This document summarizes a genetic algorithm approach for solving the unit commitment problem in power systems. The unit commitment problem aims to schedule power generating units in a cost-effective way while satisfying operational constraints. The proposed approach uses a genetic algorithm with an intelligent coding scheme to represent the on/off status of generating units over time. It also uses annular crossover and mutation genetic operators. The algorithm was tested on standard test systems and showed improvements over other approaches in reducing costs and computational time for finding solutions.
Security Constrained UCP with Operational and Power Flow ConstraintsIDES Editor
ย
An algorithm to solve security constrained unit
commitment problem (UCP) with both operational and power
flow constraints (PFC) have been proposed to plan a secure
and economical hourly generation schedule. This proposed
algorithm introduces an efficient unit commitment (UC)
approach with PFC that obtains the minimum system
operating cost satisfying both unit and network constraints
when contingencies are included. In the proposed model
repeated optimal power flow for the satisfactory unit
combinations for every line removal under given study period
has been carried out to obtain UC solutions with both unit and
network constraints. The system load demand patterns have
been obtained for the test case systems taking into account of
the hourly load variations at the load buses by adding
Gaussian random noises. In this paper, the proposed
algorithm has been applied to obtain UC solutions for IEEE
30, 118 buses and Indian utility practical systems scheduled
for 24 hours. The algorithm and simulation are carried
through Matlab software and the results obtained are quite
encouraging.
Stable Multi Optimized Algorithm Used For Controlling The Load Shedding Probl...IOSR Journals
ย
This document discusses using multi-agent based particle swarm optimization (MAPSO) and genetic algorithms (MAGA) to solve the load shedding problem in power systems. MAPSO integrates a multi-agent system with PSO, allowing agents to cooperate and compete with neighbors to find optimal load shedding solutions quickly. MAGA applies genetic algorithm concepts like reproduction, crossover and mutation to agents. The document outlines the load shedding problem formulation and constraints. It also describes PSO, genetic algorithms, multi-agent systems and how MAPSO and MAGA combine these approaches to determine the most appropriate loads to shed during under frequency or voltage conditions.
The document describes a study that uses a hybrid neuro-fuzzy (HNF) approach for automatic generation control (AGC) of a two-area interconnected power system. The HNF controller is designed using an adaptive neuro-fuzzy inference system to control frequency and tie-line power deviations. Simulation results show the HNF controller provides improved dynamic response and faster control compared to a conventional PI controller. The HNF approach can handle non-linearities in power systems while providing faster control than other conventional controllers.
Locational marginal pricing framework in secured dispatch scheduling under co...eSAT Publishing House
ย
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
ย
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
A Simple Approach for Optimal Generation Scheduling to Maximize GENCOs Profit...IJAPEJOURNAL
ย
In this paper an attempt has been made to solve the profit based unit commitment problem (PBUC) using pre-prepared power demand (PPD) table with an artificial bee colony (ABC) algorithm. The PPD-ABC algorithm appears to be a robust and reliable optimization algorithm for the solution of PBUC problem. The profit based unit commitment problem is considered as a stochastic optimization problem in which the objective is to maximize their own profit and the decisions are needed to satisfy the standard operating constraints. The PBUC problem is solved by the proposed methodology in two stages. In the first step, the unit commitment scheduling is performed by considering the pre-prepared power demand (PPD) table and then the problem of fuel cost and revenue function is solved using ABC Algorithm. The PPD table suggests the operator to decide the units to be put into generation there by reducing the complexity of the problem. The proposed approach is demonstrated on 10 units 24 hour and 50 units 24 hour test systems and numerical results are tabulated. Simulation result shows that this approach effectively maximizes the GENCOโs profit than those obtained by other optimizing methods.
VHDL Based Maximum Power Point Tracking of Photovoltaic Using Fuzzy Logic Con...IJECEIAES
ย
It is important to have an efficient maximum power point tracking (MPPT) technique to increase the photovoltaic (PV) generation system output efficiency. This paper presents a design of MPPT techniques for PV module to increase its efficiency. Perturb and Observe method (P&O), incremental conductance method (IC), and Fuzzy logic controller (FLC) techniques are designed to be used for MPPT. Also FLC is built using MATLAB/ SIMULINK and compared with the FLC toolbox existed in the MATLAB library. FLC does not need knowledge of the exact model of the system so it is easy to implement. A comparison between different techniques shows the effectiveness of the fuzzy logic controller techniques. Finally, the proposed FLC is built in very high speed integrated circuit description language (VHDL). The simulation results obtained with ISE Design Suite 14.6 software show a satisfactory performance with a good agreement compared to obtained values from MATLAB/SIMULINK. The good tracking efficiency and rapid response to environmental parameters changes are adopted by the simulation results.
IRJET- A New Approach to Economic Load Dispatch by using Improved QEMA ba...IRJET Journal
ย
This document proposes an improved Quantum behaved electro-magnetism algorithm particle swarm optimization (QEMAPSO) approach to solve the economic load dispatch (ELD) problem. The objective is to minimize the total generation cost while considering constraints like generator limits, transmission losses, and valve point effects. It formulates the ELD problem and describes the QEMA-PSO algorithm which uses QEMA to determine an initial solution that is then optimized using PSO. Results on a 6-generator IEEE 30-bus test system show that the proposed QEMA-PSO approach improves upon other methods like genetic algorithm and dance bee colony optimization in minimizing costs and emissions.
Security Constrained UCP with Operational and Power Flow ConstraintsIDES Editor
ย
An algorithm to solve security constrained unit
commitment problem (UCP) with both operational and power
flow constraints (PFC) have been proposed to plan a secure
and economical hourly generation schedule. This proposed
algorithm introduces an efficient unit commitment (UC)
approach with PFC that obtains the minimum system
operating cost satisfying both unit and network constraints
when contingencies are included. In the proposed model
repeated optimal power flow for the satisfactory unit
combinations for every line removal under given study period
has been carried out to obtain UC solutions with both unit and
network constraints. The system load demand patterns have
been obtained for the test case systems taking into account of
the hourly load variations at the load buses by adding
Gaussian random noises. In this paper, the proposed
algorithm has been applied to obtain UC solutions for IEEE
30, 118 buses and Indian utility practical systems scheduled
for 24 hours. The algorithm and simulation are carried
through Matlab software and the results obtained are quite
encouraging.
Stable Multi Optimized Algorithm Used For Controlling The Load Shedding Probl...IOSR Journals
ย
This document discusses using multi-agent based particle swarm optimization (MAPSO) and genetic algorithms (MAGA) to solve the load shedding problem in power systems. MAPSO integrates a multi-agent system with PSO, allowing agents to cooperate and compete with neighbors to find optimal load shedding solutions quickly. MAGA applies genetic algorithm concepts like reproduction, crossover and mutation to agents. The document outlines the load shedding problem formulation and constraints. It also describes PSO, genetic algorithms, multi-agent systems and how MAPSO and MAGA combine these approaches to determine the most appropriate loads to shed during under frequency or voltage conditions.
The document describes a study that uses a hybrid neuro-fuzzy (HNF) approach for automatic generation control (AGC) of a two-area interconnected power system. The HNF controller is designed using an adaptive neuro-fuzzy inference system to control frequency and tie-line power deviations. Simulation results show the HNF controller provides improved dynamic response and faster control compared to a conventional PI controller. The HNF approach can handle non-linearities in power systems while providing faster control than other conventional controllers.
Locational marginal pricing framework in secured dispatch scheduling under co...eSAT Publishing House
ย
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
ย
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
A Simple Approach for Optimal Generation Scheduling to Maximize GENCOs Profit...IJAPEJOURNAL
ย
In this paper an attempt has been made to solve the profit based unit commitment problem (PBUC) using pre-prepared power demand (PPD) table with an artificial bee colony (ABC) algorithm. The PPD-ABC algorithm appears to be a robust and reliable optimization algorithm for the solution of PBUC problem. The profit based unit commitment problem is considered as a stochastic optimization problem in which the objective is to maximize their own profit and the decisions are needed to satisfy the standard operating constraints. The PBUC problem is solved by the proposed methodology in two stages. In the first step, the unit commitment scheduling is performed by considering the pre-prepared power demand (PPD) table and then the problem of fuel cost and revenue function is solved using ABC Algorithm. The PPD table suggests the operator to decide the units to be put into generation there by reducing the complexity of the problem. The proposed approach is demonstrated on 10 units 24 hour and 50 units 24 hour test systems and numerical results are tabulated. Simulation result shows that this approach effectively maximizes the GENCOโs profit than those obtained by other optimizing methods.
VHDL Based Maximum Power Point Tracking of Photovoltaic Using Fuzzy Logic Con...IJECEIAES
ย
It is important to have an efficient maximum power point tracking (MPPT) technique to increase the photovoltaic (PV) generation system output efficiency. This paper presents a design of MPPT techniques for PV module to increase its efficiency. Perturb and Observe method (P&O), incremental conductance method (IC), and Fuzzy logic controller (FLC) techniques are designed to be used for MPPT. Also FLC is built using MATLAB/ SIMULINK and compared with the FLC toolbox existed in the MATLAB library. FLC does not need knowledge of the exact model of the system so it is easy to implement. A comparison between different techniques shows the effectiveness of the fuzzy logic controller techniques. Finally, the proposed FLC is built in very high speed integrated circuit description language (VHDL). The simulation results obtained with ISE Design Suite 14.6 software show a satisfactory performance with a good agreement compared to obtained values from MATLAB/SIMULINK. The good tracking efficiency and rapid response to environmental parameters changes are adopted by the simulation results.
IRJET- A New Approach to Economic Load Dispatch by using Improved QEMA ba...IRJET Journal
ย
This document proposes an improved Quantum behaved electro-magnetism algorithm particle swarm optimization (QEMAPSO) approach to solve the economic load dispatch (ELD) problem. The objective is to minimize the total generation cost while considering constraints like generator limits, transmission losses, and valve point effects. It formulates the ELD problem and describes the QEMA-PSO algorithm which uses QEMA to determine an initial solution that is then optimized using PSO. Results on a 6-generator IEEE 30-bus test system show that the proposed QEMA-PSO approach improves upon other methods like genetic algorithm and dance bee colony optimization in minimizing costs and emissions.
Comparison of cascade P-PI controller tuning methods for PMDC motor based on ...IJECEIAES
ย
In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
Load shedding in power system using the AHP algorithm and Artificial Neural N...IJAEMSJORNAL
ย
This paper proposes the load shedding method based on considering the load importance factor, primary frequency adjustment, secondary frequency adjustment and neuron network. Consideration the process of primary frequency control, secondary frequency control helps to reduce the amount of load shedding power and restore the systemโs frequency to the permissible range. The amount of shedding power of each load bus is distributed based on the load importance factor. Neuron network is applied to distribute load shedding strategies in the power system at different load levels. The experimental and simulated results on the IEEE 37- bus system present the frequency can restore to allowed range and reduce the damage compared to the traditional load shedding method using under frequency relay- UFLS.
Modeling and Simulation of power system using SMIB with GA based TCSC controllerIOSR Journals
ย
This document summarizes a study that uses genetic algorithms to tune a thyristor-controlled series compensator (TCSC) controller to improve the stability of a single-machine infinite-bus (SMIB) power system model. The study models the SMIB system and implements a TCSC to damp oscillations. Genetic algorithms are used to optimize the TCSC controller parameters. Simulation results show that the genetically-tuned TCSC controller more effectively damps oscillations compared to the system without a TCSC controller.
Group Search Optimization technique is used to minimize reactive power generation in a power system. The objective is to control generator voltages to reduce reactive power production while meeting system constraints. IEEE 14-bus test system is used with 4 generator buses. Generator voltages are optimized using Group Search Optimization, which finds the minimum reactive power of 5.26 MVAR after 26 iterations. Reactive power and line losses are reduced compared to the base case, showing the effectiveness of the technique in minimizing reactive power generation.
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
An Adaptive Internal Model for Load Frequency Control using Extreme Learning ...TELKOMNIKA JOURNAL
ย
The document presents a proposed adaptive internal model control scheme using an extreme learning machine for load frequency control. The controller uses a model predictive controller as the main controller combined with an adaptive extreme learning machine model as the internal model. The extreme learning machine is trained using controller output and frequency deviation data to predict frequency deviation. Simulation results on a three area power system show that the proposed internal model control with adaptive extreme learning machine model can accurately model system dynamics and effectively reduce frequency and power deviations under disturbances.
Multi Area Economic Dispatch Using Secant Method and Tie Line MatrixIJAPEJOURNAL
ย
In this paper, Secant method and tie line matrix are proposed to solve multi area economic dispatch (MAED) problem with tie line loss. Generator limits of all generators in each area are calculated at given area power demands plus export (or import) using secant method and the generator limits of all generators are modified as modified generator limits. Central economic dispatch (CED) problem is used to determine the output powers of all generators and finally power flows in all tie lines are determined from tie line matrix. Here, Secant method is applied to solve the CED problem. A modified tie line matrix is used to find power flow in each tie line and then tie line loss is calculated from the power flow in each tie line. The proposed approach has been tested on two-area (two generators in each area) system and four-area (four generators in each area) system. It is observed from various cases that the proposed approach provides optimally best solution in terms of cost with tie line loss with less computational burden.
The optimal solution for unit commitment problem using binary hybrid grey wol...IJECEIAES
ย
The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on โ39 bus IEEE test systemsโ, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
A new design of fuzzy logic controller optimized by PSO-SCSO applied to SFO-D...IJECEIAES
ย
In this article, a new strategy for the design of fuzzy logic controllers (FLC) is proposed. This strategy is based on the optimization of the FLC, by the hybridization between the particle swarm optimization algorithm (PSO) and the sine-cosine swarm optimization algorithm (SCSO), This new strategy is called FLC-PSCSO. The input-output gains and the geometric shapes of the triangular membership functions of the FLC are the objective functions to be optimized. The optimization of the latter is obtained by minimizing a cost function based on the combination of two criteria, the integral time absolute error (ITAE) and the integral absolute error (IAE). A comparison between the conventional FLC and the proposed FLC-PSCSO is made. The FLC optimized by PSCSO shows a remarkable improvement in the performance of the controlled induction motor.
Coordinated Placement and Setting of FACTS in Electrical Network based on Kal...IJECEIAES
ย
To aid the decision maker, the optimal placement of FACTS in the electrical network is performed through very specific criteria. In this paper, a useful approach is followed; it is based particularly on the use of KalaiSmorodinsky bargaining solution for choosing the best compromise between the different objectives commonly posed to the network manager such as the cost of production, total transmission losses (Tloss), and voltage stability index (Lindex). In the case of many possible solutions, Voltage Profile Quality is added to select the best one. This approach has offered a balanced solution and has proven its effectiveness in finding the best placement and setting of two types of FACTS namely Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) in the power system. The test case under investigation is IEEE-14 bus system which has been simulated in MATLAB Environment.
Performance Analysis of GA and PSO over Economic Load Dispatch ProblemIOSR Journals
ย
This document presents a performance analysis of genetic algorithm (GA) and particle swarm optimization (PSO) for solving the economic load dispatch (ELD) problem in power systems. The ELD problem aims to minimize total generation cost subject to constraints, by optimizing the power output of generators. The document implements GA and PSO to solve sample ELD problems with 6 generators, comparing the results between the two algorithms under scenarios with and without transmission losses. PSO was shown to perform better, finding lower cost solutions with better convergence than GA. The document concludes PSO is more efficient for the ELD problem due to its convergence properties.
A Comparative Study of GA tuned and PSO tuned PI Controller Based Speed Contr...paperpublications3
ย
This document presents a comparative study of using genetic algorithm (GA) and particle swarm optimization (PSO) techniques to tune the parameters of a PI controller for speed control of a brushless DC motor. It describes brushless DC motors and their advantages over conventional DC motors. It also provides details on PI controller tuning and explains the PSO and GA optimization algorithms. The results show that both PSO and GA techniques were able to optimize the PI controller parameters to improve the motor speed response. However, GA produced a slightly better fitness value than PSO based on the integral absolute error performance criterion used in the study.
This document proposes a universal algorithm for stage switching in hypercube interconnection networks used in multi-core systems. It analyzes a 4-stage 16x16 hypercube network and derives a switching algorithm where the selection bit sequence changes at each stage in a predefined manner. This algorithm is then generalized for an n-stage hypercube network to establish relationships between the selection bit patterns at different stages. The proposed universal algorithm could be used for linear switching in hypercube networks of any size to efficiently design higher order interconnection blocks for multi-core systems.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Publishing House
ย
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document presents a method for tuning PID controller parameters for an automatic voltage regulator (AVR) system using particle swarm optimization (PSO) and genetic algorithm (GA). It compares the step response results of using PSO versus GA for tuning the PID controller. The key steps are: 1) Define an objective function to minimize transient response characteristics like overshoot and settling time, 2) Minimize the objective function using PSO and GA to determine optimal PID parameters, 3) Compare the closed-loop step response results of the PSO-PID and GA-PID controllers, with the PSO-PID controller showing better performance.
Hysteresis controllers (HC) are used to limit the torque and flux in the control band in conventional configuration of direct torque control (DTC) while in the space vector pulse width modulated (SVPWM) DTC, the HC are switched to PI or PID controllers. This paper presents a modern approach for the speed control applied on a DTC of a permanent magnet synchronous motor (PMSM) using the Cuckoo Search Optimization (CSO) algorithm in order to optimize the PI speed controller parameters of the outer loop and PID flux and torque controllers of the inner loop. The system is tested at no load and with a step change in load. The performance of the controllers is presented and the results of simulation indicate a very rapid dynamic response and the system achieves the steady state (SS.) in a very short time. Also it shows that both the SS. and dynamic performances are improved by applying of the CSO algorithm. The proposed DTC simulation model of the PMSM is presented using MATLAB / SIMULINK and capable of simulating both the steady-state and dynamic response. The CSO results are compared with another control strategy that incorporates fuzzy logic controller (FLC) with DTC.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
ย
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Impact analysis of actuator torque degradation on the IRB 120 robot performan...IJECEIAES
ย
Actuators in a robot system may become faulty during their life cycle. Locked joints, free-moving joints, and the loss of actuator torque are common faulty types of robot joints where the actuators fail. Locked and free-moving joint issues are addressed by many published articles, whereas the actuator torque loss still opens attractive investigation challenges. The objectives of this study are to classify the loss of robot actuator torque, named actuator torque degradation, into three different cases: Boundary degradation of torque, boundary degradation of torque rate, and proportional degradation of torque, and to analyze their impact on the performance of a typical 6-DOF robot (i.e., the IRB 120 robot). Typically, controllers of robots are not pre-designed speci๏ฌcally for anticipating these faults. To isolate and focus on the impact of only actuator torque degradation faults, all robot parameters are assumed to be known precisely, and a popular closed-loop controller is used to investigate the robotโs responses under these faults. By exploiting MATLAB-the reliable simulation environment, a simscape-based quasi-physical model of the robot is built and utilized instead of an actual expensive prototype. The simulation results indicate that the robot responses cannot follow the desired path properly in most fault cases.
IRJET-Comparative Analysis of Unit Commitment Problem of Electric Power Syste...IRJET Journal
ย
The document presents a comparative analysis of solving the unit commitment problem (UCP) of electric power systems using dynamic programming technique. It formulates the UCP considering fuel costs, voltage stability, and imbalance limits. Dynamic programming is described as a conventional algorithm for solving deterministic problems optimally through sequential decision making. The developed algorithm is implemented on 4-unit and 10-unit power systems. Results found the dynamic programming approach provides satisfactory solutions by minimizing total generation costs while meeting operational constraints.
A Genetic Algorithm Approach to Solve Unit Commitment ProblemIOSR Journals
ย
This document describes a study that uses a genetic algorithm approach to solve the unit commitment problem of scheduling generation units in a power system over an 8-hour period. The genetic algorithm approach is able to find near-optimal solutions to the unit commitment problem and results in lower total operating costs than the traditional dynamic programming approach. The genetic algorithm approach encodes potential solutions as strings that are evaluated and evolved over generations to find low-cost solutions that satisfy constraints. The results show the genetic algorithm approach finds schedules with total costs that are $255 lower than those found by dynamic programming for the test power system.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
ย
The document presents an improved particle swarm optimization (IPSO) algorithm for solving the optimal unit commitment problem in power systems. The IPSO algorithm extends the standard PSO algorithm by using additional particle information to control mutation and mimic social behaviors. The algorithm was implemented on the IEEE 14 bus test system in MATLAB. Results showed the IPSO approach committed units to meet load demand over 24 hours while satisfying constraints, with bus voltages maintained between 1.0017 and 1.0751 per unit. Total costs including fuel, startup, and shutdown costs were minimized at each hour.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Comparison of cascade P-PI controller tuning methods for PMDC motor based on ...IJECEIAES
ย
In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
Load shedding in power system using the AHP algorithm and Artificial Neural N...IJAEMSJORNAL
ย
This paper proposes the load shedding method based on considering the load importance factor, primary frequency adjustment, secondary frequency adjustment and neuron network. Consideration the process of primary frequency control, secondary frequency control helps to reduce the amount of load shedding power and restore the systemโs frequency to the permissible range. The amount of shedding power of each load bus is distributed based on the load importance factor. Neuron network is applied to distribute load shedding strategies in the power system at different load levels. The experimental and simulated results on the IEEE 37- bus system present the frequency can restore to allowed range and reduce the damage compared to the traditional load shedding method using under frequency relay- UFLS.
Modeling and Simulation of power system using SMIB with GA based TCSC controllerIOSR Journals
ย
This document summarizes a study that uses genetic algorithms to tune a thyristor-controlled series compensator (TCSC) controller to improve the stability of a single-machine infinite-bus (SMIB) power system model. The study models the SMIB system and implements a TCSC to damp oscillations. Genetic algorithms are used to optimize the TCSC controller parameters. Simulation results show that the genetically-tuned TCSC controller more effectively damps oscillations compared to the system without a TCSC controller.
Group Search Optimization technique is used to minimize reactive power generation in a power system. The objective is to control generator voltages to reduce reactive power production while meeting system constraints. IEEE 14-bus test system is used with 4 generator buses. Generator voltages are optimized using Group Search Optimization, which finds the minimum reactive power of 5.26 MVAR after 26 iterations. Reactive power and line losses are reduced compared to the base case, showing the effectiveness of the technique in minimizing reactive power generation.
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
An Adaptive Internal Model for Load Frequency Control using Extreme Learning ...TELKOMNIKA JOURNAL
ย
The document presents a proposed adaptive internal model control scheme using an extreme learning machine for load frequency control. The controller uses a model predictive controller as the main controller combined with an adaptive extreme learning machine model as the internal model. The extreme learning machine is trained using controller output and frequency deviation data to predict frequency deviation. Simulation results on a three area power system show that the proposed internal model control with adaptive extreme learning machine model can accurately model system dynamics and effectively reduce frequency and power deviations under disturbances.
Multi Area Economic Dispatch Using Secant Method and Tie Line MatrixIJAPEJOURNAL
ย
In this paper, Secant method and tie line matrix are proposed to solve multi area economic dispatch (MAED) problem with tie line loss. Generator limits of all generators in each area are calculated at given area power demands plus export (or import) using secant method and the generator limits of all generators are modified as modified generator limits. Central economic dispatch (CED) problem is used to determine the output powers of all generators and finally power flows in all tie lines are determined from tie line matrix. Here, Secant method is applied to solve the CED problem. A modified tie line matrix is used to find power flow in each tie line and then tie line loss is calculated from the power flow in each tie line. The proposed approach has been tested on two-area (two generators in each area) system and four-area (four generators in each area) system. It is observed from various cases that the proposed approach provides optimally best solution in terms of cost with tie line loss with less computational burden.
The optimal solution for unit commitment problem using binary hybrid grey wol...IJECEIAES
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The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on โ39 bus IEEE test systemsโ, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
A new design of fuzzy logic controller optimized by PSO-SCSO applied to SFO-D...IJECEIAES
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In this article, a new strategy for the design of fuzzy logic controllers (FLC) is proposed. This strategy is based on the optimization of the FLC, by the hybridization between the particle swarm optimization algorithm (PSO) and the sine-cosine swarm optimization algorithm (SCSO), This new strategy is called FLC-PSCSO. The input-output gains and the geometric shapes of the triangular membership functions of the FLC are the objective functions to be optimized. The optimization of the latter is obtained by minimizing a cost function based on the combination of two criteria, the integral time absolute error (ITAE) and the integral absolute error (IAE). A comparison between the conventional FLC and the proposed FLC-PSCSO is made. The FLC optimized by PSCSO shows a remarkable improvement in the performance of the controlled induction motor.
Coordinated Placement and Setting of FACTS in Electrical Network based on Kal...IJECEIAES
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To aid the decision maker, the optimal placement of FACTS in the electrical network is performed through very specific criteria. In this paper, a useful approach is followed; it is based particularly on the use of KalaiSmorodinsky bargaining solution for choosing the best compromise between the different objectives commonly posed to the network manager such as the cost of production, total transmission losses (Tloss), and voltage stability index (Lindex). In the case of many possible solutions, Voltage Profile Quality is added to select the best one. This approach has offered a balanced solution and has proven its effectiveness in finding the best placement and setting of two types of FACTS namely Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) in the power system. The test case under investigation is IEEE-14 bus system which has been simulated in MATLAB Environment.
Performance Analysis of GA and PSO over Economic Load Dispatch ProblemIOSR Journals
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This document presents a performance analysis of genetic algorithm (GA) and particle swarm optimization (PSO) for solving the economic load dispatch (ELD) problem in power systems. The ELD problem aims to minimize total generation cost subject to constraints, by optimizing the power output of generators. The document implements GA and PSO to solve sample ELD problems with 6 generators, comparing the results between the two algorithms under scenarios with and without transmission losses. PSO was shown to perform better, finding lower cost solutions with better convergence than GA. The document concludes PSO is more efficient for the ELD problem due to its convergence properties.
A Comparative Study of GA tuned and PSO tuned PI Controller Based Speed Contr...paperpublications3
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This document presents a comparative study of using genetic algorithm (GA) and particle swarm optimization (PSO) techniques to tune the parameters of a PI controller for speed control of a brushless DC motor. It describes brushless DC motors and their advantages over conventional DC motors. It also provides details on PI controller tuning and explains the PSO and GA optimization algorithms. The results show that both PSO and GA techniques were able to optimize the PI controller parameters to improve the motor speed response. However, GA produced a slightly better fitness value than PSO based on the integral absolute error performance criterion used in the study.
This document proposes a universal algorithm for stage switching in hypercube interconnection networks used in multi-core systems. It analyzes a 4-stage 16x16 hypercube network and derives a switching algorithm where the selection bit sequence changes at each stage in a predefined manner. This algorithm is then generalized for an n-stage hypercube network to establish relationships between the selection bit patterns at different stages. The proposed universal algorithm could be used for linear switching in hypercube networks of any size to efficiently design higher order interconnection blocks for multi-core systems.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Publishing House
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document presents a method for tuning PID controller parameters for an automatic voltage regulator (AVR) system using particle swarm optimization (PSO) and genetic algorithm (GA). It compares the step response results of using PSO versus GA for tuning the PID controller. The key steps are: 1) Define an objective function to minimize transient response characteristics like overshoot and settling time, 2) Minimize the objective function using PSO and GA to determine optimal PID parameters, 3) Compare the closed-loop step response results of the PSO-PID and GA-PID controllers, with the PSO-PID controller showing better performance.
Hysteresis controllers (HC) are used to limit the torque and flux in the control band in conventional configuration of direct torque control (DTC) while in the space vector pulse width modulated (SVPWM) DTC, the HC are switched to PI or PID controllers. This paper presents a modern approach for the speed control applied on a DTC of a permanent magnet synchronous motor (PMSM) using the Cuckoo Search Optimization (CSO) algorithm in order to optimize the PI speed controller parameters of the outer loop and PID flux and torque controllers of the inner loop. The system is tested at no load and with a step change in load. The performance of the controllers is presented and the results of simulation indicate a very rapid dynamic response and the system achieves the steady state (SS.) in a very short time. Also it shows that both the SS. and dynamic performances are improved by applying of the CSO algorithm. The proposed DTC simulation model of the PMSM is presented using MATLAB / SIMULINK and capable of simulating both the steady-state and dynamic response. The CSO results are compared with another control strategy that incorporates fuzzy logic controller (FLC) with DTC.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
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In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Impact analysis of actuator torque degradation on the IRB 120 robot performan...IJECEIAES
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Actuators in a robot system may become faulty during their life cycle. Locked joints, free-moving joints, and the loss of actuator torque are common faulty types of robot joints where the actuators fail. Locked and free-moving joint issues are addressed by many published articles, whereas the actuator torque loss still opens attractive investigation challenges. The objectives of this study are to classify the loss of robot actuator torque, named actuator torque degradation, into three different cases: Boundary degradation of torque, boundary degradation of torque rate, and proportional degradation of torque, and to analyze their impact on the performance of a typical 6-DOF robot (i.e., the IRB 120 robot). Typically, controllers of robots are not pre-designed speci๏ฌcally for anticipating these faults. To isolate and focus on the impact of only actuator torque degradation faults, all robot parameters are assumed to be known precisely, and a popular closed-loop controller is used to investigate the robotโs responses under these faults. By exploiting MATLAB-the reliable simulation environment, a simscape-based quasi-physical model of the robot is built and utilized instead of an actual expensive prototype. The simulation results indicate that the robot responses cannot follow the desired path properly in most fault cases.
IRJET-Comparative Analysis of Unit Commitment Problem of Electric Power Syste...IRJET Journal
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The document presents a comparative analysis of solving the unit commitment problem (UCP) of electric power systems using dynamic programming technique. It formulates the UCP considering fuel costs, voltage stability, and imbalance limits. Dynamic programming is described as a conventional algorithm for solving deterministic problems optimally through sequential decision making. The developed algorithm is implemented on 4-unit and 10-unit power systems. Results found the dynamic programming approach provides satisfactory solutions by minimizing total generation costs while meeting operational constraints.
A Genetic Algorithm Approach to Solve Unit Commitment ProblemIOSR Journals
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This document describes a study that uses a genetic algorithm approach to solve the unit commitment problem of scheduling generation units in a power system over an 8-hour period. The genetic algorithm approach is able to find near-optimal solutions to the unit commitment problem and results in lower total operating costs than the traditional dynamic programming approach. The genetic algorithm approach encodes potential solutions as strings that are evaluated and evolved over generations to find low-cost solutions that satisfy constraints. The results show the genetic algorithm approach finds schedules with total costs that are $255 lower than those found by dynamic programming for the test power system.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
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The document presents an improved particle swarm optimization (IPSO) algorithm for solving the optimal unit commitment problem in power systems. The IPSO algorithm extends the standard PSO algorithm by using additional particle information to control mutation and mimic social behaviors. The algorithm was implemented on the IEEE 14 bus test system in MATLAB. Results showed the IPSO approach committed units to meet load demand over 24 hours while satisfying constraints, with bus voltages maintained between 1.0017 and 1.0751 per unit. Total costs including fuel, startup, and shutdown costs were minimized at each hour.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document provides an overview of economic dispatch and unit commitment in power systems. It discusses:
1. Economic dispatch is the process of determining generator outputs to meet demand at minimum cost, taking into account generator costs and constraints. It can be solved graphically or using the KKT conditions.
2. Unit commitment determines which generators will operate over different time periods to meet forecasted load at minimum cost, while considering generator operating constraints like minimum up/down times. It is solved using techniques like mixed integer programming and Lagrangian relaxation.
3. Mixed integer programming and Lagrangian relaxation are commonly used optimization methods for unit commitment. Mixed integer programming formulates it as an optimization problem with discrete and continuous variables.
IRJET- Swarm Optimization Technique for Economic Load DispatchIRJET Journal
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This document discusses using particle swarm optimization (PSO) technique to solve the economic load dispatch (ELD) problem in power systems. The ELD problem aims to minimize the total generation cost while satisfying constraints. PSO is applied to determine the optimal power outputs of generators. The key steps are: (1) Initialize a swarm of particles randomly within generator limits, (2) Evaluate fitness of each particle using a cost function, (3) Update particles' velocities and positions based on individual and global best positions, (4) Repeat steps 2-3 until convergence criteria is met. The method is tested on 3-unit and 6-unit systems and shown to find lower cost solutions than other algorithms like cuckoo search. PSO
This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources are intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit commitment scheduling can improve the total operating cost significantly.
Optimization of Economic Load Dispatch with Unit Commitment on Multi MachineIJAPEJOURNAL
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Economic load dispatch (ELD) and Unit Commitment (UC) are significant research applications in power systems that optimize the total production cost of the predicted load demand. The UC problem determines a turn-on and turn-off schedule for a given combination of generating units, thus satisfying a set of dynamic operational constraints. ELD optimizes the operation cost for all scheduled generating units with respect to the load demands of customers. The first phase in this project is to economically schedule the distribution of generating units using Gauss seidal and the second phase is to determine optimal load distribution for the scheduled units using dynamic programming method is applied to select and choose the combination of generating units that commit and de-commit during each hour. These precommitted schedules are optimized by dynamic programming method thus producing a global optimum solution with feasible and effective solution quality, minimal cost and time and higher precision. The effectiveness of the proposed techniques is investigated on two test systems consisting of five generating units and the experiments are carried out using MATLAB R2010b software. Experimental results prove that the proposed method is capable of yielding higher quality solution including mathematical simplicity, fast convergence, diversity maintenance, robustness and scalability for the complex ELD-UC problem.
IRJET- Optimal Power Flow Solution of Transmission Line Network of Electric p...IRJET Journal
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This document discusses using a genetic algorithm to solve the optimal power flow problem in large power transmission networks. The optimal power flow problem aims to minimize generation costs while meeting operational constraints. A genetic algorithm is proposed to solve this problem globally and efficiently. The controllable variables are divided into dynamic constraints directly impacting cost and static constraints maintained within limits by the load flow. The algorithm is tested on the IEEE 30-bus system and shown to effectively find the optimal solution. Genetic algorithms are well-suited for this problem as they can evaluate multiple solutions in parallel without requiring derivative information like traditional methods.
IRJET- Optimal Placement and Size of DG and DER for Minimizing Power Loss and...IRJET Journal
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This document summarizes research on optimizing the placement and sizing of distributed generation (DG) and distributed energy resources (DER) in a 33-bus distribution system to minimize power losses. Two optimization techniques are evaluated: Grasshopper Optimization Algorithm (GOA) and Moth Flame Optimization (MFO). MFO shows better results, identifying bus 13, 24 and 30 as optimal locations for DG, reducing losses from 0.2027 MW to 0.0715 MW at normal load. For DER, optimal locations are DG at buses 13, 25, 30 and capacitors at buses 7, 13, 30, further reducing losses to 0.0144 MW. Graphs and tables show MFO placement
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
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Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
Security Constraint Unit Commitment Considering Line and Unit Contingencies-p...IJAPEJOURNAL
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This summary provides the key details about the document in 3 sentences:
The document presents a new approach for security constrained unit commitment that considers both generator and transmission line contingencies using an incidence matrix methodology. It formulates the security constrained unit commitment problem and proposes modeling the optimal power flow using an incidence matrix to overcome challenges of admittance matrix based methods. The methodology allows easier modeling of multiple contingencies without changes to the network topology.
Electricity Generation Scheduling an Improved for Firefly Optimization AlgorithmIRJET Journal
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This document describes using an improved firefly optimization algorithm to schedule electricity generation from thermal and wind power plants. The algorithm aims to minimize total generation costs while meeting load demand and reserve requirements, taking into account wind power availability constraints and both generation unit and system constraints. It formulates the objective function and constraints for the generation scheduling problem and applies the firefly algorithm to a test system with 30 conventional units and 4 wind farms, comparing results to a particle swarm optimization method. The firefly algorithm performance is found to provide better schedules with lower total costs compared to particle swarm optimization.
Relevance of Particle Swarm Optimization Technique for the Solution of Econom...IRJET Journal
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This document presents the use of particle swarm optimization (PSO) technique to solve the economic load dispatch (ELD) problem in power systems. The ELD problem aims to schedule power plant generation outputs to meet load demand at minimum operating cost while satisfying constraints. PSO is applied by initializing generator outputs as "particles" that fly through search space to find minimum cost. Results on 5-unit and 6-unit test systems show PSO able to determine optimal outputs to meet time-varying loads at lowest cost within constraints.
This document provides a literature survey of recent methods for solving the optimal power flow (OPF) problem. It begins with an introduction to OPF, describing it as a nonlinear optimization problem that aims to optimize the operation of power systems while meeting constraints. It then categorizes OPF solution methods as conventional (e.g. Newton, gradient) or intelligent (e.g. artificial neural networks, genetic algorithms) and provides examples of each. The document surveys specific OPF formulations and solution techniques in detail. It concludes by discussing ongoing challenges for OPF methods related to power system modeling and operations in deregulated electricity markets.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
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Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
This document discusses the development of software for supplementary control of power generation systems. It begins by outlining the key requirements for supplementary control, including generation allocation, unit commitment, economic dispatch, and determining base points and participation factors. It then describes the information required for the software, such as generating unit capacities and system frequency. The software is developed to calculate the total generation capacity, maximum output, synchronized generation, base generation, and spinning reserves for each generating station. It also calculates the system and station percent regulations based on individual unit regulations. The software operates to balance generation and load while minimizing costs based on the control requirements and inputs described.
The document describes a hybrid firefly-differential evolution algorithm for solving the economic load dispatch problem. The economic load dispatch problem involves allocating generation among power plants to minimize costs while satisfying constraints. The proposed hybrid algorithm combines the differential evolution and firefly algorithms. It was tested on a 3 unit power system and showed improved efficiency and robustness compared to other existing algorithms for solving the economic load dispatch problem.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
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Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
This paper presents a particle swarm optimization (PSO) algorithm to tune the gains of an integral controller for load frequency control (LFC) in a single-area power system with multiple energy sources (thermal, hydro, gas, and wind). The objective is to minimize the integral absolute error of frequency deviations following a step load change. Simulation results show the PSO-tuned integral controller provides better transient response than an uncontrolled system, with reduced settling time, peak overshoot, and oscillations.
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Sachpazis_Consolidation Settlement Calculation Program-The Python Code and th...Dr.Costas Sachpazis
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Consolidation Settlement Calculation Program-The Python Code
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This program calculates the consolidation settlement for a foundation based on soil layer properties and foundation data. It allows users to input multiple soil layers and foundation characteristics to determine the total settlement.
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IRJET- A Genetic based Stochastic Approach for Solving Thermal Unit Commitment Problem with Intelligent Annular Genetic Algorithm
1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
ยฉ 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1511
A GENETIC BASED STOCHASTIC APPROACH FOR SOLVING THERMAL
UNIT COMMITMENT PROBLEM WITH INTELLIGENT ANNULAR
GENETIC ALGORITHM
Bilal Iqbal Ayubi1, Waseem Sajjad 2, Muhammad Asad3
1Level 7 Post Graduate diploma in electrical Engineering from City and guilds institute London, UK
2Level 7 Post Graduate diploma in electrical Engineering from City and guilds institute London, UK
3Instructor of GOVT College of Technology Faisalabad, Department of BSc Electrical Engineering Technology
(Affiliated with University of Engineering and Technology Lahore), Pakistan
---------------------------------------------------------------------***--------------------------------------------------------------------
Abstract:- Unit commitment problem has several challenges which are under active research. To schedule most cost effective
combinations of generating units, minimization of operational fuel cost and reduction in computational time for calculations are
great challenges in this combinatorial optimization problem. The present analysis work has resolved the unit commitment
downside chiefly specializing in minimum up/down time constraints; effectively handle by intelligent coding scheme. Previously
annular crossover and intelligent scheme are applied independently in research work but in proposed GA, both are apply
simultaneously in UCP. The proposed UC algorithm consists at two levels. First level searches the best on/off status schedule ofthe
generation units by using GA search and the second level deals with economic dispatch problem. Load curve data has been taken
for generating chromosomes to make initial population, annular crossover and flip bit mutation as genetic operators to produce
next population. The proposed GA operators not only produce better solution but also prevent trapping in local optimum. The
obtained results are better in terms of reduction of the search space, minimum cost solution and the convergence of algorithm
when compare with other systems available in literature. The Performance of planned algorithmic program is checked on 2
completely different 10-unit 24-h test systems and for giant scale testing 20-unit 24-h, unit commitment check systems square
measure taken into consideration.
Key Words: Unit commitment, Optimization Problem, Evolutionary programming, Intelligent Genetic Algorithm, Annular
Crossover
1. INTRODUCTION
In mathematics, optimization is the selectionofthebestsolutionfromsomesetofavailableAlternatives.Basically,optimization
problem consist of minimizing or maximizing an Objective function subject to some equality or inequality constraints. Unit
commitment is also highly complicated, non-linear, combinatorial optimization problem. The engineers have to face the
stimulating task of planning and successfully operating one of the most complex systems of engineering world. The proper
planning and economic operation of power system always have seriousmatterinpowerindustry because effectivesavingscan
be achieved over a specified time horizon. The effective operational planning of the power system includes the best utilization
of available energy resources subject to several constraints to transfer electrical energy from generating stations e.g. IPPs or
power plants to the users end without interruption of power supply at minimum costwithmaximumsafetyof equipment.Unit
commitment that conjointly known as generationSchedulingisincrediblyvital stageinoperational planningofinstallationthat
decides the on/off standing of generating units over a programing amount with minimum operating expense.t. Unit
commitment problem also having different constraints, which must be satisfies for implementation in the real life system. If
handle those constraints then we found best and feasible generation schedule to Minimize total production cost.
2. Mathematical Modeling of Unit Commitment Problem
The total production cost consist of the operating, start-upcost (costofbringingunitsonline),andshutdowncost.Theoperating
cost of a generator consists of fuel cost and maintenance cost. The fuel cost of a generator depends upon the level of generation
of that generator. When a feasible UC schedule is determined the next step is to find the optimum values of power for all
committed units, which is known as Economic Dispatch (ED). Once the dispatch levels of all committed units are obtained, the
fuel cost of each unit is calculated by using their fuel cost curves.
2.1 Objective Function
The main objective of UCP is to minimize the total operating cost over the scheduled period, which is sum of the fuel costsofthe
ON state units and the start-up costs of the OFF state units subject to the generating units and power system constraints. The
complete objective function of UCP is expressed as;
2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
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min ๐๐๐ถ =
1 1
N T
i t๏ฝ ๏ฝ
๏ฅ ๏ฅ {(๐น๐(๐๐,๐ก) + ๐๐๐ ๐,๐ก(1 โ ๐๐,๐กโ1)}๐๐,๐ก + ๐๐ท๐๐,๐ก ๐๐,๐กโ1(1 โ ๐๐,๐กโ1)} (1)
Here T is time horizon and N is expressed the number of units.
2.2 Fuel Cost
For the representation of fuel cost is most extensively used as quadratic approximation in the literature, which is primarily a
convex shaped function. The fuel cost of a generating unit is mathematically written as: The total operating cost of ๐๐กโ unit at
time t is determined by the quadratic function:
2
, , ,
$
( ) ( )i i t i i t i i t iF P a P b P c
h
๏ฝ ๏ซ ๏ซ (2)
2.2 Start-up Cost
Startup Cost is calculated by:
๐๐๐ ๐ถ๐๐ ๐ก= .
1 exp
off
i t
i i
i
x
๏ง ๏ช
๏ด
๏ฉ ๏น๏ฆ ๏ถ๏ญ
๏ซ ๏ญ๏ช ๏บ๏ง ๏ท
๏จ ๏ธ๏ซ ๏ป
(3)
Where ๐พi ๐๐๐ ๐i are the hot start and cold start coefficients in ($) respectively used in Start-up cost equation and ๐i is the time
constant of unit i. while is the de commits time of unit ๐ also including initial state.
2.3 Constraints
There are two kinds of constraints in UCP.
โข System Constraints
โข Unit Constraints
2.3.1 System Constraints
Such constraints are associated with all generators in the system, therefore they are considered as coupling constraints or
system constraints.Systemconstraintsconsistofspinningreserveandpowerbalanceconstraints.Thedetailofeachconstraintis
given as follow:
2.3.2 Power Balance Constraint
Total power generated from allcommitted unitsatanytimetmustmeettheloaddemandatthattime.Mathematicalformulation
in given as:
1
N
i๏ฝ
๏ฅ ๐i,t Ui,t=๐ท๐ก; ๐คโ๐๐๐ ๐ก=1,2,3,โฆโฆ,T (4)
2.3.3 Spinning Reserve Constraint
The spinning reserve is always necessary to maintain reliability ofsystem. The sum ofmaximum powergeneratedbyallon-line
units must be greater than or equal to sum of Load demand and spinning reserve requirement. The amount of the needful
spinning reserve is usually determined by the maximum capacity of one or two largest generating units in the systemoragiven
percentage of forecasted peak demand during the fixed time horizon
1
N
i๏ฝ
๏ฅ ๐i,t = ๐ทt + ๐ t ๐คโ๐๐๐ ๐ก = 1,2,3, โฆ,๐ (5)
๐ t is known minimum spinning reserve condition at time t.
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2.3.4 Unit Constraints
These constraints are only associated with generator not with the system therefore, they are considered as non-coupling
constraints. Each constraint is described as follows:
2.3.5 Unit Power Generation ranges
The power generated by each unit should be within its minimum and maximum bounds and mathematically formulated as
follow:
min
,i tP โค ๐๐, ๐ก โค
max
,i tP (6)
Where is the
max
,i tP known maximum power and is
min
,i tP known minimum power that unit ๐ generated at any time interval t.
2.3.6 Minimum up Time and Minimum down Time
Minimum number of hours that a unit needs to be on-line once it has been turned on is called minimum up time. Similarly,
minimum down timeis the minimum number of hours thatunit must be off-line once it has been turned off. Thus the status ofa
unit is highly dependent on the MUT and MDT constraints as shown:
First MUT defined as;
Ui,t=1:
1t
j ts
๏ญ
๏ฝ
๏ฅ (1-Ui,t)< ำถi,up for i=1,2โฆN: and t=ts+1โฆ..T (7)
Where ำถi.up is known as MUT of unit i.
And MDT defined as;
Ui,t=0;
1t
j td
๏ญ
๏ฝ
๏ฅ (1-Ui,t)< ำถi,down for i=1,2โฆโฆโฆN: and t=td+1โฆโฆ..T (8)
Where ำถi,down is the known MDT of unit i.
2.4 Proposed Methodology for UCP
In 1859 Charles Darwin presented the theory of evolution which was based on the principal of natural selection and genetics
i.e. โsurvival of the fittestโ to reach certain significant tasks.
In GA, fixed-length string is used to represent individuals or chromosomes. Each site in the stringissupposedtocharacterizea
particular feature, and the value stored in that location represents how that feature have influence in the solution.
A GA starts with the generation of random initial population. Then the fitness of each individual is calculated by a fitness
function. When the fitness is evaluated for all chromosomes, they are subjected to a process of selection in which best fit
individuals have more chances of being selected as parents. Once the parents are selected, crossover and mutation operators
are applied on them. The main reproduction operator used in GA is crossover,inwhich twoindividualsareusedas parentsand
new chromosomes are formed by swapping or crossing genetic information between these strings. Mutation is another GA
operator used for reproduction.
The GA steps used to solve the UCP in this research work along with their detail are given below;
2.4.1 Input Data for the UC Problem
The input data which is used while solving UCP by using GA can be of three types:
2.4.2 System Data
System data includes forecasted load demand in terms of real power over a schedule time horizon (T) along with the spinning
reserve (SR) requirement. The SR is taken as some percentage of forecasted load demand or a fixed amount of real power.
4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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2.4.3 Unit Data
Unit data for a UC problem contain the characteristic behavior of the cost curvesofall theindividual units.Unitdata involvethe
hot and cold start-up details, start-up and shutdown costs, maximumandminimumpowerlimits,minimumupand downtimes
and initial status of all generating units.
2.4.4 GA Parameters
This kind of input data comprises of selection of all GA parameters setting such as crossover and mutation rates, stringlength,
population size, termination criteria, fitness function and maximum number of generators etc.
2.4.5 Structure of Chromosomes
The on/off status of units are represented by binary variables i.e. โ0โ for OFF and โ1โ for ON. To represent the status of N units
over a time horizon of T hours the dimension of chromosome will be TรN.
2.4.6 Coding Scheme of Unit Commitment
In the intelligent coding scheme a binary string X translates into another binary vector representation ๐โฒ as showninFig.1the
main UCP constraints, MUT/MDT and the turbine/pumpoperatingconstraintsarealsocombinedintothese representationsas
well.
Fig .1: The coding scheme, Matrix ๐โฒ.
In Fig.1is shown that each row in the matrix expressโs the coded operating state for one unit during T-time step period. Each
row of the coded states is divided into a number of segments called here substrings, expressed as, (๐1),(๐2),and(๐3).Forthe ๐th
row each substring represents one operating state, OFF (by leading bit 0) and ON (by leading bit 1), and how much time the
OFF-state (or ON state) lasts. The length of substring ๐๐, for the ๐th unit is expressed as,
๏ป 2 max ,max1 [log ( , )],.......... .... 1
1,.................................
i in for n
i otherwisen
๏ซ ๏พ
๏ฝ (9)
In equation (9) ,maxin = max (ำถi,up ำถ๐,๐๐๐ค๐ ) for the ๐th unit. Which show that these two main constraints are included in the
chromosomes binary string and they fulfill implicitly also. For example a unit ๐ has ำถ๐, ๐ข๐=2 hours and ำถ๐, ๐๐๐ค๐=4hours, then ๐๐,
๐๐๐ฅ= 4 and ๐๐= 3. In this situation the sub string 1|01 represents that the unit is in ON state (due to the leading bit) and the up
state continues up to 3 hours, i.e., 1 hour (shown by the binary value โ01โ) plus 2-hours ำถ๐,๐ข๐. By analogy, the substring 0|11
imply that the unit is in OFF condition up to 7 hours, i.e., 3 hours (represents by โ11โ) plus 4-hours ำถ๐, down. In this way, even a
random selection of entries of the matrix does not produce infeasible solutions with respect to MUT/MDT. For taking the ำถ๐,๐ข๐
=[2,3,2,1,1] and ำถ๐,๐๐๐ค๐=[4,3,3,1,1], ๐๐ will be [3,2,2,1,1]. It is seen that for ๐๐, ๐๐๐ฅ= 1 , the sub string length decoded into 1.
Thus each bit clearly specifies the unit state in each hour. The units having ๐๐, ๐๐๐ฅ> 1, it may be possible that the corresponding
actual schedule taking the time horizon longer than 24 h, so in that case the scheduling period away from the 24th hour is
ignored. Also the initial status of generating units can be involved in the same substring coding technique.
2.4.7 Economic Dispatch and Cost calculations
The lagrangian multiplier is a classical and effective techniqueusedtosolvethe economicdispatchproblem proposedby Wood
& Wallenberg [1]. Its function is expressed as:
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๐ฟ =
i
๏ฅ ๐น๐(๐๐,๐ก) + ๐๐ (10)
Where ฮป is called undetermined lagrangian multiplier.
The required conditions for the minimum of the total production cost function are given as:
,( )
0
( )
i i t
i i
dF PL
P dP t
๏ฌ
๏ถ
๏ฝ ๏ญ ๏ฝ
๏ถ
(11)
For this required condition there must be the sum of all real power outputs must be equal to the load demand ๐ท๐ก. The
inequalities of constraints must also be satisfied. For finding the best value of ฮป we use lambda-iteration method. By usingthis
iterative method we change the value of ฮป in the systematic way:
Change the value of ฮป in the systematic way:
1- Firstly set the value of ฮป.
2- Then find the generating powers of each unit.
3-If the sum of generated powers of all units is lessthan the required demand then increase the ฮป and go to step 2.
4- If the sum of generated powers of all units is higher than the required demand then decrease the ฮป and go to step 2.
The advantage of ฮป iteration method is, for UCP its convergencetowardsglobal minimumisveryfastanditautomaticallyfulfills
the power balance constraint. Power limit constraint is handled by clamping process and find lambda again of inviolate units
for power balance constraints. So we can easily handle both economic dispatch constraints by using this method.
2.4.8 Selection
The selection operator guides towards the best solution and eliminates the lessfitindividualsbyselectingthefeasibleand best
fit chromosomes from the population. In the proposedwork binarytournamentselection[4]isappliedforfindingtheoptimum
solution. This operator picks the two individuals from the population randomly at a time and produce temporary population,
known mating pool. This selection procedure repeat itself is until the size of temporary population becomes equal to the
original population.
2.4.9 Crossover
In Genetic Algorithm, crossover is a major genetic operator which applied on two parents for the production of offspring
generation. For the implementation of this operator two parent individuals are randomly selected from the temporary
population, created after selection processandthengeneticinformationisexchangedbetweenthem.Thecrossover probability
(๐๐) is predefined for generating two new solutions.
2.4.9.1 Annular crossover:
In proposed work, for gets better convergence and solution new ring type crossover called Annular crossover has been used.
The crossover operator is applied on two parents, selected from mating pool andthengeneticinformationhasbeen exchanged
between them. Usually in GA, most common type of crossover, linear crossover is implemented on the chromosome which
expressed in the form of binary string as shown in Fig. 2(a). While in proposed crossover chromosome is represented as a
circular shape as shown in Fig. 2(b). First, for implementation of annular crossover on chromosome,definea number ๐ถ๐which
represent the starting point of crossover called locus point.
(a)String
A B C D E F G H
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(b)Ring
Fig. 2: Chromosome representation
The range of this number should be [1, L-1], Here L indicates the length of chromosome. In addition to, a number must be
defined for establishing the semi-ring length ๐ถ๐ which is exchangeduringcrossover.Thelengthofsemi-ring will be inthearray
[1,L/2]. The feasibility of solution also dependsoneffectiveexchangingofgeneticinformation.Soforthispurposethesemi-ring
length must be equal in both parent chromosomes.
Fig. 3: Proposed Crossover
2.4.9.2 Annular crossover in UCP
For the UCP, annular crossover is described by using the following steps.
1. After the selection process, from each parent selected, a unit p and a unit q are randomly chosen.
2. By using the ring representation define the scheduling of chosen units as shown in fig. 4.
3. Select the crossover point ๐ถ๐ and the length of semi-ring ๐ถ๐ randomly. For this case L is defining for the 24 hoursโ time
horizon. By taking an example where ๐ถ๐ is 22 for unit p and 18 for unit q as shown in fig. 5.
4. During crossover now exchange the genetic information in the semi ring and form new schedule for unit p and q which
shown in fig. 6.
5. Convert backs this representation into linear representation of the offspring planning schedule of unit p and q.
6. End of crossover. The annular crossover operator terminates, when the individuals in the population has been completed.
G
F
E
D
C
B
A
H
(b)Ring
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2.4.9.3 Elitism
For increase the speed of convergence, elitism is used. In which the best individuals having best fitness value are maintaining
their existence in the next generation and not to lose their useful genetic information. This proposedwork usesa certainrange
of elitism from which the best chromosomes of the population are remain a part of new population.
Fig. 4: Ring representation for unit p and q.
Fig. 5: Semi ring for the proposed Crossover
Fig. 6: Off-spring Schedule of unit p and q
2.4.9.4 Mutation
It is also a genetic operator which is used to maintain the genetic diversity from one generation to next generation. For the
modification of genetic information in the chromosome, a mutation probability P ๐ is defined in GA based problems. This
genetic operator just changes a bit which selected randomly from the matrix represents a chromosome from 0 to 1 or 1 to 0.
2.5 Main Features of proposed GA
1. This proposed algorithm not only handled small scale but also large scale system.
2. Most Constraints of UC are addressed such as:
b) Spinning Reserve Requirement
c) Minimum up Time (MUT) and Minimum down Time (MDT)
d) Maximum & Minimum Power Limits of Units
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e) Hot/Cold Start-up Cost
f) Must Run Units / Must off Units
g) Cold Start hours
h) Initial Status of Units
i) Shut down Cost
3. Methods for Economic Dispatch
โข Lambda Iteration Method
4. Intelligent generation of initial population by focusing on load curve.
5. De-commitment of excessive units using intelligent mutation operator.
6. Constraints are satisfied without using penalty term.
7. Annular Crossover.
8. Bit flip mutation
4.5 Flow Chart of proposed GA
The Flow Chart of proposed GA showing all the steps of the proposed algorithm is shown in Figure 7.
Figure 7: Flow chart of proposed Genetic Algorithm
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3. RESULTS
3.1 Result from 10-unit test system (case 1):
For this small scale test system, the number of iterations and population size are taking 50&20.Whilemutationand crossover
probabilities are set to 0.1 and 0.8 respectively.
Proposed GA is applied firstly on 10-unit test system for 24-hour time horizon, data obtained from [5]. Table 3.1 gives the
optimum power generation schedule for this test system.
Comments:
Spinning reserve taken 10% of load. Obtained operating cost is $563930, which is lowest as compare to the other
techniques mentioned in table 3.2. The cost comparison with other techniques is shown in fig 8.
3.2 Result from 10-unit test system (case 2):
The proposed algorithm also tested on second 10-unit system, which data obtained from [8]. Spinning reserve taken as 5% of
load. The results obtained from that system are also improved. Also proposed coding scheme gives the advantage to produce
feasible solution in each trial. 24-hour best UC schedule and total operating cost is given in table 3.3.
Comments:
The total operating cost for this test system is $ 560572, which is minimum cost as compare to other.
Techniques (ELR [9], EP [10], and IGA [11]) mentioned in table 3.4. The cost comparison with other techniques is shown infig
9.
3.3 Result from 20-unit test system (Case 3):
As described earlier that 20 unit system data is obtained by using proper scaling on 10-unit test system data [5] and load
demand multiplied by 2. From table 3.5, it is clearly shows that even for large scale UC problem; proposed algorithm shows
feasible and minimum cost results.
Comments:
The minimum cost obtained by this large scale unit system is $ 1124260, which is lowest cost as compare to other techniques
(IBPSO [2], BDE [7], GA [6]) mentioned in table 3.5. The best result is found from 30 independent trials. The cost comparison
with other techniques is shown in fig10
Fig 8: Cost Comparison of 10 Unit test system (Case 01)
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Fig 9: The cost comparison of 10 unit Test system (Case 02)
Fig 9: The cost comparison of 20 unit Test system (Case 03)
Table 03: Parameters for the 10 unit power system [5].
Unit ๐ ๐ท๐๐๐(๐ด๐พ) ๐ท๐๐๐(๐ด๐พ) ๐i($ /๐) ๐i($/๐ด๐พ๐) ๐i ($/๐ด๐พ๐2) ำถ๐,๐๐(๐) ำถ๐,๐ ๐๐๐(๐) ๐ธ๐($)
๐i($) ๐i(๐) ๐ฐ๐บ๐(๐)
1 455 150 0.00048 16.19 1000 8 8 4500 9000 5 8
2 455 150 0.00031 17.26 970 8 8 5000 10000 5 8
3 130 20 0.00200 16.60 700 5 5 550 1100 4 -5
4 130 20 0.00211 16.50 680 5 5 560 1120 4 -5
5 162 25 0.00398 19.70 450 6 6 900 1800 4 -6
6 80 20 0.00712 22.26 370 3 3 170 340 2 -3
7 85 25 0.00079 27.74 480 3 3 260 520 2 -3
8 55 10 0.00413 25.92 660 1 1 30 60 0 -1
9 55 10 0.00222 27.27 665 1 1 30 60 0 -1
10 55 10 0.00173 27.79 670 1 1 30 60 0 -1
1123400
1123900
1124400
1124900
1125400
1125216
1124538
1124290
1124565
1124260
Case 3
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Table 5.5: Comparison of total production cost of 20 units case 3
Number of units Total production cost ($)
IBPSO[2] BDE[7] GA[6] PLA[13] PROPOSED
20 1125216 1124538 1124290 1124565 1124260
4. CONCLUSION
Unit commitment is a non-linear, mixed integer, highly complex, combinatorial optimization problem. It is a critical step in
power system planning after short term load forecasting phase. There are many techniques in the literature are discussed
earlier and divided into three major categories, i.e. classical,meta-heuristicandhybridizedtechniques.Buteachofthemhaving
some advantages and disadvantages and requires some improvements intheiralgorithmstohandlethiscomplexoptimization
problem. In this paper, by using new proposed GA, the most difficult constraints of generation scheduling MUT/MDT is easily
handled. For economic dispatch problem, lambda iteration method is used, which easily handled, power balance and
generation limits constraints. By using direct coding scheme of GA, after several generations, it fails toyieldfeasibleresultsfor
large scale systems. The results obtained from different test systems either small or large-scale shows the effectiveness of
proposed algorithm. And it show minimum operating cost as compare to other reported methods. As a result it can easily say
that proposed GA is an effective tool to handle the UC problem without any constraint violation. The proposed GA has a high
probability to find the global solution, especially in convex formulations.
5. AKNOWLEDGEMENTS
I attribute special gratitude for my friends and family who were there for me whenever I was stuck. Prayers of my mother and
constant backing from my father were an immense source of success.
REFERENCES
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[2] Lee T, Chen C, โUnit commitment with probabilisticreserve-AnIPSapproach,โEnergyConversManage;48(2):486โ93;2007.
[3] Yuan X, Su A, Nie H, Yuan Y, Wang L, โApplication of enhanced discrete differential evolution approachtounitcommitment
problem,โ Energy Convers Manage;50(9):2449โ56; 2009.
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14. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
ยฉ 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1524
[13] A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist
competitive algorithm Navid Abdolhoseyni Saber, Mahdi Salimi*, Davar Mirabbasi,2016
AUTHORS
Bilal Iqbal Ayubi
He has done Level 7 Post Graduate
diploma in electrical Engineering
from City and guilds institute
London, UK
Waseem Sajjad
He has done Level 7 Post Graduate
diploma in electrical Engineering
from City and guilds institute
London, UK
Muhammad Asad
Instructor of GOVT College of
TechnologyFaisalabad, Department
of BSc Electrical Engineering
Technology