IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i15p6483-d1445321.html
   My bibliography  Save this article

Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory

Author

Listed:
  • Xin Huang

    (Power Grid Planning and Research Center of Guangdong Power Grid Co., Ltd., Guangzhou 526299, China)

  • Keteng Jiang

    (Tsinghua Sichuan Energy Internet Research Institute, Chengdu 610213, China)

  • Shuxin Luo

    (Power Grid Planning and Research Center of Guangdong Power Grid Co., Ltd., Guangzhou 526299, China)

  • Haibo Li

    (Tsinghua Sichuan Energy Internet Research Institute, Chengdu 610213, China)

  • Zongxiang Lu

    (Tsinghua Sichuan Energy Internet Research Institute, Chengdu 610213, China)

Abstract

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.

Suggested Citation

  • Xin Huang & Keteng Jiang & Shuxin Luo & Haibo Li & Zongxiang Lu, 2024. "Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory," Sustainability, MDPI, vol. 16(15), pages 1-31, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6483-:d:1445321
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/15/6483/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/15/6483/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Motalleb, Mahdi & Reihani, Ehsan & Ghorbani, Reza, 2016. "Optimal placement and sizing of the storage supporting transmission and distribution networks," Renewable Energy, Elsevier, vol. 94(C), pages 651-659.
    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. Motalleb, Mahdi & Thornton, Matsu & Reihani, Ehsan & Ghorbani, Reza, 2016. "A nascent market for contingency reserve services using demand response," Applied Energy, Elsevier, vol. 179(C), pages 985-995.
    2. Kotarela, F. & Kyritsis, A. & Papanikolaou, N. & Kalogirou, S.A., 2021. "Enhanced nZEB concept incorporating a sustainable Grid Support Scheme," Renewable Energy, Elsevier, vol. 169(C), pages 714-725.
    3. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    4. Mortaz, Ebrahim & Vinel, Alexander & Dvorkin, Yury, 2019. "An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid," Applied Energy, Elsevier, vol. 242(C), pages 1649-1660.
    5. Karimi, Ali & Aminifar, Farrokh & Fereidunian, Alireza & Lesani, Hamid, 2019. "Energy storage allocation in wind integrated distribution networks: An MILP-Based approach," Renewable Energy, Elsevier, vol. 134(C), pages 1042-1055.
    6. Yadav, Monika & Pal, Nitai & Saini, Devender Kumar, 2021. "Resilient electrical distribution grid planning against seismic waves using distributed energy resources and sectionalizers: An Indian's urban grid case study," Renewable Energy, Elsevier, vol. 178(C), pages 241-259.
    7. Tee, Wei Hown & Gan, Chin Kim & Sardi, Junainah, 2024. "Benefits of energy storage systems and its potential applications in Malaysia: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    8. Marczinkowski, Hannah Mareike & Østergaard, Poul Alberg, 2019. "Evaluation of electricity storage versus thermal storage as part of two different energy planning approaches for the islands Samsø and Orkney," Energy, Elsevier, vol. 175(C), pages 505-514.
    9. Nayeem Chowdhury & Fabrizio Pilo & Giuditta Pisano, 2020. "Optimal Energy Storage System Positioning and Sizing with Robust Optimization," Energies, MDPI, vol. 13(3), pages 1-20, January.
    10. Lim, Kai Zhuo & Lim, Kang Hui & Wee, Xian Bin & Li, Yinan & Wang, Xiaonan, 2020. "Optimal allocation of energy storage and solar photovoltaic systems with residential demand scheduling," Applied Energy, Elsevier, vol. 269(C).
    11. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm," Applied Energy, Elsevier, vol. 232(C), pages 212-228.
    12. Guido Carpinelli & Fabio Mottola & Christian Noce & Angela Russo & Pietro Varilone, 2018. "A New Hybrid Approach Using the Simultaneous Perturbation Stochastic Approximation Method for the Optimal Allocation of Electrical Energy Storage Systems," Energies, MDPI, vol. 11(6), pages 1-20, June.
    13. Antweiler, Werner, 2021. "Microeconomic models of electricity storage: Price Forecasting, arbitrage limits, curtailment insurance, and transmission line utilization," Energy Economics, Elsevier, vol. 101(C).
    14. Ovidiu Ivanov & Mihai-Andrei Luca & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2024. "Flexible Energy Storage for Sustainable Load Leveling in Low-Voltage Electricity Distribution Grids with Prosumers," Sustainability, MDPI, vol. 16(10), pages 1-15, May.
    15. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    16. Motalleb, Mahdi & Ghorbani, Reza, 2017. "Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices," Applied Energy, Elsevier, vol. 202(C), pages 581-596.
    17. Das, Choton K. & Bass, Octavian & Mahmoud, Thair S. & Kothapalli, Ganesh & Mousavi, Navid & Habibi, Daryoush & Masoum, Mohammad A.S., 2019. "Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    18. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    19. Tsai, Chen-Hao & Figueroa-Acevedo, Armando & Boese, Maire & Li, Yifan & Mohan, Nihal & Okullo, James & Heath, Brandon & Bakke, Jordan, 2020. "Challenges of planning for high renewable futures: Experience in the U.S. midcontinent electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    20. Benjamin Matthiss & Arghavan Momenifarahani & Jann Binder, 2021. "Storage Placement and Sizing in a Distribution Grid with High PV Generation," Energies, MDPI, vol. 14(2), pages 1-10, January.

    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:gam:jsusta:v:16:y:2024:i:15:p:6483-:d:1445321. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.