Real-time demand response in smart homes through direct lookahead approximation

Abstract

Demand Response (DR) programs play a pivotal role in managing the balance of energy supply and demand, particularly in the era of expanding Renewable Energy Resources (RES) integration into the smart grid. By encouraging consumers to shift their power usage to off-peak periods or times of high RES output, these programs alleviate grid pressure. Recent technological advancements have facilitated the deployment of DR programs in residential contexts, leveraging Home Energy Management Systems (HEMS) for controlling household appliances. However, challenges arise due to the stochastic nature of RES, fluctuating intra-day electricity prices, and varying consumer demand, making appliance scheduling a complex, dynamic problem. To navigate this issue, we introduce a direct lookahead approximation approach for HEMS. This strategy manages shiftable appliance usage and energy storage, aiming to minimize user discomfort and electricity costs. The algorithm takes into account the dynamic nature of the system, incorporating uncertain information about prices, energy production, and demand. The proposed method has demonstrated significant effectiveness, reducing electricity costs and grid consumption in a smart house by 145% and 25% respectively. Our research underscores the efficacy of sequential decision-making techniques in managing domestic energy consumption, particularly within the context of smart grids and RES integration.