Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach

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.