Algorithm For Economic Load Dispatch Research Articles

Comparison of the PSO Algorithm and JAYA Algorithm for Economic Load Dispatch

The electrical power industry has undergone the significant changes, leading to a competitive market and the need for optimal economic dispatch solutions. As energy resources becoming scarce, prices rise, environmental concerns intensify, and electricity consumption increases, classical optimization techniques struggle to handle these complex issues. This project compares particle swarm optimization (PSO) and JAYA optimization method for economic load dispatch (ELD) problems in the electrical power industry. PSO has gained recognition as an effective solution for ELD problems in the last decade. The aim of this study is to show that Jaya optimization is the best optimization methodology for ELD problems. By comparing the performance, convergence speed, and accuracy of PSO and JAYA optimization, this paper aims to provide insights into the superiority of JAYA optimization. Key Words: Economic Load dispatch, Optimization method, PSO algorithm, JAYA algorithm, Incremental fuel cost

Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

<span lang="EN-US">This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. </span>

A hunger game search algorithm for economic load dispatch

This work proposes a new approach to solve the economic load dispatch (ELD) issue in power systems by metaheuristic algorithms inspired by natural life. The problem to be resolved is to optimize the power system network with various constraints by considering the cutting in the cost of the resulting in the transmission of the electric system. The method used in this study is the hunger games search (HGS). This method duplicates the hungerdriven activity and the animal's choice of behavior. The proposed method is to add the concept of starvation as a process structure. Adaptive weights based on the concept of hunger are designed and used to simulate the effects of hunger on each trace process. To get the performance of the proposed method, this research uses mathematical methods, particle swarm optimization (PSO), differential evolution (DE), giza pyramids construction (GPC), and sine tree-seed algorithm (STSA) as a comparison. This study uses 2 case studies. In case study 1, the proposed method has a 0.16% better cost of generation than the mathematical method. Comparison of the HGS method with the PSO method in the second case study, it was found that the HGS method was 0.018% better than the PSO. From the research, it was found that the HGS method was superior.

Hybrid artificial algae algorithm for economic load dispatch

A hybrid artificial algae algorithm (HAAA) has been proposed in this paper that hybridizes the artificial algae algorithm (AAA) and simplex search method (SSM) to solve economic load dispatch problem. The AAA simulates the life cycle of microalgae which comprises helical movement, evolutionary and adaptation phases. The helical movement phase provides the exploration while the evolutionary and adaptation phases provide the exploitation of search space. The exploration provided by the helical movement completely depends upon energy and shear-force parameters. In the proposed algorithm, the AAA acts as global optimizer while SSM provides local search. The SSM improves the exploitation capability by performing a local search. Dynamic tuning of parameters enhances the exploration capability of the proposed method. An iterative heuristic repair algorithm is applied to handle the equality constraint of economic load dispatch problem. Further, the operation in prohibited operating zones is avoided heuristically. The performance of HAAA is evaluated with CEC’05 standard test functions and small, medium and large-scale economic load dispatch problems. The simulation results advocate that the proposed algorithm produces better or comparable results than the well-established and popular algorithms. Finally, the Wilcoxon sign rank test is performed to validate the results.

Improved genetic algorithm for economic load dispatch in hydropower plants and comprehensive performance comparison with dynamic programming method

This paper presents a practical genetic algorithm (GA)-based solution for solving the economic load dispatch problem (ELDP) and further compares the performance of the improved GA (IGA) with that of dynamic programming (DP). Specifically, their performance is comprehensively evaluated in terms of addressing the ELDP through a case study of 26 turbines in the Three Gorges Hydropower Plant with a focus on calculation accuracy, calculation time, and algorithm stability. Evaluation results show that the improved GA method can significantly reduce the ineffectiveness of the GA in current use and could avoid the running of the turbines in the cavitation/vibration zone, thereby ensuring the safety of the turbines during generating operations. Further, the analysis comparing the performance of the IGA and DP show that the IGA is superior to DP when a small number of turbines are involved. However, as the number of turbines increases, the IGA requires more calculation time than DP; moreover, its calculation accuracy and convergence rate are significantly reduced. It is difficult to guarantee the stability of IGA in high-dimension space even though the population grows, on account of the exponential expansion of the calculation dimension, the algorithm’s premature convergence, and the lack of a local search capability. The improvement of the GA as well as the evaluation method proposed in this paper provide a new approach for choosing and improving optimization algorithms to solve the ELDP of large-scale hydropower plants.

Closed-loop Self-adaption Reproduction Bacterial Foraging Optimization Algorithm for Economic Load Dispatch

Closed-loop Self-adaption Reproduction Bacterial Foraging Optimization Algorithm for Economic Load Dispatch

An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects

This paper presents an improved differential evolution (IDE) algorithm for economic load dispatch (ELD) problems with or without valve-point effects (VPE). The proposed IDE is different from the traditional differential evolution (DE) algorithm in three aspects: first, two mutation operators are used to generate mutant vectors; second, a dynamical crossover rate is used to update trail vectors; third, a useful population randomization is adopted to overcome the premature convergence. In addition, a modified repair process is introduced to handle the constraint violations. Eight cases are selected to testify the performance of four DE approaches on solving ELD problems. According to our empirical results, IDE makes slight improvements on the objective function values from the literature. However, it can always find the solutions satisfying the equality constraints. Most importantly, the IDE can achieve much smaller variances than other DEs on the majority of the test problems, indicating that it has strong stability on solving ELD problems.

Tournament-based harmony search algorithm for non-convex economic load dispatch problem

This paper proposes a tournament-based harmony search (THS) algorithm for economic load dispatch (ELD) problem. The THS is an efficient modified version of the harmony search (HS) algorithm where the random selection process in the memory consideration operator is replaced by the tournament selection process to activate the natural selection of the survival-of-the-fittest principle and thus improve the convergence properties of HS. The performance THS is evaluated with ELD problem using five different test systems: 3-units generator system; two versions of 13-units generator system; 40-units generator system; and large-scaled 80-units generator system. The effect of tournament size (t) on the performance of THS is studied. A comparative evaluation between THS and other existing methods reported in the literature are carried out. The simulation results show that the THS algorithm is capable of achieving better quality solutions than many of the well-popular optimization methods.

Artificial bee colony algorithm for economic load dispatch with wind power energy

This paper presents an efficient Artificial Bee Colony (ABC) algorithm for solving large scale economic load dispatch (ELD) problems in power networks. To realize the ELD, the valve-point loading effect, system load demand, power losses, ramp rate limits and prohibited operation zones are considered here. Simulations were performed on four different power systems with 3, 6, 15 and 40 generating units and the results are compared with two forms of power systems, one power system is with a wind power generator and other power system is without a wind power generator. The results of this study reveal that the proposed approach is able to find appreciable ELD solutions than those of previous algorithms.

Exchange market algorithm for economic load dispatch

Abstract This paper presents exchange market algorithm for solving economic load dispatch problems. Exchange market algorithm (EMA) is a new, robust, and strong algorithm to extract the optimal point for global optimization. Inspired by the stock exchange trading method, EMA strives to solve optimization problem. Meticulous investigation of the stock exchange methods employed by the elites in such markets has yielded to shape this algorithm. This algorithm has two searcher operators as well as two absorbent operators for individuals to be absorbed to the elite person, which leads to creation and organization of random numbers in the best way. In order to show the abilities of the EMA, this algorithm has been implemented on four test systems in different dimensions (3, 6, 15 and 40 units) with convex and nonconvex cost functions. The numerical results have been compared with the results of some new and strong algorithms. The results prove the robustness and effectiveness of the proposed algorithm and show that it could be used as a reliable tool for solving practical ELD problems.

A novel algorithm for economic load dispatch of power systems

Economic load dispatch (ELD) is of great significance for energy saving and emission reduction in power systems. However, many practical constraints and nonlinear characteristics such as valve point effects make this problem a nonlinear constrained optimization problem which is difficult to be solved by traditional optimization techniques. In order to solve this problem effectively, this paper introduces a novel method, chaotic teaching–learning-based optimization with Lévy flight (CTLBO). In the proposed CTLBO, the population is divided into two parts: one is evolved through teaching–learning process, while another part is performed by Lévy flight. Then a chaos perturbation is implemented on the randomly chosen part of population in terms of diversification. Moreover, the proposed method is combined with penalty function to address the constraints. Several numerical cases are adopted and solved to illustrate the effectiveness of the proposed algorithm. And the experimental results are analyzed and compared with existing algorithms, which show that the proposed method outperforms other algorithms and has achieved a significant improvement.

A hybrid multi-objective improved bacteria foraging algorithm for economic load dispatch considering emission

In this paper, a hybrid multiobjective improved bacterial foraging algorithm with fuzzy dominance sorting FSIBF is proposed and used to solve environmental friendly economic load dispatch problem. The fuzzy dominance-based sorting procedure is used to select the non-dominated solutions in the Pareto front. The proposed algorithm is applied to nonlinear constrained multiobjective environmental friendly economic load dispatch problem. In the proposed work, we have considered the standard IEEE 30-bus six-generator test system with fuel cost and emission as two conflicting objectives to be optimised simultaneously satisfying the system's operational constraints. The constraints imposed from the operation point of view are the limits on generator real power and reactive power outputs, bus voltages and power flow in the transmission lines. The proposed work also includes the effect of having non-smooth cost characteristics of the thermal generators which arises because of the valve point loading effect. The practical generator constraints such as ramp rate limits and prohibited operating zones are also incorporated in this work suitably.

Chaotic electromagnetism-like mechanism algorithm for economic load dispatch of power system

Chaotic electromagnetism-like mechanism algorithm (CEMA) is first proposed in this paper, which is the integration of electromagnetism-like mechanism algorithm (EMA) and chaos theory. EMA simulates the attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it moves to optimum solution according to certain criteria which need several steps. To enrich the searching behaviour and to avoid being trapped into local optimum, chaotic dynamics is incorporated into EMA. CEMA possesses excellent global optimal performance, simple programming realisation and good convergence, and it is used in economic load dispatch of power systems. Through performance comparison, it is obvious that the solution is superior to other optimisation algorithms. It can be applied to other research problems in power systems.

Enhanced probability-selection artificial bee colony algorithm for economic load dispatch: A comprehensive analysis

The prime motive of economic load dispatch (ELD) is to optimize the production cost of electrical power generation through appropriate division of load demand among online generating units. Bio-inspired optimization algorithms have outperformed classical techniques for optimizing the production cost. Probability-selection artificial bee colony (PS-ABC) algorithm is a recently proposed variant of ABC optimization algorithm. PS-ABC generates optimal solutions using three different mutation equations simultaneously. The results show improved performance of PS-ABC over the ABC algorithm. Nevertheless, all the mutation equations of PS-ABC are excessively self-reinforced and, hence, PS-ABC is prone to premature convergence. Therefore, this research work has replaced the mutation equations and has improved the scout-bee stage of PS-ABC for enhancing the algorithm's performance. The proposed algorithm has been compared with many ABC variants and numerous other optimization algorithms on benchmark functions and ELD test cases. The adapted ELD test cases comprise of transmission losses, multiple-fuel effect, valve-point effect and toxic gases emission constraints. The results reveal that the proposed algorithm has the best capability to yield the optimal solution for the problem among the compared algorithms.

Constrained Elitist Genetic Algorithm for Economic Load Dispatch: Case Study on Nigerian Power System

The Economic Load Dispatch problem concerns the determination of the optimal combination of power generators’ outputs with the lowest generation cost for a defined level of load. The problem arises from the fact that there are rated limits of generators’ power output, fuel costs of power plants are different, energy should be balanced, and plants are not located at the same distance from load centers. This work proposes the use of a novel technique - constrained elitist genetic algorithm (CEGA) in optimizing real power scheduling for the Nigerian power system. The approach was tested and evaluated against related approaches with same test data, where it exhibited superior performance to attempts so far previously reported in the literature.

Application of Bat Optimization Algorithm for Economic Load Dispatch Considering Valve Point Effects

system operation involves some kind of optimization for ensuring economy, security and stability. Economic load dispatch (ELD) is one such optimization problems and it is applied for minimizing the total fuel cost. Optimizing the fuel cost is done by properly setting the real power generation from the generators in a power system. In this work, ELD is achieved by considering two different cost functions. The bio inspired bat optimization algorithm (BOA) is used for optimally setting the values of the control variables. The BOA is a recently developed algorithm and is with less number of operators. The algorithm can be coded in any programming language easily. The proposed algorithm is tested on the standard IEEE-30 bus system and the results are compared with those of the other algorithms reported in the literature. Keywordsoptimization algorithm, Economic load dispatch, Valve point effects.

A Balance and Swap Optimization Algorithm for Economic Load Dispatch

본 논문은 경제급전의 최적화 문제를 풀기위해 균형과 교환 알고리즘을 제안하였다. 제안된 알고리즘은 첫번째로, 발전단가가 비싼 발전기는 가동을 중지시키는 개념을 도입하여 총 요구량과 총 발전량의 균형을 맞추었다. 다음으로 단위 발전량을 감소시킨 경우의 비용 감소분과 증가시킬 경우의 비용 증가분을 계산하여 최대 비용 감소분이 최소비용 증가분보다 큰 경우 두 발전기의 발전용량을 1씩 증가, 감소시키는 교환 방법을 적용하였다. 경제급전의 문제의 시험사례로 빈번히 활용되고 있는 4개 데이터 (시간당 발전비용)에 대해 제안된 알고리즘을 적용한 결과 기존의 휴리스틱 알고리즘 최적화 해를 획기적으로 감소시켜 경제적인 이익을 극대화시킬 수 있는 알고리즘을 제안하였다.

Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system

Quantum-behaved particle swarm optimization algorithm is firstly used in economic load dispatch of power system in this paper. Quantum-behaved particle swarm optimization algorithm is the integration of particle swarm optimization algorithm and quantum computing theory. The superposition characteristic and probability representation of quantum methodology are combined into particle swarm optimization algorithm. This can make a single particle be expressed by several certain probability states. And the quantum rotation gates are used to realize update operation of particles. The algorithm is simulated by two cases, which validates it can effectively solve economic load dispatch problem. Though performance comparison, it is obvious the solution is superior to that of improved particle swarm optimization algorithm and other optimization algorithms.

A Hybrid Neuro-Efficient Particle Swarm Optimization (EPSO) Algorithm for Economic Load Dispatch Considering Transmission Losses with Non-Smooth and Smooth Cost Function

A Hybrid NEURO-EPSO algorithm for solving Economic Load Dispatch Problem (ELD) with non-smooth cost function is presented in this paper. The ELD problem is a highly constrained, large scale, non-linear optimization problem, considering the equality and the inequality constraints. To demonstrate the effectiveness of the proposed algorithm, it is applied to test ELD problem with non-smooth cost function while considering the Valve-Point loading effect. Here we employ an Efficient PSO technique and the Back Propagation Algorithm(NN) to solve the ELD problem, so that faster convergence and more optimized results are obtained compared to other optimization techniques.

Particle Swarm Optimization (PSO) techniques solving Economic Load Dispatch (ELD) Problem

This paper presents a Particle Swarm Optimization (PSO) algorithm for Economic Load Dispatch (ELD) problem. The PSO, which is based on the analogy of swarms of birds, is a very robust and efficient algorithm. A swarm of particles are initiated and fly through the solution space, to find the optimal solution. The PSO algorithm belongs to Metaheuristic Optimization techniques. To show its efficiency and effectiveness, the proposed Particle Swarm Optimization (PSO) algorithm is applied to sample ELD problems composed of 4 generators.