Assessment of post-contingency congestion risk of wind power with asset dynamic ratings

Abstract

a b s t r a c tLarge scale integration of wind power can be deterred by congestion following an outage that results in constrained network capacity. Post outage congestion can be mitigated by the application of event control strategies; however they may not always benefit large wind farms. This paper investigates this problem in detail and proposes an advanced mathematical framework to model network congestion as functions of stochastic limits of network assets to capture post contingency risk of network congestion resulting through the constrained network capacity that limits high penetration of wind. The benefit of this approach is that it can limit the generation to be curtailed or re-dispatched by dynamically enhancing the network latent capacity in the event of outages or as per the need. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The case study results with large and small network models suggest that the following an outage, wind utilization under dynamic line rating can be increased considerably if the wind power producers maintain around a 15% margin of operation. IntroductionNetwork congestion is an undesirable result of insufficient capacity being available on a network to transport electricity from generation to loads. It leads to highly variable locational marginal prices (LMP) at nodes usually with high prices at load points which are affected by congestion compared to those which are not. A number of publications have used LMP as an indicator of network congestion [1][2][3]. In systems with large amount of wind power, network congestion hinders effective integration and utilization of wind as extra wind generated has to be curtailed thereby leading to uncertainty in revenue for wind power producers. The dynamic nature of wind results in large variations in power output over a short period of time, which makes effective utilization of wind an even bigger challenge in congested networks.Network congestion has a greater impact in networks under contingency. When a contingency occurs in a branch, the remaining branches in the network can experience greater loading and be at a higher risk of network congestion [4][5][6][7]. While traditional security analysis uses the N À 1 criterion this does not account for variation in output of wind leading to post contingency congestion and curtailment of wind. Therefore, even when a network seems to have no congestion and utilizes wind effectively, there is a high risk that any contingency will drastically change the situation.A number of sources agree that the true thermal capacity of a transmission line is considerably higher than the rated values [8-12] since ratings are calculated under the worst case weather assumption although such operating conditions occurs rarely in practice. It is possible to exploit this property by using dynamic line ratings (DLR) which model ...

Emergency power flow re-routing in a distribution system by using dynamic line rating

“…The effectiveness per line with DLR also installed strongest at the first few lines to then level out. V. CONCLUSION The results presented in the previous sections have shown that a congested distribution system can present different results with the implementation of DLR compared with previous studies [17]- [19]. Due to the limits and constraints in OPF formulation, it is observed that limits other than thermal, such as voltage limits, are present in the system and can have a considerable impact on the results obtained.…”

Contingency Assessment for Power System Integrated with Wind Power

“…The contingency analysis consists of three basic steps to make it simpler. They are listed below [12]:…”

A Comprehensive Overview of Dynamic Line Rating Combined with Other Flexibility Options from an Operational Point of View

“…Banerjee et al [43] conducted a congestion prevention strategy during outage conditions of both N-1 and N-2 by DLR implementation in order to reduce wind power curtailment or re-dispatch. The operational cost minimization-oriented study in [43] defined a cost variable for DLR implementation as a constraint relaxation penalty apart from the other cost components regarding conventional generators based power production, wind power uncertainty and congestion. The mentioned study in [43] also considered the stochasticity of wind power production in the evaluation as a notable difference compared to many existing DLR based operational studies.…”

“…The operational cost minimization-oriented study in [43] defined a cost variable for DLR implementation as a constraint relaxation penalty apart from the other cost components regarding conventional generators based power production, wind power uncertainty and congestion. The mentioned study in [43] also considered the stochasticity of wind power production in the evaluation as a notable difference compared to many existing DLR based operational studies. An interesting study considering also the stochasticity of wind power as well as the load demand, component availability and weather variables (wind speed, solar radiation, ambient temperature) leading to a stochastic DLR capacity as the overall resources of uncertainty in the system operation was portrayed in [44].…”