Optimal Available Transfer Capability Assessment Strategy for Wind Integrated Transmission Systems Considering Uncertainty of Wind Power Probability Distribution

Wind power prediction research shows that it is difficult to accurately and effectively estimate the probability distribution (PD) of wind power. When only partial information of the wind power probability distribution function is available, an optimal available transfer capability ( ATC ) assessment strategy considering the uncertainty on the wind power probability distribution is proposed in this paper. As wind power probability distribution is not accurately given, the proposed strategy can efficiently maximize ATC with the security operation constraints satisfied under any wind power PD function case in the uncertainty set. A distributional robust chance constrained (DRCC) model is developed to describe an optimal ATC assessment problem. To achieve tractability of the DRCC model, the dual optimization, S-lemma and Schur complement are adopted to eliminate the uncertain wind power vector in the DRCC model. According to the characteristics of the problem, the linear matrix inequality (LMI)-based particle swarm optimization (PSO) algorithm is used to solve the DRCC model which contains first and second-order moment information of the wind power. The modified IEEE 30-bus system simulation results show the feasibility and effectiveness of the proposed ATC assessment strategy.

[1]  L. Wang,et al.  Robust fast decoupled power flow , 2000 .

[2]  Ying Xiao,et al.  Available transfer capability (ATC) evaluation by stochastic programming , 2000 .

[3]  Y. Z. Sun,et al.  A hybrid stochastic approach to available transfer capability evaluation , 2001 .

[4]  Chan-Nan Lu,et al.  Method for computing probability distributions of available transfer capability , 2002 .

[5]  Thomas Ackermann,et al.  Wind Power in Power Systems , 2005 .

[6]  A. Fabbri,et al.  Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market , 2005, IEEE Transactions on Power Systems.

[7]  M.G. Da Silva,et al.  Probabilistic Assessment of Available Transfer Capability Based on Monte Carlo Method With Sequential Simulation , 2007, IEEE Transactions on Power Systems.

[8]  G.T. Heydt,et al.  Stochastic-Algebraic Calculation of Available Transfer Capability , 2007, IEEE Transactions on Power Systems.

[9]  F. Bouffard,et al.  Stochastic security for operations planning with significant wind power generation , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[10]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[11]  Y. Li,et al.  Risk based optimal strategy to split commercial components of Available Transfer Capability by particle swarm optimization method , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[12]  Adly A. Girgis,et al.  Review of available transmission capability (ATC) calculation methods , 2009, 2009 Power Systems Conference.

[13]  Bri-Mathias Hodge,et al.  Wind power forecasting error distributions over multiple timescales , 2011, 2011 IEEE Power and Energy Society General Meeting.

[14]  Ehab F. El-Saadany,et al.  Comparative study on impacts of wind profiles on thermal units scheduling costs , 2011 .

[15]  Lu Jinling,et al.  Research on available transfer capability for power system including large-scale wind farms , 2011, 2011 International Conference on Electrical and Control Engineering.

[16]  S. Tewari,et al.  A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets , 2011, IEEE Transactions on Power Systems.

[17]  Alireza Mousavi,et al.  Hybrid Mutation Particle Swarm Optimisation method for Available Transfer Capability enhancement , 2012 .

[18]  Gerard Ledwich,et al.  A multi-objective optimization approach for coordinating available transfer capability with risk control , 2012, 2012 10th International Power & Energy Conference (IPEC).

[19]  Daniel Kuhn,et al.  Distributionally robust joint chance constraints with second-order moment information , 2011, Mathematical Programming.

[20]  Juan M. Gers.  Distribution System Analysis and Automation , 2013 .

[21]  Luo Gang,et al.  Probabilistic assessment of available transfer capability considering spatial correlation in wind power integrated system , 2013 .

[22]  Hang Li,et al.  Statistical distribution for wind power forecast error and its application to determine optimal size of energy storage system , 2014 .

[23]  Liu Wenxia,et al.  Predicting available transfer capability for power system with large wind farms based on multivariable linear regression models , 2014, 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[24]  Benjamin F. Hobbs,et al.  ATC-Based System Reduction for Planning Power Systems With Correlated Wind and Loads , 2015, IEEE Transactions on Power Systems.

[25]  K. Wong,et al.  Distributionally Robust Solution to the Reserve Scheduling Problem With Partial Information of Wind Power , 2015, IEEE Transactions on Power Systems.

[26]  Ning Lu,et al.  Probabilistic-Based Available Transfer Capability Assessment Considering Existing and Future Wind Generation Resources , 2015, IEEE Transactions on Sustainable Energy.

[27]  Yiping Dai,et al.  Capacity allocation of a hybrid energy storage system for power system peak shaving at high wind power penetration level , 2015 .

[28]  Hai-Peng Ren,et al.  Immune particle swarm optimization of linear frequency modulation in acoustic communication , 2015 .

[29]  Muhammad Murtadha Othman,et al.  A Novel Approach of Rescheduling the Critical Generators for a New Available Transfer Capability Determination , 2016, IEEE Transactions on Power Systems.

[30]  Huan Xu,et al.  Distributionally robust chance constraints for non-linear uncertainties , 2014, Mathematical Programming.