IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v296y2024ics0360544224005401.html
   My bibliography  Save this article

Modeling and evaluation of probabilistic carbon emission flow for power systems considering load and renewable energy uncertainties

Author

Listed:
  • Sun, Xiaocong
  • Bao, Minglei
  • Ding, Yi
  • Hui, Hengyu
  • Song, Yonghua
  • Zheng, Chenghang
  • Gao, Xiang

Abstract

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers’ active carbon reduction in power systems.

Suggested Citation

  • Sun, Xiaocong & Bao, Minglei & Ding, Yi & Hui, Hengyu & Song, Yonghua & Zheng, Chenghang & Gao, Xiang, 2024. "Modeling and evaluation of probabilistic carbon emission flow for power systems considering load and renewable energy uncertainties," Energy, Elsevier, vol. 296(C).
  • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224005401
    DOI: 10.1016/j.energy.2024.130768
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224005401
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.130768?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. Bo Tranberg & Olivier Corradi & Bruno Lajoie & Thomas Gibon & Iain Staffell & Gorm Bruun Andresen, 2018. "Real-Time Carbon Accounting Method for the European Electricity Markets," Papers 1812.06679, arXiv.org, revised May 2019.
    2. Scarlat, Nicolae & Prussi, Matteo & Padella, Monica, 2022. "Quantification of the carbon intensity of electricity produced and used in Europe," Applied Energy, Elsevier, vol. 305(C).
    3. Sun, Xiaocong & Bao, Minglei & Guo, Chao & Ding, Yi & Zheng, Chenghang & Gao, Xiang, 2024. "An equilibrium capacity expansion model for power systems considering Gencos' coupled decisions between carbon and electricity markets," Applied Energy, Elsevier, vol. 359(C).
    4. Liu, H.B. & Jiang, C. & Jia, X.Y. & Long, X.Y. & Zhang, Z. & Guan, F.J., 2018. "A new uncertainty propagation method for problems with parameterized probability-boxes," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 64-73.
    5. He, Y.X. & Xia, T. & Liu, Z.Y. & Zhang, T. & Dong, Z., 2013. "Evaluation of the capability of accepting large-scale wind power in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 509-516.
    6. Zhao, Xiaoli & Chen, Haoran & Liu, Suwei & Ye, Xiaomei, 2020. "Economic & environmental effects of priority dispatch of renewable energy considering fluctuating power output of coal-fired units," Renewable Energy, Elsevier, vol. 157(C), pages 695-707.
    7. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Nonuniform wind farm layout optimization: A state-of-the-art review," Energy, Elsevier, vol. 209(C).
    8. Wan, Tong & Tao, Yuechuan & Qiu, Jing & Lai, Shuying, 2023. "Internet data centers participating in electricity network transition considering carbon-oriented demand response," Applied Energy, Elsevier, vol. 329(C).
    9. Sarshar, Javad & Moosapour, Seyyed Sajjad & Joorabian, Mahmood, 2017. "Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting," Energy, Elsevier, vol. 139(C), pages 680-693.
    10. Sun, Yanlong & Kang, Chongqing & Xia, Qing & Chen, Qixin & Zhang, Ning & Cheng, Yaohua, 2017. "Analysis of transmission expansion planning considering consumption-based carbon emission accounting," Applied Energy, Elsevier, vol. 193(C), pages 232-242.
    11. Onar, Sezi Cevik & Oztaysi, Basar & Otay, İrem & Kahraman, Cengiz, 2015. "Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets," Energy, Elsevier, vol. 90(P1), pages 274-285.
    12. Jiang, Kai & Yan, Xiaohe & Liu, Nian & Wang, Peng, 2022. "Energy trade-offs in coupled ICM and electricity market under dynamic carbon emission intensity," Energy, Elsevier, vol. 260(C).
    13. Shao, Changzheng & Ding, Yi & Wang, Jianhui, 2019. "A low-carbon economic dispatch model incorporated with consumption-side emission penalty scheme," Applied Energy, Elsevier, vol. 238(C), pages 1084-1092.
    14. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan & Zhang, Xian & Wang, Guibin, 2020. "Low-carbon oriented optimal energy dispatch in coupled natural gas and electricity systems," Applied Energy, Elsevier, vol. 280(C).
    15. Nazifi, Fatemeh & Trück, Stefan & Zhu, Liangxu, 2021. "Carbon pass-through rates on spot electricity prices in Australia," Energy Economics, Elsevier, vol. 96(C).
    16. Carpinelli, Guido & Caramia, Pierluigi & Varilone, Pietro, 2015. "Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems," Renewable Energy, Elsevier, vol. 76(C), pages 283-295.
    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. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan, 2022. "Robust energy systems scheduling considering uncertainties and demand side emission impacts," Energy, Elsevier, vol. 239(PD).
    19. Feng, Peiling & He, Xing, 2021. "Mixed neurodynamic optimization for the operation of multiple energy systems considering economic and environmental aspects," Energy, Elsevier, vol. 232(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. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "Low-carbon economic dispatch and energy sharing method of multiple Integrated Energy Systems from the perspective of System of Systems," Energy, Elsevier, vol. 244(PA).
    2. Wolf, Isabel & Holzapfel, Peter K.R. & Meschede, Henning & Finkbeiner, Matthias, 2023. "On the potential of temporally resolved GHG emission factors for load shifting: A case study on electrified steam generation," Applied Energy, Elsevier, vol. 348(C).
    3. Lizana, Jesus & Halloran, Claire E. & Wheeler, Scot & Amghar, Nabil & Renaldi, Renaldi & Killendahl, Markus & Perez-Maqueda, Luis A. & McCulloch, Malcolm & Chacartegui, Ricardo, 2023. "A national data-based energy modelling to identify optimal heat storage capacity to support heating electrification," Energy, Elsevier, vol. 262(PA).
    4. Da Liu & Guowei Zhang & Baohua Huang & Weiwei Liu, 2016. "Optimum Electric Boiler Capacity Configuration in a Regional Power Grid for a Wind Power Accommodation Scenario," Energies, MDPI, vol. 9(3), pages 1-13, March.
    5. Anders Arvesen & Steve Völler & Christine Roxanne Hung & Volker Krey & Magnus Korpås & Anders Hammer Strømman, 2021. "Emissions of electric vehicle charging in future scenarios: The effects of time of charging," Journal of Industrial Ecology, Yale University, vol. 25(5), pages 1250-1263, October.
    6. Li, Zhanhe & Li, Xiaoqian & Lu, Chao & Ma, Kechun & Bao, Weihan, 2024. "Carbon emission responsibility accounting in renewable energy-integrated DC traction power systems," Applied Energy, Elsevier, vol. 355(C).
    7. Braeuer, Fritz & Finck, Rafael & McKenna, Russell, 2020. "Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany," Working Paper Series in Production and Energy 44, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    8. Cheng, Xiu & Long, Ruyin & Chen, Hong & Yang, Jiahui, 2019. "Does social interaction have an impact on residents’ sustainable lifestyle decisions? A multi-agent stimulation based on regret and game theory," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Zhang, Xiaoshun & Guo, Zhengxun & Pan, Feng & Yang, Yuyao & Li, Chuansheng, 2023. "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization," Energy, Elsevier, vol. 283(C).
    10. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    12. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    13. Bruno Cárdenas & Lawrie Swinfen-Styles & James Rouse & Seamus D. Garvey, 2021. "Short-, Medium-, and Long-Duration Energy Storage in a 100% Renewable Electricity Grid: A UK Case Study," Energies, MDPI, vol. 14(24), pages 1-28, December.
    14. Jenkins, J.D. & Zhou, Z. & Ponciroli, R. & Vilim, R.B. & Ganda, F. & de Sisternes, F. & Botterud, A., 2018. "The benefits of nuclear flexibility in power system operations with renewable energy," Applied Energy, Elsevier, vol. 222(C), pages 872-884.
    15. Stephany Isabel Vallarta-Serrano & Ana Bricia Galindo-Muro & Riccardo Cespi & Rogelio Bustamante-Bello, 2023. "Analysis of GHG Emission from Cargo Vehicles in Megacities: The Case of the Metropolitan Zone of the Valley of Mexico," Energies, MDPI, vol. 16(13), pages 1-19, June.
    16. Jiang, Kai & Yan, Xiaohe & Liu, Nian & Wang, Peng, 2022. "Energy trade-offs in coupled ICM and electricity market under dynamic carbon emission intensity," Energy, Elsevier, vol. 260(C).
    17. Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    18. Segantin, Stefano & Testoni, Raffaella & Zucchetti, Massimo, 2019. "The lifetime determination of ARC reactor as a load-following plant in the energy framework," Energy Policy, Elsevier, vol. 126(C), pages 66-75.
    19. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    20. Arjuna Nebel & Christine Krüger & Tomke Janßen & Mathieu Saurat & Sebastian Kiefer & Karin Arnold, 2020. "Comparison of the Effects of Industrial Demand Side Management and Other Flexibilities on the Performance of the Energy System," Energies, MDPI, vol. 13(17), pages 1-20, August.

    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:energy:v:296:y:2024:i:c:s0360544224005401. 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/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.