This project simulates the operation of a solar-powered data center with integrated battery storage and irrigation capabilities. It models energy production, consumption, and allocation based on weather conditions, energy demands, and system configurations. The simulation is designed to optimize energy usage and analyze the economic viability of such a system.
Generates realistic weather data based on location-specific monthly averages. Simulates hourly variations in sun intensity, temperature, humidity, precipitation, cloud cover, and wind speed.
Models energy production from a solar panel array. Accounts for panel efficiency, temperature effects, and various environmental factors.
Defines energy consumption patterns for different components (irrigation, servers, GPUs). Uses Numba for optimized calculations.
Simulates charging and discharging of a battery system. Considers temperature-dependent efficiency.
Allocates energy based on priorities: irrigation, servers, battery charging, and GPUs. Handles energy deficits by discharging from the battery when necessary.
Orchestrates the entire simulation process. Runs hourly and daily simulations for a full year. Generates comprehensive reports and data for analysis.
Generates detailed reports on energy production, consumption, and financial metrics. Creates visualizations for energy balance, battery usage, and system performance.
Defines dataclasses for various configuration parameters. Provides default configurations and methods to load custom settings.
Implements a robust logging system for debugging and monitoring. Provides custom exception classes and decorators for consistent error handling.
Realistic weather simulation based on location-specific data. Detailed modeling of solar energy production considering various environmental factors. Dynamic energy allocation system with priority-based consumption. Battery storage simulation with temperature-dependent efficiency. Comprehensive financial analysis including ROI and payback period calculations. Flexible configuration system for easy customization of simulation parameters. Robust logging and error handling for reliable operation and debugging. Visualization tools for analyzing energy production, consumption, and system performance.
The simulation starts by generating weather data for a full year based on the specified location. For each hour of the year, the system:
Calculates solar energy production based on weather conditions. Determines energy needs for irrigation, servers, and potential GPU operations. Allocates energy according to priorities, using the battery for storage or supplementation as needed. Tracks battery charge levels and energy deficits.
The system generates daily, monthly, and annual reports on energy production, consumption, and financial metrics. Visualization tools create graphs and charts to illustrate system performance over time.
To run the simulation:
Ensure all dependencies are installed (NumPy, Matplotlib, Pandas, Numba). Configure the simulation parameters in config.py or prepare a custom configuration file. Run main.py to start the simulation. View the generated reports and visualizations in the specified output directory.
The simulation can be customized by modifying the configuration parameters in config.py. Key areas for customization include:
Solar park specifications (capacity, efficiency, degradation rate) Battery storage characteristics Energy consumption profiles for different components Location-specific weather data Financial parameters for ROI calculations
Potential areas for future development include:
Integration with real-time weather data APIs for more accurate simulations. Machine learning models for predictive energy management. Web-based interface for real-time monitoring and control. Expansion to include other renewable energy sources (wind, hydroelectric). More detailed modeling of data center operations and workload scheduling.
Conclusion This Solar Data Center Simulation provides a comprehensive tool for modeling and analyzing the performance of solar-powered data centers with integrated agricultural operations. It offers valuable insights into energy management, system sizing, and economic viability, making it a useful resource for researchers, engineers, and decision-makers in the fields of renewable energy and sustainable data center design.