MULTI-OBJECTIVE CAPACITY OPTIMIZATION OF RENEWABLE ENERGY POWER SYSTEM CONSIDERING TECHNO-ECONOMIC COMPARISONS OF VARIOUS ENERGY STORAGE TECHNOLOGIES

Guo Su, He Yi, Aynur Kurban, Song Guotao, Wang Haowei, Pei Huanjin

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (10) : 424-431.

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (10) : 424-431. DOI: 10.19912/j.0254-0096.tynxb.2021-0314

MULTI-OBJECTIVE CAPACITY OPTIMIZATION OF RENEWABLE ENERGY POWER SYSTEM CONSIDERING TECHNO-ECONOMIC COMPARISONS OF VARIOUS ENERGY STORAGE TECHNOLOGIES

  • Guo Su1, He Yi2, Aynur Kurban1, Song Guotao1, Wang Haowei1, Pei Huanjin1
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Abstract

This paper conducted the multi-objective capacity optimization of renewable energy power system considering techno-economic comparisons of battery, thermal energy storage, pumped hydro storage and hydrogen storage. The multi-objective capacity optimization model considers the minimization of levelized cost of energy and loss probability of power supply, which is solved by four representative multi-objective evolutionary algorithms. This paper also proposes a comprehensive metric of algorithms based on hypervolume, and the device operation characteristics and resources uncertainties are considered to improve the accuracy of simulation. The comparative results of algorithms show that the average rank of non-dominated sorting genetic algorithm is 1.6, which has the best comprehensive performance. The quantitative techno-economic comparative results of energy storage show that thermal energy storage is the most cost-effective under different reliability conditions. The sensibility analyses of different load profile and different resource level verify the effectiveness of techno-economic comparative results.

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Guo Su, He Yi, Aynur Kurban, Song Guotao, Wang Haowei, Pei Huanjin. MULTI-OBJECTIVE CAPACITY OPTIMIZATION OF RENEWABLE ENERGY POWER SYSTEM CONSIDERING TECHNO-ECONOMIC COMPARISONS OF VARIOUS ENERGY STORAGE TECHNOLOGIES[J]. Acta Energiae Solaris Sinica. 2022, 43(10): 424-431 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0314

References

[1] 谢鹏, 蔡泽祥, 刘平, 等. 考虑多时间尺度不确定性耦合影响的风光储微电网系统储能容量协同优化[J]. 中国电机工程学报, 2019, 39(24): 7126-7136.
XIE P, CAI Z X, LIU P, et al.Cooperative optimization of energy storage capacity for renewable and storage involved microgrids considering multi time scale uncertainty coupling influence[J]. Proceedings of the CSEE, 2019, 39(24): 7126-7136.
[2] JACOB S A, BANERJEE R, GHOSH C P.Sizing of hybrid energy storage system for a PV based microgrid through design space approach[J]. Applied energy, 2018, 212: 640-653.
[3] CHAUHAN A, SAINI R P.A review on integrated renewable energy system based power generation for stand-alone applications: configurations, storage options, sizing methodologies and control[J]. Renewable and sustainable energy reviews, 2014, 38: 99-120.
[4] KAABECHE A, BAKELLI Y.Renewable hybrid system size optimization considering various electrochemical energy storage technologies[J]. Energy conversion and management, 2019, 193: 162-175.
[5] 邓佃毅, 栗文义, 楚冰清, 等. 基于可靠性约束的风-光-储微网容量优化配置对配电网可靠性影响分析[J].太阳能学报, 2018, 39(9): 2439-2445.
DENG D Y, LI W Y, CHU B Q, et al.Reliability analysis of wind-photovoltaic-battery microgrids optimization configuration based on reliability constraints for distribution system[J]. Acta energiae solaris sinica, 2018, 39(9): 2439-2445.
[6] MA T, YANG H, LU L, et al.Optimal design of an autonomous solar-wind-pumped storage power supply system[J]. Applied energy, 2015, 160: 728-736.
[7] 赵波, 李得民, 吴在军, 等. 基于100%绿色能源供电目标的海岛微电网群容量优化配置[J]. 中国电机工程学报, 2021, 41(3): 932-945.
ZHAO B, LI D M, WU Z J, et al.Capacity optimal sizing of island microgrid clusters based on the 100% green energy power supply[J]. Proceedings of the CSEE, 2021, 41(3): 932-945.
[8] 邵志芳, 吴继兰. 基于动态电价风光电制氢容量配置优化[J]. 太阳能学报, 2020, 41(8): 227-235.
SHAO Z F, WU J L.Capacity configuration optimization of hydrogen production from wind and PV power based on dynamic electricity price[J]. Acta energiae solaris sinica, 2020, 41(8): 227-235.
[9] 李鹏, 韩建沛, 殷云星, 等. 电转氢作为灵活性资源的微网容量多目标优化配置[J]. 电力系统自动化, 2019, 43(17): 28-35, 139.
LI P, HAN J P, YIN Y X, et al.Multi-objective optimal capacity configuration of microgrid with power to hydrogen as flexible resource[J]. Automation of electric power systems, 2019, 43(17): 28-35, 139.
[10] 郭苏, 何意, 蒋川, 等. 风电-光伏-储热联合发电系统的多目标容量优化[J]. 太阳能学报, 2020, 41(11): 359-368.
GUO S, HE Y, JIANG C, et al.Capacity optimization of wind-photovoltaic-thermal energy storage-electric heater hybrid power sysyem[J]. Acta energiae solaris sinica, 2020, 41(11): 359-368.
[11] 汪致洵, 林湘宁, 刘畅, 等. 基于光热电站水电联产的独立海岛综合供给系统容量优化配置[J]. 中国电机工程学报, 2020, 40(16): 5192-5204.
WANG Z X, LIN X N, LIU C, et al.Optimal planning of integrated supply system in independent islands based on cogeneration of concentrating solar power plants[J]. Proceedings of the CSEE, 2020, 40(16): 5192-5204.
[12] ANOUNE K, BOUYAA M, ASTITOET A, et al.Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: a review[J]. Renewable and sustainable energy reviews, 2018, 93: 652-673.
[13] GHAFFARI A, ASKARZADEH A.Design optimization of a hybrid system subject to reliability level and renewable energy penetration[J]. Energy, 2020, 193: 116754.
[14] PETROLLESE M, COCCO D.Optimal design of a hybrid CSP-PV plant for achieving the full dispatchability of solar energy power plants[J]. Solar energy, 2016, 137: 477-489.
[15] XU X, HU W H, CAO D, et al.Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system[J]. Renewable energy, 2020, 147(Part 1): 1418-1431.
[16] JORDEHI A R.How to deal with uncertainties in electric power systems? A review[J]. Renewable and sustainable energy reviews, 2018, 96: 145-155.
[17] DEB K, PRATAP A, AGARWAL S, et al.A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE transactions on evolutionary computation, 2002, 6(2): 182-197.
[18] ZHANG Q F, LI H.MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE transactions on evolutionary computation, 2007, 11(6): 712-731.
[19] COELLO C A C, LECHUGA M S. MOPSO: A proposal for multiple objective particle swarm optimization[C]// Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, HI, USA, 2002, 2: 1051-1056.
[20] ZITZLER E, THIELE L.Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach[J]. IEEE transactions on evolutionary computation, 1999, 3(4): 257-271.
[21] WHILE L, HINGSTON P, BARONE L, et al.A faster algorithm for calculating hypervolume[J]. IEEE transactions on evolutionary computation, 2006, 10(1): 29-38.
[22] National Renewable Energy Laboratory. System advisor model[R]. SAM2020.2.29(Last Version To Include Dish Stirling and Direct Steam Power Tower CSP Models).
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