Three AI techniques, Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO), are employed to optimize the optimal composition of energy sources
This study used the combined genetic algorithm (GA) and model predictive control (MPC) to size and optimize the hybrid renewable energy PV/Wind/FC/Battery subject to certain constraints
The proposed research has to present a thorough approach for applying the evolutionary algorithm to resolve problem-based microgrid size for a specified LPSP value. The results of the
In microgrid design, people use Genetic Algorithms (GAs) to assess various parameters including the size and location of renewable energy sources, the capacity of the storage systems, the load profiles,
Comparative analyses against Particle Swarm Optimization and the Vortex Search Algorithm demonstrate the superior accuracy, stability, and computational efficiency of the proposed
Therefore, this paper presents a genetic algorithm-based approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems— node allocation and edge
Grid-tied microgrids play a crucial role by connecting renewable energy sources to the main power grid, contributing to sustainability and resilience in a balanced and effective manner.
The Crossover function is the most important operation in genetic algorithm [43]. A single point crossover function is defined to crossover a triplet of three chromosomes separately as shown in
The proposed MG consists of a Photovoltaic (PV) generator and a battery storage system and uses a Genetic Algorithm (GA) based on a one-day scheduling timeframe.
Genetic Algorithm generates demand response strategies and optimizes battery dispatch, while LightGBM forecasts solar power generation and building load consumption.
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