IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p1051-d105434.html
   My bibliography  Save this article

Non-Convex Economic Dispatch of a Virtual Power Plant via a Distributed Randomized Gradient-Free Algorithm

Author

Listed:
  • Jun Xie

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Chi Cao

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

Abstract

The economic dispatch problem of a virtual power plant (VPP) is becoming non-convex for distributed generators’ characteristics of valve-point loading effects, prohibited operating zones, and multiple fuel options. In this paper, the economic dispatch model of VPP is established and then solved by a distributed randomized gradient-free algorithm. To deal with the non-smooth objective function, its Gauss approximation is used to construct distributed randomized gradient-free oracles in optimization iterations. A projection operator is also introduced to solve the discontinuous variable space problem. An example simulation is implemented on a modified IEEE-34 bus test system, and the results demonstrate the effectiveness and applicability of the proposed algorithm.

Suggested Citation

  • Jun Xie & Chi Cao, 2017. "Non-Convex Economic Dispatch of a Virtual Power Plant via a Distributed Randomized Gradient-Free Algorithm," Energies, MDPI, vol. 10(7), pages 1-12, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1051-:d:105434
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/1051/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/7/1051/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chi Cao & Jun Xie & Dong Yue & Chongxin Huang & Jixiang Wang & Shuyang Xu & Xingying Chen, 2017. "Distributed Economic Dispatch of Virtual Power Plant under a Non-Ideal Communication Network," Energies, MDPI, vol. 10(2), pages 1-18, February.
    2. Yurii NESTEROV & Vladimir SPOKOINY, 2017. "Random gradient-free minimization of convex functions," LIDAM Reprints CORE 2851, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    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. Rakkyung Ko & Daeyoung Kang & Sung-Kwan Joo, 2019. "Mixed Integer Quadratic Programming Based Scheduling Methods for Day-Ahead Bidding and Intra-Day Operation of Virtual Power Plant," Energies, MDPI, vol. 12(8), pages 1-16, April.
    2. Jingjing Luo & Yajing Gao & Wenhai Yang & Yongchun Yang & Zheng Zhao & Shiyu Tian, 2018. "Optimal Operation Modes of Virtual Power Plants Based on Typical Scenarios Considering Output Evaluation Criteria," Energies, MDPI, vol. 11(10), pages 1-22, October.
    3. Wei-Tzer Huang & Kai-Chao Yao & Ming-Ku Chen & Feng-Ying Wang & Cang-Hui Zhu & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2018. "Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch," Energies, MDPI, vol. 11(2), pages 1-19, February.
    4. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    5. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.
    6. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).

    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. V. Kungurtsev & F. Rinaldi, 2021. "A zeroth order method for stochastic weakly convex optimization," Computational Optimization and Applications, Springer, vol. 80(3), pages 731-753, December.
    2. Furquan Nadeem & Mohd Asim Aftab & S.M. Suhail Hussain & Ikbal Ali & Prashant Kumar Tiwari & Arup Kumar Goswami & Taha Selim Ustun, 2019. "Virtual Power Plant Management in Smart Grids with XMPP Based IEC 61850 Communication," Energies, MDPI, vol. 12(12), pages 1-20, June.
    3. Jean-Jacques Forneron, 2023. "Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models," Papers 2301.07196, arXiv.org, revised Feb 2023.
    4. Stefan Wager & Kuang Xu, 2021. "Experimenting in Equilibrium," Management Science, INFORMS, vol. 67(11), pages 6694-6715, November.
    5. Buxiang Zhou & Jiale Wu & Tianlei Zang & Yating Cai & Binjie Sun & Yiwei Qiu, 2023. "Emergency Dispatch Approach for Power Systems with Hybrid Energy Considering Thermal Power Unit Ramping," Energies, MDPI, vol. 16(10), pages 1-25, May.
    6. Geovani Nunes Grapiglia, 2023. "Quadratic regularization methods with finite-difference gradient approximations," Computational Optimization and Applications, Springer, vol. 85(3), pages 683-703, July.
    7. Ghadimi, Saeed & Powell, Warren B., 2024. "Stochastic search for a parametric cost function approximation: Energy storage with rolling forecasts," European Journal of Operational Research, Elsevier, vol. 312(2), pages 641-652.
    8. Nikita Kornilov & Alexander Gasnikov & Pavel Dvurechensky & Darina Dvinskikh, 2023. "Gradient-free methods for non-smooth convex stochastic optimization with heavy-tailed noise on convex compact," Computational Management Science, Springer, vol. 20(1), pages 1-43, December.
    9. Jing Qiu & Junhua Zhao & Dongxiao Wang & Yu Zheng, 2017. "Two-Stage Coordinated Operational Strategy for Distributed Energy Resources Considering Wind Power Curtailment Penalty Cost," Energies, MDPI, vol. 10(7), pages 1-19, July.
    10. David Kozak & Stephen Becker & Alireza Doostan & Luis Tenorio, 2021. "A stochastic subspace approach to gradient-free optimization in high dimensions," Computational Optimization and Applications, Springer, vol. 79(2), pages 339-368, June.
    11. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.
    12. Jiaqi Liu & Hongji Hu & Samson S. Yu & Hieu Trinh, 2023. "Virtual Power Plant with Renewable Energy Sources and Energy Storage Systems for Sustainable Power Grid-Formation, Control Techniques and Demand Response," Energies, MDPI, vol. 16(9), pages 1-28, April.
    13. Marco Boresta & Tommaso Colombo & Alberto Santis & Stefano Lucidi, 2022. "A Mixed Finite Differences Scheme for Gradient Approximation," Journal of Optimization Theory and Applications, Springer, vol. 194(1), pages 1-24, July.
    14. Wafa Nafkha-Tayari & Seifeddine Ben Elghali & Ehsan Heydarian-Forushani & Mohamed Benbouzid, 2022. "Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects," Energies, MDPI, vol. 15(10), pages 1-20, May.
    15. Michael R. Metel & Akiko Takeda, 2022. "Perturbed Iterate SGD for Lipschitz Continuous Loss Functions," Journal of Optimization Theory and Applications, Springer, vol. 195(2), pages 504-547, November.
    16. Jingmin Wang & Wenhai Yang & Huaxin Cheng & Lingyu Huang & Yajing Gao, 2017. "The Optimal Configuration Scheme of the Virtual Power Plant Considering Benefits and Risks of Investors," Energies, MDPI, vol. 10(7), pages 1-12, July.
    17. Katya Scheinberg, 2022. "Finite Difference Gradient Approximation: To Randomize or Not?," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2384-2388, September.
    18. Jingxu Xu & Zeyu Zheng, 2023. "Gradient-Based Simulation Optimization Algorithms via Multi-Resolution System Approximations," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 633-651, May.
    19. Dvurechensky, Pavel & Gorbunov, Eduard & Gasnikov, Alexander, 2021. "An accelerated directional derivative method for smooth stochastic convex optimization," European Journal of Operational Research, Elsevier, vol. 290(2), pages 601-621.
    20. Jun Xie & Qingyun Yu & Chi Cao, 2018. "A Distributed Randomized Gradient-Free Algorithm for the Non-Convex Economic Dispatch Problem," Energies, MDPI, vol. 11(1), pages 1-15, January.

    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:10:y:2017:i:7:p:1051-:d:105434. 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.