IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v203y2023icp255-266.html
   My bibliography  Save this article

Worst CVaR based energy management for generalized energy storage enabled building-integrated energy systems

Author

Listed:
  • He, Shuaijia
  • Gao, Hongjun
  • Tang, Zao
  • Chen, Zhe
  • Jin, Xiaolong
  • Liu, Junyong

Abstract

Generalized energy storage (GES) can reduce the renewable energy curtailment, carbon emission and operational cost risk in the building-integrated energy system (BIES) energy management when faced with uncertainties of multi-energy loads and PV. In this context, GES integrated with the differentiated building thermal load-based virtual energy storage (VES) is proposed in the energy management model of BIES. Specifically, the differentiated building thermal load VES is introduced by differentiated personal factors (e.g., metabolic rate and clothing insulation) based on the predicted mean vote. Furthermore, a worst conditional value at risk (WCVaR) energy management method is developed to evaluate the operational cost risk of BIES. Meanwhile, possible probability distribution uncertainties of PV, electric and thermal loads are all fully considered by a boxlike uncertainty set. Then, the proposed WCVaR model is transformed into a linearized model by the Lagrange duality theory. Finally, the whole energy management model is solved by the CPLEX solver. Case studies and comparative analysis are performed based on a representative BIES. Numerical results show that the proposed model can reduce 33.55% of operational cost, 74.25% of carbon trading cost, and 16.96% of PV curtailment cost for BIES compared with that without GES. In addition, the conditional value at risk (CVaR) with the proposed WCVaR method ($5700.22) is between that with the traditional CVaR method considering multiple operational scenarios ($5684.68) and that with the traditional CVaR method only including the worst operational scenario ($6043.48), which indicates that the proposed WCVaR method can avoid overly-high and overly-low operational cost risks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:203:y:2023:i:c:p:255-266
    DOI: 10.1016/j.renene.2022.12.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812201802X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.12.017?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. Liu, Yangyang & Shen, Zhongqi & Tang, Xiaowei & Lian, Hongbo & Li, Jiarui & Gong, Jinxia, 2019. "Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties," Applied Energy, Elsevier, vol. 256(C).
    2. Afzali, Sayyed Faridoddin & Cotton, James S. & Mahalec, Vladimir, 2020. "Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions," Applied Energy, Elsevier, vol. 259(C).
    3. 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.
    4. de Freitas, Renan Alves & Vogel, Ederson Paulo & Korzenowski, André Luis & Oliveira Rocha, Luiz Alberto, 2020. "Stochastic model to aid decision making on investments in renewable energy generation: Portfolio diffusion and investor risk aversion," Renewable Energy, Elsevier, vol. 162(C), pages 1161-1176.
    5. Pan, Wei & Pan, Mi, 2019. "Opportunities and risks of implementing zero-carbon building policy for cities: Hong Kong case," Applied Energy, Elsevier, vol. 256(C).
    6. Tsao, Yu-Chung & Thanh, Vo-Van, 2021. "Toward blockchain-based renewable energy microgrid design considering default risk and demand uncertainty," Renewable Energy, Elsevier, vol. 163(C), pages 870-881.
    7. Yazdanparast, R. & Jolai, F. & Pishvaee, M.S. & Keramati, A., 2022. "A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions," Renewable Energy, Elsevier, vol. 184(C), pages 799-819.
    8. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    9. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    10. He, Shuaijia & Gao, Hongjun & Wang, Lingfeng & Xiang, Yingmeng & Liu, Junyong, 2020. "Distributionally robust planning for integrated energy systems incorporating electric-thermal demand response," Energy, Elsevier, vol. 213(C).
    11. Uzair, Muhammad & Rehman, Naveed ur & Yousuf, Muhammad Uzair, 2022. "Sensitivity analysis of capital and energy production cost for off-grid building integrated photovoltaic systems," Renewable Energy, Elsevier, vol. 186(C), pages 195-206.
    12. Chen, Changming & Wu, Xueyan & Li, Yan & Zhu, Xiaojun & Li, Zesen & Ma, Jien & Qiu, Weiqiang & Liu, Chang & Lin, Zhenzhi & Yang, Li & Wang, Qin & Ding, Yi, 2021. "Distributionally robust day-ahead scheduling of park-level integrated energy system considering generalized energy storages," Applied Energy, Elsevier, vol. 302(C).
    13. Zhou, Yuqi & Yu, Wenbin & Zhu, Shanying & Yang, Bo & He, Jianping, 2021. "Distributionally robust chance-constrained energy management of an integrated retailer in the multi-energy market," Applied Energy, Elsevier, vol. 286(C).
    14. Sharma, Pooja & Kolhe, Mohan & Sharma, Arvind, 2020. "Economic performance assessment of building integrated photovoltaic system with battery energy storage under grid constraints," Renewable Energy, Elsevier, vol. 145(C), pages 1901-1909.
    15. Xuan, Ang & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin, 2021. "A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables," Applied Energy, Elsevier, vol. 294(C).
    Full references (including those not matched with items on IDEAS)

    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. He, Shuaijia & Gao, Hongjun & Chen, Zhe & Liu, Junyong, 2023. "Data-driven worst conditional value at risk energy management model of energy station," Energy, Elsevier, vol. 266(C).
    2. Liu, Fan & Duan, Jiandong & Wu, Chen & Tian, Qinxing, 2024. "Risk-averse distributed optimization for integrated electricity-gas systems considering uncertainties of Wind-PV and power-to-gas," Renewable Energy, Elsevier, vol. 227(C).
    3. József Magyari & Krisztina Hegedüs & Botond Sinóros-Szabó, 2022. "Integration Opportunities of Power-to-Gas and Internet-of-Things Technical Advancements: A Systematic Literature Review," Energies, MDPI, vol. 15(19), pages 1-19, September.
    4. Zhang, Jinliang & Liu, Ziyi, 2024. "Low carbon economic scheduling model for a park integrated energy system considering integrated demand response, ladder-type carbon trading and fine utilization of hydrogen," Energy, Elsevier, vol. 290(C).
    5. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Gao, Chong & Ma, Zeyang, 2023. "A novel two-layer nested optimization method for a zero-carbon island integrated energy system, incorporating tidal current power generation," Renewable Energy, Elsevier, vol. 218(C).
    6. Ouyang, Tiancheng & Zhang, Mingliang & Wu, Wencong & Zhao, Jiaqi & Xu, Hua, 2023. "A day-ahead planning for multi-energy system in building community," Energy, Elsevier, vol. 267(C).
    7. Wang, Jiangjiang & Deng, Hongda & Qi, Xiaoling, 2022. "Cost-based site and capacity optimization of multi-energy storage system in the regional integrated energy networks," Energy, Elsevier, vol. 261(PA).
    8. Zhihan Shi & Weisong Han & Guangming Zhang & Zhiqing Bai & Mingxiang Zhu & Xiaodong Lv, 2022. "Research on Low-Carbon Energy Sharing through the Alliance of Integrated Energy Systems with Multiple Uncertainties," Energies, MDPI, vol. 15(24), pages 1-20, December.
    9. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    10. Eid Gul & Giorgio Baldinelli & Pietro Bartocci, 2022. "Energy Transition: Renewable Energy-Based Combined Heat and Power Optimization Model for Distributed Communities," Energies, MDPI, vol. 15(18), pages 1-18, September.
    11. 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).
    12. Muhammad Waseem & Muhammad Adnan Khan & Arman Goudarzi & Shah Fahad & Intisar Ali Sajjad & Pierluigi Siano, 2023. "Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges," Energies, MDPI, vol. 16(2), pages 1-29, January.
    13. Naeem, Muhammad Abubakr & Arfaoui, Nadia, 2023. "Exploring downside risk dependence across energy markets: Electricity, conventional energy, carbon, and clean energy during episodes of market crises," Energy Economics, Elsevier, vol. 127(PB).
    14. Chen, Lei & Jiang, Yuqi & Zheng, Shencong & Deng, Xinyi & Chen, Hongkun & Islam, Md. Rabiul, 2023. "A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks," Applied Energy, Elsevier, vol. 350(C).
    15. Sánchez, M.N. & Soutullo, S. & Olmedo, R. & Bravo, D. & Castaño, S. & Jiménez, M.J., 2020. "An experimental methodology to assess the climate impact on the energy performance of buildings: A ten-year evaluation in temperate and cold desert areas," Applied Energy, Elsevier, vol. 264(C).
    16. 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).
    17. 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).
    18. Baohong Jin & Zhichao Liu & Yichuan Liao, 2023. "Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model," Energies, MDPI, vol. 16(6), pages 1-21, March.
    19. 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).
    20. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).

    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:renene:v:203:y:2023:i:c:p:255-266. 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.journals.elsevier.com/renewable-energy .

    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.