Optimal Plug-In Hybrid Electric Vehicle Operation Scheduling in Smart Grids Using Walrus Optimization Algorithm

The large-scale integration of renewable energy into distribution grids is a new challenge owing to their dependence on weather conditions, which can lead to power generation fluctuations. This issue can be addressed through active engagement with power providers and consumers in smart grids. Plug-in hybrid electric vehicles (PHEVs) are consumers that can help address the issue, improve network operations, and encourage the integration of renewable energy. This study aims to schedule PHEV operations by determining the appropriate number of charging and discharging participants at suitable locations in smart grids to minimize power losses. This study uses a recently developed metaheuristic optimization technique, namely, the walrus optimization algorithm (WaOA), to solve the PHEV scheduling problem. This study tests the performance of the algorithm on a modified 33-bus distribution test system and compared it with an existing technique, namely, the gravitational search algorithm (GSA). In addition, this study considers two worst-case scenarios: A) low demand during high generation and B) high demand during no generation. Results show that the WaOA outperforms the GSA in obtaining an accurate and precise solution, but the GSA is faster in terms of the convergence rate in both scenarios. This finding indicates that the GSA is subjected to premature convergence. In the test system, the WaOA can achieve a loss reduction of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{1 1. 9 \%}$</tex> in scenario A and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{1 3. 5 \%}$</tex> in scenario B compared with the base case. This study concludes that the optimal PHEV scheduling schemes using WaOA can reduce power grid stress and enhance renewable energy integration.