Reza Noroozian , Mehrdad Abedi , Gevorg B. Gharehpetian , Seyed Hossein Hosseini ,
Volume 5, Issue 2 (6-2009)
Abstract
This paper presents the modeling and simulation of a proton exchange membrane fuel cell (PEMFC) generation system for off-grid and on-grid operation and configuration. A fuel cell DG system consists of a fuel cell power plant, a DC/DC converter and a DC/AC inverter. The dynamic model for fuel cell array and its power electronic interfacing are presented also a multi-input single output (MISO) DC/DC converter and its control scheme is proposed and analyzed. This DC/DC converter is capable of interfacing fuel cell arrays to the DC/AC inverter. Also the mathematical model of the inverter is obtained by using average technique. Then the novel control strategy of DC/AC inverter for different operating conditions is demonstrated. The simulation results show the effectiveness of the suggested control systems under both on-grid and off-grid operation modes.
M. R. Baghayipour, A. Akbari Foroud,
Volume 8, Issue 1 (3-2012)
Abstract
This paper presents a method to improve the accuracy of DC Optimal Power Flow problem, based on evaluating some nodal shares of transmission losses, and illustrates its efficiency through comparing with the conventional DCOPF solution, as well as the full AC one. This method provides three main advantages, confirming its efficiency:
1- It results in such generation levels, line flows, and nodal voltage angles that are more accurate than the conventional DCOPF solution.
2- Like the previous DCOPF problem, the new method is derived from a non-iterative DC power flow algorithm, and thus its solution requires no long run time.
3- Its formulation is simple and easy to understand. Moreover, it can simply be realized in the form of Lagrange representation, makes it possible to be considered as some constraints in the body of any bi-level optimization problem, with its internal level including the OPF problem satisfaction.
H. Mohammadian Bishe, A. Rahimi Kian, M. Sayyed Esfahani,
Volume 8, Issue 2 (6-2012)
Abstract
This paper proposes a Trust-Region Based Augmented Method (TRALM) to
solve a combined Environmental and Economic Power Dispatch (EEPD) problem. The
EEPD problem is a multi-objective problem with competing and non-commensurable
objectives. The TRALM produces a set of non-dominated Pareto optimal solutions for the
problem. Fuzzy set theory is employed to extract a compromise non-dominated solution.
The proposed algorithm is applied to the standard IEEE 30 bus six-generator test system.
Comparison of TRALM results with the various algorithms, reported in the literature shows
that the solutions of the proposed algorithm are very accurate for the EEPD problem.
R Subramanian, K Thanushkodi, A Prakash,
Volume 9, Issue 4 (12-2013)
Abstract
The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA), Differential Evolution (DE), Particle swarm optimization (PSO), Artificial Bee Colony optimization (ABC), Biogeography-Based Optimization (BBO), Bacterial Foraging optimization (BFO), Firefly Algorithm (FA) techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods.
H. Rajabi Mashhadi, S. M. Eslami, H. Modir Shanechi,
Volume 10, Issue 3 (9-2014)
Abstract
The main goal of this paper is to study statistical indices and evaluate AGC indices in power system which has large penetration of the WTGs. Increasing penetration of wind turbine generations, needs to study more about impacts of it on power system frequency control. Frequency control is changed with unbalancing real-time system generation and load . Also wind turbine generations have more fluctuations and make system more unbalance. Then AGC loop helps to adjust system frequency and the scheduled tie-line powers. The quality of AGC loop is measured by some indices. A good index is a proper measure shows the AGC performance just as the power system operates. One of well-known measures in literature which was introduced by NERC is Control Performance Standards(CPS). Previously it is claimed that a key factor in CPS index is related to standard deviation of generation error, installed power and frequency response. This paper focuses on impact of a several hours-ahead wind speed forecast error on this factor. Furthermore evaluation of conventional control performances in the power systems with large-scale wind turbine penetration is studied. Effects of wind speed standard deviation and also degree of wind farm penetration are analyzed and importance of mentioned factor are criticized. In addition, influence of mean wind speed forecast error on this factor is investigated. The study system is a two area system which there is significant wind farm in one of those. The results show that mean wind speed forecast error has considerable effect on AGC performance while the mentioned key factor is insensitive to this mean error.
S. Sivasakthi, R. K. Santhi, N. Murali Krishnan, S. Ganesan, S. Subramanian,
Volume 13, Issue 2 (6-2017)
Abstract
The increasing concern of global climate changes, the promotion of renewable energy sources, primarily wind generation, is a welcome move to reduce the pollutant emissions from conventional power plants. Integration of wind power generation with the existing power network is an emerging research field. This paper presents a meta-heuristic algorithm based approach to determine the feasible dispatch solution for wind integrated thermal power system. The Unit Commitment (UC) process aims to identify the best feasible generation scheme of the committed units such that the overall generation cost is reduced, when subjected to a variety of constraints at each time interval. As the UC formulation involves many variables and system and operational constraints, identifying the best solution is still a research task. Nowadays, it is inevitable to include power system reliability issues in operation strategy. The generator failure and malfunction are the prime influencing factor for reliability issues hence they have considered in UC formulation of wind integrated thermal power system. The modern evolutionary algorithm known as Grey Wolf Optimization (GWO) algorithm is applied to solve the intended UC problem. The potential of the GWO algorithm is validated by the standard test systems. Besides, the ramp rate limits are also incorporated in the UC formulation. The simulation results reveal that the GWO algorithm has the capability of obtaining economical resolutions with good solution quality.
O. Herbadji, L. Slimani, T. Bouktir,
Volume 15, Issue 1 (3-2019)
Abstract
In this study, a multiobjective optimization is applied to Optimal Power Flow Problem (OPF). To effectively achieve this goal, a Multiobjective Ant Lion algorithm (MOALO) is proposed to find the Pareto optimal front for the multiobjective OPF. The aim of this work is to reach good solutions of Active and Reactive OPF problem by optimizing 4-conflicting objective functions simultaneously. Here are generation cost, environmental pollution emission, active power losses, and voltage deviation. The performance of the proposed MOALO algorithm has been tested on various electrical power systems with different sizes such as IEEE 30-bus, IEEE 57-bus, IEEE 118-bus, IEEE 300-bus systems and on practical Algerian DZ114-bus system. The results of the tests proved the versatility of the algorithm when applied to large systems. The effectiveness of the proposed method has been confirmed by comparing the results obtained with those obtained by other algorithms given in the literature for the same test systems.
H. Kiani Rad, Z. Moravej,
Volume 15, Issue 3 (9-2019)
Abstract
In this paper, a new method is conducted for incorporating the forecasted load uncertainty into the Substation Expansion Planning (SEP) problem. This method is based on the fuzzy clustering, where the location and value of each forecasted load center is modeled by employing the probability density function according to the percentage of uncertainty. After discretization of these functions, the location and value of each of the new load centers are determined based on the presented fuzzy clustering based algorithm. A Genetic Algorithm (GA) is used to solve the presented optimization problem in which the allocations and capacities of new substations as well as the expansion requirements for the existing ones are determined. With the innovative presented method, the impact of uncertainty of the power and location of the predicted loads on the results of SEP is measured, and finally, it is possible to make a proper decision for the SEP. The significant features of this method can be outlined as its applicability to large-scale networks, robustness to load changes, the comprehensiveness and also, the simplicity of applying this method to various problems. The effectiveness of proposed method is demonstrated by application on a real sub-transmission system.
