The paper presents the structure of an open-source software tool that can solve an Optimal Network Reconfiguration problem for a distribution grid. The problem includes kinship constraints that permit to fully represent the logics which are behind the principal distribution automation schemes adopted by Distributor System Operators to deal with the fault detection, isolation and recovery function. The algorithm is structured to be fully open-source and it is developed on a Python-based environment. The adopted network models are also open-source and based on the Open-DSS software. Results obtained on the model of a realistically sized MV primary distribution network are shown.

An Open-Source Optimal Network Reconfiguration Tool for Improving Distribution Grid Reliability / Cometa, R.; Velini, A.; Lorusso, F.; Ricca, A.; Sbrizzai, R.; Bruno, S.. - (2024), pp. 1-6. (Intervento presentato al convegno 116th AEIT International Annual Conference, AEIT 2024 tenutosi a ita nel 2024) [10.23919/AEIT63317.2024.10736725].

An Open-Source Optimal Network Reconfiguration Tool for Improving Distribution Grid Reliability

Cometa R.;Velini A.;Lorusso F.;Sbrizzai R.;Bruno S.
2024-01-01

Abstract

The paper presents the structure of an open-source software tool that can solve an Optimal Network Reconfiguration problem for a distribution grid. The problem includes kinship constraints that permit to fully represent the logics which are behind the principal distribution automation schemes adopted by Distributor System Operators to deal with the fault detection, isolation and recovery function. The algorithm is structured to be fully open-source and it is developed on a Python-based environment. The adopted network models are also open-source and based on the Open-DSS software. Results obtained on the model of a realistically sized MV primary distribution network are shown.
2024
116th AEIT International Annual Conference, AEIT 2024
An Open-Source Optimal Network Reconfiguration Tool for Improving Distribution Grid Reliability / Cometa, R.; Velini, A.; Lorusso, F.; Ricca, A.; Sbrizzai, R.; Bruno, S.. - (2024), pp. 1-6. (Intervento presentato al convegno 116th AEIT International Annual Conference, AEIT 2024 tenutosi a ita nel 2024) [10.23919/AEIT63317.2024.10736725].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/280560
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