IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i9p3713-d1133541.html
   My bibliography  Save this article

An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm

Author

Listed:
  • Fan Li

    (School of Control Science and Engineering, Shandong University, Jingshi Road 17923, Jinan 250061, China)

  • Jingxi Su

    (School of Control Science and Engineering, Shandong University, Jingshi Road 17923, Jinan 250061, China)

  • Bo Sun

    (School of Control Science and Engineering, Shandong University, Jingshi Road 17923, Jinan 250061, China)

Abstract

This study proposes an optimal scheduling method for complex integrated energy systems. The proposed method employs a heuristic algorithm to maximize its energy, economy, and environment indices and optimize the system operation plan. It uses the k-means combined with box plots (Imk-means) to improve the convergence speed of the heuristic algorithm by forming its initial conditions. Thus, the optimization scheduling speed is enhanced. First of all, considering the system source and load factors, the Imk-means is presented to find the typical and extreme days in a historical optimization dataset. The output results for these typical and extreme days can represent common and abnormal optimization results, respectively. Thus, based on the representative historical data, a traditional heuristic algorithm with an initial solution set, such as the genetic algorithm, can be accelerated greatly. Secondly, the initial populations of the genetic algorithm are dispersed at the historical outputs of the typical and extreme days, and many random populations are supplemented simultaneously. Finally, the improved genetic algorithm performs the solution process faster to find optimal results and can possibly prevent the results from falling into local optima. A case study was conducted to verify the effectiveness of the proposed method. The results show that the proposed method can decrease the running time by up to 89.29% at the most, and 72.68% on average, compared with the traditional genetic algorithm. Meanwhile, the proposed method has a slightly increased optimization index, indicating no loss of optimization accuracy during acceleration. It can also indicate that the proposed method does not fall into local optima, as it has fewer iterations.

Suggested Citation

  • Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3713-:d:1133541
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/9/3713/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/9/3713/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Xiaoshun & Chen, Yixuan & Yu, Tao & Yang, Bo & Qu, Kaiping & Mao, Senmao, 2017. "Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems," Applied Energy, Elsevier, vol. 189(C), pages 157-176.
    2. Ye Zhao & Zhenhai Dou & Zexu Yu & Ruishuo Xie & Mengmeng Qiao & Yuanyuan Wang & Lianxin Liu, 2022. "Study on the Optimal Dispatching Strategy of a Combined Cooling, Heating and Electric Power System Based on Demand Response," Energies, MDPI, vol. 15(10), pages 1-18, May.
    3. Collins, Seán & Deane, John Paul & Poncelet, Kris & Panos, Evangelos & Pietzcker, Robert C. & Delarue, Erik & Ó Gallachóir, Brian Pádraig, 2017. "Integrating short term variations of the power system into integrated energy system models: A methodological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 839-856.
    4. Wang, Jiangjiang & Liu, Yi & Ren, Fukang & Lu, Shuaikang, 2020. "Multi-objective optimization and selection of hybrid combined cooling, heating and power systems considering operational flexibility," Energy, Elsevier, vol. 197(C).
    5. Zhang, Lizhi & Kuang, Jiyuan & Sun, Bo & Li, Fan & Zhang, Chenghui, 2020. "A two-stage operation optimization method of integrated energy systems with demand response and energy storage," Energy, Elsevier, vol. 208(C).
    6. Kamal, Rajeev & Moloney, Francesca & Wickramaratne, Chatura & Narasimhan, Arunkumar & Goswami, D.Y., 2019. "Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus," Applied Energy, Elsevier, vol. 246(C), pages 77-90.
    7. Schütz, Thomas & Schraven, Markus Hans & Fuchs, Marcus & Remmen, Peter & Müller, Dirk, 2018. "Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis," Renewable Energy, Elsevier, vol. 129(PA), pages 570-582.
    8. Yeganefar, Ali & Amin-Naseri, Mohammad Reza & Sheikh-El-Eslami, Mohammad Kazem, 2020. "Improvement of representative days selection in power system planning by incorporating the extreme days of the net load to take account of the variability and intermittency of renewable resources," Applied Energy, Elsevier, vol. 272(C).
    9. Mengmeng Qiao & Zexu Yu & Zhenhai Dou & Yuanyuan Wang & Ye Zhao & Ruishuo Xie & Lianxin Liu, 2022. "Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-31, July.
    10. Zatti, Matteo & Gabba, Marco & Freschini, Marco & Rossi, Michele & Gambarotta, Agostino & Morini, Mirko & Martelli, Emanuele, 2019. "k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization," Energy, Elsevier, vol. 181(C), pages 1051-1063.
    11. Wu, J.Y. & Wang, J.L. & Li, S. & Wang, R.Z., 2014. "Experimental and simulative investigation of a micro-CCHP (micro combined cooling, heating and power) system with thermal management controller," Energy, Elsevier, vol. 68(C), pages 444-453.
    12. Jiyuan Kuang & Chenghui Zhang & Fan Li & Bo Sun, 2018. "Dynamic Optimization of Combined Cooling, Heating, and Power Systems with Energy Storage Units," Energies, MDPI, vol. 11(9), pages 1-16, August.
    13. Cho, Heejin & Smith, Amanda D. & Mago, Pedro, 2014. "Combined cooling, heating and power: A review of performance improvement and optimization," Applied Energy, Elsevier, vol. 136(C), pages 168-185.
    14. Elsido, Cristina & Bischi, Aldo & Silva, Paolo & Martelli, Emanuele, 2017. "Two-stage MINLP algorithm for the optimal synthesis and design of networks of CHP units," Energy, Elsevier, vol. 121(C), pages 403-426.
    15. Li, Fan & Sun, Bo & Zhang, Chenghui & Liu, Che, 2019. "A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage," Energy, Elsevier, vol. 188(C).
    16. Li, Fan & Sun, Bo & Zhang, Chenghui & Zhang, Lizhi, 2018. "Operation optimization for combined cooling, heating, and power system with condensation heat recovery," Applied Energy, Elsevier, vol. 230(C), pages 305-316.
    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. Puming Wang & Liqin Zheng & Tianyi Diao & Shengquan Huang & Xiaoqing Bai, 2023. "Robust Bilevel Optimal Dispatch of Park Integrated Energy System Considering Renewable Energy Uncertainty," Energies, MDPI, vol. 16(21), pages 1-23, October.

    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. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    2. Ma, Deyin & Zhang, Lizhi & Sun, Bo, 2021. "An interval scheduling method for the CCHP system containing renewable energy sources based on model predictive control," Energy, Elsevier, vol. 236(C).
    3. Zhou, Yuan & Wang, Jiangjiang & Dong, Fuxiang & Qin, Yanbo & Ma, Zherui & Ma, Yanpeng & Li, Jianqiang, 2021. "Novel flexibility evaluation of hybrid combined cooling, heating and power system with an improved operation strategy," Applied Energy, Elsevier, vol. 300(C).
    4. Li, Yiming & Liu, Che & Zhang, Lizhi & Sun, Bo, 2021. "A partition optimization design method for a regional integrated energy system based on a clustering algorithm," Energy, Elsevier, vol. 219(C).
    5. Xiao Gong & Fan Li & Bo Sun & Dong Liu, 2020. "Collaborative Optimization of Multi-Energy Complementary Combined Cooling, Heating, and Power Systems Considering Schedulable Loads," Energies, MDPI, vol. 13(4), pages 1-17, February.
    6. Chen, Ke & Pan, Ming, 2021. "Operation optimization of combined cooling, heating, and power superstructure system for satisfying demand fluctuation," Energy, Elsevier, vol. 237(C).
    7. Hassan, Muhammed A. & Khalil, Adel & Abubakr, Mohamed, 2021. "Selection methodology of representative meteorological days for assessment of renewable energy systems," Renewable Energy, Elsevier, vol. 177(C), pages 34-51.
    8. Hong-Hai Niu & Yang Zhao & Shang-Shang Wei & Yi-Guo Li, 2021. "A Variable Performance Parameters Temperature–Flowrate Scheduling Model for Integrated Energy Systems," Energies, MDPI, vol. 14(17), pages 1-25, August.
    9. Martelli, Emanuele & Freschini, Marco & Zatti, Matteo, 2020. "Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming," Applied Energy, Elsevier, vol. 267(C).
    10. Chen, Jie & Huang, Shoujun & Shahabi, Laleh, 2021. "Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm," Applied Energy, Elsevier, vol. 298(C).
    11. Luca Urbanucci & Francesco D’Ettorre & Daniele Testi, 2019. "A Comprehensive Methodology for the Integrated Optimal Sizing and Operation of Cogeneration Systems with Thermal Energy Storage," Energies, MDPI, vol. 12(5), pages 1-17, March.
    12. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    13. Lüth, Alexandra & Seifert, Paul E. & Egging-Bratseth, Ruud & Weibezahn, Jens, 2023. "How to connect energy islands: Trade-offs between hydrogen and electricity infrastructure," Applied Energy, Elsevier, vol. 341(C).
    14. Li, Ke & Yang, Fan & Wang, Lupan & Yan, Yi & Wang, Haiyang & Zhang, Chenghui, 2022. "A scenario-based two-stage stochastic optimization approach for multi-energy microgrids," Applied Energy, Elsevier, vol. 322(C).
    15. Guillermo Rey & Carlos Ulloa & José Luís Míguez & Antón Cacabelos, 2016. "Suitability Assessment of an ICE-Based Micro-CCHP Unit in Different Spanish Climatic Zones: Application of an Experimental Model in Transient Simulation," Energies, MDPI, vol. 9(11), pages 1-13, November.
    16. Ren, Fukang & Lin, Xiaozhen & Wei, Ziqing & Zhai, Xiaoqiang & Yang, Jianrong, 2022. "A novel planning method for design and dispatch of hybrid energy systems," Applied Energy, Elsevier, vol. 321(C).
    17. Han, Zepeng & Wang, Jiangjiang & Cui, Zhiheng & Lu, Chunyan & Qi, Xiaoling, 2021. "Multi-objective optimization and exergoeconomic analysis for a novel full-spectrum solar-assisted methanol combined cooling, heating, and power system," Energy, Elsevier, vol. 237(C).
    18. Ju, Liwei & Tan, Zhongfu & Li, Huanhuan & Tan, Qingkun & Yu, Xiaobao & Song, Xiaohua, 2016. "Multi-objective operation optimization and evaluation model for CCHP and renewable energy based hybrid energy system driven by distributed energy resources in China," Energy, Elsevier, vol. 111(C), pages 322-340.
    19. Li, Ruonan & Mhaskar, Prashant & Mahalec, Vladimir, 2021. "Integration of energy systems for buildings and light industrial plants," Energy, Elsevier, vol. 233(C).
    20. Lizhi Zhang & Fan Li & Bo Sun & Chenghui Zhang, 2019. "Integrated Optimization Design of Combined Cooling, Heating, and Power System Coupled with Solar and Biomass Energy," Energies, MDPI, vol. 12(4), pages 1-21, February.

    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:16:y:2023:i:9:p:3713-:d:1133541. 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.