OPTIMIZING DEMAND RESPONSE AND RENEWABLE ENERGY INTEGRATION IN SMART HOMES: AN ILLINOIS CASE STUDY

Author

Leili Hadian Dahaj, Southern Illinois University CarbondaleFollow

Date of Award

5-1-2025

Degree Name

Master of Science

Department

Electrical and Computer Engineering

First Advisor

Sayeh, Mohammad

Abstract

This research evaluates the applicability of distributed energy resources (DERs) utilization for a residential customer and its effectiveness in reducing energy costs. In this case, a full year of hourly estimates for wind speed, ambient temperature, solar radiation, and electricity consumption are developed with a forecasting model based on artificial neural networks (ANN). The proposed methodology is assessed through a real-life example in Herrin, Illinois. The optimization model combines DER elements of solar electric panels, micro wind turbines, battery storage, and household appliances that incorporate demand response technologies. The model attempts to reduce the cost of energy while maintaining a reliable power supply and operational requirements. The optimization problem is formulated as mixed integer linear programming (MILP) and Gurobi is used as the tool to solve it.An optimized DER system was able to reduce annual energy costs by 29%. Based on the cost and benefit analysis considering initial investment, component lifetime, and other financial elements, a payback period of approximately four years is expected. Though promising, the accuracy of ANN-based forecasting does not come easily, especially with cost savings expectations in regard to wind speed predictions. This study demonstrates the applicability of DER integration in smart homes. Future studies in this area can focus on improving the electric vehicles model, improving the forecast model, and considering uncertainties in the analysis. It will improve the applicability of the solutions and lead to more realistic results.