Operation cost reduction in unit commitment problem using improved quantum bi...IJECEIAES
Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Operation cost reduction in unit commitment problem using improved quantum bi...IJECEIAES
Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Locational marginal pricing framework in secured dispatch scheduling under co...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Software Effort Estimation Using Particle Swarm Optimization with Inertia WeightWaqas Tariq
Software is the most expensive element of virtually all computer based systems. For complex custom systems, a large effort estimation error can make the difference between profit and loss. Cost (Effort) Overruns can be disastrous for the developer. The basic input for the effort estimation is size of project. A number of models have been proposed to construct a relation between software size and Effort; however we still have problems for effort estimation because of uncertainty existing in the input information. Accurate software effort estimation is a challenge in Industry. In this paper we are proposing three software effort estimation models by using soft computing techniques: Particle Swarm Optimization with inertia weight for tuning effort parameters. The performance of the developed models was tested by NASA software project dataset. The developed models were able to provide good estimation capabilities.
The modern power system around the world has grown in complexity of interconnection and
power demand. The focus has shifted towards enhanced performance, increased customer focus,
low cost, reliable and clean power. In this changed perspective, scarcity of energy resources,
increasing power generation cost, environmental concern necessitates optimal economic dispatch.
In reality power stations neither are at equal distances from load nor have similar fuel cost
functions. Hence for providing cheaper power, load has to be distributed among various power
stations in a way which results in lowest cost for generation. Practical economic dispatch (ED)
problems have highly non-linear objective function with rigid equality and inequality constraints.
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. The conventional
optimization methods are unable to solve such problems due to local optimum solution
convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been
a potential solution to the practical constrained economic load dispatch (ELD) problem. The
optimization technique is constantly evolving to provide better and faster results.
While writing the report on our project seminar, we were wondering that Science and smart
technology are as ever expanding field and the engineers working hard day and night and make
the life a gift for us
Multi-core GPU – Fast parallel SAR image generationMahesh Khadatare
This poster presents a design for parallel processing of synthetic
aperture radar (SAR) data using multi-core Graphics Processing
Units (GP-GPUs). In this design a two-dimensional image is
reconstructed from received echo pulses and their corresponding
response values. Then the comparative performance of proposed
parallel algorithm implementation is tabulated using different set of
nvidia GPUs i.e. GT, Tesla and Fermi. Analyzing experimentation
results on image of various size 1,024 x 1,024 pixels to 4,096 x
4,096 pixels led us to the conclusion that that nvidia Fermi S2050 have
maximum performance throughput .
Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch W...IJECEIAES
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
Meta-heuristic optimization techniques are important tools to define the optimal solutions for many problems. In this paper, a new advanced artificial intelligence (AI) based direct torque control (DTC) speed drives are optimally designed and implemented in real time to achieve a high performance permanent-magnet synchronous-motor (PMSM) drive. Grey wolf (GW) algorithms are used with the standard PID-based DTC (PIDDTC) and with the DTC with fuzzy logic (FDTC) based speed controllers. DSPACE DS1202 is utilized in the real-time implementation. MATLAB SIMULINK is used to simulate the steady-state (S.S.) and dynamic responses. The overall system is tested at different operating conditions for both simulation and practical work and all results are presented. A comparison between experimental and simulation results is performed and also a comparison between different applied intelligent techniques is introduced.
An Improved Particle Swarm Optimization for Proficient Solving of Unit Commit...IDES Editor
This paper presents a new approach to solving the
short-term unit commitment problem using an improved
Particle Swarm Optimization (IPSO). The objective of this
paper is to find the generation scheduling such that the total
operating cost can be minimized, when subjected to a variety
of constraints. This also means that it is desirable to find the
optimal generating unit commitment in the power system for
the next H hours. PSO, which happens to be a Global
Optimization technique for solving Unit Commitment
Problem, operates on a system, which is designed to encode
each unit’s operating schedule with regard to its minimum
up/down time. In this, the unit commitment schedule is coded
as a string of symbols. An initial population of parent
solutions is generated at random. Here, each schedule is
formed by committing all the units according to their initial
status (“flat start”). Here the parents are obtained from a predefined
set of solution’s i.e. each and every solution is adjusted
to meet the requirements. Then, a random decommitment is
carried out with respect to the unit’s minimum down times. A
thermal Power System in India demonstrates the effectiveness
of the proposed approach; extensive studies have also been
performed for different power systems consist of 10, 26, 34
generating units. Numerical results are shown comparing the
cost solutions and computation time obtained by using the
IPSO and other conventional methods like Dynamic
Programming (DP), Legrangian Relaxation (LR) in reaching
proper unit commitment.
A Novel Approach to Genetic Algorithm Based Cryptography IJORCS
Cryptography is immensely essential ingredient of network security. Public Key Cryptography, one of the most important forms of cryptography, requires the key to be unique and non- repeating. There are two ways of producing the key. One is rigorous mathematically strong algorithmic approach like AES and the other is the approach that mimics nature. The work presented explores various attempts that have been made in this direction and suggests a new technique using Genetic Algorithms. The technique has been implemented and analyzed. The results obtained are encouraging. The samples satisfy most of the tests including gap test, frequency test etc thus strengthening the belief that the algorithm is as strong, if not better than any of the mathematically strong approach.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
The Top Skills That Can Get You Hired in 2017LinkedIn
We analyzed all the recruiting activity on LinkedIn this year and identified the Top Skills employers seek. Starting Oct 24, learn these skills and much more for free during the Week of Learning.
#AlwaysBeLearning https://learning.linkedin.com/week-of-learning
Locational marginal pricing framework in secured dispatch scheduling under co...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Software Effort Estimation Using Particle Swarm Optimization with Inertia WeightWaqas Tariq
Software is the most expensive element of virtually all computer based systems. For complex custom systems, a large effort estimation error can make the difference between profit and loss. Cost (Effort) Overruns can be disastrous for the developer. The basic input for the effort estimation is size of project. A number of models have been proposed to construct a relation between software size and Effort; however we still have problems for effort estimation because of uncertainty existing in the input information. Accurate software effort estimation is a challenge in Industry. In this paper we are proposing three software effort estimation models by using soft computing techniques: Particle Swarm Optimization with inertia weight for tuning effort parameters. The performance of the developed models was tested by NASA software project dataset. The developed models were able to provide good estimation capabilities.
The modern power system around the world has grown in complexity of interconnection and
power demand. The focus has shifted towards enhanced performance, increased customer focus,
low cost, reliable and clean power. In this changed perspective, scarcity of energy resources,
increasing power generation cost, environmental concern necessitates optimal economic dispatch.
In reality power stations neither are at equal distances from load nor have similar fuel cost
functions. Hence for providing cheaper power, load has to be distributed among various power
stations in a way which results in lowest cost for generation. Practical economic dispatch (ED)
problems have highly non-linear objective function with rigid equality and inequality constraints.
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. The conventional
optimization methods are unable to solve such problems due to local optimum solution
convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been
a potential solution to the practical constrained economic load dispatch (ELD) problem. The
optimization technique is constantly evolving to provide better and faster results.
While writing the report on our project seminar, we were wondering that Science and smart
technology are as ever expanding field and the engineers working hard day and night and make
the life a gift for us
Multi-core GPU – Fast parallel SAR image generationMahesh Khadatare
This poster presents a design for parallel processing of synthetic
aperture radar (SAR) data using multi-core Graphics Processing
Units (GP-GPUs). In this design a two-dimensional image is
reconstructed from received echo pulses and their corresponding
response values. Then the comparative performance of proposed
parallel algorithm implementation is tabulated using different set of
nvidia GPUs i.e. GT, Tesla and Fermi. Analyzing experimentation
results on image of various size 1,024 x 1,024 pixels to 4,096 x
4,096 pixels led us to the conclusion that that nvidia Fermi S2050 have
maximum performance throughput .
Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch W...IJECEIAES
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
Meta-heuristic optimization techniques are important tools to define the optimal solutions for many problems. In this paper, a new advanced artificial intelligence (AI) based direct torque control (DTC) speed drives are optimally designed and implemented in real time to achieve a high performance permanent-magnet synchronous-motor (PMSM) drive. Grey wolf (GW) algorithms are used with the standard PID-based DTC (PIDDTC) and with the DTC with fuzzy logic (FDTC) based speed controllers. DSPACE DS1202 is utilized in the real-time implementation. MATLAB SIMULINK is used to simulate the steady-state (S.S.) and dynamic responses. The overall system is tested at different operating conditions for both simulation and practical work and all results are presented. A comparison between experimental and simulation results is performed and also a comparison between different applied intelligent techniques is introduced.
An Improved Particle Swarm Optimization for Proficient Solving of Unit Commit...IDES Editor
This paper presents a new approach to solving the
short-term unit commitment problem using an improved
Particle Swarm Optimization (IPSO). The objective of this
paper is to find the generation scheduling such that the total
operating cost can be minimized, when subjected to a variety
of constraints. This also means that it is desirable to find the
optimal generating unit commitment in the power system for
the next H hours. PSO, which happens to be a Global
Optimization technique for solving Unit Commitment
Problem, operates on a system, which is designed to encode
each unit’s operating schedule with regard to its minimum
up/down time. In this, the unit commitment schedule is coded
as a string of symbols. An initial population of parent
solutions is generated at random. Here, each schedule is
formed by committing all the units according to their initial
status (“flat start”). Here the parents are obtained from a predefined
set of solution’s i.e. each and every solution is adjusted
to meet the requirements. Then, a random decommitment is
carried out with respect to the unit’s minimum down times. A
thermal Power System in India demonstrates the effectiveness
of the proposed approach; extensive studies have also been
performed for different power systems consist of 10, 26, 34
generating units. Numerical results are shown comparing the
cost solutions and computation time obtained by using the
IPSO and other conventional methods like Dynamic
Programming (DP), Legrangian Relaxation (LR) in reaching
proper unit commitment.
A Novel Approach to Genetic Algorithm Based Cryptography IJORCS
Cryptography is immensely essential ingredient of network security. Public Key Cryptography, one of the most important forms of cryptography, requires the key to be unique and non- repeating. There are two ways of producing the key. One is rigorous mathematically strong algorithmic approach like AES and the other is the approach that mimics nature. The work presented explores various attempts that have been made in this direction and suggests a new technique using Genetic Algorithms. The technique has been implemented and analyzed. The results obtained are encouraging. The samples satisfy most of the tests including gap test, frequency test etc thus strengthening the belief that the algorithm is as strong, if not better than any of the mathematically strong approach.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
The Top Skills That Can Get You Hired in 2017LinkedIn
We analyzed all the recruiting activity on LinkedIn this year and identified the Top Skills employers seek. Starting Oct 24, learn these skills and much more for free during the Week of Learning.
#AlwaysBeLearning https://learning.linkedin.com/week-of-learning
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.
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.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
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.
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.
Security Constrained UCP with Operational and Power Flow ConstraintsIDES Editor
An algorithm to solve security constrained unit
commitment problem (UCP) with both operational and power
flow constraints (PFC) have been proposed to plan a secure
and economical hourly generation schedule. This proposed
algorithm introduces an efficient unit commitment (UC)
approach with PFC that obtains the minimum system
operating cost satisfying both unit and network constraints
when contingencies are included. In the proposed model
repeated optimal power flow for the satisfactory unit
combinations for every line removal under given study period
has been carried out to obtain UC solutions with both unit and
network constraints. The system load demand patterns have
been obtained for the test case systems taking into account of
the hourly load variations at the load buses by adding
Gaussian random noises. In this paper, the proposed
algorithm has been applied to obtain UC solutions for IEEE
30, 118 buses and Indian utility practical systems scheduled
for 24 hours. The algorithm and simulation are carried
through Matlab software and the results obtained are quite
encouraging.
Effects of Different Parameters on Power System Transient Stability StudiesPower System Operation
The transient stability studies plays a vital role in
providing secured operating configurations in power systems.
This paper shows an analysis of the effects of various parameters
on the transient stability studies of power system. The various
parameters for which the analysis is presented include the Fault
Clearing Time (FCT), Fault location, load increasing, machine
damping coefficient D, and Generator Armature Resistance
GAR. Under the condition that the power system is subjected to a
three-phase short-circuit fault, the Critical Clearing Time (CCT)
is calculated using numerical integration method. The analysis
has been carried out on the IEEE 30-bus test system. From this
analysis, we can conclude the importance of these different
parameters on power system transient stability studies.
PuShort Term Hydrothermal Scheduling using Evolutionary Programmingblished pa...Satyendra Singh
In this paper, Evolutionary Programming method
is used for short term hydrothermal scheduling which minimize
the total fuel cost while satisfying the constraints. This paper
developed and studies the performance of evolutionary programs
in solving hydrothermal scheduling problem. The effectiveness of
the developed program is tested for the system having one hydro
and one thermal unit for 24 hour load demand. Numerical results
show that highly near-optimal solutions can be obtained by
Evolutionary Programming.
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.
ECONOMIC LOAD DISPATCH USING GENETIC ALGORITHMIJARIIT
This paper present the application of Genetic Algorithm (GA) to Economic Load Dispatch problem of the power system. Economic Load Dispatch is one of the major optimization problems dealing with the modern power systems.ELD determines the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfactory the load demand. The objective is to minimize the total generation fuel cost and maintain the power flow within safety limits. The introduced algorithm has been demonstrated for the given test systems considering the transmission line losses.
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
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.
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.
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Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Vaccine management system project report documentation..pdfKamal Acharya
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Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
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A Genetic Algorithm Approach to Solve Unit Commitment Problem
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 7, Issue 3 (Sep. - Oct. 2013), PP 60-64
www.iosrjournals.org
www.iosrjournals.org 60 | Page
A Genetic Algorithm Approach to Solve Unit Commitment
Problem
Aditya parashar1
, Kuldeep Kumar Swankar2
1
(Electrical Engg. Department, MITS Gwalior (M.P.), INDIA)
2(Electrical Engg. Department, MITS Gwalior (M.P.), INDIA)
Abstract: In this a genetic algorithm based approach to resolve the thermal unit commitment (UC). During this
approach load demand, the most objective of getting a possible unit combination for every loading condition
whereas subjected to a range of constraints. The model during this study contains four-generation units and
also the 8-hour daily load demand. The results are compared between the dynamic programming (DP) and
genetic algorithm the achieved results prove the effectiveness, and validity of the planned approach to unravel
the large-scale UC. In the results indicating comparison of the cost solutions is using the genetic algorithm and
the Dynamic Programming. The MATLAB Tool box is used to find the result.
Keywords: Genetic Algorithm, Unit Commitment, Mat lab toolbox.
I. Introduction
It is the most important function of energy control center which determine the on/off status as well as
the real power output of the generato$/hr while minimizing the system operating cost over the planning period
subject to various physical operation and computation constraints [1]. The Unit Commitment problem is a
complex decision making process and it is difficult to develop any accurate mathematical optimization methods
capable of solving the entire for any real size system. Also, multiple constraints should be imposed which must
not be violated while finding the optimal or near optimal commitment schedule. Unit commitment (UC) is a
nonlinear mixed integer optimization problem[2]. Genetic Algorithm (GA) is a general purpose stochastic and
parallel search method, which can be used as an optimization technique for obtaining near-global optimum
solutions of given problem. This algorithm is inspired from genetics and evolution theories of natural selection
and survival of the fittest. It is an iterative procedure acting on a population of chromosomes, each chromosome
being the encoding of a candidate solution to the problem. A fitness which depends on how well it solves the
problem is associated with each chromosome [3]. Many pape$/hr have given complete solution methods for unit
commitment problem. These methods as Wood and Wollenberg, 1984.the famous traditional techniques such as
Integer Programming (Michaelwich, 1990), Dynamic Programming (Tong et. al., 1991), Branch and Bound
(wood and Wollenberg, 1984), Linear Programming (Tong et. al., 1991), Network flow programming (Tong et.
al., 1991),Lagrangian relaxation (cheng et. at. 2000), and simulated annealing (Mantawy at. al. 1999). All these
methods only provide near optimal solution. This solution affected by the limitation of the time and feasibility
of the final solution[4]. Bakirtzis et al. (1996) have developed a genetic algorithm that uses different quality
function technique to solve the unit commitment problem .The test result up to 100 generator units have been
compared with that of Dynamic programming[5] and Lagrangian relaxation methods. Swarup (2002) have
employed a new solution methodology to solve the Unit commitment problem using Genetic algorithm [6].
II. Problem Promulation
As is true for many systems, an electric power system experiences cycles. The demand for electricity is
higher during the daytime and lower during the late evening and early morning. This cyclical demand requires
that utilities companies plan for generation of power on hourly basis. The problem is fi$/hrt to decide which of
the available units to turn on, and then to determine an Economical dispatch schedule of the units. Determining
an optimal Economical dispatch schedule of a set of generating Unit Commitment Problem (UCP).
2.1.1 Fuel cost
For a given system of N generation units at hour t, the total operating fuel cost, at that particular hour is
minimized by economically dispatching the units subject to the following constraints:
(i) The total generated power by units equal to the used by load.
(ii) The power produced by each unit within bounded limits (minimum and maximum capacity).
This problem is called Economic Dispatch (ECD) and it can be stated as follows (the subscript t is omitted for
simplicity)
The FCi is the fuel cost function of the generator power output at any time interval.
FCi = ai + bipi + cipi
2
(2.1)
2. A Genetic Algorithm Approach To Solve Unit Commitment Roblem
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Where
ai, bi, ci represent unit cost coefficient.
Pi is the i unit power output [7]
2,.1.2 starts- up cost
The start-up costs relate to a on units. If the thermal unit has been off for a long period, a cold start-up cost will
be incurred. If the unit has been neoteric turned off (temperature of boiler is still high), a hot start-up cost is
used.
The Generator start up cost depends on the time the unithas been offprior tostart up. The startup cost in anygiven
time interval can berepresented byan exponential costcurve.
SCi =σi+δi{1-exp(-Toff,i/τi)} (2.2)
Where
iisthehotstartupcost.
I isthecoldstartupcost.
i istheunitcoolingtimeconstant.
Toff,iisthetimeaunithasbeenoff.
The shut down cost, SD is usually given a constant value for each unit. The total cost of production, FT for the
scheduling period is the sum of theall cost for alltheunits.
FT,i= 𝑁
𝑖=1
𝑇
𝑡=1 FCi,t+ SCi,t +SDi,t (2.3 )
There are following constraints in unit commitment problem
(i) Power equality constraint, where the total power generated must be equal to power demand PD and system
losses PL . P𝑁
𝑖=0 i u I - (PD+PL) =0 t= 1,2,..T (2.4)
(ii) Hourly spinning reserve R
P𝑁
𝑖=0 i
max
u i- (PD+PL) =R t=1,2,..T (2.5)
(iii) Generation limit constraint
Pi,t
min
≤Pi,t≤Pi,t
max
(2.6)
(iv) Start up cost
(Ton
t-1,i-MUTi)(ut-1,i-ut,i)≥0 (2.7)
(Toff
t-1,i-MDTi)(ut,i-ut-1,i)≥0 (2.8)
Ton
and Toff
is the unit on and off time , while ut,i is the unit on/ off (1,0) state [4]
III. Thermal Unit Commitment problem using Genetic algorithm
For an application of Genetic Algorithm to the Unit commitment problem, a simple binary alphabet
was chosen to encode a solution. If N represents the number of units and H the scheduling period in hou$/hr,
then with the assumption that at every hour a certain unit can be either ON or OFF, an H bit string is needed to
describe the operation schedule of a single unit. In such a string, a „1‟ at a certain location indicates that the unit
is ON at this particular hour while a „0‟ indicates that the unit is OFF. By concatenating the strings of N units, a
NH bit string is formed. Then each genotype is evaluated, and its fitness value calculated as follows:
(i) The geno-type is decoded to an N-unit / H-period operating schedule.
(ii) The fitness value F, assigned to the geno type is given in (4.1)
FT,i= 𝑁
𝑖=1
𝑇
𝑡=1 FCi,t+SCi,t+SDi,t+PFj (3.1)
PFj: penalty associated with violated constraint j
(iii)The total fuel cost, FT,i is computed as the sum of thehourly fuel costs by economically dispatching the load
demand to the operating unit for every hour t, of the scheduling period. If for some hour t, due to the operating
limits of the on line a unit, the power balance equation is not satisfied, a penalty term proportional to the power
balance violation is added to the fitness function.
(iv)Time dependent startup costs and shutdown costs can be easily computed from the information contained in
the genotype and the initial state continuous up or down time of units.
(v) For every violated constraint, a penalty term proportional to the amount of constraint violation is added to
the fitness function. The penalty multiplie$/hr, PFj are chosen sufficiently large to discourage the selection of
solution with violated constraints.
(vi)The great modeling flexibility of the GA solution to the unit commitment problem should be addressed at
this point. In the GA solution, the unit commitment problem is not decomposed either by time or by unit [9].
IV. Result And Discussion
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The values of the daily load demand of the Four-generation-units of thermal power plant are adopted form ref.
[10].
Data Table [10]
Unit
no.
Pmax
Pmin
a b c tup(h) tdown(h) Sh(hotstart)
($/hr)
Sc(cold
start)
($/hr)
Tcold
start(h)
Initial
state
Unit1 80 25 213.00 20.74 0.0018 4 2 150 350 4 -5
Unit2 250 60 585.62 16.95 0.0042 5 3 170 400 5 8
Unit3 300 75 684.74 16.83 0.0021 5 4 500 1100 5 8
Unit4 60 20 252.00 23.60 0.0034 1 1 0.00 0.02 0 -6
Load table [10]
Hour 1 2 3 4 5 6 7 8
Demand 450 530 600 540 400 280 290 500
Simulation of Result
Hour 1, Load 450 MW
Dynamic programming 10579.89 $/hr
Genetic algorithm 10534.41 $/hr
Fig.1 hour 1
Hour 2, Load 530 MW
Dynamic programming 11247.711 $/hr
Genetic algorithm 11222.44 $/hr
Fig.2 hour 2
Hour 3, Load 600 MW
Dynamic programming 11460.08 $/hr
Genetic algorithm 11407.40 $/hr