Power system security assessment with high wind penetration using the farms models based on their correlation

Abstract

Increasing the wind energy penetration in a power system presents some technical challenges to the transmission expansion planning (TEP). In the first stage of TEP, the transmission bottlenecks should be determined and ranked via security assessment. The high penetration of wind energy means a large number of wind farms exist in various geographic areas. It is found that there is a correlation between the wind speed series of different farms. Therefore, integration of the correlated wind farms into security assessment is addressed in this study. Hence, a new model of wind speed prediction is presented based on the correlated autoregressive moving average time series. The copula functions are used to make the predefined correlation among wind speed series. Here a linear correlation using a Gaussian copula is implemented. Then, the risk-based security assessment is performed using the proposed sequential time simulation. The proposed process is applied to the modified IEEE 39-bus test system which comprises 15 wind farms. Then, the transmission bottlenecks are identified and ranked regarding their overload risks. Finally, the impacts of the wind farms correlation on the risk index are investigated and those contingencies which impose the highest influence on this risk are identified.

Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power Systems

“…the EPS operation. Another approach is presented in [35], describing an overload risk assessment method based on N − 1 contingency analysis and wind penetration.…”

An Integrated LHS–CD Approach for Power System Security Risk Assessment with Consideration of Source–Network and Load Uncertainties

“…In this paper, the midpoint is selected and the corresponding Xk at this point is calculated using the inverse function as Formula (10). N points are sampled for the uncertain variable Xk (k = 1, 2, ..., K) and randomly arranged in a row, then a K × N primary sampled matrix can be obtained for K input random variables as described by Formula (11).…”

“…However, since the sampled matrix XKN is randomly arranged and the correlation is uncontrollable, the primary sampled matrix in Formula (11) needs to be properly rearranged to satisfy the assumed correlations. CD strategy has the merits of light computation burdens and high precisions, and thus in this manuscript, the CD is used to rearrange Formula (11) for obtaining satisfied correlations according to the following procedures.…”

“…In [10], Nataf transformation was used to establish a wind speed correlation model, and the effect of wind speed correlation on voltage stability margin was studied. Prof. Mehdizadeh introduced the correlation autoregressive moving average time series and copula function to predefine the correlations among wind farms, and afterward evaluated the power system static security risk with consideration of wind power correlations [11]. However, there are few reports simultaneously considering correlated wind power and load demands, as well as the uncertain network for power system risk assessment.…”

Energy storage sizing by copula modelling joint distribution for wind farm to be black‐start source

“…It decouples the individual random variables and the overall dependence structure, which makes dependence modelling and analysis much easier. been widely used in wind power scenario model of multiple WFs [21,22] and wind power forecast [23,24]. Copula functions can be conveniently constructed to adapt to diverse dependence modelling.…”