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Model Reduction for Heat Grid State Estimation

Bott, Andreas ; Friedrich, Pascal ; Rehlich, Lea ; Steinke, Florian (2021):
Model Reduction for Heat Grid State Estimation.
2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), virtual Conference, 18.10-21.10.2021, [Conference or Workshop Item]

Abstract

Traditional district heating networks have been built around few central heat production units. Now, many more decentral heat sources based on renewable energies or waste heat are being included to reduce carbon emissions. Given the resulting complex flow patterns in the network reliable and fast heat grid state estimation becomes mandatory for efficient grid control. In this paper we first show an approach to reduce computational efforts for various grid computations by summarising pipe segments of the network. We then develop a probabilistic state estimator based on the reduced model by locally linearising the non-linear grid equations around the best state estimate. We show that the linearisation approach with a significantly lower computational burden achieves a prediction quality comparable to those of a sampling-based Monte-Carlo approach that uses the full model. This allows state estimation to become an online routine even in complex heating networks.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Bott, Andreas ; Friedrich, Pascal ; Rehlich, Lea ; Steinke, Florian
Title: Model Reduction for Heat Grid State Estimation
Language: English
Abstract:

Traditional district heating networks have been built around few central heat production units. Now, many more decentral heat sources based on renewable energies or waste heat are being included to reduce carbon emissions. Given the resulting complex flow patterns in the network reliable and fast heat grid state estimation becomes mandatory for efficient grid control. In this paper we first show an approach to reduce computational efforts for various grid computations by summarising pipe segments of the network. We then develop a probabilistic state estimator based on the reduced model by locally linearising the non-linear grid equations around the best state estimate. We show that the linearisation approach with a significantly lower computational burden achieves a prediction quality comparable to those of a sampling-based Monte-Carlo approach that uses the full model. This allows state estimation to become an online routine even in complex heating networks.

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Energy Information Networks and Systems Lab (EINS)
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering
Forschungsfelder
Forschungsfelder > Energy and Environment
Forschungsfelder > Energy and Environment > Integrated Energy Systems
Event Title: 2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
Event Location: virtual Conference
Event Dates: 18.10-21.10.2021
Date Deposited: 26 Jul 2021 10:54
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