Abstract

Household battery energy storage (HBES) is expected to play an important role in the transition to decarbonized energy systems by enabling the further penetration of renewable energy technologies while assuring power grid stability. However, the hitherto high installation cost is a key barrier for further deployment of HBES. Therefore, in order to improve its economic feasibility, we will study how HBES participates in the electricity peak regulation ancillary service market (PRASM) in China, which can add new sources of income for HBES. When participating in PRASM, the market mechanism first needs to be understood, and the framework for participating in PRASM needs to be established. In this framework, HBES needs to be aggregated into a cluster by the aggregator to participate in PRASM. In this participation process, the aggregator first needs to determine the controllable capacity of HBES and analyze its uncertainty. After the upper limit of the controllable capacity is determined, the aggregator will be able to more accurately formulate the bidding strategy considering the reserve capacity and charging power allocation strategy to maximize the net income. In this paper, particle swarm optimization and chaos optimization are combined to solve this problem, and finally different scenarios are analyzed through example analysis. The results of the case analysis show that the bidding strategy considering the reserve capacity proposed in this paper can effectively reduce the output deviation value and has a relatively higher economy.

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