Optimizing Energy Systems Using Mixed-Integer Linear Programming

Dealing distributed energy resources (DERs) such as electric vehicles (EVs), photovoltaic (PV) systems, and energy storage systems (ESSs) in modern energy networks poses several challenges. Mixed-Integer Linear Programming (MILP) offers a full-bodied optimization method capable of handling both discrete and continuous variables while adhering to complex constraints. This research offers an MILP-driven energy management basis designed to lower the operational costs improve renewable energy usage, and maintain grid stability. MILP is active to list DER activities, including ESS operations and EV involvement, under various supply-demand scenarios. The simulation results demonstrate that the framework effectively reduces costs, mitigates peak demand, and improves grid reliability. This study highlights MILP's scalability and reliability as a solution to the multi-layered challenges of energy system optimization.