Optimal expansion of medium-voltage power networks because of load growth is a combinatorial problem which is important from technical and economic points of view. The planning solutions consist of installation and/or...
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Optimal expansion of medium-voltage power networks because of load growth is a combinatorial problem which is important from technical and economic points of view. The planning solutions consist of installation and/or reinforcement of high voltage/medium voltage (HV/MV) substations, feeder sections, distributed generation (DG) and storage units to expand the capacity of the network. The cost objective function of the system should be minimized subject to the technical constraints. Due to the complicacy and the complexity of the problem, it should be solved by modern optimization algorithms. In this paper, the most famous optimization algorithms for solving the distribution network planning problem are reviewed and compared, and some points are proposed to improve the performance of the algorithms. In order to compare the algorithms in practice, and verify the proposed improvement points, the numerical studies on three test distribution networks are presented. The results show that every algorithm has its own advantages and disadvantages in specific conditions. However, in general manner, the hybrid Tabu search/genetic algorithm (TS/GA) and the improved particle swarm optimization (PSO) algorithm proposed in this paper are the best choices for optimal distribution network planning. (C) 2016 Elsevier Ltd. All rights reserved.
Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation. In this paper, we systematically review the benchmarking p...
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Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation. In this paper, we systematically review the benchmarking process of optimization algorithms, and discuss the challenges of fair comparison. We provide suggestions for each step of the comparison process and highlight the pitfalls to avoid when evaluating the performance of optimization algorithms. We also discuss various methods of reporting the benchmarking results. Finally, some suggestions for future research are presented to improve the current benchmarking process.
Eight optimizer methods are combined with a perceptron neural network to achieve an optimal network and minimize errors for predicting the heat transfer rate of a ribbed triple-tube heat exchanger operating with the g...
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Eight optimizer methods are combined with a perceptron neural network to achieve an optimal network and minimize errors for predicting the heat transfer rate of a ribbed triple-tube heat exchanger operating with the graphene nanoplatelets-based nanofluid. The optimization techniques consist of Harris Hawks Optimizer (HHO), Grey Wolf Optimizer (GWO), Whale optimization Algorithm (WOA), Artificial Bee Colony (ABC), Ant Colony optimization (ACO), Ant Lion Optimizer (ALO), Biogeography-Based optimization (BBO), and Dragonfly Algorithm (DA). The required data are provided with the aid of numerical simulations. The structural parameters are considered using different rib pitches and heights. The ALO algorithm is the best method for estimating the output. The best performance of this algorithm is gained by population size of 350. By this method, the heat transfer rate is estimated with the Root Mean Square Error (RMSE) values of about 0.0310 and 0.0385 for the training and testing data samples, respectively. (C) 2020 Elsevier B.V. All rights reserved.
Microgrids interfaced with distributed generators facilitate decentralization of electric power. Bi-directional power flow due to multiple sources and dynamic behaviour of microgrids possess challenges to protection e...
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Microgrids interfaced with distributed generators facilitate decentralization of electric power. Bi-directional power flow due to multiple sources and dynamic behaviour of microgrids possess challenges to protection engineers. In this paper, an adaptive protection scheme for a central protection centre (CPC) in a microgrid is proposed. The key functions of the CPC are monitoring the microgrid, identification of fault if any, shortest path identification from a fault to the nearest operating source using Boruvka-Dijkstra graph theory algorithm. It also assigns adaptively the optimized values of time multiplier setting of relays in that path using genetic algorithm, which in turn aids in quick fault clearance. A hardware prototype is developed, tested and validated for a 7-bus microgrid network using Arduino ATmega 1280 for shortest path identification and optimized TMS value assignment for relays in that path using Raspberry Pi Model B+.
Power electronic converter (PEC)-interfaced renewable energy generators (REGs) are increasingly being integrated to the power grid. With the high renewable power penetration levels, one of the key power system paramet...
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Power electronic converter (PEC)-interfaced renewable energy generators (REGs) are increasingly being integrated to the power grid. With the high renewable power penetration levels, one of the key power system parameters, namely reactive power, is affected, provoking steady-state voltage and dynamic/transient stability issues. Therefore, it is imperative to maintain and manage adequate reactive power reserve to ensure a stable and reliable power grid. This paper presents a comprehensive literature review on the reactive power management in renewable rich power grids. Reactive power requirements stipulated in different grid codes for REGs are summarized to assess their adequacy for future network requirements. The PEC-interfaced REGs are discussed with a special emphasis on their reactive power compensation capability and control schemes. Along with REGs, conventional reactive power support devices (e.g., capacitor banks) and PEC-interfaced reactive power support devices (e.g., static synchronous compensators) play an indispensable role in the reactive power management of renewable rich power grids, and thus their reactive power control capabilities and limitations are thoroughly reviewed in this paper. Then, various reactive power control strategies are reviewed with a special emphasis on their advantages/disadvantages. Reactive power coordination between support devices and their optimal capacity are vital for an efficient and stable management of the power grid. Accordingly, the prominent reactive power coordination and optimization algorithms are critically examined and discussed in this paper. Finally, the key issues pertinent to the reactive power management in renewable rich power grids are enlisted with some important technical recommendations for the power industry, policymakers, and academic researchers.
Estimating gas source terms is essential and significant for managing a gas emission accident. optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse pro...
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Estimating gas source terms is essential and significant for managing a gas emission accident. optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
The economic dispatch problems (EDPs) in a microgrid (MG) have been extensively investigated by a variety of emerging algorithms. In this paper, we propose two newly distributed dynamic optimization algorithms to resp...
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The economic dispatch problems (EDPs) in a microgrid (MG) have been extensively investigated by a variety of emerging algorithms. In this paper, we propose two newly distributed dynamic optimization algorithms to respectively study the EDPs under both cases without and with generation constraints under a directed topology network. Two novel dynamic optimization algorithms are based on the distributed incremental cost consensus (ICC), where the mismatch between total demand and power generation is considered. Our algorithms only require the weight matrix of the directed network to be row stochastic. The theoretical analysis on the convergence of the proposed algorithms is presented by using the small gain theorem. It can be found that the algorithms are convergent at the geometric rate. Meanwhile, the power output of the generators are proved to achieve the optimal solution of EDPs based on the proposed algorithms. Finally, the corresponding conditions are also derived, and simulation studies illustrate the correctness of our results.
Adapting applications to optimally utilize available hardware is no mean feat: the plethora of choices for optimization techniques are infeasible to maximize manually. To this end, auto -tuning frameworks are used to ...
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Adapting applications to optimally utilize available hardware is no mean feat: the plethora of choices for optimization techniques are infeasible to maximize manually. To this end, auto -tuning frameworks are used to automate this task, which in turn use optimization algorithms to efficiently search the vast searchspaces. However, there is a lack of comparability in studies presenting advances in auto -tuning frameworks and the optimization algorithms incorporated. As each publication varies in the way experiments are conducted, metrics used, and results reported, comparing the performance of optimization algorithms among publications is infeasible. The auto -tuning community identified this as a key challenge at the 2022 Lorentz Center workshop on auto -tuning. The examination of the current state of the practice in this paper further underlines this. We propose a community -driven methodology composed of four steps regarding experimental setup, tuning budget, dealing with stochasticity, and quantifying performance. This methodology builds upon similar methodologies in other fields while taking into account the constraints and specific characteristics of the auto -tuning field, resulting in novel techniques. The methodology is demonstrated in a simple case study that compares the performance of several optimization algorithms used to auto -tune CUDA kernels on a set of modern GPUs. We provide a software tool to make the application of the methodology easy for authors, and simplifies reproducibility of results.
optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization tech...
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optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GANPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.
A variety of optimization algorithms has been developed for non-linear and non-convex problems in which numerous reconfigurable sensors need to be assigned to many tasks. The algorithms are based on modified gradient-...
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A variety of optimization algorithms has been developed for non-linear and non-convex problems in which numerous reconfigurable sensors need to be assigned to many tasks. The algorithms are based on modified gradient-search methods and inspired by centralized/distributed principles. Numerical evaluation of these algorithms on a statistically large set of optimization problems has shown that while each particular algorithm does not necessarily provide the optimal solution in all possible cases, some are very efficient in solving them. Distributed (agent-based) approaches are usually advocated because of their scalability and speed, but the developed centralized (synchronous) algorithms are shown to be better in terms of speed, and simultaneously in terms of effectiveness, and therefore, in terms of efficiency. (C) 2014 Elsevier Ltd. All rights reserved.
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