Search

In this paper, a nature inspired algorithm which is based on krill herd behavior is coupled with a local search technique. The proposed local search based krill herd algorithm (LSKHA) is implemented on non-convex economic dispatch problem having five generating units. This proposed algorithm allows the up-gradation of position of krill with selected individuals only instead of randomly picked individuals and it must cause the prevention of trapping of optimal solution in local minima. The variation of fuel cost is shown with respect to number of trails and optimal solution is observed with number of iterations too. The optimal solution obtained is compared with a recently implemented optimization technique in terms of fuel cost, transmission losses and statistical measures. The proposed technique has shown satisfactory improvement in optimal solution based on the result analysis.

While meeting the growing power needs of the world, use of fossil fuels has increased so much that the crises of their looming has raised now. Along with that, global warming and air pollution are also the matters of great worries for humans. All these problems can be reduced by enhancement of utilization of renewable energy resources in power generation. In many parts of the world, wind energy is present in sufficient quantity. Just one percent of wind power in the world is equivalent to 2 X 1011 W, which is about three percent of total world energy consumption rate. Role of wind energy in power generation field is increasing day by day because evolved technologies and power management techniques have started suppressing its shortcomings. In this paper, multi-objective wind– thermal power scheduling problem has been proposed and it is optimized by using α-constrained simplex method to satisfy economic and emission (NO x ,) SO 2 &CO 2 ) constraints. The credibility of presented method has been corroborated on five test systems.

Sun is the biggest source of renewable energy, which is sustainable, inexhaustible, readily available, free of cost, non-polluting and most abundant. Earth and its atmosphere are continuously accepting about 1.7 X 1017 W solar radiations. If only one percent of these radiations can be transformed into electrical energy with 10 percent efficiency, then all the energy needs of the whole world can be fulfilled. A few years ago, it wasn’t used more frequently as an electrical power source because of its unreliability. Now modern technology has changed the picture to a great extent. Solar PV has become the most competitive choice for power generation in increasing number of sites for residential and commercial purposes. Many countries have inducted ample solar power into their electrical grids to reduce their dependence on fossil fuels. In this paper, economic- environmental, solar-thermal power scheduling problem of an integrated system is recommended and it is optimized by using α - constrained simplex method, which is composed after inserting α -constrained method to nonlinear simplex method along with mutations. Justifiability of the presented method has been tested on five test systems.

Unit commitment problem (UCP) and profit based unit commitment problem (PBUCP) are two combinatorial optimization problems with several constraints in the power system optimization research. In nowadays, the PBUCP becomes more dominant over the UCP due to restructuring in power industry. In recent years, many researchers have used various evolutionary algorithms to solve the multi-constrained PBUCP but differential evolution (DE) algorithm has received less consideration. A binary differential evolution (BDE) algorithm is introduced in the present research for solving the PBUCP. Further, to enhance the performance of the proposed BDE algorithm, a synergy of the BDE algorithm and a hill climbing local search optimizer (HCLSO) is used to solve the PBUCP. In the proposed synergy, BDE algorithm explores global aspect and a HCLSO method exploits local aspect. A HCLSO is proposed in this paper which makes perturbations in unit status based on a priority of units. A new de-commitment procedure is adopted to de-commit the needlessly committed units. The competency of the proposed BDE algorithm and BDE-HCLSO algorithm has been investigated on 10-units power system for 24 hours scheduling period. Results are justified by performing a comparison with the earlier reported algorithms employed to solve the PBUCP. Further, the simulation results demonstrate that BDE-HCLSO algorithm gives superior results over BDE algorithm.

This article implements the Ion-Motion Optimization (IMO) optimizer to solve non-convex economic load dispatch (EcLD) problem in power system. The concept of Ion motion optimization is modeled from the attraction and repulsion forces among anions and cations in the real world. The mathematical model of IMO is quiet simple and very easy to implement. The liquid phase of IMO performs exploration and crystal phase simulates the exploitation. To handle the power balance equality constraint, exterior penalty method is used. Finally, EcLD problem having 13-generators is solved using ion motion optimization.

This paper presents the hybridization of particle swarm optimization (PSO) with simplex search method (SM). SM is a deterministic method which is generally used for the local search and PSO is a stochastic population based method used for global search. Thus, this paper combines the advantage of both the methods to refine the solution. Base level search is done using PSO and finally local search is done through SM to improve the overall quality of results obtained. The proposed approach is tested on multiobjective economic emission load dispatch problem and the results obtained are then compared with other available methods to show its quality and robustness. Price penalty factor method is further used to convert this multiobjective optimization problem into single objective problem. Multifuel options, valve point loading effect and transmission losses are also included in the load dispatch problem to give the practical aspect to the problem. Quality analysis is also done against classical PSO method to show the robustness of the proposed method.

In this paper a new Hybrid Artificial Algae Algorithm (HAAA) is presented. This algorithm is developed by hybridizing artificial algae algorithm and simplex search method. To check the validity of HAAA, seven Unimodal and five multimodal benchmark functions taken from cec'05 is test. The proposed HAAA is also applied to solve 40 generator economic load dispatch problem to check its real world problem solving capability. The results obtained by HAAA shows that it is a robust algorithm and may be an efficient optimizer for many other real world optimization problems.

According to no free lunch theorem, a single search technique cannot perform best under different conditions. The integration of principally similar search techniques is one of the option, that has been explored to effectively investigate the search area. In order to avoid stagnation at local minima and to enhance the search capability of particle swarm optimization, this paper proposes a technique that integrates predator prey optimization and anti-predatory particle swarm optimization. The integrated algorithm utilizes the capability of particle swarm optimization to find high quality solution, the capability of anti predatory particle swarm optimization to avoid search in worst solution regions and capability of predator prey optimization to escape from local minima under the effect of predator. The proposed algorithm has been implemented to solve economic dispatch problem. The different practical constraints such as ramp rate limits, prohibited operating zone(s) along with power balance constraint and generator limit are undertaken. The multiple fuel system is considered with transmission losses. The proposed algorithm is tested on various models of ED problems and compared with results reported in literature and found satisfactory.

Day to day growing demand of electric energy, insufficiency of energy generation resources and increasing thermal power generation cost necessitates economic thermal power generation scheduling. Emission pollutants are required to be reduced as stringent regulations has been enforced to protect environment. In this article, interactive fuzzy technique is applied to solve the real and reactive power load dispatch of thermal units to improve the economy of generation and minimal impacts of emission on environment. Weighting method is employed to convert the economic-environmental electric power load dispatch (EPLD) problem into a scalar objective optimization problem. Weights are simulated for the generation of non-inferior solutions and trade-off function is established between the conflicting objectives. To achieve the ‘best weights pattern’ regression analysis is carried out between maximum satisfaction level of the participating objectives and trade-off function. Transmission losses are obtained by performing load flow analysis. The effectiveness of the propound approach is verified on a system, consisting of 10-bus, 13 lines and thermal generators-3.

In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm which has proven to be more effective than other population based algorithms. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where designing of band-pass filter is carried out. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.