Demand response has been studied in district heating connected buildings since the rollout of smart, communicating devices has made it cost-effective to control buildings’ energy consumption externally. This research investigates optimal demand response control strategies from the district heating operator perspective. Based on earlier simulations on the building level, different case algorithms were simulated on a typical district heating system. The results show that even in the best case, heat production costs can be decreased by only 0.7%. However, by implementing hot water thermal storage in the system, demand response can become more profitable, resulting in 1.4% cost savings. It is concluded that the hot water storage tank can balance district heating peak loads for longer periods of time, which enhances the ability to use demand response strategies on a larger share of the building stock.

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The Internet of Things can be an effective way to manage the demand side and perform demand response in thermal grids. The concept provides new models for predicting demand and gathering data in a smart and inexpensive fashion. This research investigates the deployment of room-specific demand response in a districtheated office building in Southern Finland. It is fulfilled by controlling set-point temperatures on thermostatic radiator valves. By developing a predictive algorithm on a cloud platform and gathering feedback from the environment and the users, the paper presents findings about load shifting on room level and about local discomfort. The results indicate that heating power can be increased and decreased at the thermostat, even by 104% and 47% respectively. On average this accounts for 40 W/m 2 . However, the larger the set-point temperature variation is, the larger local discomfort grows. Indoor air temperature readings do not give enough evidence of thermal comfort. Hence, demand response with room-level accuracy should be deployed in areas of the building in which flexibility is least noticed.

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