Achieving 100% Evening Peak Coverage Through Pre-Event Preparation
Research Paper: "Anticipatory Deep Reinforcement Learning for Microgrid Energy Management: Achieving 100% Evening Peak Coverage Through Pre-Event Preparation"
Author: Alfonso Davila | LinkedIn | davila.alfonso@gmail.com
October 2025:
- ✅ Version 7.0 released with 98.8% load coverage across 100 test scenarios
- ✅ Best policy achieves 100% coverage (found at episode 100)
- ✅ Complete code, trained models, and data now available
✅ 98.8% Average Load Coverage (100 diverse test scenarios)
✅ 100% Best Policy Performance (episode 100)
✅ 99.2% Evening Reliability (vs 71.5% baseline)
✅ 0.13 kWh Average Unmet Energy
✅ 4.3 Year Payback Period
✅ 190% ROI (20-year projection)
✅ $7,088 Annual Savings
| Method | Load Coverage | Evening Coverage | Unmet Energy |
|---|---|---|---|
| Rule-Based Controller | 78.2% | 71.5% | 3.82 kWh |
| Tabular Q-Learning | 85.7% | 82.3% | 2.44 kWh |
| Vanilla DQN | 94.1% | 92.8% | 0.89 kWh |
| Anticipatory DQN (Ours) | 98.8% | 99.2% | 0.13 kWh |
# Clone the repository
git clone https://github.com/DynMEP/dynamic-microgrid-resilience.git
cd dynamic-microgrid-resilience
# Install dependencies
pip install -r requirements.txt# Quick demo with visualizations (100 episodes)
python cli.py demo# Train with all features and plots
python cli.py train --full --plot --economics
# Real-time training monitor
python cli.py train --episodes 1000 --plot --monitor# Evaluate on 100 diverse scenarios
python cli.py evaluate --auto-latest --scenarios 100 --plot
# Compare different battery capacities
python cli.py compare --capacities 13 15 18 20 --parallel --plot- Time-to-Event State Augmentation: Explicit temporal encoding for anticipatory behavior
- Hierarchical Reward Shaping: Extreme penalties (-400) for evening failures, bonuses for preparation
- Multi-Phase Temporal Structure: Distinct phases (daytime, pre-evening, evening, night)
- Deep Q-Network (DQN): 3-layer network (256-128-64) with experience replay
- Best Policy Tracking: Automatic saving of optimal weights during training
- Solar PV Array: 5 kW DC capacity
- Wind Turbine: 3 kW AC capacity
- Battery Storage: 18 kWh capacity, 6 kW power rating
- NEC 2023 Compliant: Articles 690, 694, 706
- Round-trip Efficiency: 95%
[
SOC_normalized, # Battery state of charge (0-1)
hour_sin, # Time encoding (cyclical)
hour_cos, # Time encoding (cyclical)
renewable_ratio, # Generation vs load ratio
net_balance, # Energy surplus/deficit
is_evening, # Evening flag (18-22h)
is_pre_evening # Preparation flag (15-17h)
]charge_full: Charge at 100% powercharge_half: Charge at 50% powerhold: No battery actiondischarge_half: Discharge at 50% powerdischarge_full: Discharge at 100% power
- Solar: Diurnal pattern with Beta-distributed cloud cover
- Wind: Weibull-distributed wind speed with cubic power curve
- Load: Residential/commercial/mixed profiles with ±10% variability
- Fast convergence: Optimal policy found at episode 100
- Stable performance: Maintains near-perfect coverage after convergence
- Efficient learning: 21 minutes on laptop CPU
- Pre-evening preparation: SOC reaches 87% by hour 15
- Evening maintenance: SOC stays above 80% during peak (18-22h)
- Perfect coverage: 100% load met in all 24 hours
- Mean Coverage: 98.8% ± 3.7%
- Perfect Scenarios: 86/100 achieve 100% coverage
- Robust: 94/100 exceed 95% target
- Failure Analysis: 6 scenarios below 95% (extreme weather conditions)
Capital Costs:
- Solar PV: $10,000 (5 kW @ $2,000/kW)
- Wind Turbine: $9,000 (3 kW @ $3,000/kW)
- Battery: $9,000 (18 kWh @ $500/kWh)
- Inverter: $1,840 (9.2 kW @ $200/kW)
- Installation: $5,968 (20%)
- Total: $35,808
Financial Metrics:
- Payback Period: 4.3 years (vs 7-10 industry standard)
- 20-Year NPV: $57,886 (5% discount rate)
- ROI: 190.1%
- IRR: 21.4%
# System status
python cli.py status
# Demo mode (100 episodes, ~1 min)
python cli.py demo
# Training
python cli.py train --episodes 1000 --plot --economics
python cli.py train --battery 20 --episodes 500 # Custom capacity
# Evaluation
python cli.py evaluate --model models/your_model.pt --scenarios 50
python cli.py evaluate --auto-latest --scenarios 100 --plot
# Battery comparison (parallel processing)
python cli.py compare --capacities 13 15 18 20 --parallel --plotfrom Dynamic_Microgrid_Resilience_v7 import MicrogridConfig, DQNAgent, train_agent
# Configure system
config = MicrogridConfig(
battery_capacity_kwh=18.0,
pv_capacity_kw=5.0,
wind_capacity_kw=3.0
)
# Train agent
agent = DQNAgent(config)
results = train_agent(agent, episodes=1000)
# Evaluate
from evaluation import evaluate_policy
metrics = evaluate_policy(agent, num_scenarios=100)
print(f"Load Coverage: {metrics['load_met_rate']:.1f}%")dynamic-microgrid-resilience/
├── Dynamic_Microgrid_Resilience_v7.py # Main DQN implementation
├── cli.py # Command-line interface
├── requirements.txt # Python dependencies
├── README.md # This file
├── LICENSE # MIT License
├── .gitignore # Git ignore rules
│
├── models/ # Saved model checkpoints
│ ├── microgrid_v7_best_model.pt
│ └── *.pt
│
├── plots/ # Generated visualizations
│ ├── training_progress.png
│ ├── hourly_performance.png
│ ├── evaluation_results.png
│ └── economic_analysis.png
│
├── results/ # Training results & logs
│ ├── *_training_progress.csv
│ ├── *_validation_results.csv
│ ├── *_resilience_detailed.csv
│ └── *_config_report.txt
│
└── docs/ # Documentation
├── API.md # API documentation
├── METHODOLOGY.md # Technical details
├── TRAINING.md # Training documentation
└── TROUBLESHOOTING.md # Troubleshooting documentation
Input (7 features)
↓
Hidden Layer 1: 256 neurons (ReLU)
↓
Hidden Layer 2: 128 neurons (ReLU)
↓
Hidden Layer 3: 64 neurons (ReLU)
↓
Output: 5 Q-values (linear)
Total Parameters: ~67,000
| Parameter | Value | Description |
|---|---|---|
| Learning Rate | 0.0002 | Adam optimizer |
| Discount Factor (γ) | 0.97 | Long-term planning |
| Batch Size | 64 | Experience replay |
| Replay Buffer | 10,000 | Transition storage |
| ε-start | 1.0 | Initial exploration |
| ε-decay | 0.9992 | Per episode |
| ε-min | 0.01 | Minimum exploration |
| Target Update | 10 episodes | Target network sync |
# Base reward (all hours)
R_base = +100 if load_met else -300
# Pre-evening preparation (hours 15-17)
R_pre_evening = +200 if SOC >= 0.75 else -250
# Evening peak (hours 18-22)
R_evening = +250 if (load_met and SOC >= 0.70) else -400
# Daytime charging (hours 6-14)
R_daytime = +50 if (charging and SOC < 0.80) else 0
# Total reward
R_total = R_base + R_pre_evening + R_evening + R_daytimeMicrogrid energy management faces a critical challenge: ensuring reliable power during evening peak demand when renewable generation is minimal. We present an anticipatory Deep Q-Network (DQN) approach that achieves 100% load coverage by learning to prepare for evening peaks hours in advance. Our method introduces a time-to-critical-event state augmentation that enables the agent to anticipate evening demand, combined with hierarchical reward shaping that heavily penalizes evening failures. On a realistic microgrid system comprising 5 kW solar PV, 3 kW wind, and 18 kWh battery storage, our approach achieves zero unmet energy with 87% pre-evening state-of-charge and maintains 80% SOC throughout evening hours. Testing across 100 diverse scenarios shows 98.8% average load coverage. Economic analysis demonstrates a 4.3-year payback period with 190% ROI over 20 years.
@article{davila2025anticipatory,
title={Anticipatory Deep Reinforcement Learning for Microgrid Energy Management:
Achieving 100\% Evening Peak Coverage Through Pre-Event Preparation},
author={Davila, Alfonso},
email={davila.alfonso@gmail.com},
year={2025}
}| Configuration | Load Coverage | Evening Coverage |
|---|---|---|
| Full Model | 98.8% | 99.2% |
| No evening penalties | 94.3% | 92.1% |
| No pre-evening rewards | 95.7% | 94.8% |
| No temporal encoding | 89.2% | 86.7% |
| Uniform rewards only | 94.1% | 92.8% |
Conclusion: All components are essential for achieving near-perfect performance.
Tested across:
- ✅ 100 diverse scenarios (varying weather, load profiles)
- ✅ 10 random seeds (convergence: 105 ± 18 episodes)
- ✅ Multiple battery capacities (13-22 kWh)
- ✅ Different locations (solar/wind profiles)
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
# Clone with examples
git clone https://github.com/DynMEP/dynamic-microgrid-resilience.git
cd dynamic-microgrid-resilience
# Install dev dependencies
pip install -r requirements-dev.txt
# Run tests
pytest tests/
# Run linter
flake8 .- 🔌 Grid-connected mode with time-of-use pricing
- 🔋 Battery degradation modeling
- 🌐 Multi-microgrid coordination
- 📱 Real-time dashboard (Flask/React)
- 🧠 Transfer learning for new locations
- 📊 Additional visualization tools
- GPU training on Windows requires CUDA toolkit
- Parallel processing on macOS slower due to spawn method
- Real-time monitor requires ANSI escape code support
See Issues for full list.
- Anticipatory DQN with pre-evening strategy
- 98.8% average load coverage
- CLI with visualization tools
- Complete documentation
- Web dashboard for live monitoring
- Grid-connected mode
- Battery health modeling
- Multi-objective optimization
- Multi-agent microgrids
- Transfer learning module
- Edge deployment (Raspberry Pi)
- Real-world pilot study
This project is licensed under the MIT License - see LICENSE file for details.
This software is free for academic and commercial use under MIT license. If you use this in production or research, please cite our paper.
- PyTorch Team: For the excellent deep learning framework
- NEC: For electrical code compliance standards (NEC 2023)
- RL Community: For foundational work on DQN and temporal credit assignment
- arXiv: For open-access preprint hosting
Author: Alfonso Davila
- Email: davila.alfonso@gmail.com
- LinkedIn: alfonso-davila-3a121087
- GitHub Issues: Report bugs or request features
- Check Documentation
- Search Issues
- Ask on Discussions
- Email for research collaboration
If you find this project useful, please consider giving it a star on GitHub! ⭐
Last updated: October 2025