Capacity optimization of wind-solar-nuclear-energy storage hybrid system considering wind and solar energy consumption

doi:10.3969/j.issn.2097-0706.2025.01.007

Abstract

Abstract:

The capacity configuration optimization of a wind-solar-nuclear-energy storage hybrid energy system was performed through a multi-objective evolutionary algorithm in this research. The hybrid energy system included photovoltaics（PV），wind turbines（WT），small modular thorium molten salt reactor（smTMSR），and thermal energy storage（TES）. The optimization objectives were to improve the stability of the electricity supply，reduce the electricity generation cost，reduce the electricity curtailment probability，and increase the fraction of renewable energy in the total power supply system（renewable energy fraction）. The PV capacity，WT capacity，and TES capacity were selected as the optimization parameters，while the local meteorological data of Wuwei city were used as input parameters. By comparing the performance of the nondominated sorting genetic algorithm（NSGA-Ⅱ，NSGA-Ⅲ）and the strength Pareto evolution algorithm（SPEA-SDE），the optimal algorithm was selected to solve the multi-objective optimization problem and obtain the Pareto solution set. The Criteria Importance Through Intercriteria Correlation（CRITIC）method was used to determine the objective weights，and the Technique for Order Preference by Similarity to Ideal Solution（TOPSIS）method was used to sort the obtained Pareto solutions，from which the best compromise solution was selected. The results demonstrated that NSGA-Ⅱ had the fastest convergence speed compared to other algorithms，but its solution set was less uniform. NSGA-Ⅲ，although slower to converge，had the most uniform solution set compared to other algorithms. The optimization results showed that the optimal capacity configuration resulted in a deficiency of power supply probability of 0.968 6%，a levelized cost of energy of 0.085 7 dollars/（kW·h）. an electricity curtailment probability of 4.898 6%，and a renewable energy share of 21.258 9%. The electricity curtailment mainly came from nuclear power，with minimal renewable energy curtailment. The sensitivity analysis results showed that the PV capacity had the most significant impact on the probability of power supply deficiency，electricity curtailment probability，and renewable energy fraction，while the WT capacity had the most significant impact on the levelized cost of energy. The wind-solar-nuclear-energy storage hybrid energy system can effectively promote renewable energy consumption and ensure the reliability of the power supply.

Key words: wind-solar-nuclear-energy storage hybrid energy system, multi-objective capacity configuration optimization, renewable energy consumption, Methods for determining index weights, TOPSIS method, sensitivity analysis

CLC Number:

TM614

NIE Xueying, CHENG Maosong, ZUO Xiandi, DAI Zhimin. Capacity optimization of wind-solar-nuclear-energy storage hybrid system considering wind and solar energy consumption[J]. Integrated Intelligent Energy, 2025, 47(1): 51-61.

Figures/Tables 21

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Table 1

Operation parameters of the hybrid energy system[6,16,28-29]

组件名称	运行参数	数值
光伏电池(PV-TD190MF5)	额定功率/W	150
	模块尺寸/mm	1 658×834×46
	组件参考效率/%	13.7
	温度系数	0.005
	参考电池温度/℃	25.0
	电池额定运行温度/℃	47.5
风机(GW82-1500)	风机轮毂高度/m	70
	风速计高度/m	10
	表面粗糙度长度/m	1.5
	额定功率/kW	1 500
	切入风速/(m·s^-1)	3
	切出风速/(m·s^-1)	22
	额定风速/(m·s^-1)	13
蓄热系统	能量耗散率	0
	蓄热效率/%	100
	放热效率/%	100
	最大蓄热容量/（MW·h）	$C T E S$
	最小蓄热容量/（MW·h）	0.05 $C T E S$
	初始蓄热容量/（MW·h）	0.05 $C T E S$
热功转化系统	热转化效率/%	40

Table 1

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Table 2

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References 29

[1]	舒印彪, 张智刚, 郭剑波, 等. 新能源消纳关键因素分析及解决措施研究[J]. 中国电机工程学报, 2017, 37(1): 1-9.
	SHU Yinbiao, ZHANG Zhigang, GUO Jianbo, et al. Study on key factors and solution of renewable energy accommodation[J]. Proceedings of the CSEE, 2017, 37(1): 1-9.
[2]	齐国愿. 可再生能源混合储能容量优化配置研究[D]. 兰州: 兰州理工大学, 2021.
	QI Guoyuan. Study on optimal allocation of hybrid energy storage capacity of renewable energy[D]. Lanzhou: Lanzhou University of Technology, 2021.
[3]	MICHAELSON D, JIANG J. Review of integration of small modular reactors in renewable energy microgrids[J]. Renewable and Sustainable Energy Reviews, 2021, 152: 111638.
[4]	张馨玉, 郭慧芳, 袁永龙. 全球小型模块化反应堆发展综述[C]. 中国核科学技术进展报告(第七卷)——中国核学会2021年学术年会论文集第8册(核情报分卷), 2021: 240-247.
	ZHANG Xinyu, GUO Huifang, YUAN Yonglong. A Review of the Development of Global Small Modular Reactors[C]. Proceedings of the 2021 Annual Academic Conference of the Chinese Nuclear Society (Volume 7) — Nuclear Intelligence Volume, 2021: 240-247.
[5]	曹亚丽, 王韶伟, 熊文彬, 等. 小型模块化反应堆特性及应用分析[J]. 核电子学与探测技术, 2014, 34(6): 801-806.
	CAO Yali, WANG Shaowei, XIONG Wenbin, et al. Features and application analysis of the small modular reactors[J]. Nuclear Electronics & Detection Technology, 2014, 34(6): 801-806.
[6]	ZHAO B C, CHENG M S, LIU C, et al. Conceptual design and preliminary performance analysis of a hybrid nuclear-solar power system with molten-salt packed-bed thermal energy storage for on-demand power supply[J]. Energy Conversion and Management, 2018, 166: 174-186.
[7]	ROSS M, BINDRA H. Estimating energy storage size for Nuclear-Renewable hybrid energy systems using data-driven stochastic emulators[J]. Journal of Energy Storage, 2021, 40: 102787.
[8]	GERKŠIČ S, VRANČIĆ D, ČALIČ D, et al. A perspective of using nuclear power as a dispatchable power source for covering the daily fluctuations of solar power[J]. Energy, 2023, 284: 128531.
[9]	BORISOVA A, POPOV D. An option for the integration of solar photovoltaics into small nuclear power plant with thermal energy storage[J]. Sustainable Energy Technologies and Assessments, 2016, 18: 119-126.
[10]	DENHOLM P, KING J C, KUTCHER C F, et al. Decarbonizing the electric sector: Combining renewable and nuclear energy using thermal storage[J]. Energy Policy, 2012, 44: 301-311.
[11]	GABBAR H A, ABDUSSAMI M R, ADHAM M I. Optimal planning of nuclear-renewable micro-hybrid energy system by particle swarm optimization[J]. IEEE Access, 2020, 8: 181049-181073.
[12]	王晓燕, 吴书泉. 基于改进粒子群优化算法的源网荷储系统容量配置研究[J]. 综合智慧能源, 2024, 46(9): 28-36. doi: 10.3969/j.issn.2097-0706.2024.09.004
	WANG Xiaoyan, WU Shuquan. Research on capacity allocation for source-grid-load-storage systems based on improved PSO[J]. Integrated Intelligent Energy, 2024, 46(9): 28-36. doi: 10.3969/j.issn.2097-0706.2024.09.004
[13]	MAHMOUDI S M, MALEKI A, REZAEI OCHBELAGH D. Techno-economic assessment of hydrogen-based energy storage systems in determining the optimal configuration of the nuclear-renewable hybrid energy system[J]. Energy Reports, 2024, 11: 4713-4725
[14]	姚芳, 杨晓娜, 葛磊蛟, 等. 风-光-氢能源系统容量优化配置研究[J]. 综合智慧能源, 2022, 44(5): 56-63. doi: 10.3969/j.issn.2097-0706.2022.05.006
	YAO Fang, YANG Xiaona, GE Leijiao, et al. Optimization of capacity allocation scheme for wind-solar-hydrogen energy system[J]. Integrated Intelligent Energy, 2022, 44(5): 56-63. doi: 10.3969/j.issn.2097-0706.2022.05.006
[15]	DAI Z M. Thorium molten salt reactor nuclear energy system (TMSR)[J]. Molten Salt Reactors and Thorium Energy, 2017:531-540.
[16]	FARES D, FATHI M, MEKHILEF S. Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system[J]. Applied Energy, 2022, 305: 117823.
[17]	EVANS D L. Simplified method for predicting photovoltaic array output[J]. Solar Energy, 1981, 27(6): 555-560.
[18]	LI J Z, LIU P, LI Z. Optimal design and techno-economic analysis of a hybrid renewable energy system for off-grid power supply and hydrogen production: a case study of West China[J]. Chemical Engineering Research and Design, 2022, 177: 604-614.
[19]	HEYDARI A, ASKARZADEH A. Optimization of a biomass-based photovoltaic power plant for an off-grid application subject to loss of power supply probability concept[J]. Applied Energy, 2016, 165: 601-611.
[20]	SRINIVAS N, DEB K. Muiltiobjective optimization using nondominated sorting in genetic algorithms[J]. Evolutionary Computation, 1994, 2(3): 221-248.
[21]	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.
[22]	ZITZLER E, LAUMANNS M, THIELE L. SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization[J]. Evolutionary Methods For Design, Optimization, and Control, 2002: 95-100.
[23]	DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601.
[24]	LI M Q, YANG S X, LIU X H. Shift-based density estimation for pareto-based algorithms in many shift-based objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(3): 348-365.
[25]	XU C B, KE Y M, LI Y B, et al. Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-Ⅱ and CRITIC-TOPSIS[J]. Energy Conversion and Management, 2020, 215: 112892.
[26]	European Commission. Photovoltaic Geographical Information System[EB/OL].(2022-03-01)[2024-08-10]. https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html#PVP.
[27]	NIE X Y, CHENG M S, ZUO X D, et al. Multi-objective capacity configuration optimization of a nuclear-renewable hybrid system[J]. Applied Thermal Engineering, 2024, 248: 123365.
[28]	HE Y, GUO S, ZHOU J X, et al. The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies[J]. Energy Conversion and Management, 2021, 229: 113779.
[29]	HE Y, GUO S, ZHOU J, et al. Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages[J]. Renewable Energy, 2022, 184: 776-790.

参数	数值
核反应堆容量/MW	10
光伏容量/MW	9.563 4
风机容量/MW	1.500 0
蓄热系统容量/（MW·h）	1 260.359 3
蓄热系统功率上限/MW	10
DPSP/%	0.968 6
LCOE/[美元·(kW·h)^-1]	0.085 7
α	0.230 4
ECP/%	4.898 6
REF/%	21.258 9