A comprehensive techno-economic analysis of standalone hybrid renewable microgrids reveals that deep reinforcement learning (DRL) outperforms conventional optimization algorithms in balancing cost, performance, and environmental impact. Researchers evaluated seven distinct microgrid configurations combining photovoltaic arrays, wind turbines, biomass generators, battery storage, electric vehicles, and diesel backup across varying weather and load scenarios.
The study employed multiple optimization techniques—including DRL, spoonbill swarm optimization, genetic algorithms, and artificial neural networks—to minimize net present cost (NPC) and levelized cost of energy (COE) while maximizing storage efficiency. The optimal configuration identified through techno-economic analysis comprises 210 kW of solar capacity, 91 kW wind generation, 25 kW biomass, 265 kWh battery storage, 22 kW EV integration, and 28 kW diesel backup. This system achieved an NPC of ₹32.3 million and COE of ₹9.23/kWh with annual carbon emissions of 408,348 kg.
DRL demonstrated superior performance across all evaluation metrics compared to conventional optimization methods. The algorithm's ability to make sequential decisions under uncertainty proved particularly valuable for managing dynamic interactions between renewable generation, storage operations, and demand response. By learning optimal operational strategies through iterative simulation, DRL effectively balanced the competing objectives of cost minimization, reliability enhancement, and emissions reduction.
The findings suggest that autonomous microgrid management systems powered by reinforcement learning can adapt to local conditions—resource availability, climate patterns, and consumption profiles—without requiring manual reconfiguration. This capability addresses a critical challenge for distributed energy deployment, particularly in remote or off-grid locations where traditional utility infrastructure is unavailable or uneconomical.
The research provides actionable guidance for energy planners and regulators designing next-generation microgrids, emphasizing the value of AI-driven optimization in achieving technical, economic, and environmental objectives simultaneously. Future work should address real-time implementation, grid connection protocols, and scalability across diverse geographies.
