Peak Shaving in District Heating Utilizing Adaptive Predictive Control

Abstract

District heating systems (DHS) are driven by the heat demands of their consumers, with higher demands giving a higher load on the heat production. While heat demands are human-dependent, they contain diurnal behaviors and weather dependencies. The diurnal behaviors contain periods with high demands causing peak loads on the heat production, which is operationally costly. This is especially true for heat pumps, a solution for DHS to include green energy, as the cost depends directly on the needed temperature. This paper presents a formulation of adaptive model predictive control (MPC) for inducing peak shaving on the production load to handle the peak load problem by using the DHS distribution network as a heat storage. It also presents a simulator model to describe the DHS. The MPC was applied to data from a case study of the DHS in Brønderslev, Denmark, showing a peak reduction of around 8%.

Keywords

• data-driven modeling
• MPC
• adaptive control
• district heating