Open AccessOptimal Recharging of EVs for Peak Power Shaving and Valley Filling using EV-Aggregator model in a Micro-grid
Author(s) -
Chinmaya Kumar Padhi,
Sweta Panda,
Gyan Ranjan Biswal
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1854/1/012016
Subject(s) - news aggregator , state of charge , peak demand , automotive engineering , computer science , scheduling (production processes) , grid , sizing , demand response , smart grid , battery (electricity) , power (physics) , mathematical optimization , reliability engineering , simulation , real time computing , engineering , electricity , electrical engineering , mathematics , art , physics , geometry , quantum mechanics , visual arts , operating system
This work presents an optimal recharging strategy for Electric vehicles (EVs) using Quadratic Programming (QP) to flatten the peak power demand on the utility. The increase in penetration of EVs in distribution systems causes a significant increase in peak power demand due to synchronization between the peak load hours and EV charging period. The optimization technique aims to assess the effects of optimal scheduling considering the initial State-of-charge (SOC) level, Demand Side Management (DSM) functionalities that meet both the grid and EV owners’ requirements. The feasibility of the scheme is verified with two case studies using different aggregator units and the effect on the system is analyzed by determining the load demands from local utilities and EVs. The aggregator units collect data from EV users with common interests such as EV battery specification, Battery SOC, recharging period and mediate with utility operators such as Transmission system operator (TSO), Distribution system operator (DSO). Further, other parameters such as peak power reduction, peak-to-average ratio (PAR), standard deviation, and peak-to-valley differences are also compared to test the effectiveness of the implemented optimization technique. The outcomes of the study show that using load demand profile and optimal rescheduling using aggregated EVs can flatten the load curve for the utility and lower the demand charges for the end-users.