摘要：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.
其他摘要：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.
