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Abstract - The scope of this paper is to present the methodology and results of integrated modelling, simulation and co-optimization of Renewable Power, Gas & Heat Systems & Markets employing PLEXOS® Integrated Energy Model and accounting for all characteristics unique to each “subsystem”. The trend towards a more interrelated energy industry calls for the need of multi-optimization models which will consider simultaneously all systems & markets components, in order to deliver better and more sophisticated Resource Planning capabilities combined with robust economic benefit and strategic development analysis.

Keywords – PLEXOS®; co- optimization; multi- optimization; power-gas-heat integrated model; integrated energy networks; Renewables Integration

Introduction

In the today’s evolving energy world, there is an undeniable uncertainty and complexity in the energy

markets. Market participants are forced to operate into extremely challenging environments

characterized by increasingly uncertain and complex market conditions. Power and gas utilities strive

to realize more value from their transmission and distribution businesses.

At the same time the developments on the natural gas exploration lead to increased dependence of

the power sector on the natural gas generation. This makes the gas-electric network coordination an

emerging challenge for regulators and policy makers, and new strategies of co-optimization of gas-

electric infrastructures are becoming of interest. Additionally there is an increased utilization of other

distributed generation technologies such as co-generation and combined heat and power, which

makes the coupling between electricity, natural gas and district heating energy systems even more

common.

This increasing utilization of gas-fired and other distributed generation, especially co- and tri-

generation, is expected to affect both the technical and economical operation of energy systems. The

conversion between different energy components (i.e. natural gas into electricity and heat)

establishes a coupling of the corresponding power flows resulting in system interactions.

Therefore, given this trend towards a more integrated energy industry, there is a prevailing need for

a single multi-optimization model which will consider simultaneously the power, gas and district heat

market components, in order to deliver better and more sophisticated Resource Planning capabilities

combined with robust economic benefit and strategic development analysis. Inevitably, there is a

need for advanced computational tools that will be able to capture and handle this complexity in the

most efficient manner and to provide viable strategic long-term and operational short-term solutions.

PLEXOS Integrated Energy Model software is such a sophisticated optimization tool that has been

designed and developed to provide this kind of solutions in complex power, gas and heat systems and

markets conditions.

Integrated Renewable Power, Gas & Heat (DH) Systems & Markets Co-ordination and Co-optimization using PLEXOS® Integrated Energy Model

Mrs Peny Panagiotakopoulou, Senior Power Systems Consultant, Energy Exemplar Europe Dr Christos Papadopoulos, Regional Director Europe

Dr

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In this study we will develop an integrated electric- gas- heat network in PLEXOS. This PLEXOS model

is capable of analysing the integrated system while accounting for characteristics unique to each

subsystem, through a straight forward problem formulation.

Aims

The aim of this paper is to present an integrated electric- gas- heat network modelled in PLEXOS. This

PLEXOS model is capable of analysing the integrated system while accounting for characteristics

unique to each subsystem, through a straight forward problem formulation. For this case study a

PLEXOS model using the Power and Gas Features was gradually developed, in order to finally account

for all the different energy components.

Theoretical Background

A. CHP & District Heat Systems

CHP technology is becoming even more popular nowadays, since it can provide a high energy supply

performance, requires less fuel to be consumed and produces less CO2 emissions per MWh. The CHP

plants can nearly double the efficiency of steam power, whereas their total efficiency can be further

improved by integration with gas- turbine plants and/ or the use of gas condensing units.

District heating (DH) is a system for distributing heat generated from one or more sources via a

network of insulated pipes carrying steam or hot water to heat buildings. The heat sources can be

different types of power stations such as industrial processing power plants which generate heat as a

by-product, energy from waste plant, heat-only boiler stations, geothermal or solar. By utilising low

grade heat which otherwise might have been wasted and displacing localised boilers district heating

systems can provide higher efficiencies and improve pollution control.

The deployment of combined heat and power (CHP) plants burning fossil fuels or biomass can be

facilitated by the use of District Heating Networks (DHNs). Such “co-generation” plants are designed

to generate electricity whilst also capturing usable heat that is produced in this process. Their

optimised design means that the overall efficiency of CHP plants can reach in excess of 80% at the

point of use.

The figure below shows a sketch of a CHP plant that supplies heat to a district heating system, chilled

water by means of absorption chillers to a district cooling system as well as electricity to the grid.

In CHP-DH systems there may be situations where there is demand for electricity but insufficient

demand for heat. This can lead to possible alternatives of either closing down the CHP plants with a

subsequent revenue loss from electricity sales or surplus heat rejected to the atmosphere. In order to

avoid such situations, thermal storages are used together with the DHNs, with several examples

existing of such large scale thermal storage systems that have been deployed across Europe (i.e.

Denmark, UK). These systems follow an increasing trend and make heat storage a more and more

widespread technology for performance optimization and increased revenue from electricity sales.

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Figure 1 – Schematic figure of a CHP plant integrated with the district heating and cooling systems3

The storage is charged when heat production is higher than the consumption, and discharged when

heat production is below the consumption, allowing for CHP plants to operate with higher flexibility,

especially when electricity prices are most favourable.

A typical CHP plant’s operation depends on the electricity network for certain periods, i.e. for

purchasing power from the grid during high demand periods / off- peak prices, or for selling surplus

power to the grid during peak hours with highest electricity prices, returning significant economic

benefits. For that reason, and as the system loads (heat and electrical) fluctuate considerably with

time of day/year, mathematical programming; and more specifically Economic Dispatch models, are

used for optimization of the district heating and cooling networks with CHP.

Figure 2 – Thermal store integrated in a DH system4

B. Gas Networks

Given the recent technological advances allowing for access to unconventional sources in shale

formations, coal beds, and sandstone formations, natural gas reserves have dramatically expanded. A

subsequent increase in natural gas production is now a reality followed by supply surplus and lower

prices, which are expected to continue resulting in relatively stable natural gas market conditions. As

a result, natural gas is a new dominant player in the energy markets.

Natural gas-fired turbines are characterized by higher efficiencies, lower capital costs, shorter

installation times, faster start up capabilities and lower CO2 emissions, which makes them an

increasingly widespread option either as baseload, intermediate or peaking units. The electric power

sector is using an increasing percentage of natural gas, and natural gas-fired electric power plants are

expected to continue to increase in importance, projected to account for 60% of capacity additions

between 2010 and 2035.

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Natural gas electricity generation relies on three basic technologies:

- Steam turbine plants, that operate like traditional coal-fuelled power plants where fossil fuel (in

this case natural gas) combustion heats water to create steam. The steam turns a turbine, which runs

a generator to create electricity. These typically have thermal efficiencies of 30 – 35%.

- Combustion turbine plants, which are generally used to meet peak electricity demand. These

operate similarly to jet engines: natural gas is combusted and used to turn the turbine blades and spin

an electrical generator. The typical size is 100 – 400 MW with a thermal efficiency around 35 – 40%.

- Combined cycle plants, which are highly efficient because they combine combustion turbines

and steam turbines; the hot exhaust from a gas-fired combustion turbine is used to create steam to

power a steam turbine. High efficiency combined cycle plants emit less than half the CO2 per

megawatt-hour as coal power plants, and operate with a 50 – 60% thermal efficiency range. A typical

natural gas combined cycle power plant has a heat rate that is about one third lower than for a

combustion turbine or gas-fired steam turbine plant.

Figure 3 – Combustion Turbine

There is an increased recognition that environmental policy and public policy in combination to the

vast shale gas developments will lead to increased reliance of the power sector on natural gas

generation. Gas Electric coordination has emerged as a complex topic for regulators and the gas and

electric sectors to confront along with the electrical system operators with concerns of present and

future potential gas constraints that impact electric system operation and reliability.

New strategies of co-optimization of electric and gas infrastructure are becoming of interest as much

of the natural gas sector growth may likely be driven by resource change and dependency of electrical

sector on pipeline network and gas network operational issue.

The PLEXOS Model

In this case study a PLEXOS Integrated power – gas – heat model has been developed (Figure 4).

The Power feature of PLEXOS has been used in order to set up an electricity demand power node,

ELECTRCITYDem, where all the power plants are located, as well as part of the electricity demand. The

Generator Class of PLEXOS is generic enough so that all types of generation resources i.e. thermal,

hydro, pump storage, wind, solar etc. are modelled under this class and the simulator infers the type

of generator from the data and relationships that are defined on it.

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A RENEWABLES power node has been also set up, where all the renewable resources (wind and solar)

are located. In the same node an Electric Boiler with heat storage facilities as well, can be connected.

Figure 4 – System Topology

The electric boiler is a type of boiler where heat2, i.e. in the form of steam, is generated using electricity

rather than through the combustion of a fuel. Electric boilers can convert electrical energy to heat

with almost 100% efficiency, but because boilers draw power from the grid their true efficiency is a

function of the overall grid production efficiency. Hence electric boilers may be economic as heat

sources at times of very low electricity price, especially if heat can be stored for later use.

An electric boiler can be created in PLEXOS by defining an “anti-generator”, which is a generator whose

generation acts as a load on the system rather than proving power. A heat load is directly defined on

the electric boiler, which represents the waste heat that must be extracted for exogenous loads.

The total demand for electricity has been defined on a regional level in PLEXOS and then split between

the two nodes, using a load participation factor, as shown in figure 5.

Figure 5 – Electricity Demand (MW) by node

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The gas market is modelled using the dedicated Gas Feature of PLEXOS, which include the gas nodes,

gas fields for the fuel production, gas storages where the gas can be injected and extracted, and the

gas pipelines for gas transportation.

In PLEXOS the gas and electric markets integrate at the gas nodes. A generator can be attached to a

gas node through the Gas Node and the Fuel Gas Node memberships. Defining both these

memberships instructs the simulator that the Generator is physically supplied with Fuel from the Gas

Node and therefore the Gas Demand will be driven by the generator’s Heat Rate and Generation

values.

Furthermore, there is the option to define additional external demand for gas, as it has been done in

this model, where Industrial and Domestic gas demands have been added using external datafiles of

hourly demand profiles.

Figure 6 – Gas Demand driven by GAS1 generator Heat Rate function

The District Heating network is approximated using the Gas Features of PLEXOS in combination with

the Constraints Class.

The demand for heat is modelled using the Gas Demand objects defined with external hourly profiles,

as shown in figure 8.

Figure 7 – External Domestic & Industrial Gas Demand (TJ)

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Figure 8 – Hourly Heat Demand (line stacked)

The District Heating network is modelled as a closed loop using the Gas Pipeline objects, and allowing

the HEATdist1, HEATdist2 and HEATdist3 pipelines being one-direction only by defining the pipeline

[Max Flow Back]= 0.

The CHP type generators, named as HEAT1, HEAT2 and HEAT3 are associated with the heat network

indirectly through the use of custom constraints. The heat production from the HEAT1 Field is set to

be equal to 0.1% of the HEAT1 generator heat production, and a similar constraint is created for the

other two CHP generators/ Fields.

The CHP plants HEAT2 and HEAT3 are assumed to be a back-pressure and an extraction turbine

accordingly. They are modelled in such a way so that the waste heat from the back-pressure turbine

is passed to the extraction turbine, using the [Heat Input/ Output] collection of PLEXOS. However,

instead of defining directly a [Heat Load] on the extraction turbine, the demand is driven by the

external heat demands defined through the District Heating network.

The amount of heat transferred from the back-pressure to the extraction turbine is equal to the fuel

input of the back-pressure less the electric output of the back-pressure (at notional maximum

efficiency). The waste heat can then be passed through a boiler before using the extraction turbine.

In PLEXOS the boiler is modelled as a component of the extraction turbine. The extraction turbine will

produce electric output from the steam output of the boiler according to its heat rate i.e. the steam

input acts just like a ‘free’ fuel source.

The Storage Class of the Gas model is used in order to set up a HEAT storage where the excess heat

can be stored in periods of higher heat production compared to consumption, and heat can be

released when the production is below the required amounts of demand. This allows the CHP plants

to generate in a more flexible manner and the system to benefit from favourable electricity prices.

Figure 9 – Heat recovery steam turbine in PLEXOS

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Problem Formulation

When defining such a network in PLEXOS the primary optimization is to find the least cost solution

given all the system constraints. When the mathematical problem formulated involves multiple

mutually exclusive opportunities, the optimal solution reported should take into account all of these

components and a co-optimization problem is solved. Co-optimization applies where optimal

decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

Once set-up, co-optimization is a natural outcome of the PLEXOS solution. In the current case study

there are three different markets considered; power, gas and heat; and the problem will be

automatically configured as a co-optimization problem where the optimal solution has to be found

considering all the mutually exclusive opportunities.

The Objective function is formulated as:

Minimize{Total system cost} = Electric production cost + Electric Demand Shortage Cost + Gas

Production cost + Natural Gas Demand Shortage Cost + Heat Production Cost + Heat Demand Shortage

Cost + Transmission Wheeling Cost + Pipeline Gas Flow Cost + Pipeline Heat Flow Cost

Subject To{System constraints}:

• [Electric Production] + [Electric Shortage] = [Electric

Demand] + [Electric Losses]

• [Electricity Transmission Constraints]

• [Electric Production] feasible

• [Gas Production] + [Gas Demand Shortage] = [Gas Demand]

+ [Gas Generator Demand]

• [Gas Pipeline Constraints]

• [Gas Production] feasible

• [Heat Production] + [Heat Demand Shortage] = [Heat

Demand] + [Heat Generator Demand]

• [Heat Pipeline Constraints]

• [Heat Production] feasible

• [Other Constraints]

Scenarios Modelled & Results analysis

We have looked at two different scenarios; a base case, where an electric boiler was not included in

the problem (case A) and one with an electric boiler in place (case B). The efficiency of the electric

boiler was assumed to be 100%.

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The unit commitment and economic dispatch of the plants is based on the marginal cost concept

(nodal load settlement method). The renewables (WIND1, WIND2, SOLAR) are the least cost

generators and that explains the fact that WIND2 is mostly used compared to the other generators in

order to meet the whole electricity demand in the least cost effective way.

The total generation as well as the dispatch of each plant changes, depending on whether the electric

boiler is used or not (Figures 10 & 11).

Figure 10 – Generation Stack curve (Case A)

Figure 11 – Generation Stack curve (Case B)

When the electric boiler is included in the system topology, the reported electricity demand on the

RENEWABLES node changes compared to the base case, since the electric boiler is defined as an “anti-

generator” (extra load). This subsequently affects the output of the wind and solar generators that

have to account for the changing demand, whereas the total MW output from the conventional plants

remains the same.

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Figure 12 – Electricity Node ‘RENEWABLES’ reported Load

The Gas Field GASProd1, located at the gas node GASProd, has to produce in order to meet the gas

demand from both the external sources (industrial & domestic) and the heat rate driven demand from

the gas generator.

Figure 11 – Gas Field Production

The total gas demands can be reported on each gas node separately as shown in figure 12. These

demands remain unchanged whether the electric boiler is in place or not.

Figure 12 – Total Gas Demand

The Heat demands on the other hand, should be covered by the Heat production as well as the Heat

Storage facility (figure 13). The Heat Pipelines are also used in order to transfer the heat from the

production/storage points to where it is required.

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Electric Boilers are usually economic as heat sources at times of very low electric price, especially if

the heat can be stored for later use. In Case B the Electric Boiler [Heat Production] increases when

electricity price decreases (Figure 14).

Figure 13 – Heat Production (Heat Fields Production & Heat Storage Net Withdrawal)

Figure 14 – Electric Boiler Heat Production in relation to Electricity Price

Figure 15 – Electricity Nodes Price (Case A & B)

This integrated power-gas-heat model is a multi-optimization problem which considers all the

different energy components and their constraints simultaneusly. PLEXOS will report one unique price

for the Region modelled taking into account all the constraints, but it will also report the shadow price

of each component individually. We can therefore obtain the Gas Nodes [Shadow Prices] as well as

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the Heat Nodes [Shadow Prices], in addition to the Electricity Nodes [Shadow Prices] (Figures 15 &

16).

Figure 16 – Gas & Heat Nodes Shadow Price

Conclusion

Creating integrated electric-gas-heat networks in PLEXOS is straight forward and can be easily done

using the dedicated features for Power and Gas modelling. The link between the different energy type

networks, can be defined using the proper type of memberships between key objects in PLEXOS

interface.

In this case study a short term multi-optimization problem was solved, taking into account the

individual techno-economic characteristics of each energy subnetwork, and analysing the system as a

whole integrated (NEXUS) network in terms of production costs.

The interactions between the different energy components were captured, as for example the relation

between heat production and storages’ charge /discharge periods, CHP plants operation during

periods of more favourable electricity prices, gas demand driven by the power network, etc.

The same network topology can be easily modelled for long term period studies, using the LT Plan

capacity expansion phase of PLEXOS, in order to assess the type, time and location of new

investments. The PLEXOS Forecasting tool can be used in order to create power, gas and heat demand

profiles for i.e. 20-30 years ahead, whereas the power, gas and heat energy components can be

modelled as expansion candidates using the appropriate expansion properties of PLEXOS (Max Units

Built, Built Cost, Economic Life, WACC, etc.). Therefore PLEXOS can be a powerful and robust

optimization tool for demand planning, strategic investment and operation decisions in the era of

integrated energy systems.

References

[1] “Article: Combined Heat and Power”, PLEXOS Wiki

[2] “Article: Heat Storage”, PLEXOS Wiki

[3] “The potential for thermal storage to reduce the overall carbon emissions from district heating

systems”, The Tyndall Centre, Tyndall Manchester

[4] “Economic and Environmental Benefits of CHP-based District Heating Systems in Sweden”,

Linköping Institute of Technology