IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i6p1395-d1356914.html
   My bibliography  Save this article

A Distributed Multi-Timescale Dispatch Strategy for a City-Integrated Energy System with Carbon Capture Power Plants

Author

Listed:
  • Huanan Liu

    (School of Information Engineering, Nanchang University, Nanchang 330031, China
    Huanan Liu and Ruoci Lu are the co-first author of this paper.)

  • Ruoci Lu

    (School of Information Engineering, Nanchang University, Nanchang 330031, China
    Huanan Liu and Ruoci Lu are the co-first author of this paper.)

  • Zhenlan Dou

    (State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)

  • Chunyan Zhang

    (State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)

  • Songcen Wang

    (National Key Laboratory of Power Grid Safety, Beijing 100192, China
    China Electric Power Research Institute, Beijing 100192, China)

Abstract

In city-integrated energy systems containing electric–thermal multi-energy sources, the uncertainty of renewable energy sources and the fluctuation of loads challenge the safe, efficient, economic and stable operation of the integrated energy systems. This paper introduces a novel approach for the operation of a carbon capture plant/CHP with PV accommodation within a city-integrated energy system. The proposed strategy aims to maximize the utilization of photovoltaic (PV) power generation and carbon capture equipment, addressing issues related to small-scale CHP climbing constraints and short-term output regulation. Additionally, this paper presents a multi-timescale optimal scheduling strategy, which effectively addresses deviations caused by PV fluctuations and load changes. This was achieved through a detailed analysis of the CHP climbing constraints, carbon capture equipment operation and real-time characteristics of PV power generation. This paper introduces a fully distributed neural dynamics-based optimization algorithm designed to address multi-timescale optimization challenges. Utilizing rolling cycles, this algorithm computes both day-ahead and real-time scheduling outcomes for urban integrated energy systems. Theoretical analyses and numerical simulations were conducted to validate the precision and efficacy of the proposed model and algorithm. These analyses quantitatively evaluate the scheduling performance of PV power generation and carbon capture CHP systems across various timescales.

Suggested Citation

  • Huanan Liu & Ruoci Lu & Zhenlan Dou & Chunyan Zhang & Songcen Wang, 2024. "A Distributed Multi-Timescale Dispatch Strategy for a City-Integrated Energy System with Carbon Capture Power Plants," Energies, MDPI, vol. 17(6), pages 1-19, March.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1395-:d:1356914
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/6/1395/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/6/1395/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abdollahi, Elnaz & Wang, Haichao & Lahdelma, Risto, 2016. "An optimization method for multi-area combined heat and power production with power transmission network," Applied Energy, Elsevier, vol. 168(C), pages 248-256.
    2. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2022. "Energy storage to solve the diurnal, weekly, and seasonal mismatch and achieve zero-carbon electricity consumption in buildings," Applied Energy, Elsevier, vol. 312(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. Rong, Aiying & Lahdelma, Risto, 2017. "An efficient model and algorithm for the transmission-constrained multi-site combined heat and power system," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1106-1117.
    2. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    3. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & Gharehpetian, G.B., 2018. "A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2128-2143.
    4. Go, Jaehyun & Byun, Jiwook & Orehounig, Kristina & Heo, Yeonsook, 2023. "Battery-H2 storage system for self-sufficiency in residential buildings under different electric heating system scenarios," Applied Energy, Elsevier, vol. 337(C).
    5. Santosh Kumar B. P. & Venkata Ramanaiah K., 2022. "An Improved Image Compression Model Enabled by Adaptive Active Contour and Supervised Learning-Based ROI Classification," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-26, January.
    6. Gabriela Scheibel Cassol & Chii Shang & Alicia Kyoungjin An & Noman Khalid Khanzada & Francesco Ciucci & Alessandro Manzotti & Paul Westerhoff & Yinghao Song & Li Ling, 2024. "Ultra-fast green hydrogen production from municipal wastewater by an integrated forward osmosis-alkaline water electrolysis system," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    7. Bloess, Andreas, 2019. "Impacts of heat sector transformation on Germany’s power system through increased use of power-to-heat," Applied Energy, Elsevier, vol. 239(C), pages 560-580.
    8. Gu, Meng & Guo, Qi & Lu, Shiliang, 2022. "Feasibility analysis of energy-saving potential of the underground ice rink using spectrum splitting sunshade technology," Renewable Energy, Elsevier, vol. 191(C), pages 571-579.
    9. Zakeri, Behnam & Virasjoki, Vilma & Syri, Sanna & Connolly, David & Mathiesen, Brian V. & Welsch, Manuel, 2016. "Impact of Germany's energy transition on the Nordic power market – A market-based multi-region energy system model," Energy, Elsevier, vol. 115(P3), pages 1640-1662.
    10. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    11. Paramjeet Kaur & Krishna Teerth Chaturvedi & Mohan Lal Kolhe, 2023. "Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique," Energies, MDPI, vol. 16(3), pages 1-17, January.
    12. Marty, Fabien & Serra, Sylvain & Sochard, Sabine & Reneaume, Jean-Michel, 2018. "Simultaneous optimization of the district heating network topology and the Organic Rankine Cycle sizing of a geothermal plant," Energy, Elsevier, vol. 159(C), pages 1060-1074.
    13. Abdollahi, Elnaz & Wang, Haichao & Lahdelma, Risto, 2019. "Parametric optimization of long-term multi-area heat and power production with power storage," Applied Energy, Elsevier, vol. 235(C), pages 802-812.
    14. Haichao Wang & Giulia Di Pietro & Xiaozhou Wu & Risto Lahdelma & Vittorio Verda & Ilkka Haavisto, 2018. "Renewable and Sustainable Energy Transitions for Countries with Different Climates and Renewable Energy Sources Potentials," Energies, MDPI, vol. 11(12), pages 1-32, December.
    15. Ali, Aliyuda & Aliyuda, Kachalla & Elmitwally, Nouh & Muhammad Bello, Abdulwahab, 2022. "Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage," Applied Energy, Elsevier, vol. 327(C).
    16. Yi, Bo-Wen & Xu, Jin-Hua & Fan, Ying, 2016. "Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model," Applied Energy, Elsevier, vol. 184(C), pages 641-658.
    17. Hailin Mu & Zhewen Pei & Hongye Wang & Nan Li & Ye Duan, 2022. "Optimal Strategy for Low-Carbon Development of Power Industry in Northeast China Considering the ‘Dual Carbon’ Goal," Energies, MDPI, vol. 15(17), pages 1-22, September.
    18. Best, Robert E. & Rezazadeh Kalehbasti, P. & Lepech, Michael D., 2020. "A novel approach to district heating and cooling network design based on life cycle cost optimization," Energy, Elsevier, vol. 194(C).
    19. Zhang, Tong & Li, Zhigang & Wu, Q.H. & Zhou, Xiaoxin, 2019. "Decentralized state estimation of combined heat and power systems using the asynchronous alternating direction method of multipliers," Applied Energy, Elsevier, vol. 248(C), pages 600-613.
    20. Abdollahi, Elnaz & Lahdelma, Risto, 2020. "Decomposition method for optimizing long-term multi-area energy production with heat and power storages," Applied Energy, Elsevier, vol. 260(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:gam:jeners:v:17:y:2024:i:6:p:1395-:d:1356914. 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.