مقاله انگلیسی رایگان در مورد برنامه‌ریزی برای خودروهای الکتریکی – IEEE 2017

Abstract

Efficient electric vehicle (EV) scheduling is a multi-objective optimization problem with conflicting customer and system operator interests, especially during vehicle-to-grid implementations. Economic charging while minimizing battery degradation and maintaining system load profiles couple the interests of these two entities. This paper focuses on identifying the relationships between these objectives and proposes to use an augmented epsilon-constrain (AUGMECON) based technique to implement two-way and three-way multi-objective optimizations. The importance of using these objectives in peak-shaving and valley-filling for an aggregated (residential) EV fleet is discussed. The proposed solution provides a look-ahead strategy into effective EV scheduling by co-optimizing multiple objectives. To provide operational guidance to utilities and customers, an optimal solution may be selected from those represented by the Pareto fronts.

Introduction

COMMERCIALIZATION and adoption of electric vehicles (EV) in the automobile market has raised concerns over their uncontrolled charging demands and their impact of the present electrical power system to serve the increasing load demand efficiently. Increases in EV load may lead to network congestion, high losses, thermal stresses, and require network reinforcements, in addition to higher operating costs at peak demands. Therefore, scheduling and control of the EV load is essential for the economic operation of the power system. Vehicle-to-grid (V2G) operations of the EVs impose additional problems due to power injection into the electric grid traditionally designed for one-way power flow. Apart from the technical challenges, well designed financial models and transactive energy frameworks are required to make V2G a feasible and lucrative option [1]-[3].