HOT|COOL NO. 2/2019 - "Smart Heating System Integration"


without jeopardizing the energy delivery to customers. From the evaluation of the results, a peak shaving potential of 17.3% could be determined with, as a derivative, a simultaneous capacity increase of 42.1% (see results on cell balancing below). MARKET INTERACTION The market interaction strategy was emulated in the Rottne demo site. Despite the lack of a real CHP, the STORM controller used input from the wood chip boilers as if they were a CHP, as a proof of concept. Based on the electricity price forecast on the power market, the STORM controller tried to move heat demand to match higher spot prices, thereby increasing the financial gain of selling electricity while still ensuring heat delivery. The primary conclusion is that the STORM controller indeed has the ability to both charge (increase heat demand of the buildings) and discharge (decrease the heat demand) alternatively in order to track the requested behaviour from the earnings/cost forecast. The ability to charge and discharge has been shown to range between 30-50 % in short-term demand on individual building level: this means that, on average, a building can modulate 30-50 % more or less energy than it normally would for a short period of time (a few hours), without people or thermostats noticing it. If this individual control ability is then coordinated among several buildings, the combined flexibility is substantial in both time and intensity. Apart from CHP optimization, there are other benefits of market interaction. If in a district heating grid with high electricity costs (e.g. those including heat pumps), the situation where the highest peak values can be flattened to the average values, this would mean a 15% price reduction on

As explained, the vDER outputs control signals suited for the substation controllers or building management systems. This is a vital feature of the STORM controller, since this means that the existing controllers in the network should not be replaced; the STORM controller is an extra control layer that collaborates with the existing controllers. It coordinates the operation of the individual, independent control systems of the connected buildings, for the sake of a certain global objective to be reached, e.g. by manipulating their setpoints. In this way, the STORM controller connects the demand and the supply sides of the network, which are normally independently controlled.


As explained, in the Rottne demonstrator, the aim was to reduce the oil consumption by the peak boilers. It should be noted that only 9 out of the about 200 consumers where connected to the STORM controller. However, these nine buildings are nevertheless the largest ones in the network and represent about one third of the total energy consumed in the network. This means on the other hand that two third of the network energy demand was uncontrollable. By controlling those 9 buildings, the peak-shaving tests resulted in a reduction in the peak consumption of 3.1% compared to the reference scenario without the STORM controller active (Figure 4). This peak heat reduction has been achieved despite an overall heat demand increase of the large uncontrolled part of the building stock in Rottne. If this influence is corrected for, then a peak heat reduction of 12.7% was determined (Figure 5).

the electricity purchase during this short period of time. Combined with the ability of the controller for charging and discharging of 40 % on average, the conclusion would be that the procurement costs of electricity could be lowered with 6 %. For the Rottne demo site, the electricity costs represent 5.9 % of the total system costs. This means that with the market interaction feature of the controller, the total operational costs can be reduced by 0.35 %. For the all-electric DHC system in Heerlen, the purchasing costs for electricity represent 21 %

of the total cost price. On the basis of the same principles, a saving of approximately 8.4 % on electricity purchases could be achieved. This option of the controller will be of great importance, especially for all electric systems such as that of Mijnwater, especially when sufficient thermal buffering has been provided in the system, making it possible to charge energy independently of the energy demand at times when the electricity price is most favourable.

Figure 4: Comparison of the Rottne network heat load with and without STORM controller.

In the Mijnwater system in Heerlen, cell balancing was the main control objective. However, by balancing the demand to the excess heat production in the cluster, less flow is extracted from the backbone network. Therefore, this results in hydraulic peak shaving on the backbone network, and an increased dT in the system. It has been found that the controller was able to reduce the flow over the entire test period for a long time


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