Multi-objective energy dispatch with deep reinforcement learning for wind–solar–thermal–storage hybrid systems
Artikel i vetenskaplig tidskrift, 2025

With the intensification of environmental pollution and energy shortage, wind–solar–thermal–storage hybrid systems have been widely considered in the advancement of sustainable energy sources. To reduce the fuel cost and carbon emissions while tracking the demanded load power, this paper proposes a novel energy dispatch strategy based on deep reinforcement learning to achieve the multi-objective task for wind–solar–thermal–storage hybrid systems. Firstly, considering the huge amount of annual load data, a characteristic day acquisition model is constructed to reduce the computational burden. To deal with the uncertainties of wind and solar data, a kernel density estimation method is proposed to obtain the seasonal power generation expectation. Then, a deep Reinforcement Learning method based on Deep Deterministic Policy Gradient is developed to achieve the multi-objective energy dispatch strategy by considering the carbon trading and fuel cost. Finally, simulation results verify that the designed method can effectively achieve multi-objective task and have better robustness performance compared with other methods.

Multi-objective energy dispatch

Deep reinforcement learning

Wind–solar–thermal–storage hybrid systems

Carbon trading

Författare

Conghao Wang

Northwestern Polytechnical University

Yan Ma

Northwestern Polytechnical University

Zhejiang University

Jingjing Xie

Northwestern Polytechnical University

Quan Ouyang

Chalmers, Elektroteknik, System- och reglerteknik

Nanjing University of Aeronautics and Astronautics

Journal of Energy Storage

2352-152X (eISSN)

Vol. 105 114635

Ämneskategorier

Energiteknik

Energisystem

DOI

10.1016/j.est.2024.114635

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Senast uppdaterat

2024-11-29