Economic Dispatch Using Modified Bat Algorithm

The economic power dispatch (EPD) is an integral part of the power system, and the major roles and purpose of its use are for achieving a reliable and efficient operation out of power system generation networks, and this operation should be obtained by minimizing the generator fuel cost. Getting optimal solutions to EPD problem requires efficient optimization algorithms. Novel Bat Algorithm (NBA) is one of the most recent methods and it has already proven its efficiency and reliability for solving the EPD problem. This paper proposes Novel Bat Algorithm (NBA) in order to solve the EPD problem based on the large scale power system. The NBA has proved its efficiency and it gave a perfect performance for small-scale systems compared with the original Bat algorithm (BA), because of considering the Doppler Effect and assumed that bat can move between various habitats. The study of the EPD for large-scale power systems is more important than study it for small ones, because all power systems in countries are considered large networks. This is why all modern studies focus on the study of EPD for large systems. To test the performance of NBA during small and large scale power system, we have applied it to many systems, including 3-thermal units, 6-thermal units, 31-Iraqi thermal units and IEEE 40-thermal units respectively, with transmission losses and generator limits, and we have made a comparison of the obtained results of NBA with other optimization methods. The Iraqi thermal units have been utilized as a new data.

Hybrid Bat & Particle Swarm Algorithm for optimization of labyrinth spillway based on half & quarter round crest shapes

Background: Regression testing is very important stage of software maintenance and evolution phase. The software keeps on updating and to preserve the software quality, it needs to be retested every time it is updated. Due to limited resources, complete testing of the software becomes tedious task. The probable solution to this problem is to execute those test cases first that are more important prior to less important test cases. Objective: Optimization methods need to be acquired for efficient test case prioritization in minimum time, while maintaining the quality of the software. Various nature-inspired algorithms like genetic algorithm, particle swarm optimization and ant colony optimization, etc. have been applied for prioritizing test cases. In this paper, we have applied a relatively new nature-inspired optimization method, namely, Bat algorithm that utilizes the echolocation, loudness and pulse emission rate of bats in prioritizing the test cases. Methods: The proposed algorithm is experimented on sample case study of timetable management system and evaluated with the help of popular evaluation metric Average Percentage of Fault Detection. Results: Results have been compared with the baseline approaches i.e. untreated, random and reverse prioritization and well-established optimization method i.e. genetic algorithm and we have found a considerable increase in the value of evaluation metric. Conclusion: This preliminary study shows that the bat algorithm have great potential to solve test case prioritization problems.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Economic Dispatch Problem (EDP) is a power system optimization problem that is required to be solved accurately using an efficient optimization technique. Hybrid optimization solutions have provided better optimum results than either deterministic or non-deterministic optimization methods. The hybridization of both Particle Swarm Optimization (PSO) and Bat Algorithm (BA) to for Hybrid Particle Swarm Bat Algorithm (H-PS-BA) optimization technique for solving EDP of Nigerian 21 thermal generating station power system was carried out in this work. The result of the work revealed that H-PS-BA performed better and gave the best optimal generation costs when compared to other methods such as PSO, Interior Point Method and BA.

The increasing adoption of solar energy as a clean and sustainable power source has intensified research efforts toward developing more efficient photovoltaic (PV) cells. These cells exhibit nonlinear characteristics that are significantly influenced by variations in irradiance and temperature. Accurate estimation of PV model parameters plays a crucial role in maximizing performance, particularly via precise maximum power point (MPP) tracking. This paper presents a new hybrid metaheuristic algorithm that combines the global exploration capability of the Bat Algorithm (BA) with local exploitation efficiency of the Crow Search Algorithm (CR) to optimize PV parameter estimation. The proposed approach is tested using Single-Diode (SDM), Double-Diode (DDM), and Triple-Diode (TDM) models based on the RTC France dataset. The hybrid model demonstrates better convergence behavior and robustness compared to conventional approaches. Qualitatively, it effectively manages parameter uncertainty; quantitatively, it achieves RMSE values of 0.00077299 (SDM), 0.0008215 (DDM), and 0.0008068 (TDM), outperforming traditional algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach

Non-convex optimization problems are prevalent in scientific and engineering applications, often characterized by multiple local minima that challenge standard optimization techniques. Global optimization methods, such as Genetic Algorithms (GA) (1992) [4] and Particle Swarm Optimization (PSO) (1995) [6], provide robust global exploration but tend to converge slowly. Conversely, local search methods, such as Newton’s method (2004) [1], (2006) [7], and the Conjugate Gradient (CG) (1964) [3] method, offer rapid convergence but are susceptible to local minima. In this paper, we present four hybrid optimization algorithms that integrate the exploratory strength of global algorithms with the refinement ability of local methods. The proposed hybrids, Genetic Algorithms (GA) + Newton, GA + CG, PSO + Newton, and PSO + CG, are evaluated against their standalone counterparts on standard benchmark functions (2013) [5], including Rosenbrock, Rastrigin, Ackley, and Himmelblau. This work builds upon our previous research on the hybridization of two global search algorithms, the Nelder–Mead (simplex) algorithm and the Bat algorithm, which resulted in the Hybrid Simplex Bat Algorithm (HSBA) [2], recently Published. Results demonstrate that the hybrid approaches consistently outperform traditional methods in terms of accuracy, convergence rate, and computational efficiency.

The quadratic assignment problem (QAP) is a well‐known challenging combinatorial optimization problem that has received many researchers' attention with varied real‐world and industrial applications areas. It is noteworthy to mention that a plethora of nature‐inspired optimization algorithms have successfully been used to solve various optimization problems, including several variants of the QAPs. In this article, a comprehensive literature review is presented to show the most relevant nature‐inspired algorithms that have been used in solving the QAP. More so, extensive experiments are conducted and analyzed to show the performance of the well‐known state‐of‐the‐art nature‐inspired meta‐heuristic optimization algorithms in solving the QAP, including the ant colony optimization (ACO), bat algorithm, genetic algorithm (GA), particle swarm optimization (PSO), and tabu search algorithm. Besides, a modified variant of the discrete PSO algorithm is implemented and compared with existing approaches. The six selected algorithms' performances, including the modified PSO, are validated on eight commonly used QAP instances of varying complexity and size, considering the quality of solutions achieved and computational time consumed by the representative algorithms. The numerical results revealed that the most competitive algorithm was ACO, while the GA appeared to be the worst performed algorithm among the six compared meta‐heuristic algorithms. However, based on the extensive analysis conducted on the tested algorithms, further improvements are suggested, including implementing new modified versions of the tested algorithms to tackle the QAP and its variant instances.

Guest Editorial: Situational awareness of integrated energy systems

In this research, a quantum computing idea based bat algorithm (QBA) is proposed to solve many-objective combined economic emission dispatch (CEED) problem. Here, CEED is represented using cubic criterion function to reduce the nonlinearities of the system. Along with economic load dispatch, emissions of SO2, NOx, and CO2 are considered as separate three objectives, thus making it a four-objective (many-objective) optimization problem. A unit-wise price penalty factor is considered here to convert all the objectives into a single objective in order to compare the final results with other previously used methods like Lagrangian relaxation (LR), particle swarm optimization, and simulated annealing. QBA is applied in six-unit power generation system for four different loads. The obtained results show QBA successfully solve many-objective CEED problem with greater superiority than other methods found in the literature in terms of quality results, robustness, and computational performance. In the end of this paper, a detailed future research direction is provided based on the simulation results and its analysis. The outcome of this research demonstrates that the inclusion of quantum computing idea in metaheuristic technique provides a useful and reliable tool for solving such many-objective optimization problem.

Mathematical optimization provides the best possible results to a problem. The latest trend in optimization, called hybrid optimization methods, combines two or more optimization techniques, deterministic and non-deterministic, with the aim of overcoming the limitation of one technique by the advantage(s) of the other technique(s). A hybrid particle swarm bat algorithm optimization technique that uses the frequency tuning technique of bat algorithm at the velocity updating stage in particle swarm optimization (PSO) was developed for avoiding premature convergence limitation of PSO using the potential of BA to escape being trapped at local optimum. Implementation of the developed optimization method for solving the combined economic emission dispatch problem of 28 Bus 7 Generators Nigeria power network showed that H-PS-BA optimization method performed better than PSO by 0.07%. Cite this article as: T. Muhammed Adekilekun, A. Gafari Abiola, O. Muniru Olajide, S. Mufutau Adewolu and B. Isaac Adekunle, "Hybrid optimization technique for solving combined economic emission dispatch problem of power systems," Turk J Electr Power Energy Syst, 2022; 2(2), 158-167.

Optimization studies in high speed turning of Ti-6Al-4V

There is a clear trend in the automotive industry to use more electrical systems in order to satisfy the ever-growing vehicular load demands. Thus, it is imperative that automotive electrical power systems will obviously undergo a drastic change in the next 10-20 years. Currently, the situation in the automotive industry is such that the demands for higher fuel economy and more electric power are driving advanced vehicular power system voltages to higher levels. For example, the projected increase in total power demand is estimated to be about three to four times that of the current value. This means that the total future power demand of a typical advanced vehicle could roughly reach a value as high as 10 kW. In order to satisfy this huge vehicular load, the approach is to integrate power electronics intensive solutions within advanced vehicular power systems. In view of this fact, this paper aims at reviewing the present situation as well as projected future research and development work of advanced vehicular electrical power systems including those of electric, hybrid electric, and fuel cell vehicles (EVs, HEVs, and FCVs). The paper will first introduce the proposed power system architectures for HEVs and FCVs and will then go on to exhaustively discuss the specific applications of dc/dc and dc/ac power electronic converters in advanced automotive power systems

Economic Power Dispatch is a critical component of the power system network (EPD). It assigns required load to online producing units while accounting for system constraints. The fundamental purpose of EPD is to lower overall power generation costs. For example, the bi-objective Combined Emission- Economic Dispatch (CE-ED) problem addresses the environmental impacts of fossil fuel power plant emissions. The bi-objective challenge reduces both fuel costs and emissions. Generators are simulated using quadratic cost functions to match their output restrictions. The non-linear, non-convex objective functions and equality and inequality requirements make the EPD issue more feasible. Recent evolutionary algorithms have improved results, especially for complex optimization problems. These methods identify global optimum solutions in non-convex and large solution domains. Recent algorithms search the solution space at random utilising numerous candidate solutions. Evolving algorithms must be able to get answers by exploring the feasible zone rather than researching the complex region. So the optimization process is speedier and consumes less calculation time while still finding the global optimum. The traditional technique fails due to premature convergence.