Jordan-Leis/Microgrid-RL

Benchmarking deep reinforcement learning algorithms for autonomous energy management in off-grid solar + battery + diesel hybrid microgrids across sub-Saharan Africa. # Microgrid-RL Benchmarking deep reinforcement learning algorithms for autonomous energy management in off-grid solar + battery + diesel hybrid microgrids across sub-Saharan Africa. Using real climate data from NASA POWER, we train and evaluate six RL frameworks to find which policies best minimize diesel consumption and blackouts while maximizing renewable utilization under realistic African conditions. An accompanying IEEE paper is available in `paper/`. --- ## What This Project Does Many rural communities in sub-Saharan Africa rely on isolated microgrids — small solar + battery + diesel hybrid power systems not connected to a national grid. Operating these systems well requires deciding every 30 minutes: how much to charge or discharge the battery, and whether (and how hard) to run the diesel generator. Bad decisions waste fuel, cause blackouts, or wear out batteries early. This project replaces hand-crafted rule-based controllers with reinforcement learning agents trained on five years of real hourly irradiance and temperature data from NASA POWER. We run a **150-run comparative study** (6 algorithms × 5 locations × 5 seeds) to determine which RL framework performs best across a range of African climates — from the arid Sahel to equatorial West Africa to the Ethiopian highlands. --- ## Key Results | Algorithm | Family | Mean Reward | Unmet Energy (kWh/yr) | Diesel (L/yr) | Battery EFC/yr | |-----------|--------|------------|----------------------|----------------|----------------| | **DDPG** | Off-policy | −134,854 ± 23,029 | **7.5 ± 24.1** | **20,007 ± 3,241** | 2,335 ± 3,916 | | SAC | Off-policy | −176,342 ± 3,080 | 0.0 ± 0.0 | 26,018 ± 1 | 3,905 ± 892 | | TQC | Off-policy | −177,736 ± 3,527 | 8.2 ± 15.3 | 26,110 ± 312 | 3,407 ± 463 | | RPPO | On-policy (RNN) | −195,672 ± 87,707 | 3,358 ± 5,001 | 13,394 ± 12,417 | 25 ± 3 | | A2C | On-policy | −342,185 ± 104,438 | 6,489 ± 4,862 | 18,503 ± 24,671 | 19 ± 6 | | PPO | On-policy | −354,415 ± 12,275 | 11,285 …