Assessment of stress in active distribution networks with asset dynamic ratings

Active distribution networks are vulnerable to random disturbances and the severity of stress of disturbances can be increased with dynamic rating of network assets, level of penetration of intermittent distributed generation (DG), and rise in customer demand. Increased stress in a distribution network can lead to major system disturbances including blackouts. This paper investigates this problem to assess how vulnerable the active networks to stresses arisen through random outages, dynamic variation of network asset ratings, demand rise, and high penetration of intermittent DG. The Monte Carlo simulation is the main driver of the assessment which incorporates dynamic rating of network assets through probabilistic modeling. The stress of the active network is recognizes as the product of network stress and the customer stress of not supplying the energy. A case study is performed and the results suggest that the active network stress can be buffered by the increased penetration of wind through strategic stations. The buffer is more effective at stressed operating conditions than the less stressed operating conditions.

[1]  S. Grijalva,et al.  Large-Scale Integration of Wind Generation Including Network Temporal Security Analysis , 2007, IEEE Transactions on Energy Conversion.

[2]  D.S. Kirschen,et al.  A probabilistic indicator of system stress , 2004, IEEE Transactions on Power Systems.

[3]  D.S. Kirschen Do Investments Prevent Blackouts? , 2007, 2007 IEEE Power Engineering Society General Meeting.

[4]  G. Harrison,et al.  DG Impact on Investment Deferral: Network Planning and Security of Supply , 2010, IEEE Transactions on Power Systems.

[5]  Danny Pudjianto,et al.  Benefits of active management of distribution network in the UK , 2005 .

[6]  I. Dobson,et al.  Risk Assessment of Cascading Outages: Methodologies and Challenges , 2012, IEEE Transactions on Power Systems.

[7]  J.D. McCalley,et al.  Power System Risk Assessment and Control in a Multiobjective Framework , 2009, IEEE Transactions on Power Systems.

[8]  J. McDonald,et al.  Customer Security Assessment in Distribution Networks With High Penetration of Wind Power , 2007, IEEE Transactions on Power Systems.

[9]  Wenyuan Li Risk assessment of power systems , 2014 .

[10]  C. Borges,et al.  Active distribution network integrated planning incorporating distributed generation and load response uncertainties , 2011, 2012 IEEE Power and Energy Society General Meeting.

[11]  Xiaofu Xiong,et al.  Power System Risk Assessment Using a Hybrid Method of Fuzzy Set and Monte Carlo Simulation , 2008 .

[12]  Magdy M. A. Salama,et al.  Adequacy assessment of distributed generation systems using Monte Carlo Simulation , 2003 .

[13]  Syed Islam,et al.  Probabilistic assessment of distribution network capacity for wind power generation integration , 2009, 2009 Australasian Universities Power Engineering Conference.

[14]  B. Jayasekara,et al.  Risk-Based Dynamic Security Assessment , 2011, IEEE Transactions on Power Systems.