Transmission system expansion planning by simulated annealing

The transmission network expansion planning is necessary for supplying the future needs, considering load growth. Furthermore, in restructured environments, transmission lines provide the required infrastructure for creating a competitive environment. In recent years, there has been a significant advancement in storage technologies. This advancement leads to using energy storage systems to postpone the construction or replacement of transmission lines. Therefore, in this paper, the problems of transmission expansion planning and energy storage systems deployment are investigated simultaneously. Considering the presence of storage devices and their effect on network operation cost, in this paper, the operation cost is modeled as an independent objective function along with investment cost. Moreover, the problems of transmission and storage expansion planning are modeled as a tri-objective optimization problem with the objectives of reducing costs and increasing the social welfare index in the power market. The multi-objective shuffled frog leaping evolutionary algorithm is used to solve these problems. The presented model for expansion planning is implemented and analyzed on IEEE 24-bus test system in the presence and absence of energy storage systems, and the effect of change in the price of energy storage systems is studied. The results of this research show that as the technology advances and the storage costs decrease, energy storage systems can play a pivotal role in reducing expansion planning costs of the power network and improving market-based indices in the restructured environment.

Although there are several well -known methods such as RCWA, FMM, for analyzing the diffraction properties of gratings, design of these optical elements with specified spectral properties is commonly a challenging problem. It is relatively not easy for the researchers to design narrow line-with diffraction filters based on guided mode resonance phenomenon with common diffraction algorithm. Simulated Annealing (SA) method is evolutionary, robust technique that has been widely utilized to design optical diffraction components. This method is inspired by the physical process of heating and controlled cooling of metal material to increase the size of its crystals and reduce their defects. The most distinctive features of this method lie in its powerful ability of convergence towards the global minimum in a reasonable computation time and the independence of the initial parameter values. In this paper, first, the physical basis of SA and its mathematical realization are introduced. Then, a Guided-Mode Resonant Grating (GMRG) filters with single layer is designed by using SA algorithm. The central wavelength of GMRG filter is locked at 532nm and its line-width is fixed at 1nm. The plane wave light radiates the grating from air cover with normal incidence. The optimized parameters are refractive indices and thicknesses of high and low material of grating, other parameters are grating period and fill factor of the grating. It is shown from our calculation that an excellent reflection spectrum with narrow line-width, high peak and low sideband can be obtained after optimizing the grating parameters. Next, a double layered GMRG filter with line-width of 4nm, which is relatively easy fabrication in experiment, is designed at central wavelength of 1064nm. The optimized parameters are grating period, groove depth, refractive index of waveguide layer and fill factor respectively. The grating substrate and waveguide layer are Sio2 and Hfo2 respectively, the grating structure is directly etched on the waveguide layer. The above grating values should be included in reasonable ranges in consideration of grating fabrication in our experiment condition. It is demonstrated from the calculations with the parameters obtained from SA optimization algorithm that the peak diffraction efficiency is more than 99% at central wavelength 1064nm and the sideband reflection is depressed at the level bellow 5% in a large wavelength range. Moreover, the parameters of a triple layer GMRG filter structure are also provided with this powerful method. Meanwhile, the results found by SA method are compared with RCWA theory.

Noise control is essential in an enclosed machine room where the noise level has to comply with the occupational safety and health act. In order to overcome a pure tone noise with a high peak value that is harmful to human hearing, a traditional reactive muffler has been used. However, the traditional method for designing a reactive muffler has proven to be time-consuming and insufficient. In order to efficiently reduce the peak noise level, interest in shape optimization of a Helmholtz muffler is coming to the forefront. Helmholtz mufflers that deal with a pure tone have been adequately researched. However, the shape optimization of multi-chamber Helmholtz mufflers that deal with a broadband noise hybridized with multiple tones within a constrained space has been mostly ignored. Therefore, this study analyzes the sound transmission loss (STL) and the best optimized design for a hybrid Helmholtz muffler under a space- constrained situation. On the basis of the plane wave theory, the four-pole system matrix used to evaluate the acoustic performance of a multi-tone hybrid Helmholtz muffler is presented. Two numerical cases for eliminating one/two tone noises emitted from a machine room using six kinds of mufflers (muffler A~F) is also introduced. To find the best acoustical performance of a space-constrained muffler, a numerical assessment using a simulated annealing (SA) method is adopted. Before the SA operation can be carried out, the accuracy of the mathematical model has been checked using the experimental data. Eliminating a broadband noise hybridized with a pure tone (130 Hz) in Case I reveals that muffler C composed of a one- chamber Helmholtz Resonator and a one-chamber dissipative element has a noise reduction of 54.9 (dB). Moreover, as indicated in Case II, muffler F, a two-chamber Helmholtz Resonator and a one-chamber dissipative element, has a noise reduction of 69.7 (dB). Obviously, the peak values of the pure tones in Case I and Case II are efficiently reduced after the muffler is added. Consequently, a successful approach in eliminating a broadband noise hybridized with multiple tones using optimally shaped hybrid Helmholtz mufflers and a simulated annealing method within a constrained space is demonstrated.

A new SA (simulated annealing) method is presented for the design of longitudinal shim coils for NMR (nuclear magnetic resonance) equipment. First, AGL (adaptive Gauss-Lobatto) quadrature used by the traditional SA method is analyzed. Two Kinds of complete elliptic integrals are introduced to integrate the spatial magnetic field, which increase the computing speed by 13.3 times. Then, the System energy calculation is extended to the field of the z-axis. The results illustrate that the coil-set efficiency designed by the new SA method is 15% higher than that of the traditional one, and the field inhomogeneity is reduced frm 1.20% to 0.49%. Finally, a prototype of coil assembly for NMR rock-core analyzer is built, and the experimental results are in good agreement with the theoretically calculated results. * Supported by the National Key Technologies Research and Development Program of China (Grant No. 001BA210A03), and Key Project of 2005 Beijing Science and Technology Programme(Grant No. D0205002...

An efficient scheduling algorithm using the Simulated Annealing (SA) method is proposed. In an actual large scheduling problem, the number of schedules searched within a reasonable time is restricted, because calculation of the starting times of jobs requires a long computation time when the problem has various types of constraints. In the proposed algorithm, two types of improvements are proposed to reduce the computation time of a scheduling algorithm: One is to reject, at an early stage of the starting time calculation, those schedules that have little possibility of being accepted in the simulated annealing method. The other is to reject unpromising schedules stochastically using the data related to the production sequence of jobs—not after the calculation of the starting times of jobs but at the step of generating a new production sequence of jobs. Thus, compared with the algorithm which selects a new production sequence randomly, better schedules can be derived in shorter computation time. The developed algorithm has been applied to a practical scheduling problem at a resin production plant, and it has become clear that this algorithm can generate significantly better schedules with a much shorter computation time.

Comparative study of optimization methods for estimation sea surface temperature and ocean wind with microwave radiometer data is conducted. The well known mesh method (Grid Search Method: GSM), regressive method, and simulated annealing method are compared. Surface emissivity is estimated with the simulated annealing and compared to the well known Thomas T. Wilheit model based emissivity. On the other hand, brightness temperature of microwave radiometer as a function of observation angle is estimated by the simulated annealing method and compares it to the actual microwave radiometer data. Also, simultaneous estimation of sea surface temperature and ocean wind speed is carried out by the simulated annealing and compared it to the estimated those by the GSM method. The experimental results show the simulated annealing which allows estimation of global optimum is superior to the other method in some extent.

Construction times of new power plants are subject to uncertainty in deregulated electricity markets and they have an important impact on the planning of the expansion of the transmission network. In this paper, we develop a model of transmission expansion planning that is based on Robust optimization and considers the uncertainty in the instant when new power plants are connected into the electricity grid. Using this transmission expansion planning model, we assess the impact of postponing the connection time of some new power plants over the system cost and the optimal network expansion plan. By obtaining the optimal solutions of the proposed model for different levels of robustness, we are able to rank the new power plants with respect to how much the delay of each project negatively affects the system cost. We illustrate our methodology and the performance of two solution approaches proposed to solve our model by using a 34-node stylized version of the main Chilean grid. In the case of the main Chilean grid, the results obtained indicate that some of the projects whose delays negatively affect the most to the system cost are the renewable power plants projected for the next ten years. This is important from the regulator viewpoint because it allows the assessment of renewable energy policies that accelerate the installation of renewable new power plants also in terms of their impact over the transmission expansion planning.

In general, power system expansion planning can be classified into three types such as generation expansion planning (GEP), transmission expansion planning (TEP) and distribution system expansion planning (DEP). Several researches have been performed in GEP and TEP. But, a huge research gap is still available in the topic of DEP. DEP can be classified into two optimization problems, namely substation expansion planning (SEP) and optimal feeder routing. This study attempts to solve real-time SEP problem for the power sector of Tamil Nadu, a state in India. Due to several design variables, the grouping of discrete and continuous variables frames the SEP problem as complicated. Hence, a promising optimization algorithm is essential to solve this complicated problem. The application of differential evolution (DE) algorithm has been presented to obtain the results with the sizes and sites of both the existing and proposed substations (SS) by satisfying the subjected constraints.KeywordsSubstation expansion planningDifferential evolutionTamil NaduLeast cost

Numerical analysis of circular straight mufflers equipped with three chambers at high-order-modes

A linearized formulation of AC multi-year transmission expansion planning: A mixed-integer linear programming approach

Environmental concerns and limits on CO2 emissions entail expanding the capacity of renewable generation units. However, the increase in the renewable generation capacity can significantly impact the hosting power system voltage stability. Therefore, the consideration of voltage stability in the expansion planning problem is of an increasing importance. In this paper, we present a multi-stage low carbon voltage stability constrained generation and transmission expansion planning (G&TEP) model. The developed G&TEP model accounts for the investment and operation costs of generation units and transmission lines, as well as the load and wind curtailment costs, and determines the optimal installation year and location for transmission lines and generation units to meet the anticipated load demand increase. The voltage collapse proximity indicator (VCPI), which is a line voltage stability index based technique, is used to account for the voltage stability of the developed expansion plans. To this end, the VCPI is incorporated in the G&TEP model to guarantee an acceptable level of voltage stability for the developed expansion plans over the planning horizon. The effectiveness of the proposed framework is validated using numerical cases studies on the IEEE 24-bus RTS test system developed in General Algebraic Modeling System (GAMS) environment.

An LSI module placement algorithm which uses neural networks is presented. A formula which determines the weighting factors of the constraint and objective function in the energy so that they converge to a vertex, a point on the edge, or an interior point of the hypercube is derived. In a comparison with simulated annealing and min-cut methods, the proposed method was as good as the min-cut method but not as good as the simulated annealing method

<span>The increasing demand of electricity and number of distributed generations connected to power system greatly influence the level of power service reliability. This paper aims at improving the reliability in an electric power distribution system by optimizing the number and location of sectionalizers using the Ant Colony Optimization (ACO) and Simulated Annealing (SA) methods. Comparison of these two methods has been based on the reliability indices commonly used in distribution system: SAIFI, SAIDI, and CAIDI. A case study has been taken and simulated at a feeder of Pujon, a place in East Java province of Indonesia, to which some distributed generators were connected. Using the existing reliability indices condition as base reference, the addition of two distributed plants, which were micro hydro and wind turbine plants, has proven to lower the indices as much as 0.78% for SAIFI, 0.79% for SAIDI, and 2.32% for CAIDI. The optimal relocation of the existing 16 sectionalizers in the network proved to decrease further the reliability indices as much as 43.96% for SAIFI, 45.52% for SAIDI, and 2.8% for CAIDI, which means bringing to much better reliability condition. The implementation of the SA method on the considered data in general resulted in better reliability indices than using the ACO method.</span>

Cancer is a disease that occurs when normal cells divide uncontrollably and attack healthy tissue. This disease is one of the leading causes of death worldwide. There are 10 million cases of cancer deaths based on data from the World Health Organization (WHO). Chemotherapy as a cancer treatment began in 1940 and has been successful since its inception. However, this treatment can be bad for the body in the long term. So, new drug designs are needed to overcome these impacts. Generally, anti-cancer drugs can be developed by considering Cyclin-Dependent Kinases 2 (CDK2) as the target. In designing a new drug, one method that can be used to accelerate the process is the quantitative structure-activity relationships (QSAR) method. This study aims to build a QSAR model for classifying anti-cancer agents from CDK2 inhibitors by using the simulated annealing (SA) and support vector machine (SVM) method. The SA method was used for feature selection, while SVM was used for the model prediction. We utilized the data set used that obtained from the ChemBL database with a total of 1.554 samples. Based on the results, we found that the best prediction model is obtained from SVM with linear and polynomial kernels with accuracy and F-1 score are 0.986 and 0.987, respectively.

Finding a representative network losses model for large-scale transmission expansion planning with renewable energy sources