Z. K. Gurgi; A. I. Ismael; R. A. Mejeed
Abstract
Solar cell efficiency considers an important part of the PV system, the parameters (Io, IL, n, Rs, and Rsh) of solar cell is the main part that effected on efficiency. The Matlab simulation program was used to estimate the three parameters' optimization values and evaluated by the Fminsearch method, ...
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Solar cell efficiency considers an important part of the PV system, the parameters (Io, IL, n, Rs, and Rsh) of solar cell is the main part that effected on efficiency. The Matlab simulation program was used to estimate the three parameters' optimization values and evaluated by the Fminsearch method, they calculated for solar cells measured from 0oC to 100oC for seven temperatures, then make comparing for the results between the Genetic Algorithm method with Neural Network Algorithm. This paper establishes the results are frequently in GA was better than NNA, with the Io being 3.0992 e-7 and IL being 3.8059 found by GA. GA is good if they have the same population size and number of iterations. The value of the objective function (fval) in GA is 0.002856 but in NNA is 0.005518. And also second objective function (fvaltemp) in GA is 0.1035 with a 0.1069 value in NNA. From the side, the execution time considers in the Fminsearch method is less than NNA and GA that being 64.9 s, 781 s, and 289 s respectively.
S. Chupradit; G. Widjaja; S. J. Mahendra; M. H. Ali; M. A. Tashtoush; A. Surendar; M. M. Kadhim; A. Y. Oudah; I. Fardeeva; F. Firman
Abstract
In recent years, as a result of remarkable increase in energy industry, discrimination between lower and higher loads as well as economic crisis which pestered a majority of countries; hence the usage of power plants became a significant issue. In addition, growing consumption of power and inexistence ...
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In recent years, as a result of remarkable increase in energy industry, discrimination between lower and higher loads as well as economic crisis which pestered a majority of countries; hence the usage of power plants became a significant issue. In addition, growing consumption of power and inexistence of valid source in satisfying the requirements has brought different problems such as diminish of fossil fuel resources, adversarial environmental influences, universal growth of Greenhouse Gases (GHGs). The associated issues have created technologies compatible with situations including Electric Vehicles (EVs). Regarding the efficiency of two-side exchange of energy within these vehicles, if there was a connection among the number of them and net under management and intelligent monitor of organization stability, so they can treat like a virtual tiny energy plant with start- up speed and free of cost. This paper presented the modeling and optimizing of the charge of electric vehicles with genetic algorithm in the presence of renewable energy sources. According to the results of this study, the cost of the HEV charge connected to the net is 75.88% less than the EV compared to the payment costs of the car (dis)charge in optimal patterns.
Application of Automatic Control in Power System
A.S. Altuma; R. Khalid; A.I. Alanssari; A. Hussien; Y.S. Mezaal; K. Al-Majdi; T. Alawsi
Abstract
Insufficient synchronization between the operational efficiency of capacitors and tap-changer transformers in regulating voltage presents a fundamental challenge in distribution networks, which in turn hinders the control performance. This challenge is caused by the inability of these two components ...
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Insufficient synchronization between the operational efficiency of capacitors and tap-changer transformers in regulating voltage presents a fundamental challenge in distribution networks, which in turn hinders the control performance. This challenge is caused by the inability of these two components to synchronize their respective operations properly. In this study, a novel control strategy is presented with the objective of achieving synchronization in the functioning of capacitors and tap transformers. Depending on the load change, various devices can be used to control the distribution network voltage. On Load Tap Changers (OLTCs) and Capacitor Banks (CBs) respond slowly to voltage changes. If the voltage changes rapidly, such devices are useless and should be avoided. Keying may shorten lifespan. This study investigated a new optimal control mechanism for coordinating tap transformers and capacitors. The optimization of tap trans- and capacitor-stage operation through the use of a Genetic Algorithm (GA) results in the reduction of superfluous switching. The limits for Point of Common Coupling (PCC) bus voltage and power factor are 0.94 and 1.02 per unit, respectively. The secondary control stage regulates the voltage of the feeder bus within the range of 0.95 to 1.05 per unit. Following the second-stage regulation of the terminal buses in the N network feeder, the third stage governs the PCC bus voltage. To prevent an infinite control loop, the voltage of the PCC bus is regulated within the range of 0.95 to 1.05 per unit (PU). These findings indicate that the optimization model is capable of achieving maximum efficiency in controlling the voltage of the distribution network. In the interim, this optimization technique produces outcomes of greater accuracy, as evidenced by a voltage value that remains consistently close to unity [Root Mean Square Error (RMSE) = 0.85] across a broad spectrum of network-loading scenarios.
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.
A.M. Alee; S. Golshannavaz; T. Ghanizadehbolandi; V. Talavat
Abstract
This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical ...
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This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical model of PV systems, wind turbines, other non-renewable distributed generations, energy storage systems, and responsive loads, a genetic algorithm (GA)-based approach is used to find the optimal placement and allocation of all units. The modeling also takes into account the uncertainty of PV units and wind turbines to represent real-world operational conditions more accurately. Additionally, although the IEEE 33-bus system is used to formulate the presented method, one can easily extend it to any other network with an arbitrary number of buses. The effectiveness of the proposed method is verified by designing three different scenarios. The simulation results obtained based on MATLAB clearly show the capability of the proposed method to improve the voltage profile and the cost of losses in ADN. This is done by properly utilizing the excess capacity of inverter-interfaced PV units as a static compensator (STATCOM), even in the absence of sunlight. The findings indicate that the inclusion of DERs and PV-STATCOM results to a notable enhancement of approximately 68.46% in power losses reduction and around 65% in the voltage deviation minimization.
M. Shabandokht-Zarami; M. Ghanbari; E. Alibeiki; M. Jannati
Abstract
The Vector Control (VC) of Y-Connected Induction Motor (YCIM) drives is entirely demanding task. Furthermore, YCIM under an Open-Circuit Fault in the Stator Coils (OCFSC) leads to deterioration of the VC. Consequently, the VC of YCIMs under an OCFSC requires a suitable design. This research focuses on ...
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The Vector Control (VC) of Y-Connected Induction Motor (YCIM) drives is entirely demanding task. Furthermore, YCIM under an Open-Circuit Fault in the Stator Coils (OCFSC) leads to deterioration of the VC. Consequently, the VC of YCIMs under an OCFSC requires a suitable design. This research focuses on an accurate and modified Field-Oriented Control (FOC) strategy for 3-phase YCIM drives under an OCFSC. Most of the recent papers studying VC of YCIMs under an OCFSC ignore the leakage inductance in the VC equations. This paper presents an alternative VC technique, considering the leakage inductance in the VC equations of YCIMs under an OCFSC. In the presented VC system, two asymmetrical Rotating Transformations (RTs) for the stator current and voltage quantities are proposed and employed. In the proposed scheme, the genetic algorithm is used to regulate the parameters of the Proportional-Integral (PI) controllers. The developed VC system provides an accurate control against an OCFSC and can be employed for different industries that need Fault-Tolerant Control (FTC) systems. The effectiveness of the proposed approach is validated through experimentation in the laboratory. The proposed control scheme gives good responses during both steady state and transient sate. In addition, the proposed VC system gives better performances during the post-fault operation compared to previous works in terms of speed and torque ripples.
Distribution Systems
R. Afshan; J. Salehi
Abstract
This paper proposes a novel hybrid Monte Carlo simulation-genetic approach (MCS-GA) for optimal operation of a distribution network considering renewable energy generation systems (REGSs) and battery energy storage systems (BESSs). The aim of this paper is to design an optimal charging /discharging scheduling ...
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This paper proposes a novel hybrid Monte Carlo simulation-genetic approach (MCS-GA) for optimal operation of a distribution network considering renewable energy generation systems (REGSs) and battery energy storage systems (BESSs). The aim of this paper is to design an optimal charging /discharging scheduling of BESSs so that the total daily profit of distribution company (Disco) can be maximized. In this study, the power generation of REGSs such as photovoltaic resources (PVs) and the network electricity prices are studied through their uncertainty natures. The probability distribution function (PDF), is used to account for uncertainties in this paper. Also, the Monte Carlo simulation (MCS) is applied to generate different scenarios of network electricity prices and solar irradiation of PVs. Optimal scheduling of BESSs can be performed by genetic algorithm (GA). In this paper, firstly, the charging and discharging state of BESSs (positive or negative sign of battery power) is determined according to the variable amount of the electricity prices and power produced from PVs, which have been obtained from the Monte Carlo simulation. Then by using the GA, optimal amount of BESSs is determined. Therefore, a hybrid MCS-GA is used to solve this problem. Numerical examples are presented to illustrate the optimal charging/discharging power of the battery for maximizing the total daily profit.
Power System Operation
S. Halilčević; I. Softić
Abstract
This paper presents an algorithm based on inter-solutions of having scheduled electricity generation resources and the fuzzy logic as a sublimation tool of outcomes obtained from the schedule inter-solutions. The goal of the algorithm is to bridge the conflicts between minimal cost and other aspects ...
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This paper presents an algorithm based on inter-solutions of having scheduled electricity generation resources and the fuzzy logic as a sublimation tool of outcomes obtained from the schedule inter-solutions. The goal of the algorithm is to bridge the conflicts between minimal cost and other aspects of generation. In the past, the optimal scheduling of electricity generation resources has been based on the optimal activation levels of power plants over time to meet demand for the lowest cost over several time periods. At the same time, the result of that type of optimization is single-dimensional and constrained by numerous limitations. To avoid an apparently optimal solution, a new concept of optimality is presented in this paper. This concept and the associated algorithm enable one to calculate the measure of a system’s state with respect to its optimal state. The optimal system state here means that the fuzzy membership functions of the considered attributes (the characteristics of the system) have the value of one. That particular measure is called the “degree of optimality” (DOsystem). The DOsystem can be based on any of the system's attributes (economy, security, environment, etc.) that take into consideration the current and/or future state of the system. The calculation platform for the chosen electric power test system is based on one of the unit commitment solvers (in this paper, it is the genetic algorithm) and fuzzy logic as a cohesion tool of the outcomes obtained by means of the unit commitment solver. The DO-based algorithm offers the best solutions in which the attributes should not to distort each other, as is the case in a strictly deterministic nature of the Pareto optimal solution.
Design System & Algorithm
R. Effatnejad; H. Aliyari; M. Savaghebi
Abstract
The Optimal Power Flow (OPF) is one of the most important issues in the power systems. Due to the complexity and discontinuity of some parameters of power systems, the classic mathematical methods are not proper for this problem. In this paper, the objective function of OPF is formulated to minimize ...
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The Optimal Power Flow (OPF) is one of the most important issues in the power systems. Due to the complexity and discontinuity of some parameters of power systems, the classic mathematical methods are not proper for this problem. In this paper, the objective function of OPF is formulated to minimize the power losses of transmission grid and the cost of energy generation and improve the voltage stability and voltage profile, considering environmental issues. Therefore, the OPF problem is a nonlinear optimization problem consisting of continuous and discontinuous variables. To solve it, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and a new hybrid algorithm combining modified Particle Swarm Optimization (PSO) and Genetic algorithm (GA) methods are proposed. In this method, each of the algorithms is performed in its procedure and generates the primary population; then, the populations are ordered and from among them, populations with the highest propriety function are selected. The first population that guesses will enter the two algorithms’ procedures for generating the new population. Note that the inputs of the two algorithms are the same; then, generates a new population. Now, there are three groups of populations: one created by modified GA, one created by modified PSO, and the other is the first initial population, and then sorted with the described sorting method.
H. Taherian; I. Nazer; E. Razavi; S. R. Goldani; M. Farshad; M. R. Aghaebrahimi
Volume 1, Issue 2 , November 2013, , Pages 136-146
Abstract
Accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. Market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. ...
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Accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. Market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. However, due to its time variant behavior and non-linear and non-stationary nature, electricity price is a complex signal. This paper presents a model for short-term price forecasting according to similar days and historical price data. The main idea of this article is to present an intelligent model to forecast market clearing price using a multilayer perceptron neural network, based on structural and weights optimization. Compared to conventional neural networks, this hybrid model has high accuracy and is capable of converging to optimal minimum. The results of this forecasting method for Market Clearing Price (MCP) of Iranian and Nord Pool Electricity Markets, as well as Locational Marginal Price (LMP) forecasting in PJM electricity market, verify the effectiveness of the proposed approach in short-term price forecasting.
M. Darabian; S. Jalilzadeh; M. Azari
Volume 1, Issue 1 , June 2013, , Pages 33-42
Abstract
This paper focuses on multi-objective designing of multi-machine Thyristor Controlled Series Compensator (TCSC) using Strength Pareto Evolutionary Algorithm (SPEA). The TCSC parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping ...
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This paper focuses on multi-objective designing of multi-machine Thyristor Controlled Series Compensator (TCSC) using Strength Pareto Evolutionary Algorithm (SPEA). The TCSC parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a SPEA algorithm. The effectiveness of the proposed controller validates on a multi-machine power system over a wide range of loading conditions. The results of the proposed controller (SPEATCSC) are compared with the Genetic Algorithm (GA) based tuned TCSC through some operating conditions to demonstrate its superior efficiency.
