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An energy management model to study energy and peak power savings from PV and storage in demand responsive buildings

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  • Sehar, Fakeha
  • Pipattanasomporn, Manisa
  • Rahman, Saifur

Abstract

Demand Response (DR) applications along with strategically deployed solar photovoltaic (PV) and ice storage systems at the building level can help reduce building peak demand and energy consumption. Research shows that no work has been carried out to study the impact of integrated control of PV and ice storage on improving building operation and energy savings in demand responsive buildings. This can enable building operators to take advantage of different electricity prices and enable utilities to spread the demand over whole day. This research presents a model to study coordinated control of building end-use loads including cooling, lighting and plug loads, together with PV and ice storage integrated with packaged air conditioning (AC) units. This is used to study their impacts on peak demand and energy consumption in a simulated medium-sized office building located in Virginia/Maryland, U.S. area. Research findings provide an improved understanding of the contribution of DR, solar PV and ice storage systems towards reducing building peak electricity demand and energy consumption while being sensitive to occupant thermal and lighting needs.

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  • Sehar, Fakeha & Pipattanasomporn, Manisa & Rahman, Saifur, 2016. "An energy management model to study energy and peak power savings from PV and storage in demand responsive buildings," Applied Energy, Elsevier, vol. 173(C), pages 406-417.
    • Handle: RePEc:eee:appene:v:173:y:2016:i:c:p:406-417
    DOI: 10.1016/j.apenergy.2016.04.039
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    15. Prasertsak Charoen & Nathavuth Kitbutrawat & Jasada Kudtongngam, 2022. "A Demand Response Implementation with Building Energy Management System," Energies, MDPI, vol. 15(3), pages 1-21, February.
    16. Luo, Na & Langevin, Jared & Chandra-Putra, Handi & Lee, Sang Hoon, 2022. "Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling," Applied Energy, Elsevier, vol. 309(C).
    17. Nicolas A. Campbell & Patrick E. Phelan & Miguel Peinado-Guerrero & Jesus R. Villalobos, 2021. "Improved Air-Conditioning Demand Response of Connected Communities over Individually Optimized Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    18. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    19. Hao, Ling & Wei, Mingshan & Xu, Fei & Yang, Xiaochen & Meng, Jia & Song, Panpan & Min, Yong, 2020. "Study of operation strategies for integrating ice-storage district cooling systems into power dispatch for large-scale hydropower utilization," Applied Energy, Elsevier, vol. 261(C).
    20. Cui, Borui & Gao, Dian-ce & Xiao, Fu & Wang, Shengwei, 2017. "Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings," Applied Energy, Elsevier, vol. 201(C), pages 382-396.
    21. Chen, Yongbao & Xu, Peng & Chen, Zhe & Wang, Hongxin & Sha, Huajing & Ji, Ying & Zhang, Yongming & Dou, Qiang & Wang, Sheng, 2020. "Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage," Applied Energy, Elsevier, vol. 280(C).
    22. Yingyue Li & Hongjun Li & Rui Miao & He Qi & Yi Zhang, 2023. "Energy–Environment–Economy (3E) Analysis of the Performance of Introducing Photovoltaic and Energy Storage Systems into Residential Buildings: A Case Study in Shenzhen, China," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
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