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Renewable Energy Integration: Bayesian Networks for Probabilistic State Estimation

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Data Analytics for Renewable Energy Integration. Technologies, Systems and Society (DARE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11325))

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Abstract

Increased availability of renewable energy sources, along with novel techniques for power flow control, open up a broad range of interesting challenges and opportunities in power flow optimization. This promises reduced power generation costs through better integration of renewable energy generators into the Smart Grid. Unfortunately, renewable generators are fundamentally variable and uncertain. This uncertainty motivates our study of probabilistic state estimation techniques in this paper. Specifically, we use probabilistic graphical models in the form of Bayesian networks to compute probabilities of power system states, thus enabling improved power flow control. Key differences between our probabilistic state estimation results as reported in this paper and similar previous efforts include: our use of Bayesian probabilistic but exact (rather than Monte Carlo) state estimation techniques; auto-generation of Bayesian networks for probabilistic state estimation; integration with corrective Security-Constrained Optimal Power Flow; and application to Distributed Flexible AC Transmission Systems. We present novel models and algorithms for probabilistic state estimation using auto-generated Bayesian networks compiled to junction trees, and report on experimental results that illustrate the scalability of our methods.

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Notes

  1. 1.

    In experiments with different ADAPT configurations and BNs, strong detection and diagnostic performance was achieved. In international diagnostics competitions arranged in 2009 and 2010, this approach had the best results in three out of four competitions in the track that used ADAPT. Please see https://c3.nasa.gov/dashlink/projects/36/ and https://c3.nasa.gov/dashlink/projects/36/ for further details.

  2. 2.

    We assume power flow control devices such as smart wires; please see http://www.smartwiregrid.com/ for details.

  3. 3.

    Details on distributed control techniques for solving DC OPF problems when transmission lines are instrumented with D-FACTS have been presented by Mohammadi, Hug, and Kar [34].

  4. 4.

    The graph definition \(\varvec{G} =(\varvec{U},\varvec{B})\) is found, for example, in an output file produced by the MatPower software, which is used in our experiments in Sect. 5. The MatPower file contains two tables called Bus Data and Branch Data. These tables contain the graph \(\varvec{G} =(\varvec{U},\varvec{B})\) discussed here along with other data utilized by CreateStateEstimator.

  5. 5.

    The junction tree algorithm works for certain hybrid BNs, namely those in which continuous (Gaussian) nodes can have both continuous and discrete parents, but discrete (multinomial) nodes can only have discrete parents.

  6. 6.

    Both relational and functional notation is used for \(\varPhi \). In other words, we say (relationally) \((B,X) \in \varPhi \) when \(\varPhi \) maps (functionally) from B to X.

  7. 7.

    For example, the IEEE 39-bus as discussed elsewhere in this paper has a total of 10 generators (8 conventional generators and 2 wind power generators) and 19 loads. In this case, there are 21 inputs to corrective SCOPF from PSE.

  8. 8.

    The data can be found in the file case39.m in the MatPower package.

  9. 9.

    For our probabilistic parameters we researched several datasets, all of which contained high-frequency data. The dataset that was chosen is from the Bonneville Power Authority (BPA) SCADA system, see http://transmission.bpa.gov/Business/Operations/Wind/default.aspx.

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Acknowledgment

This material is based, in part, upon work supported by ARPA-E. The collaboration with Prof. Gabriela Hug and Javad Mohammadi on the integration of corrective SCOPF and PSE is also acknowledged.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, USA

    Ole J. Mengshoel, Priya K. Sundararajan, Erik Reed, Dongzhen Piao & Briana Johnson

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  1. Ole J. Mengshoel
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  2. Priya K. Sundararajan
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Correspondence to Ole J. Mengshoel .

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Editors and Affiliations

  1. Masdar Institute (Khalifa University), Abu Dhabi, United Arab Emirates

    Wei Lee Woon

  2. Masdar Institute (Khalifa University), Abu Dhabi, United Arab Emirates

    Zeyar Aung

  3. Autonomous University of Madrid, Madrid, Spain

    Alejandro Catalina Feliú

  4. Massachusetts Institute of Technology, Cambridge, MA, USA

    Stuart Madnick

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Mengshoel, O.J., Sundararajan, P.K., Reed, E., Piao, D., Johnson, B. (2018). Renewable Energy Integration: Bayesian Networks for Probabilistic State Estimation. In: Woon, W., Aung, Z., Catalina Feliú, A., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. Technologies, Systems and Society. DARE 2018. Lecture Notes in Computer Science(), vol 11325. Springer, Cham. https://doi.org/10.1007/978-3-030-04303-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-04303-2_5

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