Optimal Demand Response in a building by Battery and HVAC scheduling using Model Predictive Control

Abstract

The objective of this project is to develop a load forecasting technique and demand management algorithm for a building to schedule battery and Heating Ventilation Air Conditioning system (HVAC) using the Model Predictive Control (MPC). Behind-the-meter energy storage is used for modifying the load shape and minimizing the demand charge of a building. Thermal mass of the building can also be utilized to store the heat/cool energy and HVAC is scheduled to minimize power consumption during peak times. This paper optimizes the battery schedule to minimize the monthly electricity bill. The load profile has to be forecasted and this algorithm uses a two-part forecaster where a deterministic part uses exponentially weighted moving average (EWMA) model accounting for longer term trends and a second order regression model (AR2) accounting for the short term variations. A novel mathematical model has been proposed for calculating HVAC power consumption with a given thermostat schedule. Greater savings can be realized by augmenting this algorithm with HVAC scheduling and authors are working on it minimize HVAC power consumption during peak hours without causing thermal discomfort to the residents of the building.