IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1594-d754956.html
   My bibliography  Save this article

Optimal Operation for Regional IES Considering the Demand- and Supply-Side Characteristics

Author

Listed:
  • Wenying Li

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China)

  • Ming Tang

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China)

  • Xinzhen Zhang

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China)

  • Danhui Gao

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China)

  • Jian Wang

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China)

Abstract

A regional integrated energy system (RIES) is an electricity-centric multi-energy system that can realize the mutual conversion of electricity, heat, cold, and other energy. Through multi-flexible resource interaction and the transaction of multi-investment entities, the efficiency of energy utilization can be improved. To systematize energy-consuming entities and scale photovoltaic-based renewable energy in a distribution network, the energy-consuming behavior, energy-producing schedule, and trading strategy can be coupled. Considering the interaction between the energy-consuming behavior and the uncertainty of distributed photovoltaic output, an optimal operation method for RIES is proposed on the basis of social network theory and an uncertain evolutionary game method in this paper. From the perspective of the operator, the overall profits of RIES are maximized considering the entity characteristics of both the demand and the supply side. A case study shows that the proposed method can ensure the reasonable distribution of profit among the investment entities. A closer social relationship between energy-consuming entities or a lower transaction risk cost of energy-producing entities can increase the overall energy transaction profit.

Suggested Citation

  • Wenying Li & Ming Tang & Xinzhen Zhang & Danhui Gao & Jian Wang, 2022. "Optimal Operation for Regional IES Considering the Demand- and Supply-Side Characteristics," Energies, MDPI, vol. 15(4), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1594-:d:754956
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1594/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1594/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raffay Rizwan & Jehangir Arshad & Ahmad Almogren & Mujtaba Hussain Jaffery & Adnan Yousaf & Ayesha Khan & Ateeq Ur Rehman & Muhammad Shafiq, 2021. "Implementation of ANN-Based Embedded Hybrid Power Filter Using HIL-Topology with Real-Time Data Visualization through Node-RED," Energies, MDPI, vol. 14(21), pages 1-33, November.
    2. Dongkyu Lee & Jae-Weon Jeong & Guebin Choi, 2021. "Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model," Energies, MDPI, vol. 14(10), pages 1-15, May.
    3. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Mohmmed S. Adrees, 2021. "An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    4. Aidong Zeng & Sipeng Hao & Jia Ning & Qingshan Xu & Ling Jiang, 2020. "Research on Real-Time Optimized Operation and Dispatching Strategy for Integrated Energy System Based on Error Correction," Energies, MDPI, vol. 13(11), pages 1-21, June.
    5. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.
    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. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.
    2. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    3. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    4. Nandakumar Sundararaju & Arangarajan Vinayagam & Veerapandiyan Veerasamy & Gunasekaran Subramaniam, 2022. "A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System," Sustainability, MDPI, vol. 14(9), pages 1-27, May.
    5. Dezhou Kong & Jianru Jing & Tingyue Gu & Xuanyue Wei & Xingning Sa & Yimin Yang & Zhiang Zhang, 2023. "Theoretical Analysis of Integrated Community Energy Systems (ICES) Considering Integrated Demand Response (IDR): A Review of the System Modelling and Optimization," Energies, MDPI, vol. 16(10), pages 1-22, May.
    6. Cong Liu & Yongjie Zhang, 2022. "Research on Integrated Customer-Side Energy System Planning Method Considering Carbon Emission Reduction," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    7. Mehmet Türker Takcı & Tuba Gözel, 2022. "Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    8. Hina Maqbool & Adnan Yousaf & Rao Muhammad Asif & Ateeq Ur Rehman & Elsayed Tag Eldin & Muhammad Shafiq & Habib Hamam, 2022. "An Optimized Fuzzy Based Control Solution for Frequency Oscillation Reduction in Electric Grids," Energies, MDPI, vol. 15(19), pages 1-21, September.
    9. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    10. Ying Yang & Yingjie Li & Xianyao Yan & Jianli Zhao & Chunxiao Zhang, 2021. "Development of Thermochemical Heat Storage Based on CaO/CaCO 3 Cycles: A Review," Energies, MDPI, vol. 14(20), pages 1-26, October.
    11. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    12. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Fawaz Alassery & Habib Hamam & Omar Cheikhrouhou, 2021. "A Novel Machine Learning-Based Price Forecasting for Energy Management Systems," Sustainability, MDPI, vol. 13(22), pages 1-26, 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:4:p:1594-:d:754956. 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.