{"title":"A Robust Approach to manage Demand Response for power distribution system planning","authors":"G. Celli, Luigi Sechi, G. G. Soma","doi":"10.1109/PMAPS47429.2020.9183564","DOIUrl":null,"url":null,"abstract":"The efficient development of modern distribution system requires the deployment of flexibility services provided by Distributed Energy Resources, like distributed generation, electric energy storage and demand response. This kind of planning tools have to be risk-based, in order to deal with the high level of uncertainties introduced by these new technologies. Suitable models and methodologies for the consideration of the value at risk associated to each choice are essential to compare innovative and conventional planning solutions. In the paper, Demand Response has been modelled with its possible payback effect and the optimal exploitation of this flexibility service with a predefined confidence (residual risk) has been estimated by means of a Robust Linear Programming optimization. The effectiveness of the proposed methodology is demonstrated on a simple distribution network.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PMAPS47429.2020.9183564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The efficient development of modern distribution system requires the deployment of flexibility services provided by Distributed Energy Resources, like distributed generation, electric energy storage and demand response. This kind of planning tools have to be risk-based, in order to deal with the high level of uncertainties introduced by these new technologies. Suitable models and methodologies for the consideration of the value at risk associated to each choice are essential to compare innovative and conventional planning solutions. In the paper, Demand Response has been modelled with its possible payback effect and the optimal exploitation of this flexibility service with a predefined confidence (residual risk) has been estimated by means of a Robust Linear Programming optimization. The effectiveness of the proposed methodology is demonstrated on a simple distribution network.