Research on day-ahead electricity market trading of photovoltaic and energy storage charging stations considering dual uncertainties

doi:10.3969/j.issn.2097-0706.2025.05.007

Abstract

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

CLC Number:

TM910

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.

Figures/Tables 17

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Table 2

Fig.5

Fig.6

Fig.7

Fig.8

Table 3

Value of each parameter

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

Table 3

Fig.9

Fig.10

Fig.11

Fig.12

Table 4

Table 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