Dispatch Problem In Power Systems Research Articles

Hybrid algorithm for dynamic economic dispatch with valve‐point effects

Dynamic economic dispatch problem in power system considering valve-point effects of generators is a non-smooth, non-convex and multi-dimensional constrained optimisation problem. In allusion to those characteristics, this study proposes a hybrid algorithm which integrates low-discrepancy sequences, improved shuffled frog leaping algorithm and sequential quadratic programming. The effectiveness of the proposed method has been verified by using case studies based on 5-unit, 10-unit and 30-unit test systems over a period of 24 h. The results show that the proposed method has improved solution quality and computation efficiency, compared with most current approaches.

A GA-API Solution for the Economic Dispatch of Generation in Power System Operation

This work proposes a novel heuristic-hybrid optimization method designed to solve the nonconvex economic dispatch problem in power systems. Due to the fast computational capabilities of the proposed algorithm, it is envisioned that it becomes an operations tool for both the generation companies and the TSO/ISO. The methodology proposed improves the overall search capability of two powerful heuristic optimization algorithms: a special class of ant colony optimization called API and a real coded genetic algorithm (RCGA). The proposed algorithm, entitled GAAPI, is a relatively simple but robust algorithm, which combines the downhill behavior of API (a key characteristic of optimization algorithms) and a good spreading in the solution space of the GA search strategy (a guarantee to avoid being trapped in local optima). The feasibility of the proposed method is first tested on a number of well-known complex test functions, as well as on four different power test systems having different sizes and complexities. The results are analyzed in terms of both quality of the solution and the computational efficiency; it is shown that the proposed GAAPI algorithm is capable of obtaining highly robust, quality solutions in a reasonable computational time, compared to a number of similar algorithms proposed in the literature.

An effortless hybrid method to solve economic load dispatch problem in power systems

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time.

Environmental/economic dispatch problem of power system by using an enhanced multi-objective differential evolution algorithm

An enhanced multi-objective differential evolution algorithm (EMODE) is proposed in this paper to solve environmental/economic dispatch (EED) problem by considering the minimal of fuel cost and emission effects synthetically. In the proposed algorithm, an elitist archive technique is adopted to retain the non-dominated solutions obtained during the evolutionary process, and the operators of DE are modified according to the characteristics of multi-objective optimization problems. Moreover, in order to avoid premature convergence, a local random search (LRS) operator is integrated with the proposed method to improve the convergence performance. In view of the difficulties of handling the complicated constraints of EED problem, a new heuristic constraints handling method without any penalty factor settings is presented. The feasibility and effectiveness of the proposed EMODE method is demonstrated for a test power system. Compared with other methods, EMODE can get higher quality solutions by reducing the fuel cost and the emission effects synthetically.

Self-adaptive learning based particle swarm optimization

Particle swarm optimization (PSO) is a population-based stochastic search technique for solving optimization problems over continuous space, which has been proven to be efficient and effective in wide applications in scientific and engineering domains. However, the universality of current PSO variants, i.e., their ability to achieve good performance on a variety of different fitness landscapes, is still unsatisfying. For many practical problems, where the fitness landscapes are usually unknown, employing a trial-and-error scheme to search for the most suitable PSO variant is computationally expensive. Therefore, it is necessary to develop a more adaptive and robust PSO version to provide users a black-box tool for various application problems. In this paper, we propose a self-adaptive learning based PSO (SLPSO) to make up the above demerits. SLPSO simultaneously adopts four PSO based search strategies. A probability model is used to describe the probability of a strategy being used to update a particle. The model is self-adaptively improved according to the strategies’ ability of generating better quality solutions in the past generations. In order to evaluate the performance of SLPSO, we compare it with eight state-of-the-art PSO variants on 26 numerical optimization problems with different characteristics such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise. The experimental results clearly verify the advantages of SLPSO. Moreover, a practical engineering problem, the economic load dispatch problem of power systems (ELD), is used to further evaluate SLPSO. Compared with the previous effective ELD evolutionary algorithms, SLPSO can update the best solution records.

Closure to Discussion on “Economic Load Dispatch—A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO”

In this paper an improved coordinated aggregation-based particle swarm optimization algorithm is introduced for solving the optimal economic load dispatch problem in power systems. In the improved coordinated aggregation-based particle swarm optimization algorithm each particle in the swarm retains a memory of its best position ever encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly.The ICA-PSO algorithm is tested on a number of power systems, including the systems with 6, 13, 15, and 40 generating units.

Seeker Optimization Algorithm for Optimal Reactive Power Dispatch

Optimal reactive power dispatch problem in power systems has thrown a growing influence on secure and economical operation of power systems. However, this issue is well known as a nonlinear, multimodal and mixed-variable problem. In the last decades, computation intelligence-based techniques, such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA)-based reactive power dispatch method is proposed. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, the algorithm's performance is evaluated on benchmark function optimization. Then, the SOA is applied to optimal reactive power dispatch on standard IEEE 57- and 118-bus power systems, and compared with conventional nonlinear programming method, two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms. The simulation results show that the proposed approach is superior to the other listed algorithms and can be efficiently used for optimal reactive power dispatch.

A Novel Coding Scheme for Practical Economic Dispatch by Modified Particle Swarm Approach

This paper proposes a new approach and coding scheme for solving economic dispatch problems in power systems through simulated annealing like particle swarm optimization (SA-PSO). This novel coding scheme could effectively prevent obtaining infeasible solutions through the application of stochastic search methods, thereby dramatically improving search efficiency and solution quality. Many nonlinear characteristics of power generators, and their operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones, transmission loss, and nonlinear cost functions, were all considered for practical operation. The effectiveness and feasibility of the proposed method were demonstrated by four system case studies and compared with previous literature in terms of solution quality and computational efficiency. The experiment showed encouraging results, suggesting that the proposed approach was capable of efficiently determining higher quality solutions addressing economic dispatch problems.

Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches

The objective of the Economic Dispatch Problems (EDPs) of electric power generation is to schedule the committed generating units outputs so as to meet the required load demand at minimum operating cost while satisfying all units and system equality and inequality constraints. Recently, global optimization approaches inspired by swarm intelligence and evolutionary computation approaches have proven to be a potential alternative for the optimization of difficult EDPs. Particle swarm optimization (PSO) is a population-based stochastic algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Inspired by the swarm intelligence and probabilities theories, this work presents the use of combining of PSO, Gaussian probability distribution functions and/or chaotic sequences. In this context, this paper proposes improved PSO approaches for solving EDPs that takes into account nonlinear generator features such as ramp-rate limits and prohibited operating zones in the power system operation. The PSO and its variants are validated for two test systems consisting of 15 and 20 thermal generation units. The proposed combined method outperforms other modern metaheuristic optimization techniques reported in the recent literature in solving for the two constrained EDPs case studies.

Implementing soft computing techniques to solve economic dispatch problem in power systems

Soft computing is the state-of-the-art approach to artificial intelligence and it has showed an excellent performance in solving the combined optimization problems. In this paper, issues related to the implementation of the soft computing techniques are highlighted for a successful application to solve economic dispatch (ED) 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 ED problem is discussed. The soft computing techniques, namely tabu search (TS), genetic algorithm (GA), Hopfield neural network (HNN) and multi-layered perceptron (MLP) are applied to solve the ED problem. The techniques are tested on power systems consisting of 6 and 20 generating units and the results are compared to highlight the performance of the soft computing techniques. Future directions and open-ended problems in implementation of soft computing techniques for constrained optimization problems in power system are indicated. Suggestions are presented to improve soft computing techniques.

New approach with a Hopfield modeling framework to economic dispatch

This paper presents a new Hopfield model based approach for the economic dispatch problem of power systems. To solve the economic dispatch problem using the Hopfield model, an energy function composing power mismatch, total fuel cost and the transmission line losses is defined. The weighting factors associated with the terms of the energy function can be either appropriately selected or directly estimated in the proposed model. Which, however, are determined by trial and error in the conventional Hopfield method. To minimize the value of the energy function, the computational procedures including a series of adjusting the weighting factor associated with the transmission line losses and updating the unit generations and power losses are carried out. Because the weighting factors are governed by some relationships developed, adjustment of the weighting factor is much simpler and more effective in steadily achieving solutions than adjustment of the /spl lambda/-multiplier in the lambda-iteration method for economic dispatch problems. Computational results reveal that this approach can find accurate solutions more simply and fast compared with the conventional lambda-iteration method.

New multi-objective stochastic search technique for economic load dispatch

A new multi-objective stochastic search technique (MOSST) for the multi-objective economic dispatch problem in power systems is presented. It is a highly constrained problem with both equality and inequality constraints. The MOSST heuristic has been designed as a combination of real coded genetic algorithms (GA) and simulated annealing (SA). It incorporates a genetic crossover operator BLX-/spl alpha/ and a problem specific mutation operator with a local search heuristic to provide a better search capability. Extensive simulations are carried out on standard test systems, considering various aspects, and the results are compared with other methods. These results indicate that the new MOSST heuristic converges rapidly to improved solutions. MOSST is a truly multi-objective technique, as it provides the values of various parameters for optimising different objectives, as well as the best compromise between them, all in a single run. Perturbation analysis shows that the solutions obtained by MOSST are truly pareto-optimal, i.e. no objective can be further improved without degrading the others.

Economic dispatch of generators with prohibited operating zones: a genetic algorithm approach

The work explores the use of a genetic algorithm for the solution of an economic dispatch problem in power systems where some of the units have prohibited operating zones. Genetic algorithms have a capability to provide global optimal solutions in problem domains where a complete traversion of the whole search space is computationally infeasible. Two different implementations of the genetic algorithm for the solution of this dispatch problem are presented: a standard genetic algorithm, and a deterministic crowding genetic algorithm model. The results demonstrate that the genetic algorithm can be applied successfully in the solution of problems represented with nonconvex functions.

Power System Economic Dispatch Via Network Approach

A new approach, using the bus incremental costs as the key variables to solve the economic dispatch problem of power systems is proposed in this paper. This approach maintians the system configuration, and all the relevant variables (generator schedules and line flows) can be expressed simply in terms of bus incremental costs. By the application of this approach, the problem of economic dispatch is easily converted to a concise set of quasi-linear equations resulting in a solution form which is similar to that of the electric network. This enables one to apply any readily available high speed special large system solution algorithm to handle the dispatching problem. The generation and the transmission limits also can be included in the solution process.