Improved genetic algorithm for economic load dispatch with valve-point loadings

The Economic Dispatch (ED) is one of the most important optimization problems in power systems the ultimate goal of the ED is to minimize the cost of operations in a power generation. In this paper, the Woodpecker Mating Algorithm (WMA) is used to solve the ED problem considering the nonlinear properties of generators such as valve point effects (VPE), prohibited operating zones (POZ), ramp rate limits, multiple fuel options, and transmission loss. The WMA algorithm is a novel metaheuristic algorithm inspired by the mating behavior of woodpeckers and sound intensity (a physical quantity). The WMA is implemented on six test systems of different operational dimensions and characteristics to show its capacity for solving the ED problem. The results are compared with the latest and most efficient methods introduced in the literature. Proving the efficiency of the WMA to solve the ED problem, simulation results are promising and offer the optimal fuel cost of production.

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.

A new honey bee mating optimization algorithm for non-smooth economic dispatch

Hybrid artificial algae algorithm for economic load dispatch

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

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.

Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch

Economic Load Dispatch (ELD) is one of the important optimization problem in advanced power system and control. In this paper, six and fifteen generating units in ELD problem has been considered. Prohibited zone and ramp rate limit constraints has been also considered. ELD problem has been solved using three types of optimization techniques. These optimization techniques are Particle Swarm Optimization (PSO), Particle Swarm Optimization with Constriction Factor and Inertia Weight factor Approach (PSOCFIWA) and Time Varying Acceleration Based Particle Swarm Optimization with Constriction Factor and Inertia Weight factor Approach (TVAC-PSOCFIWA) algorithms. Optimum result of each optimization techniques have been compared.

Unit Commitment is one of the most complex and difficult optimization problem in power systems. The objective of the optimal commitment is to determine the on/off states of the units in the system to meet the load demand and spinning reserve requirements at each time period, such that the overall cost of generation is minimized, while satisfying the various operational constraints. This paper presents the application of an improved genetic algorithm (GA) with crossover, mutation, and advanced operators such as repair and swap mutation to determine the commitment order of the thermal units in power generation. The proposed GA has been successfully applied to 10 and 26 generating-unit systems. Robustness of the proposed GA is demonstrated by comparison to the dynamic programming algorithm. The comparison results show the effectiveness of the proposed GA in solving the unit commitment problem with sufficient accuracy and low computational time.

This paper presents a new algorithm to solve economic dispatch (ED) as an optimization problem in power systems. A simple iterated-based algorithm has been developed. It starts with a guess and converges to the optimal point in such a way that the total generation cost of a microgrid is minimized. Since microgrid is considered as a lossless small network, the algorithm is supposed to solve the lossless economic dispatch problem. The proposed method applied on a microgrid consisting of three distributed energy resources (DERs) with convex type of cost functions. The problem was also solved by CPLEX solver as a quadratic programming problem (QP) and the obtained results of proposed algorithm were compared with its results. The comparison confirmed the capability of proposed method to solve ED problem since it was easy to implement with acceptable speed and accuracy.

In recent years, continuous and reliable electric energy supply is the objective of any power system operation. FACTS devices are promising devices from the past ten years and are becoming popular. Also FACTS devices are very effective solution for many power system transmission problems. FACTS controllers can be used for steady state voltage regulation and control, steady state power flow can be controlled on a transmission line, transient stability enhancement, along with this it also reduces the problem of sub synchronous resonance. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices play a key role in enhancing controllability and to increase power transfer capability of the network. Thyristor Controlled Series Compensator (TCSC) is an upcoming FACTS device which can be used to meet this objective. The conventional methods in solving optimization problems in power systems suffer from several limitations due to necessities of derivative existence, providing suboptimal solutions, etc. computational Intelligence plays an important role in finding the optimal solutions for multi-objective functions. A combinatorial analysis problem in power systems can be solved by modern heuristic methods in finding optimal solution. Thus, in this paper, Genetic Algorithm, a type of evolutionary computation encapsulated with heuristic approach, is proposed for finding optimal location of TCSC. IEEE standard 30bus system is considered to test the credibility of the proposed algorithm and the results obtained are subjected to analysis to determine the optimal location.

The dynamic economic dispatch (DED) problem is an extension of the conventional static load dispatch problem in the context of electrical power generation. In this paper, issues related to the implementation of the several soft computing techniques are highlighted for a successful application to solve dynamic economic dispatch (DED) problem, which is a constrained optimization problem in power systems. First of all, a survey covering the basics of the techniques is presented and then implementation of the techniques in the DED problem is discussed. The soft computing techniques, namely multi-layered perceptron neural network (MLP NN), genetic algorithm (GA), Imperialist Competitive Algorithm(ICA), particle swarm (PSO) and are applied to solve the DED problem. The Evolutionary Algorithms are tested on power system consisting 3 generating units and the results are compared together. Suggestion is presented to improve techniques.

In this paper, an effective and reliable variant of particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC) technique is proposed for the economic load dispatch problem considering valve-point loading effect. Main objective of the economic load dispatch (ELD) problem is to minimize the fuel cost by allocating the generation of the committed units subjected to the equality and inequality constraints. Equation of the economic dispatch objective function gets modified with the addition of a new parameter which represents the effect of valve-point loading. While exploring the use of PSO and its other variants to the economic dispatch problem, a number of research works have not considered the transmission losses properly. This paper represents the usefulness of the proposed PSO-TVAC algorithm to significantly reduce the fuel cost while taking into account the effect of transmission losses along with the non-convex characteristic due to valve loading. The results have been demonstrated for three-generator and ten-generator test systems.

Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valve-point effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.

Solving non-convex economic load dispatch problem via artificial cooperative search algorithm