Online Model-Free DER Dispatch Via Adaptive Voltage Sensitivity Estimation and Chance Constrained Programming

Haoyi Wang, Yingqi Liang, Yiyun Yao, Junbo Zhao, Fei Ding

Research output: Contribution to journalArticlepeer-review


This paper proposes an online data-driven distributed energy resource management system (DERMS) for distribution system optimal DER dispatch as well as voltage regulation. The key innovation is to leverage the Local Sensitivity Factor (LSF) for transforming the DER control into a computationally efficient linear programming (LP) problem. By taking real-time measurements, the estimation of LSF eliminates the need for an accurate distribution system model as well as full nodal load information, which is difficult to achieve in practice. A robust recursive least squares method is also developed to ensure the robust estimation of LSF, which is initialized using reasonable values from model-derived LSFs. This allows the system to adapt to changing operational conditions effectively. A scenario-based, chance-constrained framework is further employed to ensure voltage remains within acceptable limits in the presence of measurement and estimation uncertainties. Test results on a real-world, 759-node distribution network located in western Colorado, U.S., validate the effectiveness and robustness of the proposed control approach and demonstrate its superior performance as compared to alternative methods.
Original languageAmerican English
JournalIEEE Transactions on Power Systems
StatePublished - 2024

NREL Publication Number

  • NREL/JA-5D00-89358


  • chance-constrained optimization
  • distributed energy resource management system
  • distribution system estimation
  • local sensitivity factor
  • voltage regulation


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