Integrated bidirectional electric vehicle battery network for sustainable communities: A planning framework

Abstract

The transition towards sustainable and net-zero energy communities has become imperative in addressing the challenges of climate change and ensuring a resilient energy future. This work proposes an innovative planning framework through the development of an integrated bidirectional electric vehicle (EV) battery storage network for net-zero communities. The proposed framework is intended for neighborhood planning and integrates a bidirectional charging infrastructure that allows EV batteries to seamlessly contribute to the grid during periods of high demand or store excess renewable energy during off-peak hours. To analyze various EV microgrid integration scenarios, a combined Matlab-Simulink and EnergyPlus simulation environment is proposed to simulate EV battery networks in neighborhood settings. This study examines state of charge (SoC), and energy exchange characteristics based on specific user behaviors and charging scenarios. A neighborhood archetype of 48 single-family detached houses is considered along with five EV use profiles (EVPs) for the demonstration of the proposed method. For the considered neighborhood, in winter, EVPs have eliminated the peak loads during early morning hours (1 am–6 am) by discharging stored energy. In spring, loads exceeding the base load are observed from 1 am to 10 am, with all EVPs discharging energy until 9 am and then recharging during off-peak hours. Summer required strategic charging management, with EVPs supporting peak loads from 7 am to 6 pm. In the fall, EVPs discharged energy from 12:01 am–6 am and recharged from 10 am to 6 pm. The study introduces the EVP peak support index facilitating real-time charging adjustments and incentivizing greater participation. By leveraging this index, smart charging systems can develop algorithms to control charging times based on grid needs, ensuring efficient energy distribution and enhanced grid stability. This framework offers a robust approach to scenario generation for energy and urban planners during the neighborhood planning stages predicting energy performance and management.