Dynamic thermal rating application to facilitate wind energy integration

Kazerooni, Ali; Mutale, Joseph; Perry, Monica L.; Venkatesan, S.; Morrice, Douglas J.

doi:10.1109/ptc.2011.6019215

Cited by 47 publications

(30 citation statements)

References 5 publications

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“…However, the positive correlation between DLR and near-by wind farm production has already been observed [20]. To demonstrate the impact, the correlation between forecasting errors of wind generation and DLR is assumed to be "1".…”

Section: Correlation Between Forecasting Errorsmentioning

confidence: 99%

Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

Teng

Dupin

Michiorri

et al. 2018

IEEE Trans. Power Syst.

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Abstract--This paper analyses the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the re-dispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modelling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of DLR in supporting costeffective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyses the impact of different operational strategies, the coordination among multiple flexible technologies and installed capacity of wind generation on the benefits of DLR.

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Section: Correlation Between Forecasting Errorsmentioning

confidence: 99%

Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

Teng

Dupin

Michiorri

et al. 2018

IEEE Trans. Power Syst.

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“…738 to determine dynamic line ratings which use weather data as an input. Kazerooni et al [10] have shown that when all the stochastic variations in weather are accounted for, the thermal capacity of the line can be modelled by the generalized extreme value probability distribution and in most cases the rated line capacity is on the lower end of the possible range of thermal capacities.…”

Section: Dynamic Asset Ratingmentioning

confidence: 99%

“…Typical parameters for the probability distribution of line capacity are provided in [10]. To determine the probability distribution of line ampacity historical weather data across the line Nomenclature C g (P g ) cost of conventional generation C w (P w ) cost of wind power feed in C DLR total cost of dynamic line rating C congestion total cost of network congestion N L total number of branches in network N k number of values in discretized probability distribution of line capacity N W total number of wind generators (h pq,k , s max,pq,k ) kth Ordered pair (probability, value) representing line capacity probability distribution S sch,pq power flow in line from bus p to bus q a pq,k the dynamic line capacity discrete probability distribution c OLp unit cost of dynamic line rating P local,n adjustment of load at bus n after redispatch during congestion s jk wasted wind discrete probability distribution t jk reserve requirement discrete probability distribution c D unit cost of network congestion LMP i locational marginal price at node i LMP i,base locational marginal price at node i during uncongested base case P W total wind power generation P W,base total wind power generation during uncongested base case P D,i real power demand at bus i LMP V index measuring variation in Locational Marginal Price from base case will be necessary as per the procedure outlined in [10]. If correlation between wind speed and dynamic thermal ratings are to be accounted for, a different approach is required where the probability distribution of line capacity is conditional based on the probability of the wind speed distribution.…”

Section: Dynamic Asset Ratingmentioning

confidence: 99%

“…A number of sources agree that the true thermal capacity of a transmission line is considerably higher than the rated values [8][9][10][11][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 the thermal limit of transmission lines as stochastically varying function of internal and external real time operating conditions such as ambient temperature, level of loading, intermittent effects, and sag.…”

Section: Introductionmentioning

confidence: 99%

“…The two immediate challenges of implementing the dynamic line rating methods presented in [8][9][10][11] are the need for an online, smart monitoring system to capture real time variation and the modelling of uncertainty in constraints in optimal scheduling. While uncertainty in optimization variables can be accounted for by stochastic optimization techniques, uncertainty in constraints is more challenging to model since analytical constrained optimization techniques only allow fixed constraints.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Banerjee

Jayaweera

Islam

2015

International Journal of Electrical Power & Energy Systems

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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 ...

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