A User-centric Demand Response Framework for Residential Heating, Ventilation, and Air-conditioning Load Management

Abstract

The domestic heating, ventilation, and air-conditioning load promises a good prospect for electrical aggregators to consider it for demand response. This article presents a user-centric demand response control for scheduling the electric space heating load under a price and load uncertainty environment. The objective of the framework is to minimize a weighted sum of the expected payment, loss of comfort, and financial risk of a customer while strictly considering the end-user preferences. The household thermal behavior is modeled via an accurate two-capacity building model. The price and load uncertainty is modeled using a scenario-based stochastic programming approach. The proposed decision model is formulated as a non-linear programming problem that can be simply solved via commercially available solvers. The effectiveness of the formulation is demonstrated by applying it to a typical customer. The simulation results demonstrate that the decision mechanism allows consumers to compromise among electricity payment, thermal comfort, and risk exposure based on their thermal comfort preferences and risk priorities.