Optimal Siting of Charging Stations for Electric Vehicles Based on Fuzzy Delphi and Hybrid Multi-Criteria Decision Making Approaches from an Extended Sustainability Perspective

Huiru Zhao, Nana Li
2016 Energies  
Optimal siting of electric vehicle charging stations (EVCSs) is crucial to the sustainable development of electric vehicle systems. Considering the defects of previous heuristic optimization models in tackling subjective factors, this paper employs a multi-criteria decision-making (MCDM) framework to address the issue of EVCS siting. The initial criteria for optimal EVCS siting are selected from extended sustainability theory, and the vital sub-criteria are further determined by using a fuzzy
more » ... lphi method (FDM), which consists of four pillars: economy, society, environment and technology perspectives. To tolerate vagueness and ambiguity of subjective factors and human judgment, a fuzzy Grey relation analysis (GRA)-VIKOR method is employed to determine the optimal EVCS site, which also improves the conventional aggregating function of fuzzy Vlsekriterijumska Optimizacijia I Kompromisno Resenje (VIKOR). Moreover, to integrate the subjective opinions as well as objective information, experts' ratings and Shannon entropy method are employed to determine combination weights. Then, the applicability of proposed framework is demonstrated by an empirical study of five EVCS site alternatives in Tianjin. The results show that A3 is selected as the optimal site for EVCS, and sub-criteria affiliated with environment obtain much more attentions than that of other sub-criteria. Moreover, sensitivity analysis indicates the selection results remains stable no matter how sub-criteria weights are changed, which verifies the robustness and effectiveness of proposed model and evaluation results. This study provides a comprehensive and effective method for optimal siting of EVCS and also innovates the weights determination and distance calculation for conventional fuzzy VIKOR. Energies 2016, 9, 270 2 of 22 (EVs), as a kind of new environmentally-friendly means of transportation, are an effective way to tackle the problems related to environment pollution and fossil resource utilization [2] . Therefore, the Chinese government has devoted considerable resources to promote the adoption of electric vehicles, and has set up a target of putting five million EVs on the road by 2020 [3] . Meanwhile, a significant amount of investment has been made to subsidize EV manufacturers and buyers, build charging stations and posts, and offer tax breaks and other non-monetary incentives. Charging infrastructure, as the energy provider of electric vehicles, is critical to the development of an electric vehicle system. The availability of efficient, convenient and economic EVCSs could enhance the willingness to buy of consumers and promote the development of the sector. Low availability of charging infrastructure could hinder EV adoption, which could then in turn reduce incentives to invest in charging infrastructure development [4] . EVCS siting is the preliminary stage of EVCS construction, and has a significant impact on the service quality and operation efficiency of EVCSs during their whole life cycle. Therefore, it is essential to establish a proper framework to determine the optimal sites for EVCSs. Sustainability in the scope of energy management aims to meet present demand without compromising the energy utilization by future generations. Sustainable development can be realized by renewable resources, cleaner production and more efficient technologies. The "sustainability" in energy management is described as a long-term development integrating three pillars: economic growth, social development and environment protection [5] . To promote the sustainable development of the EV industry, optimal EVCS siting should be performed from a sustainability perspective. Moreover, concerning the diversity of advanced science and technical constraints, technology is another important perspective to determine the optimal site of EVCSs. Therefore, integrating the issues of technology, an extended concept of sustainability is proposed to determine the optimal EVCS site, which has not been addressed in previous studies. In this study, extended sustainability is employed to determine the initial evaluation criteria for optimal siting of EVCSs, which covers four perspectives, such as "economy", "society", "environment" and "technology". On this basis, 13 final sub-criteria are determined by a fuzzy Delphi method (FDM) through a series of intensive questionnaires. Considering that optimal siting of EVCSs includes multiple factors, a Multiple Criteria Decision Making model is employed to evaluate the performance of all alternatives under conflicting criteria in this study. Vlsekriterijumska Optimizacijia I Kompromisno Resenje (VIKOR) is a compensatory aggregation MCDM method, which has been used to appraise performance in many fields [6] [7] [8] . VIKOR has a simple and logical computation procedure that simultaneously considers the closeness to positive ideal as well as negative ideal solutions [9] . Due to the increasing complexity of decision-making, more and more qualitative, uncertain and imprecise factors are involved in MCDM problems [10, 11] , and thus a fuzzy VIKOR method is constructed to determine the fuzzy compromise solutions for multiple criteria, which can efficiently grasp ambiguous information as well as the essential fuzziness of human judgment [12] [13] [14] . Moreover, Grey relation analysis (GRA) is used to modify the conventional aggregating function of fuzzy VIKOR, which can better measure the distance between fuzzy numbers as well as give a ranking order of alternatives with precise numbers [15] [16] [17] [18] . On the other hand, in the application of VIKOR for optimal siting of EVCSs, weighting determination turns out to be crucial for the final ranking of alternatives. To obtain a better weights determining system for fuzzy VIKOR, a combination weights system based on subjective judgment and objective information are introduced in this study. The subjective weights are determined by experts' opinions, and the objective weights are obtained by the Shannon entropy method. Therefore, in our research, a hybrid framework on the basis of FDM, combination weights and fuzzy GRA-VIKOR methods will be employed to determine the optimal sites for EVCSs. The remainder of this paper is organized as follows: a review of the literature related to the EV industry, optimal EVCS site determination, and the main contributions of this research can be found in Section 2. In Section 3, the basic theories of related methods are elaborated. Section 4 presents the proposed framework for optimal siting of electric vehicle station. The evaluation index system for Energies 2016, 9, 270 3 of 22 optimal siting of EVCSs is established by FDM in Section 5. Section 6 performs the EVCS siting by employing combination weighting and a fuzzy GRA-VIKOR model. Results discussion and sensitivity analysis are performed to check the rationality and robustness of the proposed model and results in Section 7. Conclusions are drawn in Section 8. Literature Review The construction of electric vehicle charging stations is important in the whole life cycle of the electric vehicle industry. Meanwhile, an appropriate site and capacity for EVCS can not only benefit the related stakeholders, but also promote the sustainable development of the EV industry. Over the last decade, many studies related to the economic and environmental benefit, influence and technology in the EV industry have been conducted. Simpson [19] presented a comparison of the costs (vehicle purchase costs and energy costs) and benefits (reduced petroleum consumption) of PHEVs related to hybrid-electric and conventional vehicles. By 2011 little was known about the economic rationale for public fast chargers for electric vehicles, Schroeder et al. [20] aimed to provide an insight into the business case for this technology in a case study for Germany. Hawkins et al. [21] developed and provided a transparent life cycle inventory of conventional vehicles and electric vehicles, which verified that EVs have decreased global warming potential (GWP) relative to conventional diesel or gasoline vehicles. Matsuhashi et al. [22] developed a process-relational model to estimate lifecycle CO 2 emissions from electric vehicles (EVs) and gasoline vehicles (GVs), which indicated that the manufacture and driving of EVs produces less CO 2 emissions than that of GVs. Putrus et al. [23] analyzed the impact of electric vehicles on existing power distribution networks, including supply/demand matching and potential violations of statutory voltage limits, power quality and imbalance. Clement-Nyns et al. [24] pointed out that uncoordinated power consumption on a local scale would lead to grid problems, and computed the optimal charging profile of plug-in hybrid electric vehicles by minimizing the power losses and maximizing the main grid load. Mets et al. [25] presented smart energy control strategies based on quadratic programming for charging PHEVs, aiming to minimize the peak load and flatten the overall load profile. Rivera et al. [26] proposes a novel architecture for PEV DC charging stations by using a grid-tied neutral point clamped converter. Research focused on siting and sizing of EVCSs has received much more attention in recent years. Liu et al. [27] presented a modified primal-dual interior point algorithm to solve the optimal sizing of EV charging stations, in which environmental factors and the service radius of EV charging stations were considered. Wirges et al. [28] presented a dynamic spatial model of a charging infrastructure development for electric vehicles in the German metropolitan region of Stuttgart, and generated several scenarios of a charging infrastructure development until 2020. Jia et al. [29] introduced an optimization process for the sizing and siting of electric vehicle charging stations with minimized integrated cost of charging stations and consumers' costs, in which the charging demand and road network structure were variables. Aiming at minimizing users' losses on the way to the charging station, Ge et al. [30] determines the best location by using a Genetic Algorithm (GA) considering the traffic density and the charging station's capacity constraints. Xi et al. [31] developed a simulation-optimization model to determine the location of electric vehicle chargers, and explored the interactions between the optimization criterion and the available budget. Sathaye et al. [32] utilized a continuous facility location model for optimally siting electric vehicle infrastructure in highway corridors, and carefully dealt with the influence of demand uncertainty. Pashajavid et al. [33] proposed a scenario optimization based on a particle swarm optimization (PSO) algorithm to allocate charging stations for plug-in electric vehicles (PEVs), and a multivariate stochastic modeling methodology based on the notion of copula is provided in order to develop a probabilistic model of the load demand due to PEVs. Zi et al. [34] presented an adaptive particle swarm optimization (APSO) algorithm to optimize the siting and sizing of electric vehicle charging stations, which considered geographic information, construction costs and running costs. In order to install alternative fuel charging stations at suitable locations for alternative-fuel vehicles (AFVs), You et al. [35] developed a mixed-integer programming
doi:10.3390/en9040270 fatcat:hthz7pq7gvfg3abswo2dtn23aa