Grid Optimization of Shared Energy Storage Among Wind Farms Based on Wind Forecasting

dc.contributor.authorZhu, K.
dc.contributor.authorChowdhury, S.
dc.contributor.authorSun, Mucun
dc.contributor.authorZhang, Jie
dc.contributor.utdAuthorSun, Mucun
dc.contributor.utdAuthorZhang, Jie
dc.date.accessioned2019-07-26T17:33:53Z
dc.date.accessioned2019-09-25T16:47:36Z
dc.date.available2019-07-26T17:33:53Z
dc.date.available2019-09-25T16:47:36Z
dc.date.created2018-04
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article). Non UTD affiliates will find the web address for this item by clicking the "Show full item record" link, copying the "dc.relation.uri" metadata and pasting it into a browser.
dc.description.abstractEnergy storage is crucial for source-side renewable energy power plants for enhancing output stability and reducing mismatch between power generation and demand. However, installing large size energy storage systems for renewable energy plants may not be economic, due to high capital cost and ever-increasing human resources and maintenance cost. As a result, in this paper, a shared energy storage system among multiple wind farms is proposed to address this energy management challenge. A state-of-the-art wind power forecasting method with ensemble numerical weather prediction models is used to optimally determine the size of a shared energy storage system (ESS). A number of scenarios are performed to optimize and explore the energy storage size under different economic and storage resource sharing circumstances. The performance of ESS, namely the net revenue of power plants, is explored subject to ESS size and operating constraints of wind farms and power systems. Results of a case study show that sharing of energy storage among multiple wind farms and lower cost of storage progressively enhance the economic benefits of using storage to mitigate over-production/under-forecasting (thus curtailment) and under-production/over-forecasting scenarios.
dc.description.departmentSchool of Natural Sciences and Mathematics
dc.identifier.bibliographicCitationZhu, K., S. Chowdhury, M. Sun, and J. Zhang. 2018. "Grid optimization of shared energy storage among wind farms based on wind forecasting." Proceedings -- Transmission and Distribution Conference and Exposition, doi:10.1109/TDC.2018.8440548
dc.identifier.isbn9781538655832
dc.identifier.urihttps://hdl.handle.net/10735.1/6749
dc.language.isoen
dc.publisherInstitute Of Electrical And Electronics Engineers Inc.
dc.relation.isPartOfProceedings - Transmission and Distribution Conference and Exposition, 2018
dc.relation.urihttp://dx.doi.org/10.1109/TDC.2018.8440548
dc.rights©2018 IEEE
dc.subjectEnergy storage
dc.subjectWind power
dc.subjectWind forecasting
dc.subjectElectric utilities
dc.subjectWeather forecasting
dc.subjectRenewable energy sources
dc.titleGrid Optimization of Shared Energy Storage Among Wind Farms Based on Wind Forecasting
dc.type.genrearticle

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