Break-Even Analysis of Battery Energy Storage in Buildings Considering Time-Of-Use Rates

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dc.contributor.authorSun, Mucun
dc.contributor.authorChang, Chih-Lun
dc.contributor.authorZhang, Jie
dc.contributor.authorMehmani, A.
dc.contributor.authorCulligan, P.
dc.contributor.utdAuthorSun, Mucun
dc.contributor.utdAuthorChang, Chih-Lun
dc.contributor.utdAuthorZhang, Jie
dc.date.accessioned2019-07-12T21:43:01Z
dc.date.accessioned2019-09-25T16:47:37Z
dc.date.available2019-07-12T21:43:01Z
dc.date.available2019-09-25T16:47:37Z
dc.date.created2018-04-04
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided link to the article). Non UTD affiliates will find the web address for this item by clicking the Show full item record link and copying the "relation.uri" metadata.
dc.description.abstractAs energy consumption in residential and commercial buildings continues to grow, demand-side management (DSM) for energy systems becomes crucial, because DSM can shift energy use from peak to off-peak hours. In order to realize peak load shifting, energy storage systems (ESSs) can be integrated into buildings to store energy during off-peak hours and discharge energy in peak hours. However, installing a large number of ESSs in individual buildings can complicate DSM and increase the overall capital cost. In this paper, a cost-effective DSM strategy is proposed to address this energy management challenge. The break-even cost of battery storage in a building is explored through a process of two-step optimization in conjunction with different tariff structures. A number of scenarios are performed to conduct cost analyses of the storage-based building energy system under different time-of-use rate structures. The performance of the DSM strategy in the battery break-even cost, is explored using a particle swarm optimization algorithm based on the size of energy storage and priced-based constraints of the energy system. Results of a case study show that the proposed approach can reduce the peak-to-average ratio of the total energy demand to the total energy costs. In addition, as the percentage reductions in yearly maximum energy peaks increase, the optimal battery cost becomes progressively more economical to building owners.
dc.description.departmentSchool of Natural Sciences and Mathematics
dc.identifier.bibliographicCitationSun, M., C. -L Chang, J. Zhang, A. Mehmani, et al. 2018. "Break-even analysis of battery energy storage in buildings considering time-of-use rates." IEEE Green Technologies Conference, 10: 95-99, doi:10.1109/GreenTech.2018.00026
dc.identifier.isbn9781538651834
dc.identifier.issn2166-5478
dc.identifier.urihttps://hdl.handle.net/10735.1/6702
dc.identifier.volume2018
dc.language.isoen
dc.publisherIEEE Computer Society
dc.relation.isPartOfIEEE Green Technologies Conference, 10
dc.relation.urihttp://dx.doi.org/10.1109/GreenTech.2018.00026
dc.rights©2018 IEEE
dc.subjectEnergy storage
dc.subjectCost accounting
dc.subjectCost effectiveness
dc.subjectCost control
dc.subjectElectric batteries
dc.subjectElectric utilities
dc.subjectOffice buildings
dc.subjectEnergy systems
dc.subjectCost effectiveness
dc.titleBreak-Even Analysis of Battery Energy Storage in Buildings Considering Time-Of-Use Rates
dc.type.genrearticle

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