Modeling a realistic integrated energy hub with growing demand for electric vehicles: The case of the province of Ontario, Canada

Siroos, Ahmad; Samarghandi, Hamed

Modeling a realistic integrated energy hub with growing demand for electric vehicles: The case of the province of Ontario, Canada

dc.contributor.author	Siroos, Ahmad
dc.contributor.author	Samarghandi, Hamed
dc.date.accessioned	2025-02-26T10:29:01Z
dc.date.available	2025-02-26T10:29:01Z
dc.date.issued	2025-01-28
dc.description	0360-5442/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
dc.description.abstract	Energy hubs are multi-carrier energy management systems that efficiently distribute various forms of energy, reducing losses and environmental pollution. This paper examines Ontario, Canada, as a major energy hub, using a typical fall day pattern for energy demand. The model includes all power generation sources in Ontario: photovoltaic (PV), wind turbine (WT), nuclear, hydro, biofuel, and natural gas power plants. It also integrates the charging and discharging of electric vehicles (EVs) within the energy distribution framework. Managing the intrinsic uncertainty of the parameters is crucial for efficient operation. This study employs probabilistic functions to account for the arrival and departure hours of EVs, controlled using the Conditional Value at Risk (CVaR) method. Three methods, Information Gap Decision Theory (IGDT) with risk-seeking and risk-averse behaviors, and robust optimization, address uncertainties such as wind and solar electricity production, energy prices, and electrical, heating, and cooling demands. We compare simulation results of three scheduling scenarios for optimal energy production and dispatch. The RS-IGDT method can lead to significant losses during peak hours due to fluctuations. The robust method incurs higher costs by planning for large deviations. The RA-IGDT method balances deviations without the pessimism of the robust method, making it the recommended approach.
dc.description.sponsorship	"NSERC Discovery Grant [#2017-03743]” and the “Edwards Enhancement Chair in Business Program”
dc.description.version	Peer Reviewed
dc.identifier.citation	Siroos, A., & Hamed Samarghandi. (2025). Modeling a realistic integrated energy hub with growing demand for electric vehicles: the case of the province of Ontario, Canada. Energy, 134678–134678. https://doi.org/10.1016/j.energy.2025.134678
dc.identifier.doi	10.1016/j.energy.2025.134678
dc.identifier.uri	https://hdl.handle.net/10388/16632
dc.language.iso	en
dc.publisher	Elsevier
dc.rights	Attribution-NonCommercial 2.5 Canada	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc/2.5/ca/
dc.subject	Combined cooling
dc.subject	Heating and power
dc.subject	Energy hub
dc.subject	Electric vehicles
dc.subject	Information gap decision theory
dc.subject	Risk averse and risk seeker IGDT
dc.subject	Robust method
dc.title	Modeling a realistic integrated energy hub with growing demand for electric vehicles: The case of the province of Ontario, Canada
dc.type	Article