Appraisal of eco-friendly Preventive Maintenance scheduling strategy impacts on GHG emissions mitigation in smart grids

• Published in: Journal of cleaner production 2017-02, Vol.143, p.212-223
• Main Authors: Mollahassani-pour, Mojgan, Rashidinejad, Masoud, Abdollahi, Amir
• Format: Article
• Language: English
• Subjects: Consumer economic model; Cost reduction; Entropy technique; GHGs’ reduction; Preventive Maintenance
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2016.12.127

The electricity sector as a crucial source of GHGs (Greenhouse Gases) plays a pivotal role in energy and climate policies. In this regard, specialists endeavor to present a comprehensive and efficient plan including environmental as well as economic aspects. However, in a smart energy system, an ability to control both supply-side and demand-side is provided. This paper presents a methodology of economic and emission PM (Preventive Maintenance) scheduling while concentrating on DRRs (Demand Response Resources) as one of the aspects of smart environments to handle emitted GHGs and expenditures. The nominated structure aims to minimizing system expenditures as well as GHGs emissions over the time horizon. In one hand, PM scheduling is a highly complicated problem and, DRRs consideration makes it even more complicated; overcome this complexity, the problem is implemented in GAMS (General Algebraic Modeling System) environment. Furthermore, the compromise between multifarious targets is performed by Entropy technique to reflect system conditions, while arbitrary compromising cannot be an acceptable solution. In order to evaluate the capability of DRRs in declining GHGs as well as expenditures, it has been applied to IEEE-RTS. The results indicate that considerable reduction is occurred by increasing the number of DRRs in the system. •An MILP based multi-objective PM problem associated with voluntary DRPs is proposed.•Environmental and economic criterions’ weighting coefficient are determined by Entropy method.•Multifarious incentive values which are paid to customers in per bus are adjusted.•Most optimal location of DRPs implementation in system is nominated.•Nominal and actual potential for customers’ participation in DRPs is determined.