Assessing the effect of wind power peaking characteristics on the maximum penetration level of wind power

A probabilistic approach with considering the peaking characteristics of wind power is proposed to determine the maximum wind power penetration level (WPPL). To evaluate the effect wind peaking characteristics brought to the hosting capacity of systems, a detailed simulation of wind speed/load distributions over a long period is required. Thus, taking an hour as the time scale, the time-sharing peaking characteristics of wind power are discussed by describing the hourly distribution of wind output and load. Based on the chance constrained programming, a WPPL model is established in this study. The optimisation process is achieved using the improved particle swarm optimisation algorithm based on Monte–Carlo simulation. This proposed approach has been applied to an actual power system in central China. Simulation results show that the WPPL would be too optimistic without considering the anti-peaking characteristics of wind power. Besides various sensitivity analyses are performed to assess the effect of other parameters on the maximum penetration level.

[1]  Rong-Jong Wai,et al.  Installed capacity selection of hybrid energy generation system via improved particle-swarm-optimisation , 2014 .

[2]  Chun-Lung Chen.  Key issues of wind capacity integration in congested areas of the Taiwan power system , 2014 .

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

[4]  Bikash C. Pal,et al.  Intermittent wind generation in optimal power flow dispatching , 2009 .

[5]  S.F. Mekhamer,et al.  A Modified Particle Swarm Optimizer for the Coordination of Directional Overcurrent Relays , 2007, IEEE Transactions on Power Delivery.

[6]  Wilsun Xu,et al.  Economic Load Dispatch Constrained by Wind Power Availability: A Here-and-Now Approach , 2010, IEEE Transactions on Sustainable Energy.

[7]  Ehab F. El-Saadany,et al.  Probabilistic approach for optimal planning of distributed generators with controlling harmonic distortions , 2013 .

[8]  Luonan Chen,et al.  Optimal operation solutions of power systems with transient stability constraints , 2001 .

[9]  Pavlos S. Georgilakis,et al.  Optimal distributed generation placement under uncertainties based on point estimate method embedded genetic algorithm , 2014 .

[10]  Jeremy Firestone,et al.  The Effect of Wind Power Installations on Coastal Tourism , 2010 .

[11]  Jin Lin,et al.  A Model for Assessing the Power Variation of a Wind Farm Considering the Outages of Wind Turbines , 2012, IEEE Transactions on Sustainable Energy.

[12]  Li Guo,et al.  Multi-objective stochastic optimal planning method for stand-alone microgrid system , 2014 .

[13]  Hui Zhang,et al.  Chance Constrained Programming for Optimal Power Flow Under Uncertainty , 2011, IEEE Transactions on Power Systems.

[14]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

[15]  Noradin Ghadimi,et al.  PSO Based Fuzzy Stochastic Long-Term Model for Deployment of Distributed Energy Resources in Distribution Systems With Several Objectives , 2013, IEEE Systems Journal.

[16]  Peng Wang,et al.  Adequacy study of a wind farm considering terrain and wake effect , 2012 .

[17]  Hassan Ghasemi,et al.  Maximum penetration level of wind generation considering power system security limits , 2012 .

[18]  Chen Wang,et al.  Optimal Power Flow Solution Incorporating Wind Power , 2012, IEEE Systems Journal.

[19]  Ola Carlson,et al.  The Effect of Wind Power Integration on the Frequency of Tap Changes of a Substation Transformer , 2013, IEEE Transactions on Power Systems.

[20]  Chen Xiong,et al.  Study on degradation of straw stalk by co-fermentation of Trichoderma pseudokoningii and Phanerochaete chrysosporium. , 2009 .

[21]  Cristina L. Archer,et al.  Global Assessment of High-Altitude Wind Power , 2008 .

[22]  Enzo Sauma,et al.  Impact of high wind power penetration on transmission network expansion planning , 2012 .