{"title":"Optimal Wireless Technology Selection Approach for Sustainable Indian Smart Grid","authors":"Jignesh Bhatt, O. Jani, V. Harish","doi":"10.13052/spee1048-4236.4033","DOIUrl":null,"url":null,"abstract":"The smart grid is playing a game-changing role in achieving clean and green energy, infrastructure, and cities, which are all part of the sustainable development goals. The significance of communication infrastructure in the reliable design and operation of the smart grid is well recognized, notably for renewable integration, facilitating distributed energy resources and storage, demand response, and energy efficiency. Since choosing the optimal communication technology is a strategic decision, the problem needs careful investigation, taking into account realistic data traffic estimates to fulfill the communication needs of the applications envisaged. Even though a vast array of technologies with diverse capabilities is available to meet such communication needs, choosing the optimal wireless technology for a smart grid project remains a difficult challenge. In this context, to achieve and maximize the benefits of the smart grid and its applications, a systematic and efficient approach is necessary. This study proposes a data-driven decision-making approach for evaluating the capabilities of viable wireless technology options and selecting the most suitable option for the smart grid project at the design phase. The suggested approach and the decision-support tool were developed using a cost-function-based optimization technique. A case study of Siliguri city Indian smart grid pilot is discussed to validate the potential and aptness of the presented approach and suggest better technology alternatives as replacements. Being field data-driven, the presented optimization approach is simple, customizable, strategic, and re-usable with practical efficacy to assist decision-making.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Strategic Planning for Energy and the Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13052/spee1048-4236.4033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 1
Abstract
The smart grid is playing a game-changing role in achieving clean and green energy, infrastructure, and cities, which are all part of the sustainable development goals. The significance of communication infrastructure in the reliable design and operation of the smart grid is well recognized, notably for renewable integration, facilitating distributed energy resources and storage, demand response, and energy efficiency. Since choosing the optimal communication technology is a strategic decision, the problem needs careful investigation, taking into account realistic data traffic estimates to fulfill the communication needs of the applications envisaged. Even though a vast array of technologies with diverse capabilities is available to meet such communication needs, choosing the optimal wireless technology for a smart grid project remains a difficult challenge. In this context, to achieve and maximize the benefits of the smart grid and its applications, a systematic and efficient approach is necessary. This study proposes a data-driven decision-making approach for evaluating the capabilities of viable wireless technology options and selecting the most suitable option for the smart grid project at the design phase. The suggested approach and the decision-support tool were developed using a cost-function-based optimization technique. A case study of Siliguri city Indian smart grid pilot is discussed to validate the potential and aptness of the presented approach and suggest better technology alternatives as replacements. Being field data-driven, the presented optimization approach is simple, customizable, strategic, and re-usable with practical efficacy to assist decision-making.