A holistic power optimization approach for microgrid control based on deep reinforcement learning

Microgrid systems integrated with renewable energy sources (RES) and energy storage systems (ESS) have played a crucial role in providing more secure and reliable energy and deepening the penetration of renewables. However, optimizing the operational control of such an integrated energy system lacks the joint consideration of environmental, infrastructural and economic objectives. This paper presents a holistic data-driven power optimization approach based on deep reinforcement learning (DRL) for microgrid control, considering the multiple needs of decarbonization, sustainability and cost-efficiency. First, two control schemes, namely the prediction-based (PB) and prediction-free (PF) schemes, are devised to formulate the control problem. Second, a holistic reward function is designed to account for the operational costs, carbon emissions, peak load and battery degradation together. Third, a double dueling deep Q network (D3QN) is built to optimize the ESS charging and discharging strategies for real-time energy management. Finally, extensive simulations are conducted on a US microgrid to demonstrate the effectiveness of the proposed approach. Results show that the PB scheme outperforms the PF scheme when the prediction error is below 12.5 %, while the PF scheme becomes more effective as the error increases. It is also found that the D3QN with PB scheme can significantly reduce the annual operational cost and carbon emissions, while also greatly mitigating battery degradation and peak load.