99Figure 5-6: Pareto curve for Test case 1, showing daily building operating cost and wind generation facilitation for 11 α values ranging from 0% to 100% in increments of 10%.. 104Figur
Trang 1C ONTROL OF ENERGY STORAGE UTILISATION FOR A BUILDING INTEGRATED MICROGRID USING MULTI -
OBJECTIVE METAHEURISTIC OPTIMISATION METHODS
A thesis presented for the award of Doctor of Philosophy
BY Quang An Phan Supervisors: Dr Michael D Murphy
Dr Ted Scully
Department of Process, Energy and Transport Engineering
Cork Institute of Technology, Cork, Ireland
December 2019
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ACKNOWLEDGEMENTS
I would like to thank Cork Institute of Technology for the opportunity to conduct my PhD research I‘d also like to express my sincere gratitude to my supervisors, Dr Michael D Murphy and Dr Ted Scully, for their guidance, knowledge and patience
I would like to thank my family for all of their support From Cork Institute of Technology, I would like to thank Dr Michael Breen, Stefan Reis, Dr Conor Lynch, Dr Fan Zhang, Dr Adam O‘ Donovan, and Dr Philip Shine, for sharing their PhD experiences with me, along with the countless members of staff at Cork Institute of Technology who have helped me throughout the years
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Contents
DECLARATION I ACKNOWLEDGEMENTS II Contents III List of Figures VII List of Tables XV List of Publications XVII Nomenclature XVIII 1.1 Abbreviations XVIII 1.2 Variables XIX ABSTRACT XXI
Chapter 1 – Introduction 1
1.1 Background to research 1
1.1.1 Ireland‘s electricity use and renewable energy contribution 1
1.1.2 Microgrids and energy management 5
1.2 Problem statement 6
1.3 Research objectives 7
1.4 Research methodology 7
Chapter 2 - Literature Review 8
2.1 Introduction 8
2.2 Microgrids 9
2.2.1 Generators 9
2.2.2 Storage system 10
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2.2.3 Isolated and grid-connected Microgrids 12
2.3 Microgrid components modelling 14
2.3.1 Wind turbine energy models 14
2.3.2 Photovoltaic energy models 16
2.3.3 Lead-acid battery models 18
2.4 Energy management for Microgrids 20
2.4.1 Motivations for energy management 20
2.4.2 Energy management methodologies 21
2.5 Optimisation Algorithms 23
2.5.1 Review of optimization algorithms 23
2.5.2 Multi-objective optimisation algorithms 27
2.6 Literature review conclusion 28
Chapter 3 - Determination of a suitable optimisation method to minimise building operating costs 30
3.1 Introduction 30
3.2 Methodology 30
3.2.1 NBERT building 30
3.2.2 Research methodology 32
3.3 Energy Source Models 34
3.3.1 Photovoltaic model for 12 kWp system 34
3.3.2 Wind turbine model for a 2.5 kWp turbine 35
3.3.3 Battery bank model 36
3.3.4 Building energy consumption 37
3.3.5 Purchasing and selling price of electricity 38
3.4 Net energy use and operating costs for building 38
3.4.1 Net difference in energy production and consumption 39
3.4.2 Daily operating cost 39
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3.5 Optimisation 40
3.5.1 Piecemeal Decision Approach (PDA) 41
3.5.2 Genetic Algorithm 42
3.6 Results 45
3.7 Conclusion 49
Chapter 4 – Optimisation using multiple battery charge/discharge rates and comparison of optimisation performance for metaheuristic algorithms 51
4.1 Introduction 51
4.2 Modelling 52
4.2.1 Augmented PVS model incorporating an Rs power loss function 52
4.2.2 Wind turbine model for a 12.6 kWp turbine 53
4.2.3 Battery bank model for charge/discharge modes 54
4.3 Simulation scenarios and constraints 56
4.3.1 Simulation scenarios 56
4.3.2 Constraints 59
4.4 Optimization algorithms 62
4.4.1 Initial population 63
4.4.2 Fitness calculation 64
4.4.3 Evolve population 66
4.4.4 Stopping criterion 68
4.5 Results and discussion 68
4.5.1 Model validation 68
4.5.2 Daily operating cost of the building when not utilising a BB 74
4.5.3 Optimized daily operating cost of the building utilising the BB 75
4.5.4 Sensitivity analysis when dealing with scaled weather & electricity price data 81 4.6 Conclusion 86
Chapter 5 - Multi-objective optimisation 87
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5.1 Introduction 87
5.2 Application 88
5.3 Modelling 89
5.3.1 Building energy and electricity price 89
5.3.2 Grid wind ratio 90
5.4 Optimization 90
5.4.1 Optimization procedure 90
5.4.2 Criterion 1: Daily building operating cost 91
5.4.3 Criterion 2: Wind Generation Facilitation 91
5.4.4 Optimization constraints 92
5.4.5 Decision variables 92
5.4.6 Objective function 93
5.4.7 Genetic algorithm implementation for optimal charge/discharge schedule 95
5.4.8 WGF to COST ratio (―Yield‖) 96
5.5 Data for implementation of optimization methods 96
5.6 Scenarios for demonstration of methods 99
5.7 Results and discussion 102
5.7.1 Comparison of test cases 102
5.7.2 Analysis of all scenarios 116
Chapter 6 - GLOBAL DISCUSSION 119
6.1 Relevance to building users 122
6.2 Relevance to policymakers 123
Chapter 7 - GLOBAL CONCLUSION 124
7.1 Future Work 125
References 127
Appendix A 142
Appendix B 145
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List of Figures
Figure 1-1: Electricity production (MWh) from renewable sources (wind, hydro, biomass, biogas, PV and other (such as landfill wastes and geothermal energy) for Ireland in the
period 2010-2017 2
Figure 1-2: Electricity production (MWh) from renewable sources (wind, hydro, biomass, biogas, PV and other (such as landfill wastes and geothermal energy) for Europe in the period 2010-2017 3
Figure 1-3: Contribution of wind energy to renewable production for Ireland and the EU in the period 2010-2017 4
Figure 2-1: Isolated Micro-grid: Small autonomous hybrid power system (SAHPS) [38] 12 Figure 2-2: Grid-connected MG: System model of adaptive power management (APM) [80] 14
Figure 2-3: Typical relationship between wind speed and corresponding power delivered [81] 15
Figure 2-4: Photovoltaic panel 16
Figure 2-5: Single-diode and double-diode PV cell models [101] 17
Figure 2-6: I-V curve and Fill factor [119] 18
Figure 2-7: Electrical model for one cell of lead-acid battery [122] 19
Figure 3-1: NBERT building Clockwise from top left: Photovoltaic system; Wind turbine; Employees‘ office; Outside view of building 32
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Figure 3-2: NBERT building schematic showing the interaction between electricity generation, storage and consumption, as well as the relevant data inputs for each system 33
Figure 3-3: Genetic algorithm flow chart 44
Figure 3-4: Operating cost using the PDA and GA 46
Figure 3-5: Operating cost using the GA for FR and VR timetables 47
Figure 3-6: SOC variation using the PDA with a FR timetable 48
Figure 3-7: SOC variation using the GA with a FR timetable 48
Figure 3-8: SOC variation using the GA with a VR timetable 48
Figure 4-1: Genetic algorithm (GA) flowchart 63
Figure 4-2: Particle swarm optimization (PSO) flowchart 63
Figure 4-3: Individuals for initial population: One rate, two rate and twenty rate battery configurations 64
Figure 4-4: Individuals for initial population represented as integers: One rate, two rate and twenty rate battery configurations 64
Figure 4-5: Flowchart showing example of an individual in the population and how it was represented by integers, charge/discharge rates, state of charge, voltage and current of the battery, amount of electricity stored in/released from the battery, amount of electricity purchased from/sold to the grid, and the operating costs at each interval 65
Figure 4-6: Photovoltaic system (PVS) power validation showing simulated and measured PVS power data for 10 days in winter time 69
Figure 4-7: Photovoltaic system (PVS) power validation showing simulated and measured PVS power data for 10 days in summer time 69
Figure 4-8: Polynomial power curve fitted to wind turbine (WT) manufacturer‘s data, showing wind speeds (m/s) and corresponding power output (kW) 70
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Figure 4-9: Measured and simulated charging current and voltage versus time for standard rate C/10 71Figure 4-10: Measured and simulated charging voltage versus time using constant current charge method for C/5, C/10 and C/20 72Figure 4-11: Measured and simulated charging current versus time during constant voltage period When the charging voltage reached the voltage limit of 14.4V, the charging voltage was held constant at this voltage limit 72Figure 4-12: Measured and simulated discharging voltage versus time using constant current discharge method for D/5, D/10 and D/20 73Figure 4-13:Real time pricing, difference in electricity produced and consumed, and state
of charge of the battery bank over a 24 hour period for Configuration 1 i.e one charge and discharge rate available 75Figure 4-14: Real time pricing, difference in electricity produced and consumed, and state
of charge of the battery bank over a 24 hour period for Configuration 2 i.e two charge and two discharge rates available 76Figure 4-15: Real time pricing, difference in electricity produced and consumed, and state
of charge of the battery bank over a 24 hour period for Configuration 5 i.e five charge and five discharge rates available 76Figure 4-16: Real time pricing, difference in electricity produced and consumed, and state
of charge of the battery bank over a 24 hour period for Configuration 20 i.e twenty charge and twenty discharge rates available 77Figure 4-17: Percentage change in daily building operating costs compared to Configuration 0 over a winter week for all 20 configurations of charge and discharge rates 80
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Figure 4-18: Percentage change in daily building profit compared to Configuration 0 over
a summer week for all 20 configurations of charge and discharge rates 80Figure 4-19: Percentage change in daily building operating costs compared to Configuration 0 (average over the winter and summer week) for all 20 configurations of charge and discharge rates 81Figure 4-20: Percentage change in operating costs when scaling percentages (SP) between -25% and +25% were applied to electricity price input data 84Figure 4-21: Percentage change in operating costs when scaling percentages (SP) between -25% and +25% were applied to weather input data 85Figure 5-1: Multi-objective optimization strategy to generate an optimal charge/discharge schedule for the battery bank in a grid-connected building (NBERT) with an integrated microgrid The day-ahead real-time electricity price and grid power schedule (i.e how much electricity from the grid will be provided by wind energy), as well as day-ahead predictions for building electricity consumption and microgrid production, are all taken into account when optimizing the battery bank charge and discharge schedule This schedule is optimized based on a priority weighting factor (α) which assigns relative importance to operating cost and wind generation facilitation in the optimization process 88Figure 5-2: Procedure for calculating daily building operating cost and wind generation facilitation 92Figure 5-3: Multi-objective Genetic algorithm implementation in this study 95Figure 5-4: Representative groups for each data category for Winter: (a) PV electricity output (EPV) includes three clustered groups: W1 (Low EPV), W2 (Medium EPV), W3 (High EPV); (b) Electricity Price (EP) includes two clustered groups: W1 (Low EP), W2 (High EP); (c) Grid wind ratio (GWR) includes four clustered groups: W1 (Low GWR),
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W2 (Medium-Low GWR), W3 (Medium-High GWR), W4 (High GWR); (d) Building load (BL) includes two clustered groups: W1 (Low BL), W2 (High BL); Wind turbine output (EW) includes one group: W1 (Medium EW) 98Figure 5-5: Representative groups for each data category for Summer: (a) PV electricity output (EPV) includes three clustered groups: S1 (Low EPV), S2 (Medium EPV), S3 (High EPV); (b) Electricity Price (EP) includes two clustered groups: S1 (Low EP), S2 (High EP); (c) Grid wind ratio (GWR) includes four clustered groups: S1 (Low GWR), S2 (Medium-Low GWR), S3 (Medium-High GWR), S4 (High GWR); (d) Building load (BL) includes two clustered groups: S1 (Low BL), S2 (High BL); Wind turbine output (EW) includes one group: S1 (Medium EW) 99Figure 5-6: Pareto curve for Test case 1, showing daily building operating cost and wind generation facilitation for 11 α values ranging from 0% to 100% in increments of 10% 104Figure 5-7: Yield values for Test case 1, showing the ratio of the change in normalized wind generation facilitation to the change in normalized daily operating cost at each α value between 0% and 100% in increments of 10% 104Figure 5-8: (a) The energy difference (energy produced by renewable generation minus energy consumed by the building), electricity price and grid wind ratio at thirty minute intervals for Test case 1; (b) The corresponding state of charge (SOC) of the BB under the optimal BB schedule for each α value between 0% and 100% in increments of 10% 105Figure 5-9: Pareto curve for Test case 2, showing daily building operating cost and wind generation facilitation for 11 α values ranging from 0% to 100% in increments of 10% 106Figure 5-10:Yield values for Test case 2, showing the ratio of the change in normalized wind generation facilitation to the change in normalized daily operating cost at each α value between 0% and 100% in increments of 10% 107
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Figure 5-11: (a) The energy difference (energy produced by renewable generation minus energy consumed by the building), electricity price and grid wind ratio at thirty minute intervals for Test case 2; (b) The corresponding state of charge (SOC) of the BB under the optimal BB schedule for each α value between 0% and 100% in increments of 10% 107Figure 5-12: Pareto curve for Test case 7, showing daily building operating cost and wind generation facilitation for 11 α values ranging from 0% to 100% in increments of 10% 109Figure 5-13: Yield values for Test case 7, showing the ratio of the change in normalized wind generation facilitation to the change in normalized daily operating cost at each α value between 0% and 100% in increments of 10% 110Figure 5-14: (a) The energy difference (energy produced by renewable generation minus energy consumed by the building), electricity price and grid wind ratio at thirty minute intervals for Test case 7; (b) The corresponding state of charge (SOC) of the BB under the optimal BB schedule for each α value between 0% and 100% in increments of 10% 110Figure 5-15: Pareto curve for Test case 8, showing daily building operating cost and wind generation facilitation for 11 α values ranging from 0% to 100% in increments of 10% 112Figure 5-16: Yield values for Test case 8, showing the ratio of the change in normalized wind generation facilitation to the change in normalized daily operating cost at each α value between 0% and 100% in increments of 10% 113Figure 5-17: (a) The energy difference (energy produced by renewable generation minus energy consumed by the building), electricity price and grid wind ratio at thirty minute intervals for Test case 8; (b) The corresponding state of charge (SOC) of the BB under the optimal BB schedule for each α value between 0% and 100% in increments of 10% 113Figure 5-18: Yield values for 48 scenario combinations in winter 116Figure 5-19: Yield values for 48 scenario combinations in summer 117