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Showing 2 results for Copula
Effect of Wind Speed and Load Correlation on ELCC of Wind Turbine Generator
H. Rajabi Mashhadi, M. A. Armin,
Volume 11, Issue 3 (9-2015)
Abstract

Utilization of wind turbines as economic and green production units, poses new challenges to the power system planners, mainly due to the stochastic nature of the wind, adding a new source of uncertainty to the power system. Different types of distribution and correlation between this random variable and the system load makes conventional method inappropriate for modeling such a correlation. In this paper, the correlation between the wind speed and system load is modeled using Copula, a mathematical tool recently used in the field of the applied science. As the effect of the correlation coefficient is the main concern, the copula modeling technique allows simulating various scenarios with different correlations. The conducted simulations in this paper reveals that the wind speed correlation with the load has significant effect on the system reliability indices, such as expected energy not served (EENS) and loss of load probability (LOLP). Moreover, in this paper the effect of the correlation coefficient on the effective load carrying capability (ELCC) of the wind turbines is analyzed, too. To perform the aforementioned simulations and analyses, the modified RBTS with an additional wind farm is used.

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Stochastic Joint Optimal Distributed Generation Scheduling and Distribution Feeder Reconfiguration of Microgrids Considering Uncertainties Modeled by Copula-Based Method
M. Khajevand, A. Fakharian, M. Sedighizadeh,
Volume 16, Issue 3 (9-2020)
Abstract
Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used.  The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.