Exploiting distributed energy resources with a virtual power plant : Intelligent market participation based on forecasts

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorSierla, Seppo, Dr., Aalto University, Department of Electrical Engineering and Automation, Finland
dc.contributor.authorSubramanya, Rakshith
dc.contributor.departmentSähkötekniikan ja automaation laitosfi
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.labResearch group of Information Technologies in Industrial Automationen
dc.contributor.schoolSähkötekniikan korkeakoulufi
dc.contributor.schoolSchool of Electrical Engineeringen
dc.contributor.supervisorVyatkin, Valeriy, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
dc.date.accessioned2024-07-20T09:00:28Z
dc.date.available2024-07-20T09:00:28Z
dc.date.defence2024-08-02
dc.date.issued2024
dc.description.abstractVirtual power plants (VPPs) are a promising solution for integrating renewable energy sources, battery energy storage, and smart loads into the modern power grid. They offer an alternative to traditional centralized power generation, which is often based on fossil fuel or nuclear power, and a key characteristic of a VPP is the profitable exploitation of the distributed energy resources that it manages. This is done by trading the capacity provided by these renewable energy resources on various electricity markets. To ensure the stability of the power grid, Frequency reserve markets are used, and VPPs, especially in Northern Europe, aggregate and trade DERs on such frequency reserve markets. The industrial informatics aspects of VPPs involve coordinating a pool of intelligent Distributed Energy Resources (DERs), predicting market prices using Artificial Intelligence (AI), and developing industrial informatics architectures for VPPs in the AI era. AI is utilized to analyze extensive datasets of historical data like electricity markets or DER capacity to discern patterns and trends. This information is then leveraged to forecast future demand and supply, aiding VPPs in optimizing their operations. Similarly, with the frequency reserve market forecasts, a VPP can make better decisions about allocating resources and participating in energy markets. This dissertation explores the integration of VPPs with DERs using various industry standards. For the optimal operation and profitability of the VPPs, DER capacity and reserve market forecasting are performed and integrated into VPPs. Also, reinforcement Learning is employed for the reserve market bidding. All the proposed architectural components, such as VPP, forecasting, and DER integration, are implemented on the cloud for seamless operation. Also, a multi-tenant architecture is proposed to implement the scalability of DER integration and various Software as a Service (SaaS) integrations like forecasting to a VPP. Building continuous software engineering practices is one of the main challenges in machine learning (ML) applications. For this purpose, this work also introduces Machine Learning and Operation (MLOps) and Cloud Design Patterns (CDPs) in the context of VPP. This research contributes to realizing a more efficient, resilient, and environmentally friendly energy system by addressing the challenges of DER integration with VPP, market participation, forecasting, and cloudification of a VPP with all the sub-systems. The dissertation begins by presenting the related work in the field, establishing the context for the proposed system. Four use cases define and explain the functional and non-functional system requirements and their implementation in detail. At last, the results are presented with conclusions.en
dc.format.extent127 + app. 115
dc.identifier.isbn978-952-64-1911-4 (electronic)
dc.identifier.isbn978-952-64-1910-7 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/129540
dc.identifier.urnURN:ISBN:978-952-64-1911-4
dc.language.isoenen
dc.opnProf. Mo-Yuen Chow, Joint Institute, University of Michigan-Shanghai Jiao Tong University
dc.publisherAalto Universityen
dc.publisherAalto-yliopistofi
dc.relation.haspart[Publication 1]: Subramanya, R.; Yli-Ojanperä, M.; Sierla, S.; Hölttä, T.; Valtakari, J.; Vyatkin, V. A Virtual Power Plant Solution for Aggregating Photovoltaic Systems and Other Distributed Energy Resources for Northern European Primary Frequency Reserves. Energies 2021, 14, 1242. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202104286414. DOI: 10.3390/en14051242
dc.relation.haspart[Publication 2]: R. Subramanya, S. Sierla, M. Yli-Ojanperä, H. Makkonen, M. Pourakbari-Kasmaei and V. Vyatkin, "Interfacing Third Party Cloud Services to a Virtual Power Plant," 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Espoo, Finland, 2021, pp. 1-6. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202201261459. DOI: 10.1109/ISGTEurope52324.2021.9640200
dc.relation.haspart[Publication 3]: R. Subramanya, S. A. Sierla and V. Vyatkin, "Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals," in IEEE Access, vol. 10, pp. 54484-54506, 2022. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202208104720. DOI: 10.1109/ACCESS.2022.3176446
dc.relation.haspart[Publication 4]: Subramanya, R.; Sierla, S.; Vyatkin, V. From DevOps to MLOps: Overview and Application to Electricity Market Forecasting. Appl. Sci. 2022, 12, 9851. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202211096447. DOI: 10.3390/app12199851
dc.relation.haspart[Publication 5]: R. Subramanya, A. Harri, S. Sierla and V. Vyatkin, "Onsite Renewable Generation Time Shifting for Photovoltaic Systems," 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finland, 2023, pp. 1-6. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202401171522. DOI: 10.1109/ISIE51358.2023.10228097
dc.relation.haspart[Publication 6]: R. Subramanya, P. Räisänen, S. Sierla and V. Vyatkin, "Cloud Computing Design Patterns for MLOps: Applications to Virtual Power Plants," IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society, Singapore, Singapore, 2023, pp. 01-07. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202401171580. DOI: 10.1109/IECON51785.2023.10312212
dc.relation.ispartofseriesAalto University publication series DOCTORAL THESESen
dc.relation.ispartofseries138/2024
dc.revHästbacka, David, Assoc. Prof., Tampere University, Finland
dc.revLuvisotto, Michele, Dr., Hitachi Energy Research, Sweden
dc.subject.keywordvirtual power planten
dc.subject.keywordelectricity marketen
dc.subject.keywordbattery energy storage systemsen
dc.subject.keywordfrequency containment reserveen
dc.subject.keywordartificial intelligenceen
dc.subject.keywordreinforcement learningen
dc.subject.keywordcloud computingen
dc.subject.keywordSaaSen
dc.subject.keywordMLOpsen
dc.subject.otherElectrical engineeringen
dc.subject.otherEnergyen
dc.subject.otherInformation systemsen
dc.titleExploiting distributed energy resources with a virtual power plant : Intelligent market participation based on forecastsen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
local.aalto.acrisexportstatuschecked 2024-08-08_1438
local.aalto.formfolder2024_07_19_klo_15_29
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