An AI-powered energy system simulation framework that optimizes generation portfolios using genetic algorithms. The tool models hourly power dispatch, battery management, and source failures over multi-year horizons to evaluate reliability and economic performance of diverse energy mixes.

• AI Optimization: Uses genetic algorithms to find optimal generation mixes
• Hourly Simulation: Models power dispatch on an hour-by-hour basis over 12 years
• Multi-Source Integration: Handles conventional generators, renewable energy, and battery storage
• Reliability Analysis: Tracks power outages, load shedding, and critical load interruptions
• Economic Modeling: Calculates comprehensive costs including capital, operational, and maintenance expenses
• Battery Management: Simulates advanced charging/discharging strategies
• Failure Simulation: Models equipment failures and maintenance downtime

Manages load profiles and forecasts, processes data from Excel files, and scales energy consumption patterns for future years.

Represents different power generation sources with configurable parameters:

• Source type (conventional, renewable, battery storage)
• Capacity and rating
• Financial model (PPA or captive)
• Operational constraints
• Failure rates and maintenance requirements

Core simulation engine that:

• Dispatches power sources according to priority
• Manages spinning reserves
• Handles sudden power drops and equipment failures
• Optimizes battery storage utilization
• Calculates key performance indicators

Implements genetic algorithm optimization to explore different generation portfolios and identify optimal configurations.

1. Prepare input data files:

   • Load profiles in Excel format
   • Source definitions with technical and economic parameters
   • Project configuration settings

2. Configure simulation parameters:

   sc = Scenario(
       name="Baseline",
       client_name="Client",
       selected_sources=src_list,
       spin_reserve_perc=20,
       bess_non_emergency_use=2,
       bess_charge_hours=1,
       bess_priority_wise_use=True,
       charge_ratio_night=30
   )

3. Run the simulation:

   sc.simulate()

4. Analyze results:

   sc.aggregate_data_for_reporting()
   sc.write_yearly_data_to_csv2(output_filepath)
   print(sc.scenario_kpis)

The simulation generates comprehensive reports including:

• Yearly summary of system performance
• Hourly operational logs
• Key performance indicators:
  • Average Unit Cost ($/kWh)
  • Energy Fulfillment Ratio (%)
  • Critical Load Interruptions
  • Estimated Interruption Loss (M $)
  • Non-critical Load shedding events

• Python 3.7+
• pandas
• openpyxl
• matplotlib (for visualization)