IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v219y2023ip1s0960148123013988.html
   My bibliography  Save this article

Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling

Author

Listed:
  • Ma, Yixiang
  • Yu, Lean
  • Zhang, Guoxing
  • Lu, Zhiming
  • Wu, Jiaqian

Abstract

The uncertainty of the source-load data, accompanied by the contradiction between different goal orientations, poses a challenge to the decision-making of the scheduling scheme. To solve the issue of multi-objective optimal scheduling under the condition of source-load uncertainty, this paper proposes a multi-objective multi-energy complementary optimal scheduling scheme based on source-load uncertainty. In the proposed method, four main steps: uncertainty analysis of the source-load data, design of multi-energy complementary scheduling scheme, optimal calculation of the scheduling scheme, and multi-scenario analysis, are involved. In addition, the effect of the source-load prediction on the optimal scheduling scheme is further analyzed for management implications. In the empirical analysis, the source-load data with 15-min intervals is introduced as the sample data, and different optimization algorithms and compromise solution determination methods are selected for comparative analysis. Compared with other optimization algorithms, the proposed method has an average decrease of 22.699%, 7.587% and 22.149% in the total cost of generation (TCG), the spinning reserve cost (CSR) and the carbon emission (CE), respectively, and the average increase in the rate of new energy generation (RNE) is 11.969%. The empirical analysis shows that the proposed method outperforms all benchmark methods, which can provide valuable insights for intraday rolling scheduling under the condition of source-load uncertainty and multi-objective optimization.

Suggested Citation

  • Ma, Yixiang & Yu, Lean & Zhang, Guoxing & Lu, Zhiming & Wu, Jiaqian, 2023. "Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling," Renewable Energy, Elsevier, vol. 219(P1).
    • Handle: RePEc:eee:renene:v:219:y:2023:i:p1:s0960148123013988
    DOI: 10.1016/j.renene.2023.119483
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123013988
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119483?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. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    2. Wang, Huaqing & Xie, Zhuoshi & Pu, Lei & Ren, Zhongrui & Zhang, Yaoyu & Tan, Zhongfu, 2022. "Energy management strategy of hybrid energy storage based on Pareto optimality," Applied Energy, Elsevier, vol. 327(C).
    3. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    4. 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).
    5. Zhang, Shuang & Zhao, Tao & Xie, Bai-Chen, 2018. "What is the optimal power generation mix of China? An empirical analysis using portfolio theory," Applied Energy, Elsevier, vol. 229(C), pages 522-536.
    6. He, Liangce & Lu, Zhigang & Zhang, Jiangfeng & Geng, Lijun & Zhao, Hao & Li, Xueping, 2018. "Low-carbon economic dispatch for electricity and natural gas systems considering carbon capture systems and power-to-gas," Applied Energy, Elsevier, vol. 224(C), pages 357-370.
    7. Yang, Dongfeng & Xu, Yang & Liu, Xiaojun & Jiang, Chao & Nie, Fanjie & Ran, Zixu, 2022. "Economic-emission dispatch problem in integrated electricity and heat system considering multi-energy demand response and carbon capture Technologies," Energy, Elsevier, vol. 253(C).
    8. Ju, Liwei & Yin, Zhe & Zhou, Qingqing & Li, Qiaochu & Wang, Peng & Tian, Wenxu & Li, Peng & Tan, Zhongfu, 2022. "Nearly-zero carbon optimal operation model and benefit allocation strategy for a novel virtual power plant using carbon capture, power-to-gas, and waste incineration power in rural areas," Applied Energy, Elsevier, vol. 310(C).
    9. Hui Huang & Yingying Du & Shizhong Song & Yanlei Guo, 2020. "Key Technologies and Economic Analysis of Decentralized Wind Power Consumption: A Case Study in B City, China," Energies, MDPI, vol. 13(16), pages 1-23, August.
    10. Zhang, Jingrui & Li, Zhuoyun & Wang, Beibei, 2021. "Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing," Energy, Elsevier, vol. 223(C).
    11. Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2014. "Uncertainty handling using neural network-based prediction intervals for electrical load forecasting," Energy, Elsevier, vol. 73(C), pages 916-925.
    12. Chen, Fang & Zhou, Jianzhong & Wang, Chao & Li, Chunlong & Lu, Peng, 2017. "A modified gravitational search algorithm based on a non-dominated sorting genetic approach for hydro-thermal-wind economic emission dispatching," Energy, Elsevier, vol. 121(C), pages 276-291.
    13. Hu, Shuozhuo & Li, Jian & Yang, Fubin & Yang, Zhen & Duan, Yuanyuan, 2020. "Multi-objective optimization of organic Rankine cycle using hydrofluorolefins (HFOs) based on different target preferences," Energy, Elsevier, vol. 203(C).
    14. Suresh K. Damodaran & T. K. Sunil Kumar, 2018. "Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms," Energies, MDPI, vol. 11(2), pages 1-19, February.
    15. Yanyue Wang & Guohua Fang, 2022. "Joint Operation Modes and Economic Analysis of Nuclear Power and Pumped Storage Plants under Different Power Market Environments," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    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. Zhang, Bin & Wu, Xuewei & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: An improved soft actor–critic approach," Energy, Elsevier, vol. 271(C).
    2. Wu, Qunli & Li, Chunxiang, 2023. "Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading," Energy, Elsevier, vol. 270(C).
    3. Wang, Haibing & Zhao, Anjie & Khan, Muhammad Qasim & Sun, Weiqing, 2024. "Optimal operation of energy hub considering reward-punishment ladder carbon trading and electrothermal demand coupling," Energy, Elsevier, vol. 286(C).
    4. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.
    5. Shuozhuo Hu & Zhen Yang & Jian Li & Yuanyuan Duan, 2021. "A Review of Multi-Objective Optimization in Organic Rankine Cycle (ORC) System Design," Energies, MDPI, vol. 14(20), pages 1-36, October.
    6. Yang, Meng & Liu, Yisheng, 2023. "Research on multi-energy collaborative operation optimization of integrated energy system considering carbon trading and demand response," Energy, Elsevier, vol. 283(C).
    7. Elsir, Mohamed & Al-Sumaiti, Ameena Saad & El Moursi, Mohamed Shawky, 2024. "Towards energy transition: A novel day-ahead operation scheduling strategy for demand response and hybrid energy storage systems in smart grid," Energy, Elsevier, vol. 293(C).
    8. Wang, Fengjuan & Xie, Yachen & Xu, Jiuping, 2019. "Reliable-economical equilibrium based short-term scheduling towards hybrid hydro-photovoltaic generation systems: Case study from China," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Zhang, Kaoshe & Gao, Congchong & Zhang, Gang & Xie, Tuo & Li, Hua, 2024. "Electricity and heat sharing strategy of regional comprehensive energy multi-microgrid based on double-layer game," Energy, Elsevier, vol. 293(C).
    10. Yuxing Liu & Linjun Zeng & Jie Zeng & Zhenyi Yang & Na Li & Yuxin Li, 2023. "Scheduling Optimization of IEHS with Uncertainty of Wind Power and Operation Mode of CCP," Energies, MDPI, vol. 16(5), pages 1-17, February.
    11. Nikkhah, Saman & Rabiee, Abbas, 2018. "Optimal wind power generation investment, considering voltage stability of power systems," Renewable Energy, Elsevier, vol. 115(C), pages 308-325.
    12. 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.
    13. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    14. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    15. 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.
    16. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    17. Xiang, Yue & Guo, Yongtao & Wu, Gang & Liu, Junyong & Sun, Wei & Lei, Yutian & Zeng, Pingliang, 2022. "Low-carbon economic planning of integrated electricity-gas energy systems," Energy, Elsevier, vol. 249(C).
    18. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization," Renewable Energy, Elsevier, vol. 191(C), pages 459-492.
    19. Xiang, Yue & Wu, Gang & Shen, Xiaodong & Ma, Yuhang & Gou, Jing & Xu, Weiting & Liu, Junyong, 2021. "Low-carbon economic dispatch of electricity-gas systems," Energy, Elsevier, vol. 226(C).
    20. Rahmani, Shima & Amjady, Nima, 2017. "A new optimal power flow approach for wind energy integrated power systems," Energy, Elsevier, vol. 134(C), pages 349-359.

    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:219:y:2023:i:p1:s0960148123013988. 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.