Joint Chance Constraints Reduction Through Learning in Active Distribution Networks

Conference · Thu Feb 21 04:00:00 EST 2019

DOI:https://doi.org/10.1109/GlobalSIP.2018.8646440· OSTI ID:1506609

Baker, Kyri ^[1]; Bernstein, Andrey ^[2]

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1. University of Colorado
2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1506609
• Report Number(s): NREL/CP-5D00-73633
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
chance constraints
learning active constraints
optimal power flow