A deep reinforcement learning approach based energy management strategy for home energy system considering the time-of-use price and real-time control of energy storage system

Xiong, Shengtao; Liu, Dehong; Chen, Yuan; Zhang, Yi; Cai, Xiaoyan

doi:10.1016/j.egyr.2024.03.003

Energy Reports

2024

DOI: 10.1016/j.egyr.2024.03.003

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A deep reinforcement learning approach based energy management strategy for home energy system considering the time-of-use price and real-time control of energy storage system

Shengtao Xiong,

Dehong Liu,

Yuan Chen

et al.

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ADPA Optimization for Real-Time Energy Management Using Deep Learning

Wan,

Huang,

et al. 2024

Energies

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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.

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ADPA Optimization for Real-Time Energy Management Using Deep Learning

Wan,

Huang,

et al. 2024

Energies

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A deep reinforcement learning approach based energy management strategy for home energy system considering the time-of-use price and real-time control of energy storage system

Cited by 1 publication

References 36 publications

ADPA Optimization for Real-Time Energy Management Using Deep Learning

ADPA Optimization for Real-Time Energy Management Using Deep Learning

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