Optimal Response of Residential House Load

Abstract

Demand response control for the residential house load, which represents the largest share of load demand, has been designed with a simple logic in the past, and this could limit the gain and economic benefits which the end-users may obtain. This chapter presents a novel appliance commitment algorithm that schedules thermostatically controlled household loads based on price and consumption forecasts considering users’ comfort settings to meet an optimization objective such as minimum payment or maximum comfort. The formulation of an appliance commitment problem is described using an electrical water heater load as an example. The thermal dynamics of heating and coasting of the water heater load is modeled by physical models; random hot water consumption is modeled with statistical methods. The models are used to predict the appliance operation over the scheduling time horizon. User comfort is transformed to a set of linear constraints. Then, a novel linear-sequential-optimization-enhanced, multi-loop algorithm is used to solve the appliance commitment problem. The simulation results demonstrate that the algorithm is fast, robust, and flexible. The algorithm can be used in home/building energy-management systems to help household owners or building managers to automatically create optimal load operation schedules based on different cost and comfort settings and compare cost/benefits among schedules.