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

Artikel i vetenskaplig tidskrift, 2026

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.