K. K. Swarnkar

Bio: K. K. Swarnkar is an academic researcher. The author has contributed to research in topics: Economic dispatch & Stochastic optimization. The author has an hindex of 1, co-authored 1 publications receiving 21 citations.

Combined Economic Emission Dispatch Problem using Particle Swarm Optimization

Abstract: This paper deals with particle swarm optimization (PSO) method to solve Combined Economic emission Dispatch Problem (CEEDP)of thermal units while satisfying the constraints such as generator capacity limits, power balance and line flow limits. PSO is a stochastic optimization process based on the movement and intelligence of swarms. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units. The bi-objective problem is converted into single objective problem by introducing price penalty factor to maintain an acceptable system performance in terms of limits on generator real power outputs, transmission losses with minimum emission dispatch. The proposed approach has been evaluated on an IEEE 30-bus test system with six generators. The results obtained with the proposed approach are compared with results of genetic algorithm and other technique. Keywords

Optimal free-defect synthesis of four-bar linkage with joint clearance using PSO algorithm

Abstract: This paper presents the design of planar four-bar linkages free of order, branch and circuit defects, for the purpose of path generation, having clearances at one, two, three or all of its joints. Joint clearance is treated as a massless virtual link and its direction is known by the direction of the joint force. A Particle Swarm Optimization based algorithm is given here to solve this highly nonlinear optimization problem with some constraints, namely; the Grashof’s and free of the foregoing defects conditions. An example is included in which the optimal problem is solved for different cases; namely planar four-bar linkage having clearances at one, two, three, all of the joints and without clearance. For all the designs, the generated paths, the errors and the directions of the virtual links are plotted and are compared. Finally, we compare the optimal designs with reality.

Study of Economic Load Dispatch by Various Hybrid Optimization Techniques

Abstract: The economic load dispatch (ELD) is one of the most complex optimization problems of electrical power system. Classically, it is to identify the optimal combination of generation level of all power generating units in order to minimize the total fuel cost while satisfying the loads and losses in power transmission system. In view of the sharply increasing nature of cost of fossil fuel, energy management has gained lot of significance nowadays. Herein lies the relevance of continued research on improving the solution of ELD problem. A lot of research work have been carried out on this problem using several optimization techniques including classical, linear, quadratic, and nonlinear programming methods. The objective function of the ELD problem being of highly nonlinear and non-convex nature, the classical optimization methods cannot guarantee convergence to the global optimal solution. Some soft computing techniques like Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Clonal Selection Algorithm (CSA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Genetic Algorithm (GA), etc. are now being applied to find even better solution to the ELD problem. An interesting trend in this area is application of hybrid approaches like GA-PSO, ABC-PSO, CSA-SA, etc. and the results are found to be highly competitive. In this book chapter, we focus on the hybrid soft computing approaches in solving ELD problem and present a concise and updated technical review of systems and approaches proposed by different research groups. To depict the differences in technique of the hybrid approaches over the basic soft computing methods, the individual methods are introduced first. While the basic working principle and case studies of each hybrid approach are described briefly, the achievements of the approaches are discussed separately. Finally, the challenges in the present problem and some of the most promising approaches are highlighted and the possible future direction of research is hinted.

Renewable energy inclusion on economic power optimization using thunderstorm algorithm

Abstract: This paper presents an economic operation considered renewable energy which is optimized using thunderstorm algorithm. The problem is constrained by an emission standard and various technical limits implemented on the 62-bus system model. Simulations showed that the renewable energy inclusion penetrates to the unit commitment of generating units with strongly approach for the computational solution. This inclusion also affects to the individual power production in accordance to the fuel cost and pollutant discharge.