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dc.contributor.authorMuhandiram Arachchige, Ireshika Subodha Tharangi
dc.identifier.citationIreshika, M. A. S. T. (2023). Autonomous Demand Side Management of Electric Vehicles. (Doctoral thesis). University of Agder.en_US
dc.descriptionThere is an error in the table of content, where publication A and B have swiched places.en_US
dc.description.abstractDemand-side management approaches that exploit the temporal flexibility of electric vehicles have attracted much attention in recent years due to the increasing market penetration. These demand-side management measures contribute to alleviating the burden on the power system, especially in distribution grids where bottlenecks are more prevalent. Electric vehicles can be defined as an attractive asset for distribution system operators, which have the potential to provide grid services if properly managed. In this thesis, first, a systematic investigation is conducted for two typically employed demand-side management methods reported in the literature: A voltage droop control-based approach and a market-driven approach. Then a control scheme of decentralized autonomous demand side management for electric vehicle charging scheduling which relies on a unidirectionally communicated grid-induced signal is proposed. In all the topics considered, the implications on the distribution grid operation are evaluated using a set of time series load flow simulations performed for representative Austrian distribution grids. Droop control mechanisms are discussed for electric vehicle charging control which requires no communication. The method provides an economically viable solution at all penetrations if electric vehicles charge at low nominal power rates. However, with the current market trends in residential charging equipment especially in the European context where most of the charging equipment is designed for 11 kW charging, the technical feasibility of the method, in the long run, is debatable. As electricity demand strongly correlates with energy prices, a linear optimization algorithm is proposed to minimize charging costs, which uses next-day market prices as the grid-induced incentive function under the assumption of perfect user predictions. The constraints on the state of charge guarantee the energy required for driving is delivered without failure. An average energy cost saving of 30% is realized at all penetrations. Nevertheless, the avalanche effect due to simultaneous charging during low price periods introduces new power peaks exceeding those of uncontrolled charging. This obstructs the grid-friendly integration of electric vehicles.en_US
dc.publisherUniversity of Agderen_US
dc.relation.ispartofseriesDoctoral Dissertations at the University of Agder; no. 411
dc.relation.haspartPaper I: Ireshika, M. A. S. T., Lliuyacc-Blas, R. & Kepplinger, P. (2021). "Voltage-Based Droop Control of Electric Vehicles in Distribution Grids under Different Charging Power Levels." Energies, 14(13), 3905. doi: 10.3390/en14133905. Published version.en_US
dc.relation.haspartPaper II: Ireshika, M. A. S. T., Lliuyacc-Blas, R. & Kepplinger, P. (2019). "Autonomous demand side management of electric vehicles in a distribution grid." In 2019 7th International Youth Conference on Energy (IYCE), pp. 1-6. Preprint version.en_US
dc.relation.haspartPaper III: Ireshika, M. A. S. T., Rheinberger, K., Lliuyacc-Blas, R., Kolhe, M. L., Preißinger, M., & Kepplinger, P. (2022). "Optimal power tracking for autonomous demand side management of electric vehicles." Journal of Energy Storage, 52, 104917. doi: 10.1016/j.est.2022.104917. Published version.en_US
dc.relation.haspartPaper IV: Ireshika, M.A.S.T. & Kepplinger, P. (2022). "IEC 61851 Compliant Demand Side Management Algorithm for Electric Vehicle Charging: A MILP Based Decentralized Approach." The 13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2022). Accepted version.en_US
dc.relation.haspartPaper V: Ireshika, M.A.S.T. & Kepplinger, P. (2022). "Uncertainties in model predictive control for decentralized autonomous demand side management of electric vehicles.". Submitted to Applied Energy, Dec 2022. Submitted version. Not available in AURA.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleAutonomous Demand Side Management of Electric Vehiclesen_US
dc.typeDoctoral thesisen_US
dc.rights.holder© 2023 Muhandiram Arachchige Subodha Tharangi Ireshikaen_US
dc.subject.nsiVDP::Teknologi: 500::Miljøteknologi: 610en_US

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