Probabilistic Forecasting of Real-Time LMP and Network Congestion

The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation associated with multiparametric programs offline, the online computational cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.

[1]  Manfred Morari,et al.  Multi-Parametric Toolbox 3.0 , 2013, 2013 European Control Conference (ECC).

[2]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[3]  G. Brier VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[4]  Pei Zhang,et al.  Short-term probabilistic transmission congestion forecasting , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[5]  Ashwani Kumar,et al.  Electricity price forecasting in deregulated markets: A review and evaluation , 2009 .

[6]  Lang Tong,et al.  Stochastic coordinated transaction scheduling via probabilistic forecast , 2015, 2015 IEEE Power & Energy Society General Meeting.

[7]  A. Ott Experience with PJM market operation, system design, and implementation , 2003 .

[8]  L. Tesfatsion,et al.  Short-Term Congestion Forecasting in Wholesale Power Markets , 2011, IEEE Transactions on Power Systems.

[9]  R. Weron Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .

[10]  H. Nafisi,et al.  Determination of mean and variance of LMP using probabilistic DCOPF and T-PEM , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[11]  Efstratios N. Pistikopoulos,et al.  Multiparametric Linear Programming , 2009, Encyclopedia of Optimization.

[12]  Pierre Pinson,et al.  Global Energy Forecasting Competition 2012 , 2014 .

[13]  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.

[14]  M. Morari,et al.  Geometric Algorithm for Multiparametric Linear Programming , 2003 .

[15]  Stein-Erik Fleten,et al.  Benchmarking time series based forecasting models for electricity balancing market prices , 2015 .

[16]  Le Xie,et al.  A data-driven approach to identifying system pattern regions in market operations , 2015, 2015 IEEE Power & Energy Society General Meeting.

[17]  Lang Tong,et al.  Forecasting real-time locational marginal price: A state space approach , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[18]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[19]  Lang Tong,et al.  Probabilistic Forecast of Real-Time LMP via Multiparametric Programming , 2015, 2015 48th Hawaii International Conference on System Sciences.

[20]  Kory W. Hedman,et al.  A review of transmission switching and network topology optimization , 2011, 2011 IEEE Power and Energy Society General Meeting.

[21]  A. Roghani Araghi,et al.  A new high accuracy method for calculation of LMP as a random variable , 2009, 2009 International Conference on Electric Power and Energy Conversion Systems, (EPECS).

[22]  Fangxing Li,et al.  Probabilistic LMP Forecasting Considering Load Uncertainty , 2009, IEEE Transactions on Power Systems.