Supervised learning for optimal power flow as a real-time proxy

In this work we design and compare different supervised learning algorithms to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The motivation for quick calculation of OPF cost outcomes stems from the growing need of algorithmic-based long-term and medium-term planning methodologies in power networks. Integrated in a multiple time-horizon coordination framework, we refer to this approximation module as a proxy for predicting short-term decision outcomes without the need of actual simulation and optimization of them. Our method enables fast approximate calculation of OPF cost with less than 1% error on average, achieved in run-times that are several orders of magnitude lower than of exact computation. Several test-cases such as IEEE-RTS96 are used to demonstrate the efficiency of our approach.

[1]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[2]  W. Marsden.  I and J , 2012 .

[3]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[4]  Pierluigi Siano,et al.  Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow , 2012, IEEE Transactions on Industrial Informatics.

[5]  Labed Imen,et al.  Optimal power flow study using conventional and neural networks methods , 2015, 2015 International Conference on Renewable Energy Research and Applications (ICRERA).

[6]  Jules Thibault,et al.  Process modeling with neural networks using small experimental datasets , 1999 .

[7]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[8]  E. Vieth.  Fitting piecewise linear regression functions to biological responses. , 1989, Journal of applied physiology.

[9]  Laurine Duchesne.  Machine learning of proxies for power systems reliability management , 2016 .

[10]  Fangxing Li,et al.  Small test systems for power system economic studies , 2010, IEEE PES General Meeting.

[11]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[12]  J. Neter,et al.  Applied Linear Regression Models , 1983 .

[13]  Worawat Nakawiro,et al.  A Combined GA-ANN Strategy for Solving Optimal Power Flow with Voltage Security Constraint , 2009, 2009 Asia-Pacific Power and Energy Engineering Conference.

[14]  Probability Subcommittee,et al.  IEEE Reliability Test System , 1979, IEEE Transactions on Power Apparatus and Systems.

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[17]  Francisco Sandoval Hernández,et al.  Hopfield neural networks for optimization: study of the different dynamics , 2002, Neurocomputing.

[18]  Jiann-Liang Chen,et al.  A neural-net approach to economic power dispatch , 1993 .

[19]  L. Leemis.  Applied Linear Regression Models , 1991 .

[20]  Shie Mannor,et al.  Hierarchical Decision Making In Electricity Grid Management , 2016, ICML.

[21]  Bruce W. Schmeiser,et al.  General Hit-and-Run Monte Carlo sampling for evaluating multidimensional integrals , 1996, Oper. Res. Lett..

[22]  Panos J. Antsaklis,et al.  Artificial Neural Networks In Electric Power Industry , 1994 .

[23]  Shie Mannor,et al.  Unit Commitment Using Nearest Neighbor as a Short-Term Proxy , 2016, 2018 Power Systems Computation Conference (PSCC).

[24]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[25]  Labed Djamel,et al.  Power System Economic Dispatch Using Traditional and Neural Networks Programs , 2012 .

[26]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[27]  Shie Mannor,et al.  Distributed scenario-based optimization for asset management in a hierarchical decision making environment , 2016, 2016 Power Systems Computation Conference (PSCC).

[28]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.