thesis on hybrid renewable energy system for condo developments

ARBAMICH UNIVERSITYSCHOOL OF POST GRADUATE STUDIES

Thesis Presentation On

Design, Modeling and Analysis of Hybrid Biomass-

Photovoltaic system

for Condominium Developments

A case study of Koye Feche condominium site

On

By:- Fasil Ayele Advisor: Prof. Sudesh Parkash Sabberwa

∗ To the professors and AMU community

∗ To my Advisor

∗ My Friends

∗ My wife

Acknowledgement

∗ My wife

∗ My parents

∗ Residents of Semit condominium who took part in the work

∗ Semit waste collection micro enterprise (Abu, yadeand Adinew)

MSc. Thesis Presentation By:- Fasil Ayele Advisor: Prof. Sudesh Parkash Sabberwa Arbaminch University School Of Post Graduates

1. Introduction

2. Load profile

3. Renewable Resources

4. Design & Model

5. Simulation and Results

Presentation outline

5. Simulation and Results

6. Conclusion

7. Recommendation

8. References


∗ Motivation

∗ Aim of work

∗ Scope of study

∗ Case study site

Introduction

∗ Case study site

∗ Significance of the study


∗ Supplying Electricity to condominium developments is a big challenge to the Government

∗ Bringing new culture of distributed generation to the Ethiopian Power Industry

∗ Diversifying Hydro dominated power sector

Motivation

∗ Diversifying Hydro dominated power sector

∗ Making most out of our resources


∗ Develop a load profile for condominium developments

∗ Study available resources and their potential energy capacity

Aim of work

capacity

∗ Design, model and analyze possible Biomass-PV hybrid system for condominium developments

∗ Indicate available alternative to the problem


∗ Statistical study of the load profile from populated condominium developments

∗ Statistical study of the waste generation

∗ Rooftop PV generation potential simulation using PVWatts

Scope of study

PVWatts

∗ Simulate the optimum model using Homer


∗ The site we study under this Research is called Koye Feche

Case Study Site


∗ when finalized is expected to host 200,000 peoples

∗ The koye Feche site will have 425 G+7 buildings and 671 G+4 which sum up to be a total of 1096 buildings hosting 50,000 houses. All G+7 buildings will have 64

Case Study Site

hosting 50,000 houses. All G+7 buildings will have 64 houses and G+4from 35 to 40 houses.


∗ It is the biggest site and is expected to host around 200000 peoples whose demand for electric power will be a real challenge

∗ It is construction is being started and necessary changes in the construction can be made to implement the suggested

Case Study SiteWhy Koye Feche?

the construction can be made to implement the suggested project

∗ It is a big load and can attract the interest of power producing investors

∗ The site has enough land in the surrounding for efficient biomass bi-producing businesses


Case Study Site


∗ Indicate an alternative (help)

∗ Attract power producing Investors

Significance of the study


∗ Literature Review

∗ Methodology and procedure

∗ Load Profile curves

Load Profile


∗ There are two general categories of techniques available to model residential load demand:

∗ top-down ∗ use estimates of total residential sector energy consumption, together with other pertinent macro variables,

to attribute energy consumption to characteristics of the housing sector

∗ bottom up models

Load profiling

∗ bottom up models∗ identify the contribution of each end-use towards the aggregate energy consumption of the residential

sector

∗ Standard vs real load profile

∗ Because of a more realistic result with bottom up approaches, load profiling in this work make use of same


∗ models calculate the energy consumption of an individual or group of households and extrapolate the results to a region or nation

∗ aggregate result is generally accomplished by using a weight for each modeled house or group of houses based on its representation of the sector

Load profilingBottom up Approach

on its representation of the sector

∗ Do not rely on historical Data

∗ Common input data to bottom-up models include dwelling characteristics (e.g., size and layout, building materials, and appliances’ characteristics), weather conditions, household occupant behavior and related use of appliances, lighting use, and characteristics of heating, ventilation, and air conditioning (HVAC) systems.


∗ Bottom up models∗ In 1994 Capasso et al. propose a model forevaluating the

impact of demand side management on residential customers∗ Monte Carlo method is used to capture the relationship

between residential demand and the psychological and behavioral factors typical of the household occupants

∗ Richardson et al. introduce a Markov-chain technique to generate synthetic active occupancy patterns, based upon

Load profilingBottom up Approach

∗ Richardson et al. introduce a Markov-chain technique to generate synthetic active occupancy patterns, based upon survey data of people’s time-use in the United Kingdom.

∗ The model selected for this work is “A Highly Resolved Modeling Technique to Simulate Residential Power Demand” this model is selected because the inputs to the model can be made available with statistical load survey studies than the above models which use synthesized constants from prior studies.


Load profilingA Highly Resolved Modeling Technique to Simulate Residential Power

Demand


∗ Fantu Guta, Abebe Damte, and Tadele Ferede have studied and developed a model for the consumption pattern of a house hold. The model is a general model for households’ electricity demand in Ethiopia and specified as follows:

Load profilingRelated works

specified as follows:

Kwh =f (Popultion, Income, Price / Kwh, Priceof Oil, urban)

∗ The other available load profile pattern which is still done in a top down model is the one released by the Ethiopian Electric power Corporation in its load forecast report, which has assumed intensive use of Electric appliances by households


Load profilingRelated works


∗ Model Selected

∗ A Highly Resolved Modeling Technique to Simulate Residential Power Demand

Methodology & ProcedureLoad profiling

∗ Input variables to the model are identified

∗ Profile of cold devices

∗ Profile of HVAC devices

∗ Profiles of Living activities

∗ Profile of lighting

∗ Profile of Fixed energy use


∗ Data collection is made∗ Stage one- Building Energy survey

∗ Electrical Appliance ownership data∗ Average Electrical rating of devices determined∗ Sample size selection –creative survey systems sample size calculator with

95% confidence level and 15% confidence Interval∗ Sample size =40∗ Questionnaire Used


∗ Questionnaire Used

∗ Stage two- Activity pattern Survey∗ Activities which are identified to represent the electrical Energy

consumption of the residents∗ Cooking∗ Enjera Baking∗ Automatic washing∗ Leisure

∗ Sample selection∗ Size 9 households (3 of Each category)∗ Questionnaire used


∗ Wcold generated

∗ Average rating of cold devices 433.8W

∗ Atypical cold appliance works 27% of the time

∗ The cold appliances consumption is simulated using a Bernoulli distribution


Bernoulli distribution


∗ WHVAC generated=0


∗ W generated=0


∗ Wact generated

∗ activity pattern for each household member is generated from the activity pattern of a household collected from the site and then this pattern is converted into Wact by using power conversion factors associated with each activity. In this work a least-squares linear regression model is used to


this work a least-squares linear regression model is used to estimate these parameters. The coefficients are coined to fit a measured pattern

∗ Cooking-1225 W

∗ Enjera Baking-1200W

∗ Automatic washing-3825W

∗ Leisure-200W



WACT-single bed room



WACT-double bed room



WACT-Three bed room


∗ WLight generated

∗ For single bed =95W

∗ For Double Bed =161.33W

∗ For three bed room =243W



∗ WFIX generated



Load profilingHourly Demand Curve-single bed room


Load profilingHourly Demand Curve-double bed room


Load profilingHourly Demand Curve-three bed room


Load profilingTotal Hourly Demand Curve


Homer Load Synthesis




∗ Energy potential

Renewable Resource Study




∗ Energy potential

Resource StudyBiomass


∗ Local Biomass Resource-waste∗ Manucipal solid waste

∗ Human waste

∗ Waste in Addis Ababa-Related works

∗ The contribution to the total generation of waste by


Literature Review

∗ The contribution to the total generation of waste by the different sources is estimated to be ∗ 76% for households,

∗ 18% for commercial, institutional and industrial sources, and

∗ 6% from streets and public areas.

∗ With 80%collection potential of municipality 550t/day of MSW is collected in Addis


∗ Waste Generation Data for Addis


Literature Review


∗ Statistical survey is carried out for Semit condominium block sector 290-296

∗ The waste collecting Association collects the waste twice a week every Tuesday and Thursday. We have sorted and measured the waste collected from the condominium six blocks for a month or eight times in June and July.


Biomass Statistical survey

blocks for a month or eight times in June and July.

∗ The six blocks have two types of buildings. The first is the one with 30 homes while the second type has 20 homes. Each type has three buildings among the six. And the site hosts 150 peoples according to our survey door to door. Age difference and sex are not given special attention in the survey. Every member of the family is counted despite age and sex as an identical element




Vegetable

Paper

Rubber/

Plastic

Wood

45.9 59 43.7 88.6 44 91.3 44.1 73.2

11.25 15.6 12 10.3 16.2 16 13.5 10.8

17.55 20 20 5 9 18 14 5.9

1.35 0 0.65 10.7 0 3.2 3.9 6

1st 2nd 3rd 4th 5th 6th 7th 8th


Bone

Textile

Metals

Glass

Combusti

ble leaves

Non-

combustib

le stone

All fine

1.35 0 0.65 10.7 0 3.2 3.9 6

4.95 9.6 4.92 11 8.5 2.6 1.8 3.4

10.8 16 12 15.8 3 0.05 0.86 2.8

8.55 2.4 0.25 0 0 1 1.4 18

2.25 0.8 0 0 0.3 0 0.2 2.6

67.95 64 42 66.5 64 67 39 42

6.75 9.2 3 0 0 0 3.9 24

270 408 310 382 256 374 221.75 301

447.3 604.6 448.52 589.9 401 573.15 344.41 489.7

∗ The per capita solid waste generation is according to this survey is 0.217kg/cap/day. We have selected this value to calculate the solid waste generation potential of the project site Koye Feche. This per capita waste generation is taken because it’s found to be in the range



generation is taken because it’s found to be in the range of prior studies which is in the range 0.17-0.24kg/cap/day.

∗ Therefore the site is going to generate a daily total solid waste of 200,000cap * 0.217kg/cap/day =43,400kg/day =43.4 ton/day.


∗ In this research we did not take any data to understand the per capita generation of human waste. This is because culturally it’s unacceptable for the society to participate in such a study. Therefore we believe the data can from literature can be extended to estimate


Human waste

data can from literature can be extended to estimate the human waste generation of the site under research.

∗ healthy man generates a human waste equivalent to 0.35kg/cap/day. Therefore the Koye Feche site has a potential of generating human waste equivalent to 70,000kg/day=70tons/day


∗ In order to understand the variability associated with PV potential, a number of simulation tools have been created.

∗ Some of these specifically complement the solar

Resource StudyRooftop PV

Literature Review

∗ Some of these specifically complement the solar energy GIS models, others use interpolated solar radiation surface datasets

∗ The GIS model is the best model available to calculate the net usable area of the roof top and uses elevation data for calculating abstractions tools with respective models that can be mentioned for reference are ArcGIS, r.SUN . But these models need DEM which needs remote sensing data


∗ The best available tool that can be used here is PVWattswhich used surface solar dataset collected at ground station in the Bole airport in 10Km range from the site of study.

∗ NREL's PVWatts® Calculator is a web application


Literature Review

∗ NREL's PVWatts® Calculator is a web application developed by the National Renewable Energy Laboratory (NREL) that estimates the electricity production of a grid-connected roof- or ground-mounted photovoltaic system based on a few simple inputs. To use the calculator, you provide information about the system's location, basic design parameters, and system economics. PVWatts® calculates estimated values for the system's annual and monthly electricity production, and for the monetary value of the electricity.


∗ We know the housing development project has 425 G+7 buildings which imply that we have a net roof top area available for mounting the PV panels equal to 404,285.5m2.


404,285.5m2.

∗ Sattelite analysis of PVWatts area calculator tool revealed that given different shapes of G+4 buildings their Average area is calculated to be 341m2. There are 671 G+4 buildings on the site. Therefore we have a net usable area of 228811 m2. Total roof top area measured in a horizontal projection is therefore found to be 633,096 m2.


∗ Simulation result for sample roofs returned the following



∗ Therefore, the Potential Energy that can be generated installing a PV system in the site of study can be calculated on average Values of the sample simulation using PVWatts.


simulation using PVWatts.

∗ The total PV Energy potential thus is

Total PV Energy = Net roof top area * average energy density =633096m2*237.7 KWh/m2/year=15,048,6919.2KWh/year

∗ The average value of the per day Energy potential of the area is 483,488.85 KWh/day


∗ Biomass Plant

∗ PV plant

Design & Model


∗ Design Basis∗ The load profile studied above is constant throughout week days and

months of the year.∗ The average load which is considered as base load is to be supplied

by biomass plant.∗ The PV system is designed to meet the extra load which we

considered as peak load here.

Design And model

considered as peak load here.∗ We cannot implement highly expensive storage system so the system

designed considers grid connection.∗ The PV facility will be installed on the rooftops of the residential

buildings.∗ Considering the consumption record of the biomass fueled power

plants the local resource may fall far below the requirement. Other biomass supply chains will be available as an alternative.


∗ Biochemical power plant Design

∗ The total size of the AD is thus calculated to be

Design and model

∗ The total size of the AD is thus calculated to be

13125m3


Biomass Anaerobic

Digester

Plant

Biogas

Engine

Generat

or

AC 3-Phase

∗ AD plant Engine selection Design

∗ The total size of the Internal combustion Engine-

Design and model

∗ The total size of the Internal combustion Engine-

generator is thus calculated to be 100KW


Biomass Anaerobic

Digester

Plant

Biogas

Engine

Generat

or

AC 3-Phase

∗ Thermo chemical plant design

∗ Originally we have taken the ground to generate energy to supply load equivalent to 623,885.00Kwh. The total biomass power plant is expected to cover 623885 KWh.

∗ We have already managed to cover 2354.5KWh with our

Design and model

∗ We have already managed to cover 2354.5KWh with our anaerobic digester fueled power plant. Therefore, with the thermo chemical plant planned, we are expected to cover the remaining allocated load.

∗ KWhe= 623,885KWh -2,354.5 KWh=621530.5kWh


∗ Thermo chemical plant design

∗ The thermo chemical plant is selected to be integrated gasifier combined cycle plant. IBGCC is selected for its higher efficiency in Biomass electric generation industry.

Design and model

generation industry.


Heat recovery

steam generator

Ste

am

tur

bin

e

Generator

Biomass BFB Gasifier

and Gas cleaner

Syn-Gas

combustion

engine

Generator

∗ Design Verification-base load

Design and model


∗ Design Verification-model

Design and model


∗ Design Verification-simulation result

Design and modelBiomass Plant


∗ PV SYSTEM DESIGN

Design and model


∗ Design Verification-primary load load

Design and model


∗ Design Verification-Result

Design and model


∗ Design Verification-PV Power

Design and model


∗ Design Verification-primary load

Design and model


∗ Design Verification-served & unmet load

Design and model


∗ Design Discussion

∗ Stand alone design with out storage will leave 69% of the load unsupported.

∗ We have 81% of the PV power wasted

∗ What if Storage is included?

Design and model

∗ What if PPA exists which buys what ever is produced and available for sale and sells whenever is needed?


∗ Grid connected model

Design and model


∗ Simulation result

Design and model


∗ Proposed hybrid Energy Model

Proposed Hybrid Energy model


∗ Simulation result

simulation of Proposed hybrid system


∗ Analysis of result-PV generator



∗ Analysis of result-IBGCC generator



∗ Analysis of result-AD generator



∗ Summary of Share



∗ Analysis of Generation Vs Load



∗ The MSW needed per day for the IBCC plant is 538.53tons/day. The locally generated MSW from the developed site is 43.4tons/day. The total collectable SMW in Addis is 550ton/day therefore a supply chain from the nearby cities like Nazret, Debrezeit and collection of wood

Discussion of resource

nearby cities like Nazret, Debrezeit and collection of wood and paper factories by product is required.

∗ The total Rooftop area calculated in the design is 641903.67m2. The PVwatts estimated available area is 633096m2. We can include rooftops of common purpose buildings to fill the Gap. Therefore the PV plant can be erected without a problem


Conclusion•It is all proved that using storage less system it is not feasible to supply

the load demand with a standalone system. But it is shown that a grid-

connected Biomass-PV hybrid system with zero net energy purchase from

the grid can be developed

•The resource, the solar radiation and the area of the roof tops is found to

be sufficient for supporting the PV installation but the MSW generated

locally (on the development site) falls very short of what is needed.


locally (on the development site) falls very short of what is needed.

Therefore Biomass supply chains are necessary.

•The Energy cost of the modeled hybrid system is 0.113USD which is

actually in the international acceptable range. But cost of energy

compared to the energy sale rate of Ethiopian electric Utility shows 182 %

increase.

•Finally, it can be concluded that Hybrid renewable energy systems could

be planned and developed and can solve the problems regarding

powering the housing development projects the country is engaged with.

∗ Rooftop LiDar data study could provide better estimate of the rooftops available for solar installations and better Photovoltaic energy generation potential calculations could be made.

∗ A laboratory heating value measurements should be made for the components of the wastes so that the energy potential can be well calculated.

Recommendation

∗ A long term load study will provide us better understanding of variation in the load profile.

∗ A study of energy efficiency practices that can be adopted to the residents, either in terms of the habit of equipment use, the practice of energy utilization or pattern of use is believed to cut the demand and the feedstock to a significant amount.


∗ References

∗ Thank you for sharing me the words, the figures, the knowledge

References

knowledge


Questions?

Suggestions?Suggestions?


I Thank you


thesis on hybrid renewable energy system for condo developments

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