计及双重不确定性的光储充电站日前电力市场交易研究

doi:10.3969/j.issn.2097-0706.2025.05.007

摘要/Abstract

摘要：

为提高光储充电站（PECS）参与市场交易的中标准确度，在预出清时段设置PECS与电动汽车（EV）通过主从博弈方式确定最优交易的策略，从而在正式出清时段获取最佳收益模型。考虑到博弈过程中光伏出力与用户充电意愿双重不确定性对交易结果的影响，分别采用鲁棒优化不确定集与Takagi-Sugeno-Kang（TSK）模糊模型处理不确定性问题。建立PECS作为上层领导者、EV作为下层跟随者的主从博弈市场交易模型，以提高PECS市场收益。设计了PECS与EV之间通过博弈优化与不通过博弈优化2种情况参与日前市场的算例，博弈模型通过商业求解器CPLEX与智能优化算法求解。PECS运行前后对比表明，3个运营商收益分别提高12.59%，29.13%，20.37%，EV用户充电波动率分别降低4.4，3.6，5.2百分点，验证了模型在提高运营收益与降低充电波动性方面的有效性。

关键词: 日前电力现货市场, 主从博弈, 光储充电站, 双重不确定性, 电动汽车

Abstract:

To improve the accuracy of bid selection for PV and energy storage charging stations（PECS） in market transactions， an innovative strategy is proposed， where the optimal trading strategy is determined by PECS and electric vehicles（EV） through a leader-follower game during the pre-clearing period. This approach aims to maximize revenue during the official clearing period. Considering the uncertainties of PV output and users' willingness to charge that influence the transaction results in the game process， a robust optimization uncertainty set and the Takagi-Sugeno-Kang（TSK） fuzzy model were employed to address these uncertainties. A leader-follower market trading model was established， with PECS as the leader and EV as the follower， to improve the market income of PECS. A case study simulated two scenarios： one where PECS and EVs participated in the day-ahead market through game optimization， and another where they did so without game optimization. The game model was solved by the commercial solver CPLEX and intelligent optimization algorithm. PECS operating results showed that the profits of three operators increased by 12.59%，29.13%，20.37%，respectively，while the charging volatility of EV users decreased by 4.4，3.6 and 5.2 percentage， respectively. The results validated the effectiveness of the model in improving operating revenue and reducing charging volatility.

Key words: day-ahead electricity spot market, leader-follower game, photovoltaic and energy storage charging station, dual uncertainty, electric vehicle

中图分类号:

TM910

梁海平, 王金英. 计及双重不确定性的光储充电站日前电力市场交易研究[J]. 综合智慧能源, 2025, 47(5): 62-72.

LIANG Haiping, WANG Jinying. Research on day-ahead electricity market trading of photovoltaic and energy storage charging stations considering dual uncertainties[J]. Integrated Intelligent Energy, 2025, 47(5): 62-72.

图/表 17

图1

图2

图3

图4

表1

表2

图5

图6

图7

图8

表3

各参数取值

参数	取值
$Δ P m a x P V (t)$ /%	7.0
$∏$	24
$η P V$ /[元·(kW·h)^-1]	0.023
$η b a t t$ /[元·(kW·h)^-1]	0.050
$η c h$ / $η d i s$	0.95
$β 0$	0.85
$β 1$	1.25
$K t$	50%
K_w	1.5×10^-4
$ρ$	2.2×10^-3
$p p$ /元	43 834
$T p$ /h	2 112

表3

图9

图10

图11

图12

表4

表5

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参数	a	b	c	d
x₁	0.1	0.4	0.9	1.5
x₂	0.2	0.4	0.8	1.0
x₃	0.2	0.4	0.8	1.0

类型	光伏/kW	储能/[(kW/(kW·h)]	电网/(kW·h)	EV/辆
PECS 1	350	150/300	1 000	200
PECS 2	200	100/200	800	200
PECS 3	150	100/200	600	200

优化主体	优化前	优化后
PECS 1	23.5	19.1
PECS 2	17.8	14.2
PECS 3	16.1	10.9

优化主体	策略1	策略2
PECS 1	1 620	1 824
PECS 2	1 545	1 995
PECS 3	1 414	1 702
EV 1	4 153	3 803
EV 2	3 305	2 876
EV 3	3 360	2 979