District heating and cooling networks have great potential for energy saving, efficient thermal energy distribution and renewable energy source integration. Currently, heating systems are managed on the basis of operator experience or by using adaptive controllers, however these solutions are not suitable when there are remarkable variations in boundary conditions. In this context, Model Predictive Control is a promising strategy as it optimizes control based on the prediction of the future behavior of system dynamics and disturbances by means of simplified models. This paper presents the development of a predictive controller based on a novel Dynamic Programming optimization algorithm and aimed to supply the thermal energy to entire buildings within district heating networks. The controller is exploited to operate the district heating network of a school complex in a simulation environment (i.e. Model-in-the-Loop). Each branch connected to the network is optimized by a dedicated controller according to a multi-agent strategy. The performance of the innovative controller is compared to the results obtained by using a conventional PID controller. Conservative results show that, with the innovative controller, a reduction in fuel consumption of up to more than 7% is obtained together with up to 5 h of avoided failures of the indoor comfort constraints, depending on the season. Overall, the Model-based Predictive Controller is able to fulfill comfort requirements adequately while minimizing energy consumption. Moreover, the multi-agent approach allows these results to be extended to larger networks in future studies.

Development, analysis and application of a predictive controller to a small-scale district heating system / Saletti, C.; Gambarotta, A.; Morini, M.. - In: APPLIED THERMAL ENGINEERING. - ISSN 1359-4311. - 165:(2020), p. 114558. [10.1016/j.applthermaleng.2019.114558]

Development, analysis and application of a predictive controller to a small-scale district heating system

Saletti C.;Gambarotta A.;Morini M.
2020-01-01

Abstract

District heating and cooling networks have great potential for energy saving, efficient thermal energy distribution and renewable energy source integration. Currently, heating systems are managed on the basis of operator experience or by using adaptive controllers, however these solutions are not suitable when there are remarkable variations in boundary conditions. In this context, Model Predictive Control is a promising strategy as it optimizes control based on the prediction of the future behavior of system dynamics and disturbances by means of simplified models. This paper presents the development of a predictive controller based on a novel Dynamic Programming optimization algorithm and aimed to supply the thermal energy to entire buildings within district heating networks. The controller is exploited to operate the district heating network of a school complex in a simulation environment (i.e. Model-in-the-Loop). Each branch connected to the network is optimized by a dedicated controller according to a multi-agent strategy. The performance of the innovative controller is compared to the results obtained by using a conventional PID controller. Conservative results show that, with the innovative controller, a reduction in fuel consumption of up to more than 7% is obtained together with up to 5 h of avoided failures of the indoor comfort constraints, depending on the season. Overall, the Model-based Predictive Controller is able to fulfill comfort requirements adequately while minimizing energy consumption. Moreover, the multi-agent approach allows these results to be extended to larger networks in future studies.
Development, analysis and application of a predictive controller to a small-scale district heating system / Saletti, C.; Gambarotta, A.; Morini, M.. - In: APPLIED THERMAL ENGINEERING. - ISSN 1359-4311. - 165:(2020), p. 114558. [10.1016/j.applthermaleng.2019.114558]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2866075
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