Affiliations: [a] Power Control Center of Huolinhe Circular Economy, Inner Mongolia Huo Coal Hongjun Aluminium and Electricity Co., Ltd., Tongliao, China | [b] Engineering Technology Department, Beijing Herui Energy Storage Technology Co., Ltd., Beijing, China | [c] Digital Application Technology Research Center, State Power Investment Group Science and Technology Research Institute Co., Ltd., Beijing, China
Correspondence:
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Corresponding author: Hongfei Zhao, Digital Application Technology Research Center, State Power Investment Group Science and Technology Research Institute Co., Ltd., Beijing, China. E-mail: [email protected].
Abstract: To improve the current source-grid, load-storage microgrid coordinated optimal scheduling method, which is not ideal in terms of efficiency and effectiveness, the study combines convolutional neural network, variational modal decomposition, and long and short-term memory neural network to realize the short-term prediction of microgrid electric load. Based on this, a mathematical model having source-grid, load-storage coordinated optimal scheduling and an improved particle swarm algorithm are proposed for it. Compared with the particle swarm backpropagation model, the proposed microgrid power load short-term prediction model reduces the average absolute percentage error and root mean square error by 0.38% and 39.5%, respectively. In addition, the economic cost of the proposed power grid coordination and optimization scheduling model based on improved particle swarm optimization algorithm (IPSO) is lower, at $3954.3, and the load fluctuation is less, at 56.6 W. This indicates that the model proposed by the research institute helps to achieve self-sufficiency of electricity within the microgrid and mutual assistance between microgrids, thereby tapping into scheduling potential, and also helps to achieve economic electricity scheduling strategies, avoiding unnecessary thermal power generation and carbon dioxide emissions, and improving reliability. Therefore, the scheme proposed in the study can effectively realize the coordinated and optimal dispatch of source-network load and storage beneficial to the power enterprises.
