162 research outputs found
The optimal synthesis of scanned linear antenna arrays
In this paper, symmetric scanned linear antenna arrays are synthesized, in order to minimize the side lobe level of the radiation pattern. The feeding current amplitudes are considered as the optimization parameters. Newly proposed optimization algorithms are presented to achieve our target; Antlion Optimization (ALO) and a new hybrid algorithm. Three different examples are illustrated in this paper; 20, 26 and 30 elements scanned linear antenna array. The obtained results prove the effectiveness and the ability of the proposed algorithms to outperform and compete other algorithms like Symbiotic Organisms Search (SOS) and Firefly Algorithm (FA)
Optimization of Invasive Weed for Optimal Dimensions of Concrete Gravity Dams
Dam construction projects among the most extensive and most expensive projects are considered. It is always appropriate and optimal for such concrete structures to reduce the volume of concrete and consequently reduce construction costs is essential. In this study, invasive weed optimization software GNU octave, dimensions of concrete gravity dam Koyna located in India optimized stability constraints. For this purpose, a cross-section with a length unit consists of eight geometric parameters as input variables, and other geometric parameters were defined using these variables. The result showed that invasive weeds are well-optimized dimensions of the dam as the volume of concrete in the construction of the dam at the current level measures 3633 cubic meters that optimal dropped 3353 cubic meters, which is a mean of 7.7% of the value of the objective function (the volume of concrete in dams) is reduced. This amount of concrete decreased the construction of the dam, saving the cost and is more economical
A review of optimization approaches for controlling water-cooled central cooling systems
Buildings consume a large amount of energy across all sectors of society, and a large proportion of building energy is used by HVAC systems to provide a comfortable and healthy indoor environment. In medium and large-size buildings, the central cooling system accounts for a major share of the energy consumption of the HVAC system. Improving the cooling system efficiency has gained much attention as the reduction of cooling system energy use can effectively contribute to environmental sustainability. The control and operation play an important role in central cooling system energy efficiency under dynamic working conditions. It has been proven that optimization of the control of the central cooling system can notably reduce the energy consumption of the system and mitigate greenhouse gas emissions. In recent years, numerous studies focus on this topic to improve the performance of optimal control in different aspects (e.g., energy efficiency, stability, robustness, and computation efficiency). This paper provides an up-to-date overview of the research and development of optimization approaches for controlling water-cooled central cooling systems, helping readers to understand the new significant trends and achievements in this area. The optimization approaches have been classified as system-model-based and data-based. In this paper, the optimization methodology is introduced first by summarizing the key decision variables, objective function, constraints, and optimization algorithms. The principle and performance of various optimization approaches are then summarized and compared according to their classification. Finally, the challenges and development trends for optimal control of water-cooled central cooling systems are discussed
ΠΠΎΠ²ΡΠ΅ ΠΏΠΎΠ΄Ρ ΠΎΠ΄Ρ ΠΊ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠ΅Π½Π°ΠΌΠΈ Π½Π° ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΡΠ΅ ΡΡΠ»ΡΠ³ΠΈ
Development of new approaches to formation of analytics mechanisms for the purpose of pricing management of services is an important aspect of increasing the efficiency of transport management processes.Research aimed at improving the tools for determining the optimal parameters of the ratio of quality and price of service for formation of a competitive and efficient tariff policy continues to remain relevant and in demand in modern market conditions. The objective of the study, presented in the article, is to analyse and evaluate the prospects for implementation of the areas to improve the apparatus for assessing the price elasticity of demand for railway passenger transport services as the transition to the use of non-linear parameters in terms of customer behaviour modelling functions, as well as introduction of the most effective algorithms from the set of modern global mathematical optimisation tools.The research conclusions are based on the use of system analysis mechanisms, methods of economic and mathematical modelling and optimisation, as well as of non-parametric statistics tools.The results based on the use of an array of data on the demand of passengers of branded trains include: a comparative assessment of quality of modelling the price elasticity of demand using 15 functions that are nonlinear in terms of parameters; the most promising tools of the search for unknown parameters for non-smooth nonlinear functions for modelling the behaviour of railway customers are identified based on a three-stage procedure for comparative analysis of the performance of more than 60 optimisation algorithms (including the calculation of minima and medians for the sums of squares of modelling errors, bootstrap analysis, Kruskalβ Wallace and MannβWhitney tests, as well as the calculation of a metric specially developed by the authors for assessing the degree of superiority of one algorithm over another within the framework of non-parametric analysis).The findings seem able to be successfully used in relation to other modes of transport in solving similar problems of developing an effective toolkit for managing the prices of transport services.ΠΠ°ΠΆΠ½ΡΠΌ Π°ΡΠΏΠ΅ΠΊΡΠΎΠΌ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π½Π° ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ Π½ΠΎΠ²ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π°Π½Π°Π»ΠΈΡΠΈΠΊΠΈ Π΄Π»Ρ ΡΠ΅Π»Π΅ΠΉ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠ΅Π½Π°ΠΌΠΈ ΡΡΠ»ΡΠ³.Π ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΡΠ½ΠΎΡΠ½ΡΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠ°ΡΡ ΠΎΡΡΠ°Π²Π°ΡΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΠΈ Π²ΠΎΡΡΡΠ΅Π±ΠΎΠ²Π°Π½Π½ΡΠΌΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΠ΅ Π½Π° ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΈ ΡΡΠΎΠΈΠΌΠΎΡΡΠΈ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΠΉ ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΡΠ°ΡΠΈΡΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΠΎΠ³ΠΎ Π² ΡΡΠ°ΡΡΠ΅, β Π°Π½Π°Π»ΠΈΠ· ΠΈ ΠΎΡΠ΅Π½ΠΊΠ° ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ² ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ°ΠΊΠΈΡ
Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΉ ΠΏΠΎ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ Π°ΠΏΠΏΠ°ΡΠ°ΡΠ° ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠ΅Π½ΠΎΠ²ΠΎΠΉ ΡΠ»Π°ΡΡΠΈΡΠ½ΠΎΡΡΠΈ ΡΠΏΡΠΎΡΠ° Π½Π° ΡΡΠ»ΡΠ³ΠΈ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΏΠ°ΡΡΠ°ΠΆΠΈΡΡΠΊΠΎΠ³ΠΎ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ°, ΠΊΠ°ΠΊ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄ ΠΊ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΡΡ
ΠΏΠΎ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌ ΡΡΠ½ΠΊΡΠΈΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΈΠ· Π°ΡΡΠ΅Π½Π°Π»Π° ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡ Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΎΠΉ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ.Π€ΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠ²ΠΎΠ΄ΠΎΠ² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΎΡΠ½ΠΎΠ²ΡΠ²Π°Π΅ΡΡΡ Π½Π° ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΡΠΈΡΡΠ΅ΠΌΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΎ-ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡ Π½Π΅ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ.Π ΠΈΡΠΎΠ³Π΅, Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ°ΡΡΠΈΠ²Π° Π΄Π°Π½Π½ΡΡ
ΠΎ ΡΠΏΡΠΎΡΠ΅ ΠΏΠ°ΡΡΠ°ΠΆΠΈΡΠΎΠ² ΡΠΈΡΠΌΠ΅Π½Π½ΡΡ
ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅Π½ΠΎΠ²ΠΎΠΉ ΡΠ»Π°ΡΡΠΈΡΠ½ΠΎΡΡΠΈ ΡΠΏΡΠΎΡΠ° ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ 15 Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΡΡ
ΠΏΠΎ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌ ΡΡΠ½ΠΊΡΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅, Π² ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΡΡΡ
ΡΡΠ°ΠΏΠ½ΠΎΠΉ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠ°Π±ΠΎΡΡ Π±ΠΎΠ»Π΅Π΅ ΡΠ΅ΠΌ 60 Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ (Π²ΠΊΠ»ΡΡΠ°ΡΡΠ΅ΠΉ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅, ΡΠ°ΡΡΡΡ ΠΌΠΈΠ½ΠΈΠΌΡΠΌΠΎΠ² ΠΈ ΠΌΠ΅Π΄ΠΈΠ°Π½ Π΄Π»Ρ ΡΡΠΌΠΌ ΠΊΠ²Π°Π΄ΡΠ°ΡΠΎΠ² ΠΎΡΠΈΠ±ΠΎΠΊ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π±ΡΡΡΡΡΠ΅ΠΏ-Π°Π½Π°Π»ΠΈΠ·, ΡΠ΅ΡΡΡ ΠΡΠ°ΡΠΊΠ΅Π»Π°βΠ£ΠΎΠ»Π»Π΅ΡΠ° ΠΈ ΠΠ°Π½Π½Π°βΠ£ΠΈΡΠ½ΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ°ΡΡΡΡ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΌΠ΅ΡΡΠΈΠΊΠΈ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΏΡΠ΅Π²ΠΎΡΡ
ΠΎΠ΄ΡΡΠ²Π° ΠΎΠ΄Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π½Π°Π΄ Π΄ΡΡΠ³ΠΈΠΌ Π² ΡΠ°ΠΌΠΊΠ°Ρ
Π½Π΅ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°) ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΡ ΠΏΠΎΠΈΡΠΊΠ° Π½Π΅ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π΄Π»Ρ Π½Π΅Π³Π»Π°Π΄ΠΊΠΈΡ
Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ² ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΎΠ³ΠΎ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ°.ΠΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅ΡΡΡ, ΡΡΠΎ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Π²ΡΠ²ΠΎΠ΄Ρ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ Π΄ΡΡΠ³ΠΈΠΌ Π²ΠΈΠ΄Π°ΠΌ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ° ΠΏΡΠΈ ΡΠ΅ΡΠ΅Π½ΠΈΠΈ ΠΈΠΌΠΈ Π°Π½Π°Π»ΠΎΠ³ΠΈΡΠ½ΡΡ
Π·Π°Π΄Π°Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠ΅Π½Π°ΠΌΠΈ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΡΡ
ΡΡΠ»ΡΠ³
An application of improved salp swarm algorithm for optimal power flow solution considering stochastic solar power generation
This paper describes the use of an improved version of the Salp Swarm Algorithm, known as iSSA, to address Optimal Power Flow (OPF) issues in power system management. The iSSA is applied to OPF problems involving stochastic solar power generation, with the goal of optimizing control variables such as real power generation, voltage magnitude at generation buses, transformer tap settings, and reactive power compensation. The optimization aims to achieve three objectives: minimizing power loss, minimizing cost, and minimizing combined cost and emissions from power generation. The iSSA's performance was tested on a modified IEEE 30-bus system and compared to other recent algorithms, including SSA. The simulation results show that the iSSA outperformed all compared algorithms for all objective functions that have been derived in this study
A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges
Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field
Synthesizing multi-layer perceptron network with ant lion biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings
The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis
Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement
This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios
Optimal Design of Photovoltaic, Biomass, Fuel Cell, Hydrogen Tank Units and Electrolyzer Hybrid System for a Remote Area in Egypt
In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 , the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y
Optimal Design of Photovoltaic, Biomass, Fuel Cell, Hydrogen Tank units and Electrolyzer hybrid system for a remote area in Egypt
In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 , the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y
- β¦