Deep Learning‐Based Optimal Sizing of a Grid‐Tied Microgrid Under Real‐Time Pricing

ABSTRACT This paper presents a novel optimal sizing model for a grid‐tied microgrid operating under real‐time pricing (RTP) for electricity trading with the main grid. The model determines the optimal capacities of solar photovoltaic (PV), wind turbine (WT), battery energy storage (BES) and inverter using a deep reinforcement learning approach. A double deep Q‐network (DDQN) is proposed to solve the complex sizing problem efficiently. The sizing model incorporates a rule‐based energy management strategy, where the average of day‐ahead electricity price forecasts is used to guide the battery's state of charge (SoC) decisions. Although the model is designed to be generic, a residential building in Australia is used as a case study to validate its practical applicability. Numerical results demonstrate that the proposed method under RTP conditions achieves a lower net present cost (NPC) of electricity compared to existing sizing models from previous studies. The effectiveness and robustness of the proposed deep learning‐based approach are further confirmed through comparative analysis with other machine learning techniques and metaheuristic algorithms.