Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price

With a large number of photovoltaics (PVs) and electric vehicles (EVs) connected to the distribution network, the distribution system will face the risk of bidirectional congestion. However, the output of PV and the flexibility of EVs are uncertain, which will bring challenges to congestion management. This paper proposes a distributed optimal scheduling strategy for aggregated EVs and PV considering dynamic distribution locational marginal price (DLMP). Firstly, the static robust optimization method is used to deal with the uncertainty of EVs and PVs. Then, the dual decomposition theory is used to formulate the interactive rules between the distribution system operator (DSO) and aggregators. Finally, the distribution network congestion is alleviated according to the DLMP. The case study based on the improved ieee-33 bus system verifies the rationality and effectiveness of the strategy. The results show that the proposed strategy can effectively solve the congestion of distribution network considering the willingness of the aggregators to purchase and sell electricity.