Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization

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dc.contributor.authorGuo, Yi
dc.contributor.authorBaker, K.
dc.contributor.authorDall'Anese, E.
dc.contributor.authorHu, Z.
dc.contributor.authorSummers, Tyler H.
dc.contributor.utdAuthorGuo, Yi
dc.contributor.utdAuthorSummers, Tyler H.
dc.date.accessioned2019-08-30T19:34:49Z
dc.date.available2019-08-30T19:34:49Z
dc.date.created2018-06-27
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).
dc.description.abstractWe propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operational cost and conditional value-at-risk (CVaR) of device and network constraint violations. These decisions include scheduled power output adjustments and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming the uncertainties across the networks follow prescribed probability distributions, we assume the distributions are only observable through a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real data-generating distribution, we formulate a distributionally robust optimization OPF problem to search for power schedules and reserve policies that are robust to sampling errors inherent in the dataset. A multi-stage closed-loop control strategy based on model predictive control (MPC) is also discussed. A simpIe numerical example illustrates inherent tradeoffs between operational cost and risk of constraint violation, and we show how our proposed method offers a data-driven framework to balance these objectives.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.sponsorshipThis material is based on work supported by the National Science Foundation under grant CNS-1566127.
dc.identifier.bibliographicCitationGuo, Y., K. Baker, E. Dall'Anese, Z. Hu, et al. 2018. "Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization." Proceedings of the 2018 American Control Conference: 3840-3846, doi:10.23919/ACC.2018.8431542
dc.identifier.isbn9781538654286
dc.identifier.urihttps://hdl.handle.net/10735.1/6832
dc.identifier.volume2018
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.urihttp://dx.doi.org/10.23919/ACC.2018.8431542
dc.rights©2018 AACC.
dc.source.journalProceedings of the 2018 American Control Conference
dc.subjectRobust control
dc.subjectStochastic processes
dc.subjectDistribution (Probability theory)
dc.titleStochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
dc.type.genrearticle

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