Energy Interaction and Energy Storage Sharing Optimization Method for Users, Base Stations, and Charging Stations

doi:10.16183/j.cnki.jsjtu.2023.407

Abstract

Abstract:

The internal energy optimization within a single entity of industrial users, base stations, and charging stations is constrained by local power supply and demand limitations, resulting in low utilization of flexible resources such as energy storage and insufficient energy utilization efficiency. To address these issues, an energy sharing and interactive optimization method is proposed for industrial users, base stations, and charging stations based on the quantification of their complementarity and a game-based pricing incentive mechanism. First, a complementary quantification model is developed based on the analysis of the characteristics of industrial users, base stations, and charging stations, using the standard deviation of net load as a complementary indicator. Then, considering the adjustable capabilities of air conditioning and electric vehicles, as well as the proactive decision-making abilities of industrial users, charging stations, and base stations, a master-slave game-based pricing model is established to incentivize the sharing of energy storage and energy interaction among these entities. Next, incorporating 0-1 integer variables, a solution method utilizes the adaptive differential evolution algorithm combined with the mixed-integer optimization theory. Finally, case studies validate that optimizing the energy storage and energy dispatch of industrial users, base stations, and charging stations in different time periods can effectively leverage their complementarity, enhance the economic benefits of each entity, improve the utilization of idle flexible resources, and enhance the overall energy self-consistency of the system.

Key words: shared energy storage, Stackelberg game, flexible resources, base station, electric vehicle

CLC Number:

TM732

HU Long, FANG Baling, FAN Feilong, CHEN Dawei, LI Xinxi, ZENG Run. Energy Interaction and Energy Storage Sharing Optimization Method for Users, Base Stations, and Charging Stations[J]. Journal of Shanghai Jiao Tong University, 2025, 59(7): 877-888.

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交易方式	时段	电价/[元· (kW·h)^-1]
从电网购电价	峰:10:00—15:00、18:00—23:00	1.350
	平:7:00—10:00、15:00—18:00	0.820
	谷:0:00—7:00、23:00—24:00	0.423
上网电价	0:00—24:00	0.352

优化方法	互补匹配度	互补性指数
方法2	0.86	29.10
方法3	0.72	32.49
方法4	0	50.17
本文方法	0.94	9.85

优化方法	互济率/%	储能利用率/%	储能损耗成本/元
方法1	0	38.77	21.32
方法2	66.01	52.36	15.22
方法3	59.85	45.26	17.86
本文方法	78.69	58.57	12.76

优化方法	成本/元				总成本/元
优化方法	用户	充电站	基站	聚合商	总成本/元
方法1	1117.7	3.4	-72.39	0	1048.71
方法2	1062.8	-23.6	-72.39	24.40	966.81
方法3	1078.4	3.4	-107.07	4.82	974.73
本文方法	1009.0	-41.6	-122.39	38.22	845.01

优化方法	新能源消纳率/%	环境成本/元	峰均比
方法1	80.47	187.68	3.27
方法2	90.64	112.53	2.78
方法3	85.55	135.20	2.89
本文方法	97.52	56.85	2.45