R.K. Avvari; V. Kumar D M
Abstract
In this paper, a new hybrid decomposition-based multi-objective evolutionary algorithm (MOEA) is proposed for the optimal power flow (OPF) problem including Wind, PV, and PEVs uncertainty with four conflicting objectives. The proposed multi-objective OPF (MOOPF) problem includes minimization of the total ...
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In this paper, a new hybrid decomposition-based multi-objective evolutionary algorithm (MOEA) is proposed for the optimal power flow (OPF) problem including Wind, PV, and PEVs uncertainty with four conflicting objectives. The proposed multi-objective OPF (MOOPF) problem includes minimization of the total cost (TC), total emission (TE), active power loss (APL), and voltage magnitude deviation (VMD) as objectives and a novel constraint handling method, which adaptively adds the penalty function and eliminates the parameter dependence on penalty function evaluation is deployed to handle several constraints in the MOOPF problem. In addition, summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of MOEA. Further, a fuzzy min-max method is utilized to get the best-compromised values from Pareto-optimal solutions. The impact of intermittence of Wind, PV, and PEVs integration is considered for optimal cost analysis. The uncertainty associated with Wind, PV, and PEV systems are represented using probability distribution functions (PDFs) and its uncertainty cost is calculated using the Monte-Carlo simulations (MCSs). A commonly used statistical method called the ANOVA test is used for the comparative examination of several methods. To test the proposed algorithm, standard IEEE 30, 57, and 118-bus test systems were considered with different cases and the acquired results were compared with NSGA-II and MOPSO to validate the suggested algorithm's effectiveness
Application of Automatic Control in Power System
А. Sadratdin; W.K. Al-Azzawi; B.M. Ali; A.N. Obeed; N.A. Hussien; A.M. Shareef; Kadhum Al-Majdi; A.S. Ibrahim
Abstract
This study investigates a hybrid electric system that utilizes novel energy sources and is subject to variable production and uncertainty. The study proposes a multi-objective optimization methodology using Genetic Algorithm (GA) to optimize energy source consumption and utilization, accounting for variations ...
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This study investigates a hybrid electric system that utilizes novel energy sources and is subject to variable production and uncertainty. The study proposes a multi-objective optimization methodology using Genetic Algorithm (GA) to optimize energy source consumption and utilization, accounting for variations in production/load levels across different time intervals. The proposed approach enables the end-user to achieve desired operational outcomes while adhering to specified constraints, taking into account both economic constraints and environmental considerations. The study explores the implementation of intelligent electric energy management in a model electric motor system that incorporates various electric energy generators, including solar cells, fuel cells, micro-turbines, and batteries. The optimization problem was formulated with multi-objectives of minimizing operating cost and environmental pollution. The presented approach demonstrated that the energy management system or electrical system operator is a proficient mechanism. Ultimately, the investigation has resulted in the development of an intelligent energy management system aimed at enhancing the efficiency of the energy production and storage sampling and planning system. The findings of the optimization clearly demonstrate an inverse link between the operating costs and pollution emissions in the system under study.
P. Omidi; S. Abazari; S.M. Madani
Abstract
The relay coordination problem of directional overcurrent has been an active research issue in distribution networks and power transmission. In general, the problem of relay coordination is the nonlinearity of the optimization problem, which increases or decreases with different network structures. ...
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The relay coordination problem of directional overcurrent has been an active research issue in distribution networks and power transmission. In general, the problem of relay coordination is the nonlinearity of the optimization problem, which increases or decreases with different network structures. This paper presents a new method with directional overcurrent relay coordination approach to reduce the operating time of the relays between the primary and backup relays by using hybrid programming of ILP (interval linear programming) and DE (differential evolution). Due to the difference in short circuit current level from grid connected to the isolated mode, therefore, it is necessary to use a reliable protection solution to reduce this discrimination time and also to prevent the increase of coordination time interval (CTI). The ability of the objective function used in this paper is to reduce the discrimination time of primary and backup relays and simultaneously reduce the operating time of primary and backup relays by introducing a new method. The basic parameters of the directional overcurrent relay (DOCR) such as time multiplier setting (TMS) and plug setting (PS) have been adjusted such that the relays operation time should be optimized. Optimization is based on a new objective function, described as a highly constrained non-linear problem to simultaneously minimize operating time in backup and primary relays. A function of penalty is also used to check the problem constraints in case the backup relay time is fewer than that of the main relay. The method is implemented on modified IEEE 14- and 30-bus distribution networks. The results demonstrate the efficiency of the method, and the values are optimal compared to those of other algorithms. MATLAB program has also been used to simulate optimization.
A. Jasemi; H. Abdi
Abstract
As a basic tool in power system control and operation, the optimal power flow (OPF) problem searches the optimal operation point via minimizing different objectives and maintaining the control variables within their applicable regions. In recent years, this problem has encountered many challenges due ...
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As a basic tool in power system control and operation, the optimal power flow (OPF) problem searches the optimal operation point via minimizing different objectives and maintaining the control variables within their applicable regions. In recent years, this problem has encountered many challenges due to the presence of renewable energy sources, which has led introducing of a combinatorial type of power networks known as AC/DC hybrid power systems. In this paper, the OPF problem is proposed in an AC/DC hybrid microgrid, including wind power plants. For the first time, the mentioned problem is considered as a multi-objective optimization problem via minimizing fuel cost and emission. The problem is modeled while considering the power flow equations, the voltage limits in AC and DC buses, the AC voltage angle limits, and the firing angle of the converters. Also, due to the uncertain power generated by wind power plants, the probabilistic OPF problem is modeled by the five-point estimation method. To solve the problem, the "fmincon" function in MATLAB software is used by applying the IP algorithm. The simulation case study on a 13-bus sample MG verifies the effectiveness of the proposed method. The numerical results confirm that increasing the wind farm capacity from 14.54 MW to 113 MW, will be led to increasing the fuel cost from 10% to 61%, in case of including the power losses compared to the condition in which they are neglected. It is also observed that in terms of different weights, the total air pollution including the power losses is 2.30 to 2.40 times higher than the total pollution without electrical losses
H. Shayeghi; M. Alilou
Abstract
In the last years, microgrids have been introduced for better managing the overall power network. The two-way communication between supplier and consumer sides of a smart microgrid causes to better apply the demand side management methods to this type of system. For this reason, the multi-objective demand ...
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In the last years, microgrids have been introduced for better managing the overall power network. The two-way communication between supplier and consumer sides of a smart microgrid causes to better apply the demand side management methods to this type of system. For this reason, the multi-objective demand side management of a smart microgrid is investigated in this study. The economic and environmental indices of the microgrid are considered as the primary objective functions of the proposed demand side management method. The load variations of the microgrid are improved based on the applied demand response program. The operator of the microgrid can provide the demand of the system using a wind turbine, photovoltaic panel, diesel generator, micro turbine, fuel cell, energy storage system and the upstream network. The stochastic behavior of renewable units is also considered to evaluate the proposed method in a more realistic condition. The combination of the multi-objective ant lion optimization algorithm and the analytical hierarchy process method is utilized to solve the demand side management problem. Numerical results, which are obtained from evaluating the proposed method in a sample microgrid, demonstrate the high efficiency of the proposed demand side management method in improving the economic and environmental indices of the microgrid.
Distribution Systems
M.A. Tavakoli Ghazi Jahani; P. Nazarian; A. Safari; M.R. Haghifam
Abstract
Network reconfiguration is a nonlinear optimization procedure which calculates a radial structure to optimize the power losses and improve the network reliability index while meeting practical constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with ...
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Network reconfiguration is a nonlinear optimization procedure which calculates a radial structure to optimize the power losses and improve the network reliability index while meeting practical constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with the objective functions of minimization of power losses and improvement of reliability index. The optimization problem is solved by multi-objective grasshopper optimization algorithm (MOGOA) which is one of the most modern heuristic optimization tools. To solve an optimization problem, the suggested algorithm mathematically mimics and formulates the behavior of grasshopper swarms. The modifying comfort zone coefficient needs grasshoppers to balance exploration and exploitation, which helps the MOGOA to find an exact approximation of global optimization and not trapped in local optima. The efficiency of the suggested technique is approved regarding the 33-bus and 69-bus test systems. Optimization results expressed that the suggested technique not only presents the intensified exploration ability but also has a better solution compared with previous algorithms.
Distribution Systems
Hossein Shayeghi; Masoud Alilou
Volume 3, Issue 2 , December 2015, , Pages 131-146
Abstract
In this paper, simultaneous placement of distributed generation, capacitor bank and protective devices are utilized to improve the efficiency of the distribution network. The objectives of the problem are reduction of active and reactive power losses, improvement of voltage profile and reliability indices ...
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In this paper, simultaneous placement of distributed generation, capacitor bank and protective devices are utilized to improve the efficiency of the distribution network. The objectives of the problem are reduction of active and reactive power losses, improvement of voltage profile and reliability indices and increasing distribution companies’ profit. The combination of firefly algorithm, particle swarm optimization and analytical hierarchy process is proposed to solve the multi-objective allocation problem. The proposed method is implemented on IEEE 69-bus and also an actual 22-bus distribution systems in Tehran-Iran. Test results approve the effectiveness of the proposed method for improved reliability and network performance of the distribution network.
Mostafa Sedighizadeh; Mahdi mahmoodi
Volume 3, Issue 1 , June 2015, , Pages 56-70
Abstract
In distribution systems, in order to diminish power losses and keep voltage profiles within acceptable limits, network reconfiguration and capacitor placement are commonly used. In this paper, the Hybrid Shuffled Frog Leaping Algorithm (HSFLA) is used to optimize balanced and unbalanced radial distribution ...
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In distribution systems, in order to diminish power losses and keep voltage profiles within acceptable limits, network reconfiguration and capacitor placement are commonly used. In this paper, the Hybrid Shuffled Frog Leaping Algorithm (HSFLA) is used to optimize balanced and unbalanced radial distribution systems by means of a network reconfiguration and capacitor placement. High accuracy and fast convergence are the highlighted points of the proposed approach because of solving the multi-objective reconfiguration and capacitor placement in fuzzy frame work. These objectives are the minimization of total network real power losses, the minimization of buses voltage violation, and load balancing in the feeders. Each objective is transferred into fuzzy domain using membership function and fuzzified separately. Then, the overall fuzzy satisfaction function is formed and considered as a fitness function. To gain the optimal solution, the value of this function will be maximized. In the literature, several reconfiguration and capacitor placement methods have been investigated, which are implemented separately. However, there are few studies which simultaneously apply these two strategies. The proposed algorithm has been implemented in three IEEE test systems (two balanced and one unbalanced systems). Numerical results obtained by simulation show that the performance of the HSFLA algorithm is much higher than several other meta-heuristic algorithms.
R. Ghanizadeh; A. Jahandideh shendi; M. Ebadian; M. Golkar; A. Ajami
Volume 1, Issue 2 , November 2013, , Pages 110-123
Abstract
In this paper, a novel compensator based on Magnetically Controlled Reactor with Fixed Capacitor banks (FC-MCR) is introduced and then power system stability in presence of this compensator is studied using an intelligent control method. The problem of robust FC-MCR-based damping controller design is ...
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In this paper, a novel compensator based on Magnetically Controlled Reactor with Fixed Capacitor banks (FC-MCR) is introduced and then power system stability in presence of this compensator is studied using an intelligent control method. The problem of robust FC-MCR-based damping controller design is formulated as a multi-objective optimization problem. The multi-objective problem is concocted to optimize a composite set of two eigenvalue-based objective functions comprising the desired damping factor, and the desired damping ratio of the lightly damped and undamped electromechanical modes. The controller is automatically tuned by optimization of an eigenvalue-based multi-objective function using Honey Bee Mating Optimization (HBMO) to simultaneously shift the lightly damped and undamped electromechanical modes to a prescribed zone in the s-plane so that the relative stability is guaranteed and the time domain specifications concurrently secured. The effectiveness of the proposed controller in damping low frequency oscillations under different operating conditions is demonstrated through eigenvalue analysis and nonlinear time simulation studies. The results show that the tuned HBMO-based FC-MCR controller which is designed by using the proposed multi-objective function has an outstanding capability in damping power system low frequency oscillations and significantly improves the power systems dynamic stability.
H. R. Imanijajarmi; A. Mohamed; H. Shareef
Volume 1, Issue 1 , June 2013, , Pages 54-62
Abstract
This paper presents an innovative active power filter design method to simultaneously compensate the current harmonics and reactive power of a nonlinear load. The power filter integrates a passive power filter which is a RL low-pass filter placed in series with the load, and an active power filter which ...
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This paper presents an innovative active power filter design method to simultaneously compensate the current harmonics and reactive power of a nonlinear load. The power filter integrates a passive power filter which is a RL low-pass filter placed in series with the load, and an active power filter which comprises an RL in series with an IGBT based voltage source converter. The filter is assumed to inject a current into the connection node of the load and grid to eliminate current harmonics and its reactive part. The voltage source converter is placed in a hysteresis feedback control loop to generate a harmonic current. The bandwidth and output amplitude of the hysteresis controller are optimized with the inductance of RL filters. Three objective functions are considered in the optimization problem, which include minimizing of current total harmonic distortion, maximizing of power factor, and minimizing of the IGBT bridge current. For solving the optimization problem, two well-known multi-objective evolutionary algorithms are applied, namely, non-dominated sorting genetic algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). Test results showed that the SPEA2 technique exhibited a better performance in comparison to NSGA-II relative to the objectives.
