of working, which can be described in a method.Section 3.1 portrays the underlying principles in the development. Existing development methodswill be described in section 3.2 and section 3.3 looks for valuable lessons in other energy models.Section 3.4 will synthesize the useful aspects of the performed desk research, which will lead to thesuggested development method for the ETM development described in section 3.5. The conclusionsof the chapter are given in section 3.6.

Throughout the chapter the distinction is made between two parts of the development:

Model development The model development deals with the simplified representation of the reality. Thiscontains all the issues that are paramount in model development: The demarcation,level of detail, way of simplification and the manner in which data is aggregated to therequired input data.

Software development The software development includes everything that facilitates the model, such as thedatabase in where the model is stored, the calculations, and the complete interface thatpresents the results and provides the possibility to changes model variables.

The development of the ETM is the combination of both model and the software development.Chapter 2 has shown that when the two elements are developed separately, the software can be

incompatible with the model. In order to realize a successful model, the way of thinking and the wayof working in the software has to be compatible with that of the model.

3.1 Underlying principles

In both the model development and the software development underlying principles exist that arebasis for the theory. The principles for the two types of development will be discussed here:

Model development The underlying principle that is used in this thesis is that of systems thinking. Here the

system is considered as part of a large system, rather than as an isolated entity. Asystems thinking approach is systemic (holistic instead of in pieces) and/ or rationalsystematic (stepwise instead of intuitive) [Flood and Carson, 1988]. With a systemsthinking approach the problem is always addressed within the context of itsenvironment. Each system has its own characteristics which determines its behavior, butis also dependent on the interaction with other systems. Using systems thinking inmodels, the distinction can be made between quantitative and qualitative models. TheETM can be positioned as a quantitative model, as it focuses on the technical systemwhere all variables can be quantified and calculated in order to achieve a certain goal.The assumptions for the qualitative aspects of the energy system are made by the userof the model.



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Software development The way of thinking that needs to be adopted in the software development is the objectoriented (OO) thought process [Weisfeld, 2008]. This is because Ruby on Rails, thesoftware that is used for the model makes use of Object Oriented Programming (OOP),as most modern software does [Weisfeld, 2008]. OOP is a programming paradigm thatwas developed to increase the reusability and maintainability code [Ambler, 1998]. InOOP all data is stored in objects, which belong to a certain class. Objects attributes beused to define characteristics of the object. Code written in OO software does notautomatically use the advantages of OOP, but when used correctly it increasestransparency and reduces the amount of code required [Ambler, 1998]. An exampleoften used to illustrate OOP is a library; Books, shelves and authors can be defined asclasses, where the individual books are seen as objects in the book class [Frishberg,2001].

Although the underlying way of thinking for both developments has a different perspective, there is acommon ground. Both developments describe relations between the objects involved, and theinfluence these have on each other. The common ground of the two ways of thinking can be used if the systems thinking can in some way be combined with the object oriented thought process in thedevelopment of a combined method.

3.2 Existing methods

Existing development methods can help to create a method for combined model and softwaredevelopment. Although no methods specific to the combination of model and software developmenthave been found, separate methods for both model development and software development canprovide useful features. This section will describe the results of the desk research into thedevelopment methods, which presents two useable methods for model development in section3.2.1, and four methods for software development in 3.2.2. For these methods an overview will begiven in section 3.2.3. Here is concluded that none of the methods can be used directly, but somefeatures are useful for a development method for the ETM.

3.2.1 Model development methods

This research focuses on two development methods for model construction, namely the model cycle

[van Daalen et al., 1999] and the “Co -Evolutionary Method for Modeling Large Scale Socio-TechnicalSystem Evolution” *Ni kolic, 2009]. Furthermore, model development theories described by Balci[1994], Jakeman et al. [2006], and de Jong [2002] will be briefly discussed.

A wide range of model development methods exist. Knepell [1993] and Banks et al. [1987] review arange of model development methods, including some designed for development of specific models.Many of the reviewed development methods show similarities with the model cycle described by vanDaalen et al. [1999]. Because of this, this research assumes that the model cycle represents themajority of model development methods used in literature.



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The model cycleA useful method for model development is the model cycle, which represents the commonlyaccepted process of model development. There are different versions of this cycle, but most consistof the following basic steps [van Daalen et al., 1999]:

Defining goal and functionIn this step, the purpose of the model is described.

ConceptualizationHere, the structure and complexity are determined. This can be done through a choiceof modeling methodology. Also, a conceptual model can be made.

Model constructionIn this phase, a quantitative model is created. In contrast with the conceptual model,this is based on the actual data available.

Model assessment During assessment, the model is verified and validated. In verification, the model ischecked for logical errors, whereas in validation, the functionality of the model is tested.

Model use/ experimentationIn the final step is tested if the model can be used to obtain the goal of the model.

When the model does not satisfy the requirements, the steps may have to be revisited. Feedbackfrom the users can for example be used as input for a new cycle in the method. This is visualized infigure 4.

Figure 4: The Model Cycle [van Daalen et al. , 1999]

Methods similar to the model cycleBalci [1994] uses a variation of the model cycle to give insight in validation, verification and testingtechniques that can be used between each of the phases of the development [Balci, 1994]. Thearticle provides an overview of the steps in the model development process, each with thoroughlydescribed options for validation, verification and testing techniques. The importance of usingvalidation and verification is also explained in detail.The phases, processes and techniques to verify and validate are visualized in a cycle similar to themodel cycle, as figure 29 in appendix IV shows. The article does not provide the steps for modeldevelopment in a substantiated method, but the described techniques are useful for verification andvalidation in the ETM development.



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3.2.2 Software development methods

Multiple software development techniques have been developed over the years. As computers andsoftware have become increasingly more advanced the development methods changed as well. Indiscussing the software development methods, it can be noted that the methods have similarities

with the model development methods.

Both the evolution of methods and the similarity with modeling methods can be seen over the nextpages, where four software development methods are discussed. The four methods described resultfrom quick research into influential software development methods. Various other developmentmethods were found, but based on the amount of references to the methods these four seemed tohave been the most influential in software development.

The methods are ordered by age, so the evolution over the years can be recognized. The oldestdevelopment method with significant influence was the waterfall model, first introduced by Royce in

1970.

The waterfall modelThe sequential steps from the waterfall development method originate from the manufacturingindustries where changes after the initial design were costly. Therefore the method has sequentialsteps that are not revisited once they are completed, avoiding expensive redesign. Although differentversions exist, the basic steps in the waterfall model are [Royce, 1970];

Determine requirements Analysis and design Implementation Verification Maintenance

The approach is document driven, which means that it makes it easy for the manager to track theproject’s progress *Martin, 1999+. Although the waterfall model is still well known in softwaredevelopment, the strict separation between phases often make it an example of how softwaredevelopment should not be managed. This is because requirements and the design are neverperfectly described the first time around, and phases must be frequently revisited with new-foundperspectives from the implementation phase. Like the basic steps, variations of the visualization of

the waterfall model exist. In figure 5 , Royce’s original depiction of the method is shown.



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Figure 5: The waterfall model [Royce, 1970] The V-modelRecognizing the need to look back at the initial requirements, the V-model was developed inGermany in 1986 [Boehm, 1988]. The V-model is similar to the waterfall model, as the basic steps arethe same. The difference between the V-model and the waterfall model is that after theimplementation step, the following steps look back at the design to test if it fulfills the initialrequirements. The process of looking back to the requirements is a first step towards the loop that iscreated in Boehm’s spiral model.

The steps in the V model are [FHWA, 2005]:

Concept of operations Requirements and architecture Detailed design Implementation Integration, test and verification System verification and validation Operations and maintenance

These steps can be visualized as follows;

Figure 6: The V-model [FHWA, 2005]



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Boehm's Spiral modelBoehm’s spiral mode l [Boehm, 1988] is designed for large software development projects, wherelarge risks are involved if the wrong choices are made in the development of the software. Boehmrecognized the risks of development using the waterfall method in 1988, and tried to combine theadvantages of several development theories into his development method [Boehm, 1988].

One of the most important aspects is that it is risk-driven, as each iteration contains a risk analysis tomake sure the correct alternative is chosen. This emphasis on risk management has to do with themagnitude of the projects involved. One iteration was originally suggested to be six months to twoyears, so an extra iteration due to incorrect risk-analysis could result in high costs. The steps of thedevelopment method are [Boehm, 1988];

1. Determine objectives, alternatives, constraints2. Evaluate alternatives, identify & resolve risks3. Develop, verify next level product

4. Plan next phases

In Boehm’s original paper, the development process was visualized as follows;

Figure 7: The Spiral model [Boehm, 1988]



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Iterative and Incremental Development (IID)The basic idea of IID is sim ilar to that of Boehm’s spiral model; the design cannot be correctlyspecified directly at the start of the process. The method really is a combination of two concepts thathave become accepted in software development over the years; iterative and incrementaldevelopment. In iterative development preliminary versions of the product are released to getfeedback early in the design process. With incremental development separate pieces of the softwareare developed simultaneously. Although the concept IID is commonly known and accepted, theindividual steps of this method cannot been traced back to one specific piece of literature or year.Hung has defined the steps in iterative design as follows [Hung, 2007]:

Planning Requirements Analysis & Design Implementation Testing Evaluation

The steps can be visualized as follows:

Figure 8: Visualization of iterative development [Hung, 2007]

In contrast with the previous software development methods, this visualization does not reflectwhen the design is completed, and shows a continuing development of the product. The combinationof iterative with incremental development allows multiple processes as depicted in figure 8 to runsimultaneously. Compared to Boe hm’s Spiral model, IID has a shorter iteration time, which is weeksor even days rather than months. Combined with the fact that in modern software developmentverification of the code is done simultaneously with coding itself, the model does not have to focuson risk as much.

Verification of the code while coding is called Test Driven Development. This is performed by firstwriting the expected results from the code before the actual code is written. The tests areautomatically run with every change of the code, which provides the programmer with feedback onwhether or not the code results in the pre-defined expected outcome.



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3.2.3 Overview of the development methods

None of the discussed development methods can be directly applied for the ETM development,because all methods either describe model or software development, whereas chapter 2 has shownthat a combined development method is required for the ETM. Still, most methods have useful

features that can be used to develop a suitable development method for the ETM.

The methods for software development are not very different from the model developmentmethods. Both types include a planning with specification of requirements, implementation andevaluation. The similarity between the models is visualized in the table in appendix V, where thesteps of the development methods are compared. The cyclical aspect of the methods is notvisualized in this table.

Cyclical aspect of development methodsThe model cycle, Boehm’s spiral model and IID are all visualized in a c ycle. Modern software

development appears to have evolved into an iterative process. It was learned that requirementschange over time, and that a single loop through the steps is usually not enough to fulfill therequirements. Therefore, multiple repetitions of the design steps are done before the software isreleased. In the model cycle, repetition of the process is only required if deemed necessary duringmodel use.In table 1, the useful features of the development methods apart from the cyclical aspect aresummarized. Like the cyclical aspect, these features will be integrated in the suggested developmentmethod as much as possible.

Table 1: Useful features of the development methods

Development method Feature AdvantageModel cycle Specific steps for model

development Assures proper consideration

of model properties

Co-evolutionary method Project breakdown intosmall pieces

Emphasis on validation

Allows simultaneousdevelopment of model parts

Provides support for modelresults

The Waterfall model A clear distinction betweenprocess activities

Provides easy-to-managemilestones

The V model Reflection on the initialrequirements

Enables to deal with changingrequirements

Boehm’s s piral model Separate planning phase

Representation of cumulative effort

Gives insight in relationshipbetween process activities

Insight in progress easesprocess management

Iterative and IncrementalDevelopment

Incremental development

Revisit of all developmentphases in each loop

Short iterations

Allows simultaneousdevelopment processes

Able to deal with changinggoals and requirements

Provides output for feedbackearly in the process



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Similar modelsThe fact that verification and validation in models is stressed by all of these authors is interesting. Tosee how a similar model has dealt with verification and validation, similar models will be discussed.Brief research into similar energy models has resulted in an overview of eight models, which aresummarized in table 2.

Table 2: Summary of energy models found

Model name 4 Purpose Scope1. PRIMES Generate future energy scenarios European energy market, using

input from economic models2. LEAP Profile economic conditions,

benchmark, target industries anddevelop economic developmentstrategies

Economy and policy scenarios forthe US

3. RETScreen Evaluate the energy production andsavings, costs, emission reductions,financial viability and risk for varioustypes of technologies

Residential, commercial andinstitutional buildings,communities and industrialfacilities and processes.

4. Green-X Economic market and policyassessment to promote renewableenergy sources

Electricity production in the EU

5. HOMER Designing and analyzing hybrid powersystems

Economic and technical aspects of conventional and renewableenergy technologies

6. GETOnline Exploration of policy andtechnology options from aclimate perspective

Energy required for electricity, heatand transport globally

7. In zukunftleben

Supply information on potentialenergy savings

Transport and buildings inGermany

8. McKay Development of future energyscenarios

Energy use in the UK

From these models, the PRIMES model seems to have the best documentation on the developmentof the model. Also, the mathematical character of this model is most similar to that of the ETM.Therefore, this model is researched to find information on the development process that has been

used, with specific attention to the verification and validation processes.

4 These models can be found online at:1. www.e3mlab.ntua.gr 2. www.leapmodel.com 3. www.retscreen.net 4. www.green-x.at 5. www.homerenergy.com

6. dhcp2-pc011117.fy.chalmers.se 7. www.in-zukunft-leben.de 8. www.decc.gov.uk/en/content/cms/what_we_do/lc_uk/2050/2050.aspx

http://www.e3mlab.ntua.gr/


http://www.leapmodel.com/


http://www.retscreen.net/


http://www.green-x.at/


http://www.homerenergy.com/


http://dhcp2-pc011117.fy.chalmers.se/


http://www.in-zukunft-leben.de/


http://www.decc.gov.uk/en/content/cms/what_we_do/lc_uk/2050/2050.aspx












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The PRIMES modelDeveloped by the National Technical University Athens (NTUA), t he PRIMES model is an “energymarket equilibrium engineering-economic model that is used for the long term and the study of structural changes in ene rgy markets” *NTUA, 2010+. The PRIMES model would fit in about the sameof Jebaraj’s categories as the ETM. An important difference between the PRIMES model and the ETMis that PRIMES calculates a scenario based on today’s trends, where the ETM merely calculates theresults of one future scenario based on the user’s assumptions . To calculate the future scenario, thePRIMES model uses input data from EUROSTAT and three other models [NTUA, 2010];

Global trends on energy prices (from the POLES model) Global availability of energy resources (from the POLES model) Macro-economic development (from the GEM-E3 model)

The relation between PRIMES and the models it interacts with is shown in figure 9.

Figure 9: The PRIMES model in relation with other models [NTUA, 2010]

The forecast in PRIMES is based on calculated equilibriums for several energy carriers, based ontrends in supply and demand. The assumptions that are taken to calculate this equilibrium herecannot be changed by the user, but have been tested for sensitivity.

To predict future equilibriums for each of the energy carriers many variables are used. Where thisequilibrium will be found of course depends on the assumptions in all of these variables for bothdemand and supply. As can be seen in the figure, the variables that are used from other models areoften again a result from other models, which also have their own assumptions.

The PRIMES model is an agent-based model. The GEM-E3 reference manual states that “at anequilibrium point, supply equals demand in all markets and prices are such that all agents optimisetheir behaviour ” *NTUA, 2010, p19]. Because of the sheer number of variables in the models, thisoptimization is therefore dependent on many assumptions that the user can disagree with.Furthermore, the quote already entails the assumption that all actors optimize their behavior. If auser disagrees with this assumption, all other assumed relations are irrelevant.



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PRIMES model developmentResearching the development process of PRIMES, little information could be found on theconsequent steps in the process. Based on the reference manual, some characteristics of the PRIMESmodel development can be mentioned [NTUA, 2010b].

The model is based on System Dynamics, where numerous complex equations are definedthat use different variables in the system.

The model is a result of an incremental process, where small parts of the model weredeveloped independent of each other to be combined later on.

The model is a result of an iterative process, where feedback of earlier versions has beenused to update the requirements.

The model has been extensively peer reviewed for validation and used sensitivity analysis tofind the most influential variables.

In a review of the PRIMES model, Apsimon [2010] proposes to improve the transparency of the

model. She concludes the model is a “s ophisticated state of the art model for energy projectionstreating each country in the EC 27 on the same basis ”, but is concerned about the transparency of the complicated model, the assumptions and data used, and the dissemination of results [Apsimon,2010].

Apsimon’s review of the PRIMES model shows that even when a model has been properly verifiedand validated but lacks the description of these activities, the users of the model can be skepticalabout the accuracy of this analysis and still have low confidence it the results. From this can beconcluded that even with well-performed verification and use of extensive peer-reviews, theassumptions can still be questioned if the level of transparency it not high enough. This means that a

high level of transparency is required for models such as PRIMES and the ETM.

3.4 Combining the lessons

Lessons can be drawn from the initial two attempts of the ETM development, from research intodevelopment methods, and from development of other models. How these lessons will be applied inthe suggested development method for the ETM will be explained here.

3.4.1 Initial attempts

The first two attempts to develop the online ETM have shown that development of software can beunsuccessful if it is incompatible with the way the model has been developed. A method is requiredthat makes the software development compatible with the model development. Also, requirementsfor the model and software development may be conflicting. Therefore, planning and determinationof requirements of both model and software development are combined in the suggested method.

3.4.2 Development methods

The table on page 33 provides an overview of useful aspects of the researched developmentmethods. How these elements will be used in the suggested method, will be discussed for model and

software development methods separately. The important cyclical aspect is described first, as it canbe found in both model and software development methods.



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The cyclical aspectThree of the researched development methods describe the development as a cycle. This resultsfrom the experience that multiple iterations are needed to meet requirements, especially whenthese requirements change over time. Because of changing requirements, some cycles may fail tomeet requirements or become useless. The lesson from IID, or iterative development morespecifically, is that although these incomplete iterations may not contribute to the end result, theydo lead to more insight in the requirements. Another lesson from IID, in this case from incrementaldevelopment, is that multiple processes can run simultaneously.

Using a cycle as basis for the suggested method means that the process steps can be repeatedmultiple times in order to meet the requirements. The cyclical aspect can also be used to visualizethe repetitive iterations through the development phases. Boehm [1988] describes thatsimultaneous development processes can be visualized as parallel spiral cycles, adding a thirddimension to the cycle. The concept of using iterations for the development is an important aspect of

the suggested method. To further specify the suggested method, lessons from model and softwaredevelopment methods will be used.

Model development methodsAs the suggested method will be used for the development of models, proper consideration of modelproperties is important in the development. Therefore the model cycle will be used to provide thesteps for model development iterations. The final step of the model cycle, model use, is not includedin the suggested method, as the use of the model is not part of its development. Still, feedback fromusers can be used to change the requirements in further development.

One useful feature of the co-evolutionary method, breaking down the project into small pieces, iscomparable with the incremental aspect of IID. Here too parts of the project can be developedsimultaneously to be combined in a later stage. As the suggested method describes both model andsoftware development, simultaneous development is a useful contribution. The emphasis the co-evolutionary method puts on validation will be used in the suggested method as well, because theresulting support for the outcomes is an important goal of the ETM.

Software development methodsFrom the software development methods, the spiral model and IID provide the most features for thesuggested method. Both useful features of the spiral model are used. The separate planning phase,which gives insight in the relationship between process activities, is particularly useful. As thesuggested method will combine model and software development, insight is required to understandinterdependencies between model and software development.

The second useful feature, representation of cumulative effort in the development is useful toprovide insight in the overall progress. Although the completion of iterations through thedevelopment cycle cannot be used as milestones, it does give insight in the development speed,which is useful in process management [Martin, 1999].

Both useful features of IID have been integrated as well. Iterations will be kept short, which has theadvantage early prototypes can be released to provide feedback early in the process. Secondly, all



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development phases will be revisited each iteration. In combination with the short iteration time,frequent revisit of all process steps will make the process able to deal with changing requirements.

The useful feature from the waterfall model, clear distinction of the process activities to provideeasy-to-manage milestones, is used to distinct phases which reflect the progress through thesuggested method. Because these phases may be revisited in a later stage, this does not createactual milestones, but does provide insight in the development progress. The V- model’s reflection oninitial requirements is already incorporated in the cyclical aspect of the suggested method, whichalso results in the ability to deal with changing requirements.

The steps in software development differ from the model development steps. Model developmentsteps like demarcation and formalization are specific to model development and not relevant whendeveloping software. Examining the overview in appendix V, the steps from IID seem to be mostsuitable for the suggested method, based on the characteristics of the suggested method describedin this section.

3.4.3 Other models

Research into other models has shown the importance of continuous validation and verification.Without it, model development errors (which do inevitably occur) will accumulate to the point wherethey cannot be traced back to its cause, which results in useless models. Even when models areproperly verified and validated, lack of a proper description of the validation process can result in lowconfidence in the models results with the user.

Besides the importance of verification and validation for the model itself, this means it is also

important to document this process for the acceptance of the outcomes of the model. Wellperformed verification and validation gives confidence in the model results to a certain extent, butskeptical users will want to verify and validate the model themselves before they trust the results.

As the ETM is used to give insight in complex relationships in the energy system, it would thereforebe best if users could repeat the verification and validation process themselves. In conclusion, theresearch into other models results into three lessons for the development:

Include proper verification and validation in the development process Include documentation of verification and validation in the development process

Create the possibility for users to verify the results themselves

3.5 Description of the Object Oriented Modeling method

The overview of the development methods in section 3.2.3 has shown that model development andsoftware development are closely related. Both the model and software development make use of the principle that everything can be described as an entity with certain attributes. This principle isused in the first iteration of the method to create compatibility between the model and the OOsoftware. Therefore the first iteration is the key of the Object Oriented Modeling (OOM) method. It

will be described in section 3.5.3, after the model steps have been mentioned.



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Both model and software developmentThe method consists of different steps for model and software development. Therefore, the type of the iteration is determined in the planning phase. This means that the planning phase is the same forboth model and software development, but the other phases have different steps.

Figure 10: Basis of the OOM method visualization

Visualization of the methodA visualization of the method makes it easier for the user to understand the relation between thedifferent steps, especially since the method uses sub-processes for incremental development todevelop different parts of the model simultaneously. In the case of multiple synchronous processes,a new spiral is used for each individual sub-development. The basis of the visualization of the ObjectOriented Modeling (OOM) method is depicted in figure 10.

The method starts in the middle of the figure and progresses over the line through the phases of thedevelopment. The distance from the center represents the cumulative effort put in the development,increasing over time. When the process completes the four phases it returns to the first phase,planning, to start a new iteration.

It is possible the process is terminated before it has completed all phases. This would mean the linewould stop halfway through a cycle, leaving the product with the result of the previous iterations, if

any.Development phasesThe OOM method consists of four phases, each consisting of three steps. The first phase is combinedfor both model and software development, in which is the iteration type is chosen. The first step of the model is planning, which has the following steps:

Phase 1: Planning1. Update requirements

The first step is to make an update of the requirements of the modeling software. Hereall requirements that need to be met can be listed.



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2. Prioritize requirementsNow a list of requirements has been created, the developer can prioritize them. This canbe done together with the client if necessary. By selecting the most importantrequirements that have not yet been met, it is clear what is to be done next.

3. Plan iterationA planning of the iteration concludes the planning phase. This includes the choice foreither a further development of the model or the software. This choice depends on therequirements with the highest priority.

Because the consequent steps in the method depend on whether is chosen to further develop themodel or the software, the two developments will be discussed separately here. First, the steps of model development iterations will be explained.

3.5.1 Model development iterations

The last three phases of the model development consist of the following steps;

Phase 2: Conceptualization1. Demarcation

The first step of conceptualization is to make a demarcation. Here a choice has to bemade what part of reality needs to be modeled. All variables that can significantly affectthe system’s state need to be included. A higher number of variables can increase thereality of the model, but too many variables will overcomplicate it. Therefore the modelneeds to be as simple as possible, without harming reality. The demarcation determinesthe amount of variables, not the complexity in which they are modeled.

2. Describe objects and relationshipsThe second step is to describe the objects in the system and their characteristics. In thedescription, the object can be detailed to a high level as this is formalized in the nextphase. When the objects are described, the relation between them needs to bedescribed. More accuracy in the description of the relationship results in a moreaccurate model, but can increase the complexity.

3. Choose modeling characteristics Finally, the characteristics need to be chosen for each addition to the model.Additions can have different modeling characteristics. Some variables will for examplebe described that change over time, where others remain constant. The modelingcharacteristics can affect the formalization in the next step.

Phase 3: Specification1. Formalization

Based on the conceptualization, the new input data for the model extension needs to beformalized to work according to the model characteristics. Objects, attributes, relations,time elements and processes are noted in a form the model can calculate with it. As themethod is iterative, the model may start out simple while adding more complexity inlater stages.

2. Data gathering & processingWhen the model has been properly formalized, the variables and their relations have tobe quantified. Data has to be gathered, and may need to be adapted before the model



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can use it. This is because the model could use standardized units its database that differfrom the units that are gathered.

3. ImplementationThe data in the correct form can now be implemented. This means that new objects canbe created with attributes and the relations can be specified. Finally the data may needto be imported into a database in order to test the results.

Phase 4: Model testing1. Verification

With verification, is it substantiated that the model is transformed from one form intoanother, as intended, with sufficient accuracy [Balci, 1994]. Comparing the results withthe results of previous iterations can help to find errors.

2. ValidationAfter verification, the outcomes of the model can be validated. Structural validation canbe used to test if the model behaves as expected when extreme values are used as inputin the model, as you can often expect what would happen in extreme cases in reality.Replicative validation can be done by checking if the model response to changes invariables compare with the realistic situation.

3. Integration and documentationWhen the result of the iteration is properly validated, it can be moved from the testingenvironment to the actual model in use. The description of additional is documented incase unexpected behavior arises in later iterations or for review purposes. Thedocumentation of the validation is especially important when users of the model cannotrepeat the validation steps himself.

Using the steps from model development the visualization of the iteration can be completed;

Figure 11: Visualization of a model iteration in the OOM method

The process looks different when the planning phase concludes the software has to be developed inmore detail. This will be discussed in the next section.



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3.5.2 Software development iterations

The steps for the software development iterations of the method have been created based on thesoftware development methods. They are described here starting from phase 2, as the first phase,planning, is the same for both model and software development.

Phase 2: Design1. Analysis

The analysis is done to look for the best approach, structure and design to satisfy theobjectives of the iteration.

2. SpecificationIn this step the software structure is specified, such as where data will be stored. Thiscan be in a database, or in the code itself. Also the required classes and methods aremapped out according to the choices in this step.

3. Design tests

Just before coding, the exact specifications of the design are written in tests on thesmallest scale of the application. The design of these tests makes sure the code behavesthe way it is supposed to do in all circumstances during the development. These are thetests in what is called Test Driven Development.

Phase 3: Development1. Coding & verification

Now that the design phase is completed, the development starts with the coding of thedesign. The tests that are written in the previous step will provide feedback on whetherthe code provides the expected behavior, verifying it instantly.

2. IntegrationWhen the code works as required, it is integrated into the existing software.

3. OptimizationThe new code can be optimized in combination with the existing code to reduce thenumber of calculations and increase speed. The code can also be optimized to increaseflexibility and maintainability, so that the application can be changed easier later.

Phase 4: Implementation1. Validation

The new addition of the application is checked to see if performance is as expectedbased on the specifications and replicative validation. When proper test have beenwritten in the design phase, structural validation has already been performed.

2. EvaluationDuring evaluation is checked whether the addition satisfies the requirements set in theplanning phase. Also, the effect of the newly written code on other dependent modulesis evaluated, just as the overall performance of the application.

3. DocumentationThe addition is documented to understand the workings in a later phase or for otherprogrammers.



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The steps of the software development method are visualized using the same logic as the modeldevelopment method. This results in the following depiction:

Figure 12: Visualization of a software iteration in the OOM method

3.5.3 Defining the network structure

In the first iteration of the OOM method, the model and software development are combined in thecreation of a network structure. This will be the basic structure for both the model and the software,

and consists of a directed graph where the nodes are defined in objects, and edges are defined inlinks.

Building the network structureAs the network structure will be basis for the model calculations, it must represent the mostimportant aspect of the model. In energy models, this can be the flow of energy through the system.In other models, a different feature can be used for the network structure. Mathematical modelsoften describe relationships between entities with a common ground. In the discussion of this thesisseveral examples are given to illustrate the wide range of network structures that can beconstructed.

To define the network structure, OOP is used. All objects are defined in one class, with attributes toassign useful specifications. All links are defined in a separate class. The complete network structurecan so be defined in these two classes. Attributes can be assigned to the links to define specificationsas well.

Standardized calculationWith the network structure in place, a standardized calculation method must be made that cancalculate the effect of changes in the attributes of the structure. The links between the objects itself do not change anymore, but the attributes that define the flow over the links can be used to

calculate the influence on the related objects.



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With the network structure defined, the basis for continued development is ready. The requirementsfor both model and software can now be updated in the start of a new iteration.

The process of constructing a network structure can best be illustrated with an example. Thebeginning of chapter 4 will show how the network structure was constructed in the ETMdevelopment.

3.6 Conclusion

The way of thinking that is used for the development method for the ETM must fit in both systemsthinking as the object oriented thought process. The OOM method combines the way of working of various researched development methods into one integrated method for both model and softwaredevelopment. The key element of the method is in the construction of the network structure in thefirst iteration.

Now the OOM method has been described, its use in the ETM development is described. Thedescription of the development process in the next chapter continues from the where chapter 2ended.



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Chapter 4: Applying the OOM method

In this chapter the third and final approach in ETM development will be discussed. Section 4.1describes the ‘converter concept ’ that is the network structure for the model. Section 4.2 describeshow the OOM method applies this concept in the ETM development. In section 4.3 the resultingdesign will be described in detail, and section 4.4 discusses the use of the OOM method in thefurther development of the model.

4.1 The converter concept

The first two attempts for the development of the online model did not seem to lead to a modular ortransparent model. During the process of porting there was a clear need for further visualrepresentation of the relationships between the elements in the model, especially for programmers

unfamiliar with energy systems. Although the UML described in section 2.2 did provide sometransparency, a useful representation of the energy system was needed to further clarify theinterrelationships between the elements in the energy system. Several representations were found,such as the ones in appendix VI, but none of these have the same scope or level of detail as the ETM.

Visualizing the energy systemTo create more insight, a visualization of the energy system was created with the same scope andlevel of detail as the existing ETM. This visualization shows the energy flow between elements of theenergy system modeled in the ETM. A part of the visualization is displayed in figure 13 below, whichshows the relationship between the national gas network, electricity producing facilities, the power

grid and the heat network. Power plants are not specified in this figure for the sake of simplicity.

Figure 13: A part of the visualization of the energy system

In figure 13, arrows between the elements indicate the direction of the energy flow. For instance, gasflows from the ‘national gas network ’ to the power plants, which in turn supply electricity to the ‘HVpower grid ’. This grid is also fed by electricity from interconnectors (‘electricity import/export’) ,

allowing import and export of electricity.



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Using this logic, the main elements of the Excel model were structured in one large diagram. Usingthe arrow width to show the amount of energy, it gives a quick overview of the largest energy flowsin the system. This kind of representation is called a ‘Sankey diagram ’. An early version of the Sankeydiagram for the complete build-up of the ETM is shown in figure 14.

Figure 14: One of the first Sankey diagrams showing objects

The objective of the Sankey diagram was to show the relationships between the objects. Because the

objects both supply and demand energy, they were named Converters. Converters describe therelationship between input and output in a standardized way using the first law of thermodynamics:the conservation of energy. Using this principle, every entity in the diagram can be seen as aConverter object, which can convert one form of energy to another. For example, a coal plantconverts coal into electricity and loss (heat that cannot be used), and a gas fired heater converts gasinto useable heat and loss. Bearing in mind that energy is never destroyed but dissipates into theenvironment, the ‘losses ’ are included in the equation to maintain the energy balance.

This concept was well received by the project’s team members, and it was decided to further developthis concept to see if it could be used as basis of the software calculations. Together with the project

leader a set of basic calculations was created that could calculate all the required values for themodel, based on the thermodynamic laws. Textbox 1 describes how the calculations methods arederived from the thermodynamic laws.



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Textbox 1: Applying the thermodynamic laws for the calculation software

The thermodynamic laws of nature always hold. When modeled correctly, these laws can thereforebe used for every energy conversion in the model in the same way, as long as all the variables aredescribed as an energy converter and has the required attributes. The thermodynamic laws are a

result of the properties of energy and are used as the basis for the calculation rules of the software.How these properties result in the basic calculation rules for the converters is explained using asimple converter as an example below. In this example we consider a gas fired heater with anefficiency of 80%.

Conservation of energyMost importantly, energy is always conserved. Energy losses do occur, but when these are added inthe energy balance, the total amount of energy will not change. This is also considered in thecalculations. As the model is designed to calculate the impact of changes in demand of energy, itcalculates the fuel demand from the heat demand. This means that in order to calculate the demandfor natural gas, the heat demand is divided by the efficiency. As the amount of heat is required inthis example is 80, the amount of gas needed is 80 / 80% = 100. For each of the defined energydemands, the model calculates the required supply of energy from the very left of the system. Usingthe conversions defined for each of the converters, it finally reaches a demand of primary energy atthe very right end.

Energy carriersThe form of energy is not relevant for the energy balance calculation, as all of these forms can beexpressed in mega joules (MJ). However, the carrier in which the energy is transported is defined inorder to assign attributes to these carriers such as cost and CO 2 properties. The form of energy canalso be derived from the energy carrier when required. In the example the natural gas containschemical energy, where usable heat and loss both consist of thermal energy.

Quality of energyThis fact that energy can change from carrier, is used in many energy conversions in the model. Thesecond law of thermodynamics which states that entropy can only increase (meaning the ‘quality’ of energy can only decrease), is not included in the calculation software yet. Theoretically, this law

could be used to make a better comparison between technologies. Heat pumps for example makeuse of ambient heat, which makes the term efficiency hard to use and compare betweentechnologies. The use of entropy could therefore be a useful addition for the model to compare theincrease of entropy between technologies.

Based on these relatively simple rules, the software is designed to calculate through the wholesystem. Since these calculations are based on a thermodynamic law, they will not have to bechanged and are fixed in the calculation part of the software called the kernel.

Figure 15: A gas heater as represented in the model



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Calculation engineWith the converters structured in the Sankey diagram, it became possible to use the new calculationmethod to determine the values of the ‘ converter demands’ based on given conversion rates andshares. To test the energy converter concept, a mock-up model of 10 energy converters was built forwhich a programmer wrote software to calculate the according values. After solving someprogramming difficulties, the calculation engine returned the expected outcomes in the mock-upmodel.

As the calculation method is consistent for all of the energy converters, this meant that the wholesystem could be calculated in the same fashion. Therefore, the choices made in the initial model arethe foundation of the calculation engine, or kernel, determining its workings. The choices weredocumented in a presentation together with the project leader to inform new staff on the workingsof the software. A visualization of the initial calculation engine is shown in figure 16 and thecomplete presentation can be found in appendix VII.

Figure 16: Representation of the calculation engine

Object classesWith the basic calculation engine working, the existing Excel model had to be converted to a formatthat could be read by the kernel. In order to do this, three new classes were defined that togethercreate the basis of the new model. The four classes are clarified with the example of textbox 1;

ConvertersClass of objects that convert one form of energy to another, such as the gas fired heater.

ConversionsClass that defines the energy conversions and efficiencies for each converter. For a gasheater in the example the conversion is defined as: 100 gas input, 80 heat output and 20loss output.

LinksClass that defines the links between converters that transfers the energy. For the links inthe example is defined that the gas is taken from the national gas network to the gasheater, and the heat from the gas heater is supplied to the household heat demand.

Energy carriersClass that defines the energy carriers in the model, such as heat, gas, and electricity.

These object can be used as attributes to the Links.



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These four classes define the main energy balance of the model for which the kernel calculates allthe corresponding values.

All entities in the Excel model were described as converters, and attributes were assigned to theconverters. Initially, the energy demand was the only attribute assigned, to calculate the energy

balance. The resulting network structure represents the energy system and is referred to as thegraph. Iteratively, the model was extended to ensure the resulting values still corresponded with thevalidated data.

Assigning attributesTo supply information on the costs and CO 2 emissions, additional attributes were assigned to theconverters and energy carriers. The calculated values for costs and CO 2 were validated bycomparison with the original Excel model. For both cost and CO 2 calculations, improvements to thekernel had to be made before the values corresponded with the validated data. After someprogramming iterations the new model design produced similar outcomes to the old Excel model.

Final demand as basisBecause the calculation engine was designed to calculate everything based on the demand, somecalculations had to be done differently than in the Excel model, which uses data from the StatisticsNetherlands (CBS) on final demand as its basis. When an alternative to a predominant technology ischosen, the Excel model calculates the impact this alternative would have on the final demand basedon the difference in fuel consumption with the current technology. For the new design, the CBS datais only used initially to calculate the current situation, after which the final demands are calculated bythe kernel. This is illustrated in figure 17.

For example, CBS provides the household ’s final energy demand for heating split in the energycarriers that are used to provide this heat, i.e. the amount of gas that is needed for the centralheating in households. In reality, the household just needs a certain amount of heat, regardless of how it is supplied. In the Netherlands most heat is currently supplied by gas heaters, but this could

just as well be supplied by systems using other carriers with different efficiencies, such as heatpumps or micro-CHPs.

Figure 17: Difference between the calculations of the Excel and the online model



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Usable heat demandIn order to calculate how much would be saved with alternative heating technologies a ‘usable heatdemand ’ was specified. This usable demand can usually not be found in literature or reports, so it isinitially calculated based on the shares of the technologies used and their efficiencies. With this value(and the shares and efficiencies of the converters that supply this demand), the kernel can calculatehow much of the energy carriers, such as gas and electricity, are used in total. This should then of course match the final demand as supplied by the CBS. The value for usable heat demand cannot beverified directly, but when the values calculated for final demand match the verified CBS data thedemand for ‘usable heat’ in reality is not really relevant.

An example is given in figure 18, where the usable ‘household heat demand ’ can be calculated fromthe final demand. In this hypothetical example CBS states the final use for heating is 820 MJ gas and125 MJ electricity. Assume that 75% of heat in households is supplied by gas fired heaters, 10% byelectric heaters, another 10% by heat pumps and 5% by micro-CHPs. For sake of simplicity of thisexample we state the following: heaters are 100% efficient, the heat pump has a ‘Coefficient of Performance ’ (CoP) of 4 and the micro-CHP heat/electricity ratio is 5 to 2. Based on the shares andefficiencies, we can calculate that the heating demand is 1000 MJ and that 75 MJ ambient heat is‘used ’.

The calculated usable demand now makes it easy to determine what the effect would be if shares orefficiencies of the heating technologies would change in the future.

Figure 18: Example of the usable household heat demand

Other usable demandsFor every part of the model where the user can select alternatives, a ‘usable demand ’ wasconstructed. For example: in the sector households a certain lighting demand is calculated that needsto be filled by the available types of lighting, and in the transport sector a demand of transport wasdetermined that needs to be satisfied.

The concept of ‘usable demands’ is important in the model. These usable demands make it possibleto compare alternative technologies. With further expansion of the model, more usable demands

may need to be created to compare technologies that supply a certain demand that is not normallyspecified, such as the actual cooling demand of offices.



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4.2 The development process

As noted earlier, the project consists of two parts;

Modeling the energy system (Model development)

Programming software designed to calculate the model and show its results (Softwaredevelopment)

In order to integrate the two types of development in the same project, the Object OrientedModeling (OOM) method has been developed. In this section the OOM method will be comparedwith practice. Because the OOM method will result in a product where the two parts of the modelare closely entwined, it is hard to assign the parts of the end-product to either the software or themodel, as most of the end-product consists of both.

Whereas the product cannot be assigned to the model or software, the process can be split in eithermodel or software development, as it has either been created by a model iteration or a softwareiteration. The first four iterations of the development can be visualized using the OOM method as isdone in figure 19. The model and software development do not necessarily have to alternate,multiple iterations of the same kind of development can exist as well.

Figure 19: The first four iterations, alternating model and software development

The first step of the OOM method is started by creating the correct requirements for the model.With the requirements sorted, the phases of the model were planned. The first four iterations can be

described as follows:

1. Conception of the converter based concept2. Calculate the values for the mock-up model as a proof of concept with a software iteration3. Further specification of the model, where a part of the Excel is converted to the new

converter based concept4. A software iteration to display all the values calculated in the second iteration

These method steps will be explained in more detail later on. First will be explained how the idea of the OOM method came into existence.



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Creation of the OOM methodAs noted in section 4.1, the project of creating the online model had two prior unsuccessfulapproaches before a successful approach was found that could be used to create the newapplication. From the perspective of the OOM method, the two first attempts only focused onsoftware development without considering the model design.

It was thought that since the model had already been made anyway, it only needed to be convertedto an online application. The outcome of these first two approaches forced the team to think aboutanother approach. With the overviews of the workings of the original Excel model, it became clearthat the software could not be built separate from the model. This knowledge led to a successfulproduct using the third approach.

The basis of the first stepIt is worth it to look at the beginning of the third approach in more detail. Here the design of theconverter concept could be used in combination with the object oriented design of the software.

The manner in which the network structure is described in the software is the reason why theconverter concept works well. Defining the links between the converter objects made it possible tocalculate the complete network structure in a standardized fashion.

In retrospect, this moment can be seen as a key point of the development, as this aligned the modeldevelopment with the software. The creation of a network structure can therefore be seen as thefirst iteration in the OOM method. The steps that followed are described with the help of the OOMmethod in the next section.

4.2.1 Software development iterations

With the software constructed from scratch, there was a lot of flexibility in how to create the model,not limited by design choices of existing software. The design process will be described using thesoftware steps from the OOM method that has been described in chapter 3.

Figure 20: A software iteration in the OOM method



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Because software development using the OOM method is an iterative process, the steps of thedevelopment technique will be done more than once. Even within a single iteration different stepsmay be revisited, as at some point it may be required to take a few steps back. The process istherefore not as one-directional as depicted in the model. That being said, the process is describedhere using the four phases of the OOM method.

PlanningAccording to the adapted development technique the initial requirements have to be determinedfirst. In practice, the requirements are a combination of the possibilities the Excel model already hadand wishes to deal with its shortcomings. As these requirements are just the initial requirements andcan be updated every iteration, they did not have to be exhaustive at this point. The highest prioritywas to create a model that would work online and creates more transparency and maintainability.For the software, this meant that a system would have to be developed that could calculate thecorrect outcomes for different inputs.

DesignFor the first software design, a small graph was set up with only 10 variables and relationships. Thegraph is the structure of the objects in the model with the links to describe the relationship betweenthem. The small graph was specified with values for demand and shares. The purpose of this mock-up model was to have a specified energy-balance for which the calculation engine could calculate thecorresponding values. In theory the design could calculate this, now it had to be coded.

Development The simple mock-up model was completely pre-calculated so it would be working correctly when theoutcome of the kernel’s calculations would match the pre -calculated values. With agile development,

tests are included while writing the software, so the results are verified right away. After variousattempts the software written by the programmers could indeed calculate the correct values. Withthe basic calculations working correctly, another programmer completely rewrote the code to makeit more transparent.

ImplementationAt this stage, the only thing required from the software was the correct calculation of the values thatwere already defined in the mock-up model. With the basic kernel working, its speed was evaluatedand the basics of the kernel were documented.

The third and fourth iterationOnce the kernel proved to work correctly, the second iteration was completed. Now the mostimportant requirement for the application was to reproduce values from the Excel model as soon aspossible. This meant the whole graph needed to be built based on the existing model. The thirditeration therefore used steps of the model development, where a small part of the existing modelwas converted to the new converter-based format. This iteration was purposely kept short to testthat the calculation engine worked correctly, this time with real data. Fortunately the Excel modelprovided a lot of values that could be used to validate the outcome. In a fourth iteration the softwarewas developed further to show the exact values for each of the converters and the links betweenthem. This made it possible to quickly find the errors in the input database. A couple iterations later,

the complete energy balance from the Excel model was translated to the converter based model.



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With many code corrections in the coding & verification phase, the model was eventually successfullytransferred to the new system.

The following iterationsFrom this point on, both the software development and the model development are driven by the

wish to include additional scope or detail. First, a wish to add functionality arises. This will lead to anew requirement for the interface to show this information. If possible, a new query can be made toextract the information from the kernel if the needed information is available in the existing graph. If the information is not available however, either the software or the model needs to be developedfurther. When the required data is already available without adjustments to the graph it takesanother software iteration to further develop the interface. If more information is required than isavailable in the graph, the model needs to be extended. For this, a model development iteration of the OOM method is performed. To understand how a model iteration is performed, the followingsection describes the exact steps of a model iteration.

4.2.2 Model development iterations

For the development of the model, the model development cycle can be used. To clarify how thismethod can be used to develop the model, an example is given in textbox 2. As each of the additionsis a result of a single iteration through the method, the complete model can be seen as a result of themultiple iterations of the incremental development. This makes the requirements for the completemodel a combination of the requirements of the different iterations.

Textbox 2: A hypothetical example of using the spiral model to increase the model's level of detail

The requirement exists to give the user of the model the option to see the effect of alternativemeans of heating in households. This choice is not presently given in the interface of the model.The fact that this information is not given in the interface does not necessarily mean it is not includedin the graph. If it is, the information can be collected from the calculation engine and displayed in theinterface. If it is not, the graph will have to be extended. For this extension the spiral as shown infigure 21 can be used.

Planning1. Update requirements

The new requirement is to show alternative means of heating in households.2. Prioritize requirements

If other or conflicting requirements also exist, they have to be prioritized.3. Plan iteration

As the desired addition to the model requires an addition to the model, this iterationwill use the steps of the model development. In this step, a time plan is made for allsteps needed to perform this extension as well.



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Figure 21: A model iteration of the OOM method

Conceptualization1. Demarcation

First, a demarcation needs to be made in how much detail this question needs to beanswered. For example, it can be decided that only a limited number of heatingtechnologies are implemented because the others are not significant or no informationcan be found on them. It is also important to look at the available data to determinewhat technologies should be included in the model.

2. Describe objects and relationsThen, the technologies and relationships between the technologies are defined. Of course, the alternative heating technologies supply heat to the created heat demand,but also the energy suppliers need to be linked. Whereas for central heating the neededgas comes from the national gas network, the energy supply for an electric heatercomes from the electricity grid. More complicated converters are micro-CHPs, which,beside heat, also supply electricity, and heat pumps, which do not only use electricitybut ambient heat as well. Whereas in some other models the energy flow from theenvironment is not taken into account, it has to be in this model in order to maintainbalance in the energy equation.

3. Choose modeling characteristics

For every addition to the model it needs be determined how it should be modeled. Inthis case it needs to be determined how the alternative means of heating will beincluded in the model. Specified has to be if the technology specifications will changeover time, if it depends on other variables in the model, or what options the device has.New energy flows will have a continuous character, where costs may be discrete.

Specification1. Formalization

Now that the heating technologies are described, they need to be specified. This is doneby assigning attributes to them. Information on efficiencies, capacities, costs, currentmarket share and other information are assigned to the newly made converters.

Because the model has made simplifications of the real world, the gathered data mayhave to be formalized for the model to calculate with it.



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For example, formalization of a gas fired heater in households is to specify its efficiencybased on the converter concept. This efficiency is different from data on gas firedheaters that is usually supplied, which is calculated on the lower caloric value of gas andcan therefore be over 100%. As the model calculations are based on the actual amount

of energy in the carrier, the efficiency needs to be formalized, which is this case means itis based on the upper caloric value of gas. 2. Data gathering & processing

With the assumptions the information needed now has to be collected. For example, itcan be found that an average heater costs 120 euro per kW, the average size is 20 kWand it converts 92% of the available energy from the gas into usable heat. The gatheredinformation has to be processed in order for the model to use it. As everything in themodel is expressed in MJ, the values have to be converted to MJ as well. For theexample this would mean that the investment cost would be 0.120 euro per MJ/s andthe capacity is 0.020 MJ/s. The efficiency can be used as directly, with the addition thatthere is an 8% loss and the input is 100% natural gas to create the energy balance.

Next to the attributes of the converter, the market shares of the technologies have to bespecified to the links. These shares determine how the usable heat demand is dividedover the links to the technologies.

3. ImplementationFinally, the data has to be implemented. This means that attributes can be assigned tothe correct object and all values are added to the database which can be uploaded tothe server.

Model Testing1. Verification

When the kernel has calculated all values for the modeled energy system, the values can

be verified. First of all, this consists of checking whether the correct values have beencalculated for the technologies. This is to see if no errors have been made indetermining the shares or efficiencies, and to check if the calculation engine stilloperates correctly.

2. ValidationAfter the data is verified, the addition can be validated by using the model output to seeif the influence of the technologies is as it should be. For example, the electricity useshould go up when gas fired heaters are replaced with electric heaters and the use of sustainable energy use should go up when solar water heaters are used. Next to thecorrect response, the extent to which factors are influenced can be validated as well.Unexpected values can indicate that there are still some errors in the data or falseassumptions have been made.

3. Integration and documentationWhen Quintel is confident the addition works as it should, it can be integrated from thetest model into the model on the website. The additional heating technologies in themodel are documented, with special attention on the description of how this addition isvalidated.



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4.3 The product

In this section, the new design will be described in detail. The focus of this thesis is the developmentand specifications of the model, but as it is shows the design that has resulted from the OOMmethod, the complete project is described in this section.

4.3.1 The elements of the new design

As described previously, the project consists of both model and software development. The completemodel is described in databases. In order to show the results of the calculations in the interface aconnection is needed between the interface and the databases. The elements of the application andthe communication between them can be visualized as follows, where the arrows represent theinformation flow between the different parts of the application:

Figure 22: The design elements

The flows and the elements will be described here, starting with the application’s basis, the database and kernel.

4.3.2 Database and kernel

The basis of the new design is the database, in which all data of the energy balance is stored. Thisincludes converters, the links between them and the corresponding attributes. The database holdsinformation about the graph, from which the kernel can calculate all required data. The kernel is thecalculation engine of the model. It is built to calculate the energy balance using the ‘usable’ demand,the links between the variables and the efficiencies. This energy balance can then be visualized in agraph. This graph is the simplified reflection of the energy system, the actual model. Now theobjective of the model is that users can adjust a variable in the model to see how this influences theenergy system. This is done by recalculating the entire graph with the altered variables. By comparingthe existing graph with the original, the differences can be easily calculated. The original graph isstored separately in the database so it can be compared with the adapted graph.

4.3.3 Graph Query Language

With all of the values available in the graph, users need to be able to see the results of the model andsee the effect of changing variables. This means the user-interface must be able to show data fromthe graph and accept input to calculate a new graph. The connection between the interface and thedatabase is managed by the Graph Query Language (GQL). The GQL can get the required information



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from the calculated values, or change variables for which a new graph can be calculated by thekernel.

At the time of writing the GQL is still under development, to make even more advanced queriespossible. Eventually, a graph in the interface can be created using a query that will, for example, ask

for the amount of fuel used for the heat production in the industry. Put in the correct syntax, theGQL will understand the question and return the corresponding value. Using the intelligent GQLmakes it easier to interact with the database, as the GQL will sum the total of all heat producers inthe industry rather than having to specify all the individual converters. The advantage is that whenother converters for heat production are added in industry, the query does not have to be changed.

4.3.4 Interface

The interface actually consists of two parts; the front-end and the back- end. The ‘front -end’ interfacesupplies the users with the model results and the gives the possibility to change the input variables.

The ‘back -end’ of the model is a part that can only be accessed by Quintel staff. This is theadministrator area, which is totally different from the front-end. The difference can be visualized asfollows:

Figure 23: Visualization of the front-end and the back-end of the interface

First the back-end will be discussed, as this is most relevant for the subject of this thesis. After this abrief description of the front-end will be given.

The back-endWhen logged in on the back-end of the model, a lot of functions are available. The current functionscan be categorized into three purposes;

1. Uploading new model versions2. Advanced analysis3. Changing the front-end interface

Each of these functions will be described below.

Uploading new model versionsFirst of all, the back-end interface allows new model versions to be uploaded. The applicationautomatically extracts a newly uploaded zip-file to the correct places in the database, after which thenew version is available in the overview. Here the new version of the model can be accessed to see if it generally produces the same results. Tools can be used to compare versions with each other to

quickly see where differences occur. Also, new areas can be defined for models of countries,provinces, or cities. To get more details from a specific model, options exist for analysis.



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Advanced analysisSeveral useful tools have been developed by the programmers to create a better understanding of the energy flows in the system. Most importantly, a complete list of all converters can be shown withthe corresponding energy flows. For each converter more detailed information is given on its owndetail page. Here all assigned attributes are listed, as well as the results of the calculations based onthe attributes. For example, the production cost per MWh electricity is calculated for each of thepower plant converters, using its own attributes in combination with other attributes, such as thefuel price.

Figure 24: Visualization of converters in relation with energetic use of gas in households

Graph visualizationAnother useful tool is the possibility to create a visualization of a given converter and its relationshipwith its surrounding converters in the energy system. This can especially be helpful to understand

the energy system, as the flow through the whole energy system can be easily followed. In figure 24, the relationship between converters linked with energetic use of gas in households is shown. Thisautomated visualization is similar to the Sankey diagram, albeit rotated 90 degrees clockwise.

The visualization gives a lot of information that helps to understand the energy system, just like theSankey diagram. As a visualization of the entire system would be too large and contain too muchinformation to be easily understood, only converters that are no more than three conversions apartfrom the selected converter are shown. That means for each converter a different visualization iscreated. Each of the blocks represents a converter, with the energy flow expressed in PJ. The arrowsbetween converters show where the energy comes from, with the type of link and the size of theflow. Also the energy carrier is expressed with a number and by the color of the arrow.



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The front-endThe front-end is the part that is visible for all visitors to the website. As was mentioned earlier, thegoal of the model is to give insight into the energy system to as many people as possible and it istherefore made publicly available. With a wide variation of users with different levels of knowledge,it is hard to design an interface that is both comprehensive and easy to use; this could be the subjectof a thesis itself. The description of the interface in this chapter is therefore only to describe theoptions the user has available to change variables in the model, without the discussion of why thesespecific variables are chosen.

IntroductionWhen a user first reaches the website, the model will be introduced and the user will be asked tochoose a country and a future year. Currently, the user can choose between 5 countries and the 12provinces in the Netherlands for the year 2020, 2030, 2040 or 2050. This future year will be the yearthe scenario will be built for. After selecting an area and future year, the user can choose to startwith one of the four main categories;

Policy Demand Costs Supply

When selected, an introductory screen for the category is shown.

Main layout When a category has been chosen the user sees the standard layout, as is shown in figure 26. This

layout is a result from the team ’s efforts to create a design in which the user can see the wholemodel in one page without information overload, to keep a high level of transparency.

In the screen heading (1), a search box can be used to quickly find a specific slider or information inthe model. Also, the scenario can be reset to the start values and saved, or a previously savedscenario can be loaded. The four main categories (2) always remain visible as tabs in the top of thescreen as well. Users can navigate to every category and topic at all times.

Within the main categories, the variables are organized per topic in the left side of the screen (3),and then grouped per type in the middle (4). Per topic, the sliders are shown of the variables that canbe altered. Because a subgroup is chosen three times, the number of variables displayed at once isquite small, usually no more than 5 at the time. Within the categories the layout is kept the same sothe user can keep a clear overview of the model.



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ds

Figure 26: The main elements in the layout of the interface (in June 2010)

At the right of the screen, a chart is displayed where the effect of the user’s choices can be seen (5).The most important indicators of the created scenario are shown in the ‘dashboard’ at the bottom of the screen (6). When users change a variable, the indicators are automatically updated.

The indicators provide information on:

Energy use: The change in primary energy demand; CO2 emissions: Change of CO 2 emissions compared to 1990; Dependence: Percentage of energy that is imported from abroad; Costs: Total annual costs of fuels, electricity production facilities and heating devices; Bio-footprint: Total required land for the production of all the biomass used in the country; Renewables: Percentage of renewable energy used in the total energy use; Policies met: Number of the policies set by the user that are currently met.

To further help the user, each of the sliders contains additional information when clicked. Thisprovides a more detailed definition, assumptions that are made for the slider, the potential impact or

information on the historical development of the value.

4 53

1

6

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4.4 Future development of the ETM

As was mentioned in chapter 2, Quintel has the ambition to extent the ETM to other countries. As aresult of the implementation of the converter concept additional features have become possible aswell. Potential ETM additions that have been made possible by the network structure are:

OptimizationWith the converter concept, the kernel can calculate the results for each change of thevariables. With an optimization script, the model can be used to optimize a defined setof variables to reach a specific goal.

Automated generation of Sankey diagramsA Sankey diagram enables the user to get a quick overview the main flows through thesystem. As all the flows are calculated in the network of converters, a Sankey diagramcould be automatically created for any future scenario the user has created.

Calculation of CO 2 abatement curves of specific technologiesAs the system calculates the CO 2 emission of each of the individual converters, it is

possible to quickly compare alternatives to reduce CO 2 emissions. With the costs of thealternatives defined, it becomes possible to create CO 2 abatement curves for eachscenario.

Coupling of the ETM with other modelsAt this moment, the sliders of the user interface are used for all input variables of themodel. The converter concept can be used to link the ETM with the output of othermodels. Also, the output of the ETM can be as input for other models.



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Chapter 5: Conclusions, recommendations and discussion

The Energy Transition Model (ETM) was developed to create insight in the relationships in the energysystem, and can be used to explore future energy scenarios. The first version of the model wasdeveloped in Excel, but could not fulfill Quintel’s requireme nts. In the process to develop a newversion of the model online, two attempts were unsuccessful in meeting the transparency andflexibility requirements. To find a method that could be used to further develop the ETM, thefollowing research question was constructed:

What method should be used for the further development of the ETM to fulfill Quintel’s

requirements?

Because existing methods appear not suitable for the combined development of model andsoftware, the OOM method is proposed for the further development of the ETM. Most importantreasons to suggest the OOM method are because the method makes full use of the properties of thesoftware, and provides insight in the relationship between model and software development.

The reasoning behind this conclusion is further detailed in section 5.1. Recommendations for Quinteland other model developers are described in section 5.2 and section 5.3 further discusses the resultsof the thesis.

5.1 Conclusions

Two sub questions have been formulated to answer the main question in this thesis. The first sub-

question was:

What are Quintel’s requirements for the ETM?

Based on the vision and mission of Quintel, an overview was created of the broader goals of Quintelto be able to determine specific requirements for the ETM. On a high level, the requirements can besummarized as follows:

Accessibility The model needs to be easily accessible to educate as many people as possible,preferably over the internet and without requirements of plug-ins or downloads which

can withhold people to use it. Transparency

The model needs to be very transparent so the model can be easily validated during thedevelopment. A high level of transparency also increases the credibility of the model,and improves the educational function of the model.

Flexibility A high level of flexibility is required because the model will be continuously underdevelopment. This includes higher levels of modeling detail, as well as expansion toinclude other countries and entities (such as municipalities, harbors, etc.).



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Because these requirements could not be met by a model in Excel, Quintel decided to develop anonline model. The development of an online model that is available over the internet provides thepossibility meet the accessibility requirement. Technical aspects of the website, such as platformsand browsers compatibility and performance issues may still affect the accessibility of the model, butare outside the scope of this thesis. Transparency and flexibility do not automatically increase whenan online model is used. These requirements are the focus of this thesis, as they need to be a resultof the model design.

In the first approach to convert the existing Excel model to an online version is attempted to describeall relationships between the input and output of the model in a single equation. In the secondapproach, the relationships as defined in Excel were ‘ported’ to Ruby software. In both attempts wasquickly realized that the requirements could not be met with the chosen approach. This indicatesthat the choice for transparent and flexible software such as Ruby does not automatically result in atransparent and flexible model. To look for a method that can be used for the development of atransparent and flexible model, the second question was:

What methods are available for the further development of the ETM?

The development of the ETM appears to be a combination of both model development and softwaredevelopment. As literature on this combined development could not be found, desk research intoexisting methods for separate model development and software development was performed. Thisresulted in an overview of methods with useful features, which have all been used in thedevelopment of the suggested method.

Although some methods do have useful features for the development of the ETM, no method can

directly be used for the ETM development because none of the existing methods combine systemthinking with the object oriented approach. The first attempts have revealed the need for a methodthat combines the two underlying principles. Practice and research with the purpose to create such amethod have supplied three lessons:

Practice learned that an integrated approach of model and software development isessential. Attempts to develop software separately from the model were unsuccessful inleading to a product that could meet the requirements.

Research into existing development methods provided a structure for both model andsoftware development that starts with the basic steps of requirements and planning and

concludes with validation. Also, it shows that using multiple iterations is essential for bothmodel and software development to provide feedback early in the process for adjustingrequirements and continuous verification of the results. Combination of the methods intoone supplies insight in the relationship between process activities, easing processmanagement.

Literature on existing energy models has shown the importance of verification. Criticism onthe PRIMES model [Apsimon, 2010] has also shown that transparency of the model must alsobe high enough that users can verify the models data and calculations. The possibility forusers to verify the results themselves strengthens the models credibility.

From these lessons, the OOM method was developed. This method is applicable to combined modeland software developments that use object oriented (OO) software. Because both developments use



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the same method, the requirements and planning is determined for the project as a whole. The OOMmethod draws out the steps in the design of model and software as shown in figure 27.

Because of the combined planning phase for model and software development, the OOM methodgives insight in the integrated process, simplifying communication between stakeholders in the

process. Because of the iterative character of the method, each modification to the model orsoftware is verified in the development process.

Figure 27: Steps in both the model and software iterations of the OOM method

With the OOM method as the suggested method for the development of the ETM, the last researchquestion was:

Can the suggested method for the further ETM development fulfill Quintel’s requirements?

So far, the product seems to fulfill Quintel’s requirements. This however does not mean that thisresult can be attributed to the OOM method, or that the requirements will continue to be met in thefurther development. Therefore, the resulting product and process will have to be discussed in moredetail.

The productThe OOM method has resulted in a product which fulfills Quintel’s requirements. This success can be

ascribed to the conception of the converter concept, which is a network based structure that usessimple input-output calculations founded on the law of conservation of energy. This has resulted in amodel that is both flexible and transparent.

The high level of flexibility is a result of the modularity of the converters structured in the network.As each of the converters is defined as an object in the software, the attributes can easily bemodified. Because the same calculation method is used in the complete network of converters,changes or additions to the model can be made without having to change the calculation method.

The model has a high level of transparency because the results of all the calculation steps can be

queried from the converter attributes. This transparency also contributes to the credibility of themodel, as unexpected outcomes can be traced back to the input variables step by step.



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The development processThe OOM method contributes to the final product through improvement of the developmentprocess. The model requirements have been met, but this does not necessarily mean that this is aresult of the OOM method; the model requirements may have been met using another method aswell. Therefore it is at least as important to discuss the effects of the OOM method on the ETMdevelopment process.The way of thinking that is introduced by the OOM method has shaped the ETM’s developmentprocess in such a way that the further software development will not compromise the modelrequirements.

5.2 Recommendations

Recommendations are split into recommendations for Quintel and recommendations for developersinvolved in other model developments.

Recommendations for Quintel 1. Refrain from using exceptions

The converter based concept has resulted in a model that fulfills the requirements. Still,exceptions in the standardized calculated method or structure can endanger bothtransparency and flexibility. Exceptions (such as been made for furnaces providing bothspace heating and hot water) must therefore be prevented as much as possible. Sourceswhich supply data in a different format than the model ’s should always be formalized tofit the calculation method and structure. Although the energy system will workaccording to the same principles everywhere, countries can use different definitions for

energy use. To validate the model by comparing its results with the data source, queriesshould be altered to reproduce data according to the countries’ definitions, not themodel structure.

2. If necessary, the network structure can be reconsidered to provide the most realisticrepresentation of the energy system

As the network structure has originally been designed to reproduce data from the Excelmodel, which uses Dutch definitions for energy use, it may not be the most optimalstructure for other countries. For example, the network structure has been designed tocalculate CHP use as defined in the Netherlands. As Germany uses a different definition,the input now has to be transformed to fit the chosen structure. It is suggested toreconsider the basic network structure to prevent transparency problems when is notedthat other countries use different definitions for energy use.

3. Use the validation and verification options as much as possibleAlmost all literature presses the importance of validation and verification, also after theinitial development process. Criticism on the PRIMES model [Apsimon, 2010] shows thatverification and validation is not only important for the developers, but also affects theuser’s confidence in the model. The credibility of the model would increase if theverification tools are also available for the model users, so they can verify the numbersare being calculated correctly.



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Recommendations for developers of other models 1. Use OO software with a network structure

Based on the development of the ETM it can be recommended to use the strengths of OOP when developing software for a model. A prerequisite is that OO software needs tobe used, but as most modern software supports OOP this should not be a problem.When a network structure can be created, the combination with OO software canprovide useful advantages. With links defined between objects, a directed graph iscreated. When attributes are assigned to objects and links in the graph, the standardizedcalculation can be made that calculates the flow through the links of the structuredobjects. In the ETM, this approach has led to a highly transparent and flexible model.

2. Use the OOM development method for combined model-software developmentsBoth model and software development methods already exist. When separate methodsare used with incompatible basic principles, the process result may not meet therequirements. To align the basic design principles the OOM method is developed. This

also results in better insight in the relationship between the parts of the design. Whenthe OOM method is used a network structure will be created, which has the advantagesdescribed above.

3. Document validationIndependent of the used development method (if any) is recommended to createproper documentation of the model validation process. Literature on modeling showsthat validation is often insufficiently documented. The importance of validation itself isevident, but a proper documentation of this process can contribute to the credibility of the model.

When developing a model using the OOM method or a network structure, experiences from thedevelopment of the ETM for Quintel may be useful for developers of other models as well, as it is thefirst model in which the OOM method is used. As stated earlier, the fact that ETM currently satisfiesQuintel’s requirements does not validate t he method. Therefore it can be useful to discuss the valueof the OOM method.

5.3 Discussion

At the start of the project, the ETM already existed in Excel. For this design, a lot of modeling choiceshad already been made. Also for the online model, several design choices had already been made byQuintel before the research has started, such as the choice for Ruby on Rails. Although Quintelprovided a lot of freedom in design options for the project, this situation limited the developmentspace.

Beginning of the research projectThe first month of the project was used to analyze the Excel model. A structured plan of action forthe online development was not conceived, mainly because it was not clear what problems currentlyexisted and which could be expected during the development of the online model.To start the online development, Quintel suggested an approach. As no alternative approaches were

available, the suggested approach was followed. With hindsight, it may have been better to take



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time to consider possible approaches. Given the situation the initiation of the suggested approach isunderstandable, as Quintel had experience in both modeling and software development.

Execution of the research projectThe OOM method was developed simultaneously with the ETM, where the developments of the

OOM method and the ETM development have supported each other. Some of the pitfalls in the ETMdevelopment have contributed to the development of the OOM method, and some of the lessonsfrom the OOM method have contributed in the ETM development. It is important to understand thatthe way of thinking introduced by the OOM method has already provided useful insight for the ETMdevelopment, but the method may require further analysis and sophistication to improve its practicalrelevance.

Result of the research projectThe OOM method presented in this thesis has been very helpful in the development of the ETM. If the OOM method will equally helpful in similar development processes is unclear, as the OOMmethod was not developed until after the start of the ETM development. With two unsuccessfulattempts, the ETM development has shown that it can be hard to develop software for a specificmodel. Here, the fact that the first two approaches were unsuccessful has led to useful insight thatwas used in the further development. Therefore, it is questionable if the OOM method would haveled to a successful product if used as the first approach. Additional research is required to know if theuse of the OOM-method can also help the start of the development process.

Application of the OOM methodBecause the modeling steps in the OOM method are similar to the steps in the existing model cycle,

the added value of the method comes from the integration with the software development method,by using the network structure. The OOM method can be used in the combined development of models in which a model structure can be defined, and object oriented software is used. This isillustrated in figure 28, where the ETM development can be positioned in the dark overlap.

Figure 28: Application of the OOM method



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Value of the OOM methodTo evaluate the effect of the OOM method properly, developments of models using the OOMmethod can be compared with developments of similar models that have not used the OOMmethod.

Model development in OO software is rarely described in literature. Furthermore, the chance is smallthat the OOM method will be used in one of these developments in the near future. Therefore, it isunlikely that the added value of the OOM method will be determined this way in the short term. Thevalue of the key element in the OOM method, the network structure, may be easier to validate.First, the importance of this network structure is discussed.

Using the strengths of OO softwareThis thesis describes a model development method that makes use of the properties of OO softwareby creating a network structure for a model. Although most of the programming languages that arecurrently being used support OOP, this does not mean that models developed in this language

automatically make use of the advantages, as is shown by the two unsuccessful approaches.

In OO software, relationships in the model can still be described using normal references. Thedifference is that when a network structure is used, the relationships (or links) between the objectsin the model are formalized in a separate class, such as the Links class in the ETM. These links makesit possible that a standardized calculation method can be used for the complete network structure.

Defining the network structureThe network structure in OO software is defined in the links between the objects. In the case of theETM, links represent a flow of energy between objects that act as energy converters. The law of conservation of energy can be used for the standardized calculation method to compute the flowthrough the network of converters. This network structure has proven to work well for energy flowsin the ETM, but the same principle may be useful in other models. As long as the relationships can beformalized in a network, a network structure can be used. Because this is the case in manymathematical models, a network structure can be applied in many models that are being developedin OO software. To demonstrate a network structure can be defined for various types of models,some examples are given:

Distribution models

In distribution models, the flow of a product can be simulated through a distributionnetwork such as oil through pipelines, or blood through a vascular system. In theseexamples, objects can represent storage or nodes. Attributes for both objects and linkscan used to define properties such as capacity, temperature or length.

(Macro) Economic modelsIn (macro) economic models, the flow of money can be modeled through a network of actors, where links can define transactions. Actors can be assigned attributes such asprice sensitivity, customer type and loyalty.

Resource modelsIn resource models usage of scarce resources can be modeled a network, such aspalladium use in technologies, or water use in a dry country. Here, possible objects areusers of the resource and links can define the resource distribution. Attributes like



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desired volume, quality requirement, method of transport and/or quality can be definedto both objects and links.

Transportation modelsIn transportation models a network structure can be used to simulate transport, forpeople or goods between junctions or decision moments. Possible attributes includetransport cost, level of comfort and/or speed.

Environmental modelsIn environmental models, numerous networks can be considered. For example, thecarbon cycle or pollution of mercury can be modeled in a structure of bodies thatcontain the substance, with links that describes the exchange. Possible attributes areconcentration or volume for objects, and exchange rate for links.

Process modelsIn process models, a network structure of actions can be created, where links define thepossibilities for subsequent actions. By defining attributes such as duration, occurrence,

costs or knowledge required, a network structure can be used to calculate effects of changes in these variables.

Application of the network structure is most obvious for models involving infrastructure, but it can beapplied in many more cases, as shown in previous examples. As the use of OO software was aprerequisite, the network structure will only work in this type of software.



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Chapter 6: References

6.1 Interviews

Jacobs, W., 2010, an interview on the future ETM development on August 29 of 2010 Schoenmakers, D., 2010, an interview on the development process before the start of the

project on May 17 of 2010

6.2 Literature

Apsimon, H., 2010, NIAM proposal to improve transparency of PRIMES , TFIAM presentationat Palais des Nations, Geneva, Switzerland

Balci, O., 1990, Guidelines for successful simulation studies , Proceedings 1990 Winter

Simulation Conference, IEEE, Piscataway, NJ, page 25-32 Balci, O., 1994, Validation, verification and testing techniques throughout the lifetime of a

simulation study , Annals of Operations Research 53 , USA Banks, J., Gerstein, D., Searles, S., 1987, Modeling processes, validation, and verification of

complex simulations: A survey, Methodology and Validation, SCS, San Diego, CA, page 13-18 Boehm, B., 1988, A Spiral Model of Software Development and Enhancement , IEEE Computer,

Vol. 21, No. 5, 1988, pp. 61-72 De Brujin, H., ten Heuvelhof, E., In 't Veld, R., 2005, Process Management: Why Project

Management Fails , Kluwer Academic Publishers Cockburn, A., 2008, Using Both Incremental and Iterative Development . CrossTalk USAF

Software Technology Support Center (STSC) 21, page 27-30 Van Daalen, C., Thissen, W, Verbraeck, A, 1999, Methods for the Modeling and Analysis of

Alternatives, Handbook of Systems engineering and Management , John Wiley & Sons, NewYork, Chapter 26, page 1037-1076

Deeter, C., and Hoffman, A., 1978, Energy Related Mathematical Models , Texas ChristianUniversity, Fort Worth, Texas, USA

Department of Defense (DoD), 2001, Systems Engineering Fundamentals , DefenseAcquisition University Press

Department of Defense (DoD), 2004, Defense Acquisition Guidebook Ch. 4: System of Systems

Engineering , Washington DC Energy Information Administration (AIA), 2010, International Energy Outlook 2010 Gates, R. et al., 2005, Oil shockwave-oil crisis executive simulation , National Commission on

Energy Policy and S ecuring America’s Future Energy Federal Highway Administration (FHWA), 2005, Clarus Concept of Operations , US department

of Transportation, Publication No. FHWA-JPO-05-072 Flood, R., Carson, E., 1988, Dealing with Complexity: an Introduction to the Theory and

Application of Systems Science . Plenum Press, New York Hirsch, R., 2007, Mitigation of maximum world oil production: Shortage scenarios ,

Management Information Services, Inc., Alexandria, VA, USA



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Jakeman, A., Letcher, R., Norton, J., 2006, Ten iterative steps in development and evaluationof environmental models , Environmental Modelling and Software 21, page 602 –614

De Jong, K., Wesseling, C., Karssenberg, D., 2002, Improving the model development cycle by automatic configuration of modelling tools , Proceedings iEMSs 2002, Integrated Assessmentand Decision Support, Lugano, Switzerland

Jordan, M., 1998, Learning in graphical models , Kluwer Academic Publishers Kavrakoglu, I., 1987, Energy models , European Journal of Operational Research 28, page 121-

131 Knepell, P., Arangno, D., 1993, Simulation Validation: A Confidence Assessment Methodology ,

Monograph 3512-04, IEEE Computer Society Press, Los Alamitos, CA Landry, M., Malouin, J., and Oral, M., 1983, Model variation in operations research , European

Journal of Operational Research 14, page 207-220 Larman, C. and Basili, V., 2003, Iterative and Incremental Development: A Brief History , IEEE

Computer Society 36, page 47-56

Luft, G., 2009, Dependence on Middle East energy and its impact on global security , theInstitute for the Analysis of Global Security, Springer Science

McNeil, B., Matear, R., 2008, Southern Ocean acidification: A tipping point at 450-ppmatmospheric CO 2, Proceedings of the National Academy of Sciences 105, 18860-18864.

Nikolic, I., 2009, Co-evolutionary method for modeling large scale socio-technical systemsevolution, thesis, TU Delft

National Technical University of Athens (NTUA), 2010a, The GEM-E3 model: ReferenceManual , EC Programme JOULE

Royce, W., 1970, Managing the development of large software systems , Proceedings of the9th International Conference on Software Engineering 1987, page 328-338

Verschuuren, P., Doorewaard, H., 2003, Het ontwerpen van een onderzoek , Lemma BV,Utrecht

De Vries, L., Correljé, A., Knops, H., 2009, Electricity, Market design and policy choices ,Reader SPM9541, Faculty of Technology, Policy and Management, Delft University of Technology

Washington, W., Knutti, R., Meehl, G., Teng, H., Tebaldi, C., Lawrence, D., Buja, L., Strand, W.,2009, How much climate change can be avoided by mitigation? , Geophysical ResearchLetters 36

Weisfeld, M., 2008, The Object-Oriented Thought Process, Third Edition , Addison-Wesley,

USA

6.3 Websites

Ambler, S., 1998, A Realistic Look at Object-Oriented Reuse¸ http://www.drdobbs.com/184415594 , last visited October 29, 2010

Energy Information Administration (EIA), 2009, International Energy Outlook 2009 ,http://www.eia.doe.gov/oiaf/ieo/ , last visited December 29, 2009

Environmental Protection Agency (EPA), Particulate Matter ,

http://www.epa.gov/oar/particlepollution/ , last visited December 29, 2009

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Appendix I: Excel Slider overview



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Appendix II: Tab 2.i of the Excel model



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Appendix III: The UML-diagram



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Appendix IV: Variations on the modelcycle

Figure 29: The life cycle of a simulation study [Balci, 1990]



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Figure 30: Iterative relationship between model building steps [Jakeman et al., 2006]

Figure 31: Model development cycle with tools used [De Jong et al., 2002]



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Appendix V: Overview of thedevelopment method steps



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Appendix VIII: The Excel model

development of the energy transition model - introduction of the object oriented modeling method

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