Real-time Energy Management Algorithm for PV-assisted Charging Station Considering Demand Response

摘要

Photovoltaic (PV)-assisted charging station is one of important charging facilities served for Electric Vehicles (EV). To minimize the operation cost of photovoltaic (PV)-assisted electric vehicle (EV) charging station, an energy management considering demand response (DR) strategy is proposed. The wavelet neural network (WNN) is utilized to forecast the price based on history data and the forecasting result is regarded as the basic price vector. Real-time price is utilized to replace basic price in current time slot to form the new price vector (NPV). The feasible energy demand region (FEDR) model is utilized to calculate the lower bounds and upper bounds dynamically. The dynamic linear programming (DLP) algorithm is utilized to calculate the optimal charging energy schedule based on the NPV and FEDR model. A comprehensive result obtained from comparison simulations has shown that the proposed ADR strategy is excellent in reducing cost, improving PV self-consumption and mitigating charging peak load on grid.