Load Scheduling for Residential Consumer loads using Mixed Integer Linear Programming

Abstract

As compared to traditional flat electricity rates, real-time electricity pricing methods have the potential provide the economic and environmental benefits. Real time pricing gives consumers the option of reducing their electricity bills by responding to pricing that varies depending on the time of day. Load scheduling for the residential consumer loads is carried out using mixed integer linear programming (MILP). The proposed optimization-based model in this paper aims to reduce the total electricity bill also ensuring the consumer comfort. The objective is to minimize both electrical peak load and electricity cost subject to various constraints. Proposed method effectively minimizes the energy consumption cost for day-ahead time horizon according to the forecasted electricity price using mixed integer linear programming Residential users can profit from the usage of renewable energy to improve energy efficiency and reduce their reliance on the grid while lowering their electricity bills. To efficiently control the load, a solar PV system is connected along with utility supply to feed the residential loads. The optimization is also carried out in the presence of RES. The usefulness of the suggested model in greatly decreasing power costs and peak load is demonstrated by simulation results considering different scenarios.