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Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm

Author

Listed:
  • Qiang Zhou

    (Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China)

  • Jianmei Zhang

    (Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China)

  • Pengfei Gao

    (Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China)

  • Ruixiao Zhang

    (Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China)

  • Lijuan Liu

    (Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China)

  • Sheng Wang

    (Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China)

  • Lin Cheng

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Wei Wang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Shiyou Yang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

This paper develops a two-stage robust optimization (TSRO) model for prosumers considering multiple uncertainties from the sustainable energy of wind power generation and load demand and extends the existing nested column-and-constraint generation (C&CG) algorithm to solve the corresponding optimization problem. First, considering the impact of these uncertainties on market trading strategies of prosumers, a box uncertainty set is introduced to characterize the multiple uncertainties; a TSRO model for prosumers considering multiple uncertainties is then constructed. Second, the existing nested C&CG algorithm is extended to solve the corresponding optimization problem of which the second-stage optimization is a bi-level one and the inner level is a non-convex optimization problem containing 0–1 decision variables. Finally, a case study is solved. The optimized final overall operating cost of prosumers under the proposed model is CNY 3201.03; the extended algorithm requires only four iterations to converge to the final solution. If a convergence accuracy of 10 −6 is used, the final solution time of the extended algorithm is only 9.75 s. The case study result shows that prosumers dispatch the ESS to store surplus wind power generated during the nighttime period and release the stored electricity when the wind power generation is insufficient during the daytime period. It can contribute to promoting the local accommodation of renewable energy and improving the efficiency of renewable energy utilization. The market trading strategy and scheduling results of the energy storage system (ESS) are affected by multiple uncertainties. Moreover, the extended nested C&CG algorithm has a high convergence accuracy and a fast convergence speed.

Suggested Citation

  • Qiang Zhou & Jianmei Zhang & Pengfei Gao & Ruixiao Zhang & Lijuan Liu & Sheng Wang & Lin Cheng & Wei Wang & Shiyou Yang, 2023. "Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9769-:d:1174369
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    References listed on IDEAS

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    1. Zhou, Siyu & Han, Yang & Yang, Ping & Mahmoud, Karar & Lehtonen, Matti & Darwish, Mohamed M.F. & Zalhaf, Amr S., 2022. "An optimal network constraint-based joint expansion planning model for modern distribution networks with multi-types intermittent RERs," Renewable Energy, Elsevier, vol. 194(C), pages 137-151.
    2. Ke Shang & Felix T. S. Chan & Stephen Karungaru & Kenji Terada & Zuren Feng & Liangjun Ke, 2020. "Two-Stage Robust Optimization for the Orienteering Problem with Stochastic Weights," Complexity, Hindawi, vol. 2020, pages 1-15, November.
    3. Sen, Souvik & Ganguly, Sourav, 2017. "Opportunities, barriers and issues with renewable energy development – A discussion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1170-1181.
    4. Nathphol Khaboot & Chitchai Srithapon & Apirat Siritaratiwat & Pirat Khunkitti, 2019. "Increasing Benefits in High PV Penetration Distribution System by Using Battery Enegy Storage and Capacitor Placement Based on Salp Swarm Algorithm," Energies, MDPI, vol. 12(24), pages 1-20, December.
    5. Kendall Mongird & Vilayanur Viswanathan & Patrick Balducci & Jan Alam & Vanshika Fotedar & Vladimir Koritarov & Boualem Hadjerioua, 2020. "An Evaluation of Energy Storage Cost and Performance Characteristics," Energies, MDPI, vol. 13(13), pages 1-53, June.
    6. Alam, Muhammad Raisul & St-Hilaire, Marc & Kunz, Thomas, 2019. "Peer-to-peer energy trading among smart homes," Applied Energy, Elsevier, vol. 238(C), pages 1434-1443.
    7. Chen, Kaixuan & Lin, Jin & Song, Yonghua, 2019. "Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model," Applied Energy, Elsevier, vol. 242(C), pages 1121-1133.
    8. Yael Parag & Benjamin K. Sovacool, 2016. "Electricity market design for the prosumer era," Nature Energy, Nature, vol. 1(4), pages 1-6, April.
    9. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    10. Carlos Adrian Correa-Florez & Andrea Michiorri & Georges Kariniotakis, 2019. "Comparative Analysis of Adjustable Robust Optimization Alternatives for the Participation of Aggregated Residential Prosumers in Electricity Markets," Energies, MDPI, vol. 12(6), pages 1-27, March.
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    Cited by:

    1. Chunyang Hao & Yibo Wang & Chuang Liu & Guanglie Zhang & Hao Yu & Dongzhe Wang & Jingru Shang, 2023. "Research on Two-Stage Regulation Method for Source–Load Flexibility Transformation in Power Systems," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    2. Yi Zhang & Tian Lan & Wei Hu, 2023. "A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response," Sustainability, MDPI, vol. 15(19), pages 1-22, October.

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