[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
The 9th International Energy Conference
..
:: Search published articles ::
Showing 11 results for Genetic Algorithm

A Pourshafie, M Saniei, S.s Mortazavie,
Volume 12, Issue 2 (7-2009)
Abstract

Today, the increasing need for maximum utilization of existing transmission systems and developing new types of static and controllable reactive power Compensating devices, the necessity of shunt capacitors in the distribution system is made more and more evident. Because the loads of a distribution network was not permanently employed and change during every hours a day and to prevent increases in network voltage due to capacitors existence, capacitor switching must done effectively so that in times that voltage increases, the installed capacity can be controlled. In this paper a control scheme to determine the optimal capacity of switched capacitors in distribution networks using genetic algorithm is presented and optimized capacity with the aim of maximizing the benefits from network and HV / MV transformers loss reduction, with considering costs in Purchase, installation and maintenance of capacitors were obtained. Finally, the maximum number of capacitor banks on each load node, with considering the daily changes, optimum mode of capacitors switching is determined.
Alireza Salehinia, Mahmoudreza Haghifam, Majid Shahabi,
Volume 14, Issue 3 (10-2011)
Abstract

A wide penetration of DG in distribution system, operation and designed of these networks has changed. The reactive power management and optimal capacitor placement are the most important characteristics in distribution networks on which the impact of the DGs should be studied. Type of distributed generation (DG) technology, modeling and their diverse capacity can affect the optimal capacitor placement in the distribution systems. In this research fixed and switchable capacitors allocation in distribution networks in the presence of scattered generation units is presented. As a cost function in optimization procedure, cost of energy loss, installation and purchase costs of capacitors and cost of peak power loss are considered. Genetic Algorithm with new coding and operators are used for optimization. One of the significant characteristics of the proposed GA–based method is providing the switching table for allocated capacitors in various load levels.
Hosein Sadeghi, Mahdi Zolfaghari, Hosein Sohrabi, Younes Salmani,
Volume 15, Issue 2 (7-2012)
Abstract

Energy demand management has very importance in economic security and planning. To identify the energy demand affecting factors and energy demand prediction can help Policy makers and activists in the energy market to improve market performance and better economic decisions and high fuel security. Recently, new techniques have been developed to economic variables prediction and modeling. Among these techniques Genetic algorithm and Particle Swarm Optimization are the best known and most widely used in literature including economy. Therefore, in this study the genetic algorithm and particle Swarm Optimization is used for energy demand estimation and prediction in the form of linear and exponential and then their performance in each of the models evaluated. The results indicate that accuracy and efficiency of the particle swarm optimization in both of exponential and linear forms is better than genetic algorithm. In addition, among the different forms the exponential form estimated with the particle swarm optimization is the best way to predict the future energy demand.

Volume 16, Issue 4 (1-2014)
Abstract

Because of constraint in fossil fuel and incremental cost of energy, photovoltaic (PV) systems attract researchers’ special attention. One of the most important issues in this decade is obtaining the maximum power in PV systems. Maximum power point tracking (MPPT) in PV system is a technique to get the maximum available energy of PV module. This paper proposed an intelligent control method using fuzzy logic controller and optimization of its parameters by Genetic Algorithms (GAs) which are search algorithms based on the mechanism of natural selection and genetics. Simulation results show better performance of Optimized fuzzy logic controller under variable weather conditions.
Mr Saman Ahmadi, Mr S.m.t Bathaee,
Volume 17, Issue 4 (1-2015)
Abstract

In recent years, the various usage of fuel cells in vehicles has absorbed attention in industry and academic area. The fuel cells, are considered as a modern power sources in transportation. Hybridization of the fuel cell system with a secondary peaking power source is an effective approach to overcome the disadvantages of the fuel-cell-alone-powered vehicles. Two of the possible combinations is fuel cell/ battery and Fuel cell/ battery/ ultra-capacitor composition. Typically, one energy source is storage like battery and ultra-capacitor, and the other is conversion of a fuel to energy like fuel cell. Obviously, the performance of the drive train relies mainly on control quality. This work presents an enhanced method for distributing power demand through the hybrid power sources. Fuzzy logic control is considered for this purpose. Genetic algorithm has implemented for tuning this strategy by means of off-line simulation in two combined driving cycle and evaluation in 4 separated driving cycles. Multiple objective fitness function deals with effective parameters for getting reliable and acceptable results from optimization process. Finally, this optimized strategy present a fully-advanced method for energy management system of fuel cell hybrid vehicles.
, , ,
Volume 18, Issue 2 (7-2015)
Abstract

Developing energy estimation models is one of the most important steps in macro-planning for providing sustainable energy with the purpose of economic development and social welfare. Also, it has always been noted by energy policymakers and analysts. Residential and commercial sectors are the largest energy consumers in Iran and it is very important to estimate energy demand of the sectors. In the present study energy demand of residential and commercial sectors of Iran has been estimated using linear and exponential functions and the coefficients are obtained from genetic algorithm. 54 different scenarios with various inputs have been investigated. Data from the years 1968 to 2011 are used to develop models and select the suitable scenario. Results show that an exponential model with inputs including total value added minus that of the oil sector, value of made buildings, total number of households and consumer energy price index is the most suitable model. Using the selected scenario, energy demand of residential and commercial sectors is estimated up to the year 2032. The results show that the energy demand of the sectors will achieve a level of about 1180 million barrel of oil equivalent per year by 2023.
Hamdi Abdi, Mansour Moradi, Ali Rostami,
Volume 18, Issue 4 (1-2016)
Abstract

Application of distributed generation particularly wind power plants in power systems, is rapidly increasing due to their various advantages. Considering the effects of these new resource impacts in power system planning, particularly TEP problem is inevitable, due to their essential role in changing the optimal plans. In this paper, a multi-objective optimization method is presented using DC load-flow in order to present a strong model for solving TEP problem. Considering uncertainties of wind farms generation and network load in the form of probabilistic variables, as well as the investment and maintenance costs in the objective function are the main advantages of the proposed algorithm. Multi-objective optimization problem has been analyzed using Imperialist Competitive Algorithm (ICA) which is one of the newest evolutionary optimization algorithms applied to power systems. To study the effects of wind farms on TEP problem and validate the proposed method, necessary changes have been made in RTS-IEEE 24-bus network and the presented algorithm has been applied to them. The results have been compared to optimum plans which were searched by GA Algorithm


, Pouria Maghouli, ,
Volume 19, Issue 4 (1-2017)
Abstract

Cascading failures and blackouts are the most significant threats for power system security. If the process of cascading failures proceeds by further line tripping, the system will face uncontrolled islanding. Establishing of uncontrolled islands with deficiency in active or reactive power balance is the main reasons for system blackouts.

Controlled islanding is an active and effective way of avoiding catastrophic wide area blackouts. It is usually considered as a constrained combinatorial optimization problem. However, the combinatorial explosion of the solution space that occurs for large power systems increases the complexity of this problem. This study proposes a controlled islanding algorithm using genetic algorithm to find a suitable islanding solution for preventing the initiation of wide area blackouts by un-damped electromechanical oscillations or voltage instability. The objective function used in this controlled islanding algorithm is the minimal power-flow disruption. In this study to improve voltage stability after separation, minimal active and reactive power disruption is considered.

 The sole constraint applied to this solution is related to generator coherency. In the first step of the algorithm, the generator nodes are grouped using normalized spectral clustering, based on their dynamic models, to produce groups of coherent generators. In the second step of the algorithm, GA search for islanding solution that provides the minimum power-flow disruption while satisfying the constraint of coherent generator groups is determined by grouping all nodes. Simulation results, obtained using MATLAB and DigSilent software on the IEEE 39 bus test systems show that the proposed algorithm is efficient and have a well performance regarding voltage and transient stability when solving the controlled islanding problem. Also, results compared with spectral clustering algorithm that is newest and strongest research in this field.


Ramin Ghasemi Asl, Mohammad Amin Javadi, Mehdi Khalaji,
Volume 21, Issue 1 (6-2018)
Abstract

In this study, a combined cycle power plant with a nominal capacity of 500 MW, including two gas units and one steam unit, was considered by the mathematical model of thermodynamic modeling and the results of the modeling were controlled by the design information of the system. Then, the objective functions are optimized by considering the decision variables. In this multi-objective optimization that has been carried out by Non-Dominated Sorting Genetic Algorithm (NSGA-II), three objective functions of exergy efficiency, CO2 emission and produced power costs composing of the cost of injected fuel into combustion chamber ,cost of exergy destruction, investment cost and cost of environmental pollutants have been studied. The results indicate that the efficiency of combined cycle power plant depends on design parameters including gas turbine input temperature, compressor pressure ratio, and pinch point temperature and any change occurring in these parameters may lead to noticeable change in objective functions, so that the efficiency of this power plant is increased after optimization up to 8.12 % and heat rate is correspondingly reduced from 7233 (kJ/kWh) to 7023 (kJ/kWh). Similarly, exergy destruction in total system shows 7.23 reduction.


Davood Manzoor, Mahdi Ghaemi-Asl, Ahmad Norouzi,
Volume 21, Issue 2 (7-2018)
Abstract

As the electricity industry has changed and became more competitive, the electricity price forecasting has become more important. Investors need to estimate future prices in order to take proper strategy to maintain their market share and to maximize their profits. In the economic paradigm, this goal is pursued using econometric models. The validity of these models is judged by their forecasting errors. This paper is an effort to compare the forecasting power of Artificial Neural Network (ANN), Genetic Algorithm (GA) and ARIMA models for hourly electricity prices in Iran electricity market. According to the results, ANNs has the best forecasting performance followed by GA in the second place and ARIMA model in the third place.
Aliyeh Kazemi, Raja Bashirzadeh, Sara Aryaee,
Volume 22, Issue 4 (2-2020)
Abstract

This study aims to forecast Iran's electricity demand by using meta-heuristic algorithms, and based on economic and social indexes. To approach the goal, two strategies are considered. In the first strategy, genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA) are used to determine equations of electricity demand based on economic and social indexes consisted of population, gross domestic product (GDP), electricity price, and electricity consumption during the years 1968 to 2015. In this regard, linear and nonlinear models are developed. In the second strategy, artificial neural networks (ANNs) trained by meta-heuristic algorithms (GA, PSO, and ICA) are used to forecast electricity demand. The results show that nonlinear PSO with %2.85 mean absolute percentage error (MAPE) is a suitable model to forecast Iran's electrical energy demand. Iran's electricity demand would reach 324 terawatt-hours (TWh) up to the year 2025.

Page 1 from 1     

نشریه انرژی ایران Iranian Journal of Energy
Persian site map - English site map - Created in 0.06 seconds with 35 queries by YEKTAWEB 4714