A microgrid scheduling method based on the improved jellyfish search algorithm
Junnan Zhang, Hua Fu · 2024
Microgrid optimization scheduling, a pivotal facet of intelligent grid optimization, is crucial in curbing energy consumption, mitigating environmental degradation, and trimming user electricity expenses. The overarching aim of microgrids extends beyond mere power supply adequacy to encompass enhanced economic viability and environmental sustainability. Hence, this study sets forth a multi-objective optimization scheduling paradigm tailored for microgrids operating in islanded mode. This framework holistically integrates the operational costs and environmental impact considerations of microgrid systems. The constellation of distributed generation assets within microgrid systems encompasses photovoltaic arrays, wind turbines, diesel engines, microturbines, and batteries. To effectively tackle the proposed optimization conundrum, an enhanced jellyfish search algorithm is posited in alignment with the structural nuances and attributes of the model. Two distinct operation scheduling strategies are scrutinized, and their scheduling outcomes are deliberated across varied optimization objectives. Simulation outcomes underscore the model's efficacy and supremacy in curtailing user electricity expenditures, decreasing environmental pollution, and fostering the optimized operation of microgrids.