A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems
Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected sy...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Haack, J. [verfasserIn] Narayan, A. [verfasserIn] Patil, A.D. [verfasserIn] Klaes, M. [verfasserIn] Braun, M. [verfasserIn] Lehnhoff, S. [verfasserIn] de Meer, H. [verfasserIn] Rehtanz, C. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Electric power systems research - Amsterdam [u.a.] : Elsevier Science, 1977, 212 |
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| Übergeordnetes Werk: |
volume:212 |
| DOI / URN: |
10.1016/j.epsr.2022.108356 |
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| Katalog-ID: |
ELV008475016 |
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| 520 | |a Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. | ||
| 650 | 4 | |a Automata | |
| 650 | 4 | |a Cyber–physical energy systems | |
| 650 | 4 | |a Disturbance propagation | |
| 650 | 4 | |a Finite state machine | |
| 650 | 4 | |a Hybrid systems modelling | |
| 650 | 4 | |a ICT-enabled grid services | |
| 700 | 1 | |a Narayan, A. |e verfasserin |0 (orcid)0000-0003-3593-1780 |4 aut | |
| 700 | 1 | |a Patil, A.D. |e verfasserin |0 (orcid)0000-0002-4115-8379 |4 aut | |
| 700 | 1 | |a Klaes, M. |e verfasserin |0 (orcid)0000-0001-7567-3208 |4 aut | |
| 700 | 1 | |a Braun, M. |e verfasserin |4 aut | |
| 700 | 1 | |a Lehnhoff, S. |e verfasserin |0 (orcid)0000-0003-2340-6807 |4 aut | |
| 700 | 1 | |a de Meer, H. |e verfasserin |4 aut | |
| 700 | 1 | |a Rehtanz, C. |e verfasserin |0 (orcid)0000-0002-8134-6841 |4 aut | |
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| 936 | b | k | |a 52.52 |j Thermische Energieerzeugung |j Wärmetechnik |
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| allfields |
10.1016/j.epsr.2022.108356 doi (DE-627)ELV008475016 (ELSEVIER)S0378-7796(22)00526-0 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Haack, J. verfasserin (orcid)0000-0001-5202-1039 aut A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. Automata Cyber–physical energy systems Disturbance propagation Finite state machine Hybrid systems modelling ICT-enabled grid services Narayan, A. verfasserin (orcid)0000-0003-3593-1780 aut Patil, A.D. verfasserin (orcid)0000-0002-4115-8379 aut Klaes, M. verfasserin (orcid)0000-0001-7567-3208 aut Braun, M. verfasserin aut Lehnhoff, S. verfasserin (orcid)0000-0003-2340-6807 aut de Meer, H. verfasserin aut Rehtanz, C. verfasserin (orcid)0000-0002-8134-6841 aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 212 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:212 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 212 |
| spelling |
10.1016/j.epsr.2022.108356 doi (DE-627)ELV008475016 (ELSEVIER)S0378-7796(22)00526-0 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Haack, J. verfasserin (orcid)0000-0001-5202-1039 aut A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. Automata Cyber–physical energy systems Disturbance propagation Finite state machine Hybrid systems modelling ICT-enabled grid services Narayan, A. verfasserin (orcid)0000-0003-3593-1780 aut Patil, A.D. verfasserin (orcid)0000-0002-4115-8379 aut Klaes, M. verfasserin (orcid)0000-0001-7567-3208 aut Braun, M. verfasserin aut Lehnhoff, S. verfasserin (orcid)0000-0003-2340-6807 aut de Meer, H. verfasserin aut Rehtanz, C. verfasserin (orcid)0000-0002-8134-6841 aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 212 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:212 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 212 |
| allfields_unstemmed |
10.1016/j.epsr.2022.108356 doi (DE-627)ELV008475016 (ELSEVIER)S0378-7796(22)00526-0 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Haack, J. verfasserin (orcid)0000-0001-5202-1039 aut A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. Automata Cyber–physical energy systems Disturbance propagation Finite state machine Hybrid systems modelling ICT-enabled grid services Narayan, A. verfasserin (orcid)0000-0003-3593-1780 aut Patil, A.D. verfasserin (orcid)0000-0002-4115-8379 aut Klaes, M. verfasserin (orcid)0000-0001-7567-3208 aut Braun, M. verfasserin aut Lehnhoff, S. verfasserin (orcid)0000-0003-2340-6807 aut de Meer, H. verfasserin aut Rehtanz, C. verfasserin (orcid)0000-0002-8134-6841 aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 212 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:212 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 212 |
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10.1016/j.epsr.2022.108356 doi (DE-627)ELV008475016 (ELSEVIER)S0378-7796(22)00526-0 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Haack, J. verfasserin (orcid)0000-0001-5202-1039 aut A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. Automata Cyber–physical energy systems Disturbance propagation Finite state machine Hybrid systems modelling ICT-enabled grid services Narayan, A. verfasserin (orcid)0000-0003-3593-1780 aut Patil, A.D. verfasserin (orcid)0000-0002-4115-8379 aut Klaes, M. verfasserin (orcid)0000-0001-7567-3208 aut Braun, M. verfasserin aut Lehnhoff, S. verfasserin (orcid)0000-0003-2340-6807 aut de Meer, H. verfasserin aut Rehtanz, C. verfasserin (orcid)0000-0002-8134-6841 aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 212 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:212 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 212 |
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10.1016/j.epsr.2022.108356 doi (DE-627)ELV008475016 (ELSEVIER)S0378-7796(22)00526-0 DE-627 ger DE-627 rda eng 620 DE-600 52.52 bkl 53.31 bkl 53.39 bkl Haack, J. verfasserin (orcid)0000-0001-5202-1039 aut A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. Automata Cyber–physical energy systems Disturbance propagation Finite state machine Hybrid systems modelling ICT-enabled grid services Narayan, A. verfasserin (orcid)0000-0003-3593-1780 aut Patil, A.D. verfasserin (orcid)0000-0002-4115-8379 aut Klaes, M. verfasserin (orcid)0000-0001-7567-3208 aut Braun, M. verfasserin aut Lehnhoff, S. verfasserin (orcid)0000-0003-2340-6807 aut de Meer, H. verfasserin aut Rehtanz, C. verfasserin (orcid)0000-0002-8134-6841 aut Enthalten in Electric power systems research Amsterdam [u.a.] : Elsevier Science, 1977 212 Online-Ressource (DE-627)308447549 (DE-600)1502242-0 (DE-576)259271047 nnns volume:212 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.52 Thermische Energieerzeugung Wärmetechnik 53.31 Elektrische Energieübertragung 53.39 Elektrische Energietechnik: Sonstiges AR 212 |
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Enthalten in Electric power systems research 212 volume:212 |
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Enthalten in Electric power systems research 212 volume:212 |
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Thermische Energieerzeugung Wärmetechnik Elektrische Energieübertragung Elektrische Energietechnik: Sonstiges |
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findex.gbv.de |
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Automata Cyber–physical energy systems Disturbance propagation Finite state machine Hybrid systems modelling ICT-enabled grid services |
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620 |
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Electric power systems research |
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Haack, J. @@aut@@ Narayan, A. @@aut@@ Patil, A.D. @@aut@@ Klaes, M. @@aut@@ Braun, M. @@aut@@ Lehnhoff, S. @@aut@@ de Meer, H. @@aut@@ Rehtanz, C. @@aut@@ |
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2022-01-01T00:00:00Z |
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a hybrid model for analysing disturbance propagation in cyber–physical energy systems |
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A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems |
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Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. |
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Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. |
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Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services. |
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