Application Of Multi-Layered Perceptron Neural Network (MLPNN) With Consideration Losses And Emission Dispatch

Nowadays, optimum generation and consumption of energy are considered as two important problems. Economic Dispatch that is the most optimum is one of important problems in power system. This paper presents a multi-layered perceptron neural network (MLPNN) method to solve the combined Losses and emission dispatch problem. Therefore, knowing Problem-Solving in Economic Dispatch is a necessity. First, the Economic Dispatch is explained and then neural network is reviewed. Next, different kinds of neural networks are mentioned by using in Economic Dispatch

Application Of Multi-Layered Perceptron Neural Network (MLPNN) With Consideration Losses And Emission Dispatch

Nowadays, optimum generation and consumption of energy are considered as two important problems. Economic Dispatch that is the most optimum is one of important problems in power system. This paper presents a multi-layered perceptron neural network (MLPNN) method to solve the combined Losses and emission dispatch problem. Therefore, knowing Problem-Solving in Economic Dispatch is a necessity. First, the Economic Dispatch is explained and then neural network is reviewed. Next, different kinds of neural networks are mentioned by using in Economic Dispatch

Antlion optimization algorithm for optimal non-smooth economic load dispatch

This paper presents applications of Antlion optimization algorithm (ALO) for handling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints

Modified moth swarm algorithm for optimal economic load dispatch problem

In this study, optimal economic load dispatch problem (OELD) is resolved by a novel improved algorithm. The proposed modified moth swarm algorithm (MMSA), is developed by proposing two modifications on the classical moth swarm algorithm (MSA). The first modification applies an effective formula to replace an ineffective formula of the mutation technique. The second modification is to cancel the crossover technique. For proving the efficient improvements of the proposed method, different systems with discontinuous objective functions as well as complicated constraints are used. Experiment results on the investigated cases show that the proposed method can get less cost and achieve stable search ability than MSA. As compared to other previous methods, MMSA can archive equal or better results. From this view, it can give a conclusion that MMSA method can be valued as a useful method for OELD problem

Algorithm for calculating the analytic solution for economic dispatch with multiple fuel units

AbstractThe problem of economic dispatch with multiple fuel units has been widely addressed via different techniques using approximate methods due to the exponential complexity of full enumeration in the underlying combinatory problem. A method has recently been outlined by Min et al. (2008)[12], that allows the problem to be solved in an exact way in polynomial time. In this paper, we present an alternative technique and take this idea further, studying and comparing two algorithms of polynomial complexity: basic recurrence and divide-and-conquer. Moreover, we provide the exact solution to the problem by Lin and Viviani (1984)[1], that constitutes the traditional test for all approximate methods and present a comprehensive survey of several heuristic approaches

Solving practical economic load dispatch problem using crow search algorithm

The practical economic load dispatch problem is a non-convex, non-smooth, and non-linear optimization problem due to including practical considerations such as valve-point loading effects and multiple fuel options. An optimization algorithm named crow search algorithm is proposed in this paper to solve the practical non-convex economic load dispatch problem. Three cases with different economic load dispatch configurations are studied. The simulation results and statistical analysis show the efficiency of the proposed crow search algorithm. Also, the simulation results are compared to the other reported algorithms. The comparison of results confirm the high-quality solutions and the effectiveness of the proposed method for solving the non-convex practical economic load dispatch problem

Hybrid optimization algorithm to solve the nonconvex multiarea economic dispatch problem

In this paper, multiarea economic dispatch (MAED) problems are solved by a novel straightforward process. The solved MAED problems include transmission losses, tie-line constraints, multiple fuels, valve-point effects, and prohibited operating zones in which small, medium, and large scale test systems are involved. The methodology of tackling the problems consists in a new hybrid combination of JAYA and TLBO algorithms simultaneously to take the advantages of both to solve even nonsmooth and nonconvex MAED problems. In addition, a new and simple process is used to tackle with the interaction between areas. The objective is to economically supply demanded loads in all areas while satisfying all of the constraints. Indeed, by combining JAYA and TLBO algorithms, the convergence speed and the robustness have been improved. The computational results on small, medium, and large-scale test systems indicate the effectiveness of our proposed algorithm in terms of accuracy, robustness, and convergence speed. The obtained results of the proposed JAYA-TLBO algorithm are compared with those obtained from ten well-known algorithms. The results depict the capability of the proposed JAYA-TLBO based approach to provide a better solution.fi=vertaisarvioitu|en=peerReviewed

Non-convex constrained economic power dispatch with prohibited operating zones and piecewise quadratic cost functions

This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms