An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results

Abstract

This paper presents a holistic innovative solution for the transformation of the current district heating and cooling systems to automated more efficient systems. A variety of technological advancements have been developed and integrated to support the effective energy management of future district heating and cooling sector. First, we identify and discuss the main challenges and needs that are in line with the EU objectives and policy expectations. We give an overview of the main parts that our solution consists of, with emphasis on the forecasting tools and an advanced control system that addresses unit commitment and economic load dispatch problems. The proposed control approach employs distributed and scalable optimisation algorithms for optimising the short-term operations of a district heating and cooling plant subject to technical constraints and uncertainties in the energy demand. To test the performance and validate the proposed control system, a district heating plant with multiple energy generation units and real-life heat load data were used. Simulation experiments were also used to evaluate the benefits of using thermal storage units in district heating systems. The results show that the proposed method could achieve significant cost savings when energy storage is employed. The proposed control strategy can be applied for both operating optimally district heating plants with storage and supporting investment planning for new storage units.

A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling

“…Approaches such as the one presented in the present study provide various new findings and deeper knowledge on the data-driven models' behavior. The data-driven model could also be integrated within a system's optimization mechanism [28] as a means to capture the current or even predicted storage capacity of the DHC network. Target for this modelling approach is to provide a fast-and-dirty implementation to any thermal system, where there is a limited amount of information regarding the details of the TES plant's operation, other than temperature measurements, by trying to achieve the best possible accuracy for a data-driven model.…”

“…Aside from ANNs, other various ML models were applied for the prediction of energy demand [22]; Bayesian nets and reinforcement methods were used for heat load prediction in district heating systems [23,24], fuzzy networks were implemented for the prediction of energy demand concerning renewable energy systems [25], SVMs were employed for predictive energy management between solar energy source and an energy storage [26] and ensembles of online ML algorithms were used for operational demand forecasting in DH systems [18,27] as representative examples. The need of incorporating ML-based forecasting algorithms within advanced control strategies has been highlighted in [28,29] for the transformation of the current district heating and cooling systems to more efficient automated systems.…”

Innovative Technologies for District Heating and Cooling: InDeal Project

“…In order to automate the advanced control concept and solve the decision problems in near real-time, a preliminary integration plan has been designed that allows for reliable data exchange between the modelling/control framework and the information captured by the AI-based supporting tools. More information about the proposed control framework can be found in [9].…”

Model Predictive Control of a Substation of a District Heating System for Enhanced Environmental and Economic Performance

“…The benefits of applying advanced control approaches such as MPC [14] in DH systems are well understood [10,15,16]. In [15], a simplified building heating system model is developed and implemented with MPC to demonstrate a case study of 95-flat communal heating system.…”