Proposed a mathematical model for optimizing the profits and emissions while setting dynamic prices of electricity. A bilevel & multi-objective model is proposed for maximizing profits of retailer, minimizing the emissions produced, & minimizing the total cost of customers. Our models use Time-of-Use(TOU) price structure of Demand Response Management. We use following as the input data parameters.

1. demand_profile.csv contains the hourly demand for electricity in kWh. The Rows represent customers & each column corresponds hours beginning from 00:00 to 23:00.
2. fuel_properties.csv contains the attributes of fuels used in the thermal power plants. The attributes are unit costs, unit electricity produced, unit emissions prodcued.

Model Building.ipynb contains the Python script for data storing and optimization building. Here I have built several user defined function to solve the models. Please note here to execute this file you first need to install the mathematical solver Gurobi. For installation please visit Gurobi

• There is one variable in 'Model Building.ipynb' capacities which means --> maximum percentage of supply available of each fuel type. For example, capacities = [.50, .60, .20] means that 50% of total electricity demand of customer can be satisfied using coal and similarly 60% by natural gas and so on.

Use Data Visualization.ipynb for the visualzation & summary of the results obtained from the models. For any further queries please contact me.

• This model is a work of Masters' thesis titled 'Bilevel and Multi-objective Optimization of Electricity Price Setting with Carbon Emission Consideration'. To read click here

I have written this python code as a novice and it can be improved or made efficient to some extent.