Dieu Ngoc Vo

Cuckoo search algorithm using different distributions for short-term hydrothermal scheduling with cascaded hydropower plants

This paper proposes a cuckoo search algorithm (CSA) using different distributions for solving short-term hydrothermal scheduling (ST-HTS) problem with cascaded hydropower plants. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. The advantages of the CSA method are few control parameters and effective for optimization problems with complicated constraints. In the proposed CSA, three distributions have been used including Lévy distribution, Gaussian distribution and Cauchy distribution. The proposed methods have been tested on one system having one thermal plant and four cascaded hydropower plants scheduled in twenty-four subintervals and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is a very favorable method for solving the short term hydrothermal scheduling problems with cascaded hydropower plants.

Cuckoo Optimization Algorithm for Optimal Power Flow

This paper proposes a cuckoo optimization algorithm (COA) method for solving optimal power flow (OPF) problem. The proposed method is inspired from the life of the family of cuckoo. In the proposed method, there are two main components including mature cuckoos and cuckoo’s eggs. During the survival competition, the survived cuckoo societies immigrate to a better environment and restart the process. The cuckoo’s survival effort hopefully converges to a state that there is only one cuckoo society with the same maximum profit values. The COA method has been tested on the IEEE 30, 57, and 118- bus systems with three kinds of objective function and the obtained results have been compared to those from conventional particle swarm optimization (PSO) method. The result comparison has shown that the proposed method can obtain better optimal solution than the conventional PSO. Therefore, the proposed COA could be a useful method for implementation in solving the OPF problem.

Multi-Objective Short-Term Fixed Head Hydrothermal Scheduling Using Augmented Lagrange Hopfield Network

This paper proposes an augmented Lagrange Hopfield network (ALHN) based method for solving multi-objective short term fixed head hydrothermal scheduling problem. The main objective of the problem is to minimize both total power generation cost and emissions of NOx, SO₂, and CO₂ over a scheduling period of one day while satisfying power balance, hydraulic, and generator operating limits constraints. The ALHN method is a combination of augmented Lagrange relaxation and continuous Hopfield neural network where the augmented Lagrange function is directly used as the energy function of the network. For implementation of the ALHN based method for solving the problem, ALHN is implemented for obtaining non-dominated solutions and fuzzy set theory is applied for obtaining the best compromise solution. The proposed method has been tested on different systems with different analyses and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is very efficient for solving the problem with good optimal solution and fast computational time. Therefore, the proposed ALHN can be a very favorable method for solving the multi-objective short term fixed head hydrothermal scheduling problems.

An effective cuckoo search algorithm for large-scale combined heat and power economic dispatch problem

This paper develops an effective cuckoo search algorithm (ECSA) for searching optimal solutions for the problem of combined heat and power economic dispatch. The main task of the problem is to determine the optimal value of power of the pure power generators, of the heat of the pure heat generators and of both power and heat of cogenerators so that fuel cost is minimized while exactly meeting power and heat demands and power and heat limits as well as the complicated feasible operating zone of cogenerators. The proposed ECSA is a newly improved version of conventional cuckoo search algorithm to improve the quality of solutions and reduce the maximum number of iterations based on two modified techniques. The first technique is based on the ratio of the difference between the fitness function value of each solution and the lowest fitness function value of the best current solution to the lowest one to determine an effective operation for producing the second new solution generation. The second technique aims to integrate both previous and current solutions into one group and sort them in the descending order of fitness value. The effectiveness of ECSA has been validated via six cases corresponding to six test systems where the scale of the systems is ranged from the smallest system with four units to the largest one with forty-eight units with valve point loading effects. The comparisons of obtained results with other existing methods have indicated that the proposed ECSA is very effective and robust for finding optimal solutions for the CHPED problem.

Modified Bat Algorithm for Combined Economic and Emission Dispatch Problem

The paper proposes a new modified Bat algorithm (MBA) for solving combined economic and emission load dispatch (CEED) problems where transmission power losses are considered. The MBA is first developed in the paper by modifying the several modifications on the conventional Bat algorithm (BA) in aim to improve the performance of the BA. The MBA is tested on two different systems with the transmission power losses. The performance of the MBA is evaluated by comparing obtained results with BA and other existing algorithms available in the study. As a result, it can be concluded that the MBA outperforms the BA and is very strong for solving the CEED problem.

Meta-Heuristic Algorithms for Solving Hydrothermal System SchedulingProblem Considering Constraints in Transmission Lines

This paper presents the applications of Cuckoo search algorithm (CSA) for solving the problem of optimal power flow for hydrothermal system (OPF-HTS) where IEEE 30-bus test system with both thermal plants and hydropower plants is considered. The problem is first developed in the paper by the combination of optimal power flow (OPF) problem and short-term hydrothermal scheduling (STHTS) problem and it becomes much more complicated than the two sub-problems because it includes all constraints of transmission grid from the comer and all hydraulic constraints from the later in addition to the multi optimal subintervals. In order to validate the performance of the CSA when applied to the problem, another existing meta-heuristic algorithm, Particle swarm optimization has been employed to solve the same problem and compare the obtained results. The analysis on the obtained results has indicated that the CSA is more effective and robust than PSO. Consequently, it can be sated that CSA is a very efficient method for solving the problem.

One rank cuckoo search algorithm for short-term hydrothermal scheduling with reservoir constraint

In this paper, two one rank cuckoo search algorithm (ORCSA) based methods are first proposed for solving the short-term hydrothermal scheduling (ST-HTS) problem. The main objective of the ST-HTS problem is to minimize total generation fuel cost over a schedule time while satisfying equality constraints including power balance equations, total water discharge constraint and inequality constraints including reservoir storage limits and the operation limits of the hydropower plants and thermal generators. The proposed ORCSA is effective for solving optimization problems based on the improvement of the conventional cuckoo search algorithm. Two distributions including Lévy distribution and Cauchy distribution are used in the ORCSA method resulting in two versions of ORCSA including ORCSA-Lévy and ORCSA-Cauchy. Moreover, the bound by best solution mechanism is also proposed to handle inequality constraints to speed up its computational process. Test result comparison with other methods has indicated that the proposed ORCSA methods are very favorable for solving the short term hydrothermal scheduling problems with reservoir constraint.

The application of an effective cuckoo search algorithm for optimal scheduling of hydrothermal system considering transmission constraints

This paper presents an implementation of an effective cuckoo search algorithm (ECSA) for solving hydrothermal scheduling (ST-FH-HTS) problems considering transmission power losses, nonconvex fuel cost function of thermal units, and transmission grid constraints such as the voltage of load buses, voltage of generator buses, capacity of transmission lines. The ECSA method has been developed based on the conventional cuckoo search algorithm (CCSA) which is a recently developed meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other birds of other species for solving optimization problems. In the ECSA method, new eggs generated via Lévy flights are replaced partially, and the newly generated eggs are then evaluated and ranked at once. Moreover, there is a boundary by the best solution technique proposed for replacing the invalid dimension in order to improve convergence rate and performance. The performance of ECSA has been investigated via comparisons with other methods by testing on eleven systems. In addition, the ECSA and other popular meta-heuristic algorithms such as CCSA, conventional particle swarm optimization, conventional differential evolution, and conventional Bat algorithm have been tested on a large-scale system with 50 thermal units and four hydrounits considering constraints from an IEEE 118-bus transmission line grid. The result comparisons from ECSA with other methods for 12 test systems have revealed that ECSA method is very efficient for solving ST-FH-HTS problems. Therefore, the ECSA can be a favorable method for solving the ST-FH-HTS problems.

Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems

In the power system operation, the reduction of the power loss in distribution systems has significance in the reduction of operating cost. In this paper, a novel chaotic stochastic fractal search (CSFS) method is implemented for determining the optimal siting, sizing, and number of distributed generation (DG) units in distribution systems. The objective of the optimal DG placement problem is to minimize the power loss in distribution systems subject to the constraints such as power balance, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. The proposed CSFS method improves the performance of the original SFS by integrating chaotic maps into it. On the other hand, ten chaotic maps are utilized to replace the random scheme of the original SFS to enhance its performance in terms of accuracy of solution and convergence speed, corresponding to ten chaotic variants of the SFS where variant being chosen is the best chaotic variant regarding search performance. For solving the problem, the CSFS is implemented to simultaneously find the optimal siting and sizing of DG units and the optimal number of DG units will be obtained via comparing optimal results from different numbers of DG in the problem. The proposed method is tested on the IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the CSFS are verified by comparing to those from the original SFS and other methods in the literature. The result comparisons indicate that the proposed CSFS method can obtain higher quality solutions than the original SFS version and many other methods in the literature for the considered cases of the test systems. Moreover, the incorporation of chaos theory allows performing the search process at higher speeds. Therefore, the proposed CSFS method can be a very promising method for solving the problem of optimal placement of DG units in distribution systems.

Solving Bi-Objective Short-Term Cascaded Hydrothermal Scheduling Problem Using Modified Cuckoo Search Algorithm

This paper presents a modified cuckoo search algorithm (MCSA) for solving bi-objective short-term cascaded hydrothermal scheduling (BO-STCHTS) problem. The objective of the problem is to determine the optimal operation for thermal plants and a cascaded reservoir system while satisfying all constraints including electrical constraints of both hydro and thermal plants and hydraulic constraints of reservoirs so that the total generation of fuel cost and pollutant emission from thermal power plants are minimized. The MCSA has been developed by modifying the search strategy via Levy flights to improve the performance of the conventional cuckoo search algorithm for solving the problem. The result comparison from a test system with nonconvex fuel cost function and cascaded reservoir between the proposed MCSA and other methods in the literature has shown that the MCSA is very efficient for the BO-STCHTS problem. Therefore, the proposed NCSA can be a efficient method for solving the nonconvex BO-STCHTS problem.