Deep reinforcement learning approach for hybrid renewable energy systems optimization

The sizing of hybrid renewable energy systems (HRES) is a major challenge faced in contemporary energy research. The optimal configuration based on the specific consumption requirements is essential for strategic energy planning. Effective sizing must balance the investment costs, reliability, environmental impacts, and greenhouse gas emissions while satisfying the expected energy requirements. This study proposes a novel multi-criteria sizing approach based on deep reinforcement learning (DRL). The DRL agent is guided by a reward function that integrates three essential performance metrics: energy cost (LCOE), renewable energy fraction (REF), and the loss of power supply probability (LPSP). A penalty function is also included to consider the reliance on external sources, such as diesel generators and the public grid, promoting greater autonomy and renewable usage. The DRL-based approach was implemented and tested on three distinct demand profiles, using hourly data for one year. A comparative analysis was conducted against three established methods: particle swarm optimization (PSO), multi-objective PSO (MOPSO), and non-dominated sorted genetic algorithm (NSGA-II). The results indicate that DRL significantly outperforms all the benchmark methods in terms of economic efficiency. DRL achieves a significant reduction in the energy costs, ranging from 21.33 % to 30.09 % when compared with PSO, 27.89 %–30.27 % when compared with MOPSO, and 27.63 %–28.47 % when compared with NSGA-II. These findings demonstrate that DRL presents a robust and adaptive framework for the sizing and operational control of HRES. DRL presents more autonomous, cost-effective, and scalable renewable energy solutions by minimizing the energy costs while maintaining the system reliability.