A two-stage framework for the joint planning and operation of battery-integrated renewable generation in microgrids coupled with energy hubs and electric vehicle parking lots

The transition toward clean energy necessitates the integration of renewable energy resources (RERs) into modern power grids. However, the inherent uncertainties in RER generation and demand pose challenges for system operators, requiring flexible resources such as batteries and adaptive prosumers to enhance operational resilience. To address these challenges, this paper proposes a comprehensive two-stage optimization model for integrating RERs within microgrids, leveraging the conditional value at risk (CVaR) technique to mitigate uncertainty-related risks. The first stage, implemented in MATLAB, optimizes the location and capacity of assets using an improved gray wolf optimizer (IGWO). The second stage, formulated in GAMS and solved via CPLEX, manages distribution network operations while incorporating newly installed assets, a linear AC power flow model, and short-term uncertainties. The proposed framework is tested on a modified IEEE 69-bus distribution system with four microgrids. Simulation results show that the model effectively enhances the flexibility of energy hubs, parking lots, and batteries, leading to a 23.8 % increase in RER hosting capacity, a 56.76 % reduction in operating costs, and an 11.46 % decrease in CO2 emissions. Additionally, the IGWO algorithm improves convergence speed by 40.38 % compared to the standard GWO algorithm, demonstrating its computational efficiency.