{"title":"利用IEEE RTS测试系统的实例数据,对电力市场预测值形成的电力系统进行模型开发","authors":"J. Tchórzewski","doi":"10.1109/EEM.2014.6861268","DOIUrl":null,"url":null,"abstract":"The paper presents results of electrical power system identification shaped by selected values of the electrical power market, which identification is conducted using IEEE RTS test data. Development of such a model is an extremely important step of designing electrical power system development, especially as regards forecasting power of generators as well as fixed and variable costs of system development, etc. The EE system model was obtained by taking six input variables, i.a. total existing active power and forecast demand for active power increase as well as three output variables, i.a. forecast power of generators and forecast additional fixed and variable costs. Parametric models and models in state space were obtained. State variable courses were shown.","PeriodicalId":261127,"journal":{"name":"11th International Conference on the European Energy Market (EEM14)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Model development of the electrical power system shaped by forecast values of the electricity market using example data of the IEEE RTS test system\",\"authors\":\"J. Tchórzewski\",\"doi\":\"10.1109/EEM.2014.6861268\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents results of electrical power system identification shaped by selected values of the electrical power market, which identification is conducted using IEEE RTS test data. Development of such a model is an extremely important step of designing electrical power system development, especially as regards forecasting power of generators as well as fixed and variable costs of system development, etc. The EE system model was obtained by taking six input variables, i.a. total existing active power and forecast demand for active power increase as well as three output variables, i.a. forecast power of generators and forecast additional fixed and variable costs. Parametric models and models in state space were obtained. State variable courses were shown.\",\"PeriodicalId\":261127,\"journal\":{\"name\":\"11th International Conference on the European Energy Market (EEM14)\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"11th International Conference on the European Energy Market (EEM14)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EEM.2014.6861268\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"11th International Conference on the European Energy Market (EEM14)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EEM.2014.6861268","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Model development of the electrical power system shaped by forecast values of the electricity market using example data of the IEEE RTS test system
The paper presents results of electrical power system identification shaped by selected values of the electrical power market, which identification is conducted using IEEE RTS test data. Development of such a model is an extremely important step of designing electrical power system development, especially as regards forecasting power of generators as well as fixed and variable costs of system development, etc. The EE system model was obtained by taking six input variables, i.a. total existing active power and forecast demand for active power increase as well as three output variables, i.a. forecast power of generators and forecast additional fixed and variable costs. Parametric models and models in state space were obtained. State variable courses were shown.