This paper proposes a compositional modeling framework for the optimal energy management of a district network. The focus is on cooling of buildings, which can possibly share resources to the purpose of reducing maintenance costs and using devices at their maximal efficiency. Components of the network are described in terms of energy fluxes and combined via energy balance equations. Disturbances are accounted for as well, through their contribution in terms of energy. Different district configurations can be built, and the dimension and complexity of the resulting model will depend both on the number and type of components and on the adopted disturbance description. Control inputs are available to efficiently operate and coordinate the district components, thus enabling energy management strategies to minimize the electrical energy costs or track some consumption profile agreed with the main grid operator.

A compositional modeling framework for the optimal energy management of a district network

Ioli, Daniele;Falsone, Alessandro;Papadopoulos, Alessandro Vittorio;Prandini, Maria
2019-01-01

Abstract

This paper proposes a compositional modeling framework for the optimal energy management of a district network. The focus is on cooling of buildings, which can possibly share resources to the purpose of reducing maintenance costs and using devices at their maximal efficiency. Components of the network are described in terms of energy fluxes and combined via energy balance equations. Disturbances are accounted for as well, through their contribution in terms of energy. Different district configurations can be built, and the dimension and complexity of the resulting model will depend both on the number and type of components and on the adopted disturbance description. Control inputs are available to efficiently operate and coordinate the district components, thus enabling energy management strategies to minimize the electrical energy costs or track some consumption profile agreed with the main grid operator.
2019
Building thermal regulation; Compositional systems; Energy management; Smart grid modeling; Control and Systems Engineering; Modeling and Simulation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Industrial and Manufacturing Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1059015
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