Data-driven real-time dynamic pricing for dual-PV-grid-powered bidirectional electric vehicle charging

Abstract

Transportation electrification is revolutionizing a progressive transition to attain net zero goals. Renewable energy integration emerges as a promising solution to reduce both the dependency on fossil fuels and control global warming. With the advancements in electric vehicle (EV) technology, formulating an effective EV charging tariff is crucial. Therefore, this work proposes a real-time dynamic tariff framework for a grid-tied solar photovoltaic (PV)-based EV charging system. It incorporates various system parameters, including the electricity market rates, battery state of charge levels, and the congestion rates at the charging station. The framework is flexible to operate in stand-alone mode during grid outages, which is not uncommon in developing countries, or during high electricity market rates. The principle objective is to incentivize EV users with an optimal choice of sources based on their availability and price levels. The proposed framework is scalable from household charging to a distribution licensee, significantly improving its financial health, or in forming a microgrid. The artificial neural network is employed to evaluate the system parameters in the framework. Furthermore, the model optimizes the rate to be charged from alternate sources during an outage. The proposed optimization enhances the financial viability of distribution licensees by 87.5% on average, along with maximizing the benefits to consumers by an average savings of 50%.