Study of Demand Response Based on Model Predictive Control

Part 1-Verification of Demand Response Using Predicted Real Time Price

Abstract

Demand response (DR) is considered an effective method to address the shortage of electricity supply capacity during peak hours due to power outages caused by natural disasters and sudden failures as well as the effects of global warming, etc. Maintaining room thermal comfort is an essential element of DR. However, it is necessary to identify the impact of each comfort index and establish control based on uncertain electricity charge. In this paper, model predictive control (MPC) utilizing prediction by artificial neural networks was used to evaluate the performance of MPC based on DR by changing the control method, constraint conditions, and heat source pump mass flow rate. The results indicate that DR-MPC, which adjusts the heat source pump mass flow rate at each high and low electricity metered rate, can be used to maintain thermal comfort and level electricity load.