风电-光伏-抽蓄-电制氢多主体能源系统增益的合作博弈分配策略

doi:10.16183/j.cnki.jsjtu.2022.531

摘要/Abstract

摘要：

根据清洁能源示范基地的建设需求,提出基于合作博弈论的风电-光伏-抽蓄-电制氢多主体能源系统联合优化运行的增益分配策略.为兼顾系统运行安全性,构建上网出力互补性评价指标.风电、光伏、抽蓄、电制氢利益主体通过内部电量交易进行合作,以系统运行收益最大为优化目标构建联合优化调度模型.根据调度结果,应用合作博弈论中的最大最小成本法分配系统合作增量收益.利用风电-光伏-抽蓄-电制氢清洁能源示范基地12利益主体系统算例进行仿真验证,结果表明联合优化运行可实现各利益主体自身收益正增长,抽蓄库容、上网电价以及运行安全性需求会影响系统的合作增量收益.

关键词: 风电-光伏-抽蓄-电制氢多主体能源系统, 短期调度, 增益分配策略, 合作博弈论, 最大最小成本法

Abstract:

To meet the construction demand of clean energy demonstration bases, a gain allocation strategy for the joint optimization operation of wind-solar-pumped storage-hydrogen multi-stakeholder energy system based on the cooperative game theory is proposed. In order to take into consideration the security of system operation, evaluation indicators for the complementarity of on-grid output are constructed. The stakeholders of wind, solar, pumped storage, and power-to-hydrogen cooperate through the internal electricity transaction to construct a joint scheduling model with the optimization goal of maximizing the operation benefits. Then, the minimum cost remaining saving (MCRS) method in the cooperative game theory is applied to allocate the synergistic benefits based on the scheduling results. The simulation results of a 12-stakeholder wind-solar-pumped storage-hydrogen clean energy demonstration base show that each stakeholder can derive positive gains through joint operation, and the reservoir capacity of pumped storage station, on-grid price and operation security demand will affect the cooperative synergistic benefits of the system.

Key words: wind-solar-pumped storage-hydrogen multi-stakeholder energy system, short term dispatch, allocation mechanism of synergistic gains, cooperative game theory, minimum cost remaining saving (MCRS) method

中图分类号:

TM732

段佳南, 谢俊, 邢单玺. 风电-光伏-抽蓄-电制氢多主体能源系统增益的合作博弈分配策略[J]. 上海交通大学学报, 2024, 58(6): 872-880.

DUAN Jia’nan, XIE Jun, XING Shanxi. A Cooperative Game Allocation Strategy for Wind-Solar-Pumped Storage-Hydrogen Multi-Stakeholder Energy System[J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 872-880.

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参考文献 22

[1]	李佳琪, 徐潇源, 严正. 大规模新能源汽车接入背景下的电氢能源与交通系统耦合研究综述[J]. 上海交通大学学报, 2022, 56(3): 253-266. doi: 10.16183/j.cnki.jsjtu.2021.464
	LI Jiaqi, XU Xiaoyuan, YAN Zheng. A review of coupled electricity and hydrogen energy system with transportation system under the background of large-scale new energy vehicles access[J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 253-266.
[2]	李玲芳, 陈占鹏, 胡炎, 等. 基于灵活性和经济性的可再生能源电力系统扩展规划[J]. 上海交通大学学报, 2021, 55(7): 791-801. doi: 10.16183/j.cnki.jsjtu.2020.024
	LI Lingfang, CHEN Zhanpeng, HU Yan, et al. Expansion planning of renewable energy power system considering flexibility and economy[J]. Journal of Shanghai Jiao Tong University, 2021, 55(7): 791-801.
[3]	ZHANG Z W, ZHOU L W, SUN Y, et al. Joint optimal dispatching strategy of pumped storage and thermal power units with large-scale wind power integration[C]//2021 IEEE 5th Conference on Energy Internet and Energy System Integration. Taiyuan, China: IEEE, 2021: 2166-2171.
[4]	TIAN S P, HE J, XIN Y J, et al. Research on day-ahead optimal dispatch considering the mutual constraint of pumped storage unit[C]//2020 International Conference on Intelligent Computing, Automation and Systems. Chongqing, China: IEEE, 2020: 255-260.
[5]	黄强, 郭怿, 江建华, 等. “双碳”目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509. doi: 10.16183/j.cnki.jsjtu.2021.272
	HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[6]	张刘冬, 殷明慧, 卜京, 等. 基于成本效益分析的风电-抽水蓄能联合运行优化调度模型[J]. 电网技术, 2015, 39(12): 3386-3392.
	ZHANG Liudong, YIN Minghui, BU Jing, et al. A joint optimal operation model of wind farms and pumped storage units based on cost-benefit analysis[J]. Power System Technology, 2015, 39(12): 3386-3392.
[7]	姜枫, 鲍峰, 姬联涛, 等. 考虑抽蓄-风-光-火联合系统运行经济效益的日前优化调度研究[J]. 水力发电, 2022, 48(1): 99-105.
	JIANG Feng, BAO Feng, JI Liantao, et al. Research on optimization of day-ahead dispatching of pumped storage-wind power-photovoltaic-thermal power hybrid system considering operating economic benefit[J]. Water Power, 2022, 48(1): 99-105.
[8]	林俐, 岳晓宇, 许冰倩, 等. 计及抽水蓄能和火电深度调峰效益的抽蓄-火电联合调峰调用顺序及策略[J]. 电网技术, 2021, 45(1): 20-32.
	LIN Li, YUE Xiaoyu, XU Bingqian, et al. Sequence and strategy of pumped storage-thermal combined peak shaving considering benefits of pumped storage and deep regulation of thermal power[J]. Power System Technology, 2021, 45(1): 20-32.
[9]	郭志忠, 叶瑞丽, 刘瑞叶, 等. 含抽水蓄能电站的可再生能源电网优化调度策略[J]. 电力自动化设备, 2018, 38(3): 7-15.
	GUO Zhizhong, YE Ruili, LIU Ruiye, et al. Optimal scheduling strategy for renewable energy system with pumped storage station[J]. Electric Power Automation Equipment, 2018, 38(3): 7-15.
[10]	王开艳, 罗先觉, 贾嵘, 等. 充分发挥多能互补作用的风蓄水火协调短期优化调度方法[J]. 电网技术, 2020, 44(10): 3631-3641.
	WANG Kaiyan, LUO Xianjue, JIA Rong, et al. Short-term coordinated scheduling of wind-pumped-hydro-thermal power system with multi-energy complementarities[J]. Power System Technology, 2020, 44(10): 3631-3641.
[11]	WU X, LI H Y, WANG X L, et al. Cooperative operation for wind turbines and hydrogen fueling stations with on-site hydrogen production[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2775-2789.
[12]	BAN M F, BAI W C, SONG W L, et al. Optimal scheduling for integrated energy-mobility systems based on renewable-to-hydrogen stations and tank truck fleets[J]. IEEE Transactions on Industry Applications, 2022, 58(2): 2666-2676.
[13]	HU Z Y, CHEN L, GAN D Q, et al. Allocation of unit start-up costs using cooperative game theory[J]. IEEE Transactions on Power Systems, 2006, 21(2): 653-662.
[14]	麻秀范, 余思雨, 朱思嘉, 等. 基于多因素改进Shapley的虚拟电厂利润分配[J]. 电工技术学报, 2020, 35(Sup.2): 585-595.
	MA Xiufan, YU Siyu, ZHU Sijia, et al. Profit allocation to virtual power plant members based on improved multifactor shapley value method[J]. Transactions of China Electrotechnical Society, 2020, 35(Sup.2): 585-595.
[15]	XIE J, ZHANG L Q, CHEN X Y, et al. Incremental benefit allocation for joint operation of multi-stakeholder wind-PV-hydro complementary generation system with cascade hydro-power: An Aumann-Shapley value method[J]. IEEE Access, 2020, 8: 68668-68681.
[16]	张丽琴, 谢俊, 张秋艳, 等. 基于Shapley值抽样估计法的风-光-水互补发电增益分配方法[J]. 电力自动化设备, 2021, 41(9): 126-132.
	ZHANG Liqin, XIE Jun, ZHANG Qiuyan, et al. Synergistic benefit allocation method for wind-solar-hydro complementary generation with sampling-based Shapley value estimation method[J]. Electric Power Automation Equipment, 2021, 41(9): 126-132.
[17]	FARIA E, BARROSO L A, KELMAN R, et al. Allocation of firm-energy rights among hydro plants: An Aumann-Shapley approach[J]. IEEE Transactions on Power Systems, 2009, 24(2): 541-551.
[18]	夏依莎, 刘俊勇, 刘继春, 等. 基于电量共享的梯级水光蓄联合发电系统优化调度策略[J]. 电力自动化设备, 2021, 41(9): 118-125.
	XIA Yisha, LIU Junyong, LIU Jichun, et al. Optimal scheduling strategy of cascaded hydro-photovoltaic-pumped storage hybrid generation system based on electric energy sharing[J]. Electric Power Automation Equipment, 2021, 41(9): 118-125.
[19]	ZHANG L Q, XIE J, CHEN X Y, et al. Cooperative game-based synergistic gains allocation methods for wind-solar-hydro hybrid generation system with cascade hydropower[J]. Energies, 2020, 13(15): 3890.
[20]	ZHOU B R, GENG G C, JIANG Q Y. Hydro-thermal-wind coordination in day-ahead unit commitment[J]. IEEE Transactions on Power Systems, 2016, 31(6): 4626-4637.
[21]	李健华, 刘继春, 陈雪, 等. 含可再生能源的多能互补发电系统容量配置方法[J]. 电网技术, 2019, 43(12): 4387-4398.
	LI Jianhua, LIU Jichun, CHEN Xue, et al. Capacity allocation of multi-energy complementary system including renewable energy[J]. Power System Technology, 2019, 43(12): 4387-4398.
[22]	HAN L Y, MORSTYN T, MCCULLOCH M. Incentivizing prosumer coalitions with energy management using cooperative game theory[J]. IEEE Transactions on Power Systems, 2019, 34(1): 303-313.

抽蓄库容缩放系数	合作前收益/ 万元	合作后收益/ 万元	增量收益/ 万元
0.1	-413.90	-278.77	135.13
0.2	-399.96	-246.04	153.93
0.3	-386.03	-218.32	167.71
0.5	-358.16	-177.29	179.13
1.0	-288.48	-109.35	179.13
1.5	-218.80	-40.16	179.13

上网电价场景设置	合作前收益/ 万元	合作后收益/ 万元	增量收益/ 万元
增加0.5元/(kW·h)	-288.30	-109.32	178.98
基础场景	-288.48	-109.35	179.13
减少0.5元/(kW·h)	-288.66	-109.38	179.27

上网电价场景设置	合作前收益/ 万元	合作后收益/ 万元	增量收益/ 万元
与工业电价正相关	-255.54	-107.03	148.52
与工业电价负相关	-247.73	-94.59	153.13

分配方法	n主体系统理论联盟求解数量/个	本算例实际联盟求解数量/个	计算耗时/s
Shapley值法	2ⁿ-1	4095	85285
MCRS法	2n+1	25	304