Implementation of Reinforcement Learning Environment for Hybrid Renewable Energy Systems

The hybrid renewable energy systems (HRES) must be capable of responding to intermittent supply by photovoltaics/wind, the health of storage, and time-dependent tariffs. Instantaneous, rule-based dispatch cannot operate under such stochastic couplings. Some of the reinforcement learning (RL) experiments rely on ad-hoc simulators, ineffective constraint management, and training regimes, which are not operational in latency. The study has offset this gap by creating a modular and physics-consistent RL environment of HRES that is capable of combining forecast-aware state-construction, battery/grid-safety screening, and reproducible benchmarking of Q-Learning, DQN, PPO, and a novel Hybrid Policy Gradient Algorithm (HPGA). Environment includes projection of constraints (SoC, ramp, feeder limits), multi-objective reward shaping (cost, curtailment, aging), as well as a co-simulation loop that reveals non-stationarity of real market and weather. The platform has shown the final outputs of a representative microgrid (200 kW PV, 150 kW wind, 500kWh BESS). The PPO has 91.4 percent renewable utilization and 15.8 percent operating cost reduction over a rule base as compared to HPGA, which boosts convergence stability and battery stress. Decision latency reaches 32 ms/step (PPO) versus 47 (DQN) and 62 (Q-Learning), and policies stabilize within ≈480 episodes compared with ≈700 for tabular methods. By coupling rigorous physical constraints with sample-efficient RL updates, the proposed environment enables deployable, low-latency control and provides a transparent testbed for future algorithms advancing HRES operation toward higher renewable penetration, lower lifecycle cost, and robust grid support.