Optimization for Electricity Generation Using Python

The optimization of electricity generation using Python and Gurobi involves formulating a model to maximize power production from hydro, solar, and wind sources while considering environmental factors like water flow, temperature, sunshine, and rainfall. The problem is modeled using linear programming, and data is collected monthly to simulate energy generation for a year. Gurobi Optimization, a powerful solver, is used to determine the best allocation of energy sources, subject to constraints like available water flow for hydro, sunlight for solar, and wind conditions for wind power. The baseline generation is calculated for each month, and the optimization model adjusts the power generation to maximize efficiency. The model's objective is to maximize the total power generated, and constraints are applied to ensure that power generation does not exceed environmental limits. After optimizing the monthly, quarterly, and yearly power generation, the savings from optimization compared to baseline generation are calculated. The results show that optimized generation leads to a 40.95% savings in total power. The model dynamically adjusts the energy mix based on price fluctuations, with the final output showing optimal generation per energy source. A more realistic approach can be incorporated by using price data for each energy source. The optimization results help improve overall power generation and provide insights into how different factors affect the optimal energy mix for electricity generation.

[Straight-Data-Science]

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[RAZZKIRAN]

Video quality should improve, now it is 720p, use 4k videos, and
Print visuals, i mean print graphs for result analysis

[Straight-Data-Science]

You can download the source code, as an HTML file, from here: