Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds through Learning

Journal Article · Thu Mar 07 00:00:00 EST 2019 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/TSG.2019.2903767· OSTI ID:1506618

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

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

This paper considers distribution systems with a high penetration of distributed, renewable generation and addresses the problem of incorporating the associated uncertainty into the optimal operation of these networks. Joint chance constraints, which satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints, leading to a chance-constrained optimal power flow problem. Departing from the computationally-heavy scenario-based approaches or approximations that transform the joint constraint into conservative deterministic constraints, this paper develops a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and accurately and efficiently approximates bounds on the joint chance constraint iteratively. In particular, the proposed framework improves upon the classic methods based on the union bound (or Boole's inequality) by generating a much less conservative set of single chance constraints that also guarantees the satisfaction of the original joint constraint. The proposed framework is evaluated numerically using the IEEE 37-node test feeder, focusing on the problem of voltage regulation in distribution grids.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 1506618
• Report Number(s): NREL/JA-5D00-73517
• Journal Information: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 6 Vol. 10; ISSN 1949-3053
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
Monte Carlo methods
estimation
optimization
power systems
uncertainty
upper bound
voltage control