Robust Placement and Sizing of Charging Stations From a Novel Graph Theoretic Perspective

Abstract

This paper proposes analytical approaches to extend the capacity of existing networks of electric vehicles (EVs) by placement of additional charging stations (CSs) as well as determining the sizes of existing and new CSs in order to handle future expansions of EVs. The EV flow at CSs is modeled by a graph where nodes are potential locations for CSs and edges are uncertain parameters representing the variable EV flow at CSs. The required extra CS locations are explored by transforming the CS placement problem into a controllability framework addressed by maximum matching principle (MMP). To find the sizes of each CS, the graph of CS network is partitioned featuring only one CS in each subgraph. The size of CS in each subgraph is then determined by transforming the problem into the problem of robust stability of a system with uncertain parameters where each parameter is associated with an edge of subgraph. The zero exclusion principle is then tested for the related Kharitonov rectangles and polygonal polynomials of closed loop system with selected feedback gain as CS capacity. The proposed analytical approach is tested on the existing Tesla CS Network of Sydney. The locations of extra required CSs as well as the sizes of existing and new CSs are determined to maintain the waiting times at all stations below the threshold level.

Charging Station Planning for Electric Vehicles

“…Besides, previous studies have not dealt with key parameters such as SoC, the EV driving range, and the Average Travel Distance (ATD) of EVs simultaneously and an impact analysis considering these parameters has not been undertaken. Furthermore, most of the works have either used only a simulated system [16] or a real system [6,[17][18][19][20][21], but their investigations for CS need estimation are limited, as an analysis is not made for future requirements in these areas. This could be attributed to the reason, that these investigations require large-scale simulations, considering the planning area population or density, SoC, EV Driving Range, EV penetration (EVP), and the Average Travel Distance (ATD) of EVs, to estimate the needs for the future.…”

“…In most of the works, CS need estimation is undertaken for either a simulated distribution system [16] or for some city planning areas in India [17], China [19], Singapore [18], Australia [21], Japan [20], the UK [6] and the US [22]. However, limited investigations [20,23] have been done which consider the EVP in the planning areas and also the trend analysis on distances versus several installed CSs.…”

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

“…The progress of energy is a main objective in human life [1][2][3][4][5]. Electrical vehicles (EV) are spread out in most countries due to the emissions of gases from internal combustion vehicles [6]. The management strategy and design of the placement of a charging station (CS) play an imperative part in distribution network reliability, power loss and voltage stability [7], the coordination between the distribution network in the road network and the layout of EV charging stations.…”