Seyed Ashkan Nejati, B. Chong, Mahyar Alinejad, Shahriar Abbasi
{"title":"Optimal scheduling of electric vehicles charging and discharging in a smart parking-lot","authors":"Seyed Ashkan Nejati, B. Chong, Mahyar Alinejad, Shahriar Abbasi","doi":"10.1109/UPEC50034.2021.9548226","DOIUrl":null,"url":null,"abstract":"With large penetration and uncontrolled charging and discharging of Electric Vehicles (EVs) due to rapid increasing demand for clean energy, the upstream grid may face many technical issues such as energy security and reliability risk. Therefore, it is essential to carry out smart scheduling for charging and discharging of EVs. In this paper, a residential parking-lot scheme using 200 EVs as an example is developed to schedule the charging and discharging of EVs which is based on the initial and final value of the State Of Charge (SOC) of all EVs requested by the owner one day in advance, and the information the expected arrival and departure time to the parking-lot. However, errors most probably exist in this scheme where owners’ requests cannot be met because of random behaviours of the other EV owners which will require the imposition of penalty. An optimisation problem is formulated to maximise the Smart Parking-Lot (SPL) profit considering random behaviours EV owners and the penalty imposed. In this paper, the aim is achieved by defining some random behaviours and penalty flexibility. The related optimising problem is solved by using the Particle Swarm Optimisation (PSO) algorithm. The effectiveness of the proposed method is verified through four different scenarios; in the first scenario, some random behaviours of EV owners are considered. In the last three scenarios, some flexibilities in penalising of the EV owners have been considered. The simulation results of all scenarios are compared and used to demonstrate the features of the proposed scheduling method.","PeriodicalId":325389,"journal":{"name":"2021 56th International Universities Power Engineering Conference (UPEC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 56th International Universities Power Engineering Conference (UPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UPEC50034.2021.9548226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
With large penetration and uncontrolled charging and discharging of Electric Vehicles (EVs) due to rapid increasing demand for clean energy, the upstream grid may face many technical issues such as energy security and reliability risk. Therefore, it is essential to carry out smart scheduling for charging and discharging of EVs. In this paper, a residential parking-lot scheme using 200 EVs as an example is developed to schedule the charging and discharging of EVs which is based on the initial and final value of the State Of Charge (SOC) of all EVs requested by the owner one day in advance, and the information the expected arrival and departure time to the parking-lot. However, errors most probably exist in this scheme where owners’ requests cannot be met because of random behaviours of the other EV owners which will require the imposition of penalty. An optimisation problem is formulated to maximise the Smart Parking-Lot (SPL) profit considering random behaviours EV owners and the penalty imposed. In this paper, the aim is achieved by defining some random behaviours and penalty flexibility. The related optimising problem is solved by using the Particle Swarm Optimisation (PSO) algorithm. The effectiveness of the proposed method is verified through four different scenarios; in the first scenario, some random behaviours of EV owners are considered. In the last three scenarios, some flexibilities in penalising of the EV owners have been considered. The simulation results of all scenarios are compared and used to demonstrate the features of the proposed scheduling method.