微电网评估中节点负荷演化的遗传算法

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-01 DOI:10.1109/ICRERA.2016.7884412

S. Korjani, M. Porru, A. Serpi, A. Damiano

{"title":"微电网评估中节点负荷演化的遗传算法","authors":"S. Korjani, M. Porru, A. Serpi, A. Damiano","doi":"10.1109/ICRERA.2016.7884412","DOIUrl":null,"url":null,"abstract":"One of the on-going research topic in smart grid planning and assessment is the definition of suitable time evolution of load profiles in micro grids by using the information about the network topology and the available electrical measurements. This paper presents an approach for a heuristic definition of nodal load profiles in micro grids when the available measurements are not exhaustive for its state evaluation. In particular, in order to develop the preliminary micro grids assessment, a Genetic Algorithm (GA) has been employed to determine possible evolution of nodal load profiles that satisfy the power system constraints and input measurements. In order to verify the effectiveness of proposed methodology a real micro grid has been considered as case of study. The micro grid has been simulated in Digsilent and the used GA has been implemented in Matlab environment. Finally, Digsilent Programming Language (DPL) has been employed for interfacing the GA with Digsilent.","PeriodicalId":287863,"journal":{"name":"2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A genetic algorithm for the definition of nodal load time evolutions in micro grids assessment\",\"authors\":\"S. Korjani, M. Porru, A. Serpi, A. Damiano\",\"doi\":\"10.1109/ICRERA.2016.7884412\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the on-going research topic in smart grid planning and assessment is the definition of suitable time evolution of load profiles in micro grids by using the information about the network topology and the available electrical measurements. This paper presents an approach for a heuristic definition of nodal load profiles in micro grids when the available measurements are not exhaustive for its state evaluation. In particular, in order to develop the preliminary micro grids assessment, a Genetic Algorithm (GA) has been employed to determine possible evolution of nodal load profiles that satisfy the power system constraints and input measurements. In order to verify the effectiveness of proposed methodology a real micro grid has been considered as case of study. The micro grid has been simulated in Digsilent and the used GA has been implemented in Matlab environment. Finally, Digsilent Programming Language (DPL) has been employed for interfacing the GA with Digsilent.\",\"PeriodicalId\":287863,\"journal\":{\"name\":\"2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRERA.2016.7884412\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRERA.2016.7884412","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

利用微电网拓扑信息和可用的电测量数据，确定微电网负荷分布的合适时间演化是智能电网规划与评估的研究热点之一。本文提出了一种启发式定义微电网节点负荷分布的方法，当可用的测量值不能详尽地用于微电网状态评估时。特别是，为了进行初步的微电网评估，采用遗传算法(GA)来确定满足电力系统约束和输入测量的节点负载分布的可能演变。为了验证所提方法的有效性，以实际微电网为例进行了研究。在Digsilent中对微电网进行了仿真，并在Matlab环境下实现了所采用的遗传算法。最后，采用Digsilent编程语言(DPL)实现了GA与Digsilent的接口。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A genetic algorithm for the definition of nodal load time evolutions in micro grids assessment

One of the on-going research topic in smart grid planning and assessment is the definition of suitable time evolution of load profiles in micro grids by using the information about the network topology and the available electrical measurements. This paper presents an approach for a heuristic definition of nodal load profiles in micro grids when the available measurements are not exhaustive for its state evaluation. In particular, in order to develop the preliminary micro grids assessment, a Genetic Algorithm (GA) has been employed to determine possible evolution of nodal load profiles that satisfy the power system constraints and input measurements. In order to verify the effectiveness of proposed methodology a real micro grid has been considered as case of study. The micro grid has been simulated in Digsilent and the used GA has been implemented in Matlab environment. Finally, Digsilent Programming Language (DPL) has been employed for interfacing the GA with Digsilent.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)

自引率

0.00%

发文量