Spatial Clustering for Temporal Power Ramp Balance and Wind Power Estimation
2015 (English)In: Green Technologies Conference (GreenTech), 2015 Seventh Annual IEEE, 2015, 214-220 p.Conference paper (Refereed)Text
Power estimation and power ramp estimation is of crucial importance in renewable energy applications especially for wind power plants that is going to be the focus of this study. Intermittent supply of wind power generation can cause power ramps which are sudden change of power production in time. This is an important problem in power system that aims to keep the load and generation balance. Unbalance in the system can lead to price volatility and grid security issues that will create power stability problems and financial losses. Herein a spatial clustering methodology for improving spatio-temporal relations are investigated to improve wind power estimation and power ramp estimation. To validate the data with the model that will be used in clustering analysis, spatial results of Computational Fluid Dynamics (CFD) tool simulations are employed to test suggested methodology in space. CFD results generate the input data for space clustering process. Via the CFD generated spatial information, the relationship between spatial clustering to wind power ramp rate characteristics of wind Turbines are introduced for the spatio-temporal power and power ramp rate relations. Spatial relations for power ramp characteristics of each node in wind resource map is introduced. In space scales, K-means algorithm is used to create spatial Power Ramp Rate (PRR) based clusters to define most related clusters in space so that impact of each spatial cluster can be introduced.
Place, publisher, year, edition, pages
2015. 214-220 p.
, IEEE Green Technologies Conference, ISSN 2166-546X
computational fluid dynamics;pattern clustering;power engineering computing;wind power plants;wind turbines;CFD;K-means algorithm;PRR;clustering analysis;computational fluid dynamics;power ramp estimation;power ramp rate relations;space clustering process;spatial clustering;spatial power ramp rate;temporal power ramp balance;wind power estimation;wind turbines;Data models;Estimation;Time series analysis;Wind power generation;Wind speed;Wind turbines;Power ramp;data mining;power estimation;spatial clustering;temporal clustering;wind power
Other Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:uu:diva-268967DOI: 10.1109/GREENTECH.2015.39ISI: 000380903700031ISBN: 9781479988846OAI: oai:DiVA.org:uu-268967DiVA: diva2:899722
Green Technologies Conference (GreenTech), 2015 Seventh Annual IEEE