Integrated optimization of smart building energy consumption in microgrids using linearized real-time control strategies

Abstract

This research develops a model to reduce main grid electricity costs and boost local demand and generation within a microgrid, adhering to operational constraints. It uses a mixed-integer nonlinear programming (MINLP) framework to manage heating, ventilation, air conditioning, lighting, photovoltaic generation, and energy storage while ensuring indoor comfort. A rolling horizon strategy was employed to simplify the original model, accompanied by pre-processing in EnergyPlus software utilizing linearization techniques, culminating in a Mixed-Integer Linear Programming approximation. Linearization yields an optimally solvable model that is appropriate for real-time energy management applications. We performed simulations under decentralized and centralized schemes for a 13-bus microgrid with uncontrollable loads and smart buildings. This study conducted a scalability analysis for the 34-bus microgrid case. The rolling horizon method successfully handled uncertainties in demand and reduced the amount of data needed for forecasting across five different consumption models, which included various combinations of photovoltaic units and energy storage systems. The findings indicated a 16 % decrease in peak power demand and an error margin when comparing linearized results with actual data, showcasing notable enhancements in cost efficiency and stability. The testing provided insights into optimal configurations for each region, validating the model's effectiveness in enhancing microgrid reliability, sustainability, cost-effectiveness, and occupant comfort.