scholarly journals Generating Set Search Method with Optimal Bind Tolerance for Solving the Economic Load Dispatch Problem of Thermal Stations

2021 ◽  
Author(s):  
Ikenna Onyegbadue ◽  
Cosmas Ogbuka ◽  
Theophilus Madueme
Keyword(s):  
Mesh Size ◽  
Economic Cost ◽  
Function Evaluation ◽  
Economic Load Dispatch ◽  
Maximum Function ◽  
Stopping Criteria ◽  
Generating Set ◽  
Initial Mesh ◽  
Contraction Factor ◽  
Search Approach

A non-derivative direct search approach called Generating Set Search (GSS) algorithm with varying bind tolerance to solve non-convex Economic Load Dispatch problem of the thermal stations in Nigeria is presented. A complete poll was carried out with initial mesh size of 1.0, expansion factor of 2.0 and contraction factor of 0.5. The binding tolerance was varied from 100 – 2200 with an increment of 100. The stopping criteria were based on the following: mesh tolerance of 0.000001, maximum iteration of 1500 and maximum function evaluation of 30000. The Economic Load Dispatch of 2500 MW, 3000 MW, 3500 MW and 4000 MW produced optimal solutions at binding tolerances of 500, 600, 1100, and 1600 respectively. The economic cost (measured in quantity of fuel) obtained for the dispatch of 2500 MW, 3000 MW, 3500 MW and 4000 MW were 83577.6936190168 MMBTU/hr, 83577.6936667599 MMBTU/hr, 83577.6937160183 MMBTU/hr and 83577.694264612 MMBTU/hr respectively. The evaluations carried out on the function in order to obtain the best solution were 1484, 5709, 6895 and 7556 for 2500 MW, 3000 MW, 3500 MW and 4000 MW of load respectively. Although the optimal bind tolerances had more iterations and evaluations, these can be traded off for the best solutions offered.<br>

scholarly journals Generating Set Search Method with Optimal Bind Tolerance for Solving the Economic Load Dispatch Problem of Thermal Stations

2021 ◽  
Author(s):  
Ikenna Onyegbadue ◽  
Cosmas Ogbuka ◽  
Theophilus Madueme
Keyword(s):  
Mesh Size ◽  
Economic Cost ◽  
Function Evaluation ◽  
Economic Load Dispatch ◽  
Maximum Function ◽  
Stopping Criteria ◽  
Generating Set ◽  
Initial Mesh ◽  
Contraction Factor ◽  
Search Approach

A non-derivative direct search approach called Generating Set Search (GSS) algorithm with varying bind tolerance to solve non-convex Economic Load Dispatch problem of the thermal stations in Nigeria is presented. A complete poll was carried out with initial mesh size of 1.0, expansion factor of 2.0 and contraction factor of 0.5. The binding tolerance was varied from 100 – 2200 with an increment of 100. The stopping criteria were based on the following: mesh tolerance of 0.000001, maximum iteration of 1500 and maximum function evaluation of 30000. The Economic Load Dispatch of 2500 MW, 3000 MW, 3500 MW and 4000 MW produced optimal solutions at binding tolerances of 500, 600, 1100, and 1600 respectively. The economic cost (measured in quantity of fuel) obtained for the dispatch of 2500 MW, 3000 MW, 3500 MW and 4000 MW were 83577.6936190168 MMBTU/hr, 83577.6936667599 MMBTU/hr, 83577.6937160183 MMBTU/hr and 83577.694264612 MMBTU/hr respectively. The evaluations carried out on the function in order to obtain the best solution were 1484, 5709, 6895 and 7556 for 2500 MW, 3000 MW, 3500 MW and 4000 MW of load respectively. Although the optimal bind tolerances had more iterations and evaluations, these can be traded off for the best solutions offered.<br>

scholarly journals An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2770
Author(s):  
Mokhtar Said ◽  
Ali M. El-Rifaie ◽  
Mohamed A. Tolba ◽  
Essam H. Houssein ◽  
Sanchari Deb
Keyword(s):  
Economic Cost ◽  
Economic Load Dispatch ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Best Value ◽  
Sine Cosine Algorithm ◽  
Cost Of Production ◽  
Swarm Algorithm ◽  
Main Item ◽  
Emission Dispatch

Economic Load Dispatch (ELD) is a complicated and demanding problem for power engineers. ELD relates to the minimization of the economic cost of production, thereby allocating the produced power by each unit in the most possible economic manner. In recent years, emphasis has been laid on minimization of emissions, in addition to cost, resulting in the Combined Economic and Emission Dispatch (CEED) problem. The solutions of the ELD and CEED problems are mostly dominated by metaheuristics. The performance of the Chameleon Swarm Algorithm (CSA) for solving the ELD problem was tested in this work. CSA mimics the hunting and food searching mechanism of chameleons. This algorithm takes into account the dynamics of food hunting of the chameleon on trees, deserts, and near swamps. The performance of the aforementioned algorithm was compared with a number of advanced algorithms in solving the ELD and CEED problems, such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA). The simulated results established the efficacy of the proposed CSA algorithm. The power mismatch factor is the main item in ELD problems. The best value of this factor must tend to nearly zero. The CSA algorithm achieves the best power mismatch values of 3.16 × 10−13, 4.16 × 10−12 and 1.28 × 10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the ELD problem. The CSA algorithm achieves the best power mismatch values of 6.41 × 10−13 , 8.92 × 10−13 and 1.68 × 10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the CEED problem. Thus, the CSA algorithm was found to be superior to the algorithms compared in this work.

Performance analysis

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Areej Ahmad Alsaadi ◽  
Wadee Alhalabi ◽  
Elena-Niculina Dragoi
Keyword(s):  
Performance Analysis ◽  
Evolutionary Computation ◽  
Search Algorithm ◽  
Function Evaluation ◽  
World Congress ◽  
Maximum Function ◽  
Content Type ◽  
Hybrid Function ◽  
Depth Study ◽  
Almost All

Purpose Differential search algorithm (DSA) is a new optimization, meta-heuristic algorithm. It simulates the Brownian-like, random-walk movement of an organism by migrating to a better position. The purpose of this paper is to analyze the performance analysis of DSA into two key parts: six random number generators (RNGs) and Benchmark functions (BMF) from IEEE World Congress on Evolutionary Computation (CEC, 2015). Noting that this study took problem dimensionality and maximum function evaluation (MFE) into account, various configurations were executed to check the parameters’ influence. Shifted rotated Rastrigin’s functions provided the best outcomes for the majority of RNGs, and minimum dimensionality offered the best average. Among almost all BMFs studied, Weibull and Beta RNGs concluded with the best and worst averages, respectively. In sum, 50,000 MFE provided the best results with almost RNGs and BMFs. Design/methodology/approach DSA was tested under six randomizers (Bernoulli, Beta, Binomial, Chisquare, Rayleigh, Weibull), two unimodal functions (rotated high conditioned elliptic function, rotated cigar function), three simple multi-modal functions (shifted rotated Ackley’s, shifted rotated Rastrigin’s, shifted rotated Schwefel’s functions) and three hybrid Functions (Hybrid Function 1 (n=3), Hybrid Function 2 (n=4,and Hybrid Function 3 (n=5)) at four problem dimensionalities (10D, 30D, 50D and 100D). According to the protocol of the CEC (2015) testbed, the stopping criteria are the MFEs, which are set to 10,000, 50,000 and 100,000. All algorithms mentioned were implemented on PC running Windows 8.1, i5 CPU at 1.60 GHz, 2.29 GHz and a 64-bit operating system. Findings The authors concluded the results based on RNGs as follows: F3 gave the best average results with Bernoulli, whereas F4 resulted in the best outcomes with all other RNGs; minimum and maximum dimensionality offered the best and worst averages, respectively; and Bernoulli and Binomial RNGs retained the best and worst averages, respectively, when all other parameters were fixed. In addition, the authors’ results concluded, based on BMFs: Weibull and Beta RNGs produced the best and worst averages with most BMFs; shifted and rotated Rastrigin’s function and Hybrid Function 2 gave rise to the best and worst averages. In both parts, 50,000 MFEs offered the best average results with most RNGs and BMFs. Originality/value Being aware of the advantages and drawbacks of DS enlarges knowledge about the class in which differential evolution belongs. Application of that knowledge, to specific problems, ensures that the possible improvements are not randomly applied. Strengths and weaknesses influenced by the characteristics of the problem being solved (e.g. linearity, dimensionality) and by the internal approaches being used (e.g. stop criteria, parameter control settings, initialization procedure) are not studied in detail. In-depth study of performance under various conditions is a “must” if one desires to efficiently apply DS algorithms to help solve specific problems. In this work, all the functions were chosen from the 2015 IEEE World Congress on Evolutionary Computation (CEC, 2015).

Physical and Mental Impairment-of-function Evaluation

1972 ◽  
Author(s):  
Lee Gurel ◽  
Margaret W. Linn ◽  
Bernard S. Linn
Keyword(s):  
Function Evaluation ◽  
Mental Impairment

A Fast Scheduling Method to Solve Economic Load Dispatch Problem

2020 ◽  
Vol 13 (1) ◽  
pp. 12
Author(s):  
Conny K. Wachjoe ◽  
Hermagasantos Zein ◽  
Jangkung Raharjo
Keyword(s):  
Economic Load Dispatch ◽  
Scheduling Method

Beveridge, Bismarck and Southern European Health Care Systems: Can We Decide Which is the Best in EU-15? A Statistical Analysis

2017 ◽  
Vol 1 (1) ◽  
pp. 41
Author(s):  
Angeliki Moisidou
Keyword(s):  
Health Care ◽  
Statistical Analysis ◽  
Empirical Analysis ◽  
Access To Health Care ◽  
High Performance ◽  
Social Protection ◽  
Health Care Systems ◽  
Health Sector ◽  
Economic Cost ◽  
Care Systems

A statistical analysis has been conducted with the aim to elucidate the effect of health care systems (HSs) on health inequalities assessed in terms of (a) differential access to health care services and (b) varying health outcomes among different models of HSs in EU-15 ((Beveridge: UK, IE, SE, FI, DK), (Bismarck: DE, FR, BE, LU, AT, NL), (Southern European model: GR, IT, ES, PT)). In the effort to interpret the results of the empirical analysis, we have ascertained systematic differences among the HSs in EU-15. Specifically, it is concluded that countries with Beveridge HS can be characterized more efficient (than average) in the most examined correlations, showing particularly high performance in the health sector. Similarly, countries with Bismarck HS record fairly satisfactory performance, but simultaneously they display more structural weaknesses compared with the Beveridge model. In addition, our empirical analysis has shown that adopting Bismarck model requires higher economic cost, compared with the Beveridge model, which is directly financed by taxation. On the contrary, in the countries with Southern European HS, the lowest performances are generally identified, which can be attributed to the residual social protection that characterizes these countries. The paper concludes with a synthesis of the empirical findings of our research. It proposes some directions for further research and presents a set of implications for policymakers regarding the planning and implementation of appropriate policies in order to tackle health inequality within HSs.

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