This paper presents a Teaching-Learning-Based Algorithm (TLBO) to solve Economic Load Dispatch (ELD) problems involving dierent linear and non-linear constraints. The problem formulation also considers non-convex objective functions including the eect of valve-point loading and the multi-fuel option of large-scale thermal plants. Many diculties, such as multimodality, dimensionality and dierentiability, are associated with the optimization of large scale non-linear constraint based non-convex Economic load Dispatch problems. TLBO is a population-based technique which implements a group of solutions to proceed to the optimum solution. TLBO uses two dierent phases, ' Teacher Phase' and ' Learner Phase', and uses the mean value of the population to update the solution. Unlike other optimization techniques, TLBO does not require any parameter to be tuned, thus, making its implementation simpler. TLBO uses the best solution of the iteration to change the existing solution in the population, thereby increasing the convergence rate. In the present paper, Teaching-Learning-Based Optimization (TLBO) is applied to solve such types of complicated problems eciently and eectively, in order to achieve a superior quality solution in a computationally ecient way. Simulation results show that the proposed approach outperforms several existing optimization techniques. Results also proved the robustness of the proposed methodology.

In this paper, Brent method is proposed to solve dynamic Economic Dispatch (DED) problem with transmission losses. The proposed algorithm involves the selection of the values of incremental fuel costs (lambda) and then the evaluation of optimal lambda is done by Brent method. The constraint of ramp rate limits distinguishes the DED problem from traditional static Economic Dispatch (ED) problem. The DED problem divides the entire Dispatch period into a number of small time intervals and then static Economic Dispatch problem is solved in each interval by incorporating the ramp rate limits. The proposed method has been tested on 6- and 15- units. The simulation results of the proposed method are compared with conventional lambda iterative method. The simulation results show that the proposed method achieves qualitative solution with less computational time than the conventional lambda iterative method.

The main objective of this paper is to develop a distributed model using grid environment through which the Economic load Dispatch solutions of multi-area power systems can be obtained continuously. Grid computing is a viable solution in order to exploit the enormous amount of computing power available across Internet to solve large interconnected power system problems. A grid service model is proposed for Economic load Dispatch, which provides the solutions at specific intervals of time. The proposed model is designed in such a way that any node in the grid can provide the Economic load Dispatch (ELD) solution. The node which serves as a server node at a specific instance in the grid can obtain the power system data from other client grid nodes and responds with Economic load Dispatch solutions. The proposed model is highly distributed and has inherent features of scalability, reliability and also uses all available computing power, hence Economic feasibility of analyzing power system problems was taken care implicitly.

This paper presents a new approach to Economic Dispatch (ED) problems with nonconvex cost functions using Memetic Algorithm (MA). The practical ED problem have nonconvex cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. In this paper, MA with three different local searches is suggested to deal with the equality and inequality constraints in the ED problem. To validate the results obtained by proposed MAs, a Real Genetic Algorithm (RGA) and an MA adopted from the literature are applied for comparison. Also, the results obtained by MAs and RGA are compared with the previous approaches reported in the literature. The results show that the MAs produce optimal or nearly optimal solutions for all study systems.