This paper proposes a novel optimization method for wind power investment to find the optimal location and sizing of multiple wind farms considering both the economic and security aspects of power system operation and planning. The proposed approach maintains the system's security against transient instabilities while improving the voltage profile in the network and minimizing the cost resulting from the investment of wind farms and their operation with thermal units. The transient stability assessment is performed for the power system, considering the uncertainties due to its wind power generation. To model these uncertainties, Taguchi's orthogonal array testing method is utilized. Using Taguchi's method, all the uncertainties in an optimization problem are modeled with only a few representative testing scenarios, and thus, it provides computation efficacy. Moreover, an enhanced hybrid algorithm combining the particle swarm and gray-wolf optimization methods is developed to obtain efficient results in solving the problems formulated. The proposed wind power investment approach is implemented on the New England 39-bus test system, and the results show its effectiveness in providing a reliable and economic wind investment strategy for both investors and operators in the long-term operation and planning of the power system.
Summary In this study, an improved variant of chicken swarm optimization (CSO), named I‐CSO, is proposed to find the unknown parameters of the proton exchange membrane fuel cell (PEMFC) models. Although the basic CSO has a well‐established population hierarchy mechanism that gives it an important advantage over its competitors, it suffers from premature convergence and can be easily trapped into the local optima because of inadequate use of population information in the update rule of the rooster's position. In the proposed I‐CSO, this shortcoming is addressed by introducing a new learning strategy for the roosters, which play leadership roles in the foraging behavior of the chicken swarm, to improve the algorithm convergence capability. Moreover, an adaptive inertia weight is introduced to make the algorithm more stable by striking a better balance between the exploration and exploitation phase. The sum of absolute error between the actual and estimated voltage outputs of the stack is suggested as the objective function to perform the optimization. Besides the suggested one, two other objective functions are also used to evaluate the impact of objective function choice on the optimization results. The test of the method is performed on two commercial PEMFCs, which are BCS 500‐W Stack and NedStack PS6, and the results of I‐CSO are compared with those of other competitive algorithms published in the literature. The final results show that the use of the proposed I‐CSO with the suggested objective function demonstrates excellent performance in estimating the PEMFC model parameters with fewer errors.
Güç sistemlerinde meydana gelen simetrik ve simetrik olmayan kısa devre arızaları şebekenin güç dengesini bozarak güç sistemini kararsız hale getirebilmektedir. Bu durumun önüne geçmek ve sistemin kararlı halde kalma yeteneğini geliştirmek için çeşitli arıza akımı sınırlayıcı yapıları, özellikle yenilenebilir enerji kaynakları içeren güç sistemlerinde sıkılıkla kullanılmaktadır. Nitekim, bu çalışmada, güç sistemlerinde meydana gelen kısa devre arızalarına karşı süperiletken arıza akımı sınırlayıcı (SAAS) yapısının en uygun yerleşim yeri bulunmaya çalışılmıştır. Güç sistemi modeli olarak 3 makinalı, 9 baralı batı sistemi koordinasyon konseyi (WSCC) test sistemi kullanılmış ve analizler MATLAB'da gerçekleştirilmiştir.
Studies on transient stability constrained optimal power flow (TSCOPF) have become crucial for power systems to guarantee their dynamic securities against credible contingencies, while their optimum operations are to be continuously projected under changing conditions. However, the current approach to the TSCOPF problem is not sufficient to meet the expectations of a modern power system because it suffers from uncertainties mainly due to the rapid and large integration of distributed energy sources. This study proposes a novel method using the information-gap decision theory (IGDT) technique to solve the TSCOPF problem in the presence of uncertainties due to the penetration of wind farms. The IGDT is a nonprobabilistic decision-making method that can be easily implemented to handle uncertainty in optimisation problems. While presenting applicable strategies, it does not require any information about the historical data, probability density function or membership function of the uncertain parameters. The proposed method offers an analysis for the economic dispatch in a power system with wind energy resources while providing robustness against transient instabilities and uncertainties in power generation. To demonstrate the effectiveness of the proposed method, it is implemented on New England 39-bus and IEEE 118bus test systems.
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