Distributed Electricity Trading Mechanism of Multi-operatorVirtual Power Plant Based on Nash Bargaining Method

doi:10.12204/j.issn.1000-7229.2022.03.015

Abstract

Abstract:

With the power market sales side increasingly open, different stakeholders in virtual power plants (VPP) can increase economic benefits through electricity trading. Firstly, for a type of VPP that includes multiple operating entities such as distributed new energy operator, energy storage operator, and aggregator of prosumers, a cooperative game-based energy trading mechanism among multiple operating entities is proposed in this paper, and the total operating cost of VPP is minimized. Secondly, taking the operating cost of each operating entity’s separate transaction with the distribution network as the breakdown point of the negotiation, the Nash bargaining method is used to solve the electricity transaction volume and revenue transfer among various operating entities, so as to maintain the enthusiasm of each operating entity to participate in the cooperation. Taking into account the non-convexity of the Nash bargaining model and the privacy security of each operating entity, the bargaining equilibrium problem is converted into two convex sub-problems, and the ADMM algorithm is used to solve them. Finally, a simulation example further verifies that the proposed method can effectively reduce the operating cost of each operating entity, and provides a reference scheme for the design of the electricity transaction mechanism in VPP.

Key words: virtual power plant, cooperative game, energy transaction, Nash bargaining, Pareto-optimal, alternating direction multiplier method

CLC Number:

TM73

WANG Shuai, SHUAI Xuanyue, WANG Zhidong, WANG Xiuli, ZHANG Liang, QU Ying, SONG Xinfu. Distributed Electricity Trading Mechanism of Multi-operatorVirtual Power Plant Based on Nash Bargaining Method[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(3): 141-148.

Figures/Tables 7

Fig.1 Framework of multi-operator power trading in VPP

Table 1 Time-of-use price of the grid元/(kW·h)

Fig.2 Curve of wind turbine output in DGO,PV output and load forecast in PA in a day

Fig.3 Curve of DGO-side unit output, CESO-sideenergy storage battery output, and PA-side loadadjustment in a day under two modes

Fig.4 Curve of power transmission betweenoperators in a day under mode 2

Fig.5 Curve of power interaction between eachoperator and the main grid in a day under two modes

Table 2 Operating costs of all operators under two modes and profit transfer during the bargaining process 元

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