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.
Normally, the character of the wind energy as a renewable energy sources has uncertainty in generation. To resolve the Optimal Power Flow (OPF) drawback, this paper proposed a replacement Hybrid Multi Objective Artificial Physical Optimization (HMOAPO) algorithmic rule, which does not require any management parameters compared to different meta-heuristic algorithms within the literature. Artificial Physical Optimization (APO), a moderately new population-based intelligence algorithm, shows fine performance on improvement issues. Moreover, this paper presents hybrid variety of Animal Migration Optimization (AMO) algorithmic rule to express the convergence characteristic of APO. The OPF drawback is taken into account with six totally different check cases, the effectiveness of the proposed HMOAPO technique is tested on IEEE 30-bus, IEEE 118-bus and IEEE 300-bus check system. The obtained results from the HMOAPO algorithm is compared with the other improvement techniques within the literature. The obtained comparison results indicate that proposed technique is effective to succeed in best resolution for the OPF drawback.
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.
Small Signal Stability Improvement and Congestion Management Using PSO Based ...IDES Editor
In this paper an attempt has been made to study the
application of Thyristor Controlled Series Capacitor (TCSC)
to mitigate small signal stability problem in addition to
congestion management of a heavily loaded line in a
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
the power flows in the network and can help in improvement
of small signal stability aspect. It can also provide relief to
congestion in the heavily loaded line. However, the
performance of any FACTS device highly depends upon its
parameters and placement at suitable locations in the power
network. In this paper, Particle Swarm Optimization (PSO)
method has been used for determining the optimal locations
and parameters of the TCSC controller in order to damp small
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
limit power flow congestion. The results of simulation reveals
that TCSC controllers, placed optimally, not only mitigate
small signal oscillations but they can also alleviate line flow
congestion effectively.
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
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.
This document provides an overview of economic dispatch and unit commitment in power systems. It discusses:
1. Economic dispatch is the process of determining generator outputs to meet demand at minimum cost, taking into account generator costs and constraints. It can be solved graphically or using the KKT conditions.
2. Unit commitment determines which generators will operate over different time periods to meet forecasted load at minimum cost, while considering generator operating constraints like minimum up/down times. It is solved using techniques like mixed integer programming and Lagrangian relaxation.
3. Mixed integer programming and Lagrangian relaxation are commonly used optimization methods for unit commitment. Mixed integer programming formulates it as an optimization problem with discrete and continuous variables.
IRJET-Comparative Analysis of Unit Commitment Problem of Electric Power Syste...IRJET Journal
The document presents a comparative analysis of solving the unit commitment problem (UCP) of electric power systems using dynamic programming technique. It formulates the UCP considering fuel costs, voltage stability, and imbalance limits. Dynamic programming is described as a conventional algorithm for solving deterministic problems optimally through sequential decision making. The developed algorithm is implemented on 4-unit and 10-unit power systems. Results found the dynamic programming approach provides satisfactory solutions by minimizing total generation costs while meeting operational constraints.
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.
Normally, the character of the wind energy as a renewable energy sources has uncertainty in generation. To resolve the Optimal Power Flow (OPF) drawback, this paper proposed a replacement Hybrid Multi Objective Artificial Physical Optimization (HMOAPO) algorithmic rule, which does not require any management parameters compared to different meta-heuristic algorithms within the literature. Artificial Physical Optimization (APO), a moderately new population-based intelligence algorithm, shows fine performance on improvement issues. Moreover, this paper presents hybrid variety of Animal Migration Optimization (AMO) algorithmic rule to express the convergence characteristic of APO. The OPF drawback is taken into account with six totally different check cases, the effectiveness of the proposed HMOAPO technique is tested on IEEE 30-bus, IEEE 118-bus and IEEE 300-bus check system. The obtained results from the HMOAPO algorithm is compared with the other improvement techniques within the literature. The obtained comparison results indicate that proposed technique is effective to succeed in best resolution for the OPF drawback.
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.
Small Signal Stability Improvement and Congestion Management Using PSO Based ...IDES Editor
In this paper an attempt has been made to study the
application of Thyristor Controlled Series Capacitor (TCSC)
to mitigate small signal stability problem in addition to
congestion management of a heavily loaded line in a
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
the power flows in the network and can help in improvement
of small signal stability aspect. It can also provide relief to
congestion in the heavily loaded line. However, the
performance of any FACTS device highly depends upon its
parameters and placement at suitable locations in the power
network. In this paper, Particle Swarm Optimization (PSO)
method has been used for determining the optimal locations
and parameters of the TCSC controller in order to damp small
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
limit power flow congestion. The results of simulation reveals
that TCSC controllers, placed optimally, not only mitigate
small signal oscillations but they can also alleviate line flow
congestion effectively.
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
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.
This document provides an overview of economic dispatch and unit commitment in power systems. It discusses:
1. Economic dispatch is the process of determining generator outputs to meet demand at minimum cost, taking into account generator costs and constraints. It can be solved graphically or using the KKT conditions.
2. Unit commitment determines which generators will operate over different time periods to meet forecasted load at minimum cost, while considering generator operating constraints like minimum up/down times. It is solved using techniques like mixed integer programming and Lagrangian relaxation.
3. Mixed integer programming and Lagrangian relaxation are commonly used optimization methods for unit commitment. Mixed integer programming formulates it as an optimization problem with discrete and continuous variables.
IRJET-Comparative Analysis of Unit Commitment Problem of Electric Power Syste...IRJET Journal
The document presents a comparative analysis of solving the unit commitment problem (UCP) of electric power systems using dynamic programming technique. It formulates the UCP considering fuel costs, voltage stability, and imbalance limits. Dynamic programming is described as a conventional algorithm for solving deterministic problems optimally through sequential decision making. The developed algorithm is implemented on 4-unit and 10-unit power systems. Results found the dynamic programming approach provides satisfactory solutions by minimizing total generation costs while meeting operational constraints.
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.
Many traditional optimization methods have been successfully used from years to deal with ELD problem. However these techniques have limitations in many aspects as they provide inaccurate results. The objective is to minimize total fuel cost of power generation so as to meet the power demands to satisfy all constraints. In present paper, the parameters of the fuzzy logic are tuned using genetic algorithms. By using GA with fuzzy logic leads to an intelligent dimension for ELD solution space to obtain an optimum solution for ELD
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.
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 presents a traditional approach called the lambda iteration method to solve the economic load dispatch (ELD) problem considering generator constraints. The ELD problem aims to minimize the total fuel cost while meeting demand and generator constraints. The lambda iteration method is implemented on a three-unit and six-unit system, with and without transmission losses, in MATLAB. The results show that considering transmission losses provides a more accurate solution to the ELD problem compared to ignoring losses. The lambda iteration method provides an effective traditional technique for solving the ELD problem.
Genetic Algorithm for Solving the Economic Load DispatchSatyendra Singh
In this paper, comparative study of two approaches, Genetic Algorithm
(GA) and Lambda Iteration method (LIM) have been used to provide
the solution of the economic load dispatch (ELD) problem. The ELD
problem is defined as to minimize the total operating cost of a power
system while meeting the total load plus transmission losses within
generation limits. GA and LIM have been used individually for solving
two cases, first is three generator test system and second is ten
generator test system. The results are compared which reveals that GA
can provide more accurate results with fast convergence characteristics
and is superior to LIM.
Comparative study of the price penalty factors approaches for Bi-objective di...IJECEIAES
One of the main objectives of electricity dispatch centers is to schedule the operation of available generating units to meet the required load demand at minimum operating cost with minimum emission level caused by fossil-based power plants. Finding the right balance between the fuel cost the green gasemissionsis reffered as Combined Economic and Emission Dispatch (CEED) problem which is one of the important optimization problems related the operationmodern power systems. The Particle Swarm Optimization algorithm (PSO) is a stochastic optimization technique which is inspired from the social learning of birds or fishes. It is exploited to solve CEED problem. This paper examines the impact of six penalty factors like "Min-Max", "Max-Max", "Min-Min", "Max-Min", "Average" and "Common" price penalty factors for solving CEED problem. The Price Penalty Factor for the CEED is the ratio of fuel cost to emission value. This bi-objective dispatch problem is investigated in the Real West Algeria power network consisting of 22 buses with 7 generators. Results prove capability of PSO in solving CEED problem with various penalty factors and it proves that Min-Max price penalty factor provides the best compromise solution in comparison to the other penalty factors.
This document discusses optimization of power system operation through techniques like unit commitment and economic dispatch. It begins with an introduction to meeting varying electricity demand. It then outlines the steps of optimization including long-term planning, unit commitment for hourly/monthly decisions, and economic dispatch for instantaneous dispatch. Unit commitment deals with deciding which generation units to operate to satisfy demand while considering constraints. Common solution methods for unit commitment include priority lists, heuristics, mixed integer programming and dynamic programming. The document provides examples of advances in these areas and practical software used for optimization.
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
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.
This document proposes a multi-objective framework for short-term scheduling of a microgrid considering cost minimization and emission minimization objectives. It formulates the problem as a mixed integer nonlinear program with constraints including power balance and unit generation limits. The Normal Boundary Intersection method is employed to solve the multi-objective problem and generate a Pareto front of optimal solutions. Simulation results are presented comparing the proposed approach to other methods.
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.
This document discusses economic dispatch in power systems. It begins with an introduction that defines economic dispatch and optimal power flow problems. It then discusses various constraints in economic dispatch problems, including generator limits, transmission line limits, and reserve requirements. Different economic dispatch problems are examined, including ones that neglect transmission losses and include losses. The document also discusses unit commitment problems and provides an example of calculating the optimal dispatch to minimize total generation costs.
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.
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
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.
Numerical simulation of Hybrid Generation System: a case studyIRJET Journal
This document summarizes a study that simulates a hybrid power generation system for an area in Tamanrasset, Algeria using HOMER software. The system combines wind turbines, photovoltaic panels, diesel generators, and batteries. Solar radiation, wind speed, and load data for the area are presented. The simulation process and components of the hybrid system are defined in HOMER. Simulation results will validate the technical and economic feasibility of the hybrid system to reduce dependence on diesel generators and lower emissions.
Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a non-linear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.
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.
Solution of Combined Heat and Power Economic Dispatch Problem Using Different...Arkadev Ghosh
This document presents a study that uses the Mine Blast Algorithm (MBA) and Bare Bones Teaching Learning Based Optimization (BBTLBO) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The CHPED problem involves determining the optimal power and heat allocation among generation units to minimize costs while considering constraints. The document describes the mathematical formulation of the CHPED problem and provides an example simulation on a 7 generator system. The results show that both MBA and BBTLBO algorithms find low-cost solutions for the CHPED problem and outperform other algorithms in terms of solution quality and convergence speed.
The document discusses assessing spinning reserve requirements in a deregulated power system. It defines key terms like spinning reserve, spot market, and day-ahead market. It describes a test power system with 3 generating zones and a 49-step load forecast uncertainty model. It outlines assumptions and develops a cost model to minimize hourly costs based on spinning reserve levels and constraints. The results show that spinning reserve requirements are affected by the load forecast uncertainty percentage, spot market price, spinning reserve price, and generator reloading limits. Future work could incorporate generator failure rates and do a cost-benefit analysis of requirements based on load forecast uncertainty.
Green Buildings Overview and Analysis of Energy Efficient Buildingpaperpublications3
Abstract: The challenges our planet faces, particularly climate change and sustainable economic development, are global in nature and so require global solutions. The building sector, which consumes as much as 40% of world’s energy, 12% of its water and contributes 40% of its waste sent to landfill, is the major part of this global problem. Reducing energy use in buildings saves resources and money while reducing pollution and CO2 in the atmosphere. It also leverages even greater savings at power plants. For the average 33-percent-efficient coal-fired power plant, saving a unit of electricity in a building saves three units of fuel at the power plant. So to reduce green house gas emission, government promotes new buildings construction and to retrofit existing buildings while satisfying low energy criteria. This means improving energy efficiency of buildings and energy systems, developing sustainable building concepts and promoting renewable energy sources. “Green” or “sustainable” buildings use key resources like energy, water, materials, and land more efficiently than buildings that are just built to code. With more natural light and better air quality, green buildings typically contribute to improved employee and student health, comfort, and productivity. A green building depletes the natural resources to the minimum during its construction and operation. In this paper an over view of green building is discussed.
Abstract: A cool property that high temperature superconductors possess is something explained by Meissner Effect. Which makes possible the levitation of superconducting magnets, when a magnetic pole of the same polarity is placed facing the former.
In a complete vacuum if the superconductor is rotated at some particular speed probably very less than “c” some point on the rotating object might just be able to cross the temporal barrier.
Many traditional optimization methods have been successfully used from years to deal with ELD problem. However these techniques have limitations in many aspects as they provide inaccurate results. The objective is to minimize total fuel cost of power generation so as to meet the power demands to satisfy all constraints. In present paper, the parameters of the fuzzy logic are tuned using genetic algorithms. By using GA with fuzzy logic leads to an intelligent dimension for ELD solution space to obtain an optimum solution for ELD
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.
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 presents a traditional approach called the lambda iteration method to solve the economic load dispatch (ELD) problem considering generator constraints. The ELD problem aims to minimize the total fuel cost while meeting demand and generator constraints. The lambda iteration method is implemented on a three-unit and six-unit system, with and without transmission losses, in MATLAB. The results show that considering transmission losses provides a more accurate solution to the ELD problem compared to ignoring losses. The lambda iteration method provides an effective traditional technique for solving the ELD problem.
Genetic Algorithm for Solving the Economic Load DispatchSatyendra Singh
In this paper, comparative study of two approaches, Genetic Algorithm
(GA) and Lambda Iteration method (LIM) have been used to provide
the solution of the economic load dispatch (ELD) problem. The ELD
problem is defined as to minimize the total operating cost of a power
system while meeting the total load plus transmission losses within
generation limits. GA and LIM have been used individually for solving
two cases, first is three generator test system and second is ten
generator test system. The results are compared which reveals that GA
can provide more accurate results with fast convergence characteristics
and is superior to LIM.
Comparative study of the price penalty factors approaches for Bi-objective di...IJECEIAES
One of the main objectives of electricity dispatch centers is to schedule the operation of available generating units to meet the required load demand at minimum operating cost with minimum emission level caused by fossil-based power plants. Finding the right balance between the fuel cost the green gasemissionsis reffered as Combined Economic and Emission Dispatch (CEED) problem which is one of the important optimization problems related the operationmodern power systems. The Particle Swarm Optimization algorithm (PSO) is a stochastic optimization technique which is inspired from the social learning of birds or fishes. It is exploited to solve CEED problem. This paper examines the impact of six penalty factors like "Min-Max", "Max-Max", "Min-Min", "Max-Min", "Average" and "Common" price penalty factors for solving CEED problem. The Price Penalty Factor for the CEED is the ratio of fuel cost to emission value. This bi-objective dispatch problem is investigated in the Real West Algeria power network consisting of 22 buses with 7 generators. Results prove capability of PSO in solving CEED problem with various penalty factors and it proves that Min-Max price penalty factor provides the best compromise solution in comparison to the other penalty factors.
This document discusses optimization of power system operation through techniques like unit commitment and economic dispatch. It begins with an introduction to meeting varying electricity demand. It then outlines the steps of optimization including long-term planning, unit commitment for hourly/monthly decisions, and economic dispatch for instantaneous dispatch. Unit commitment deals with deciding which generation units to operate to satisfy demand while considering constraints. Common solution methods for unit commitment include priority lists, heuristics, mixed integer programming and dynamic programming. The document provides examples of advances in these areas and practical software used for optimization.
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
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.
This document proposes a multi-objective framework for short-term scheduling of a microgrid considering cost minimization and emission minimization objectives. It formulates the problem as a mixed integer nonlinear program with constraints including power balance and unit generation limits. The Normal Boundary Intersection method is employed to solve the multi-objective problem and generate a Pareto front of optimal solutions. Simulation results are presented comparing the proposed approach to other methods.
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.
This document discusses economic dispatch in power systems. It begins with an introduction that defines economic dispatch and optimal power flow problems. It then discusses various constraints in economic dispatch problems, including generator limits, transmission line limits, and reserve requirements. Different economic dispatch problems are examined, including ones that neglect transmission losses and include losses. The document also discusses unit commitment problems and provides an example of calculating the optimal dispatch to minimize total generation costs.
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.
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
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.
Numerical simulation of Hybrid Generation System: a case studyIRJET Journal
This document summarizes a study that simulates a hybrid power generation system for an area in Tamanrasset, Algeria using HOMER software. The system combines wind turbines, photovoltaic panels, diesel generators, and batteries. Solar radiation, wind speed, and load data for the area are presented. The simulation process and components of the hybrid system are defined in HOMER. Simulation results will validate the technical and economic feasibility of the hybrid system to reduce dependence on diesel generators and lower emissions.
Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a non-linear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.
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.
Solution of Combined Heat and Power Economic Dispatch Problem Using Different...Arkadev Ghosh
This document presents a study that uses the Mine Blast Algorithm (MBA) and Bare Bones Teaching Learning Based Optimization (BBTLBO) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The CHPED problem involves determining the optimal power and heat allocation among generation units to minimize costs while considering constraints. The document describes the mathematical formulation of the CHPED problem and provides an example simulation on a 7 generator system. The results show that both MBA and BBTLBO algorithms find low-cost solutions for the CHPED problem and outperform other algorithms in terms of solution quality and convergence speed.
The document discusses assessing spinning reserve requirements in a deregulated power system. It defines key terms like spinning reserve, spot market, and day-ahead market. It describes a test power system with 3 generating zones and a 49-step load forecast uncertainty model. It outlines assumptions and develops a cost model to minimize hourly costs based on spinning reserve levels and constraints. The results show that spinning reserve requirements are affected by the load forecast uncertainty percentage, spot market price, spinning reserve price, and generator reloading limits. Future work could incorporate generator failure rates and do a cost-benefit analysis of requirements based on load forecast uncertainty.
Green Buildings Overview and Analysis of Energy Efficient Buildingpaperpublications3
Abstract: The challenges our planet faces, particularly climate change and sustainable economic development, are global in nature and so require global solutions. The building sector, which consumes as much as 40% of world’s energy, 12% of its water and contributes 40% of its waste sent to landfill, is the major part of this global problem. Reducing energy use in buildings saves resources and money while reducing pollution and CO2 in the atmosphere. It also leverages even greater savings at power plants. For the average 33-percent-efficient coal-fired power plant, saving a unit of electricity in a building saves three units of fuel at the power plant. So to reduce green house gas emission, government promotes new buildings construction and to retrofit existing buildings while satisfying low energy criteria. This means improving energy efficiency of buildings and energy systems, developing sustainable building concepts and promoting renewable energy sources. “Green” or “sustainable” buildings use key resources like energy, water, materials, and land more efficiently than buildings that are just built to code. With more natural light and better air quality, green buildings typically contribute to improved employee and student health, comfort, and productivity. A green building depletes the natural resources to the minimum during its construction and operation. In this paper an over view of green building is discussed.
Abstract: A cool property that high temperature superconductors possess is something explained by Meissner Effect. Which makes possible the levitation of superconducting magnets, when a magnetic pole of the same polarity is placed facing the former.
In a complete vacuum if the superconductor is rotated at some particular speed probably very less than “c” some point on the rotating object might just be able to cross the temporal barrier.
Technical Aspects of EHV XLPE Cable End Termination and on Site Brief Procedurepaperpublications3
Abstract: The objective of this paper is to provide an source of information on EHV XLPE cable end termination processes. It also describes the brief procedure of 220 KV XLPE single core (1×1200 Cu corrugated Al sheath) cable termination at 400 KV GSS Heerapura (Rajasthan Vidyut Prasaran Nigam Ltd) Jaipur (Raj) India. The realization of high density power transmission in major city areas requires the effective utilization of limited underground space available so the different versions of the termination are designed for operation under severe outdoor conditions and operation voltage up to 550 KV.
Non-Contact Temperature Measurement System Based on Embeddedpaperpublications3
This document describes the design and implementation of a non-contact temperature measurement system based on an embedded ARM LPC2148 hardware platform. The system uses an infrared sensor and signal processing unit to measure temperature without physical contact. An IIC (Inter-Integrated Circuit) bus allows communication between the temperature measurement system and the ARM microcontroller. Software was developed to control the IIC communication, including transmitting temperature commands and receiving the measured temperature data. Experimental results showed the system has high stability, speed and precision for non-contact temperature measurement applications like fault diagnosis and performance testing.
Power Quality Improvement of DC Drive by Reduction of Circulating Currentpaperpublications3
Abstract: The paper presents power quality improvement of DC drives by reduction of circulating current in parallal operation of active filters based on hysteresis current control. As it is a well-known fact that power quality determines the fitness of electrical power to consumer devices, hence an effort has been made to improve power quality in this work. Simulation with the help of MATLAB/Simulink has been done and results obtained are discussed in detail to verify the theoretical results. The multipulse converter was connected with DC drives and was run at no load condition to find out the transient and steady state performances. FFT analysis has been performed and Total Harmonic Distortion (THD) results obtained at different pulses are shown here.
Este documento describe una sesión de actividad física para un grupo de 26 estudiantes de tercer grado con el objetivo general de mejorar la convivencia y el respeto entre los estudiantes. La sesión de 50 minutos incluye ejercicios iniciales como "Mi espacio" y "Todos en círculos" para fomentar la aceptación y las relaciones entre los estudiantes. La parte principal consiste en una gymkana de tareas domésticas en grupos mixtos para enseñar la responsabilidad. Los ejercicios finales son "¿
This document summarizes Andy's introduction to Kotlin, including information about himself and his experience with Kotlin. It discusses some key features of Kotlin like null safety, smart casts, properties, method extensions, lambda expressions, and dependency injection. The document provides code examples for converting Java code to Kotlin and demonstrates multiple class definitions, data classes, property types, null handling, and other Kotlin syntax. It encourages learning Kotlin by using it directly in projects rather than just reading about it.
Management of patients_with_venous_leg_ulcers_final_2016GNEAUPP.
This document provides clinical practice statements for the management of patients with venous leg ulcers. It aims to identify barriers and facilitators to implementing best practices based on guidelines. The document was created by an international expert working group to account for differences in healthcare systems worldwide and address gaps in current guidelines. Clinical practice statements cover the differential diagnosis and assessment of leg ulcers, treatment delivery including dressings and invasive options, referral structures, secondary prevention after healing, and outcome monitoring. The goal is to improve leg ulcer management and enhance the patient experience.
Abstract: The coin-based mobile battery charger developed in this paper is providing a unique service to the rural public where grid power is not available for partial/full daytime and a source of revenue for site providers. The coin-based mobile battery charger can be quickly and easily installed outside any business premises. The mobile phone market is a vast industry, and has spread into rural areas as a essential means of communication. While the urban population use more sophisticated mobiles with good power batteries lasting for several days, the rural population buy the pre-owned mobile phones that require charging frequently. Many times battery becomes flat in the middle of conversation particularly at inconvenient times when access to a standard charger isn't possible. The coin-based mobile battery chargers are designed to solve this problem. The user has to plug the mobile phone into one of the adapters and insert a coin; the phone will then be given a micro-pulse for charging. It does not bring a mobile from 'dead' to fully charged state. The charging capacity of the mobile is designed with the help of pre-defined values. It is, of course, possible to continue charging the mobile by inserting more coins. This compact and lightweight product is designed to cater for the growing number of rural mobile users worldwide. A suitable microcontroller is programmed for all the controlling applications. The source for charging is obtained from solar energy and back up storage battery in case of non-availability of solar energy.
Comparison of Tuning Methods of PID Controllers for Non-Linear Systempaperpublications3
Abstract: Modern days have seen vast developments in the field of controller’s .There are various controllers developed these days with various different specifications. But the only drawback is that, there is no fixed method for the tuning of these controllers, which is necessary for controlling of the system based on the variation of the input or for the changes in the system. In order to overcome this drawback, in this paper we have compared various tuning methods of PID controller for non-linear system. As a non-linear system we have taken the dc motor as a system. For the particular DC motor controller transfer function has been determined and control parameters such as Proportional Gain, Integral Time and Derivative time are identified. They are numerous methods of developing a Proportional Integral and Derivative (PID) Controller, amongst them some methods are adopted in this paper and Comparisons of Time Domain specifications of those controllers has been carried out.
Abstract: Electrocardiogram is a machine that is used for the detection and the analysis of the peaks of the ECG signal. ECG signal is used for the detection of various diseases related to the heart. The cardiac arrhythmia shows abnormalities of heart that is considered as the major threat to the human. The peaks that are present in the ECG signal are used for detection of the disease. The R peak of the ECG signal is used for the detection of the disease, the arrhythmia is detected as Tachycardia and Bradycardia. This paper presents a study of the ECG signal, peaks and of the various techniques that are used for the detection of disease.
A Power Control Scheme for UPQC for Power Quality Improvementpaperpublications3
Abstract: The proliferation of power electronics based equipmenthas produced a significant impact on the quality of electric power supply. Conventional power quality mitigation equipment is proving to be inadequate for an increasing number of applications, and this fact has attracted the attention of power engineers to develop dynamic and adjustable solutions to power quality problems. This has led to development of Custom Power Devices (CPD).One modern and very promising CPD that deals with both load current and supply voltage imperfections is the Unified Power Quality Conditioner (UPQC).
This paper investigated the development of UPQC control schemes and algorithms for power quality improvement and implementation of a versatile control strategy to enhance the performance of UPQC. The proposed control scheme gives better steady-state and dynamic response. The validity of the proposed control method is verified by means of MATLAB/SIMULINK.
Complete Notes on Companies Ordinance, Paper LL.B. Part II.
.....................All students are advised to download and Prepare yourself. Shah Muhammad Zarkoon.
University Law College Quetta.
Analysis and Comparisons of Different Type of WCES- A Literature Reviewpaperpublications3
Abstract: With very rapid development of wind power technologies and significant growth of wind power capacity installed worldwide, various wind turbine concepts have been developed. The wind energy conversion system is required to be more cost-competitive, so that comparisons of different wind generator systems are necessary. A literature review of different types wind energy conversion systems is presented. First, the modern wind turbines are described with respect to both their control features and drive train types, and their advantages and disadvantages are described. Then, the quantitative comparison and market penetration of different wind generator systems are presented. The promising permanent magnet generator types are also investigated. After that the ongoing trends of wind generator systems and related comparison criteria are discussed.
Shunt Faults Detection on Transmission Line by Waveletpaperpublications3
Abstract: Transmission line fault detection is a very important task because major portion of power system fault occurring in transmission system. This paper represents a fast and reliable method of transmission line shunt fault detection. MATLAB Simulink use for modeled an IEEE 9-bus test power system for case study of various faults. In proposed work Daubechies wavelet is applied for decomposition of fault transients. The application of wavelet analysis helps in accurate classification of the various fault patterns. Wavelet entropy measure based on wavelet analysis is able to observe the unsteady signals and complexity of the system at time-frequency plane.
The result shows that the proposed method is capable to detect all the shunt faults.
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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- 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.
The optimal solution for unit commitment problem using binary hybrid grey wol...IJECEIAES
The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
Multi objective economic load dispatch using hybrid fuzzy, bacterialIAEME Publication
The document summarizes a research paper that proposes a new approach for solving the economic load dispatch problem using a hybrid fuzzy, bacterial foraging-Nelder–Mead algorithm. The economic load dispatch problem minimizes generation costs while satisfying load demand under system constraints. The proposed approach considers generation costs, spinning reserve costs, and emission costs simultaneously. It also accounts for valve-point effects, prohibited operating zones, and other practical constraints. A hybrid bacterial foraging and Nelder–Mead algorithm combined with fuzzy logic is used to solve the optimization problem. Simulation results show the advantages of the proposed method in reducing total system costs.
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources are intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit commitment scheduling can improve the total operating cost significantly.
Optimal Operation of Wind-thermal generation using differential evolutionIOSR Journals
This document presents an optimal operation model for a wind-thermal power generation system using differential evolution (DE). DE is an evolutionary algorithm inspired by biological evolution that can solve complex constrained optimization problems. The paper formulates the economic dispatch problem to minimize total generation cost of the wind and thermal plants subject to various constraints like power balance, generator limits, ramp rates, and valve point loading effects. Five different DE mutation strategies are analyzed for solving the wind-thermal economic dispatch problem on a test system with 10 thermal units. The results show that the best mutation strategy and control parameter values (mutation rate and crossover rate) depend on the problem and can significantly impact the solution quality and consistency obtained by the DE algorithm.
Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using P...theijes
This document summarizes the application of particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem for Nigeria's thermal power stations. PSO is used to determine the optimal allocation of total power demand among generating units to minimize total fuel costs while satisfying constraints. The PSO algorithm is applied to a 1999 model of Nigeria's power network and results are compared to other heuristic methods. PSO efficiently distributes load to minimize costs and overcomes limitations of traditional optimization techniques for non-linear power system problems.
A Genetic Algorithm Approach to Solve Unit Commitment ProblemIOSR Journals
This document describes a study that uses a genetic algorithm approach to solve the unit commitment problem of scheduling generation units in a power system over an 8-hour period. The genetic algorithm approach is able to find near-optimal solutions to the unit commitment problem and results in lower total operating costs than the traditional dynamic programming approach. The genetic algorithm approach encodes potential solutions as strings that are evaluated and evolved over generations to find low-cost solutions that satisfy constraints. The results show the genetic algorithm approach finds schedules with total costs that are $255 lower than those found by dynamic programming for the test power system.
A Decomposition Aggregation Method for Solving Electrical Power Dispatch Prob...raj20072
This document proposes a decomposition/aggregation method to solve large-scale economic dispatch problems with many generators. It decomposes a power system into areas, each containing generators and loads. An evolutionary programming technique optimizes dispatch in each area locally. The area solutions are then aggregated to solve the overall system problem while minimizing total cost. The method is demonstrated on 5-bus and 26-bus test systems decomposed into two areas each. Local area problems are solved as subproblems, while the overall system solution is the "master problem". Results are compared to a centralized approach. The decomposition/aggregation method shows promise in solving economic dispatch with large numbers of generators.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
Unit Commitment Problem in Electrical Power System: A Literature Review IJECEIAES
Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.
stability of power flow analysis of different resources both on and off gridrehman1oo
This document presents a power flow optimization strategy model for a distribution network that considers source, load, and storage. The model aims to minimize total cost, voltage deviation, and power losses over time periods determined through k-means clustering of an equivalent load curve. A particle swarm optimization algorithm is used to solve the multi-objective optimization model subject to power flow, voltage, and other constraints. The model is tested on an IEEE 33-node system and is shown to improve economic and reliability performance compared to a fixed weighting approach.
Combining both Plug-in Vehicles and Renewable Energy Resources for Unit Commi...IOSR Journals
This document presents a study that combines plug-in electric vehicles with vehicle-to-grid technology (V2G), renewable energy resources like wind and solar, and existing power plants, to optimize unit commitment in smart grids. The goal is to minimize total costs and emissions. A genetic algorithm is used to optimize scheduling of generation units, V2G vehicles providing spinning reserves, and time-varying renewable sources over a 24-hour period to meet load demand at lowest cost while satisfying constraints. Simulation results validate that integrating V2G and renewable energy sources can effectively reduce costs and emissions for the smart grid.
A Generalized Multistage Economic Planning Model for Distribution System Cont...IJERD Editor
This document presents a generalized multistage economic planning model for distribution systems containing distributed generation (DG) units. The model minimizes total investment and operation costs over a planning horizon divided into multiple periods, taking into account load growth, equipment capacities and voltages limits. Constraints include power flow equations and logical constraints relating planning periods. The model is applied to a sample 11kV distribution network with one substation, 23 load buses and 32 feeders over 4 annual periods. The mixed integer nonlinear optimization problem is solved using LINGO software to obtain the least-cost expansion plan.
A Simple Approach for Optimal Generation Scheduling to Maximize GENCOs Profit...IJAPEJOURNAL
In this paper an attempt has been made to solve the profit based unit commitment problem (PBUC) using pre-prepared power demand (PPD) table with an artificial bee colony (ABC) algorithm. The PPD-ABC algorithm appears to be a robust and reliable optimization algorithm for the solution of PBUC problem. The profit based unit commitment problem is considered as a stochastic optimization problem in which the objective is to maximize their own profit and the decisions are needed to satisfy the standard operating constraints. The PBUC problem is solved by the proposed methodology in two stages. In the first step, the unit commitment scheduling is performed by considering the pre-prepared power demand (PPD) table and then the problem of fuel cost and revenue function is solved using ABC Algorithm. The PPD table suggests the operator to decide the units to be put into generation there by reducing the complexity of the problem. The proposed approach is demonstrated on 10 units 24 hour and 50 units 24 hour test systems and numerical results are tabulated. Simulation result shows that this approach effectively maximizes the GENCO’s profit than those obtained by other optimizing methods.
Implementation effects of economics and market operations based model for tra...nooriasukmaningtyas
The main objective of this paper is to introduce power system economic operations in traditionally integrated power systems and market operations in deregulated power systems and study its effects. The power system economic operation is mathematically treated as an optimization problem. Also, a function of economic operation is to minimize generation cost, transmission losses, and so on, subject to power system operation constraints. In this paper, we start from generation cost formulations and introduce traditional economic dispatch model, optimal power flow model, and unit commitment model. With the deregulation of the power industry, integrated power system is unbundled to generation, transmission, and distribution. Electricity is traded in the wholesale market. Small customers purchase energy from electricity retailers through the retail market. The electricity market is operated for energytrading while satisfying power system operation requirements. Electricity market is mathematically modelled as an optimization problem that is subject to power system operation constraints and market operation constraints.
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- 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.
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A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
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Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
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metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Swarm Optimization Technique
1. ISSN 2349-7815
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Paper Publications
Optimal Unit Commitment Based on Economic
Dispatch Using Improved Particle Swarm
Optimization Technique
Neha Thakur1
, L. S. Titare2
1,2
Deptt. of Electrical Engineering, JEC, Jabalpur (M.P.), India
Abstract: In this paper, an algorithm to solve the optimal unit commitment problem under deregulated
environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique accounting
economic dispatch constraints. In the present electric power market, where renewable energy power plants have
been included in the system, there is a lot of unpredictability in the demand and generation. This paper presents an
improved particle swarm optimization algorithm (IPSO) for power system unit commitment with the
consideration of various constraints. IPSO is an extension of the standard particle swarm optimization algorithm
(PSO) which uses more particles information to control the mutation operation, and is similar to the social society
in that a group of leaders could make better decisions. The program was developed in MATLAB and the proposed
method implemented on IEEE 14 bus test system.
Keywords: Unit Commitment, Particle Swarm Optimisation. Best individual particle, Best group particle, Voltage
Security.
1. INTRODUCTION
Over the years, power systems had seen an immense shift from isolated systems to huge interconnected systems. These
interconnected power systems are more reliable and at the same time have brought up many challenges in the operation
from economics and system security perspective. Power systems can be divided into three main sub-systems called the
Generation, Transmission and the Distribution systems apart from the power consumption at the end. The behaviour of all
sub-systems is interdependent. Each of the sub-systems has its own behavioural attributes and constraints which govern
overall system operation. Power systems have expanded the reach over a large geography for years to supply and cater to
the ever increasing load demand. With this vast spread due to continuously growing power requirements, every utility in
the world is facing a problem in reliable operation of system.
The need to supply of electricity to consumers with utmost importance towards reliability inclines utilities to plan at every
level. In addition to reliability, an aspect that concerns utilities in planning is the economics involved in system operation.
From the stage of power generation to the supply at consumer level, there exist many economic considerations. Thus, the
planning steps followed should enable system reliable operation while optimizing the economics needed. The power
system is subjected to a varying electric load demand with peaks and valleys at different times in a day completely based
on human requirements. This urges the company to commit (turn ON) sufficient number of generating units to cater to
this varying load at all times. The option of committing all of its units and keeping them online all the time to counter
varying nature of load is economically detrimental [1] for the utilities.
A literature survey on unit commitment reveals that several methods have been developed to solve unit commitment [2,
3].They include dynamic programming method, It is a stochastic search method which searches for solution from one
state to the other. The feasible states are then saved [4, 5]. Dynamic programming was the earliest optimization-based
method to be applied to the UC problem. It is used extensively throughout the world. It has the advantage of being able to
2. ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
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Paper Publications
solve problems of a variety of sizes and to be easily modified to model characteristics of specific utilities. But the
disadvantage of this method is curse of dimensionality. i.e., the computational effort increases exponentially as problem
size increases and solution is infeasible and its suboptimal treatment of minimum up and downtime constraints and time-
dependent start-up costs. Lagrange Relaxation method, In this method the constraints are relaxed using Lagrange
multipliers. Unit commitment is written as a cost function involving a single unit and coupling constraints. Solution is
obtained by adjoining coupling constraints and cost by Lagrange multipliers. Mixed Integer Linear Programming method,
the method is widely used in the commitment of thermal units. It uses binary variables (0 or 1) to represent start up, shut
down and on/off status of units. Even it guarantees optimal solution in finite number of steps; it fails when number of
units increases because they require large memory space and suffer from great computational delay [6]. While considering
the priority list method for the committing the units, replication time and memory are saved, and it can also be pertained
in a genuine power system. In contrast, the priority list method has shortcomings that consequence into suboptimal
solutions since it won’t consider each and every one of the possible combinations of generation [7].
Section -2 presents problem formulation. Section-3 presents problem solution using DP algorithm. Section-4 gives
implementation of developed algorithm on IEEE-14-bus system and section-5 gives conclusion.
2. FORMULATION OF UNIT COMMITMENT PROBLEM
Unit commitment can be defined as the selection of generators that must be operated to meet the forecasted load demand
on the system over a period of time so that fuel cost is minimum [9,10]. The Unit Commitment Problem (UCP) is to
determine a minimal cost turn-on and turn-off schedule of a set of electrical power generating units to meet a load demand
[12] while satisfying a set of operational constraints. It is a well-known problem in power industry and helps in saving
fuel cost if units are committed correctly so that fuel cost is saved.
A. Need for Unit Commitment:
(i) Enough units will be committed to supply the load.
(ii) To reduce loss or fuel cost.
(iii) By running the most economic unit load can be supplied by that unit operating closer to its better efficiency.
B. Factors Considered In Unit Commitment:
(i) For finding the nature of fluctuating load as well as to commit the units accordingly a graph is drawn between load
demand and hours of use. This graph is known as load curve. In the solution load pattern for M period is formed using
load curve.
(ii)The possible numbers of units are committed to meet the load.
(iii)The load dispatch is calculated for all feasible combinations and operating limits of the units have to be calculated.
Unit Commitment is considered as a complex optimization problem where the aim is to minimize the objective function
in the presence of heavy constraints The objective function is given by Minimize Total cost = Fuel cost + Start-up cost
+Shut down cost
C. The input-output characteristic of a generating unit is obtained by combining directly the input-output characteristics of
boiler and that of turbine-generator set [13]. A typical input-output characteristic also called fuel cost curve of a thermal
generating unit is convex as shown in Fig. 1
Figure 1. Input-output characteristics of thermal generator
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This non liner curve can be approximated to a quadratic equation (1)
( ) (1)
Where ( ) represents the cost function, is the power output and , and are the coefficients of input-output
characteristic of ith unit. These cost coefficients are determined experimentally. The constant is equivalent to the fuel
consumption or cost incurred in operating the unit without power output. The slope of this input-output curve is called the
incremental fuel cost of unit.
Start- up cost: When the unit is at rest, some energy is required to bring the unit online. It is maximum when the unit is at
cold start (start- up cost when cooling). The unit is given sufficient energy input to keep it at operating temperature (start-
up cost when banking). So it requires some energy input to keep it at operating temperature.
Shut down cost: It is the cost for shutting down the unit. Sometimes during the shutdown period boiler may be allowed to
cool down naturally and thus no shut down cost will be incurred.
The two costs are as shown, and are compared while determining the UC schedule and a best approach among them is
chosen [1].
Start-up cost for cold start: ( ⁄
) (2)
Start-up cost for hot start: (3)
Where STC is the Start-up cost, Cc is the cold start cost in MBtu, F is the fuel cost, Cf is the fixed cost that includes crew
expenses and maintenance expenses, Ct is cost in Mbtu/hour for maintaining the unit at operating temperature, α is the
thermal time constant of the unit and t the time in hours the unit was allowed to cool. Shutdown cost is generally taken as
a constant value.
D. constraints in unit commitment [11]:
1. Power balance: the total generated load and demand at corresponding hours must be equal
∑ (4)
2. Minimum capacity committed: : It is the total power available from all units synchronised on the system minus present
loads plus the losses. It is given by
∑ (5)
3. Thermal constraints: The temperature and pressure of units increase gradually as the units are started. So they must be
synchronised before bringing online.
4. Must run units: Some of the units must be given a must run status in order to provide voltage support for the network.
For such units =1.
5. Minimum up/down time:
(6)
(7)
6. Unit generation limits: The generated power of a unit should be within its minimum and maximum power limits.
(8)
7. Ramp rate constraints: The ramp rate constraint ensures that sufficient ramp rate capacity is committed to accommodate
required generation changes. Any generation changes beyond the required changes are due strictly to economics of the
committed generators.
(9)
8. Fuel constraints: The constraint means limited availability of fuel or burning of some amount of fuel.
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Objective function: so the total cost can be represented by
∑ ∑ [ ( ) ( ) ] (10)
3. PROBLEM SOLUTION USING PARTICLE SWARM OPTIMISATION METHOD
Since all the previous methods suffer from dimensionality and computation problems, a new method has been evolved in
solving the unit commitment. It is known as Particle Swarm Optimisation method.[15].The method was developed by
simulation of social model. The method is inspired from social behaviour such as “bird flocking‖” or “fish schooling”[8].
The method consists of a group of particles in a given dimension moving towards optimal solution. The particles move
based on their previous best position, the position of neighbours and the best among all particles [14].Each particle move
towards the optimal solution based on its previous best position given by Pbest, position of other particles and the best
among all the other particles given by Gbest. The search is continued until a globally best solution is obtained or specific
number of iteration is reached.
A. Algorithm of PSO:
It is known that a particle in the swarm flies through hyperspace and alters its position over the time iteratively, according
to its own experience and that of its neighbours. Velocity is the factor responsible for this and which reflects the social
interaction. If xj represents particle x in iteration j, it is modified for the next iteration or it can be said that it is moved to a
new location as shown, where vj+1 is the velocity term derived for j+1 iteration.
(11)
A particle x flying in hyperspace has a velocity v. The best success attained by the particle is stored as pbest and the best
among all the particles in the swarm is stored as gbest.
Step1: Initialize the swarm or population Pop randomly of desired size, let K in the hyperspace.
* +
Step 2: Calculate the fitness value of each particle f(xij).
Step 3: Compare the fitness of each particle with its own best attained thus far as illustrated below
if ( ) {
( )
(12)
else : no change in pbest and
Step 4: Compare the fitness values of all particles and find gbest as shown
if ( ) {
( )
(13)
else : no change in gbest and
Step 5: Change the velocity of each particle for the next iteration as under, where w is inertia weight, c1, c2 are constants,
rand is random variable which assumes uniformly distributed values between 0 and 1.
( ) ( ) (14)
Step 6: Move each particle to a new position
(15)
Step 7: Repeat step 2 to 6 until convergence.
Inertia weight w: Controls the influence of previous velocity on the new velocity. Large inertia weights cause larger
exploration of search space, while smaller inertia weights focus the search on a smaller region. Typical PSO starts with a
maximum inertia weight wmax which decreases over iterations to a minimum value wmin as shown.
5. ISSN 2349-7815
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(16)
Where it represents the current iteration count and itmax is the maximum iterations allowed.
Reference [15] gives the best values of wmax and wmin as 0.9 and 0.4 respectively for most of the problems.
B. Advantages Of PSO Compared To Conventional Methods:
1. Easy to implement and potential to achieve a high quality solution with stable convergence characteristics.
2. The particles are treated as volume less and each particle update position and velocity according to its own experience
and partners experience.
3. PSO is more capable of maintaining diversity of the swarm.
4. One of reasons that PSO is attractive is that there are very few parameters to adjust [16]
Figure 2. Flow Chart of PSO Applied To Unit Commitment
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4. TEST SYSTEM AND SIMULATION RESULTS
Table 1 shows the 24 hour UC schedule for standard IEEE 14 bus test data given in Appendix B. Results given in the
table are self-explanatory with hourly load demand, unit status, and power output from each committed unit. Total cost of
UC schedule along with hourly production costs and total transitional cost are listed. In order to indicate the effectiveness
of proposed UC algorithm, the maximum and minimum load bus voltages attained during every hour in the system are
shown in the Table 2 that follows the voltages at the load buses in the system during 24 hour time period attained as high
as 1.0751 PU and as low as 1.0017 PU.
Table 1. UC for IEEE 14 Bus Test System
Hour Load Unit Status Power Output (MW) Cost($)
(MW) X10^31 2 3 6 8 1 2 3 6 8
1 181.30 0 1 1 1 1 0 33.47 64.54 43.90 39.99 0.9618
2 170.94 1 0 1 1 1 60.00 0 52.70 27.19 32.14 0.9506
3 150.22 1 1 1 1 0 62.84 20.00 47.64 21.20 0 0.8415
4 103.60 1 0 0 1 1 64.49 0 0 22.23 18.00 0.6392
5 129.50 1 0 1 1 1 58.06 0 32.00 14.45 25.99 0.7710
6 155.40 1 0 1 1 0 71.19 0 54.79 30.92 0 0.8321
7 181.30 1 0 1 0 0 104.1 0 80.00 0 0 0.9551
8 202.02 1 0 1 0 0 125.7 0 80.00 0 0 1.0823
9 212.38 1 0 1 1 0 117.6 0 80.00 18.00 0 1.1479
10 227.92 1 0 1 1 0 115.1 0 80.00 36.00 0 1.2155
11 230.51 1 1 1 0 1 116.1 20.00 80.00 0 18.00 1.2824
12 217.56 1 0 1 0 1 104.1 0 80.00 0 36.00 1.1681
13 207.20 1 0 1 1 1 83.40 0 66.35 18.00 41.16 1.1271
14 196.84 1 0 1 0 0 120.4 0 80.00 0 0 1.0496
15 227.92 1 1 1 0 1 113.4 20.00 80.88 0 18.00 1.2667
16 233.10 1 1 1 0 1 89.37 37.90 72.54 0 36.00 1.2584
17 220.15 1 0 1 1 1 88.59 0 71.11 18.00 44.29 1.1944
18 230.51 1 1 1 1 1 76.95 20.00 61.76 36.00 37.70 1.2616
19 243.46 1 0 1 1 1 86.76 0 70.44 45.00 43.23 1.3026
20 253.82 1 1 1 1 1 82.58 20.00 67.30 45.00 41.11 1.3782
21 259.00 1 0 1 1 1 94.07 0 77.19 45.00 45.00 1.3857
22 233.10 1 1 1 0 1 94.16 20.00 76.45 0 45.00 1.2638
23 225.33 1 0 1 0 1 102.8 0 80.00 0 45.00 1.2067
24 212.38 1 1 1 1 0 97.98 20.00 79.35 18.00 0 1.1559
Transitional Cost 2.7198
Total Cost 29.418
Table 2. Hourly Min. and Max. Load Bus Voltages for IEEE 14 Bus Test Systems
Hour Vmax Vmin Hour Vmax Vmin
1 1.0779 1.0358 13 1.0704 1.0275
2 1.071 1.0286 14 1.0325 0.9974
3 1.0651 1.0269 15 1.0529 1.0114
4 1.0737 1.0323 16 1.0535 1.0123
5 1.0722 1.0307 17 1.0700 1.0273
6 1.0631 1.0270 18 1.0709 1.0291
7 1.036 1.0135 19 1.0704 1.0277
8 1.0353 1.0144 20 1.0705 1.0293
9 1.0659 1.0209 21 1.0697 1.0275
10 1.0626 1.0210 22 1.0541 1.0151
11 1.0659 1.0244 23 1.053 1.0127
12 1.0546 1.0204 24 1.0582 1.0239
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5. CONCLUSION
The optimal unit commitment of thermal systems resulted in enormous saving for electrical utilities. The formulation of
unit commitment was discussed and the solution is obtained using the Particle Swarm Optimization method. It is found
that the total operating cost obtained from the solution of unit commitment using particle swarm optimization is minimum
compared to the outcomes obtained from conventional methods. And also the computation time is less.
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