Multi-objective capacity allocation optimization method of photovoltaic EV charging station considering V2G

Abstract

Large-scale electric vehicles (EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid (V2G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use (TOU) price; Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-II and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.

Keywords

vehicle to grid (V2G) / capacity configuration optimization / time-to-use (TOU) price / multi-objective optimization / NSGA-II algorithm / NSGA-SA algorithm

References

[1] ZefrehM S, ToddT D, KarakostasG. Energy provisioning and operating costs in hybrid solar-powered infrastructure [J]. IEEE Transactions on Sustainable Energy, 2014, 5(3): 986-994

[2] Ai-OgailiA S, HashimT J T, TahmatN A, RamasamyA K, MarsadekM B, FaisalM, HannanM A. Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charing: Challenges and recommendations [J]. IEEE Access, 2019, 7: 128353-128371

[3] LiuJ-z, ChenH, ZhangW, YurkovichB, RizzoniG. Energy management problems under uncertainties for grid-connected microgrids: A chance constrained programming approach [J]. IEEE Transactions on Smart Grid, 2017, 8(6): 2585-2596

[4] SainsN S, Ai-AnbagiI. Optimal charging and discharging for EVs in a V2G participation under critical peak conditions [J]. IET Electrical Systems in Transportation, 2018, 8(2): 136-143

[5] YangH-j, WangL, ZhangY-y, TaiH-m, MaY-h, ZhouM. Reliability evaluation of power system considering time of use electricity pricing [J]. IEEE Transactions on Power Systems, 2019, 34(3): 1991-2002

[6] LIU Xiao-ling, WANG Zhao-jie, GAO Feng, WU Jiang, GUAN Xiao-hong, ZHOU Dian-min. Response behaviors of power generation and consumption in energy intensive enterprise under time-of-use price [J]. Power System Automation, 2014(8): 41–49. DOI: https://doi.org/10.7500/AEPS20130502007. (in Chinese)

[7] YangH-m, YangS-p, XuY, CaoE-b, LaiM-y, ZhaoY-D. Electric vehicle route optimization considering TOU electricity price by learnable partheno-genetic algorithm [J]. IEEE Transactions on Smart Grid, 2015, 6(2): 657-666

[8] LiuH-w, WangS-t, LiuG-c, ZhangJ-w, WenS-F. SARAP algorithm of multi-objective optimal capacity configuration for WT-PV-DE-BES Stand-Alone Microgrid [J]. IEEE Access, 2020, 8: 126825-126838

[9] HuanL, MehrdadE, ZhangZ-J. Configuration optimization and analysis of a large scale PV/Wind system [J]. IEEE Transactions on Sustainable Energy, 2017, 8(1): 84-93

[10] RavadaneghS N, OskueeM R, KarimiM. Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties [J]. Journal of Central South University, 2017, 24(8): 1837-1849

[11] WuM, SunL-j, ZhangH, WangW, LuoG-M. Research on optimal storage capacity of DC micro-grid system in PV station [J]. The Journal of Engineering, 2017, 2017(13): 859-864

[12] TAYLOR M J, ALEXANDER A. Evaluation of the impact of plug-in electric vehicle loading on distribution system operations [C]// IEEE Power & Energy Society General Meeting. Calgary, 2009: 1–6. DOI: https://doi.org/10.1109/PES.2009.5275317.

[13] XuS, LiY. An optimization model of peak-valley price time interval for guiding the orderly charging and discharging of electric vehicles [J]. Power Demand Side Management, 2018, 20(5): 11-15(in Chinese)

[14] AkbariM, ShojaeefardM H, AsadiP, KhalkhaliA. Hybrid multi-objective optimization of microstructural and mechanical properties of B₄C/A356 composites fabricated by FSP using TOPSIS and modified NSGA-II [J]. Transactions of Nonferrous Metals Society of China, 2017, 27(11): 2317-2333

[15] SuC, LiuY. Multi-objective imperfect preventive maintenance optimisation with NSGA-II [J]. International Journal of Production Research, 2020, 58(13): 4033-4049

[16] WangL, LiX-H. Study of distortion correction of bp neural network based on improved genetic simulated annealing algorithm [J]. Computer Measurement and Control, 2019, 27(5): 77-81(in Chinese)

[17] LuX-y, LiuN, ChenZ, ZhangJ-h, XiaoX-N. Multi-objective optimal scheduling for PV-Assisted charging of electric vehicles [J]. Transactions of China Electrotechnical Society, 2014, 29(8): 46-56(in Chinese)

[18] MaR, LetuH, YangK, WangT, ChenL. Estimation of surface shortwave radiation from himawari-8 satellite data based on a combination of radiative transfer and deep neural network [J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(8): 5304-5316

[19] AlsadiS Y, NassarY F. Estimation of solar irradiance on solar fields: An analytical approach and experimental results [J]. IEEE Transactions on Sustainable Energy, 2017, 8(4): 1601-1608

[20] YuD-x, ZhangJ-h, WangX-y, GaoY. Optimal capacity configuration of grid-connected Wind-PV-storage hybrid power generation system [J]. Journal of Power System and Automation, 2019, 31(10): 59-65(in Chinese)