IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9489-d1003365.html
   My bibliography  Save this article

Optimal Power Flow with Stochastic Solar Power Using Clustering-Based Multi-Objective Differential Evolution

Author

Listed:
  • Derong Lv

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Guojiang Xiong

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China
    Guizhou University Institute of Engineering Investigation & Design Co., Ltd., Guiyang 550025, China)

  • Xiaofan Fu

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Yang Wu

    (Guizhou Electric Power Grid Dispatching and Control Center, Guiyang 550002, China)

  • Sheng Xu

    (Guizhou Electric Power Grid Dispatching and Control Center, Guiyang 550002, China)

  • Hao Chen

    (Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System, Quanzhou 362216, China)

Abstract

Optimal power flow is one of the fundamental optimal operation problems for power systems. With the increasing scale of solar energy integrated into power systems, the uncertainty of solar power brings intractable challenges to the power system operation. The multi-objective optimal power flow (MOOPF) considering the solar energy becomes a hotspot issue. In this study, a MOOPF model considering the uncertainty of solar power is proposed. Both scenarios of overestimation and underestimation of solar power are modeled and penalized in the form of operating cost. In order to solve this multi-objective optimization model effectively, this study proposes a clustering-based multi-objective differential evolution (CMODE) which is based on the main features: (1) extending DE into multi-objective algorithm, (2) introducing the feasible solution priority technique to deal with different constraints, and (3) combining the feasible solution priority technique and the merged hierarchical clustering method to determine the optimal Pareto frontier. The simulation outcomes on two cases based on the IEEE 57-bus system verify the reliability and superiority of CMODE over other peer methods in addressing the MOOPF.

Suggested Citation

  • Derong Lv & Guojiang Xiong & Xiaofan Fu & Yang Wu & Sheng Xu & Hao Chen, 2022. "Optimal Power Flow with Stochastic Solar Power Using Clustering-Based Multi-Objective Differential Evolution," Energies, MDPI, vol. 15(24), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9489-:d:1003365
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9489/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9489/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaobing Yu & Xianrui Yu & Yiqun Lu & Jichuan Sheng, 2018. "Economic and Emission Dispatch Using Ensemble Multi-Objective Differential Evolution Algorithm," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    2. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    3. Shi, Bin & Yan, Lie-Xiang & Wu, Wei, 2013. "Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction," Energy, Elsevier, vol. 56(C), pages 135-143.
    4. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    5. Xiong, Guojiang & Shuai, Maohang & Hu, Xiao, 2022. "Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization," Energy, Elsevier, vol. 244(PB).
    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. Lai, Wenhao & Zheng, Xiaoliang & Song, Qi & Hu, Feng & Tao, Qiong & Chen, Hualiang, 2022. "Multi-objective membrane search algorithm: A new solution for economic emission dispatch," Applied Energy, Elsevier, vol. 326(C).
    2. Xu Chen & Shuai Fang & Kangji Li, 2023. "Reinforcement-Learning-Based Multi-Objective Differential Evolution Algorithm for Large-Scale Combined Heat and Power Economic Emission Dispatch," Energies, MDPI, vol. 16(9), pages 1-23, April.
    3. Hossein Nourianfar & Hamdi Abdi, 2022. "Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique," Sustainability, MDPI, vol. 14(6), pages 1-26, March.
    4. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
    5. Grzegorz Drałus & Damian Mazur & Jacek Kusznier & Jakub Drałus, 2023. "Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation," Energies, MDPI, vol. 16(18), pages 1-23, September.
    6. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
    7. José Manuel Gámez Medina & Jorge de la Torre y Ramos & Francisco Eneldo López Monteagudo & Leticia del Carmen Ríos Rodríguez & Diego Esparza & Jesús Manuel Rivas & Leonel Ruvalcaba Arredondo & Alejand, 2022. "Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning," Sustainability, MDPI, vol. 14(15), pages 1-14, July.
    8. 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.
    9. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    10. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    11. Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).
    12. Jean-Nicolas Louis & Stéphane Allard & Freideriki Kotrotsou & Vincent Debusschere, 2020. "A multi-objective approach to the prospective development of the European power system by 2050," Post-Print hal-02376337, HAL.
    13. Zou, Dexuan & Gong, Dunwei & Ouyang, Haibin, 2023. "The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant," Applied Energy, Elsevier, vol. 351(C).
    14. Isaac Gallardo & Daniel Amor & Álvaro Gutiérrez, 2023. "Recent Trends in Real-Time Photovoltaic Prediction Systems," Energies, MDPI, vol. 16(15), pages 1-17, July.
    15. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    16. Izadbakhsh, Maziar & Gandomkar, Majid & Rezvani, Alireza & Ahmadi, Abdollah, 2015. "Short-term resource scheduling of a renewable energy based micro grid," Renewable Energy, Elsevier, vol. 75(C), pages 598-606.
    17. 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.
    18. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    19. Zailan, Roziah & Lim, Jeng Shiun & Manan, Zainuddin Abdul & Alwi, Sharifah Rafidah Wan & Mohammadi-ivatloo, Behnam & Jamaluddin, Khairulnadzmi, 2021. "Malaysia scenario of biomass supply chain-cogeneration system and optimization modeling development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    20. Monica Borunda & Adrián Ramírez & Raul Garduno & Gerardo Ruíz & Sergio Hernandez & O. A. Jaramillo, 2022. "Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning," Energies, MDPI, vol. 15(23), pages 1-25, November.

    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:15:y:2022:i:24:p:9489-:d:1003365. 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.