Risk‐based security assessment of transmission line overloading considering spatio‐temporal dependence of load and wind power using vine copula

Khuntia, Swasti R.; Rueda, José L.; Meijden, Mart A. M. M. van der

doi:10.1049/iet-rpg.2018.6091

Cited by 15 publications

(7 citation statements)

References 26 publications

(35 reference statements)

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“…Recently, power system has undergone drastic changes related to privatization and installation of new devices and technologies such as renewable generation sources [4,5]. The privatization process pushed electric power networks to work near their limits, especially with the demand for continuous loads [5,6]. In addition, the digital revolution in the fields of communications, software , computer systems and artificial intelligence systems provided advanced technologies that developed the infrastructure for various different industrial sectors, including the electric power sector.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Security Assessment For Power System Using Attribute Selection Technique

Alhubaity

Khalel

Al-Rawe

2022

IJEEI

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The evaluation of the dynamic security of the electrical power system after the occurrence of disturbances in the network is one of the most important tools that the control center uses to maintain the system in a safe operating mode, as well as prevent cases of system out of control and cases of complete shutdown. With the annual increase in the size of the electrical system and its distribution over a very wide geographical area, this led to a new challenge to assess dynamic security assessment (DSA), which is dealing with a huge and varied amount of data that requires processing in a very short time. To address these challenges, this study presented a new technique of artificial intelligence, which is the attribute selection technique, to reduce the size of this data and thus improve the accuracy and speed of results. This method relied on the combination of decision tree (DT) algorithms and a technique attribute selection in the data obtained from the test system IEEE-30Bus Model. The results of this method showed a significant reduction in the number of data used, which amounted to 77.27% of the total data, which led to an improvement in the classification accuracy, as the classification accuracy reached 97.39%. This reduction is very important when dealing in the online operating environment, as it saves the time necessary to reach the most accurate evaluation decision and thus issue gives a greater opportunity to take the appropriate decision in the event of disturbances and keep the power system in a secure case.

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Section: Introductionmentioning

confidence: 99%

Dynamic Security Assessment For Power System Using Attribute Selection Technique

Alhubaity

Khalel

Al-Rawe

2022

IJEEI

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“…In addition, Sun et al [23] used vine copulas to the spatial dependencies of load and wind power for studying the transmission expansion planning problem. Khuntia et al [24] evaluated line overloading risks for the high wind penetrated systems using vine copulas to capture both spatial and temporal dependences between the load and wind power. In [25][26], a vine copula was employed to model the joint distribution of wind power from various wind farms and the point estimate methods were used to reduce the computational burden of the PPF.…”

Section: Introductionmentioning

confidence: 99%

Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion

et al. 2022

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As the use of renewable energy is continuously increasing, power systems are currently exposed to greater uncertainty and variability, which can lead to severe power system stability issues. Therefore, a power system analysis tool should be devised to assess the impact of renewable energy integration along with an accurate modeling of their stochastic characteristics. In this study, an advanced probabilistic power flow (PPF) method is developed using vine copulas that captures the complex dependency of the stochastic wind power generated from multiple wind sites. The proposed method also involves the use of a function for selecting the probability models of wind speeds by regions in a sophisticated manner. The effectiveness of the proposed method is tested on an IEEE bus system as well as, on a South Korean power system with thousands of buses and transmission lines using PSS/E with Python API. The simulations demonstrate that the proposed method can more accurately evaluate the power system risks with the sophisticated modeling of wind power in multiple sites as compared to the deterministic approach or the PPF with independent sampling.

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“…To construct parametric models for multivariate distributions, vine copulas are a versatile tool which has been used in the literature for similar problems. For example, in Guo et al (2021); Khuntia et al (2019); Xiao et al (2020), copulas are applied for dependency modeling of wind power in conjunction with OPF. Furthermore, in Xu et al (2021), Gaussian copulas are used to determine uncertainty sets for an OPF problem with chance constraints.…”

Section: Introductionmentioning

confidence: 99%

Robust DC optimal power flow with modeling of solar power supply uncertainty via R-vine copulas

et al. 2022

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We present a robust approximation of joint chance constrained DC optimal power flow in combination with a model-based prediction of uncertain power supply via R-vine copulas. It is applied to optimize the discrete curtailment of solar feed-in in an electrical distribution network and guarantees network stability under fluctuating feed-in. This is modeled by a two-stage mixed-integer stochastic optimization problem proposed by Aigner et al. (Eur J Oper Res (2022) https://doi.org/10.1016/j.ejor.2021.10.051). The solution approach is based on the approximation of chance constraints via robust constraints using suitable uncertainty sets. The resulting robust optimization problem has a known equivalent tractable reformulation. To compute uncertainty sets that lead to an inner approximation of the stochastic problem, an R-vine copula model is fitted to the distribution of the multi-dimensional power forecast error, i.e., the difference between the forecasted solar power and the measured feed-in at several network nodes. The uncertainty sets are determined by encompassing a sufficient number of samples drawn from the R-vine copula model. Furthermore, an enhanced algorithm is proposed to fit R-vine copulas which can be used to draw conditional samples for given solar radiation forecasts. The experimental results obtained for real-world weather and network data demonstrate the effectiveness of the combination of stochastic programming and model-based prediction of uncertainty via copulas. We improve the outcomes of previous work by showing that the resulting uncertainty sets are much smaller and lead to less conservative solutions while maintaining the same probabilistic guarantees.

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Risk‐based security assessment of transmission line overloading considering spatio‐temporal dependence of load and wind power using vine copula

Cited by 15 publications

References 26 publications

Dynamic Security Assessment For Power System Using Attribute Selection Technique

Dynamic Security Assessment For Power System Using Attribute Selection Technique

Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion

Robust DC optimal power flow with modeling of solar power supply uncertainty via R-vine copulas

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