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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
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    References listed on IDEAS

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    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).
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    Citations

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    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).

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