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.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
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.
Optimization of Economic Load Dispatch with Unit Commitment on Multi MachineIJAPEJOURNAL
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- A Genetic based Stochastic Approach for Solving Thermal Unit Commitmen...IRJET Journal
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.
Profit based unit commitment for GENCOs using Parallel PSO in a distributed c...IDES Editor
In the deregulated electricity market, each
generating company has to maximize its own profit by
committing suitable generation schedule termed as profit
based unit commitment (PBUC). This article proposes a
Parallel Particle Swarm Optimization (PPSO) solution to the
PBUC problem. This method has better convergence
characteristics in obtaining optimum solution. The proposed
approach uses a cluster of computers performing parallel
operations in a distributed environment for obtaining the
PBUC solution. The time complexity and the solution quality
with respect to the number of processors in the cluster are
thoroughly tested. The method has been applied to 10 unit
system and the results show that the proposed PPSO in a
distributed cluster constantly outperforms the other methods
which are available in the literature.
IJERD(www.ijerd.com)International Journal of Engineering Research and Develop...IJERD Editor
This document presents a fuzzy-logic based approach to solve the unit commitment problem in power generation systems. The unit commitment problem aims to determine the optimal on/off schedule of generating units to minimize operating costs while meeting demand and constraints. The proposed approach models key factors like generator load capacity, fuel costs, and startup costs as fuzzy variables. It then uses fuzzy logic techniques to determine a commitment schedule. The approach is demonstrated on a case study of a 4-unit thermal power plant in Turkey. Results are compared to dynamic programming to show the fuzzy logic approach provides preferable solutions with less computational time.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
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.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
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 document reviews various maximum power point tracking (MPPT) techniques for photovoltaic systems. It discusses 17 different MPPT techniques, comparing them based on their method (direct control, sampling, modulation), variables tracked (voltage, current), required circuitry (analog, digital), need for tuning, relative cost, and hardware complexity. The techniques range from simple hill-climbing methods like perturb and observe to more advanced intelligent techniques using fuzzy logic, neural networks, and particle swarm optimization. The document concludes that fuzzy logic and other hybrid/intelligent techniques provide good performance for rapidly changing temperature and irradiance conditions with fast response and less fluctuation, though they require more complex hardware.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
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.
Optimization of Economic Load Dispatch with Unit Commitment on Multi MachineIJAPEJOURNAL
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- A Genetic based Stochastic Approach for Solving Thermal Unit Commitmen...IRJET Journal
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.
Profit based unit commitment for GENCOs using Parallel PSO in a distributed c...IDES Editor
In the deregulated electricity market, each
generating company has to maximize its own profit by
committing suitable generation schedule termed as profit
based unit commitment (PBUC). This article proposes a
Parallel Particle Swarm Optimization (PPSO) solution to the
PBUC problem. This method has better convergence
characteristics in obtaining optimum solution. The proposed
approach uses a cluster of computers performing parallel
operations in a distributed environment for obtaining the
PBUC solution. The time complexity and the solution quality
with respect to the number of processors in the cluster are
thoroughly tested. The method has been applied to 10 unit
system and the results show that the proposed PPSO in a
distributed cluster constantly outperforms the other methods
which are available in the literature.
IJERD(www.ijerd.com)International Journal of Engineering Research and Develop...IJERD Editor
This document presents a fuzzy-logic based approach to solve the unit commitment problem in power generation systems. The unit commitment problem aims to determine the optimal on/off schedule of generating units to minimize operating costs while meeting demand and constraints. The proposed approach models key factors like generator load capacity, fuel costs, and startup costs as fuzzy variables. It then uses fuzzy logic techniques to determine a commitment schedule. The approach is demonstrated on a case study of a 4-unit thermal power plant in Turkey. Results are compared to dynamic programming to show the fuzzy logic approach provides preferable solutions with less computational time.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
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.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
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 document reviews various maximum power point tracking (MPPT) techniques for photovoltaic systems. It discusses 17 different MPPT techniques, comparing them based on their method (direct control, sampling, modulation), variables tracked (voltage, current), required circuitry (analog, digital), need for tuning, relative cost, and hardware complexity. The techniques range from simple hill-climbing methods like perturb and observe to more advanced intelligent techniques using fuzzy logic, neural networks, and particle swarm optimization. The document concludes that fuzzy logic and other hybrid/intelligent techniques provide good performance for rapidly changing temperature and irradiance conditions with fast response and less fluctuation, though they require more complex hardware.
Application of AHP algorithm on power distribution of load shedding in island...IJECEIAES
This paper proposes a method of load shedding in a microgrid system operated in an Island Mode, which is disconnected with the main power grid and balanced loss of the electrical power. This proposed method calculates the minimum value of the shed power with reference to renewable energy sources such as wind power generator, solar energy and the ability to control the frequency of the generator to restore the frequency to the allowable range and reduce the amount of load that needs to be shed. Computing the load importance factor (LIF) using AHP algorithm supports to determine the order of which load to be shed. The damaged outcome of load shedding, thus, will be noticeably reduced. The experimental results of this proposed method is demonstrated by simulating on IEEE 16-Bus microgrid system with six power sources.
Load Shifting Technique on 24Hour Basis for a Smart-Grid to Reduce Cost and P...IRJET Journal
This document summarizes a research paper that proposes a load shifting technique using particle swarm optimization to reduce costs and peak demand in a smart grid. The technique shifts loads from peak hours to off-peak hours on a daily basis. Simulation results show that applying the load shifting technique to residential, commercial, and industrial loads in a smart grid reduces both the overall operational cost and peak load demand. The particle swarm optimization algorithm performs better than genetic algorithms at minimizing costs and shifting loads to reduce peaks.
Compromising between-eld-&-eed-using-gatool-matlabSubhankar Sau
Creating a compromising points between economic load dispatch & emission created from the plant to minimising those effects.
these are created by using MATLAB and GATOOL .
taking Weighted Sum Method,also Pareto optimal curve.
created by: SUBHANKAR SAU
DESIGN A TWO STAGE GRID CONNECTED PV SYSTEMS WITH CONSTANT POWER GENERATION A...Journal For Research
The many Different techniques for Maximum Power Point Tracking of Photovoltaic arrays are discussed. This constant power generation is produced by limiting to a set limit around the MPPT, which makes the panel to reach around the MPPT, this makes the voltage and the current to stabilize so oscillations are reduced. These methods provide a better efficiency but suffer from sudden solar irradiance changes. Hence to alleviate the issues of the variation power a robust controller based on the Input Output Linearizer along with the constant power control is introduced. This controller can ensure a fast transition between maximum power point tracking regardless of the solar irradiance levels, high performance and stable operation are always achieved by the proposed control strategy. It can regulate the PV output power according to any set point and force the PV systems to operate around MPPT which enhances the efficiency.
Economic Load Dispatch Optimization of Six Interconnected Generating Units Us...IOSR Journals
This document describes using particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem of optimizing the operation of six interconnected generating units. ELD aims to minimize total generation costs while satisfying constraints. PSO is applied to find optimal unit outputs that minimize cost, accounting for transmission losses. The proposed PSO approach is compared to genetic algorithms and conventional methods on a test system, showing PSO provides better solutions faster. Key steps of the PSO algorithm for ELD are initializing particles, evaluating fitness at each iteration, and updating personal and global best positions to iteratively improve solutions.
The document summarizes a study comparing time series and artificial neural network (ANN) methods for short-term load forecasting of Covenant University, Nigeria. Load data from October 15-16, 2012 was used to develop forecasting models using moving average, exponential smoothing (time series methods) and ANN. The ANN model with inputs of previous load, time of day, day of week and weekday/weekend proved most accurate with a mean absolute deviation of 0.225, mean squared error of 0.095 and mean absolute percent error of 8.25, making it the best forecasting method according to the error measurements.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithmpaperpublications3
Abstract: Flexible Alternating Current Transmission Systems (FACTS) devices represents a technological development in electrical power systems to have a tendency to generate the power with minimum price and less time that fulfill our requirement according to our need. Now a days Flexible AC Transmission System (FACTS) devices play a vital role in boost the power of system performance and power transfer capability. TCSC is an important member of family. In practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics, so its placement is very important. This paper represent a meta heuristic hybrid Algorithm of Artificial Bee Colony (ABC) and Differential Evolution (DE) for finding the best placement and parameter setting of Thyristor Controlled Series capacitor to attain optimum power flow (OPF) of grid network. The proposed technique is tested at IEEE-30 bus test System. Result shows that the selected technique is one of the best for placement of TCSC for Secured optimum Power Flow (OPF).
Keywords: Optimal placement, Severity index, stressed power system, System loadability, TCSC, Hybrid DE/ABC.
Title: TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithm
Author: Ritesh Diwan, Preeti Sahu
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
IRJET- A Survey on Optimization Technique for Congestion Managenment in Restr...IRJET Journal
This document discusses congestion management in restructured power markets using generator rescheduling optimization techniques. It presents particle swarm optimization (PSO) as an effective method to select generators and reschedule their power outputs to minimize congestion costs. PSO works by having particles represent potential solutions that move through the search space, guided by their own experience and the experiences of neighboring particles. The algorithm iteratively improves the candidate solutions until an optimal rescheduling plan is found. The technique aims to reduce participating generator numbers and reschedule their outputs at minimum cost while respecting power balance and operating constraints. PSO is well-suited for this problem due to its simplicity, efficiency, and ability to handle non-convex problems.
Economic Load Dispatch for Multi-Generator Systems with Units Having Nonlinea...IJAPEJOURNAL
This document presents an economic load dispatch problem that uses the Gravity Search Algorithm to minimize total generation costs for multi-generator power systems. It discusses how practical constraints like valve point loading, multi-fuel operation, and forbidden zones result in non-ideal, non-continuous generator cost curves. The Gravity Search Algorithm is applied to find the optimal dispatch schedule that accounts for these realistic cost functions and minimizes the total cost of generation while satisfying demand. The algorithm is tested on sample power systems and able to find solutions within acceptable timeframes that outperform traditional optimization methods for large, complex problems.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
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.
Comparison of cascade P-PI controller tuning methods for PMDC motor based on ...IJECEIAES
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.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
IRJET-Power Quality Improvement in Grid Connected Wind Energy Conversion Syst...IRJET Journal
This document summarizes research on using a unified power quality conditioner (UPQC) to improve power quality in grid-connected wind energy conversion systems. The connection of large wind farms can cause power quality issues like voltage sags/swells and harmonics. A UPQC, which consists of series and shunt voltage source inverters connected back-to-back via a DC link, can mitigate both voltage-related issues from the grid and current-related issues from the load. Simulation results using MATLAB/Simulink show that a UPQC is effective at compensating for voltage dips, swells and harmonics, thus improving the quality of power supplied to the load.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Grid Connected Electricity Storage Systems (2/2)Leonardo ENERGY
Development and use of Renewable Energy Sources is one of the key elements in European Electricity Research. However, connecting energy sources such as photovoltaics and wind turbines to the electricity grid causes significant effects on these networks. Bottlenecks are stability, security, peaks in supply & demand and overall management of the grid. Energy storage systems provide means to overcome technical and economic hurdles for large-scale introduction of distributed sustainable energy sources. The GROW-DERS project (Grid Reliability and Operability with Distributed Generation using Flexible Storage) investigates the implementation of (transportable) distributed storage systems in the networks. The project is funded by the European Commission (FP6) and the consortium partners are KEMA, Liander, Iberdrola, MVV, EAC, SAFT, EXENDIS, CEA-INES and IPE.
In this project 3 storage systems (2 Li-ion battery systems and a flywheel) have been demonstrated at different test locations in Europe. Additionally, a dedicated software tool, PLATOS (PLAnning Tool for Optimizing Storage), has been developed by KEMA to optimize the energy management of electricity networks using storage. For each network, the location, size and type of storage systems is evaluated for all possible configurations and the most attractive option is selected.
El documento resume las visitas a tres museos - el Museo Confenalco, Las Novias del Gato en Tejada, y el Museo La Tertulia. En el Museo Confenalco, se analizaron fotografías y se aprendió sobre la cultura. Las Novias del Gato en Tejada exhibe esculturas de gatas que compiten por el amor de un gato. El Museo La Tertulia muestra obras sobre invasión extraterrestre, contaminación ambiental, y mapas de Colombia hechos por niños de otros países.
Communication is an important issue when caring for someone with dementia. This document from Alzheimer's Australia provides tips for communicating effectively with someone who speaks Arabic as their primary language. Key recommendations include speaking clearly and simply, using familiar objects or photos to help convey meaning, and involving family members who can interpret if the person has difficulty understanding.
This document provides information about math and science formulas. It includes the formulas for area of basic shapes like circles, squares, triangles, and cubes. It also includes formulas for volume, speed, distance, time, pressure, density, and acceleration. Users are prompted with questions about each formula and can select an answer button to check their response.
This document provides a personal timeline of the author's experiences with educational technology from kindergarten through college and his career goals. It outlines some of the key technologies the author encountered at different stages of his education, including playing Oregon Trail in kindergarten, using Napster in high school, and using iPods in college. It also describes his experience learning to use a video camera and interests in using smart boards and developing a video production studio for students. The document includes links to websites about the history of educational technology.
Este documento describe la tecnología actual como la posibilidad de prescindir del ratón al interactuar con el computador y el uso de dispositivos táctiles. También describe una empresa ecuatoriana llamada People Web que desarrolla aplicaciones para pantallas táctiles compatibles con Windows. Finalmente, explica que la tecnología digital pone fin a la firma manual a través del uso de firmas digitales emitidas por el Banco Central del Ecuador.
Application of AHP algorithm on power distribution of load shedding in island...IJECEIAES
This paper proposes a method of load shedding in a microgrid system operated in an Island Mode, which is disconnected with the main power grid and balanced loss of the electrical power. This proposed method calculates the minimum value of the shed power with reference to renewable energy sources such as wind power generator, solar energy and the ability to control the frequency of the generator to restore the frequency to the allowable range and reduce the amount of load that needs to be shed. Computing the load importance factor (LIF) using AHP algorithm supports to determine the order of which load to be shed. The damaged outcome of load shedding, thus, will be noticeably reduced. The experimental results of this proposed method is demonstrated by simulating on IEEE 16-Bus microgrid system with six power sources.
Load Shifting Technique on 24Hour Basis for a Smart-Grid to Reduce Cost and P...IRJET Journal
This document summarizes a research paper that proposes a load shifting technique using particle swarm optimization to reduce costs and peak demand in a smart grid. The technique shifts loads from peak hours to off-peak hours on a daily basis. Simulation results show that applying the load shifting technique to residential, commercial, and industrial loads in a smart grid reduces both the overall operational cost and peak load demand. The particle swarm optimization algorithm performs better than genetic algorithms at minimizing costs and shifting loads to reduce peaks.
Compromising between-eld-&-eed-using-gatool-matlabSubhankar Sau
Creating a compromising points between economic load dispatch & emission created from the plant to minimising those effects.
these are created by using MATLAB and GATOOL .
taking Weighted Sum Method,also Pareto optimal curve.
created by: SUBHANKAR SAU
DESIGN A TWO STAGE GRID CONNECTED PV SYSTEMS WITH CONSTANT POWER GENERATION A...Journal For Research
The many Different techniques for Maximum Power Point Tracking of Photovoltaic arrays are discussed. This constant power generation is produced by limiting to a set limit around the MPPT, which makes the panel to reach around the MPPT, this makes the voltage and the current to stabilize so oscillations are reduced. These methods provide a better efficiency but suffer from sudden solar irradiance changes. Hence to alleviate the issues of the variation power a robust controller based on the Input Output Linearizer along with the constant power control is introduced. This controller can ensure a fast transition between maximum power point tracking regardless of the solar irradiance levels, high performance and stable operation are always achieved by the proposed control strategy. It can regulate the PV output power according to any set point and force the PV systems to operate around MPPT which enhances the efficiency.
Economic Load Dispatch Optimization of Six Interconnected Generating Units Us...IOSR Journals
This document describes using particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem of optimizing the operation of six interconnected generating units. ELD aims to minimize total generation costs while satisfying constraints. PSO is applied to find optimal unit outputs that minimize cost, accounting for transmission losses. The proposed PSO approach is compared to genetic algorithms and conventional methods on a test system, showing PSO provides better solutions faster. Key steps of the PSO algorithm for ELD are initializing particles, evaluating fitness at each iteration, and updating personal and global best positions to iteratively improve solutions.
The document summarizes a study comparing time series and artificial neural network (ANN) methods for short-term load forecasting of Covenant University, Nigeria. Load data from October 15-16, 2012 was used to develop forecasting models using moving average, exponential smoothing (time series methods) and ANN. The ANN model with inputs of previous load, time of day, day of week and weekday/weekend proved most accurate with a mean absolute deviation of 0.225, mean squared error of 0.095 and mean absolute percent error of 8.25, making it the best forecasting method according to the error measurements.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithmpaperpublications3
Abstract: Flexible Alternating Current Transmission Systems (FACTS) devices represents a technological development in electrical power systems to have a tendency to generate the power with minimum price and less time that fulfill our requirement according to our need. Now a days Flexible AC Transmission System (FACTS) devices play a vital role in boost the power of system performance and power transfer capability. TCSC is an important member of family. In practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics, so its placement is very important. This paper represent a meta heuristic hybrid Algorithm of Artificial Bee Colony (ABC) and Differential Evolution (DE) for finding the best placement and parameter setting of Thyristor Controlled Series capacitor to attain optimum power flow (OPF) of grid network. The proposed technique is tested at IEEE-30 bus test System. Result shows that the selected technique is one of the best for placement of TCSC for Secured optimum Power Flow (OPF).
Keywords: Optimal placement, Severity index, stressed power system, System loadability, TCSC, Hybrid DE/ABC.
Title: TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithm
Author: Ritesh Diwan, Preeti Sahu
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
IRJET- A Survey on Optimization Technique for Congestion Managenment in Restr...IRJET Journal
This document discusses congestion management in restructured power markets using generator rescheduling optimization techniques. It presents particle swarm optimization (PSO) as an effective method to select generators and reschedule their power outputs to minimize congestion costs. PSO works by having particles represent potential solutions that move through the search space, guided by their own experience and the experiences of neighboring particles. The algorithm iteratively improves the candidate solutions until an optimal rescheduling plan is found. The technique aims to reduce participating generator numbers and reschedule their outputs at minimum cost while respecting power balance and operating constraints. PSO is well-suited for this problem due to its simplicity, efficiency, and ability to handle non-convex problems.
Economic Load Dispatch for Multi-Generator Systems with Units Having Nonlinea...IJAPEJOURNAL
This document presents an economic load dispatch problem that uses the Gravity Search Algorithm to minimize total generation costs for multi-generator power systems. It discusses how practical constraints like valve point loading, multi-fuel operation, and forbidden zones result in non-ideal, non-continuous generator cost curves. The Gravity Search Algorithm is applied to find the optimal dispatch schedule that accounts for these realistic cost functions and minimizes the total cost of generation while satisfying demand. The algorithm is tested on sample power systems and able to find solutions within acceptable timeframes that outperform traditional optimization methods for large, complex problems.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
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.
Comparison of cascade P-PI controller tuning methods for PMDC motor based on ...IJECEIAES
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.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
IRJET-Power Quality Improvement in Grid Connected Wind Energy Conversion Syst...IRJET Journal
This document summarizes research on using a unified power quality conditioner (UPQC) to improve power quality in grid-connected wind energy conversion systems. The connection of large wind farms can cause power quality issues like voltage sags/swells and harmonics. A UPQC, which consists of series and shunt voltage source inverters connected back-to-back via a DC link, can mitigate both voltage-related issues from the grid and current-related issues from the load. Simulation results using MATLAB/Simulink show that a UPQC is effective at compensating for voltage dips, swells and harmonics, thus improving the quality of power supplied to the load.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Grid Connected Electricity Storage Systems (2/2)Leonardo ENERGY
Development and use of Renewable Energy Sources is one of the key elements in European Electricity Research. However, connecting energy sources such as photovoltaics and wind turbines to the electricity grid causes significant effects on these networks. Bottlenecks are stability, security, peaks in supply & demand and overall management of the grid. Energy storage systems provide means to overcome technical and economic hurdles for large-scale introduction of distributed sustainable energy sources. The GROW-DERS project (Grid Reliability and Operability with Distributed Generation using Flexible Storage) investigates the implementation of (transportable) distributed storage systems in the networks. The project is funded by the European Commission (FP6) and the consortium partners are KEMA, Liander, Iberdrola, MVV, EAC, SAFT, EXENDIS, CEA-INES and IPE.
In this project 3 storage systems (2 Li-ion battery systems and a flywheel) have been demonstrated at different test locations in Europe. Additionally, a dedicated software tool, PLATOS (PLAnning Tool for Optimizing Storage), has been developed by KEMA to optimize the energy management of electricity networks using storage. For each network, the location, size and type of storage systems is evaluated for all possible configurations and the most attractive option is selected.
El documento resume las visitas a tres museos - el Museo Confenalco, Las Novias del Gato en Tejada, y el Museo La Tertulia. En el Museo Confenalco, se analizaron fotografías y se aprendió sobre la cultura. Las Novias del Gato en Tejada exhibe esculturas de gatas que compiten por el amor de un gato. El Museo La Tertulia muestra obras sobre invasión extraterrestre, contaminación ambiental, y mapas de Colombia hechos por niños de otros países.
Communication is an important issue when caring for someone with dementia. This document from Alzheimer's Australia provides tips for communicating effectively with someone who speaks Arabic as their primary language. Key recommendations include speaking clearly and simply, using familiar objects or photos to help convey meaning, and involving family members who can interpret if the person has difficulty understanding.
This document provides information about math and science formulas. It includes the formulas for area of basic shapes like circles, squares, triangles, and cubes. It also includes formulas for volume, speed, distance, time, pressure, density, and acceleration. Users are prompted with questions about each formula and can select an answer button to check their response.
This document provides a personal timeline of the author's experiences with educational technology from kindergarten through college and his career goals. It outlines some of the key technologies the author encountered at different stages of his education, including playing Oregon Trail in kindergarten, using Napster in high school, and using iPods in college. It also describes his experience learning to use a video camera and interests in using smart boards and developing a video production studio for students. The document includes links to websites about the history of educational technology.
Este documento describe la tecnología actual como la posibilidad de prescindir del ratón al interactuar con el computador y el uso de dispositivos táctiles. También describe una empresa ecuatoriana llamada People Web que desarrolla aplicaciones para pantallas táctiles compatibles con Windows. Finalmente, explica que la tecnología digital pone fin a la firma manual a través del uso de firmas digitales emitidas por el Banco Central del Ecuador.
Accent Windows is a family-owned Colorado company that has been in business since 1982, offering factory direct pricing on custom manufactured windows with many glazing options and the best lifetime guarantees in the industry, including coverage for material, labor, workmanship, glass breakage, and seal failure that transfers for the lifetime of the homeowner without service fees.
Este documento apresenta 5 atividades para uma lição de português da 1a série do ensino médio. A primeira atividade pede para conjugar verbos em todos os tempos e modos. A segunda atividade pede para ler um poema e fazer uma análise. A terceira atividade propõe redigir um artigo de opinião sobre os malefícios do cigarro para jovens. A quarta atividade pede para redigir um texto sobre a interferência do poder público no combate à obesidade. A quinta atividade pede para
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
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.
IRJET- Optimal Power Flow Solution of Transmission Line Network of Electric p...IRJET Journal
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 Generation Scheduling for Thermal UnitsIRJET Journal
This document summarizes a research paper that develops an optimal short-term generation scheduling for 10 generating units using particle swarm optimization (PSO). The scheduling problem is formulated to minimize operating costs while satisfying constraints like power balance, unit limits, minimum up/down times, and spinning reserve requirements. PSO is described as an evolutionary algorithm that finds the global best solution by updating particle velocities and positions based on the particle's own experience and the experience of neighboring particles. The steps of applying PSO to the scheduling problem are outlined, with particles initialized randomly within unit limits and then updated iteratively until an optimal schedule is found.
IRJET- Optimal Generation Scheduling for Thermal UnitsIRJET Journal
This document summarizes a research paper that develops an optimal short-term generation scheduling model for 10 generating units using particle swarm optimization (PSO). The objective is to minimize total operating costs including fuel costs and start-up costs while satisfying constraints like power balance, generator limits, minimum up/down times, and reserve requirements. PSO is applied to obtain the optimal scheduling by updating the velocity and position of "particles" representing generator outputs over iterations. Results show PSO efficiently finds near-optimal solutions and provides economic benefits compared to other techniques for solving short-term generation scheduling problems.
A new approach to the solution of economic dispatch using particle Swarm opt...ijcsa
This document presents a new approach to solving the economic dispatch problem using particle swarm optimization combined with simulated annealing (PSO-SA). The economic dispatch problem aims to minimize the total generation cost while satisfying constraints like power demand and generator limits. Previous solutions had limitations. The authors propose using PSO-SA to find high quality solutions more efficiently. PSO is able to find global optima but can get trapped in local optima. SA helps avoid this through probabilistic jumping. The authors combine PSO and SA techniques to leverage their benefits while overcoming individual limitations. They test the PSO-SA method on three generator systems and find it provides better results than traditional and other computational methods.
IRJET- Voltage Stability, Loadability and Contingency Analysis with Optimal I...IRJET Journal
This document discusses contingency analysis and optimal placement of renewable distributed generators (RDGs) using continuation power flow analysis to improve voltage stability and loadability. It presents a methodology to determine the optimal location and mix of different RDG technologies (solar, wind, fuel cells) on the IEEE 9-bus test system using the Power System Analysis Toolbox (PSAT). Reactive power performance indices are calculated for different line contingencies to identify critical buses. The results show that optimally placing RDGs can enhance voltage stability and increase the maximum loadability point compared to the base case without RDGs.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Reconfiguration and Capacitor Placement in Najaf Distribution Networks Sector...IRJET Journal
This document discusses reconfiguring distribution networks and optimally placing capacitors in Najaf, Iraq's distribution systems to improve performance. It provides background on distribution system design using software like CYM_Dist to minimize losses through reconfiguration and reactive power support. The document outlines the proposed methodology, which includes load allocation, network reconfiguration to reduce losses while respecting constraints, and identifying optimal capacitor placement and sizing. It reviews relevant literature on techniques like sensitivity analysis and heuristic optimization for reconfiguration and capacitor placement. The methodology is then applied to the 11kV Al Jamiea distribution system in Najaf as a case study.
This document summarizes a research paper that proposes a new approach for solving the economic dispatch problem in power systems using a hybrid particle swarm optimization and simulated annealing algorithm. The paper introduces economic dispatch and describes previous solution methods. It then presents the new hybrid algorithm, which combines the global search capabilities of particle swarm optimization with the probabilistic jumping of simulated annealing to find high-quality solutions faster. The paper applies the method to test cases and finds it performs better than traditional and other computational techniques at determining low-cost generation schedules that satisfy operational constraints.
The document proposes a new approach for solving the economic dispatch problem in power systems using a hybrid particle swarm optimization and simulated annealing algorithm. It begins with introductions to economic dispatch and optimization techniques like particle swarm optimization and simulated annealing. It then describes the economic dispatch problem formulation, including the objective of minimizing generation cost while satisfying constraints. The document proposes a novel hybrid algorithm that combines the salient features of particle swarm optimization and simulated annealing to generate high-quality solutions efficiently. It presents the particle swarm optimization, simulated annealing and hybrid algorithms in detail. The effectiveness of the proposed approach is demonstrated through case studies on different power systems.
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.
IRJET- Performance Improvement of the Distribution Systems using Meta-Heu...IRJET Journal
This document summarizes a research paper that proposes using a Whale Optimization Algorithm (WOA) to optimize the control of photovoltaic (PV) systems connected to distribution networks in Egypt. The high penetration of PV systems can cause voltage issues in distribution grids. The paper aims to enhance distribution network performance by optimally designing a proportional-integral controller for PV inverters using WOA. WOA is inspired by the hunting behavior of humpback whales and is applied to minimize voltage deviations across the network. The effectiveness of the WOA-designed controller is evaluated through simulations and compared to controllers designed using genetic algorithms.
A Review on Various Techniques Used for Economic Load Dispatch in Power Systemijtsrd
The document discusses various techniques used to solve the economic load dispatch (ELD) problem in power systems. The ELD problem involves determining the optimal power output of generators to minimize generation costs while meeting demand and operating constraints. The document reviews several methods that have been used to solve the ELD problem, including lambda iteration, gradient search, Newton's method, linear programming, dynamic programming, neural networks, evolutionary algorithms, particle swarm optimization, and other metaheuristic techniques. It provides details on how each method approaches solving the optimization problem posed by economic load dispatch.
This document describes an automatic power factor correction (APFC) system using a capacitive bank. The system measures the power factor of an electrical load using a microcontroller. If the power factor is low, indicating poor efficiency, the microcontroller switches capacitors into the system from the capacitive bank to compensate and improve the power factor towards unity. The document provides details on the components, operation, and simulation of the APFC system to automatically correct the power factor without manual intervention. This improves efficiency and reduces costs for both utilities and customers.
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.
This document presents a two-stage model for daily volt/var control (VVC) of distribution systems that includes distributed energy resources (DERs) like wind turbines and synchronous machine-based distributed generations. The first stage is a day-ahead market that minimizes electrical energy costs and gas emissions from generation units to determine an initial schedule. The second stage examines this schedule from an operational perspective to determine optimal daily dispatches of VVC devices while minimizing losses, adjustment of scheduled active powers, and depreciation costs. It uses Benders decomposition to solve the mixed integer nonlinear programming problem, and tests the approach on two distribution test networks.
IRJET- A Comparative Study of Economic Load Dispatch Optimization MethodsIRJET Journal
This document presents a comparative study of different optimization methods for solving the economic load dispatch (ELD) problem in power systems. The ELD problem involves minimizing generation costs while meeting demand, and is formulated as a non-linear optimization problem with constraints. Various conventional and evolutionary algorithms have been used to solve ELD, but more recently bio-inspired algorithms like flower pollination algorithm and Jaya optimization have shown better performance. The paper evaluates these nature-inspired algorithms and compares their results for the ELD problem to demonstrate their effectiveness.
This document discusses the application of distributed generation (DG) in power system planning and design. It first provides background on increasing electricity demand and the traditional centralized model of power systems. It then describes various techniques used to optimize voltage profiles and reduce losses, including load flow analysis, economic load dispatch, genetic algorithms, and particle swarm optimization. As an example, these techniques are applied to IEEE's 30-bus test system to find the optimal placement and size of DG units while maintaining voltage limits and minimizing transmission losses. The results show benefits like improved voltage profiles and reduced losses when DG is incorporated into the system.
A presentation on economic load dispatchsouravsahoo28
This document contains a presentation on economic load dispatch by Sourav Sahoo. It discusses distributing load between generating units and plants to minimize costs. It introduces the lambda iteration method for solving economic dispatch problems and considers transmission losses. In summary, it outlines that economic dispatch determines the lowest cost generation allocation, lambda iteration efficiently solves this, and transmission losses are accounted for with penalty factors.
Locational marginal pricing framework in secured dispatch scheduling under co...eSAT Publishing House
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.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
1. Nishant Chaturvedi et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 3( Version 2), March 2014, pp.24-31
www.ijera.com 24 | P a g e
A Novel Approach for Economic Load Dispatch Problem Based
On GA and PSO
Nishant Chaturvedi1
, A. S. Walkey2
1
Research Scholar, Department of Electrical & Electronics Engineering
2
Associate Professor, Department of Electrical & Electronics Engineering National Institute of Technical
Teachers‘ Training and Research, Bhopal (M.P.)
Abstract
Economic load dispatch (ELD) is an important issue in the operation of power system, and several models by
using different techniques have been used to solve these problems. Some traditional approaches are utilized to
find out the optimal solution of non-linear problem. More recently, the soft computing techniques have received
more attention and were used in a number of successful and practical applications. Genetic algorithm and
particle swarm optimization are the most popular algorithms in term of optimization. The PSO techniques have
drawn much attention from the power system community and been successfully applied in many complex
optimization problems in power systems. This paper find out the advantages of application of Genetic algorithm
(GA) and Particle Swarm Optimization (PSO) in specific to the economic load dispatch problem. Here, an
attempt has been made to find out the minimum cost by using GA and PSO using the data of fifteen generating
units. Comparison of both algorithm is shown here with a standard example when considering Loss and No Loss
Conditions.
Keywords –Genetic algorithm, PSO, Economic Load Dispatch.
I. INTRODUCTION
The Economic Load Dispatch (ELD)
problem is one of the fundamental issues in power
system operation. The ELD problem involves the
solution of two different problems. The first of these
is the Unit Commitment or predispatch problem
wherein it is required to select optimally out of the
available generating sources to operate, to meet the
expected load and provide a specified margin of
operating reserve over a specified period of time. The
second aspect of economic dispatch is the on-line
economic dispatch wherein it is required to distribute
the load among the generating units actually
paralleled with the system in such manner as to
minimize the total cost of supplying the minute-to-
minute requirements of the system.
The main objective is to reduce the cost of
energy production taking into account the
transmission losses. While the problem can be solved
easily if the incremental cost curves of the generators
are assumed to be monotonically increasing piece-
wise linear functions, such an approach will not be
workable for nonlinear functions in practical systems.
In the past decade, conventional optimization
techniques such as lambda iterative method, linear
programming and quadratic programming have been
successfully used to solve power system optimization
problems such as Unit commitment and Economic
load dispatch. For highly non-linear and
combinatorial optimization problems, the
conventional methods are facing difficulties to locate
the global optimal solution. To overcome these
difficulties, some intelligent methods are used which
are iterative techniques that can search not only local
optimal solutions but also a global optimal solution
depending on problem domain and execution time
limit. They are general-purpose searching techniques
based on principles inspired from the genetic and
evolution mechanisms observed in natural systems
and populations of living beings. These methods have
the advantage of searching the solution space more
thoroughly. The main difficulty is their sensitivity to
the choice of parameters. Among intelligent methods,
PSO is simple and promising. It requires less
computation time and memory. It has also standard
values for its parameters. In this paper the Particle
Swarm Optimization (PSO) is proposed as a
methodology for economic load dispatch. The results
are compared with the traditional method i.e. Genetic
Algorithm (GA).
II. FORMULATION OF ECONOMIC LOAD
DISPATCH PROBLEM
Input Output Characteristic Parameters
The parameters of the input-output
characteristic of any generating unit can be
determined by the following approaches:
Based on the experiments of the generating
unit efficiency.
RESEARCH ARTICLE OPEN ACCESS
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Based on the historic records of the
generating unit operation.
Based on the design data of the generating
unit provided by manufacturer.
In the Practical power systems, we can
easily obtain the fuel statistic data and power output
statistics data. Through analysing and computing data
set (𝐹𝑘, 𝑃𝑘 ), we can determine the shape of the input-
output characteristic and the corresponding
parameters.
A. System Constraints
Generally there are two types of constraints [1]:
1. Equality constraints
2. Inequality constraints
1. Equality Constraints
The equality constraints are the basic load
flow equations of active and reactive power [1]
𝑃𝑖 − 𝑃𝐷 − 𝑃𝐿
𝑁
𝑖=1
= 0
2. Inequality Constraints
Following are the inequality constraints:
i. Generator Constraints
The KVA loading of a generator can be
represented as 𝑃2 + 𝑄2. The KVA loading should
not exceed a pre-specified value to limit the
temperature rise. The maximum active power
generated ‗P‘ from a source is also limited by thermal
consideration to keep the temperature rise within
limits. The minimum power generated is limited by
the flame instability of the boiler. If the power
generated out of a generator falls below a pre-
specified value𝑃 𝑚𝑖𝑛 , the unit is not put on the bus bar.
𝑃 𝑚𝑖𝑛 ≤ 𝑃 ≤ 𝑃𝑚𝑎𝑥
The maximum reactive power is limited by
overheating of rotor and minimum reactive power is
limited by the stability limit of machine. Hence the
generator reactive powers Q should not be outside the
range stated by inequality for its stable operation.
𝑄 𝑚𝑖𝑛 ≤ 𝑄 ≤ 𝑄 𝑚𝑎𝑥
ii. Voltage Constraints
The voltage magnitudes and phase angles at
various nodes should vary within certain limits. The
normal operating angle of transmission should lie
between 30 to 45 degrees for transient stability
reasons. A higher operating angle reduces the
stability during faults and lower limit of delta assures
proper utilization of the available transmission
capacity.
iii. Running Spare Capacity Constraints
These constraints are required to meet:
The forced outages of one or more
alternators on the system &
The unexpected load on the system.
The total generation should be such that in addition to
meeting load demand and various losses a minimum
spare capacity should be available i.e.
𝐺 ≥ 𝑃𝑝 + 𝑃𝑠𝑜
Where,𝐺 is the total generation and𝑃𝑠𝑜 is some pre-
specified power. A well planned system has
minimum𝑃𝑠𝑜 [1].
iv. Transmission Line Constraints
The flow of active and reactive power through
the transmission line circuit is limited by the thermal
capability of the circuit and is expressed as.
𝐶𝑝 ≤ 𝐶𝑝𝑚𝑎𝑥
Where𝐶𝑝𝑚𝑎𝑥 is the maximum loading capacity of
the𝑃 𝑡ℎ
line [1].
v. Transformer tap settings
If an auto-transformer is used, the minimum
tap setting could be zero and maximum one, i.e.
0 ≤ 𝑡 ≤ 1.0
Similarly for a two winding transformer if tapping
are provided on the secondary side,
0 ≤ t ≤ n where n is the ratio of transformation [1].
vi. Network security constraints
If initially a system is operating
satisfactorily and there is an outage, may be
scheduled or forced one, it is natural that some of the
constraints of the system will be violated. The
complexity of these constraints (in terms of number
of constraints) is enhanced when a large system is
being analyzed. In this a study is to be made with
outage of one branch at a time and then more than
one branch at a time. The natures of the constraints
are same as voltage and transmission line constraints
[1].
B. Optimum Load Dispatch
The optimum load dispatch problem
involves the solution of two different problems. The
first of these is the unit commitment or pre dispatch
problem wherein it is required to select optimally out
of the available generating sources to operate to meet
the expected load and provide a specified margin of
operating reserve over a specified period time. The
second aspect of economic dispatch is the on line
economic dispatch whereas it is required to distribute
load among the generating units actually paralleled
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with the system in such manner as to minimize the
total cost of supplying the minute to minute
requirements of the system. The objective of this
work is to find out the solution of nonlinear on line
economic dispatch problem by using PSO algorithm.
C. Cost Function
The Let𝐶𝑖mean the cost, expressed for
example in dollars per hour, of producing energy in
the generator unit I. the total controllable system
production cost therefore will be,
𝐶 = 𝐶(𝑖)𝑁
𝑖=1 INR/hr
The generated real power𝑃𝐺𝑖 accounts for the
major influence on𝐶𝑖. The individual real generation
are raised by increasing the prime mover torques, and
this requires an increased expenditure of fuel. The
reactive generations𝑄 𝐺𝑖 do not have any measurable
influence on𝐶𝑖because they are controlled by
controlling by field current.
The individual production cost𝐶𝑖of
generators unit I is therefore for all practical purposes
a function only of𝑃𝐺𝑖 , and for the overall controllable
production cost, we thus have,
𝐶 = 𝐶(𝑖) 𝑃𝐺𝑖
𝑁
𝑖=1
When the cost function𝐶can be written as a sum
of terms where each term depends only upon one
independent variable.
III. PROPOSED METHODOLOGY
A. Genetic Algorithm
GA handles a population of possible
solutions. Each solution is represented through a
chromosome, which is just an abstract representation.
Coding all the possible solutions into a chromosome
is the first part, but certainly not the most
straightforward one of a Genetic Algorithm. A set of
reproduction operators has to be determined, too.
Reproduction operators are applied directly on the
chromosomes, and are used to perform mutations and
recombination over solutions of the problem [12].
Appropriate representation and reproduction
operators are really something determinant, as the
behaviour of the GA is extremely dependents on it.
Frequently, it can be extremely difficult to find a
representation, which respects the structure of the
search space and reproduction operators, which are
coherent and relevant according to the properties of
the problems.
Selection is supposed to be able to compare
each individual in the population. Selection is done
by using a fitness function. Each chromosome has an
associated value corresponding to the fitness of the
solution it represents. The fitness should correspond
to an evaluation of how good the candidate solution
is [13]. The optimal solution is the one, which
maximizes the fitness function. Genetic Algorithms
deal with the problems that maximize the fitness
function. But, if the problem consists in minimizing a
cost function, the adaptation is quite easy. Either the
cost function can be transformed into a fitness
function, for example by inverting it; or the selection
can be adapted in such way that they consider
individuals with low evaluation functions as better.
Once the reproduction and the fitness
function have been properly defined, a Genetic
Algorithm is evolved according to the same basic
structure. It starts by generating an initial population
of chromosomes. This first population must offer a
wide diversity of genetic materials. The gene pool
should be as large as possible so that any solution of
the search space can be engendered. Generally, the
initial population is generated randomly [15].Then,
the genetic algorithm loops over an iteration process
to make the population evolve. Each iteration
consists of the following steps:
1. Evaluation
Initially many individual solutions are
randomly generated to form an initial population. The
population size depends on the nature of the problem,
but typically contains several hundreds or thousands
of possible solutions. Traditionally, the population is
generated randomly, allowing the entire range of
possible solutions. Occasionally, the solutions may
be "seeded" in areas where optimal solutions are
likely to be found.
2. Truncation Selection
Truncation selection is a selection method
used in genetic algorithms to select potential
candidate solutions for recombination.In truncation
selection the candidate solutions are ordered by
fitness, and some proportionof the fittest individuals
are selected and reproduced 1/p times.
3. Crossover
Crossover is a genetic operator used to vary
the programming of a chromosome or chromosomes
from one generation to the next.
Figure 1: Crossover Operation
Parent 1 Feasible00 1 1
Parent 2 Feasible10 0 1
Child 1 Feasible00 0 1
Child2 In-feasible10 1 1
Crossover
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Assume a problem of four items has a full
feasible random population. When it performs
crossover using two feasible solution as parents, it
generates to children, it could happen that one of it or
both are not feasible as shown in figure 1.
It is analogous to reproduction and
biological crossover, upon which genetic algorithms
are based. Cross over is a process of taking more than
one parent solutions and producing a child solution
from them [16].
4. Mutation
Mutation is a genetic operator used to
maintain genetic diversity from one generation of a
population of genetic algorithm chromosomes to the
next. It is analogous to biological mutation.
Figure 2: Mutation Operation
Mutation alters one or more gene values in a
chromosome from its initial state. In mutation, the
solution may change entirely from the previous
solution. Hence GA can come to better solution by
using mutation.
The basic genetic algorithm is as follows:
-[start] Genetic random population of n
chromosomes (suitable solutions for the problem)
- [Fitness] Evaluate the fitness f(x) of each
chromosome x in the population
-[New population] Create a new population by
repeating following steps until the New population is
complete
- [selection] select two parent chromosomes from a
population according to their fitness (the better
fitness, the bigger chance to get selected).
- [crossover] with a crossover probability, cross over
the parents to form new offspring (children). If no
crossover was performed, offspring is the exact copy
of parents.
- [Mutation] with a mutation probability, mutate new
offspring at each locus (position in chromosome)
- [Accepting] Place new offspring in the new
population.
- [Replace] Use new generated population for a
further sum of the algorithm.
The Genetic algorithm process is discussed through
the GA cycle [16]
Figure 3: Genetic Algorithm cycle
Reproduction is the process by which the
genetic material in two or more parent is combined to
obtain one or more offspring. In fitness evaluation
step, the individual‘s quality is assessed. Mutation is
performed to one individual to produce a new version
of it where some of the original genetic material has
been randomly changed. Selection process helps to
decide which individuals are to be used for
reproduction and mutation in order to produce new
search points.
B. Particle Swam Optimization
Particle Swarm Optimization (PSO) is a
technique used to explore the search space of a given
problem to find the settings or parameters required to
maximize or minimize a particular objective.
PSO shares many similarities with
evolutionary computation techniques such as Genetic
Algorithms (GA). The system is initialized with a
population of random solutions and searches for
optima by updating generations. However, unlike
GA, PSO has no evolution operators such as
crossover and mutation. In PSO, the potential
solutions, called particles, fly through the problem
space by following the current optimum particles.
This technique, first described by James
Kennedy and Russell C. Eberhart in 1995, originates
from two separate concepts: the idea of swarm
intelligence based off the observation of swarming
habits by certain kinds of animals (such as birds and
fish); and the field of evolutionary computation. The
PSO algorithm works by simultaneously maintaining
several candidate solutions in the search space.
During each iteration of the algorithm, each
candidate solution is evaluated by the objective
function being optimized, determining the fitness of
that solution. Each candidate solution can be thought
of as a particle ―flying‖ through the fitness landscape
finding the maximum or minimum of the objective
function. Initially, the PSO algorithm chooses
Calculation/
Manipulation
Reproduction
Mate
Offspring
Decoded
String
Population
(Chromosomes)
Selection
Generic
Operations
(Fitness
function)
New
Generati
on
Parent
s
Feasible
Mutation
10 0 1
Infeasible 1 100
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candidate solutions randomly within the search space.
It should be noted that the PSO algorithm has no
knowledge of the underlying objective function, and
thus has no way of knowing if any of the candidate
solutions are near to or far away from a local or
global maximum or minimum.
The PSO algorithm simply uses the
objective function to evaluate its candidate solutions,
and operates upon the resultant fitness values. Each
particle maintains its position, composed of the
candidate solution and its evaluated fitness, and its
velocity. Additionally, it remembers the best fitness
value it has achieved thus far during the operation of
the algorithm, referred to as the individual best
fitness, and the candidate solution that achieved this
fitness, referred to as the individual best position or
individual best candidate solution.
Finally, the PSO algorithm maintains the
best fitness value achieved among all particles in the
swarm, called the global best fitness, and the
candidate solution that achieved this fitness, called
the global best position or global best candidate
solution. The PSO algorithm consists of just three
steps, which are repeated until some stopping
condition is met:
1. Evaluate the fitness of each particle.
2. Update individual and global best fitness‘s
and positions.
3. Update velocity and position of each
particle.
The first two steps are fairly trivial. Fitness
evaluation is conducted by supplying the candidate
solution to the objective function. Individual and
global best fitness and positions are updated by
comparing the newly evaluated fitness against the
previous individual and global best fitness, and
replacing the best fitness and positions as necessary.
The velocity and position update step is responsible
for the optimization ability of the PSO algorithm. The
velocity of each particle in the swarm is updated
using the following equation:
𝑣𝑖 𝑡 + 1 = 𝑤𝑣𝑖 𝑡 + 𝑐1 𝑟1 𝑥𝑖 𝑡 − 𝑥𝑖 𝑡
+ 𝑐2 𝑟2[𝑔 𝑡 − 𝑥𝑖 𝑡 ]
Figure 4: Flow chart of PSO
Each of the three terms of the velocity
updateequation have different roles in the PSO
algorithm. This process is repeated until some
stopping condition is met.Somecommon stopping
conditions include: a pre-set number of iterations of
the PSO algorithm, a number of iterations since the
last update of the global best candidate solution, or a
predefined target fitness value.
IV. SIMULATION AND RESULTS
We considered a standard problem for
fifteen generator system. The cost characteristic
equation for all fifteen units are as given below:
UNIT 1: F1 = 0.000299*P1^2 + 10.1*P1 +671 Rs/Hr
150 MW < P1 < 455 MW
UNIT 2: F2 = 0.000183*P2^2 + 10.2*P2 + 574
Rs/Hr 150 MW < P2 < 455 MW
UNIT 3: F3 = 0.001126*P3^2 + 8.8*P3 + 374 Rs/Hr
20 MW < P3 < 130 MW
UNIT 4: F4 = 0.001126*P4^2 + 8.8*P4 + 374 Rs/Hr
20 MW < P4 < 130 MW
UNIT 5: F5 = 0.000205*P5^2 + 10.4*P5 + 461
Rs/Hr 150 MW < P5 < 470 MW
UNIT 6: F6 = 0.000301*P6^2 + 10.1*P6 + 630
Rs/Hr 135 MW < P6 < 460 MW
UNIT 7: F7 = 0.000364*P7^2 + 9.8*P7 + 548 Rs/Hr
135 MW < P7 < 465 MW
UNIT 8: F8 = 0.000338*P8^2 + 11.2*P8 + 227
Rs/Hr 60 MW < P8 < 300 MW
UNIT 9: F9 = 0.000807*P9^2 + 11.2*P9 + 173
Rs/Hr 25 MW < P9 < 162 MW
UNIT 10: F10 = 0.001203*P10^2 + 10.7*P10 + 175
Rs/Hr 25 MW < P10 < 160 MW
UNIT 11: F11 = 0.003586*P11^2 + 10.2*P11 + 186
Rs/Hr 20 MW < P11 < 80 MW
UNIT 12: F12 = 0.005513*P12^2 + 9.9*P12 + 230
Rs/Hr 20 MW < P12 < 80 MW
UNIT 13: F13 = 0.000371*P13^2 + 13.1*P13 + 225
Rs/Hr 25 MW < P13 < 85 MW
UNIT 14: F14 = 0.001929*P14^2 + 12.1*P14 + 309
Rs/Hr 15 MW < P14 < 55 MW
UNIT 15: F15 = 0.004447*P15^2 + 12.4*P15 + 323
Rs/Hr 15 MW < P15 < 55 MW
Transmission Loss Bmn matrix for the above
equations is as follows:
B
=
1.4
1.2
0.7
−0.1
−0.3
−0.1
−0.1
−0.1
−0.3
−0.5
−0.3
−0.2
0.4
0.3
−0.1
1.2
1.5
1.3
0.0
−0.5
−0.2
0.0
0.1
−0.2
−0.4
−0.4
0.0
0.4
1.0
−0.2
0.7
1.3
7.6
−0.1
−1.3
−0.9
−0.1
0.0
−0.8
−1.2
−1.7
0.0
−2.6
11.1
−2.8
−0.1
0.0
−0.1
3.4
−0.7
−0.4
1.1
5.0
2.9
3.2
−1.1
0.0
0.1
0.1
−2.3
−0.3
−0.5
−1.3
−0.7
9.0
1.4
−0.3
−1.2
−1.0
−1.3
0.7
−0.2
−0.2
−2.4
−0.3
−0.1
−0.2
−0.9
−0.4
1.4
1.6
0.0
−0.6
−0.5
−0.8
1.1
−0.1
−0.2
−1.7
0.3
−0.1
0.0
−0.1
1.1
−0.3
0.0
1.5
1.7
1.5
0.9
−0.5
0.7
0.0
−0.2
−0.8
−0.1
0.1
0.0
5.0
−1.2
−0.6
1.7
16.8
8.2
7.9
−2.3
−3.6
0.1
0.5
−7.8
−0.3
−0.2
−0.8
2.9
−1.0
−0.5
1.5
8.2
12.9
11.6
−2.1
−2.5
0.7
−1.2
−7.2
−0.5
−0.4
−1.2
3.2
−1.3
−0.8
0.9
7.9
11.6
20.0
−2.7
−3.4
0.9
−1.1
−8.8
−0.3
−0.4
−1.7
−1.1
0.7
1.1
−0.5
−2.3
−2.1
−2.7
14.0
0.1
0.4
−3.8
16.8
−0.2
0.0
0.0
0.0
−0.2
−0.1
0.7
−3.6
−2.5
−3.4
0.1
5.4
−0.1
−0.4
2.8
0.4
0.4
−2.6
0.1
−0.2
−0.2
0.0
0.1
0.7
0.9
0.4
−0.1
10.3
−10.1
2.8
0.3
1.0
11.1
0.1
−2.4
−1.7
−0.2
0.5
−1.2
−1.1
−3.8
−0.4
−10.1
57.8
−9.4
−0.1
−0.2
−2.8
−2.6
−0.3
0.3
−0.8
−7.8
−7.2
−8.8
16.8
2.8
2.8
−9.4
128.3
And the system load is 2630 MW.
No
Start
Generation on initial searching points of each agent
Evaluation of searching
points of each agent
Modification of each searching
points by state equation
Reach maximum iteration
Stop
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A. Scenario 1: Neglecting System Loss
In this case B = 0.
On simulating our program the results we get are as
follows:
For Genetic algorithm
G1 331.341
G2 351.055
G3 52.133
G4 47.497
G5 374.177
G6 380.865
G7 347.692
G8 209.330
G9 80.103
G10 89.362
G11 20.321
G12 20.300
G13 25.603
G14 15.135
G15 15.084
Cost: 29953.6646 INR
Loss: 0 MW
For Particle Swarm optimization
G1 422.069
G2 416.387
G3 130.000
G4 130.000
G5 150.000
G6 419.265
G7 465.000
G8 60.000
G9 25.000
G10 25.000
G11 21.249
G12 41.030
G13 25.000
G14 15.000
G15 15.000
Cost: 29441.3778 INR
Loss: 0 MW
B. Scenario 2: Considering System Loss
On simulating our program the results we get are as
follows:
For Genetic algorithm
G1 330.556
G2 366.239
G3 46.937
G4 47.668
G5 379.599
G6 357.450
G7 366.297
G8 205.215
G9 87.093
G10 74.658
G11 20.359
G12 20.494
G13 25.863
G14 15.184
G15 16.396
Cost: 29955.4757 INR
Loss: 0.0066798 MW
For Particle Swarm optimization
G1 422.072
G2 416.384
G3 130.000
G4 130.000
G5 150.000
G6 419.271
G7 465.000
G8 60.000
G9 25.000
G10 25.000
G11 21.246
G12 41.031
G13 25.000
G14 15.000
G15 15.000
Cost: 29441.4192 INR
Loss: 0.0039904 MW
Figure 5: Convergence graph for Genetic Algorithm
Figure 6: Convergence graph for PSO
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V. CONCLUSION
Economic load dispatch in electric power
sector is an important task, as it is required to supply
the power at the minimum cost which aids in profit-
making. As the efficiency of newly added generating
units are more than the previous units the economic
load dispatch has to be efficiently solved for
minimizing the cost of the generated power.
In this paper both conventional GA and PSO
based economic dispatch of load for generation cost
reduction were comparatively investigated on two
sample networks (15 generator system with loss and
without loss). The results obtained were satisfactory
for both approaches but it was shown that the PSO
performed better than GA from the economic
viewpoints. This is because of the better convergence
criteria and efficient population generation of PSO.
A future recommendation can be made for
GA and PSO to solve ELD problems as the use of
new efficient operators to control and enhance the
efficiency of instantaneous population for better and
fast convergence.
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