A DNN-based predictive optimization method for operating integrated EV charging stations with PV and battery storage

Newly built EV charging stations are usually equipped with battery storage system and PV panels, which can maximize the usage of cleanable power energy, and provide great capabilities for meeting various control objectives of the power grid. Operated properly, the controllable load of such charging stations can be used to provide ancillary services in an electricity market. This paper presents a novel predictive optimization method that adopts deep neural network (DNN) for forecasting EV loads and electricity market prices and optimally operating storage systems. The DNN-based forecasting methods consist of key function modules of data collection/cleaning, feature engineering, model training with parameter tuning, and adaptive updates for ensuring long term effectiveness. The economic operation of an EV charging station is formulated as a nonlinear multi-objective optimization problem with multiple constraints. Detailed mathematical optimization models considering time-of-use electricity and peak shaving market are provided. Comprehensive case studies are conducted on an actual EV charging station using real operation data that verifies the effectiveness of the proposed models and methods.