Research on Data-Driven Operation Control of Secondary Loop of District Heating System

Abstract

District heating system (DHS) is a major part of urban energy system. The operation control of heating substation and its corresponding secondary loop is a challenging topic. This paper proposes a new method of secondary loop operation control via a data-driven approach by dividing the process into two parts: primary loop valve opening (PLVO) prediction and corresponding secondary loop supply temperature (SLST) prediction. The goal is to provide both a time sequence of enhanced valve opening and its expected supply water temperature. Both models are trained with multilayer perceptron (MLP) and extreme gradient boosting (XGBoost) methods with consideration of the impact of input and prediction length. A demo heating substation and its secondary loop in Zhengzhou city are selected to illustrate the effectiveness of this approach. The application results show that the enhanced PLVO strategy proposed in this paper could reduce the supply water temperature deviation from the expected SLST and adapt to the change of ambient conditions. Compared with the existing operation strategy, the proposed approach reduces the supply water temperature deviation from 12.68% to 3.18% in an overheating scenario and from 6.18% to 3.87% in an insufficient heating scenario.