Using HBMO Algorithm to Optimal Sizing & Sitting of Distributed Generation in Power System

This paper analyzes of HBMO placement method efficiency in comparison with PSO and GA in order to sizing and sitting of distributed generation in distribution power system. These algorithms for optimization in this paper is tested on IEEE 33 bus reconfigured test system. The proposed objective function considers active power losses and the voltage profile in nominal load of system. In order to use of optimization algorithms, at first, placement problem is written as an optimization problem which includes the objective function and constraints, and then to achieve the most desirable results, Optimization methods is applied to solve the problem. High performance of the proposed algorithm in mention system is verified by simulations in MATLAB software and in order to illustrate of feasibility of proposed method will accomplish

Using HBMO Algorithm to Optimal Sizing & Sitting of Distributed Generation in Power System

oai:ojs.portalgaruda.org:article/179This paper analyzes of HBMO placement method efficiency in comparison with PSO and GA in order to sizing and sitting of distributed generation in distribution power system. These algorithms for optimization in this paper is tested on IEEE 33 bus reconfigured test system. The proposed objective function considers active power losses and the voltage profile in nominal load of system. In order to use of optimization algorithms, at first, placement problem is written as an optimization problem which includes the objective function and constraints, and then to achieve the most desirable results, Optimization methods is applied to solve the problem. High performance of the proposed algorithm in mention system is verified by simulations in MATLAB software and in order to illustrate of feasibility of proposed method will accomplish

Short-term optimal hydro-thermal scheduling using clustered adaptive teaching learning based optimization

In this paper, Clustered Adaptive Teaching Learning Based Optimization (CATLBO) algorithm is proposed for determining the optimal hourly schedule of power generation in a hydro-thermal power system. In the proposed approach, a multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, net head and power generation is considered. Constraints such as power balance, water balance, reservoir volume limits and operation limits of hydro and thermal plants are considered. The feasibility and effectiveness of the proposed algorithm is demonstrated through a test system, and the results are compared with existing conventional and evolutionary algorithms. Simulation results reveals that the proposed CATLBO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques

Short term complex hydro thermal scheduling using integrated PSO-IBF algorithm

In this article, an integrated evolutionary technique such as particle swarm optimization (PSO) algorithm and improved bacterial foraging algorithm (IBFA) have been developed to provide an optimum solution to the scheduling problem with complex thermal and hydro generating stations. PSO algorithm is framed based on the intelligent behavior of the fish school and a flock of birds and the optimal solution in the multidimensional search region is achieved by assigning a random velocity to each potential solution (called the particle). BFA is designed by following the prey-seeking (chemotactic) nature of E. coli bacteria. This technique is followed in an improved manner to get the convergence rate in dynamic for a hyperspace problem by implementing a chemotactic step in a linearly decreased way instead of the static one. The effectiveness of this integrated algorithm is evaluated by using it in a complex thermal and hydro generating system. In this testing system, multiple numbers of cascaded reservoirs in hydro plants have a time coupling effect and thermal power units have a valve point loading effect. The simulation results indicate its merits by comparing it with other meta-heuristic techniques related to the fuel cost required to generate the thermal power.

Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value

Evaluación mediante indicadores clave de rendimiento del despacho económico hidrotérmico resuelto por medio de técnicas heurísticas

El presente artículo resuelve el problema de coordinación hidrotérmica mediante la utilización de técnicas heurísticas como alternativa a los métodos de optimización exactos. Las técnicas utilizadas son el método de enjambre de partículas, algoritmos genéticos con sus variantes de selección por ruleta y torneo y el novedoso y reciente algoritmo lobo gris. El objetivo del modelo de optimización planteado radica en la minimización de la función objetivo referente al costo de combustibles de las centrales térmicas considerando, además, el efecto de punto de válvula que le da un toque más realista al problema. La metodología de solución propuesta incluye penalizaciones en la función objetivo relacionadas a las violaciones de las restricciones de balances de potencia y el balance dinámico de los reservorios. El despacho económico se ejecuta para un sistema de prueba compuesto por múltiples centrales térmicas y varias centrales hidroeléctricas con reservorios en cascada. Los algoritmos desarrollados se implementaron en el software Matlab. Adicionalmente, se evalúa innovadoramente los resultados logrados por cada técnica heurística mediante la utilización de diferentes indicadores clave de rendimiento.This paper solves the hydrothermal scheduling problem by using heuristic techniques as an alternative to exact optimization methods. The techniques used are the particle swarm method, genetic algorithms with its variants of selection by roulette and tournament and the novel and recent grey wolf algorithm. The objective of the proposed optimization model lies in the minimization of the objective function regarding fuel costs of thermal power plants, also considering the valve point effect that gives a more realistic touch to the problem. The proposed solution methodology includes penalties in the objective function related to the violations of constraints of the power balance and dynamic balance of reservoirs. The economic dispatch is executed for a test system composed of multiple thermal power plants and several hydroelectric power plants with cascaded reservoirs. The algorithms developed were implemented in the Matlab software. Additionally, the results achieved by each heuristic technique are innovatively evaluated using different key performance indicators

Security-Constrained Unit Commitment Based on a Realizable Energy Delivery Formulation

Security-constrained unit commitment (SCUC) is an important tool for independent system operators in the day-ahead electric power market. A serious issue arises that the energy realizability of the staircase generation schedules obtained in traditional SCUC cannot be guaranteed. This paper focuses on addressing this issue, and the basic idea is to formulate the power output of thermal units as piecewise-linear function. All individual unit constraints and systemwide constraints are then reformulated. The new SCUC formulation is solved within the Lagrangian relaxation (LR) framework, in which a double dynamic programming method is developed to solve individual unit subproblems. Numerical testing is performed for a 6-bus system and an IEEE 118-bus system on Microsoft Visual C# .NET platform. It is shown that the energy realizability of generation schedules obtained from the new formulation is guaranteed. Comparative case study is conducted between LR and mixed integer linear programming (MILP) in solving the new formulation. Numerical results show that the near-optimal solution can be obtained efficiently by the proposed LR-based method