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CVaR risk-based optimization framework for renewable energy management in distribution systems with DGs and EVs

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  • Wu, Jiekang
  • Wu, Zhijiang
  • Wu, Fan
  • Tang, Huiling
  • Mao, Xiaoming

Abstract

A method based on chance constrained second-order cone programming (CCSOCP) is presented for the optimal risk value control of power loss in distribution systems with the distributed generation (DG) of renewable energy systems and electric vehicles (EVs). The charging power of the EV is seen as a random variable, and the risk value of the power loss – due to the uncertainties in the power output of distributed generation of renewable energy systems and charging power of electric vehicles – is studied. Moreover, a second-order cone programming based method is also presented to constrain the potential risk of power loss to an acceptable range by optimally coordinating the power output of DG and the EV charging power in a distribution system. A conditional value at risk (CVaR) model for the power loss of distribution systems is presented and CVaR is taken as a constraint to control the risk value of power loss due to uncertainties in DG and EV charging. The results of a test on a 69-node system are used to verify the validity of the risk control method proposed in this paper.

Suggested Citation

  • Wu, Jiekang & Wu, Zhijiang & Wu, Fan & Tang, Huiling & Mao, Xiaoming, 2018. "CVaR risk-based optimization framework for renewable energy management in distribution systems with DGs and EVs," Energy, Elsevier, vol. 143(C), pages 323-336.
    • Handle: RePEc:eee:energy:v:143:y:2018:i:c:p:323-336
    DOI: 10.1016/j.energy.2017.10.083
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    Cited by:

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    8. Yilu Wang & Zixuan Jia & Jianing Li & Xiaoping Zhang & Ray Zhang, 2021. "Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk," Energies, MDPI, vol. 14(21), pages 1-16, October.

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