Dynamic charging of electric vehicles integrating renewable energy: a multi‐objective optimisation problem. Issue 2 (5th April 2019)

• Record Type: Journal Article
• Authors: Humfrey, Harry; Sun, Hongjian; Jiang, Jing
• Is Part Of: IET smart grid. Volume 2:Issue 2(2019)
• Page Start: 250
• Page End: 259
• Publication Date: 2019-04-05
• Subjects: optimisation -- battery powered vehicles -- renewable energy sources -- power grids -- electric vehicle charging -- demand side management; local renewable generation -- multiobjective optimisation problem -- energy retailer -- power grid -- allocation method -- local renewable energy integration -- average Gini coefficient reduction -- electric vehicles -- range anxiety -- acceptable range -- EV technology -- carbon emissions -- dynamic charging system -- demand‐side management method -- EVs dynamically -- energy reduction -- first‐come‐first‐served allocation method -- carbon dioxide emission reduction -- smooth demand profile -- time 24.0 hour -- CO2; B0260 Optimisation techniques -- B8110B Power system management, operation and economics -- B8520 Transportation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0066
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : Dynamically charging electric vehicles (EVs) have the potential to significantly reduce range anxiety and decrease the size of battery required for acceptable range. However, with the main driver for progressing EV technology being the reduction of carbon emissions, consideration of how a dynamic charging system would impact these emissions is required. This study presents a demand‐side management method for allocating resources to charge EVs dynamically considering the integration of local renewable generation. A multi‐objective optimisation problem is formulated to consider individual users, an energy retailer and a regulator as players with conflicting interests. A 19% reduction in the energy drawn from the power grid is observed over the course of a 24 h period when compared with a first‐come‐first‐served allocation method. This results in a greater reduction in CO2 emissions of 22% by considering the power grid's make‐up at each time interval. Furthermore, a 42% reduction in CO2 emissions is achieved compared to a system without local renewable energy integration. By varying the weights assigned to the players' goals, the method can reduce overall demand at peak times and produce a smoother demand profile. System fairness is shown to improve with an average Gini coefficient reduction of 4.32%.