Guodong Wu, Diangang Hu, Lijuan Liu, and Guangqing Bao
New energy consumption, particle swarm optimisation, microgrid, optimisation, model
With the rapid development of new energy technology, how to effectively integrate and consume these energy sources has become a major challenge for power grid scheduling and management. To solve the optimisation problem of new energy consumption in microgrids (Mgs), this study proposes to improve the particle swarm optimisation (PSO) algorithm and apply it to the Mg combination optimisation model. Based on the PSO algorithm, a simulated annealing (SA) algorithm is introduced to construct a SA particle swarm algorithm for new energy consumption. At the same time, combined with a Mg example in a certain city in China, a comparison was made through experiments with multi-objective PSO (MOPSO), traditional particle swarm algorithm, and sparrow search algorithm (SSA). The experimental results showed that the SAPSO algorithm ensured that the new energy consumption rate of the Mg system reached 100%, to achieve the effect of environmental protection and energy conservation. The research results of this study have achieved the economic and environmental goals of Mg operation while meeting user load demands and have played an important role in the field of new energy consumption. The study optimised the consumption plan of new energy, improved the utilisation rate of renewable energy, reduced the operating cost of Mg systems, and provided important theoretical and practical guidance for Mg management. ∗ College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China; e-mail: [email protected]; [email protected] ∗∗ State Grid Gansu Electric Power Company, Lanzhou, 730030, China; e-mail: hudg [email protected] ∗∗∗ State Grid Gansu Electric Power Company Electric Power Science Research Institute, Lanzhou, 730030, China; e-mail: [email protected] Corresponding author: Diangang Hu
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