A Stackelberg Game-based Optimization Strategy for Integrated Energy Systems Incorporating Wind-solar Power Scenario Generation

doi:10.11930/j.issn.1004-9649.202502017

Abstract

Abstract:

In view of the characteristics of multi-physical system coupling and multi-stakeholder involvement in the operation of integrated energy systems (IES), this paper conducts a study on the optimization strategy of the leader-follower integrated energy systems considering the uncertainty and correlation of wind and solar output, aiming to enhance the energy utilization rate and operational efficiency of the IES with high permeability of new energy while taking into account the operational demands of various entities involved. Firstly, a scenario generation method based on mixed Copula function theory is presented to obtain typical daily scenarios of wind and PV power generation. Secondly, a multi-user integrated energy service provider model is developed and an integrated demand response model for electricity and heat loads is established. And then, the integrated energy sales department, acting as the leader, publishes energy selling prices and demand response compensation rates to users; the park energy aggregation department and energy storage stations, acting as followers, regulate the output of energy coupling conversion equipment, electricity exchange between parks, and the charging and discharging of energy storage stations. Finally, case studies demonstrate that the proposed methods and strategies can effectivey enhance the economic benefits of the integrated energy system.

Key words: mixed Copula, stackelberg game, integrated demand response, scenario generation, electrical interaction, IGDT

WEN Sihai, XIAN Yuesheng, HAN Yang, LIU Qunying, CHEN Shuheng. A Stackelberg Game-based Optimization Strategy for Integrated Energy Systems Incorporating Wind-solar Power Scenario Generation[J]. Electric Power, 2025, 58(11): 72-87.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202502017

https://www.electricpower.com.cn/EN/Y2025/V58/I11/72

Figures/Tables 13

References 28

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参数	时段
参数	00:00— 07:00	08:00— 12:00	13:00— 23:00
配电网购售电价/(元·(kW·h)^–1)	0.31	0.64	1
园区间购售电价/(元·(kW·h)^–1)	0.19	0.42	0.67
集中风电场售电价/(元·(kW·h)^–1)	0.25	0.53	0.84
储能电站购售电价/(元·(kW·h)^–1)	0.26	0.54	0.85
天然气价/(元·m^–3)	1.8	2.9	3.8

参数	时段
参数	00:00— 07:00	08:00— 12:00	13:00— 23:00
配电网购售电价/(元·(kW·h)^–1)	0.31	0.64	1
园区间购售电价/(元·(kW·h)^–1)	0.19	0.42	0.67
集中风电场售电价/(元·(kW·h)^–1)	0.25	0.53	0.84
储能电站购售电价/(元·(kW·h)^–1)	0.26	0.54	0.85
天然气价/(元·m^–3)	1.8	2.9	3.8

设备名称	英语缩写	能量转换效率	运行损耗系数/ (元·(kW·h)^–1)
燃气轮机	GT	0.35	0.021
燃气锅炉	GB	0.9	0.026
余热锅炉	WHB	0.68	0.016
溴化锂制冷机	LBAC	0.72	0.013
储能电站	ESS	0.98	0.013
空调	AC	3	0.015
光伏机组	PV	/	0.039
风电机组	WT	/	0.039
配电网	/	/	0.003

设备名称	英语缩写	能量转换效率	运行损耗系数/ (元·(kW·h)^–1)
燃气轮机	GT	0.35	0.021
燃气锅炉	GB	0.9	0.026
余热锅炉	WHB	0.68	0.016
溴化锂制冷机	LBAC	0.72	0.013
储能电站	ESS	0.98	0.013
空调	AC	3	0.015
光伏机组	PV	/	0.039
风电机组	WT	/	0.039
配电网	/	/	0.003

Copula函数类型	参数	参数估计值	K-S校验值				平方欧式距离和$ {d^2} $	AIC	BIC
Copula函数类型	参数	参数估计值	$ {U_1} $	$ {p_1} $	$ {U_2} $	$ {p_2} $	平方欧式距离和$ {d^2} $	AIC	BIC
Gaussian	$ {\theta _{\rm Ga}} $	0.9313	0	0.0828	0	0.5006	32.2602	–1948.9227	–1890.8426
Clayton	$ {\theta _{\rm C}} $	3.4814	1	0.0082	1	0.0039	42.5377	–1764.3040	–1706.2239
Gumbel	$ {\theta _{\rm G}} $	6.4684	0	0.6303	0	0.3607	31.5718	–2699.7685	–2641.6884
Frank	$ {\theta _{\rm F}} $	21.3129	0	0.5230	1	0.1020	31.0751	–2182.4469	–2124.3668
Mixed	$ {\lambda _{\rm Ga}} $	0.1498	0	0.8552	0	0.9108	30.9761	–5031.3016	–4566.6610
	$ {\lambda _{\rm C}} $	0.1265
	$ {\lambda _{\rm G}} $	0.0427
	$ {\lambda _{\rm F}} $	0.6809
	$ {\theta _{\rm Ga}} $	0.9604
	$ {\theta _{\rm C}} $	0.8331
	$ {\theta _{\rm G}} $	529.6581
	$ {\theta _{\rm F}} $	81.8658