Development and Evaluation of an Online Home Energy Management Strategy for Load Coordination in Smart Homes with Renewable Energy Sources
Xiaoling Chen, Cory Miller, Mithun Goutham, Prasad Dev Hanumalagutti, Rachel Blaser, Stephanie Stockar
TL;DR
This work tackles the challenge of coordinating residential loads in the presence of on-site renewables and stationary storage under real-time constraints. It introduces an online, decentralized HEM strategy based on a hierarchical sequential MPC solved with a genetic algorithm, incorporating deferral and temperature discomfort costs. Through year-long simulations across diverse house sizes and locations, the approach achieves about 5–6% reduction in grid cost and defers roughly half of flexible loads, largely by leveraging energy storage and renewable generation. The findings highlight the practical impact on consumer costs and grid reliability, while also identifying location- and size-dependent factors and outlining avenues for future work on battery degradation and extended metrics.
Abstract
In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50\% of the flexible loads, which is possible due to the use of the stationary energy storage.