Payal Vyankat Dahiwale;Zakir H. Rather;Indradip Mitra
{"title":"A Distance Metric Based Approach for Analyzing, Assessing, and Strengthening EV Policy","authors":"Payal Vyankat Dahiwale;Zakir H. Rather;Indradip Mitra","doi":"10.1109/TEMPR.2024.3454413","DOIUrl":null,"url":null,"abstract":"In realizing seamless mass adoption of electric vehicles (EVs), the government plays a vital role by framing and implementing various EV related policies. These policies, if framed adequately, play a key role in achieving electrification of transportation in States. The literature review suggests that there is a pressing need to develop a unified method to analyze the EV policy of a State and determine its effectiveness as well as key interventions to strengthen EV policy. Therefore, to determine the quality and the effectiveness of EV policies, a quantitative analysis of EV policies by using the Hamming distance and the L1 norm (Manhattan distance) has been proposed in this paper. The policies are analyzed based on essential interventions to promote and strengthen electric mobility. This paper proposes an L1 norm-based EV policy analysis that assigns a remoteness score to indicate the status of the existing EV policy and its implementation when compared to the ideal EV policy. The paper proposes an approach that is based on three methods to improve the remoteness score: cost of intervention implementation, benefit of the intervention to the EV ecosystem, and the number of move/steps that are required to reduce the remoteness score. The proposed approach as applied to various Indian State EV policies has identified the States whose EV policies have lower remoteness score, thereby highlighting the States having relatively better EV policies. The analysis of these methods for optimal intervention selection when implemented for various State EV policies shows that the intervention implementation cost-based method is the most economical one. This paper formulates a linear optimization problem with the objective of intervention-implementation cost minimization to develop an economical roadmap to reduce the remoteness score. Recommendations for EV policy makers and stakeholders using an optimization based quantitative analysis of EV policies in India are also presented in this paper.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 1","pages":"46-58"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Energy Markets, Policy and Regulation","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10664029/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In realizing seamless mass adoption of electric vehicles (EVs), the government plays a vital role by framing and implementing various EV related policies. These policies, if framed adequately, play a key role in achieving electrification of transportation in States. The literature review suggests that there is a pressing need to develop a unified method to analyze the EV policy of a State and determine its effectiveness as well as key interventions to strengthen EV policy. Therefore, to determine the quality and the effectiveness of EV policies, a quantitative analysis of EV policies by using the Hamming distance and the L1 norm (Manhattan distance) has been proposed in this paper. The policies are analyzed based on essential interventions to promote and strengthen electric mobility. This paper proposes an L1 norm-based EV policy analysis that assigns a remoteness score to indicate the status of the existing EV policy and its implementation when compared to the ideal EV policy. The paper proposes an approach that is based on three methods to improve the remoteness score: cost of intervention implementation, benefit of the intervention to the EV ecosystem, and the number of move/steps that are required to reduce the remoteness score. The proposed approach as applied to various Indian State EV policies has identified the States whose EV policies have lower remoteness score, thereby highlighting the States having relatively better EV policies. The analysis of these methods for optimal intervention selection when implemented for various State EV policies shows that the intervention implementation cost-based method is the most economical one. This paper formulates a linear optimization problem with the objective of intervention-implementation cost minimization to develop an economical roadmap to reduce the remoteness score. Recommendations for EV policy makers and stakeholders using an optimization based quantitative analysis of EV policies in India are also presented in this paper.