Forecast PV Generation, EV Charging Schedule and Energy Load Demand for Optimal Power Scheduling in P2P Nanogrid Network

This paper presents optimal power scheduling for peer-to-peer (P2P) nanogrid networks incorporating photovoltaic (PV) and electric vehicle (EV) resources. The aim is to minimize energy cost, maximize self-consumption of PV power, and reduce grid dependency through coordinated scheduling of EV charging and P2P energy trading. In this approach, EVs are considered not only as loads but also as energy-sharing partners, contributing to the dynamic energy landscape of the network. The primary objective is to develop an optimal solution for the power scheduling in the nanogrid network. After forecasting, the dataset are given to an optimization module using particle swarm optimization (PSO) to minimize the energy costHere, PV generation data, EV charging schedules, and energy load demand are forecasted using Bidirectional - Long Short Term Memory (BD-LSTM) models. An objective function was formulated for optimal power scheduling, and optimized using PSO.