This paper describes the progress of the Horizon 2020 STORM-project, started in March 2015. The objective of this project is to develop and demonstrate a generic district heating and cooling network controller. Generic in this sense means that the controller is applicable to widely spread 3rd generation, but also very innovative 4th generation networks. Therefore, the controller will be tested in a traditional district heating scheme but also in an advanced low-temperature network. The controller influences the demand of the network to achieve a certain objective. STORM focuses on three objectives: (i) a peak shaving objective used to minimize the use of often polluting peak boilers; (ii) a cell balancing objective striving to balance a cluster of consumers to producers of excess heat or/cold; and (iii) a market interaction objective, applicable for heat/cold producers with a connection to the electrical grid (heat pumps or CHPs), maximizing the profit for the producer by switching these devices based on the electricity price. To guarantee the generic applicability of the controller, self-learning control techniques are used. These techniques have the advantage that they 'learn' the behavior of the network by themselves without the need to be extensively tuned at the installation (plug-and-play installation). To date, the focus was on algorithms for a forecast module of the heat/cold consumption of the network. These algorithms are already implemented in the controller platform and were running in real-time in one of the demonstration sites. Also, a tracking module was already developed and is currently tested. This module which will try to match the actual network consumption to this optimal consumption profile by distributing the control signals to the right heat consumers. Next step is the implementation of a planning module, which determines the optimal consumption profile, taken into account the forecasts.
Vanhoudt, D., Claessens, B., Desmedt, J., & Johansson, C. (2017). Status of the Horizon 2020 Storm Project. In Energy Procedia (Vol. 116, pp. 170–179). Elsevier Ltd. https://doi.org/10.1016/j.egypro.2017.05.065