Joint Chance Constraints Reduction Through Learning in Active Distribution Networks

Andrey Bernstein, Kyri Baker

Research output: Contribution to conferencePaperpeer-review

7 Scopus Citations

Abstract

Due to an increase in distributed generation and controllable loads, distribution networks are frequently operating under high levels of uncertainty. Joint chance constraints, which seek to satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints for optimization and control of these networks. Due to the complexity of evaluating these constraints directly, sampling approaches or approximations can be used to transform the joint chance constraint into deterministic constraints. However, sampling techniques may be extremely computationally expensive and not suitable for physical networks operating on fast timescales, and conservative approximations may needlessly result in a much higher cost of system operation. The proposed framework aims to provide a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and uses this additional information to accurately and efficiently approximate the joint chance constraint directly.

Original languageAmerican English
Pages922-926
Number of pages5
DOIs
StatePublished - 2 Jul 2018
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: 26 Nov 201829 Nov 2018

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/TerritoryUnited States
CityAnaheim
Period26/11/1829/11/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

NREL Publication Number

  • NREL/CP-5D00-73633

Keywords

  • Chance constraints
  • Learning active constraints
  • Optimal power flow

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