Optimized Renewable Energy Control for Multi-Function Buildings with Shiftable Loads

Abstract

The increasing integration of distributed and intermittent renewable energy sources, such as solar and wind, is transforming the energy landscape, posing significant challenges for reliably delivering power to loads that fluctuate throughout the day and year. This research focuses on the development of a detailed model for a small-scale microgrid that powers a multi-functional building, incorporating a combination of renewable energy systems like wind turbines and photovoltaic panels, supplemented by battery and thermal storage, with grid power serving as a backup. The inclusion of both residential and commercial spaces within the building is intended to create a more balanced and predictable energy demand profile. Using a blend of historical meter readings and simulation-based modeling, two annual load profiles are developed to represent the building’s electricity and hot-water consumption patterns. To meet these demands, battery storage is employed for electricity, while hot-water needs are managed through a central thermal storage system. A carefully designed energy dispatch algorithm is implemented to prioritize the distribution of available renewable power either directly to the load or to storage systems while respecting operational constraints related to power flow and storage capacity. The system design aims to optimize the sizing of the microgrid components to minimize total costs while achieving high renewable energy penetration and minimizing curtailment. Simulation results reveal that a multi-functional building, due to its more consistent load throughout the day and across seasons, achieves a lower energy cost per unit delivered compared to a purely residential building, making it a more cost-effective solution for integrating renewable energy.  The study uses typical domestic and commercial load patterns based on historical data to simulate realistic energy demand. Residential loads peak in the mornings and evenings, while commercial loads peak during daytime hours. Seasonal changes are also considered to reflect annual load variation.