Proposes an innovative approach to radiality representation in electrical distribution system reconfiguration, enhancing efficiency and computational performance.
This paper explores multi-objective stochastic programming to optimize energy management in grid-connected unbalanced microgrids, including renewable energy and EV integration.
This paper presents a scenario-based convex programming model to enhance reconfiguration capabilities in electrical distribution systems by planning reserve branches.
A hybrid model combining stochastic programming and information gap decision theory for managing uncertainties in renewable generation and energy demand within microgrids.
This work enhances algebraic methods for assessing the reliability of electrical distribution systems, offering robust metrics for system performance evaluation.
This study explores robust joint planning of electrical distribution systems with electric vehicle charging stations, addressing uncertainties in demand and generation.
The paper introduces a multistage long-term planning framework for electrical distribution systems, evaluating multiple alternatives and optimization scenarios.
This paper presents a mixed-binary linear programming model for distribution system expansion planning, providing insights into optimization and resource allocation.