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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
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