Appliance Commitment for Household Load Scheduling

TLDR

This paper presents a novel appliance commitment algorithm that schedules thermostatically controlled household loads based on price and consumption forecasts while respecting users' comfort settings to minimize payment or maximize comfort. The authors formulate the appliance commitment problem using an electrical water heater example, model its thermal dynamics and random hot‑water demand, transform comfort into linear constraints, and solve the resulting problem with a novel linear‑sequential‑optimization‑enhanced multiloop algorithm. Simulations show the algorithm is fast, robust, and flexible, enabling home/building energy‑management systems to automatically generate optimal load schedules and compare cost‑benefit trade‑offs.

Abstract

This paper 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, multiloop 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.