{"title":"利用电网支持目标优化太阳能光伏发电容量的分配","authors":"V. Vermeulen, J. Strauss, H. Vermeulen","doi":"10.1109/POWERAFRICA.2017.7991224","DOIUrl":null,"url":null,"abstract":"This paper presents the results of an optimisation analysis aimed at exploring the geographical allocation of solar PV generation capacity in the context of grid support considerations such as average daily yield and variability, with particular focus on the full year scenario compared to the high demand season. All problem cases were evaluated using a combination of genetic algorithm (GA) and pattern search techniques implemented via MATLAB's Global Optimization Toolbox. A hybrid pattern search method incorporating a GA search step produced the best overall solution quality, but could benefit from parameter adjustment to improve its solution consistency. The optimal allocations found for all objective functions are generally distinctly different for the full year scenario and the high demand season, indicating potential advantages in applying a seasonally variable feed-in tariff for solar PV generation. Meanwhile the objectives of daily energy maximisation and variability minimisation were found to produce contrasting results.","PeriodicalId":6601,"journal":{"name":"2017 IEEE PES PowerAfrica","volume":"67 1","pages":"202-207"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimisation of the allocation of solar PV generation capacity using grid support objectives\",\"authors\":\"V. Vermeulen, J. Strauss, H. Vermeulen\",\"doi\":\"10.1109/POWERAFRICA.2017.7991224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the results of an optimisation analysis aimed at exploring the geographical allocation of solar PV generation capacity in the context of grid support considerations such as average daily yield and variability, with particular focus on the full year scenario compared to the high demand season. All problem cases were evaluated using a combination of genetic algorithm (GA) and pattern search techniques implemented via MATLAB's Global Optimization Toolbox. A hybrid pattern search method incorporating a GA search step produced the best overall solution quality, but could benefit from parameter adjustment to improve its solution consistency. The optimal allocations found for all objective functions are generally distinctly different for the full year scenario and the high demand season, indicating potential advantages in applying a seasonally variable feed-in tariff for solar PV generation. Meanwhile the objectives of daily energy maximisation and variability minimisation were found to produce contrasting results.\",\"PeriodicalId\":6601,\"journal\":{\"name\":\"2017 IEEE PES PowerAfrica\",\"volume\":\"67 1\",\"pages\":\"202-207\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE PES PowerAfrica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/POWERAFRICA.2017.7991224\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE PES PowerAfrica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/POWERAFRICA.2017.7991224","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimisation of the allocation of solar PV generation capacity using grid support objectives
This paper presents the results of an optimisation analysis aimed at exploring the geographical allocation of solar PV generation capacity in the context of grid support considerations such as average daily yield and variability, with particular focus on the full year scenario compared to the high demand season. All problem cases were evaluated using a combination of genetic algorithm (GA) and pattern search techniques implemented via MATLAB's Global Optimization Toolbox. A hybrid pattern search method incorporating a GA search step produced the best overall solution quality, but could benefit from parameter adjustment to improve its solution consistency. The optimal allocations found for all objective functions are generally distinctly different for the full year scenario and the high demand season, indicating potential advantages in applying a seasonally variable feed-in tariff for solar PV generation. Meanwhile the objectives of daily energy maximisation and variability minimisation were found to produce contrasting results.