Fuzzy Logic Based Optimization Algorithm Design for Active Distribution Grid Power Balance Scheduling Strategy

This study presents a novel approach to optimizing power balance scheduling strategies in active distribution grids through the design and evaluation of a fuzzy logic-based optimization algorithm. With the increasing integration of renewable energy sources and the proliferation of electric vehicles, efficient management of power flow within distribution grids has become paramount. Traditional optimization methods often struggle to handle the uncertainties and complexities inherent in real-world grid operations. In response, fuzzy logic offers a flexible and adaptive framework capable of accommodating imprecise or uncertain information. Leveraging the principles of fuzzy logic and genetic algorithms, this study proposes a robust and resilient optimization approach tailored to the specific requirements of active distribution grids. The study begins with an overview of the challenges associated with power balance scheduling in active distribution grids, emphasizing the need for innovative optimization techniques. It introduces the fundamentals of fuzzy logic and genetic algorithms, highlighting their relevance to power grid management. Subsequently, a fuzzy logic-based optimization algorithm is proposed, designed to optimize power flow while adhering to operational constraints. Through comprehensive experimental evaluation, the effectiveness and practical feasibility of the proposed algorithm are assessed under various grid scenarios. Key performance metrics, including mean deviation from optimal power balance, standard deviation, and constraint violations, are analyzed to provide insights into the algorithm's performance. The results demonstrate the superiority of the fuzzy logic-based optimization algorithm over traditional methods, showcasing its ability to minimize discrepancies between power generation and consumption while maintaining grid stability and reliability.