A day-ahead market pricing model for load aggregators based on potential game

doi:10.3969/j.issn.2097-0706.2023.11.008

Abstract

Abstract:

As residential electricity consumption continues to increase and new power systems are under construction， demand response has become an important means to ensure the safety and stability of the superior power grid，and higher requirements on demand side are proposed by the power grid. Load aggregators play an increasingly important role as the intermediary between the system and users. To keep the stability of the aggregators' day-ahead bidding market and motivate users to participate in demand response， the load resources participating in demand response are taken as generalized demand-side resources. Then， a demand response mechanism based on price incentives is proposed， and a hierarchical trading model including the superior grid， load aggregators and users is established based on game theory and potential game. The revenue models of users and load aggregators are made based on the user response mechanism and day-ahead market bidding mechanism. The models can simplify the multi-objective optimization problem to single-objective convex quadratic programming problems through function construction. The proposed algorithm can converge to Nash equilibria in a short time and maintain the convergence under large-scale demand responses. Proven by the practical cases， the proposed model can improve the profit of load aggregators and the revenue of users at the same time.

Key words: new power system, demand response, electrical power market, non-cooperative game, potential game, load aggregator, day-ahead market bidding, Nash equilibrium

CLC Number:

TK01⁺8:TM73

MEI Wenqing, LIU Xiaofeng, WANG Jiacheng, TAN Mengling. A day-ahead market pricing model for load aggregators based on potential game[J]. Integrated Intelligent Energy, 2023, 45(11): 62-69.

Figures/Tables 9

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LA	场景1	场景2
1	9 711.34	9 676.50
2	11 404.43	11 341.67
3	12 917.33	12 673.65

LA	用户	场景1	场景2
1	A类用户	0.563 3	0.538 9
	B类用户	0.287 0	0.271 9
	C类用户	0.191 3	0.181 3
2	A类用户	0.473 2	0.472 5
	B类用户	0.237 0	0.236 9
	C类用户	0.158 0	0.157 9
3	A类用户	0.453 0	0.440 1
	B类用户	0.226 5	0.220 2
	C类用户	0.151 0	0.146 8