Optimal scheduling strategy for virtual power plants with aggregated user-side distributed energy storage and photovoltaics based on CVaR-distributionally robust optimization

This paper addresses the management and operational challenges posed by installing distributed photovoltaic (PV) and energy storage resources for industrial, commercial, and residential customers. In many regions, virtual power plant (VPP) aggregators are faced with the difference between two different tariff policies when aggregating such distributed energy resources (DERs), a consideration that is overlooked in several existing studies. A VPP business model is proposed in which an electricity retailer aggregates these DERs. The proposed business model introduces a strategy to participate in the spot energy market to utilize spread arbitrage, which accommodates both tariff systems while considering the interests of VPPs and users. Therefore, a two-stage optimal scheduling and bidding strategy model is developed. A distributionally robust optimization (DRO) approach is used in the model of stage I to cope with electricity price uncertainty. Considering the risk that electricity price poses to market bids, a DRO based on the conditional value at risk is developed for the model of stage II. Using Guangdong Province as a case study, the proposed business model and strategy are validated using the results of numerical computations involving a practical case that combines actual data associated with an electricity retailer and spot electricity market transactions in Guangdong Province. The results indicate that when users have access to 50 % of the benefits of the VPP, they can obtain a boost of about 1 % compared to the pre-aggregation.