A hybrid robust-stochastic optimization framework for optimal energy management of electric vehicles parking lots

Morteza Nazari-Heris, Mohammad Amin Mirzaei, Somayeh Asadi, Behnam Mohammadi-Ivatloo, Kazem Zare, Houtan Jebelli

Research output: Contribution to journalArticlepeer-review

Abstract

The integration of electric vehicles (EVs) into the energy industry has introduced an unprecedented complexity into the energy supply system due to the uncertain nature of such vehicles. The uncertainty of arrival and departure times, as well as the state of charge (SOC) in the arrival and departure of the EVs, is a big challenge in the entry of such vehicles and their facilities into energy networks. These days, a novel approach known as the EVs smart parking lot (SPL) is widely studied in the energy industry looking to manage the charging and discharging electricity of EVs as well as energy supply issues. This study proposes an SPL equipped with heat and power sources, including renewable and non-renewable technologies such as wind turbines, locally installed generating facilities consisting of combined heat and power (CHP) plants, micro-turbines, and heat and power storage systems. The operator of the SPL, in addition to supplying its electricity for sale to the power market, can sell the heat generated by the CHP units locally to maximize its profit. In addition, the proposed model for the SPL can handle the uncertain nature of EV arrivals and departures and the associated SOC level. It can also manage wind-power output and gauge optimal power prices based on hybrid robust-stochastic programming, which is implemented in a case study to confirm its practicality and effectiveness. The analysis shows the effectiveness of the proposed hybrid robust-stochastic operation model in maximizing the profit of the SPL driver and managing the uncertainty level of the system parameters.

Original languageEnglish (US)
Article number101467
JournalSustainable Energy Technologies and Assessments
Volume47
DOIs
StatePublished - Oct 2021

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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