&21752/2)(1(5*<6725$*(87,/,6$7,21)25$ %8,/',1*,17(*5$7('0,&52*5,'86,1*08/7,- 2%-(&7,9(0(7$+(85,67,&237,0,6$7,210(7+2'6 A thesis presented for the award of Doctor of Philosophy BY Quang An Phan Supervisors: Dr. Ted Scully Department of Process, Energy and Transport Engineering Cork Institute of Technology, Cork, Ireland December 2019 '(&/$5$7,21 I declare that this thesis has not previously been submitted for a degree at Cork Institute of Technology, Ireland or any other university. I declare that the work contained in this thesis is my own. Quang An Phan December, 2019 I $&.12:/('*(0(176 I would like to thank Cork Institute of Technology for the opportunity to conduct my PhD UHVHDUFK ,¶G DOVR OLNH WR H[SUHVV P\ sincere gratitude to my supervisors, Dr Michael D.
Murphy and Dr Ted Scully, for their guidance, knowledge and patience. I would like to thank my family for all of their support. From Cork Institute of Technology, I would like to thank Dr Michael Breen, Stefan Reis, Dr Conor Lynch, Dr )DQ=KDQJ'U$GDP2¶'RQRYDQDQG'U3KLOLS6KLQHIRUVKDULQJWKHLU3K'H[SHULHQFHV with me, along with the countless members of staff at Cork Institute of Technology who have helped me throughout the years. II &RQWHQWV DECLARATION.
III List of Figures. VII List of Tables. XV List of Publications. XXI Chapter 1 ± Introduction .1 Background to research.
1 ,UHODQG¶VHOHFWULFLW\XVHDQGUHQHZDEOHHQHUJ\FRQWULEXWLRQ .2 Microgrids and energy management. 7 Chapter 2 - Literature Review .3 Isolated and grid-connected Microgrids .3 Microgrid components modelling .1 Wind turbine energy models .2 Photovoltaic energy models .3 Lead-acid battery models .4 Energy management for Microgrids .1 Motivations for energy management .2 Energy management methodologies.1 Review of optimization algorithms .2 Multi-objective optimisation algorithms .6 Literature review conclusion. 28 Chapter 3 - Determination of a suitable optimisation method to minimise building operating costs .3 Energy Source Models .1 Photovoltaic model for 12 kWp system .2 Wind turbine model for a 2.3 Battery bank model.4 Building energy consumption.5 Purchasing and selling price of electricity.4 Net energy use and operating costs for building .1 Net difference in energy production and consumption .2 Daily operating cost .1 Piecemeal Decision Approach (PDA). 49 Chapter 4 ± Optimisation using multiple battery charge/discharge rates and comparison of optimisation performance for metaheuristic algorithms.1 Augmented PVS model incorporating an Rs power loss function .2 Wind turbine model for a 12.3 Battery bank model for charge/discharge modes .3 Simulation scenarios and constraints.5 Results and discussion .2 Daily operating cost of the building when not utilising a BB .3 Optimized daily operating cost of the building utilising the BB .4 Sensitivity analysis when dealing with scaled weather & electricity price data 81 4.
86 Chapter 5 - Multi-objective optimisation .1 Building energy and electricity price .2 Grid wind ratio .2 Criterion 1: Daily building operating cost .3 Criterion 2: Wind Generation Facilitation .7 Genetic algorithm implementation for optimal charge/discharge schedule. 95 :*)WR&267UDWLR ³<LHOG´ .5 Data for implementation of optimization methods .6 Scenarios for demonstration of methods .7 Results and discussion .1 Comparison of test cases .2 Analysis of all scenarios. 116 Chapter 6 - GLOBAL DISCUSSION .1 Relevance to building users .2 Relevance to policymakers. 123 Chapter 7 - GLOBAL CONCLUSION.
145 VI /LVWRI)LJXUHV 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; (PSOR\HHV¶RIILFH 2XWVLGHYLHZRIEXLOGLQJ.
32 VII 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- 3RO\QRPLDO SRZHU FXUYH ILWWHG WR ZLQG WXUELQH :7 PDQXIDFWXUHU¶V GDWD showing wind speeds (m/s) and corresponding power output (kW). 70 VIII Figure 4-9: Measured and simulated charging current and voltage versus time for standard rate C/10. 71 Figure 4-10: Measured and simulated charging voltage versus time using constant current charge method for C/5, C/10 and C/20.
72 Figure 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. 72 Figure 4-12: Measured and simulated discharging voltage versus time using constant current discharge method for D/5, D/10 and D/20. 73 Figure 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.
one charge and discharge rate available. 75 Figure 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. two charge and two discharge rates available. 76 Figure 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.
five charge and five discharge rates available. 76 Figure 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. twenty charge and twenty discharge rates available. 77 Figure 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 IX 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. 80 Figure 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. 81 Figure 4-20: Percentage change in operating costs when scaling percentages (SP) between -25% and +25% were applied to electricity price input data. 84 Figure 4-21: Percentage change in operating costs when scaling percentages (SP) between -25% and +25% were applied to weather input data.
85 Figure 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. 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 schedXOH LV RSWLPL]HG EDVHG RQ D SULRULW\ ZHLJKWLQJ IDFWRU Į ZKLFK DVVLJQV UHODWLYH importance to operating cost and wind generation facilitation in the optimization process.
88 Figure 5-2: Procedure for calculating daily building operating cost and wind generation facilitation. 92 Figure 5-3: Multi-objective Genetic algorithm implementation in this study. 95 Figure 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), X 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). 98 Figure 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).
99 Figure 5-6: Pareto curve for Test case 1, showing daily building operating cost and wind JHQHUDWLRQIDFLOLWDWLRQIRUĮYDOXHVUDQJLQJIURPWRLQLQFUHPHQWVRI 104 Figure 5-7: Yield values for Test case 1, showing the ratio of the change in normalized ZLQG JHQHUDWLRQ IDFLOLWDWLRQ WR WKH FKDQJH LQ QRUPDOL]HG GDLO\ RSHUDWLQJ FRVW DW HDFK Į value between 0% and 100% in increments of 10%. 104 Figure 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 scheGXOHIRUHDFKĮYDOXHEHWZHHQDQGLQLQFUHPHQWVRI. 105 Figure 5-9: Pareto curve for Test case 2, showing daily building operating cost and wind JHQHUDWLRQIDFLOLWDWLRQIRUĮYDOXes ranging from 0% to 100% in increments of 10%. 106 Figure 5-10:Yield values for Test case 2, showing the ratio of the change in normalized wind generation facilitation to the change in normali]HG GDLO\ RSHUDWLQJ FRVW DW HDFK Į value between 0% and 100% in increments of 10%.
107 XI 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 RSWLPDO%%VFKHGXOHIRUHDFKĮYDOXHEHWZHHQDQGLQLQFUHPHQWVRI. 107 Figure 5-12: Pareto curve for Test case 7, showing daily building operating cost and wind JHQHUDWLRQIDFLOLWDWLRQIRUĮYDOXHVUDQJLQJIURPWRLQLQFUHPHQWVRI 109 Figure 5-13: Yield values for Test case 7, showing the ratio of the change in normalized ZLQG JHQHUDWLRQ IDFLOLWDWLRQ WR WKH FKDQJH LQ QRUPDOL]HG GDLO\ RSHUDWLQJ FRVW DW HDFK Į value between 0% and 100% in increments of 10%. 110 Figure 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 RSWLPDO%%VFKHGXOHIRUHDFKĮYDOXHEHWZHHQDQGLQLQFUHPHQWVRI .