Key points are not available for this paper at this time.
The expansion of electric vehicle (EV) adoption, seasonal changes in power demand, and the adoption of renewable energy sources (RES) have posed new, intricate operational problems within distribution networks. These include additional power losses, excess yield gaps, drops in voltage levels, and reduced operational flexibility. This work propose a complete optimization model that consists of several steps to enhance the performance and resilience of radial distribution systems (RDS) with respect to daily and seasonal variations using the Transit Search Optimization (TSO) algorithm. Such strategies include optimal allocation of RES, placement of electric vehicle charging stations (EVCS), dynamic network reconfiguration, and improved unit commitment strategies to enhance power quality, mitigate losses, and maintain stable voltage levels. A multi-objective function opts to achieve optimal RES hosting capacity together with optimal tie-switch settings across scenario-based summer, winter, spring, and fall shifts. Simulations were done on both a modified IEEE 33-bus system and a practical 51-bus distribution network. A multi-function framework on TSO is employed for optimal dynamic reconfiguration strategy to improve the performance of the distribution network considering the dynamic behavior of power profiles of wind speed and solar irradiation over a 24-hour period with varying seasons. The aim of the proposed methodology is to optimize the system performance throughout the day under hourly changes in loads and weather conditions. Technical and economic benefits for selection of RES and reconfiguration strategy applied to boost system effectiveness and accommodate more customers. The results of this study show that joint placement of RES integrated with seasonal reconfiguration done markedly enhances efficiency, improves voltage and the voltage stability index, increases operational flexibility, and guarantees reliable supply during peak EV charging and periods of low renewable generation. The optimization also demonstrates robustness against generation and demand uncertainties.
Atef et al. (Wed,) studied this question.