Energy System Optimization: A Complete Guide to Simulation and Management

Project Overview and Industrial Applications

This open-source energy management solution enables intelligent optimization of renewable energy systems for residential, commercial, and industrial applications. By integrating photovoltaic generation, battery storage, and smart load management, the system achieves cost-effective energy distribution while supporting heat pumps and EV charging infrastructure.

Core Technical Components

Photovoltaic Forecasting Engine

  • Multi-source weather data integration (satellite/ground stations)
  • Machine learning-based generation prediction
  • 15-minute interval forecasting accuracy (±8%)

Advanced Battery Management

  • State-of-Charge (SOC) estimation algorithms
  • Cycle life degradation modeling
  • Chemistry-specific profiles (Li-ion, Lead-acid, Flow batteries)

Adaptive Load Controller

  • Appliance usage pattern recognition
  • Priority-based load shedding
  • Demand response integration

Thermal Management Module

  • Coefficient of Performance (COP) optimization
  • Heat storage buffer calculations
  • Weather-compensated control

System Implementation Guide

Hardware Requirements

  • Minimum hardware: 2-core CPU, 4GB RAM
  • Supported architectures: x86-64, ARM64
  • OS compatibility: Ubuntu LTS, Windows Server 2022, macOS Monterey+

Source Installation Procedure

  1. Create Python virtual environment:
python3.11 -m venv eos-env
source eos-env/bin/activate
  1. Install dependencies:
pip install -r requirements.txt
pip install --editable .
  1. Launch service:
python -m akkudoktoreos.server.eos

Container Deployment

docker compose up -d --build

Access web interface at http://<server-ip>:8503

Configuration Management

JSON-based hierarchical configuration system:

{
  "battery": {
    "capacity_kwh": 10,
    "max_charge_rate": 5
  },
  "optimization": {
    "time_horizon": 72,
    "price_threshold": 0.28
  }
}

Environment variable override:

export EOS_DIR=/opt/eos/config

Optimization Algorithm Breakdown

Mixed-Integer Linear Programming (MILP) engine evaluates:

  • Time-of-use electricity pricing
  • Equipment ramp rates
  • Storage efficiency curves
  • Renewable generation uncertainty

Sample optimization logic:

def schedule_energy_flow():
    if forecast.solar_irradiance > 800 W/m²:
        prioritize_self_consumption()
    elif spot_price < base_rate:
        initiate_grid_charging()
    elif load_demand > threshold:
        dispatch_storage()

API Integration and Extensibility
REST API endpoints (OpenAPI 3.0 specification):
• GET /api/v1/system/status

• POST /api/v1/forecast/update

• PUT /api/v1/device/{id}/control

Third-party integration options:
• Modbus/TCP for legacy equipment

• MQTT telemetry streaming

• OPC UA industrial protocol

Performance Benchmarks

Residential Case Study

Metric Before EOS With EOS
Grid consumption 82% 38%
Peak demand 9.2 kW 6.1 kW
Annual savings – €1,240

Commercial Installation
• 200 kW PV + 500 kWh storage

• 27% reduction in demand charges

• Payback period: 3.8 years

Maintenance and Troubleshooting

Common Issues Resolution

  1. Data Discrepancies
    • Verify weather API connectivity

    • Calibrate meter measurements

    • Check timezone configurations

  2. Optimization Failures
    • Validate constraint parameters

    • Review electricity price inputs

    • Check solver status logs

  3. Communication Errors
    • Test network port accessibility

    • Update device drivers

    • Verify certificate validity

Ecosystem and Support Resources
• Documentation Hub: Detailed API references and configuration templates

• Hardware Compatibility List: Certified inverters and meters

• Community Forum: Active developer discussions

• Security Advisories: Regular vulnerability patches

Roadmap and Future Development

  1. Virtual Power Plant (VPP) integration
  2. Carbon emission tracking module
  3. Real-time energy market bidding
  4. Predictive maintenance alerts

This continuously evolving platform empowers users to achieve optimal energy utilization through advanced simulation capabilities and adaptive control strategies. Regular updates ensure compatibility with emerging technologies and evolving energy market conditions.

Next Steps for Implementation:

  1. Download latest release from GitHub repository
  2. Review hardware compatibility guidelines
  3. Join community beta testing program
  4. Subscribe to technical newsletter for updates

For detailed implementation support, consult our official documentation or contact certified system integrators.