ADPA Optimization for Real-Time Energy Management Using Deep Learning

Zhengdong Wan, Yan Huang, Liangzheng Wu and Chengwei Liu ()
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Zhengdong Wan: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China
Yan Huang: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China
Liangzheng Wu: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China
Chengwei Liu: Central Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Wuhan 430071, China

Energies, 2024, vol. 17, issue 19, 1-13

Abstract: The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.

Keywords: microgrid; real-time pricing; deep learning; adaptive dynamic programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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