Showing 3 results for Monte Carlo
M. Esfand Abadi, M. H. Miran Baygi, A. Mahloojifar, S. Moghimi,
Volume 1, Issue 4 (10-2005)
Abstract
In this paper, thermal effects of laser irradiance on biological tissue is
investigated using computer simulations. Earlier attempts in this field made use of finite
difference and finite element techniques. Here a novel approach is adopted to improve the
results. The effect of our implicit approach on the convergence procedure and accuracy of
results, with different timing steps, is explored. Monte Carlo method is used in combination
with the finite volume algorithm in order to obtain a profile of light distribution and heat
transport in tissue. It is shown that implicit finite volume technique has not only acceptable
accuracy, but also high stability for different timing steps.
B. Adineh, H. Rajabi Mashhadi, M. E. Hajiabadi,
Volume 10, Issue 2 (6-2014)
Abstract
The main goal of this paper is to structurally analyze impact of DSM programs on reliability indices. A new approach is presented to structurally decompose reliability index Expected Energy Not Supplied (EENS) by using Monte Carlo simulation. EENS is decomposed into two terms. The first term indicates EENS which is caused by generation contingencies. The second term indicates EENS which is caused by transmission and generation contingencies. The proposed approach can be used to indicate appropriate buses for applying DSM. Furthermore, networks are studied at two levels HLI and HLII. Studies show that in some networks reliability indices are affected mostly at the HLI level. While in some other networks, reliability indices are influenced mostly at the HLII level. It means that in these networks, reliability indices are affected by transmission contingencies. Then, it is shown that the implementation of load shifting is effective in some networks and buses. These are the ones which their EENS is more influenced by generation contingencies. However it is not effective in the ones which their EENS is more influenced by transmission contingencies. The simulation results on the IEEE-RTS and Khorasan network show the efficiency of the proposed approach.
M.a Armin, H Rajabi Mashhadi,
Volume 11, Issue 4 (12-2015)
Abstract
Wind energy penetration in power system has been increased very fast and large amount of capitals invested for wind farms all around the world. Meanwhile, in power systems with wind turbine generators (WTGs), the value of Available transfer capability (ATC) is influenced by the probabilistic nature of the wind power. The Mont Carlo Simulation (MCS) is the most common method to model the uncertainty of WTG. However, the MCS method suffers from low convergence rate. To overcome this shortcoming, the proposed technique in this paper uses a new formulation for solving ATC problem analytically. This lowers the computational burden of the ATC computation and hence results in increased convergence rate of the MCS. Using this fast technique to evaluate the ATC, wind generation and load correlation is required to get into modeling. A numerical method is presented to consider load and wind correlation. The proposed method is tested on the modified IEEE 118 bus to analyze the impacts of the WTGs on the ATC. The obtained results show that wind generation capacity and its correlation with system load has significant impacts on the network transfer capability. In other words, ATC probability distribution is sensitive to the wind generation capacity.
