Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach. (1st January 2021)

• Record Type: Journal Article
• Authors: Zhang, Guozhou; Hu, Weihao; Cao, Di; Liu, Wen; Huang, Rui; Huang, Qi; Chen, Zhe; Blaabjerg, Frede
• Is Part Of: Energy conversion and management. Volume 227(2021)
• Publication Date: 2021-01-01
• Subjects: Hybrid energy system -- Energy management -- Information entropy theory -- Cost reduction -- Deep reinforcement learning; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.113608
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: The dynamic energy management of hybrid energy system is studied. Uncertainty of renewable energy, demand side and price are considered. A data-driven deep reinforcement learning method is applied. The flexibility adjustment of battery and water tank is achieved. Abstract: Significant dependence on fossil fuels and freshwater shortage are common problems in remote and arid regions. In this context, the operation of a wind-solar-diesel-battery-reverse osmosis hybrid energy system has become a suitable option to solve this problem. However, owing to the uncertainties of renewable energy availability and load demand, it is a challenge for operators to develop an energy management scheme for such a system. This study aims to determine a real-time dynamic energy management strategy considering the uncertainties of the system. To this end, the energy management of a hybrid energy system is presented as an optimal control objective, and multi-targets are considered along with constraints. The information entropy theory is introduced to calculate the weight factor for the trade-off between different targets. Then, a deep reinforcement learning algorithm is adopted to solve this problem and obtain the optimal control policy. Finally, the proposed method is applied to a typical hybrid energy system, and numerous data are applied to train an agent to obtain the optimal energy management policy. Simulation results demonstrate that a well-trained agent can provide a better control policy and reduce costs by up to 14.17% in comparison with other methods.