Risk-based dispatch optimization of microgrids considering the uncertainty in EV driving patterns

Mohseni, S; Brent, AC

Risk-based dispatch optimization of microgrids considering the uncertainty in EV driving patterns

Mohseni, S

Brent, AC

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2022, 00, pp. 1-6
Issue Date:: 2022-07-04

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Field	Value	Language
dc.contributor.author	Mohseni, S https://orcid.org/0000-0001-7367-3757
dc.contributor.author	Brent, AC
dc.date	2022-06-12
dc.date.accessioned	2023-07-08T21:08:55Z
dc.date.available	2023-07-08T21:08:55Z
dc.date.issued	2022-07-04
dc.identifier.citation	2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2022, 00, pp. 1-6
dc.identifier.isbn	9781665412117
dc.identifier.uri	http://hdl.handle.net/10453/171352
dc.description.abstract	The large scale electrification of the transport sector will result in increasingly peaky loads which necessitates the system integration of additional capital intensive storage infrastructure in renewables rich microgrids of the future In this context the vehicle to grid V2G technology can provide an effective platform to unlock the so called storage on wheels potential of electric vehicles EVs thereby reducing the need for cost prohibitive grid scale storage However the dispatch optimization of electricity networks integrating a high share of V2G enabled EVs is challenging given the uncertainties in forecasts of the associated timing of charging and discharging In response this paper introduces a novel mixed integer linear programming based model for the optimal operation of a grid connected microgrid MG integrating solar photovoltaic and wind turbine generation systems which are supported by a hydrogen based energy storage system whilst assuming a high level of EV penetration To this end the information gap decision theory IGDT is employed to evaluate the impact of risk averse RA and risk seeking RS strategies against the possible aggregated EV charging discharging scenarios where limited information is available about relevant driving patterns Importantly the simulation results from a test case community MG system indicate that the RA and RS strategies are associated with a representative daily MG operation profit deviation of 21 and 19 compared to the most likely risk neutral strategy respectively
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
dc.relation.ispartof	2022 17th International Conference on Probabilistic Methods Applied to Power Systems
dc.relation.isbasedon	10.1109/pmaps53380.2022.9810595
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Risk-based dispatch optimization of microgrids considering the uncertainty in EV driving patterns
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Manchester, United Kingdom
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
utslib.copyright.status	closed_access	*
dc.date.updated	2023-07-08T21:08:54Z
pubs.finish-date	2022-06-15
pubs.publication-status	Published
pubs.start-date	2022-06-12
pubs.volume	00

Abstract:

The large scale electrification of the transport sector will result in increasingly peaky loads which necessitates the system integration of additional capital intensive storage infrastructure in renewables rich microgrids of the future In this context the vehicle to grid V2G technology can provide an effective platform to unlock the so called storage on wheels potential of electric vehicles EVs thereby reducing the need for cost prohibitive grid scale storage However the dispatch optimization of electricity networks integrating a high share of V2G enabled EVs is challenging given the uncertainties in forecasts of the associated timing of charging and discharging In response this paper introduces a novel mixed integer linear programming based model for the optimal operation of a grid connected microgrid MG integrating solar photovoltaic and wind turbine generation systems which are supported by a hydrogen based energy storage system whilst assuming a high level of EV penetration To this end the information gap decision theory IGDT is employed to evaluate the impact of risk averse RA and risk seeking RS strategies against the possible aggregated EV charging discharging scenarios where limited information is available about relevant driving patterns Importantly the simulation results from a test case community MG system indicate that the RA and RS strategies are associated with a representative daily MG operation profit deviation of 21 and 19 compared to the most likely risk neutral strategy respectively

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http://hdl.handle.net/10453/171352