Demand response of space heating using model predictive control in an educational office building

Aleksi Mäki,Juha Jokisalo,Risto Kosonen,S.I Tanabe,J Kurnitski,H Zhang,I Nastase,M.C Gameiro Da Silva,G Cao,L Mazzarela,C Inard,P Wargocki

doi:10.1051/e3sconf/201911103067

Abstract

The building sector plays a remarkable role in decreasing of the overall global CO2 emissions since as much as 30% from the total global CO2 emission are generated in buildings. Demand response provides one possibility to tackle the problem. It can be used to decrease CO2 emissions in entire energy system in addition to providing energy cost savings for building owners and energy companies. In this study, the demand response potential was estimated in an educational office building that was heated by district heating. The potential was defined in respect of energy cost savings, energy flexibility and thermal comfort. Model predictive control was developed, which utilized the dynamic hourly district heating prices. The MPC algorithm written in the Matlab software, predicted the future heating demand while the optimization algorithm NSGA-II minimized the heating energy cost, maximized the energy flexibility and maintained acceptable thermal comfort by changing the space heating temperature setpoints. The operation of the MPC algorithm was tested in the IDA ICE 4.8 simulation software. As a result, the annual district heating energy costs could be reduced by 4.2% compared to the reference case with constant space heating temperature setpoint of 21 °C. The maximum flexibility factor attained was 14%. Acceptable level of thermal comfort was maintained throughout the simulation time.

Highlights

Demand response constructs a group of methods where the end-user’s energy load is modified to decrease the aggregated overall CO2 emissions of the energy production and to enhance the efficiency of the whole energy system
The results from the model predictive algorithm (MPC) algorithm implemented DR cases are analysed in respect of energy cost savings, energy flexibility and thermal comfort
The results show that the highest energy cost savings were obtained from the DR case O1.2

Summary

Introduction

Demand response constructs a group of methods where the end-user’s energy load is modified to decrease the aggregated overall CO2 emissions of the energy production and to enhance the efficiency of the whole energy system. This study focused in the price-based model where the building’s load was modified according to the dynamic district heating price. From the building owner’s point of view, the objective in demand response is to decrease the annual energy costs by maintaining indoor environmental quality that supports the building’s usage. Several studies have shown that the decreased level of IEQ decreases the occupant’s productivity [1,2] and it may cause health hazards [3] which both would cancel out the attained energy cost savings. The DR should be controlled so that the IEQ would always be maintained at acceptable level

Objectives

Methods

Results

Discussion

Conclusion