---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Shortfall of Energy resources, increasing power generation price issue and ever-growing demand of electricity necessitates best **economic** **dispatch** in today‘s power systems. The major issue in power system is **economic** **load** **dispatch** (ELD) problem. Mainly it is an optimization problem and to reduce total generation cost of units is its main objective, while satisfying constraints. **Economic** **load** **dispatch** is the short-term determination of the most effective result of style of electrical power generation facilities, to satisfy the system **load**, at the bottom potential value, subject to transmission and operational constraints. This analysis paper tries to denote the numerical particularisation of **Economic** **load** **dispatch** issue arrangement utilizing delicate registering methodology in electrical era structure considering all completely different physical and power evoked system imperatives.

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This is the modified version variant of classical PSO. Since classical PSO lacks with global search ability at last stage of iteration. It’s have one of the major disadvantage that if once it set to local optimization than cannot gives global solution of the problem. In this study proposed moderate random search strategy with PSO. This approach not only gives better performance than classical PSO but also enhance the global search ability of particle also improving the convergence time [17]. Hao Gao and Wenbo was first introduced PSO with Moderate random search strategy in the year 2011[17]. It is initialize same as PSO when using for the solution of nonlinear **economic** **load** **dispatch**. In case of PSO the velocity of particles almost zero at the last stage of iteration, so [17] suggested that only update of particle position is sufficient to get optimal solution of the ELD problem with valve point loading effect.

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The Conventional **economic** **load** **dispatch** (ELD) problem of power generation involves allocation of power generation to different generating units to minimize the operating cost subject to diverse equality and inequality constraints of the power system. The solution of ELD gives optimal generation of generating units that satisfy the system power balance equation and generation limit constraints. The **Economic** **load** **dispatch** problem can be formulated mathematically as follows:

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For most of energy companies, particularly the electricity utilities, the **Economic** **Load** **Dispatch** (ELD) problem is one of the fundamental issues in power system operation. It is the use of the optimization techniques in this power system problem that has served the energy companies to lower their operating generation costs throughout decades. Not only that these optimization methods help them reduce costs, it also assists the utility planners and decision makers to make better and faster decisions that improve the quality of the delivery of the service.

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Abstract— This paper presents a new efficient approach to **Economic** **Load** **Dispatch** (ELD) problems with non-convex cost functions using PSO-ANN. The practical ELD problems have non-smooth cost functions considering valve point loading effects with equality and inequality constraints that make the problem of finding the global optimum difficult using any traditional mathematical approach. Therefore, Particle Swarm Optimization (PSO) technique is used for generating training data for the neural network. This paper proposes a PSO-ANN for the fast on-line implementation of the **economic** **load** **dispatch** problem. Test results of system with 3 generating units are given to illustrate the effectiveness of the proposed method.

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Present day power systems have the problem of deciding how best to meet the varying power demand that has a daily and weekly cycle in order to maintain a high degree of economy and reliability. Among the options that are available for an engineer in choosing how to operate the system, **economic** **load** **dispatch** (ELD) is the most significant. ELD is a computational process whereby the total required generation is distributed among the generating units in operation so as to minimize the total generation cost, subject to **load** and operational constraints. The objective of ELD is to minimize the total generation cost of a power system for a given **load** while satisfying various constraints [1].

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ABSTRACT:Grey Wolf Optimization (GWO) is a new meta-heuristic inspired by grey wolves. The leadership hierarchy and hunting mechanism of the grey wolves is mimicked in GWO. In this paper, GWO is proposed to solve the **economic** **load** **dispatch** (ELD) problem with valve-point effects. To demonstrate the effectiveness of the proposed approach, the numerical studies have been performed for twostandard test systems, i.e. six andfifteen generating unit systems, respectively. The results show that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literature.

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ABSTRACT: This paper developed a new method to solve the **economic** **load** **dispatch** (ELD) considering the valve- point effects in power systems. The method is based on a hybrid particle swarm optimization and gravitational search algorithm (hybrid PSO-GSA) techniques. The fundamentally of this algorithm is to combine the ability of social thinking in PSO with the local search capability of GSA. The hybrid PSO-GSA technique is applied to a thirteen unit test system to illustrate the effectiveness of the proposed algorithm. The results show that the proposed algorithms certainly produce more optimal solution when compared results of other optimization algorithms reported in literature. KEYWORDS: Particle swarm optimization, gravitational search algorithm, **economic** **load** **dispatch**, valve-point effects.

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Abstract: This paper describes gravitational search algorithm for solving the non convex **Economic** **load** **Dispatch** (ELD) problem. The main objective of **economic** **load** **dispatch** problem is to generate the required amount of power so that the total operating cost of system is minimized, while satisfying **load** demand and system equality and inequality Constraints. Different heuristic optimization methods have been proposed to solve this problem in previous study. So in this paper, gravitational search algorithm (GSA) based on law of gravity and mass interaction is proposed. This proposed approach has been tested on 3, 38 test systems. Simulation results of proposed approach are compared with some well-known heuristic search methods. The obtained results verify the efficiency of the proposed method with minimum computational time in solving various nonlinear functions Keyword: **Economic** **dispatch**, gravitational search algorithm, equality and inequality Constraints.

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Direct Search (DS) methods are evolutionary algorithms used to solve constrained optimization problems. DS methods do not require information about the gradient of the objective function while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This study examines the usefulness of a constrained pattern search algorithm to solve well-known power system **Economic** **Load** **Dispatch** problem (ELD) with a valve-point effect. For illustrative purposes, the proposed PS technique has been applied to various test systems to validate its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been assessed and investigated through comparison with results reported in literature. The outcome is very encouraging and suggests that pattern search (PS) may be very useful in solving power system **economic** **load** **dispatch** problems.

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If transmission losses are very small or generators are connected close enough to the **load** centre, the transmission losses can be neglected. But when generating units are situated at different distance from the **load**, the transmission losses plays very important role and the **economic** **dispatch** will be affected by cost of different transmission losses.The concept of **economic** **load** **dispatch** considering transmission losses has been considered in my thesis.The proposed methods are used to find the optimal results while working with operating constraints in the ELD.

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Abstract: The **Economic** **Load** **Dispatch** (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for 3,6,13 and 15 generator test case systems. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony optimization (ABC), Biogeography-Based Optimization (BBO), Bacterial Foraging Optimization (BFO), Firefly Algorithm (FA) techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods. Keywords: Artificial Bee Colony Optimization, Biogeography-Based Optimization, **Economic** **Load** **Dispatch**, Firefly Algorithm, Genetic Algorithm, Particle Swarm Optimization.

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Abstract: Electrical power systems are designed and operated to meet the continuous variation of power demand. The main aim of modern electric power utilities is to provide high-quality reliable power supply to the consumers at the lowest possible cost while meeting the limits and constraints imposed on the generating units and environment. These constraints formulates the **economic** **load** **dispatch** (ELD) problem for finding the optimal combination of the output power of all the online generating units that minimizes the total fuel cost, while satisfying an equality constraint and a set of inequality constraints. This paper proposes application of PSO trained Anfis for solving **economic** **load** **dispatch** problem. Particle swarm optimization (PSO) is a population based stochastic optimization technique, inspired by social behavior of bird flocking or fish schooling. The proposed approach has been examined and tested with the numerical results of optimal scheduling of generation with three and six generating units. The results of the proposed PSO-ANFIS algorithm are compared with that of other techniques such as Lambda-Iteration Method and PSO Method and compared to both cases; the proposed algorithm outperforms the solution. Keywords: ANFIS, OSG, ELD, PSO, Lambda iteration

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The **economic** **load** **dispatch** plays an important role in the operation of power system. The main objective of this paper is to determine the optimal combination of power outputs of all generating units so as to meet the required demand at minimum cost while satisfying all types of constraints. In this paper the lambda iteration method and the two main types evolutionary optimization technique genetic algorithm and particle swarm optimization which are generic population based probabilistic search optimization algorithms and can be applied to real world problem are respectively applied to solve an ELD problem and at last the comparison between all three method has been presented. The PSO provides the generation level such that the generation level is coming out to be lower than the cost resulted with genetic algorithm method.

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Electrical power systems are designed and operated to meet the continuous variation of power demand. In power system, minimization of operation cost is very important. **Economic** **load** **dispatch** (ELD) is a method to schedule the power generator outputs with respect to the **load** demands and to operate the power system most economically or in other words the main objective is to allocate the optimal power generation of different units at the lowest cost possible while meeting all system constraints [1].

In modern world electric power system, there are many problems such as optimal power flow, **Economic** **Load** **Dispatch** (ELD), Unit Commitment (UC) etc. the **economic** **load** **dispatch** problem is one of the major problem in the power system. The objective is to fulfill the **load** demand by reducing the cost and satisfying the environmental constraints. Over the years many techniques have been applied to solve the ELD problems such as conventional methods and non-conventional technique have been used to solve the ELD problems. Conventional methods have simple mathematical models and high search speed but they are failed or unable to solve many complex problems due to local optimum solution and facing difficulties to locate the global optimal solution. Recently, the interest have been focused on the other method based on artificial intelligence to solve the **economic** **load** **dispatch** problem, these are Particle Swarm Optimization, Artificial Bee Colony, Genetic Algorithm, Neural Networks, Evolutionary Programming and many more.

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industrial, agricultural, entertainment, information and communication sectors etc depend on electrical energy. In fact, the modem economy is totally dependent on the electricity as a basic input. This is turn has led to the increase in the number of power generating stations and their capacities and the consequent increase in power transmission lines which connect the generating stations to the **load** centres. Interconnections between generating systems are also equally important for reliable and supply quantity of power system which also provide flexibility in system operation. Among different issues in power system operation, **economic** **load** **dispatch** (ELD) and optimal power flow (OPF) problem constitute a major part.

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