IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v243y2019icp274-287.html
   My bibliography  Save this article

Flexible dispatch of a building energy system using building thermal storage and battery energy storage

Author

Listed:
  • Niu, Jide
  • Tian, Zhe
  • Lu, Yakai
  • Zhao, Hongfang

Abstract

The increasing development of renewable energy sources requires more flexible technologies to be applied in building energy systems and a flexible controlled resource for the power grid. This work focuses on investigating the flexibility potential of building thermal storage and battery energy storage. Firstly, an autoregressive model with exogenous inputs is proposed to forecast the dynamic cooling demand and, based on that, a mixed integer linear model is formulated to optimize the dispatch of building energy systems with minimal operating costs. A factory building located in Huizhou, China is used as a case study. The results show that the ARX model can accurately predict the thermal load hourly. The model’s level of fit is above 81%. The optimization objectives influence the development of the flexibility potential when building thermal storage and battery energy storage are considered. In this work, the economic objective is applied first to discuss the flexibility potential. The results show that, in this studied case, the operational cost decreased by 5.3% by using battery energy storage and further decreased by 4.0% by using building thermal storage. However, the results also reveal that unilaterally pursuing minimal operational costs results in larger peak valley difference of feeder power. The peak-valley difference of the feeder power increased from 714 kW to 1245 kW and 1689 kW respectively when the battery energy storage and building thermal storage were employed for the economic dispatch of the building energy system. Therefore, this work retests the flexibility potential of battery energy storage and building thermal storage by adding a constraint for feeder power difference. The results show that the operational costs can be still reduced greatly without damaging the stability of the power feeder.

Suggested Citation

  • Niu, Jide & Tian, Zhe & Lu, Yakai & Zhao, Hongfang, 2019. "Flexible dispatch of a building energy system using building thermal storage and battery energy storage," Applied Energy, Elsevier, vol. 243(C), pages 274-287.
  • Handle: RePEc:eee:appene:v:243:y:2019:i:c:p:274-287
    DOI: 10.1016/j.apenergy.2019.03.187
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191930604X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.03.187?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
    2. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    3. Junker, Rune Grønborg & Azar, Armin Ghasem & Lopes, Rui Amaral & Lindberg, Karen Byskov & Reynders, Glenn & Relan, Rishi & Madsen, Henrik, 2018. "Characterizing the energy flexibility of buildings and districts," Applied Energy, Elsevier, vol. 225(C), pages 175-182.
    4. Dominković, D.F. & Gianniou, P. & Münster, M. & Heller, A. & Rode, C., 2018. "Utilizing thermal building mass for storage in district heating systems: Combined building level simulations and system level optimization," Energy, Elsevier, vol. 153(C), pages 949-966.
    5. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    6. Tian, Zhe & Niu, Jide & Lu, Yakai & He, Shunming & Tian, Xue, 2016. "The improvement of a simulation model for a distributed CCHP system and its influence on optimal operation cost and strategy," Applied Energy, Elsevier, vol. 165(C), pages 430-444.
    7. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    8. Renaldi, R. & Kiprakis, A. & Friedrich, D., 2017. "An optimisation framework for thermal energy storage integration in a residential heat pump heating system," Applied Energy, Elsevier, vol. 186(P3), pages 520-529.
    9. Jin, Ming & Feng, Wei & Marnay, Chris & Spanos, Costas, 2018. "Microgrid to enable optimal distributed energy retail and end-user demand response," Applied Energy, Elsevier, vol. 210(C), pages 1321-1335.
    10. Arteconi, Alessia & Ciarrocchi, Eleonora & Pan, Quanwen & Carducci, Francesco & Comodi, Gabriele & Polonara, Fabio & Wang, Ruzhu, 2017. "Thermal energy storage coupled with PV panels for demand side management of industrial building cooling loads," Applied Energy, Elsevier, vol. 185(P2), pages 1984-1993.
    11. Klein, Konstantin & Herkel, Sebastian & Henning, Hans-Martin & Felsmann, Clemens, 2017. "Load shifting using the heating and cooling system of an office building: Quantitative potential evaluation for different flexibility and storage options," Applied Energy, Elsevier, vol. 203(C), pages 917-937.
    12. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
    13. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    14. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    15. Bolívar Jaramillo, Lucas & Weidlich, Anke, 2016. "Optimal microgrid scheduling with peak load reduction involving an electrolyzer and flexible loads," Applied Energy, Elsevier, vol. 169(C), pages 857-865.
    16. Zhang, Yang & Campana, Pietro Elia & Yang, Ying & Stridh, Bengt & Lundblad, Anders & Yan, Jinyue, 2018. "Energy flexibility from the consumer: Integrating local electricity and heat supplies in a building," Applied Energy, Elsevier, vol. 223(C), pages 430-442.
    17. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    18. Stinner, Sebastian & Huchtemann, Kristian & Müller, Dirk, 2016. "Quantifying the operational flexibility of building energy systems with thermal energy storages," Applied Energy, Elsevier, vol. 181(C), pages 140-154.
    19. Li, Xiwang & Malkawi, Ali, 2016. "Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions," Energy, Elsevier, vol. 112(C), pages 1194-1206.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Hong & Zhao, Yue & Gu, Chenghong & Ge, Shaoyun & Yang, Zan, 2021. "Adjustable capability of the distributed energy system: Definition, framework, and evaluation model," Energy, Elsevier, vol. 222(C).
    2. Shabnam Homaei & Mohamed Hamdy, 2021. "Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes," Energies, MDPI, vol. 14(10), pages 1-32, May.
    3. Song, Yuguang & Xia, Mingchao & Chen, Qifang, 2023. "The robust synchronization control scheme for flexible resources considering the stochastic and delay response process," Applied Energy, Elsevier, vol. 343(C).
    4. Zhang, Xiang & Saelens, Dirk & Roels, Staf, 2022. "Estimating dynamic solar gains from on-site measured data: An ARX modelling approach," Applied Energy, Elsevier, vol. 321(C).
    5. Chong Shao & Bolin Zhang & Bo Wei & Wenfei Liu & Yong Yang & Zhaoyuan Wu, 2023. "A Health-Aware Energy Storage Sharing Mechanism for a Renewable Energy Base," Energies, MDPI, vol. 16(14), pages 1-22, July.
    6. Lyu, Cheng & Jia, Youwei & Xu, Zhao, 2021. "Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles," Applied Energy, Elsevier, vol. 299(C).
    7. Luo, Yongqiang & Zhang, Ling & Liu, Zhongbing & Yu, Jinghua & Xu, Xinhua & Su, Xiaosong, 2020. "Towards net zero energy building: The application potential and adaptability of photovoltaic-thermoelectric-battery wall system," Applied Energy, Elsevier, vol. 258(C).
    8. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    9. Sarah O’Connell & Marcus Martin Keane, 2021. "Development of a Framework for Activation of Aggregator Led Flexibility," Energies, MDPI, vol. 14(16), pages 1-15, August.
    10. Angizeh, Farhad & Ghofrani, Ali & Zaidan, Esmat & Jafari, Mohsen A., 2022. "Adaptable scheduling of smart building communities with thermal mapping and demand flexibility," Applied Energy, Elsevier, vol. 310(C).
    11. He, Shuaijia & Gao, Hongjun & Tang, Zao & Chen, Zhe & Jin, Xiaolong & Liu, Junyong, 2023. "Worst CVaR based energy management for generalized energy storage enabled building-integrated energy systems," Renewable Energy, Elsevier, vol. 203(C), pages 255-266.
    12. Yin, Linfei & Qiu, Yao, 2022. "Long-term price guidance mechanism of flexible energy service providers based on stochastic differential methods," Energy, Elsevier, vol. 238(PB).
    13. Naghikhani, Ali & Hosseini, Seyed Mohammad Hassan, 2022. "Optimal thermal and power planning considering economic and environmental issues in peak load management," Energy, Elsevier, vol. 239(PA).
    14. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
    15. Ajagekar, Akshay & Decardi-Nelson, Benjamin & You, Fengqi, 2024. "Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 355(C).
    16. van der Meer, Dennis & Wang, Guang Chao & Munkhammar, Joakim, 2021. "An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic," Applied Energy, Elsevier, vol. 283(C).
    17. Ali Saberi Derakhtenjani & Andreas K. Athienitis, 2021. "Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems," Energies, MDPI, vol. 14(4), pages 1-19, February.
    18. Goldsworthy, M. & Moore, T. & Peristy, M. & Grimeland, M., 2022. "Cloud-based model-predictive-control of a battery storage system at a commercial site," Applied Energy, Elsevier, vol. 327(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Yang & Campana, Pietro Elia & Yang, Ying & Stridh, Bengt & Lundblad, Anders & Yan, Jinyue, 2018. "Energy flexibility from the consumer: Integrating local electricity and heat supplies in a building," Applied Energy, Elsevier, vol. 223(C), pages 430-442.
    2. Maitanova, Nailya & Schlüters, Sunke & Hanke, Benedikt & von Maydell, Karsten, 2024. "An analytical method for quantifying the flexibility potential of decentralised energy systems," Applied Energy, Elsevier, vol. 364(C).
    3. Clauß, John & Stinner, Sebastian & Sartori, Igor & Georges, Laurent, 2019. "Predictive rule-based control to activate the energy flexibility of Norwegian residential buildings: Case of an air-source heat pump and direct electric heating," Applied Energy, Elsevier, vol. 237(C), pages 500-518.
    4. Lv, Chaoxian & Yu, Hao & Li, Peng & Wang, Chengshan & Xu, Xiandong & Li, Shuquan & Wu, Jianzhong, 2019. "Model predictive control based robust scheduling of community integrated energy system with operational flexibility," Applied Energy, Elsevier, vol. 243(C), pages 250-265.
    5. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    6. Matthias Eydner & Lu Wan & Tobias Henzler & Konstantinos Stergiaropoulos, 2022. "Real-Time Grid Signal-Based Energy Flexibility of Heating Generation: A Methodology for Optimal Scheduling of Stratified Storage Tanks," Energies, MDPI, vol. 15(5), pages 1-31, February.
    7. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    8. Jin, Xiaolong & Wu, Qiuwei & Jia, Hongjie, 2020. "Local flexibility markets: Literature review on concepts, models and clearing methods," Applied Energy, Elsevier, vol. 261(C).
    9. Xiaoyi Zhang & Weijun Gao & Yanxue Li & Zixuan Wang & Yoshiaki Ushifusa & Yingjun Ruan, 2021. "Operational Performance and Load Flexibility Analysis of Japanese Zero Energy House," IJERPH, MDPI, vol. 18(13), pages 1-19, June.
    10. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    11. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.
    12. Awan, Muhammad Bilal & Sun, Yongjun & Lin, Wenye & Ma, Zhenjun, 2023. "A framework to formulate and aggregate performance indicators to quantify building energy flexibility," Applied Energy, Elsevier, vol. 349(C).
    13. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    14. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    15. Monika Hall & Achim Geissler, 2020. "Load Control by Demand Side Management to Support Grid Stability in Building Clusters," Energies, MDPI, vol. 13(19), pages 1-15, October.
    16. Finck, Christian & Li, Rongling & Zeiler, Wim, 2019. "Economic model predictive control for demand flexibility of a residential building," Energy, Elsevier, vol. 176(C), pages 365-379.
    17. Chu, Wenfeng & Zhang, Yu & He, Wei & Zhang, Sheng & Hu, Zhongting & Ru, Bingqian & Ying, Shangxuan, 2023. "Research on flexible allocation strategy of power grid interactive buildings based on multiple optimization objectives," Energy, Elsevier, vol. 278(PB).
    18. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    19. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    20. Ali Saberi Derakhtenjani & Andreas K. Athienitis, 2021. "Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems," Energies, MDPI, vol. 14(4), pages 1-19, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:243:y:2019:i:c:p:274-287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.