A Solution of Optimal Power Flow Incorporating Wind Generation and Power Grid Uncertainties

This paper proposes a novel approach for the solution of optimal power flow with consideration of uncertainties caused by wind generation and various factors in the power grid. Regarding the uncertainties studied here, multiple types of uncertainty modeling techniques are applied during research. Evidence theory and extended affine arithmetic are employed and mixed as the framework of uncertainty propagation to fuse probability distributions, possibility distributions, and intervals so as to obtain the best possible probability bounds, and the dependence among variables is handled by copula theory and affine arithmetic. Moreover, the uncertainty of wind farm active power and the characteristic of wind farm reactive power are modeled and integrated into the power flow calculation. An enhanced particle swarm optimization algorithm with introduction of fitness comparison and constraint handling techniques under the evidence theory framework is applied to the solution of this problem. The proposed model and method are tested on the IEEE 30-bus standard test system and a real-sized 183-bus power system to demonstrate the validity and effectiveness.

[1]  Pu Li,et al.  Probabilistic analysis for optimal power flow under uncertainty , 2010 .

[2]  Frédéric Messine,et al.  A General Reliable Quadratic Form: An Extension of Affine Arithmetic , 2006, Reliab. Comput..

[3]  C. Cañizares,et al.  Probabilistic Optimal Power Flow in Electricity Markets Based on a Two-Point Estimate Method , 2006, IEEE Transactions on Power Systems.

[4]  Anna M. Bonner,et al.  Acknowledgments , 2019, The Neurodiagnostic journal.

[5]  Jean Goubault-Larrecq,et al.  A generalization of p-boxes to affine arithmetic , 2011, Computing.

[6]  P. Embrechts,et al.  Chapter 8 – Modelling Dependence with Copulas and Applications to Risk Management , 2003 .

[7]  Libao B. Shi,et al.  An Analytical Solution for Wind Farm Power Output , 2014, IEEE Transactions on Power Systems.

[8]  Didier Dubois,et al.  Joint Propagation and Exploitation of Probabilistic and Possibilistic Information in Risk Assessment , 2006, IEEE Transactions on Fuzzy Systems.

[9]  Wu Jun-ling A JOINT ITERATION METHOD FOR LOAD FLOW CALCULATION OF POWER SYSTEM CONTAINING UNIFIED WIND FARM AND ITS APPLICATION , 2005 .

[10]  Xiaohong Guan,et al.  Application of a fuzzy set method in an optimal power flow , 1995 .

[11]  Kumaraswamy Ponnambalam,et al.  Probabilistic optimal power flow , 1998, Conference Proceedings. IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.98TH8341).

[12]  Zheng Xu,et al.  Fuzzy power flow solution considering wind power variability and uncertainty , 2015 .

[13]  Enrico Zio,et al.  Genetic Algorithms in the Framework of Dempster-Shafer Theory of Evidence for Maintenance Optimization Problems , 2015, IEEE Transactions on Reliability.

[14]  Masoud Rashidinejad,et al.  Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach , 2015 .

[15]  V. Miranda,et al.  Fuzzy modelling of power system optimal load flow , 1991, [Proceedings] Conference Papers 1991 Power Industry Computer Application Conference.

[16]  Enrico Zio,et al.  Literature review of methods for representing uncertainty , 2013 .

[17]  Bijay Ketan Panigrahi,et al.  Optimal Power Flow with Multiple Data Uncertainties , 2013 .

[18]  Jürgen Branke,et al.  Experimental Analysis of Bound Handling Techniques in Particle Swarm Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[19]  Y. X. Ni,et al.  Effects of wind power variability and intermittency on power flow , 2012, 2012 IEEE Power and Energy Society General Meeting.

[20]  Robert C. Williamson,et al.  Probabilistic arithmetic. I. Numerical methods for calculating convolutions and dependency bounds , 1990, Int. J. Approx. Reason..

[21]  Alfredo Vaccaro,et al.  An Affine Arithmetic-Based Framework for Uncertain Power Flow and Optimal Power Flow Studies , 2017, IEEE Transactions on Power Systems.

[22]  Scott Ferson,et al.  Constructing Probability Boxes and Dempster-Shafer Structures , 2003 .

[23]  A. Schellenberg,et al.  Enhancements to the Cumulant Method for Probabilistic Optimal Power Flow Studies , 2009, IEEE Transactions on Power Systems.

[24]  Yie-Tone Chen,et al.  Optimal power flow by a fuzzy based hybrid particle swarm optimization approach , 2011 .

[25]  Alfredo Vaccaro,et al.  A Novel Affine Arithmetic Method to Solve Optimal Power Flow Problems With Uncertainties , 2014, IEEE Transactions on Power Systems.

[26]  Qing Xiao,et al.  Solving Probabilistic Optimal Power Flow Problem Using Quasi Monte Carlo Method and Ninth-Order Polynomial Normal Transformation , 2014, IEEE Transactions on Power Systems.

[27]  Yixin Ni,et al.  Uncertain Power Flow Analysis Based on Evidence Theory and Affine Arithmetic , 2018, IEEE Transactions on Power Systems.

[28]  C. Chung,et al.  A Novel Probabilistic Optimal Power Flow Model With Uncertain Wind Power Generation Described by Customized Gaussian Mixture Model , 2016, IEEE Transactions on Sustainable Energy.

[29]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization: Velocity initialization , 2012, 2012 IEEE Congress on Evolutionary Computation.

[30]  Reza Safabakhsh,et al.  A novel stability-based adaptive inertia weight for particle swarm optimization , 2016, Appl. Soft Comput..