Table1_A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm.DOCX

• Main Authors: Xianjing Zhong, Xianbo Sun, Yuhan Wu
• Format: Dataset
• Language: unknown
• Published: 2024
• Subjects: Nuclear Engineering; Carbon Sequestration Science; Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels); Carbon Capture Engineering (excl. Sequestration); Non-automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels); Chemical Engineering not elsewhere classified; Power and Energy Systems Engineering (excl. Renewable Power); Renewable Power and Energy Systems Engineering (excl. Solar Cells); Energy Generation; Conversion and Storage Engineering; Nuclear Engineering (incl. Fuel Enrichment and Waste Processing and Storage); Chemical Sciences not elsewhere classified; wind–solar-storage AC/DC microgrid; hybrid energy storage system; optimal configuration; bi-layer optimal model; multi-strategy hybrid improvement of Beluga Whale Algorithm; Beluga; Beluga whale; Beluga*
• Online Access: https://doi.org/10.3389/fenrg.2024.1336205.s001; https://figshare.com/articles/dataset/Table1_A_bi-layer_optimization_method_of_the_grid-connected_microgrid_based_on_the_multi-strategy_of_the_beluga_whale_algorithm_DOCX/25302679

Endeavoring to enhance the penetration rate of renewable energy sources, concurrently ensuring economic and operational stability, this study proposes a novel bi-layer optimization method of the wind–solar-storage AC/DC microgrid (MG). First, by incorporating a superordinate electric and seasonal hydrogen hybrid energy storage system (E&SHESS), the topology structure of the microgrid is established. Subsequently, to rectify the intrinsic limitations of the conventional beluga whale optimization (BWO) algorithm, this paper proposes a multi-strategy hybrid improvement to BWO (MHIBWO). This innovative improvement integrates an MTent strategy, a step size adjustment mechanism, and a crisscross strategy. Then, constructing a bi-layer iterative model based on the topology, annual net income and grid-connected friendliness are introduced as optimization objectives for the outer and inner layers, respectively, utilizing MHIBWO and CPLEX for resolution. Through a nested iteration of the two layers, the model outputs the capacity scheme with the best performance of economy and stability. Finally, the simulation unequivocally demonstrated the superiority of MHIBWO and the model proposed. In addition, based on the real data of the Elia power station, the validity of the method in operation is tested using the fuzzy C-means algorithm (FCMA) to extract and aggregate typical days, thereby presenting a sophisticated solution for the field of microgrids optimization configuration.