Customized Critical Peak Rebate Pricing Mechanism for Virtual Power Plants
Wen Chen, Jing Qiu, Qingmian Chai
Abstract
To address the increasing impact of extreme temperatures (ETs) on the power system and electricity supply, demand response (DR) programs are widely used to reduce peak demands at ETs. The customized pricing mechanisms are required for an effective DR to target the potential consumers accurately. This paper proposes a frequency control ancillary service and critical peak rebate (FCAS-CPR) strategy based on cumulative prospect theory (CPT) for a virtual power plant (VPP) in coupled FCAS and DR markets. In order to develop a customized pricing model in the proposed strategy, the load clustering technique is applied. This aims to classify the loads based on the typical characteristics of consumers’ reactions to ETs. The consumers’ load reactions to ETs are described by the peak temperature sensitivity and the lag length. Then, the subjective decisions of irrational consumers are reflected based on the CPT. Last, the interaction between VPP consumers and the retailer is modeled as a Stackelberg game and solved by the Salp Swarm Algorithm (SSA). The simulation results verify the effectiveness of the proposed FCAS-CPR pricing mechanism, which can efficiently reduce the peak loads to mitigate impacts of ETs on power systems, while achieving a win-win outcome in maximizing the utilities of both the retailer and VPP consumers.