A hierarchical and modular agent-oriented framework for power systems co-simulations
During the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid “smart”. Often, these domain-specific simulators have been then coupled with co-simulation platforms to test...
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| Vydáno v: | Energy Informatics Ročník 5; číslo Suppl 4; s. 48 - 21 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Cham
Springer International Publishing
01.12.2022
Springer Nature B.V SpringerOpen |
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| ISSN: | 2520-8942, 2520-8942 |
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| Abstract | During the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid “smart”. Often, these domain-specific simulators have been then coupled with co-simulation platforms to test new scenarios. In parallel, agent-oriented approaches have been introduced to test distributed control strategies and include social and behavioural aspects typical of the consumer side. Rarely, simulators of the physical systems have been coupled with these innovative techniques, especially when social and psychological aspects have been considered. In order to ease the re-usability of these simulators, avoiding re-coding everything from scratch, we propose a hierarchical and modular agent-oriented framework to test new residential strategies in the energy context. If needed, the presented work enables the user to select the desired level of details of the agent-based framework to match the corresponding physical system without effort to test very different scenarios. Moreover, it allows adding on top of the physical data, behavioural aspects. To this end, the characteristics of the framework are first introduced and then different scenarios are described to demonstrate the flexibility of the proposed work: (i) a first stand-alone scenario with two hierarchy levels, (ii) a second co-simulation scenario with a photovoltaic panel simulator and (iii) a third stand-alone scenario with three hierarchy levels. Results demonstrate the flexibility and ease of use of the framework, allowing us to compare several scenarios and couple new simulators to build a more and more complex environment. The framework is in the early stages of its development. However, thanks to its properties in the future it could be extended to include new actors, such as industries, to get the full picture. |
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| AbstractList | During the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid “smart”. Often, these domain-specific simulators have been then coupled with co-simulation platforms to test new scenarios. In parallel, agent-oriented approaches have been introduced to test distributed control strategies and include social and behavioural aspects typical of the consumer side. Rarely, simulators of the physical systems have been coupled with these innovative techniques, especially when social and psychological aspects have been considered. In order to ease the re-usability of these simulators, avoiding re-coding everything from scratch, we propose a hierarchical and modular agent-oriented framework to test new residential strategies in the energy context. If needed, the presented work enables the user to select the desired level of details of the agent-based framework to match the corresponding physical system without effort to test very different scenarios. Moreover, it allows adding on top of the physical data, behavioural aspects. To this end, the characteristics of the framework are first introduced and then different scenarios are described to demonstrate the flexibility of the proposed work: (i) a first stand-alone scenario with two hierarchy levels, (ii) a second co-simulation scenario with a photovoltaic panel simulator and (iii) a third stand-alone scenario with three hierarchy levels. Results demonstrate the flexibility and ease of use of the framework, allowing us to compare several scenarios and couple new simulators to build a more and more complex environment. The framework is in the early stages of its development. However, thanks to its properties in the future it could be extended to include new actors, such as industries, to get the full picture. Abstract During the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid “smart”. Often, these domain-specific simulators have been then coupled with co-simulation platforms to test new scenarios. In parallel, agent-oriented approaches have been introduced to test distributed control strategies and include social and behavioural aspects typical of the consumer side. Rarely, simulators of the physical systems have been coupled with these innovative techniques, especially when social and psychological aspects have been considered. In order to ease the re-usability of these simulators, avoiding re-coding everything from scratch, we propose a hierarchical and modular agent-oriented framework to test new residential strategies in the energy context. If needed, the presented work enables the user to select the desired level of details of the agent-based framework to match the corresponding physical system without effort to test very different scenarios. Moreover, it allows adding on top of the physical data, behavioural aspects. To this end, the characteristics of the framework are first introduced and then different scenarios are described to demonstrate the flexibility of the proposed work: (i) a first stand-alone scenario with two hierarchy levels, (ii) a second co-simulation scenario with a photovoltaic panel simulator and (iii) a third stand-alone scenario with three hierarchy levels. Results demonstrate the flexibility and ease of use of the framework, allowing us to compare several scenarios and couple new simulators to build a more and more complex environment. The framework is in the early stages of its development. However, thanks to its properties in the future it could be extended to include new actors, such as industries, to get the full picture. |
| ArticleNumber | 48 |
| Author | Bottaccioli, Lorenzo Patti, Edoardo Macii, Alberto De Vizia, Claudia |
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| Cites_doi | 10.1109/JSYST.2021.3135236 10.1109/MELECON48756.2020.9140496 10.1016/j.engappai.2019.03.008 10.1016/j.rser.2016.04.063 10.1016/j.rser.2019.05.038 10.1177/0037549720923401 10.1109/JSYST.2017.2726350 10.1007/978-3-319-46909-6_24 10.1016/j.erss.2018.10.021 10.1109/TII.2011.2158841 10.1109/ACCESS.2019.2927446 10.1109/ACCESS.2018.2886201 10.1016/j.rser.2019.01.036 10.1109/SGMS.2011.6089027 10.1016/j.enbuild.2020.109900 10.1002/er.1136 |
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Selectra. https://luce-gas.it/guida/bolletta/luce/imposte SousaTSoaresTPinsonPMoretFBarocheTSorinEPeer-to-peer and community-based markets: a comprehensive reviewRenew Sustain Energy Rev201910436737810.1016/j.rser.2019.01.036 The_AnyLogic_Company. AnyLogic. The AnyLogic Company. https://www.anylogic.com/. Accessed 1 May 2022 Carmichael R, Schofield J, Woolf M, Bilton M, Ozaki R, Strbac G (2014) Residential consumer attitudes to time-varying pricing. “Low Carbon London” LCNF project: Imperial College London Scherfke S (2018) mosaik-aiomas-demo. https://gitlab.com/mosaik/examples/mosaik-aiomas-demo ByrkaKJedrzejewskiASznajd-WeronKWeronRDifficulty is critical: the importance of social factors in modeling diffusion of green products and practicesRenew Sustain Energy Rev20166272373510.1016/j.rser.2016.04.063 MahmoodITul ainQNasirHAJavedFAguadoJAA hierarchical multi-resolution agent-based modeling and simulation framework for household electricity demand profileSimulation202096865567810.1177/0037549720923401 IRENA (2019) International Renewable Energy Agency AD (ed) Renewable power generation costs in 2018. IRENA. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/May/IRENA_Renewable-Power-Generations-Costs-in-2018.pdf GME. 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In: 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON), pp 529–534 BottaccioliLDi CataldoSAcquavivaAPattiERealistic multi-scale modeling of household electricity behaviorsIEEE Access201972467248910.1109/ACCESS.2018.2886201 VanDamKNikolicILukszoZAgent-based modelling of socio-technical systems2012BerlinSpringer Science and Business Media HansenPLiuXMorrisonGMAgent-based modelling and socio-technical energy transitions: a systematic literature reviewEnergy Res Soc Sci201949415210.1016/j.erss.2018.10.021 Schütte S, Scherfke S, Tröschel M. Mosaik: a framework for modular simulation of active components in Smart Grids. In: 2011 IEEE 1st SGMS. 2011;pp 55–60 De Vizia C, Patti E, Macii E, Bottaccioli L (2022) A user-centric view of a demand side management program: from surveys to simulation and analysis. IEEE Syst J TruongTMAmblardFGaudouBBlancCSVinhPCBarolliLCFBM—a framework for data driven approach in agent-based modeling and simulationNature of computation and communication2016ChamSpringer International Publishing26427510.1007/978-3-319-46909-6_24 ARERA (2018) Gli oneri generali di sistema fino al 31.12.2017. ARERA. https://www.arera.it/it/elettricita/onerigenerali.htm European_Commission. Bridge. European_Commission. https://bridge-smart-grid-storage-systems-digital-projects.ec.europa.eu/. Accessed 01 Feb 2021 VelleiMLe DréauJAbdelouadoudSYPredicting the demand flexibility of wet appliances at national level: the case of FranceEnergy Build202021410990010.1016/j.enbuild.2020.109900 PaateroJVLundPDA model for generating household electricity load profilesInt J Energy Res200630527329010.1002/er.1136 WalzbergJDandresTMerveilleNCherietMSamsonRAssessing behavioural change with agent-based life cycle assessment: application to smart homesRenew Sustain Energy Rev201911136537610.1016/j.rser.2019.05.038 K VanDam (244_CR28) 2012 244_CR23 244_CR25 244_CR21 244_CR20 TM Truong (244_CR26) 2016 244_CR16 T Sousa (244_CR24) 2019; 104 244_CR8 L Bottaccioli (244_CR3) 2018; 12 244_CR9 K Byrka (244_CR5) 2016; 62 244_CR6 I Mahmood (244_CR15) 2020; 96 244_CR7 A Micolier (244_CR17) 2019; 82 244_CR12 244_CR1 L Bottaccioli (244_CR4) 2019; 7 244_CR2 244_CR14 244_CR13 DS Schiera (244_CR22) 2019; 7 244_CR10 J Walzberg (244_CR30) 2019; 111 P Palensky (244_CR19) 2011; 7 244_CR27 M Vellei (244_CR29) 2020; 214 P Hansen (244_CR11) 2019; 49 JV Paatero (244_CR18) 2006; 30 |
| References_xml | – reference: TruongTMAmblardFGaudouBBlancCSVinhPCBarolliLCFBM—a framework for data driven approach in agent-based modeling and simulationNature of computation and communication2016ChamSpringer International Publishing26427510.1007/978-3-319-46909-6_24 – reference: Argonne_National_Laboratory. Repast. Argonne National Laboratory. https://repast.github.io/docs.html. Accessed 12 Apr 2021 – reference: De Vizia C, Patti E, Macii E, Bottaccioli L (2022) A user-centric view of a demand side management program: from surveys to simulation and analysis. IEEE Syst J – reference: PaateroJVLundPDA model for generating household electricity load profilesInt J Energy Res200630527329010.1002/er.1136 – reference: PalenskyPDietrichDDemand side management: demand response, intelligent energy systems, and smart loadsIEEE Trans Ind Inform20117338138810.1109/TII.2011.2158841 – reference: Uri W (1999) NetLogo. NetLogo. http://ccl.northwestern.edu/netlogo/. Accessed 15 Feb 2022 – reference: Scherfke S (2014) aiomas’ documentation. Stefan Scherfke. https://aiomas.readthedocs.io/en/latest – reference: VanDamKNikolicILukszoZAgent-based modelling of socio-technical systems2012BerlinSpringer Science and Business Media – reference: Schütte S, Scherfke S, Tröschel M. Mosaik: a framework for modular simulation of active components in Smart Grids. In: 2011 IEEE 1st SGMS. 2011;pp 55–60 – reference: IRENA (2019) International Renewable Energy Agency AD (ed) Renewable power generation costs in 2018. IRENA. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/May/IRENA_Renewable-Power-Generations-Costs-in-2018.pdf – reference: BottaccioliLDi CataldoSAcquavivaAPattiERealistic multi-scale modeling of household electricity behaviorsIEEE Access201972467248910.1109/ACCESS.2018.2886201 – reference: Carmichael R, Schofield J, Woolf M, Bilton M, Ozaki R, Strbac G (2014) Residential consumer attitudes to time-varying pricing. “Low Carbon London” LCNF project: Imperial College London – reference: Le MT, Nguyen TL, Tran QT, Besanger Y, Hoang TT (2020) A co-simulation approach for validating agent-based distributed algorithms in smart grid. In: 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON), pp 529–534 – reference: SchieraDSMinutoFDBottaccioliLBorchielliniRLanziniAAnalysis of rooftop photovoltaics diffusion in energy community buildings by a novel GIS- and agent-based modeling co-simulation platformIEEE Access20197934049343210.1109/ACCESS.2019.2927446 – reference: ARERA (2018) Gli oneri generali di sistema fino al 31.12.2017. ARERA. https://www.arera.it/it/elettricita/onerigenerali.htm – reference: GME. Gestore Mercati Energetici. https://www.mercatoelettrico.org/It/Default.aspx – reference: HansenPLiuXMorrisonGMAgent-based modelling and socio-technical energy transitions: a systematic literature reviewEnergy Res Soc Sci201949415210.1016/j.erss.2018.10.021 – reference: VelleiMLe DréauJAbdelouadoudSYPredicting the demand flexibility of wet appliances at national level: the case of FranceEnergy Build202021410990010.1016/j.enbuild.2020.109900 – reference: Czekster RM (2020) Tools for modelling and simulating the Smart Grid. CoRR. https://arxiv.org/abs/2011.07968 – reference: MahmoodITul ainQNasirHAJavedFAguadoJAA hierarchical multi-resolution agent-based modeling and simulation framework for household electricity demand profileSimulation202096865567810.1177/0037549720923401 – reference: European_Commission. Bridge. European_Commission. https://bridge-smart-grid-storage-systems-digital-projects.ec.europa.eu/. Accessed 01 Feb 2021 – reference: ByrkaKJedrzejewskiASznajd-WeronKWeronRDifficulty is critical: the importance of social factors in modeling diffusion of green products and practicesRenew Sustain Energy Rev20166272373510.1016/j.rser.2016.04.063 – reference: The_AnyLogic_Company. AnyLogic. The AnyLogic Company. https://www.anylogic.com/. Accessed 1 May 2022 – reference: WalzbergJDandresTMerveilleNCherietMSamsonRAssessing behavioural change with agent-based life cycle assessment: application to smart homesRenew Sustain Energy Rev201911136537610.1016/j.rser.2019.05.038 – reference: BottaccioliLPattiEMaciiEAcquavivaAGIS-based software infrastructure to model PV generation in fine-grained spatio-temporal domainIEEE Syst J20181232832284110.1109/JSYST.2017.2726350 – reference: ISTAT. Multiscope on families: use of time—microdates for public use. ISTAT. https://www.istat.it/it/archivio/202531. Accessed 01 Feb 2022 – reference: MicolierATaillandierFTaillandierPBosFLi-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information ModellingEng Appl Artif Intell201982445910.1016/j.engappai.2019.03.008 – reference: Scherfke S (2018) mosaik-aiomas-demo. https://gitlab.com/mosaik/examples/mosaik-aiomas-demo – reference: SousaTSoaresTPinsonPMoretFBarocheTSorinEPeer-to-peer and community-based markets: a comprehensive reviewRenew Sustain Energy Rev201910436737810.1016/j.rser.2019.01.036 – reference: Masci F. Accise ed IVA: le imposte in Bolletta Luce. Selectra. https://luce-gas.it/guida/bolletta/luce/imposte – ident: 244_CR1 – ident: 244_CR8 doi: 10.1109/JSYST.2021.3135236 – ident: 244_CR14 doi: 10.1109/MELECON48756.2020.9140496 – volume: 82 start-page: 44 year: 2019 ident: 244_CR17 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2019.03.008 – ident: 244_CR20 – volume: 62 start-page: 723 year: 2016 ident: 244_CR5 publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2016.04.063 – volume: 111 start-page: 365 year: 2019 ident: 244_CR30 publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2019.05.038 – ident: 244_CR6 – ident: 244_CR10 – volume: 96 start-page: 655 issue: 8 year: 2020 ident: 244_CR15 publication-title: Simulation doi: 10.1177/0037549720923401 – ident: 244_CR16 – ident: 244_CR2 – volume: 12 start-page: 2832 issue: 3 year: 2018 ident: 244_CR3 publication-title: IEEE Syst J doi: 10.1109/JSYST.2017.2726350 – ident: 244_CR12 – ident: 244_CR13 – start-page: 264 volume-title: Nature of computation and communication year: 2016 ident: 244_CR26 doi: 10.1007/978-3-319-46909-6_24 – volume: 49 start-page: 41 year: 2019 ident: 244_CR11 publication-title: Energy Res Soc Sci doi: 10.1016/j.erss.2018.10.021 – volume: 7 start-page: 381 issue: 3 year: 2011 ident: 244_CR19 publication-title: IEEE Trans Ind Inform doi: 10.1109/TII.2011.2158841 – volume: 7 start-page: 93404 year: 2019 ident: 244_CR22 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2927446 – volume: 7 start-page: 2467 year: 2019 ident: 244_CR4 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2886201 – volume: 104 start-page: 367 year: 2019 ident: 244_CR24 publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2019.01.036 – volume-title: Agent-based modelling of socio-technical systems year: 2012 ident: 244_CR28 – ident: 244_CR23 doi: 10.1109/SGMS.2011.6089027 – ident: 244_CR21 – ident: 244_CR25 – ident: 244_CR27 – ident: 244_CR7 – volume: 214 start-page: 109900 year: 2020 ident: 244_CR29 publication-title: Energy Build doi: 10.1016/j.enbuild.2020.109900 – ident: 244_CR9 – volume: 30 start-page: 273 issue: 5 year: 2006 ident: 244_CR18 publication-title: Int J Energy Res doi: 10.1002/er.1136 |
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| SubjectTerms | Agent-based modelling Agent-oriented programming Co-simulation Computer Science Flexibility Information Systems and Communication Service Photovoltaics Plug-and-play Psychological factors Residential energy Simulation Simulators Smart grid Software |
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| Title | A hierarchical and modular agent-oriented framework for power systems co-simulations |
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