An Improved Optimal Forecasting Algorithm for Comprehensive Electric Vehicle Charging Allocation. Issue 10 (31st July 2019)

• Record Type: Journal Article
• Authors: Abbas, Farukh; Feng, Donghan; Habib, Salman; Rasool, Aazim; Numan, Muhammad
• Is Part Of: Energy technology. Volume 7:Issue 10(2019:Oct.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-31
• Subjects: artificial neural networks -- large-scale electric vehicles -- multiobjective scheduling; Energy development -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1002/ente.201900436
• Languages: English
• ISSNs: 2194-4288

Abstract

Abstract : The anticipation of large‐scale electric vehicles (EVs) charging and discharging load can bring security and reliability challenges to the power system. As a smart load, EV requires an intelligently designed scheduling and pricing algorithm that takes into account the stochastic EVs' user behavior, grid charging capacity, battery characteristics, and real‐time electricity price variations. Herein, a multiobjective comprehensive stand‐alone solution is proposed considering a dynamic pricing model to intelligently regulate EVs charging/discharging schedule. An improved optimal forecasting approach is utilized to precisely predict the load variations by utilizing historical load and weather data. The proposed alternative heuristic charging strategy optimally configures solution indices and provides a tradeoff between considered evaluation parameters taken from the perspective of both power suppliers and EV users, thus mitigating the effect of uncontrolled charging introduced by stochastic charge–discharge activities. The objective is to shift the peak hours' load to nonpeak hours with a reduction in average‐to‐peak ratio, minimize charging cost, and maximize the availability of charging capacity for pledging traveling plans determined by EV users. Different EV penetrations are tested to validate the performance of the proposed solution under massive EV integration, with a driving pattern obtained from the Beijing National Travel Survey. Abstract : A comprehensive standalone framework is proposed, which optimally controls the electric vehicle (EV) charging pattern with the formulation of charging schemes, considering corresponding forecasted load‐based dynamic EV charging prices.