Sujate Jantarang

Diversity control approach to ant colony optimization for unit commitment problem

This paper presents a new method to solve the constrained unit commitment problem by applying ant colony optimization (ACO) based on the diversity control approach. The pheromone updating rule is modified to control the diversification by adopting a simple mechanism for random selection in ACO. The proposed method is tested on the 10-unit test system with a scheduling time horizon of 24 hours. The numerical results show an economical saving in the total operating cost when compared to the previous literature results. Moreover, two types of the proposed diversity control technique have the features of easy implementation and a better convergence rate superior to a standard ACO.

Reactive tabu search for optimal power flow under constrained emission dispatch

This paper presents an efficient method to obtain the optimal power flow (OPF) problem under constrained emission dispatch by applying reactive tabu search (RTS) algorithm. The RTS is developed as a derivative-free optimization technique in solving constrained emission OPF problem significantly reduces the computational burden with the strategies that make the search process robust and fast. The effectiveness of the proposed approach has been demonstrated through the IEEE 30-bus, 6-generator, test system. The simulation results reveal that the proposed RTS can yield highly optimal solution and tan reduce computational execution time superior to a standard tabu search. Moreover, the proposed method provides better solution than previous literatures with promising results.

Hybrid ant system/priority list method for unit commitment problem with operating constraints

This paper presents a new method to solve unit commitment problem with operating constraints using a hybrid ant system/priority list method (HASP). The proposed methodology employs ant system in cooperating with the priority list method to find unit commitment solution as means of mutually combining the advantages of them in that a flexibility of the priority list method is reinforced, while AS algorithm can gain the benefit of using bias information for improving its performance during search process. The simulation results show that the proposed HASP is capable of obtaining satisfactory solution within reasonable computational time. Moreover, the proposed HASP can be extensively applied to handle the ramp rate limits and the operating reserve constraint, which are important for modern power system operation.

A solution to unit commitment problem using hybrid ant system/priority list method

This paper presents a new method to solve unit commitment problem with operating constraints using a hybrid ant system/priority list method (HASP). The proposed methodology employs ant system in cooperating with the priority list method to find unit commitment solution as means of mutually combining the advantages of them in that a flexibility of the priority list method is reinforced, while AS algorithm can gain the benefit of using bias information for improving its performance during search process. The simulation results show that the proposed HASP is capable of obtaining satisfactory solution within reasonable computational time.

Relativity Pheromone Updating Strategy in Ant Colony Optimization for Constrained Unit Commitment Problem

This paper presents a development of the enhanced ant colony optimization (EACO) based on a novel approach of the relativity pheromone updating strategy (RPUS) for solving constrained unit commitment problem which cooperates with the candidate path management module (CPMM) embedded the effective repairing heuristic module (ERHM) in reducing search space and recovering a feasible optimality region so that a high quality solution can be acquired in a very early iterative. The adoption of RPUS not only enhances the search convergence of EACO, but also provides relatively pheromone information that is suitably exploited for a good guidance of search process. The EACO algorithms have been performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show better economical saving in the total operating cost when compared to the previous literature results. Furthermore, the proposed EACO topology can remarkably speed up the computation time of ant colony optimization algorithms, which is favorable for a large-scale UC problem implementation.

Selective self-adaptive approach to ant system for solving unit commitment problem

This paper presents a novel approach to solve the constrained unit commitment problem using Selective Self-Adaptive Ant System (SSAS) for improving search performance by automatically adapting ant populations and their transition probability parameters, which cooperates with Candidate Path Management Module (CPMM) and Effective Repairing Heuristic Module (ERHM) in reducing search space and recovering a feasible optimality region so that a high quality solution can be acquired in a very early iterative. The proposed SSAS algorithm not only enhances the convergence of search process, but also provides a suitable number of the population sharing which conducts a good guidance for trading-off between the importance of the visibility and the pheromone trail intensity. The proposed method has been performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show the most economical saving in the total operating cost when compared to the previous literature results. Moreover, the proposed SSAS topology can remarkably speed up the computation time of ant system algorithms, which is favorable for a large-scale unit commitment problem implementation.

Relative Velocity Updating in Parallel Particle Swarm Optimization Based Lagrangian Relaxation for Large-scale Unit Commitment Problem

This paper presents an effectiveness of combined parallel relative particle swarm optimization (PRPSO) and Lagrangian relaxation (LR) for a large-scale constrained unit commitment (UC) problem in electric power system. The proposed algorithm incorporates PRPSO with a new relative velocity updating (RVU) approach to tradeoff the solution of each slave processing unit. The parallel algorithm based on the synchronous parallel implementation is developed to consider the neighborhoods decomposition of multiple particle swarm optimizers. The proposed PRPSO divides the neighborhood into sub-neighborhood so that computational effort is reduced and UC solutions are remarkably improved. The proposed method is performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show an economical saving in the total operating cost when compared to the previous literature results. Moreover, the proposed PRPSO based RVU scheme can considerably speed up the computation time of a traditional PSO, which is favorable for a large-scale UC problem implementation.

Generation Expansion Planning Including Biomass Energy Sources with Global Environmental Consideration Using Improved Tabu Search

This paper presents a development of improved Tabu search (ITS) and its application to a least-cost generation expansion planning (GEP) including biomass energy sources under global environmental impact consideration. A carbon tax is taken into account as counter measure to reduce CO2 emission for introducing and promoting biomass energies to GEP problem based conventional fossil-fuel plants. The proposed ITS is conducted by the proposed a self-reforming candidate list strategies to improve search performance of a standard TS. The ITS approach is applied to the test system with 15 existing power plants, 5 types of conventional fossil-fuel and 2 types of biomass energy over a 14-year planning period. The simulation results reveal that the proposed ITS not only can yield optimal solution as well as dynamic programming, but also can remarkably reduce computational time. Moreover, the proposed method provides better solution superior to a standard Tabu search with promising results.

Automatic circuit simplification for meaningful symbolic analysis using the genetic algorithm

This paper demonstrates the use of the genetic algorithm to simplify both the final symbolic transfer functions and the initial equivalent circuits so that meaningful and understandable results can be obtained. The resulting simplified circuits are only as complicated as necessary to give the required accuracy in the results, which will also help in giving a better graphical insight into the circuits under consideration. This highly efficient algorithm has also been integrated into a symbolic circuit analysis package.

Enhancement of 3-D reconstruction from 2-D images using single camera

This paper presents an enhancement technique for 3-D image reconstruction from two 2-D images obtained from single CCD rotation camera. This technique eliminates the effects of non-symmetrical in the camera. The 3-D image reconstruction employs a conventional stereo technique in which the first image is used as a reference. In addition, this paper presents a novel technique for searching the same position in two images using new normalized sum of squared difference (NSSD-New), which gives less position error as compared with existing techniques. Experimental results demonstrate superiority of our approach in 3-D image reconstruction in comparison with the other techniques.