{"title":"Identification of Most Significant Parameter of Impact of Climate Change and Urbanization on Operational Efficiency of Hydropower Plant","authors":"Priyanka Majumder, A. K. Saha","doi":"10.4018/IJEOE.2019070103","DOIUrl":null,"url":null,"abstract":"The operational performance of hydropower plants (HPPs) is largely affected as the output from the plant entirely depends on the rainfall and demand from consumers both of which are compromised due to the vulnerability in climatic patterns and rapid change in urbanization rate. Although, not all the parameters are equally affected and the present study aims to find the degree of impact on the various correlated parameters on which production efficiency of HPPs varies. In this aspect, a neural network concept was used as decision making tool to identify the most significant parameters with respect to change in climate, urbanization along with machine failure because as a combined effect of the first two parameters, the probability of machine failure will also increase. The result from the study provides an opportunity to mitigate the impact that can be caused as a result of climate change impact and change in rate of urbanization. According to the result it was found that Efficiency of Generators is the most significant parameter of impact of climate change and urbanization on operational efficiency of hydropower plant. The result from the scenario analysis suggested that if the A2 scenario becomes true in 2061-70 there will be a maximum decrease in the OE and if land use scenario: PR story line is found to be adopted in the future world of 2020-30 the change in OE will be the greatest (an increase of 6.056%) compared to any other scenario developed for the impact of urbanization followed by land use change scenario of the 2031-40 decade, which will be equal to an increase of 5.247% compared to the baseline.","PeriodicalId":43245,"journal":{"name":"International Journal of Energy Optimization and Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4018/IJEOE.2019070103","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Energy Optimization and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJEOE.2019070103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 2
Abstract
The operational performance of hydropower plants (HPPs) is largely affected as the output from the plant entirely depends on the rainfall and demand from consumers both of which are compromised due to the vulnerability in climatic patterns and rapid change in urbanization rate. Although, not all the parameters are equally affected and the present study aims to find the degree of impact on the various correlated parameters on which production efficiency of HPPs varies. In this aspect, a neural network concept was used as decision making tool to identify the most significant parameters with respect to change in climate, urbanization along with machine failure because as a combined effect of the first two parameters, the probability of machine failure will also increase. The result from the study provides an opportunity to mitigate the impact that can be caused as a result of climate change impact and change in rate of urbanization. According to the result it was found that Efficiency of Generators is the most significant parameter of impact of climate change and urbanization on operational efficiency of hydropower plant. The result from the scenario analysis suggested that if the A2 scenario becomes true in 2061-70 there will be a maximum decrease in the OE and if land use scenario: PR story line is found to be adopted in the future world of 2020-30 the change in OE will be the greatest (an increase of 6.056%) compared to any other scenario developed for the impact of urbanization followed by land use change scenario of the 2031-40 decade, which will be equal to an increase of 5.247% compared to the baseline.