Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators
This work presents a novel strategy, designed from the distribution system operator viewpoint, aimed at estimating the hosting capacity in electric distribution systems when controllable plug‐in electric vehicles are in place. The strategy seeks to determine the maximum wind‐based distributed genera...
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| Veröffentlicht in: | IET generation, transmission & distribution Jg. 15; H. 2; S. 359 - 370 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.01.2021
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| ISSN: | 1751-8687, 1751-8695 |
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| Abstract | This work presents a novel strategy, designed from the distribution system operator viewpoint, aimed at estimating the hosting capacity in electric distribution systems when controllable plug‐in electric vehicles are in place. The strategy seeks to determine the maximum wind‐based distributed generation penetration by coordinating, on a forecast basis, the dispatch of electric vehicle aggregators, the operation of voltage regulation devices, and the active and reactive distributed generation power injections. Different from previous works, the proposed approach leverages controllable features of electric vehicles taking into account technical electric vehicle characteristics, driving behaviour of electric vehicle owners, and electric vehicle energy requirements to accomplish their primary purpose. The presented strategy is formulated as a two‐stage stochastic mixed‐integer linear programming problem. The first stage maximises the distributed generation installed capacity, while the second stage minimises the energy losses during the planning horizon. Probability density functions are used to describe the uncertainties associated with renewable distributed generation, conventional demand, and electric vehicle driving patterns. Obtained results show that controlling the power dispatched to electric vehicle aggregators can increase the distributed generation hosting capacity by up to 15% (given a 40% electric vehicle penetration), when compared to an uncontrolled electric vehicle approach. |
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| AbstractList | This work presents a novel strategy, designed from the distribution system operator viewpoint, aimed at estimating the hosting capacity in electric distribution systems when controllable plug‐in electric vehicles are in place. The strategy seeks to determine the maximum wind‐based distributed generation penetration by coordinating, on a forecast basis, the dispatch of electric vehicle aggregators, the operation of voltage regulation devices, and the active and reactive distributed generation power injections. Different from previous works, the proposed approach leverages controllable features of electric vehicles taking into account technical electric vehicle characteristics, driving behaviour of electric vehicle owners, and electric vehicle energy requirements to accomplish their primary purpose. The presented strategy is formulated as a two‐stage stochastic mixed‐integer linear programming problem. The first stage maximises the distributed generation installed capacity, while the second stage minimises the energy losses during the planning horizon. Probability density functions are used to describe the uncertainties associated with renewable distributed generation, conventional demand, and electric vehicle driving patterns. Obtained results show that controlling the power dispatched to electric vehicle aggregators can increase the distributed generation hosting capacity by up to 15% (given a 40% electric vehicle penetration), when compared to an uncontrolled electric vehicle approach. Abstract This work presents a novel strategy, designed from the distribution system operator viewpoint, aimed at estimating the hosting capacity in electric distribution systems when controllable plug‐in electric vehicles are in place. The strategy seeks to determine the maximum wind‐based distributed generation penetration by coordinating, on a forecast basis, the dispatch of electric vehicle aggregators, the operation of voltage regulation devices, and the active and reactive distributed generation power injections. Different from previous works, the proposed approach leverages controllable features of electric vehicles taking into account technical electric vehicle characteristics, driving behaviour of electric vehicle owners, and electric vehicle energy requirements to accomplish their primary purpose. The presented strategy is formulated as a two‐stage stochastic mixed‐integer linear programming problem. The first stage maximises the distributed generation installed capacity, while the second stage minimises the energy losses during the planning horizon. Probability density functions are used to describe the uncertainties associated with renewable distributed generation, conventional demand, and electric vehicle driving patterns. Obtained results show that controlling the power dispatched to electric vehicle aggregators can increase the distributed generation hosting capacity by up to 15% (given a 40% electric vehicle penetration), when compared to an uncontrolled electric vehicle approach. |
| Author | Sabillon, Carlos Padilha‐Feltrin, Antonio Venkatesh, Bala Quijano, Darwin A. Melgar‐Dominguez, Ozy D. |
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| Cites_doi | 10.3390/en13081965 10.1109/TSG.2015.2494371 10.1109/TSTE.2016.2598400 10.1109/61.25627 10.1023/A:1021805924152 10.1109/TPWRS.2009.2031223 10.3390/electronics9040552 10.1109/TII.2011.2158841 10.1109/TPWRS.2003.814891 10.1016/j.renene.2016.10.051 10.1049/iet-gtd.2017.0557 10.1109/TSTE.2017.2695195 10.3390/en11112981 10.1109/TPWRS.2014.2320895 10.1109/TSG.2017.2715259 10.1109/TSTE.2017.2754421 10.1109/TPWRS.2013.2256937 10.1109/TSG.2018.2820026 10.3390/en10020200 10.1049/iet-rpg.2018.5389 10.1109/TII.2018.2829710 10.1007/978-1-4614-0237-4 10.1016/j.renene.2018.07.008 10.1109/TII.2016.2619065 |
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| SubjectTerms | Control of electric power systems Optimisation techniques Other topics in statistics Probability and statistics Voltage control |
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| Title | Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators |
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