This project’s main goal is a linear programming model that re-envisions ambitious yet feasible renewable energy goals for key regions by focusing on the trade-offs between different fossil-free energy sources.  For this, the project focuses on the data of four common fossil-free energy sources: wind, solar, nuclear and hydro, as well as relevant variables such as Cost, Reliability, Existing Energy Mix and Public Approval.

Focus: LP deals specifically with linear equations and inequalities. This means it works with problems where relationships are represented as straight lines (hence 'linear').

Objective: It always aims to find the maximum or minimum value of a linear equation, known as the objective function. For example, maximizing profit or minimizing cost.

Method: LP uses specific mathematical methods, like the Simplex algorithm, to find the best solution.

Constraints: All constraints in LP are linear (straight-line relationships). For instance, you can't spend more than a certain budget, or you need at least a certain amount of some ingredient.

Solutions: Solutions in LP are often numerical and can include fractions or decimals.

Imagine you have a map with various paths and you need to find the shortest way to a treasure. Each path has its own rules, like how much weight you can carry or how fast you can travel. The Simplex algorithm helps you navigate these paths and rules to find the most efficient route to the treasure.

In technical terms, it deals with equations representing constraints (like the rules of each path) and a goal (like reaching the treasure in the shortest time). The algorithm iteratively explores vertices on a multidimensional shape (the map), checking at each step if it's closer to the best solution. It's like checking each intersection on a map to see if you're closer to the treasure. This continues until it finds the most efficient route, giving you the best solution to your problem.

Diet Problem (DP) involves finding the most cost-effective diet that meets all nutritional requirements. Imagine you have a list of foods, each with its own nutritional content and cost. Your goal is to choose a combination of these foods that provides all the necessary nutrients (like vitamins, proteins, carbohydrates, etc.) for the least possible cost. You set up equations for each nutrient, ensuring your diet doesn't fall short or exceed what's needed. Then, you use linear programming to minimize the total cost while satisfying these nutritional constraints. It's like solving a puzzle where you balance your health needs against your budget, finding the best possible dietary plan.