IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v227y2021ics0360544221006356.html
   My bibliography  Save this article

A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China

Author

Listed:
  • Lu, Xiaohui
  • Yang, Yang
  • Wang, Peifang
  • Fan, Yiming
  • Yu, Fangzhong
  • Zafetti, Nicholas

Abstract

In this research, a new approach has been suggested for providing an optimization bidding strategy in the day-ahead market for case research in China. This research uses a newly developed version of Emperor Penguin Optimizer (CEPO) to govern the fitness function of all individuals based on the Market Clearing Price (MCP) probability function. The resemblance amounts between each day and the next day are used for clustering. The clustering is performed based on the well-known subtractive clustering methodology. A simulation model using the probability function in MCP estimates the fitness function of the generated strategies. The results indicate that this method proposed is a statistically effective bidding design in China’s day-ahead market related to two other plans from the literature.

Suggested Citation

  • Lu, Xiaohui & Yang, Yang & Wang, Peifang & Fan, Yiming & Yu, Fangzhong & Zafetti, Nicholas, 2021. "A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China," Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221006356
    DOI: 10.1016/j.energy.2021.120386
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221006356
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120386?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. Paria Akbary & Mohammad Ghiasi & Mohammad Reza Rezaie Pourkheranjani & Hamidreza Alipour & Noradin Ghadimi, 2019. "Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 1-26, January.
    2. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    3. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    4. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad, 2020. "Optimal operating strategy of high-temperature heat and power storage system coupled with a wind farm in energy market," Energy, Elsevier, vol. 210(C).
    5. Arabkoohsar, A. & Andresen, G.B., 2017. "Dynamic energy, exergy and market modeling of a High Temperature Heat and Power Storage System," Energy, Elsevier, vol. 126(C), pages 430-443.
    6. Ostadi, Bakhtiar & Motamedi Sedeh, Omid & Husseinzadeh Kashan, Ali, 2020. "Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model," Energy, Elsevier, vol. 191(C).
    7. Aliabadi, Danial Esmaeili & Kaya, Murat & Şahin, Güvenç, 2017. "An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms," Energy Policy, Elsevier, vol. 100(C), pages 191-205.
    8. Motamedi Sedeh, Omid & Ostadi, Bakhtiar, 2020. "Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price," Energy Policy, Elsevier, vol. 145(C).
    9. Wang, Longze & Liu, Jinxin & Yuan, Rongfang & Wu, Jing & Zhang, Delong & Zhang, Yan & Li, Meicheng, 2020. "Adaptive bidding strategy for real-time energy management in multi-energy market enhanced by blockchain," Applied Energy, Elsevier, vol. 279(C).
    10. Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
    11. Fang, Yuchen & Zhao, Shuqiang, 2020. "Look-ahead bidding strategy for concentrating solar power plants with wind farms," Energy, Elsevier, vol. 203(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Yuanzheng & Huang, Jingjing & Liu, Yun & Zhao, Tianyang & Zhou, Yue & Zhao, Yong & Yuen, Chau, 2022. "Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation: A Singapore electricity market study," Energy, Elsevier, vol. 254(PA).
    2. Yu, Liying & Wang, Peng & Chen, Zhe & Li, Dewen & Li, Ning & Cherkaoui, Rachid, 2023. "Finding Nash equilibrium based on reinforcement learning for bidding strategy and distributed algorithm for ISO in imperfect electricity market," Applied Energy, Elsevier, vol. 350(C).

    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. Motamedi Sedeh, Omid & Ostadi, Bakhtiar, 2020. "Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price," Energy Policy, Elsevier, vol. 145(C).
    2. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    3. Liu, Lijun & Qian, Jin & Hua, Li & Zhang, Bin, 2022. "System estimation of the SOFCs using fractional-order social network search algorithm," Energy, Elsevier, vol. 255(C).
    4. Kavita Jain & Muhammed Basheer Jasser & Muzaffar Hamzah & Akash Saxena & Ali Wagdy Mohamed, 2022. "Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market," Mathematics, MDPI, vol. 10(12), pages 1-19, June.
    5. Wu, Cong & Li, Jiaxuan & Liu, Wenjin & He, Yuzhe & Nourmohammadi, Samad, 2023. "Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm," Applied Energy, Elsevier, vol. 345(C).
    6. Jun Dong & Dongran Liu & Xihao Dou & Bo Li & Shiyao Lv & Yuzheng Jiang & Tongtao Ma, 2021. "Key Issues and Technical Applications in the Study of Power Markets as the System Adapts to the New Power System in China," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
    7. Xing, Yazhou & Zhang, Su & Wen, Peng & Shao, Limin & Rouyendegh, Babak Daneshvar, 2020. "Load prediction in short-term implementing the multivariate quantile regression," Energy, Elsevier, vol. 196(C).
    8. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
    9. Yang, Zixuan & Liu, Qian & Zhang, Leiyu & Dai, Jialei & Razmjooy, Navid, 2020. "Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm," Energy, Elsevier, vol. 212(C).
    10. Yang, Peiwen & Dong, Jun & Lin, Jin & Liu, Yao & Fang, Debin, 2021. "Analysis of offering behavior of generation-side integrated energy aggregator in electricity market:A Bayesian evolutionary approach," Energy, Elsevier, vol. 228(C).
    11. Hou, Rui & Li, Shanshan & Wu, Minrong & Ren, Guowen & Gao, Wei & Khayatnezhad, Majid & gholinia, Fatemeh, 2021. "Assessing of impact climate parameters on the gap between hydropower supply and electricity demand by RCPs scenarios and optimized ANN by the improved Pathfinder (IPF) algorithm," Energy, Elsevier, vol. 237(C).
    12. Yuan, Zhi & Wang, Weiqing & Wang, Haiyun & Mizzi, Scott, 2020. "Combination of cuckoo search and wavelet neural network for midterm building energy forecast," Energy, Elsevier, vol. 202(C).
    13. Sun, Xianke & Wang, Gaoliang & Xu, Liuyang & Yuan, Honglei & Yousefi, Nasser, 2021. "Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm," Energy, Elsevier, vol. 237(C).
    14. Li, Yalun & Gao, Xinlei & Feng, Xuning & Ren, Dongsheng & Li, Yan & Hou, Junxian & Wu, Yu & Du, Jiuyu & Lu, Languang & Ouyang, Minggao, 2022. "Battery eruption triggered by plated lithium on an anode during thermal runaway after fast charging," Energy, Elsevier, vol. 239(PB).
    15. Ren, Xiaojun & Wu, Yongtang & Hao, Dongmin & Liu, Guoxu & Zafetti, Nicholas, 2021. "Analysis of the performance of the multi-objective hybrid hydropower-photovoltaic-wind system to reduce variance and maximum power generation by developed owl search algorithm," Energy, Elsevier, vol. 231(C).
    16. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
    17. Yin, Linfei & Sun, Zhixiang, 2021. "Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems," Applied Energy, Elsevier, vol. 300(C).
    18. Xu, Xiao & Hu, Weihao & Liu, Wen & Du, Yuefang & Huang, Rui & Huang, Qi & Chen, Zhe, 2021. "Look-ahead risk-constrained scheduling for an energy hub integrated with renewable energy," Applied Energy, Elsevier, vol. 297(C).
    19. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    20. Sadi, M. & Arabkoohsar, A., 2019. "Exergoeconomic analysis of a combined solar-waste driven power plant," Renewable Energy, Elsevier, vol. 141(C), pages 883-893.

    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:227:y:2021:i:c:s0360544221006356. 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.