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Adoption of smart grid technologies: An analysis of interactions among barriers

Journal

RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 33, Issue -, Pages 554-565

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2014.02.030

Keywords

Barriers; Green House Gases (GHG); Interpretative Structural Modeling (ISM); MICMAC analysis; Smart grid technologies adoption

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Global electricity demands are increasing at rapid pace. Energy supply, their usage and technologies involved need to be more economical, environment friendly and socially sustainable. Efforts are being done all over the globe to reduce this greenhouse effect; and renewable energy technologies to combat climate changes, which require extensive changes to the current electricity generation and distribution systems. To meet this goal, it is required to optimize the grid operations and available resources to meet the ever increasing energy demands in an efficient, effective and environment sustainable way. It has been found that smart grid technologies have not been so popular due to some obstacles that are hindering its maturation and rapid deployment. An attempt has been made to identify and analyze the barriers to implement smart grid technologies adoption. Twelve relevant barriers towards implementation of smart grid technologies have been identified from extensive literature review and duly validated with experts' (from academia and industry) opinions. Also, valuable experts' opinions have been utilized to identify contextual relationships among these important barriers and a hierarchical model has been developed based on Interpretive Structural Modeling methodology. Matrice d'Impacts Croises-Multipication Applique' an Classment (MICMAC) analysis has also been used to: classify the barriers based upon dependence and driving power; and validate developed ISM based model. Lack of Regulatory Framework barrier has been identified as driver or independent level barrier i.e. most important bottom level barrier hindering adoption of smart grid technologies. The developed structured model will help to understand interrelationships and interdependencies among the identified barriers to implement smart grid technologies. Different solutions for handling these identified barriers have also been suggested in the paper. Organizations involved in power generation and distribution may be benefited by understanding of these barriers, their interactions and suggested mitigation solutions towards effective adoption of smart grid technologies. (C) 2014 Elsevier Ltd. All rights reserved.

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