| EVChargeEnv: An OpenEnv Benchmark for EV Charging Optimization | |
| 1. Motivation | |
| As AI agents move from static prediction to acting autonomously in dynamic environments, we need richer environments than toy grids and games. One domain that naturally combines uncertainty, long-horizon planning, and multi-objective decision-making is electric vehicle (EV) charging. | |
| EVChargeEnv is my contribution to the OpenEnv Challenge. It simulates a simplified but realistic EV charging process where an agent must decide how much to charge at each timestep while adapting to fluctuating electricity prices and grid load. | |
| The core objective is: | |
| Reach full battery while minimizing cost and avoiding grid overload. | |
| This makes EVChargeEnv a clean and interpretable environment that still contains meaningful complexity. | |
| --- | |
| 2. Environment Design | |
| EVChargeEnv exposes a continuous-control RL task with a 4-dimensional state: | |
| - charge_level ∈ [0, 1] – battery state of charge | |
| - price ∈ [0, 1] – dynamic energy price | |
| - grid_load ∈ [0, 1] – current grid stress/instability | |
| - time_step_norm ∈ [0, 1] – normalized timestep | |
| Agents output a continuous charging rate between 0 and 1. | |
| Scenarios | |
| To test robustness, EVChargeEnv includes three difficulty modes: | |
| - easy – smooth price/load curves and short episodes | |
| - medium – balanced volatility (default scenario) | |
| - hard – noisy price/load dynamics and slower charging | |
| --- | |
| 3. Reward Function | |
| The reward balances several competing factors: | |
| - progress_reward – reward for increasing charge | |
| – cost_penalty – charging during high prices costs more | |
| – overload_penalty – charging when grid load is high is discouraged | |
| – time_penalty – each step costs a tiny penalty to encourage faster execution | |
| This encourages agents to: | |
| - charge aggressively during low-price, low-load periods | |
| - slow or stop charging during peak price/load | |
| - finish charging efficiently | |
| --- | |
| 4. Implementation and OpenEnv Integration | |
| The environment is implemented using gymnasium and structured to reflect OpenEnv specifications. Key files include: | |
| - env/ev_charge_env.py – main environment logic | |
| - openenv.yaml – metadata describing observation/action spaces, rewards, and termination criteria | |
| - run_evaluation.py – produces standardized JSON outputs for assessment | |
| Example output: | |
| { | |
| "avg_reward": -1.23, | |
| "avg_steps": 31.2, | |
| "episodes": 5 | |
| } | |
| Published on the Hugging Face Hub: | |
| https://huggingface.co/oozan/EVChargeEnv | |
| --- | |
| 5. Baseline Agents and Training | |
| Baselines included: | |
| Random Baseline – ignores price/load. | |
| Price-Aware Baseline – charges more when price is low. | |
| PyTorch Policy-Gradient Agent – small neural model trained with REINFORCE. | |
| The learned agent shows: | |
| - improved reward | |
| - sensible patterns | |
| - adaptation to medium scenario | |
| --- | |
| 6. Running the Environment | |
| Install: | |
| pip install -r requirements.txt | |
| Evaluate: | |
| python run_evaluation.py | |
| Heuristic baseline: | |
| python run_price_aware_evaluation.py | |
| Train agent: | |
| python train_evchargeenv_pg.py | |
| Notebook also included for Colab execution. | |
| --- | |
| 7. Future Improvements | |
| Possible extensions: | |
| - Day/night pricing cycles | |
| - Renewable energy influence | |
| - Emergency events / blackouts | |
| - Battery degradation modeling | |
| - PPO/SAC/LLM-based agents | |
| - Visualization tools | |
| --- | |
| Final Thoughts | |
| EVChargeEnv is: | |
| - simple | |
| - realistic | |
| - modular | |
| - OpenEnv-compliant | |
| It provides a practical environment for research on planning and resource optimization under uncertainty. | |
| Repo: | |
| https://huggingface.co/oozan/EVChargeEnv | |