The augment of ecological shield and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating renewable energy sources into existing power system. **Wind** power is becoming worldwide a significant component of the power generation portfolio. Profuse literatures have been reported for the **thermal** **Unit** **Commitment** (UC) **solution**. In this work, the UC problem has been formulated by integrating **wind** power generators along with **thermal** power system. The **Wind** Generator **Integrated** UC (WGIUC) problem is more complex in nature that necessitates a promising optimization tool. Hence, the modern bio-inspired algorithm namely, **Grey** **Wolf** Optimization (GWO) algorithm has been chosen as the main optimization tool and real coded scheme has been incorporated to handle the operational constraints. The standard test systems are used to validate the potential of the GWO algorithm. Moreover, the ramp rate limits are also included in the mathematical WGIUC formulation. The simulation results prove that the intended algorithm has the capability of obtaining economical resolutions with good **solution** quality.

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More recently, some authors have proposed other methods to solve this problem. Authors in [6] and [7] present the model of a voltage-source converter suit- able for OPF **solution** of HVDC **using** Newton Raph- son Algorithm (NRA) and a sequential method was introduced [8]. A new approach for load flow analy- sis of **integrated** HVDC power systems **using** sequential modified Gauss-Seidel method was reported [9]. In [10], a multi-terminal HVDC power flow with a conventional AC power flow has been proposed. In [11], a steady- state multi-terminal HVDC model for power flow has been developed and it includes converter limits, as well as different converter topologies. Other authors have solved this problem by applying new techniques, such as Artificial Bee Colony (ABC) algorithm [12], Genetic Algorithm (GA) [13] and Backtracking Search Algo- rithm (BSA) [14]. Authors in [15] proposed an OPF in order to minimize the losses in a multi-terminal HVDC grid. Application of transient stability constraints for OPF, to a transmission system including an HVDC, was proposed [16]. Authors in [17] applied an informa- tion gap decision theory to the OPF model for the op- timal operation of AC-DC systems with offshore **wind** farms.

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In recent past, a number of initiatives have been taken to enhance the utilization of **wind** power in the electric power generation sector. Limited predictability and variability of **wind** power makes the operation of power system is problematic. The **Wind** **Integrated** **Thermal** Scheduling (WITS) problem plays a vital role in generating green power. The optimal selection and optimal dispatch of **thermal** units require to be modified based on **wind** farm output. This makes WITS is a complex optimization problem, that has to identify the optimal schedule of generating units while satisfying all prevailing constraints. GWO algorithm is used to determine the generating schedule of **thermal** units. By observing Table 3, it can be understood that the minimum up/down time constraints and initial status of units are satisﬁed for all **thermal** generating units. First two **thermal** units are committed for whole scheduling horizon, because these units have high **commitment** priorities than other **thermal** units. They function as

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A common drawback to meta-heuristic methods is that, in general, the optimization performance is highly dependent on fine parameter tuning. However, the pro- posed approach outperforms these methods in term of convergence speed to the best **solution**. Moreover, the use of OPF is extended to include the study of renew- able energy systems like **wind** power, which becomes more and more useful in recent power networks, and many studies are made to integrate this natural power efficiently to a power system. Ranjit and Jadhav in [19], as well as Maskar et al. in [20], presented a study of OPF problem in a system incorporating **wind** power sources, **using** modified ABC algorithm named Gbest guided ABC algorithm; the method showed good re- sults for fuel cost optimization case, and voltage profile enhancement, then under **wind** condition the total op- erating cost is optimized efficiently, compared to other methods. The method presented some benefits con- cerning reserve coefficient adjustment when consider- ing imbalance cost of **wind** power. Meanwhile, Shanhe et al. [21] presented a new economic dispatch technique based on PSO-GSA algorithm for a power system in- cluding two **wind** power sources; the method was tested on a six generators’ system connected with two stochas- tic **wind** power sources. The test yielded good results compared with other results found in the literature with different methods especially for cost and emission reduction. Panda and Tripathy [22], and Mishra and Vignesh [23] introduced another OPF algorithm based on security constrained OPF **solution** of **wind**-**thermal** generation system **using** modified bacteria foraging al- gorithm. The method was tested on the same system stated in [18], in which the **wind** power variability was modelled incorporating conventional **thermal** generat- ing system. Recent works in [19], [24] and [25] pre- sented better results and faster convergence character- istics **using** **Grey** **Wolf** **Optimizer** algorithm. **Grey** **Wolf** **Optimizer** (GWO) algorithm mimics the behaviour of **grey** wolves in nature by simulating their leadership hierarchy, through haunting, searching for, encircling, and attacking the prey [26].

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ABSTRACT: Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive **solution** under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced **Grey** **Wolf** **Optimizer** (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition **using** single diode model and EGWO is applied to track global maximum power point. The proposed method was programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method were analyzed and compared with GWO and PSO algorithms. It was observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.

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In this paper, a new meta-heuristic algorithm, called **grey** **wolf** optimization (GWO) is presented to solve combined economic and emission dispatch (CEED) problem considering transmission losses. GWO is inspired by **grey** wolves, to mimic the hierarchy of leadership and hunting mechanism of **grey** wolves in nature. The effectiveness of the proposed algorithm has been tested on the standard IEEE 30-bus test system and the results were compared with other methods reported in recent literature. The simulation results show that the proposed algorithm outperforms previous optimization methods.

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Abstract: The performance of a multi-layer neural network (MLP) depends how it is optimized. The optimization of MLP including its structure is tedious one as there is no explicit rules for deciding number of layers and number of neurons in each layer. Further, if the error function is multi-modal the conventional way of **using** gradient descent rule may give only local optimal solutions which may result in poorer performance of the network. In this paper a novel way is adopted to optimize the MLP in which a recently developed meta-heuristic optimization technique, Gray **wolf** **optimizer** (GWO) is used to optimize the weights of the MLP network. Meta-heuristic algorithms are known to be very efficient in finding globally optimal solutions of highly non-linear optimization problems. In this work the optimization of MLP is done by variation of hidden neurons layer wise and best performance is obtained **using** GWO algorithm. The ultimate optimal structure of MLP network so obtained is 13-6-1 where 13 is the number of neurons in the input layer, 6 is the number of neurons in the hidden layer and 1 is the number of neuron in the output layer. Single hidden layer is found to give better results as compared to more hidden layers. The performance of the optimized GWO-MLP network is investigated on three different datasets namely UCI Cleveland Benchmark Dataset, UCI Statlog Benchmark Dataset and Ruby Hall Clinic Local Dataset. On comparison the performance of the proposed approach is found to be superior to all other already reported works in terms of accuracy and MSE.

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The significant of this thesis can be divided into two fields including segmentation algorithm and medical. The significant of the thesis in terms of segmentation algorithm is that the proposed algorithm can determine the optimized initial point values for segmentation process of the medical image automatically. Moreover, the blurred area around the edges is further enhanced. Therefore, the quality of the final segmentation result is getting improved. In this thesis, the large number of cases are processed in a short time having the almost same accuracy. The segmentation process utilizing the method presented in this thesis becomes easier with less human intervention. The significant of the project in terms of medical field is that treatment planning by medical expert will be easier because the brain relate diseases such as Alzheimer can be found out by measuring brain White Matter (WM) region **using** segmentation algorithm.

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An extensive literature reviews reveals that a good numbers of meta-heuristic algorithms have been proposed in order to cater this matter. In [11-13], the LSSVM is hybrid with a Swarm Intelligence (SI) algorithm, namely Particle Swarm Optimization (PSO) for parameter tuning. In the studies, the efficiency of PSO- LSSVM is realized in different problem domain which includes nuclear science, shipping and water drainage and irrigation respectively. On the other hand, the capability of Genetic Algorithm (GA), which is a dominant algorithm in Evolutionary Algorithm (EA) class was tested in several function estimation problems, which includes in [14, 15]. Meanwhile, in [16], the LSSVM is hybridized with Fruit Fly Optimization (FFO) [17] for electric load predictive analysis. In the study, the FFO which is inspired from the food searching behaviour is employed as an **optimizer** to LSSVM. Later, the FFO-LSSVM is compared against several identified techniques which include single LSSVM and regression technique. Final results suggested that the FFO-LSSVM is capable to produce lower error rate relative to several identified metrics.

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control is established in the interconnected power system of hydro, **thermal**, and **wind** for solving the problem of frequency instability in this paper. Besides, the improved **grey** **wolf** optimization algorithm (GWO) is presented based on the offspring **grey** **wolf** **optimizer** (OGWO) search strategy to handle local convergence for the GWO algorithm in the later stage. The experimental results show that the improved **grey** **wolf** algorithm has a superior optimization ability for the standard test function. The traditional proportional integral derivative (PID) controller cannot track the random disturbance of **wind** power in the hydro, **thermal**, and **wind** interconnected power grid. However, the proposed OGWO dynamically adjusts the PID controller control parameters to follow the **wind** power random disturbance, regional frequency deviation, and tie-line power deviation.

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emphasis on the behavioral linkage between parents and their offsprings. It is powerful optimization technique which does not first and second derivates of objective function [30]. The main stages of EP are initialization, creation of offspring vectors by mutation and finally competition and selection to evaluate the optimal **solution**, so common underlying idea that come out is given a population of individuals or parents, environmental pressure causes the natural selection based on survival of fittest and finally reach the global optimum point [31]. In EP recombination or mutation is applied to each candidate or parent resulted into one or more new candidates (offspring) which competes with main parents on the basis of their fitness values and selected to undergo mutation for the next generation. This process repeats until search reaches the global optimal point.

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A definitive pastime of dark wolves is chasing technique. The wolves are engaged with chasing for getting their prey. At the start, they can't discover the prey where it's far in spite of the reality that they want to find out the vicinity of prey. Among all wolves, alpha performs the lead hobby to direct all specific wolves. Beta and delta serves to alpha for deciding on desire. At lengthy closing, the dim wolves with the direction of essential wolves accomplish the region of prey i.E., the remarkable best association. These all are mounted via utilizing the below conditions. The 3 nice preparations are achieved in complete technique. For that, the state of affairs of dim wolves is updated over every iterations by **using** the equations (5) to (11).

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Abstract—This work focuses on implementing the optimal power flow (OPF) problem, considering **wind**, solar and hy- dropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled **using** Weibull, Log- normal and Gumbel probability density functions. The system- wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic **grey** **wolf** optimization (GWO) algorithm is proposed, in order to enhance the algorithm’s exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE- 30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions.

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In this paper a genetic algorithm approach is used to solve profit based **unit** **commitment** problem under deregulated environment. The profit based **unit** **commitment** under deregulation involves determining the time intervals for **commitment** of generating units for an individual power producer to maximize his profit considering the effect of spot market prices. To validate the proposed algorithm a system with 10 **unit** data has been considered with usual **unit** constraints.

In recent years, web services as computational models were developed quickly and played significant roles in e-commerce and web-based services. Therefore, the use of convenient and fast web service with atomic functionality has increased. However, for an application consisting of tasks, a combination of web services is used to execute the tasks where each task (called abstract task) is meant for a specific function. For each task, there are a number of candidate web services with the same functionality but with different quality characteristics. An optimal **solution** for execution of an application is a set of selected web services whose combination is the most

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Table II shows the start-up time for the various types of conventional plant on the system. This can vary for each fuel type because of different characteristics for different plant with the same fuel. Here, flexible units are defined as those that can come online in less than one hour. It can be seen that the inflexible mid merit plant cannot start in less than one hour, and therefore are classified as not flexible, as are base loaded gas and coal units. Data for **wind**, load and **unit** characteristics is taken from [24], and used with the STT to produce scenario trees for the scheduling model. The system modelled has a peak demand of 9600MW and a minimum de- mand of 3500MW in 2020. Interconnection to Great Britain is assumed to be 1000MW. The Great Britain electricity system is modelled by grouping together similar units in blocks, so there are large blocks for nuclear, coal, CCGT, etc, with **wind** providing approximately 12% of electricity demand. **Wind** and load is assumed to be perfectly forecast in Great Britain. The interconnector is operated on a day ahead basis only, i.e, import or export is fixed at noon every day for the following day, and cannot be altered intra-day, i.e. when the system rolls forward, the exchange scheduled on the interconnector can not be changed. The average replacement reserve for the system is shown in Fig. 2 for varying frequencies of rolling. This was calculated based on the percentile of total forecast error which most closely matches the current demand for replacement

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The objective of UC with V2G is to minimize the total operating cost over the time horizon while the hourly load demand and spinning reserve are met and emission, and to improve system reserve and reliability. The cost includes mainly fuel cost and start-up cost[8]. Usually large cheap units are used to satisfy base load demand of a system. Most of the time, large units are therefore on and they have slower ramp rates. On the other hand, small units have relatively faster ramp rates. Besides, each **unit** has different cost and emission characteristics that depend on amount of power generation, fuel type, generator **unit** size, technology and so on. Gridable vehicles of V2G technology will reduce dependencies on small/micro expensive units. But number of gridable vehicles in V2G is much higher than small/micro units. So profit, emission, spinning reserve, reliability of power systems vary on scheduling optimization quality. UC with V2G is a large-scale and complex optimization problem. The objective of the UC with V2G is to minimize total operation cost and emission, where cost includes mainly fuel cost and start-up cost [6].

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ABSTRACT: **Unit** **commitment** problem (UCP) plays a key role in the power system operation and control. This paper deals with the mathematical formulation of conventional UCP and its **solution** with priority list (PL) approach **using** GABC algorithm. In this study, three different PL approaches are deliberated, namely, cost priority, power priority and hybrid priority to attain optimum **solution**. The optimal results ofUCP with different PL approaches are compared over a scheduling period which yields that the power priority and hybrid priority methods provide better result compared to the conventional cost priority approach for standard IEEE 10-**unit** system.

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It is a stochastic search method which searches for **solution** from one state to the other. The feasible states are then saved [1,11,19]. Dynamic programming was the earliest optimization-based method to be applied to the UC problem. It is used extensively throughout the world. It has the advantage of being able to solve problems of a variety of sizes and to be easily modified to model characteristics of specific utilities[11].But the disadvantage of this method is curse of dimensionality. ie, the computational effort increases exponentially as problem size increases and **solution** is infeasible[4] and its suboptimal treatment of minimum up and downtime constraints and time-dependent startup costs[11].

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Zadeh introduced the concept of fuzzy sets in 1965 as a mathematical means of describing vagueness in linguistics. It was later developed by mathematical researchers in 1970[11]. The idea may be considered as a generalization of classical set theory. UC is a complex decision-making process [1,3,5]which operates appropriate units at different hours and schedules the outputs of the committed units to meet a predicted demand, such that the operating cost is minimized. Due to the uncertainty of the demand and outages of generating units, fuzzy is used to represent the uncertainty.The method is an intelligence based technique that quantifies linguistic terms so that variables are treated as continuous. It establishes the relation between input and output according to some fuzzy control rules.eg by **using** ―if-then‖. The result is defuzzified to obtain numerical **solution** [4].But the disadvantage of this method is that it cannot handle large scale systems [5].

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