Economic Dispatch Problem In Power Systems Research Articles

Distributed dual gradient tracking for economic dispatch in power systems with noisy information

Distributed algorithms can be efficiently used for solving economic dispatch problem (EDP) in power systems. To implement a distributed algorithm, a communication network is required, making the algorithm vulnerable to noise which may cause detrimental decisions or even instability. In this paper, we propose an agent-based method which enables a fully distributed solution of the EDP in power systems with noisy information exchange. Through the novel design of the gradient tracking update and introducing suppression parameters, the proposed algorithm can effectively alleviate the impact of noise and it is shown to be more robust than the existing distributed algorithms. The convergence of the algorithm is also established under standard assumptions. Moreover, a strategy are presented to accelerate our proposed algorithm. Finally, the algorithm is tested on several IEEE bus systems to demonstrate its effectiveness and scalability.

Solving Power System Economic Dispatch Problem Based on Genetic Algorithm

With the rapid development of the world economy, the demand for power load is also growing rapidly. The power system economic dispatch problem studied in this paper is to reduce the fuel cost of generator units on the premise of satisfying some series of constraints of power system. In this paper, genetic algorithm is adopted as the solution strategy for power system economic dispatch problem, and the penalty function is used to deal with the equality constraint condition in the power system economic dispatch problem. Finally, we use the genetic algorithm to solve the IEEE-9 notes power system economic dispatch problem and give the corresponding simulation results. The simulation results show that the genetic algorithm can solve the power system economic dispatch problem effectively.

Hybrid Mean-Variance Mapping Optimization for Non-Convex Economic Dispatch Problems

The economic dispatch (ED) is one of the important optimization problems in power system generation for fuel cost saving. This paper proposes a hybrid variant of mean-variance mapping optimization (MVMO-SH) for solving such problem considering the non-convex objective functions. The new proposed method is a hybrid variant of the original mean-variance mapping optimization algorithm (MVMO) with the embedded local search and multi-parent crossover to enhance its global search ability and improve solution quality for optimization problems. The proposed MVMO-SH is tested on different non-convex ED problem including valve point effects, multiple fuels and prohibited operating zones characteristics. The result comparisons from the proposed method with other methods in the literature have indicated that the proposed method is more robust and provides better solution quality than the others. Therefore, the proposed MVMO-SH is a promising method for solving the complex ED problems in power systems.

Method for a Parallel Solution of a Combined Economic Emission Dispatch Problem

The power system simulation software tools are traditionally designed for serial codes and optimized using single-processor computers. They are inadequate in terms of computational efficiency and execution time for the ever-increasing complexity of the power grid. Due to the above-mentioned sequential computing demerits, this paper used MATLAB data parallelism message passing interface software to execute the Lagrange's and Particle Swarm Optimization (PSO) algorithms in parallel with multiple processor units with different and large data sets for the solution of the Combined Economic Emission Dispatch (CEED) problem. The two important advantages of using parallel computing approach to solve the power system economic dispatch problem are 1) to increase the efficiency (solution quality) and 2) to reduce the execution time (speed-up) of the parallelization process for the CEED problem solution. The comparison between the Lagrange's and PSO data-parallel solution quality and execution time is presented for the CEED problem for Institute of Electrical and Electronic Engineers (IEEE) 30 bus and IEEE 118 bus systems. The paper contributes to the on-line real-time market analyses of the deregulated power system, which need improved solution quality and a fast computation process to solve the power system energy management (CEED) problems for proper discussion and decision making at the control center level.

Swarm based mean-variance mapping optimization for convex and non-convex economic dispatch problems

In power system generation, the economic dispatch (ED) is used to allocate the real power output of thermal generating units to meet the required load demand so as the total cost of thermal generating units is minimized. This paper proposes a swarm based mean-variance mapping optimization $$(\hbox {MVMO}^{S})$$ for solving the ED problems with convex and nonconvex objective functions. The proposed method is the extension of the original single particle mean-variance mapping optimization by initializing a set of particles. The special feature of the proposed method is a mapping function applied for the mutation based on the mean and variance of n-best population. The proposed $$\hbox {MVMO}^{S}$$ is tested on various systems and the obtained results are compared to those from many other optimization methods in the literature. Test results have shown that the proposed method can obtain better solution quality than the other methods. Therefore, the proposed $$\hbox {MVMO}^{S}$$ is a potential method for efficiently solving the convex and nonconvex ED problems in power systems.

A self-adaptive chaos and Kalman filter-based particle swarm optimization for economic dispatch problem

It is challenging to obtain global minima for practical economic dispatch (ED) problem with heavy constraints. Traditional PSO is easy to fall into local optimum when it is applied to solve the ED problem; therefore, in this paper, an efficient self-adaptive chaos and Kalman filter-based particle swarm optimization algorithm (SCKF-PSO) is proposed to solve economic dispatch (ED) problem while considering minimizing the cost with various equality and inequality constraints. The algorithm adopts both the learning mechanism of PSO and the estimation strategy of Kalman filter to update the position of the particle, which can improve the convergence performance. Moreover, a novel self-adaptive chaotic strategy is utilized to increase the diversity of the population. The feasibility of SCKF-PSO algorithm is illustrated by testing on several benchmark functions and three different ED problems in power systems. The simulation results show that compared with previous approaches reported in the literature, the proposed SCKF-PSO can obtain higher quality solutions with stability and efficiency in the ED problem.

Extreme learning machine based genetic algorithm and its application in power system economic dispatch

In this paper a novel optimization algorithm, which utilizes the key ideas of both genetic algorithm (GA) and extreme learning machine (ELM), is proposed. Traditional genetic algorithm employs genetic operations, such as selection, mutation and crossover to generate the optimal solution. In practice, the child solutions generated by crossover and mutation are largely random and therefore cannot ensure the fast convergence of the algorithm. To tackle the weakness of traditional GA, the ELM is introduced to estimate the nonlinear functional relationships between the parent population and child population generated by genetic operations. The trained downward-climbing and upward-climbing ELMs are then employed to generate candidate solutions, which forms the new population together with the solutions given by genetic operations. The proposed algorithm is applied to the power system economic dispatch problem. As demonstrated in case studies, the modified genetic algorithm is able to locate local minima faster and escape from local minima with a greater probability. The proposed algorithm can therefore ensure the faster convergence and provide more economical dispatch plans.

A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems

This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem.

Application of pattern search method to power system security constrained economic dispatch with non-smooth cost function

Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED) with non-smooth cost function. Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using three different test systems. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED). In addition, valve-point effect loading and total system losses are considered to further investigate the potential of the PS technique. Based on the results, it can be concluded that the PS has demonstrated ability in handling highly nonlinear discontinuous non-smooth cost function of the SCED.

Power System Security Constrained Economic Dispatch Using Real Coded Quantum Inspired Evolution Algorithm

This paper presents a new optimization technique based on quantum computing principles to solve a security constrained power system economic dispatch problem (SCED). The proposed technique is a population-based algorithm, which uses some quantum computing elements in coding and evolving groups of potential solutions to reach the optimum following a partially directed random approach. The SCED problem is formulated as a constrained optimization problem in a way that insures a secure- economic system operation. Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) is then applied to solve the constrained optimization formulation. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that RQIEA is very applicable for solving security constrained power system economic dispatch problem (SCED). possibly discontinuous. Both continuous and discrete variables may be involved in the problem. In this paper, a new optimization method is presented. The developed method is novel in the concept of searching mechanism to find the optimum. The proposed Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) is based on quantum computing principles which incorporate the ideas stemmed from the behavior of quantum computing elements encapsulated in a structured evolution process. Quantum computing is based on several phenomena of the quantum world that are fundamentally different from those encountered in classical computing. Such phenomena are complex probability amplitudes, quantum interference, quantum parallelism, quantum entanglement and the unitary nature of quantum evolution. The main objective of this study is to introduce the proposed Real Coded Quantum-Inspired Evolution Algorithm to the subject of power system dynamic security with the most economical operating conditions. In this study RQIEA is employed to solve the security constrained economic dispatch problem (SCED). This new approach will be used to minimize the system objective cost function satisfying a set of constraints.

Augmented Hopfield network for unit commitment and economic dispatch

The Hopfield network has been applied to the power system economic dispatch problem with very promising results. However, it has been found that the unit commitment problem cannot be tackled accurately within the framework of the conventional Hopfield network. This is due to the fact that both discrete and continuous terms must be considered to fully model the problem. This paper presents an augmented network architecture with a new form of interconnection between neurons giving a more general energy function containing both discrete and continuous terms. A comprehensive cost function for the unit commitment problem is developed and mapped to this energy function. Results show that this technique outperforms previous neural network methods. The new method also compares favourably with Lagrangian relaxation. Detailed results for a power system with thermal, hydro and pumped storage units are presented.