Julien Walzberg, Thomas Dandres, Nicolas Merveille, M. Cheriet, R. Samson
{"title":"住宅电力需求波动的环境评估","authors":"Julien Walzberg, Thomas Dandres, Nicolas Merveille, M. Cheriet, R. Samson","doi":"10.1109/STICT.2019.8789377","DOIUrl":null,"url":null,"abstract":"Including dynamic aspects in the environmental assessment of power systems allows computing the environmental benefits of demand-side management strategies for the smart grid which could not be assessed with static data such as shifting part of the demand from one period to another. Several methodological approaches have been developed in life cycle assessment to account for dynamic aspects, but none has given much attention to the demand side of the equation. However, demand is also prone to fluctuate in time and its misrepresentation may lead to additional errors. In this study, a stochastic approach was applied to model the fluctuating residential power demand of Canadians' homes. An hourly and a yearly average electricity mix were then used to compute the environmental impacts of the hourly or yearly average homes' electricity demand. Finally, an approach combining an average and a marginal hourly electricity mix was then proposed to assess the benefits of a simple demand side management strategy: the shifting of homes' dryers loads up to two hours later than usual. Results show that assuming a constant demand or electricity mix both leads to errors which may be as high as 150% depending on the period of the month assessed. Moreover, using an hourly average electricity mix to set up the demand side strategy increases climate change impact by 0.6% whereas using a marginal mix decreases climate change impact by 10%.","PeriodicalId":209175,"journal":{"name":"2019 IEEE Sustainability through ICT Summit (StICT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Environmental Assessment of Fluctuating Residential Electricity Demand\",\"authors\":\"Julien Walzberg, Thomas Dandres, Nicolas Merveille, M. Cheriet, R. Samson\",\"doi\":\"10.1109/STICT.2019.8789377\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Including dynamic aspects in the environmental assessment of power systems allows computing the environmental benefits of demand-side management strategies for the smart grid which could not be assessed with static data such as shifting part of the demand from one period to another. Several methodological approaches have been developed in life cycle assessment to account for dynamic aspects, but none has given much attention to the demand side of the equation. However, demand is also prone to fluctuate in time and its misrepresentation may lead to additional errors. In this study, a stochastic approach was applied to model the fluctuating residential power demand of Canadians' homes. An hourly and a yearly average electricity mix were then used to compute the environmental impacts of the hourly or yearly average homes' electricity demand. Finally, an approach combining an average and a marginal hourly electricity mix was then proposed to assess the benefits of a simple demand side management strategy: the shifting of homes' dryers loads up to two hours later than usual. Results show that assuming a constant demand or electricity mix both leads to errors which may be as high as 150% depending on the period of the month assessed. Moreover, using an hourly average electricity mix to set up the demand side strategy increases climate change impact by 0.6% whereas using a marginal mix decreases climate change impact by 10%.\",\"PeriodicalId\":209175,\"journal\":{\"name\":\"2019 IEEE Sustainability through ICT Summit (StICT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Sustainability through ICT Summit (StICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STICT.2019.8789377\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Sustainability through ICT Summit (StICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STICT.2019.8789377","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Environmental Assessment of Fluctuating Residential Electricity Demand
Including dynamic aspects in the environmental assessment of power systems allows computing the environmental benefits of demand-side management strategies for the smart grid which could not be assessed with static data such as shifting part of the demand from one period to another. Several methodological approaches have been developed in life cycle assessment to account for dynamic aspects, but none has given much attention to the demand side of the equation. However, demand is also prone to fluctuate in time and its misrepresentation may lead to additional errors. In this study, a stochastic approach was applied to model the fluctuating residential power demand of Canadians' homes. An hourly and a yearly average electricity mix were then used to compute the environmental impacts of the hourly or yearly average homes' electricity demand. Finally, an approach combining an average and a marginal hourly electricity mix was then proposed to assess the benefits of a simple demand side management strategy: the shifting of homes' dryers loads up to two hours later than usual. Results show that assuming a constant demand or electricity mix both leads to errors which may be as high as 150% depending on the period of the month assessed. Moreover, using an hourly average electricity mix to set up the demand side strategy increases climate change impact by 0.6% whereas using a marginal mix decreases climate change impact by 10%.