CAPACITY CONFIGURATION AND MULTI-OBJECTIVE DECISION-MAKING FOR WIND-PV-CHP SYSTEM WITH MOLTEN SALT HEAT STORAGE

Wang Jiale, Zhou Yuangui, Guo Haoyu, Wang Luchun, Wan Luoqiang, Jia Fei

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 599-607.

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 599-607. DOI: 10.19912/j.0254-0096.tynxb.2025-0791

CAPACITY CONFIGURATION AND MULTI-OBJECTIVE DECISION-MAKING FOR WIND-PV-CHP SYSTEM WITH MOLTEN SALT HEAT STORAGE

  • Wang Jiale1, Zhou Yuangui1,2, Guo Haoyu1, Wang Luchun3, Wan Luoqiang3, Jia Fei1
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Abstract

In order to alleviate the technical challenges of renewable energy accommodation and synergistic supply of multi-energy loads in the context of global climate governance, this paper proposes a wind-photovoltaic-molten salt thermal energy storage-electric heater hybrid renewable cogeneration system, which uses electric heater-thermal energy storage-power block to regulate wind and photovoltaic output and supply power load, as well as uses thermal energy storage-heat exchanger to supply heat load. This paper comprehensively considers the economy, reliability, energy efficiency and environmental performance to establish a many-objective capacity optimization model. The model aims to minimize levelized cost of energy, loss of power and heat supply probability, curtailment rate of wind and PV power and whole lifecycle equivalent carbon emissions as objectives, uses the non-dominated sorting genetic algorithm(NSGA-Ⅲ) to solve the four-objective problem, then uses principal component analysis to reduce dimension, finally uses TOPSIS decision-making method to obtain the optimal-compromise solution. The results of case study show that the objective function of the cogeneration system proposed in this paper is reduced by 4.7%, 31.8%, 7.5% and 26.4%, respectively, compared to the stand-alone power system.

Key words

wind-solar cogeneration system / molten salt thermal storage / high-dimensional multi-objective optimization / principal component analysis / TOPSIS

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Wang Jiale, Zhou Yuangui, Guo Haoyu, Wang Luchun, Wan Luoqiang, Jia Fei. CAPACITY CONFIGURATION AND MULTI-OBJECTIVE DECISION-MAKING FOR WIND-PV-CHP SYSTEM WITH MOLTEN SALT HEAT STORAGE[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 599-607 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0791

References

[1] Liu H C, Geng Z, Gu Y J, et al.A regional integrated energy system with a coal-fired CHP plant, screw turbine and solar thermal utilization: scenarios for China[J]. Energy Conversion and Management, 2020, 212: 112812.
[2] 陈胜, 卫志农, 孙国强, 等. 电-气互联综合能源系统安全分析与优化控制研究综述[J]. 电力自动化设备, 2019, 39(8): 3-11.
Chen S, Wei Z N, Sun G Q, et al.Review on security analysis and optimal control of electricity-gas integrated energy system[J]. Electric Power Automation Equipment, 2019, 39(8): 3-11.
[3] 孙亮, 李佳雯, 杨心贺, 等. 含相变储能的冷热电联供型微网多目标优化配置[J]. 电力自动化设备, 2021, 41(10): 60-66.
Sun L, Li J W, Yang X H, et al.Multi-objective optimal configuration of combined cooling, heating and power microgrid with phase change material energy storage[J]. Electric Power Automation Equipment, 2021, 41(10): 60-66.
[4] 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.
[5] Ren G R, Wan J, Liu J F, et al.Spatial and temporal assessments of complementarity for renewable energy resources in China[J]. Energy, 2019, 177: 262-275.
[6] 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 system[J]. Acta Energiae Solaris Sinica, 2020, 41(11): 359-368.
[7] 刘忠, 陈星宇, 邹淑云, 等. 计及碳排放的风-光-抽水蓄能系统容量优化配置方法[J]. 电力系统自动化, 2021, 45(22): 9-18.
Liu Z, Chen X Y, Zou S Y, et al.Optimal capacity configuration method for wind-photovoltaic-pumped-storage system considering carbon emission[J]. Automation of Electric Power Systems, 2021, 45(22): 9-18.
[8] 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.
[9] Bloess A.Modeling of combined heat and power generation in the context of increasing renewable energy penetration[J]. Applied Energy, 2020, 267: 114727.
[10] Maleki A, Hafeznia H, Rosen M A, et al.Optimization of a grid-connected hybrid solar-wind-hydrogen CHP system for residential applications by efficient metaheuristic approaches[J]. Applied Thermal Engineering, 2017, 123: 1263-1277.
[11] Chen Y Z, Wang J, Lund P D.Thermodynamic performance analysis and multi-criteria optimization of a hybrid combined heat and power system coupled with geothermal energy[J]. Energy Conversion and Management, 2020, 210: 112741.
[12] 宁光涛, 李琳玮, 何礼鹏, 等. 面向绿色海岛微型综合能源系统的储能系统容量规划方法[J]. 电力自动化设备, 2021, 41(2): 8-15.
Ning G T, Li L W, He L P, et al.Capacity planning method of energy storage system for micro integrated energy system in environmental friendly islands[J]. Electric Power Automation Equipment, 2021, 41(2): 8-15.
[13] Guo S, Liu D Y, Chen X Y, et al.Model and control scheme for recirculation mode direct steam generation parabolic trough solar power plants[J]. Applied Energy, 2017, 202: 700-714.
[14] 邢作霞, 丑佳明, 郭珊珊, 等. 基于混合神经网络的风电场测风数据插补方法的研究[J]. 太阳能学报, 2025, 46(5): 458-464.
Xing Z X, Chou J M, Guo S S, et al.Research on wind farm wind measurement data interpolation method based on hybrid neural network[J]. Acta Energiae Solaris Sinica, 2025, 46(5): 458-464.
[15] 刘尚奇, 胡健, 张晓杰, 等. 含光伏和储氢的电-氢集成化能源站容量配置[J]. 太阳能学报, 2023, 44(8): 171-179.
Liu S Q, Hu J, Zhang X J, et al.Capacity configuration of integrated electricity charging and hydrogen refueling station containing photovoltaic and hydrogen storage[J]. Acta Energiae Solaris Sinica, 2023, 44(8): 171-179.
[16] Abdelshafy A M, Jurasz J, Hassan H, et al.Optimized energy management strategy for grid connected double storage (pumped storage-battery) system powered by renewable energy resources[J]. Energy, 2020, 192: 116615.
[17] Deb K, Saxena D K.Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems[C]//Proceedings of the 2006 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). Vancouver, Canada, 2006: 3353-3360.
[18] Muhsen D H, Nabil M, Haider H T, et al.A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods[J]. Energy, 2019, 174: 1158-1175.
[19] 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.
[20] 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.
[21] Bader J, Zitzler E.HypE: an algorithm for fast hypervolume-based many-objective optimization[J]. Evolutionary Computation, 2011, 19(1): 45-76.
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