Location and Expansion of Electric Bus Charging Stations Based on Gridded Affinity Propagation Clustering and a Sequential Expansion Rule

Abstract

With the escalating contradiction between the growing demand for electric buses and limited supporting resources of cities to deploy electric charging infrastructure, it is a great challenge for decision-makers to synthetically plan the location and decide on the expansion sequence of electric charging stations. In light of the location decisions of electric charging stations having long-term impacts on the deployment of electric buses and the layout of city traffic networks, a comprehensive framework for planning the locations and deciding on the expansion of electric bus charging stations should be developed simultaneously. In practice, construction or renovation of a new charging station is limited by various factors, such as land resources, capital investment, and power grid load. Thus, it is necessary to develop an evaluation structure that combines these factors to provide integrated decision support for the location of bus charging stations. Under this background, this paper develops a gridded affinity propagation (AP) clustering algorithm that combines the superiorities of the AP clustering algorithm and the map gridding rule to find the optimal candidate locations for electric bus charging stations by considering multiple impacting factors such as land cost, traffic conditions, and so on. Based on the location results of the candidate stations, the expansion sequence of these candidate stations is proposed. In particular, a sequential expansion rule for planning the charging stations is proposed that considers the development trends of the charging demand. To verify the performance of the gridded AP clustering and the effectiveness of the proposed sequential expansion rule, an empirical investigation of Guiyang City, the capital of Guizhou province in China, is conducted. The results of the empirical investigation demonstrate that the proposed framework that helps find optimal locations for electric bus charging stations and the expansion sequence of these locations are decided with less capital investment pressure. This research shows that the combination of gridded AP clustering and the proposed sequential expansion rule can systematically solve the problem of finding the optimal locations and deciding on the best expansion sequence for electric bus charging stations, which denotes that the proposed structure is pretty pragmatic and would benefit the government for long-term investment in electric bus station deployment.

A Method for the Identification of Critical Interstop Sections in Terms of Introducing Electric Buses in Public Transport

“…Electric buses have range limitations, which are related to battery capacity [24]. To use this type of fleet on existing bus routes, it is necessary to know the specifications of the buses [25].…”

“…An important aspect is how these vehicles are charged, which can be performed in depots by cable charging, at bus stop stations by pantograph charging, or on roads by inductive charging. In terms of cost, the most efficient option is to charge vehicles in the depots [24].…”

“…Other parameters related to the local landform (elevation and depression), the road network, the availability of charging infrastructure, and the weather, the level of energy required is influenced by the need to use heating and air conditioning [27]. Existing bottlenecks on public transport routes may preclude the introduction of electric buses due to the longer time required to cover a relatively short route due to traffic congestion in these places [24].…”

Construction Planning and Operation of Battery Swapping Stations for Electric Vehicles: A Literature Review

“…In order to solve this dilemma, the government has invested a lot of energy in infrastructure deployment. Service operation of electric vehicle charging stations has become a hot issue for scholars; for example, the deployment of charging stations [7][8][9], electric vehicle interaction with power systems [10][11][12] and the VRP problem with charging stations [13][14][15][16][17][18]. However, as the main mode of energy supply for electric vehicles, charging mode also has two main problems that restrict the industrialization of electric vehicles.…”

Affinity Propagation Based on Structural Similarity Index and Local Outlier Factor for Hyperspectral Image Clustering

“…Compared with classical centroid-based clustering, AP is insensitive to the initial centers and outliers. Therefore, it is widely used in face recognition [32], HSI classification [33], fault detection [34], and many other fields [35][36][37]. At the same time, many scholars have carried out in-depth studies of AP and put forward improved versions.…”