Day-Ahead Market Clearing Model and Computational Performance Optimization Method with Distributed Photovoltaic Aggregation Entities

With the rapid development and popularization of distributed photovoltaic power generation, aggregating a large number of distributed photovoltaic systems to participate in the electricity market has become an effective way. This article proposes a day-ahead market model and the optimal dispatch method considering distributed photovoltaic aggregation. It first introduces the CVaR (Conditional Value at Risk) theory to quantify the additional scheduling costs caused by the grid connection of distributed photovoltaic aggregation entities. Then, a day-ahead market clearing model considering distributed photovoltaic aggregation entities is constructed, which minimizes power generation costs while maximizing the benefits of photovoltaic aggregation entities while meeting the safety constraints of the electricity market. Finally, using actual data and case studies from a high proportion of distributed photovoltaic access areas, the effectiveness and feasibility of the proposed model and algorithm are verified. This article contributes to the research and practice of a day ahead market clearing model and computational performance optimization method containing distributed photovoltaic aggregation entities, which is of great significance for promoting the sustainable development of distributed photovoltaic power generation systems and the effective operation of the electricity market.