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.
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.
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.
Resource aware wind farm and D-STATCOM optimal sizing and placement in a dist...IJECEIAES
Doubly fed induction generators (DFIG) based wind farms are capable of providing reactive power compensation. Compensation capability enhancement using reactors such as distributed static synchronous compensator (D-STATCOM) while connecting distribution generation (DG) systems to grid is imperative. This paper presents an optimal placement and sizing of offshore wind farms in a coastal distribution system that is emulated on an IEEE 33 bus system. A multi-objective formulation for optimal placement and sizing of the offshore wind farms with both the location and size constraints is developed. Teaching learning algorithm is used to optimize the multi-objective function constraining on the capacity and location of the offshore wind farms. The proposed formulation is a multi-objective problem for placement of the wind generator in the power system with dynamic wind supply to the power system. The random wind speed is generated as the input and the wind farm output generated to perform the optimal sizing and placement in the distributed system. MATLAB based simulation developed is found to be efficient and robust.
IMPROVED SWARM INTELLIGENCE APPROACH TO MULTI OBJECTIVE ED PROBLEMSSuganthi Thangaraj
Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Metaheuristic optimization techniques especially Improved Particle Swarm Optimization (IPSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of IPSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. This paper illustrates successful implementation of the Improved Particle Swarm Optimization (IPSO) to Economic Load Dispatch Problem (ELD). Power output of each generating unit and optimum fuel cost obtained using IPSO algorithm has been compared with conventional techniques. The results obtained shows that IPSO algorithm converges to optimal fuel cost with reduced computational time when compared to PSO and GA for the three, six and IEEE 30 bus system.
A Hybrid Control Scheme for Fault Ride-Through Capability using Line-Side Con...Suganthi Thangaraj
As the wind power installations are increasing in number, Wind Turbine Generators (WTG) are required to have Fault Ride-Through (FRT) capabilities. Lately developed grid operating codes demand the WTGs to stay connected during fault conditions, supporting the grid to recover faster back to its normal state. In this paper, the generator side converter incorporates the maximum power point tracking algorithm to extract maximum energy from wind turbine system. A hybrid control scheme for energy storage systems (ESS) and braking choppers for fault ride-through capability and a suppression of the output power fluctuation is proposed for permanent-magnet synchronous generator (PMSG) wind turbine systems. During grid faults, the dc-link voltage is controlled by the ESS instead of the line-side converter (LSC), whereas the LSC is exploited as a STATCOM to inject reactive current into the grid for assisting in the grid voltage recovery. A simple model of the proposed system is developed and simulated in MATLAB environment. The effectiveness of the system is validated through extensive simulation results
Bulk power system availability assessment with multiple wind power plants IJECEIAES
The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
Multi-Objective Optimization Based Design of High Efficiency DC-DC Switching ...IJPEDS-IAES
In this paper we explore the feasibility of applying multi objective stochastic
optimization algorithms to the optimal design of switching DC-DC
converters, in this way allowing the direct determination of the Pareto
optimal front of the problem. This approach provides the designer, at
affordable computational cost, a complete optimal set of choices, and a more
general insight in the objectives and parameters space, as compared to other
design procedures. As simple but significant study case we consider a low
power DC-DC hybrid control buck converter. Its optimal design is fully
analyzed basing on a Matlab public domain implementations for the
considered algorithms, the GODLIKE package implementing Genetic
Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated
Annealing (SA). In this way, in a unique optimization environment, three
different optimization approaches are easily implemented and compared.
Basic assumptions for the Matlab model of the converter are briefly
discussed, and the optimal design choice is validated “a-posteriori” with
SPICE simulations.
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.
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.
Resource aware wind farm and D-STATCOM optimal sizing and placement in a dist...IJECEIAES
Doubly fed induction generators (DFIG) based wind farms are capable of providing reactive power compensation. Compensation capability enhancement using reactors such as distributed static synchronous compensator (D-STATCOM) while connecting distribution generation (DG) systems to grid is imperative. This paper presents an optimal placement and sizing of offshore wind farms in a coastal distribution system that is emulated on an IEEE 33 bus system. A multi-objective formulation for optimal placement and sizing of the offshore wind farms with both the location and size constraints is developed. Teaching learning algorithm is used to optimize the multi-objective function constraining on the capacity and location of the offshore wind farms. The proposed formulation is a multi-objective problem for placement of the wind generator in the power system with dynamic wind supply to the power system. The random wind speed is generated as the input and the wind farm output generated to perform the optimal sizing and placement in the distributed system. MATLAB based simulation developed is found to be efficient and robust.
IMPROVED SWARM INTELLIGENCE APPROACH TO MULTI OBJECTIVE ED PROBLEMSSuganthi Thangaraj
Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Metaheuristic optimization techniques especially Improved Particle Swarm Optimization (IPSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of IPSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. This paper illustrates successful implementation of the Improved Particle Swarm Optimization (IPSO) to Economic Load Dispatch Problem (ELD). Power output of each generating unit and optimum fuel cost obtained using IPSO algorithm has been compared with conventional techniques. The results obtained shows that IPSO algorithm converges to optimal fuel cost with reduced computational time when compared to PSO and GA for the three, six and IEEE 30 bus system.
A Hybrid Control Scheme for Fault Ride-Through Capability using Line-Side Con...Suganthi Thangaraj
As the wind power installations are increasing in number, Wind Turbine Generators (WTG) are required to have Fault Ride-Through (FRT) capabilities. Lately developed grid operating codes demand the WTGs to stay connected during fault conditions, supporting the grid to recover faster back to its normal state. In this paper, the generator side converter incorporates the maximum power point tracking algorithm to extract maximum energy from wind turbine system. A hybrid control scheme for energy storage systems (ESS) and braking choppers for fault ride-through capability and a suppression of the output power fluctuation is proposed for permanent-magnet synchronous generator (PMSG) wind turbine systems. During grid faults, the dc-link voltage is controlled by the ESS instead of the line-side converter (LSC), whereas the LSC is exploited as a STATCOM to inject reactive current into the grid for assisting in the grid voltage recovery. A simple model of the proposed system is developed and simulated in MATLAB environment. The effectiveness of the system is validated through extensive simulation results
Bulk power system availability assessment with multiple wind power plants IJECEIAES
The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
Multi-Objective Optimization Based Design of High Efficiency DC-DC Switching ...IJPEDS-IAES
In this paper we explore the feasibility of applying multi objective stochastic
optimization algorithms to the optimal design of switching DC-DC
converters, in this way allowing the direct determination of the Pareto
optimal front of the problem. This approach provides the designer, at
affordable computational cost, a complete optimal set of choices, and a more
general insight in the objectives and parameters space, as compared to other
design procedures. As simple but significant study case we consider a low
power DC-DC hybrid control buck converter. Its optimal design is fully
analyzed basing on a Matlab public domain implementations for the
considered algorithms, the GODLIKE package implementing Genetic
Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated
Annealing (SA). In this way, in a unique optimization environment, three
different optimization approaches are easily implemented and compared.
Basic assumptions for the Matlab model of the converter are briefly
discussed, and the optimal design choice is validated “a-posteriori” with
SPICE simulations.
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
Evaluation of IEEE 57 Bus System for Optimal Power Flow AnalysisIJERA Editor
The analysis of load flow in a network under steady state operation is challenging task especially subjected to
inequality constraints in which the system operates. No doubt, that the load flow system analysis is an important
aspect for power system analysis and design. The basic analysis technique for power flow is to find different
parameters including magnitude and phase angle of voltage at each bus with active and reactive power flows in
each transmission lines. Thus, load flow analysis is important numerical analysis for any power system. In this
regard, this experiment is studied to evaluate IEEE 57 bus system for optimal flow analysis.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
Reactive Power Planning is a major concern in the
operation and control of power systems This paper compares
the effectiveness of Evolutionary Programming (EP) and
New Improved Differential Evolution (NIMDE) to solve
Reactive Power Planning (RPP) problem incorporating
FACTS Controllers like Static VAR Compensator (SVC),
Thyristor Controlled Series Capacitor (TCSC) and Unified
power flow controller (UPFC) considering voltage stability.
With help of Fast Voltage Stability Index (FVSI), the critical
lines and buses are identified to install the FACTS controllers.
The optimal settings of the control variables of the generator
voltages,transformer tap settings and allocation and parameter
settings of the SVC,TCSC,UPFC are considered for reactive
power planning. The test and Validation of the proposed
algorithm are conducted on IEEE 30–bus system and 72-bus
Indian system.Simulation results shows that the UPFC gives
better results than SVC and TCSC and the FACTS controllers
reduce the system losses.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
Heuristic remedial actions in the reliability assessment of high voltage dire...IJECEIAES
Planning of high voltage direct current (HVDC) grids requires inclusion of reliability assessment of alternatives under study. This paper proposes a methodology to evaluate the adequacy of voltage source converter/VSCHVDC networks. The methodology analyses the performance of the system using N-1 and N-2 contingencies in order to detect weaknesses in the DC network and evaluates two types of remedial actions to keep the entire system under the acceptable operating limits . The remedial actions are applied when a violation of these limits on the DC system occurs; those include topology changes in the network and adjustments of power settings of VSC converter stations. The CIGRE B4 DC grid test system is used for evaluating the reliability/adequacy performance by means of the proposed methodology in this paper. The proposed remedial actions are effective for all contingencies; then, numerical results are as expected. This work is useful for planning and operation of grids based on VSC-HVDC technology.
Optimal Placement of D-STATCOM Using Hybrid Genetic and Ant Colony Algorithm ...IJAPEJOURNAL
In this work, a modern algorithm by hybrid genetic algorithm and ant colony algorithm is designed to placement and then simulated to determine the amount of reactive power by D-STATCOM. Also this method will be able to minimize the power system losses that contain power loss in transmission lines. Furthermore, in this design a IEEE 30-bus model depicted and three D-STATCOM are located in this system according to Economic Considerations. The optimal placement of each D-STATCOM is computed by the ant colony algorithm. In order to optimize placement for each D-STATCOM, two groups of ant are selected, which respectively located in near nest and far from the nest. Moreover, for every output simulation of D-STATCOM that is used to produce or absorb of reactive power, a genetic algorithm to minimizing the total network losses is applied. Finally, the result of this simulation shows net losses reduction about 150% that it verifies the new algorithm performance.
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.
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.
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.
MPPT for PV System Based on Variable Step Size P&O AlgorithmTELKOMNIKA JOURNAL
This paper presents some improvements on the Perturb and Observe (P&O) method to overcome the common drawbacks of conventional P&O method. The main advantage of this modified algorithm is its simplicity with higher accuracy results, compared to the conventional methods. The operation of the entire solar Maximum Power Point Tracking (MPPT) system was observed through two different approaches, which are through MATLAB/Simulink simulation and hardware implementation. A small scale of hardware design, which consists of solar PV cell, boost converter and Arduino Mega2560 microcontroller, had been utilized for further verification on the simulation results. The simulation results that was carried out by this modified P&O algorithm showed improvement and a promising performance: faster convergence speed of 0.67s, small oscillation at steady state region and promising efficiency of 95.23%. Besides, from the hardware results, the convergence time of the power curve was able to maintain at 0.2s and give almost zero oscillation during steady state. It is envisaged that the method is useful in future research of Photovoltaic (PV) system.
Fuzzy and predictive control of a photovoltaic pumping system based on three-...journalBEEI
In this work, an efficient control scheme for a double stage pumping system is proposed. On the DC side, a three-level boost converter is employed to maximize the photovoltaic power and to step-up the DC-link voltage. For maximum power point tracking, the classical incremental conductance method is substituted by a fuzzy logic controller. The designed controller estimates the optimal step size which speeds up the tracking process and improves the accuracy of the extracted photovoltaic power. Afterwards, the voltages across the three-level boost converter (TLBC) capacitors are balanced by phase shifting the applied duty ratios. On the motor pump side, a two-level inverter drives the motor pump with the cascaded nonlinear predictive control. The predictive controller is preferred over the conventional field-oriented control because it accelerates the torque response and resists to the change of the engine parameters. The designed controllers are evaluated using MATLAB/Simulink, and compared with the conventional controllers (incremental conductance algorithm and field-oriented control). The robust control scheme of the entire system has increased the hydraulic power by up to 23% during the system start-up and up to 10% in steady state.
Exergy Based Performance Analysis of FGPS (NTPC Faridabad)Santosh Verma
Compute energy and exergy flows using the thermodynamic property values with the real time operation parameters at terminal points of crucial systems and evaluate exergy destruction at different systems
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Integration of the Renewable Energy to the Grid by Considering Small ...IJECEIAES
In recent decades, one of the main management’s concerns of professional engineers is the optimal integration of various types of renewable energy to the grid. This paper discusses the optimal allocation of one type of renewable energy i.e. wind turbine to the grid for enhancing network’s performance. A multi-objective function is used as indexes of the system’s performance, such as increasing system loadability and minimizing the loss of real power transmission line by considering security and stability of systems’ constraints viz.: voltage and line margins, and eigenvalues as well which is representing as small signal stability. To solve the optimization problems, a new method has been developed using a novel variant of the Genetic Algorithm (GA), specifically known as Non-dominated Sorting Genetic Algorithm II (NSGAII). Whereas the Fuzzy-based mechanism is used to support the decision makers prefer the best compromise solution from the Pareto front. The effectiveness of the developed method has been established on a modified IEEE 14-bus system with wind turbine system, and their simulation results showed that the dynamic performance of the power system can be effectively improved by considering the stability and security of the system.
Design methodology of smart photovoltaic plant IJECEIAES
In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink.
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, 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
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
Evaluation of IEEE 57 Bus System for Optimal Power Flow AnalysisIJERA Editor
The analysis of load flow in a network under steady state operation is challenging task especially subjected to
inequality constraints in which the system operates. No doubt, that the load flow system analysis is an important
aspect for power system analysis and design. The basic analysis technique for power flow is to find different
parameters including magnitude and phase angle of voltage at each bus with active and reactive power flows in
each transmission lines. Thus, load flow analysis is important numerical analysis for any power system. In this
regard, this experiment is studied to evaluate IEEE 57 bus system for optimal flow analysis.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
Reactive Power Planning is a major concern in the
operation and control of power systems This paper compares
the effectiveness of Evolutionary Programming (EP) and
New Improved Differential Evolution (NIMDE) to solve
Reactive Power Planning (RPP) problem incorporating
FACTS Controllers like Static VAR Compensator (SVC),
Thyristor Controlled Series Capacitor (TCSC) and Unified
power flow controller (UPFC) considering voltage stability.
With help of Fast Voltage Stability Index (FVSI), the critical
lines and buses are identified to install the FACTS controllers.
The optimal settings of the control variables of the generator
voltages,transformer tap settings and allocation and parameter
settings of the SVC,TCSC,UPFC are considered for reactive
power planning. The test and Validation of the proposed
algorithm are conducted on IEEE 30–bus system and 72-bus
Indian system.Simulation results shows that the UPFC gives
better results than SVC and TCSC and the FACTS controllers
reduce the system losses.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
Heuristic remedial actions in the reliability assessment of high voltage dire...IJECEIAES
Planning of high voltage direct current (HVDC) grids requires inclusion of reliability assessment of alternatives under study. This paper proposes a methodology to evaluate the adequacy of voltage source converter/VSCHVDC networks. The methodology analyses the performance of the system using N-1 and N-2 contingencies in order to detect weaknesses in the DC network and evaluates two types of remedial actions to keep the entire system under the acceptable operating limits . The remedial actions are applied when a violation of these limits on the DC system occurs; those include topology changes in the network and adjustments of power settings of VSC converter stations. The CIGRE B4 DC grid test system is used for evaluating the reliability/adequacy performance by means of the proposed methodology in this paper. The proposed remedial actions are effective for all contingencies; then, numerical results are as expected. This work is useful for planning and operation of grids based on VSC-HVDC technology.
Optimal Placement of D-STATCOM Using Hybrid Genetic and Ant Colony Algorithm ...IJAPEJOURNAL
In this work, a modern algorithm by hybrid genetic algorithm and ant colony algorithm is designed to placement and then simulated to determine the amount of reactive power by D-STATCOM. Also this method will be able to minimize the power system losses that contain power loss in transmission lines. Furthermore, in this design a IEEE 30-bus model depicted and three D-STATCOM are located in this system according to Economic Considerations. The optimal placement of each D-STATCOM is computed by the ant colony algorithm. In order to optimize placement for each D-STATCOM, two groups of ant are selected, which respectively located in near nest and far from the nest. Moreover, for every output simulation of D-STATCOM that is used to produce or absorb of reactive power, a genetic algorithm to minimizing the total network losses is applied. Finally, the result of this simulation shows net losses reduction about 150% that it verifies the new algorithm performance.
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.
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.
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.
MPPT for PV System Based on Variable Step Size P&O AlgorithmTELKOMNIKA JOURNAL
This paper presents some improvements on the Perturb and Observe (P&O) method to overcome the common drawbacks of conventional P&O method. The main advantage of this modified algorithm is its simplicity with higher accuracy results, compared to the conventional methods. The operation of the entire solar Maximum Power Point Tracking (MPPT) system was observed through two different approaches, which are through MATLAB/Simulink simulation and hardware implementation. A small scale of hardware design, which consists of solar PV cell, boost converter and Arduino Mega2560 microcontroller, had been utilized for further verification on the simulation results. The simulation results that was carried out by this modified P&O algorithm showed improvement and a promising performance: faster convergence speed of 0.67s, small oscillation at steady state region and promising efficiency of 95.23%. Besides, from the hardware results, the convergence time of the power curve was able to maintain at 0.2s and give almost zero oscillation during steady state. It is envisaged that the method is useful in future research of Photovoltaic (PV) system.
Fuzzy and predictive control of a photovoltaic pumping system based on three-...journalBEEI
In this work, an efficient control scheme for a double stage pumping system is proposed. On the DC side, a three-level boost converter is employed to maximize the photovoltaic power and to step-up the DC-link voltage. For maximum power point tracking, the classical incremental conductance method is substituted by a fuzzy logic controller. The designed controller estimates the optimal step size which speeds up the tracking process and improves the accuracy of the extracted photovoltaic power. Afterwards, the voltages across the three-level boost converter (TLBC) capacitors are balanced by phase shifting the applied duty ratios. On the motor pump side, a two-level inverter drives the motor pump with the cascaded nonlinear predictive control. The predictive controller is preferred over the conventional field-oriented control because it accelerates the torque response and resists to the change of the engine parameters. The designed controllers are evaluated using MATLAB/Simulink, and compared with the conventional controllers (incremental conductance algorithm and field-oriented control). The robust control scheme of the entire system has increased the hydraulic power by up to 23% during the system start-up and up to 10% in steady state.
Exergy Based Performance Analysis of FGPS (NTPC Faridabad)Santosh Verma
Compute energy and exergy flows using the thermodynamic property values with the real time operation parameters at terminal points of crucial systems and evaluate exergy destruction at different systems
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Integration of the Renewable Energy to the Grid by Considering Small ...IJECEIAES
In recent decades, one of the main management’s concerns of professional engineers is the optimal integration of various types of renewable energy to the grid. This paper discusses the optimal allocation of one type of renewable energy i.e. wind turbine to the grid for enhancing network’s performance. A multi-objective function is used as indexes of the system’s performance, such as increasing system loadability and minimizing the loss of real power transmission line by considering security and stability of systems’ constraints viz.: voltage and line margins, and eigenvalues as well which is representing as small signal stability. To solve the optimization problems, a new method has been developed using a novel variant of the Genetic Algorithm (GA), specifically known as Non-dominated Sorting Genetic Algorithm II (NSGAII). Whereas the Fuzzy-based mechanism is used to support the decision makers prefer the best compromise solution from the Pareto front. The effectiveness of the developed method has been established on a modified IEEE 14-bus system with wind turbine system, and their simulation results showed that the dynamic performance of the power system can be effectively improved by considering the stability and security of the system.
Design methodology of smart photovoltaic plant IJECEIAES
In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink.
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, 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
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, 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.
The Journal will bring together leading researchers, engineers and scientists in the domain of interest from around the world. Topics of interest for submission include, but are not limited to
International Journal of Humanities and Social Science Invention (IJHSSI)inventionjournals
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, 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
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
All manuscripts are subject to rapid peer review. Those of high quality (not previously published and not under consideration for publication in another journal) will be published without delay.
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, 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
International Journal of Humanities and Social Science Invention (IJHSSI)inventionjournals
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, 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 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
Economic Load Dispatch for Multi-Generator Systems with Units Having Nonlinea...IJAPEJOURNAL
Economic Load Dispatch aims at distributing the load demand between various generation stations in a system such that the total cost of generation is minimum. This is of vital importance since it not only reduces the operation cost of the generation utility but also helps in conserving fast dwindling energy resources. Modern day power systems are large interconnected systems with a large number of generator units each having its own cost curve. Ideally the cost function of a unit is a quadratic function of the power generated by the unit and the cost curve obtained is a smooth parabola. But in practice cost curves deviate from the idealised one due the several reasons such as valve point effect, multi fuel operation, existence of forbidden zones etc. and as such may not be continuous or analytic. Also for a large interconnected system it becomes essential to consider the effect of transmission losses. Conventional numerical method based approaches work well with systems without losses but for large systems with losses obtaining convergence becomes difficult as the number of iterations required as well as the computational time are very high. These methods fail entirely if non ideal cost curves are considered. Hence soft computing based methods become essential. Here Gravity Search Algorithm(GSA) has been used to for finding economic load scheduling in a multi generator system, given a certain load demand, and taking into consideration the effects of practical constraints on the idealised load curve. The algorithms for finding the economic scheduling has been written in Matlab and has provided satisfactory results based on the given tolerance values. Also the traditional and soft computing based approaches have been compared to demonstrate the advantages of one over the other.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Optimal 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.
A Review on Various Techniques Used for Economic Load Dispatch in Power Systemijtsrd
Electrical power plays a pivotal role in the modern world to satisfy various needs. It is therefore very important that the electrical power generated is transmitted and distributed efficiently in order to satisfy the power requirement. The Economic Load Dispatch ELD problem is the most significant problem of optimization in forecasting the generation amongst thermal generating units in power system. Pankaj Verma | Manish Prajapati "A Review on Various Techniques Used for Economic Load Dispatch in Power System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49830.pdf Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/49830/a-review-on-various-techniques-used-for-economic-load-dispatch-in-power-system/pankaj-verma
Economic dispatch by optimization techniquesIJECEIAES
The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
Optimal Power Flow With UPFC Using Fuzzy- PSO With Non-Smooth Fuel Cost FunctionIJERA Editor
This paper presents an efficient and reliable evolutionary based approach to solve the Optimal Power Flow problem in electrical power network. The Particle Swarm Optimization method is used to solve optimal power Flow problem in power system by incorporating a powerful and most versatile Flexible Alternating Current Transmission Systems device such as Unified power Flow Controller. It is a new device in FACTS family and has great flexibility that can control Active power, Reactive power and voltage magnitudes simultaneously. In this paper optimal location is find out using Fuzzy approach and control settings of UPFC are determined by PSO. The proposed approach is examined on IEEE-30 bus system with different objective function that reflects fuel cost minimization and fuel cost with valve point effects. The test results show the effectiveness of robustness of the proposed approachcompared with the existing results in the literature.
Optimal power flow with distributed energy sources using whale optimization a...IJECEIAES
Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSOGWO).
Evolutionary algorithm solution for economic dispatch problemsIJECEIAES
A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints, these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed, the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
A039101011
1. International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org Volume 3 Issue 9 ǁ September2014 ǁ PP.01-011
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Solving economic dispatch with valve point loading effects by using optimization techniques G. Chandrakala1, V. Ramakrishna2 Jan Bhasha Shaik3 1(Department of Electrical and Electronics Engineering, Audisankara Institute of Technology & Science, Andhra Pradesh, India) 2(Department of Electrical and Electronics Engineering, College, Sri Venkateswara University, Andhra Pradesh, India) 3(Department of Electrical and Electronics Engineering, Audisankara Institute of Technology & Science, Andhra Pradesh, India) ABSTRACT : The economic dispatch problem with valve point loading effects may cause a small change in the objective function formulation. Due to valve point loading effects mechanism, complexity will come into picture and some other additionalities will include. Hence, we use strong optimization techniques to determine the minimum fuel cost for generation. The proposed optimization technique is based on a hybrid shuffled differential evolution (SDE) algorithm which combines the benefits of shuffled frog leaping algorithm and differential evolution to give optimal solution. The SDE algorithm integrates a novel differential mutation operator specifically designed to effectively address the problem under study. In order to validate the proposed methodology, detailed simulation results obtained on two standard test systems are presented and discussed. A comparative analysis with other settled nature-inspired solution algorithms demonstrates the superior performance of the proposed methodology in terms of both solution accuracy and convergence and performances. KEYWORDS: Differential evolution, Nonconvex economic dispatch, Shuffled frog leaping algorithm, Valve point loading effects.
I. INTRODUCTION
A certain load demand existing at any instant of time in a power system may be supplied in an infinite number of configurations. In the load flow problem if the specified variable P,V at generator buses are allowed to vary in a region constrained by practical consideration(upper and lower limits of active and reactive power, bus voltage limit), then for a certain P-Q values of load buses there results an infinite number of load flow solutions each pertaining to one set of values of specified P,V(control variables). The best choice in some sense of the values of control variables leads to the best load flow solution. Operating economy is naturally predominant in determining the best choice; though there are several others equally important factors (which we shall not consider here for simplicity) should be given consideration. Economic operation of power systems calls for the selection of the best operating configuration that gives maximum operating economy or minimum operating cost. The total operating cost includes fuel, labour, and maintenance costs, but for simplicity we shall assume that the only cost that we need to consider are fuel costs for power production as these makes the major portion of the total operating (variable) cost and are directly related to the value of power output. The reactive power generation has no appreciable influence on the fuel consumption and the fuel cost is critically dependent on real power generation. Fuel cost characteristics (fuel cost vs net active power output) of different units may be different giving different economic efficiency. So the problem of selecting the optimum operating configuration reduces to the problem of finding an optimal combination of generating units to run and to allocate these real power generations. Obviously power generation by hydro units is much cheaper and can give much better operating economy. But the operation of such plants are dependent on the availability of water which is however restricted and subject to seasonal variations. In those systems, where both thermal and hydro sources are available, economy can be achieved by properly mixing the two types of generations. The problem of economic operation of a power system or optimal power flow can be state as: Allocating the load (MW) among the various units of generating stations and among the various generating stations in such ways that, the overall cost of generation for the given load demand is minimum.
This is an optimization problem, the objective of which is to minimize the power generation cost function subject to the satisfaction of a given set of linear and non-linear equality and inequality constraints. The problem is analyzed, solved and then implemented under online condition of the power system. The input data
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for the problem comes from conventional power flow study. For a given load demand, power flow study can be used to calculate of active and reactive power generations, line flows and losses. The study also furnishes some control parameters such as the magnitude of voltage and voltage phase differences. The economic scheduling problem can be understood as an outcome of multiple power flow studies, where a particular power flow studies result is considered more appropriate in terms of cost of generation. The solution to this problem cannot be optimal unless otherwise all the constraints of the system are satisfied. We discuss the economic scheduling problem in the following sections, but first we consider the constraints that need to be addressed. In order to try and overcome some of aforesaid limitations more sophisticated solution algorithms have been proposed in literature. In particular paper [5] proposes the application of a dynamic programming based algorithm. Although this algorithm has no restrictions on the shape of the cost curve, it performances tends to deteriorate as the number of generators increases [5]. In particular the ED problem solution considering valve point effects have been addressed by; evolutionary programming (EP) [6]; improved fast EP (IFEP) [7] ; genetic algorithm [3]; particular swarm optimization (PSO) combined with the SQP method (PSO-SQP) [8]; improved coordinated aggregation-based PSO (ICA-PSO) [9]; quantum-inspired particular swarm optimization (QPSO) [10]; combining of chaotic differential evolution quadratic programming (DEC-SQP) [11]; firefly algorithm (FA) [12]. Differential evolution (DE) is an evolutionary computation method for optimizing non-linear and non- differentiable continuous space functions developed by Storn and Price [13]. DE may occasionally stop proceeding toward the global optimum even though the population has not converged to a local optimum. This situation is usually referred to as stagnation. DE also suffers from the problem of premature convergence, where the population converges to some local optima of a multimodal objective function, losing its diversity. Shuffled frog leaping algorithm (SFLA) is a newly developed memetic metaheuristic algorithm for combinatorial optimization, which has simple concept, few parameters, high performance, and easy programming [14]. Recently, SFLA and its variants have been successfully applied to various fields of power system optimization[15-18]. The main benefits of SFLA is its fast convergence while its main drawbacks are mainly due to the insufficient learning mechanism for the swarm that could lead to noncomprehensive solution domain exploration. In order to overcome the intrinsic limitations of DE and SFLA, emphasizing at the same time their benefits, an innovative technique called shuffled differential evolution (SDE) characterized by a novel mutation operator has been designed. The main contributions of this paper are: (1) Presenting a novel mutation operator to enhance the search ability of the SDE. And the mutation operator is specific to this work and has been never presented in the previous search works in the area. (2) Applying the proposed methodology to two benchmark ED problems with valve point loading effects and the results are presented. (3) The best results obtained from the solution of the ED problem by adopting the SDE algorithm are compared to those published in the recent state-of-the art literatures.
II. OPTIMIZATION PROBLEM FORMATION FOR ECONOMIC LOAD DISPATCH
The input-output characteristic of the whole generating unit system can be obtained by combining directly the input-output characteristic of the boiler and the input-output characteristic of the turbine-generator unit. It is a smooth convex curve, which is shown in Fig. 1
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Fig.1 Input-output Characteristic of generating unit The primary objective of ELD problem is to determine themost economic loading of the generating units such that the loaddemand in the power system can be met [3]. Additionally, theELD planning must be performed satisfying different equalityand inequality constraints. In general, the problem is formulatedas follows. Consider a power system having N generating units, eachloaded to PiMW. The generating units should be loaded in sucha way that minimizes the total fuel cost FT while satisfying thepower balance and other constraints. Therefore, the classic ELDproblem can be formulated as an optimization process with theobjective: (1) where the fuel input–power output cost function of isith unit is represented by the function Fi. The most simplified fuel cost function Fi(Pi) for generator i loaded with PiMW is approximated by a quadratic function as follows: (2) Where ai, biand ciare the fuel cost coefficients of the ithgeneratic unit. i = 1, 2, ……N 2.1. Economic Dispatch problem considering valve-point loading effect For more rational and precise modeling of fuel cost function, the above expression of cost function is to be modified suitably. The generating units with multi-valve steam turbines exhibit a greater variation in the fuel- cost functions [3]. The valve opening process of multi-valve steam turbines produces a ripple-like effect in the heat rate curve of the generators. These “valve-point effect” are illustrated in Fig.2. Fig. 2. Valve Point loading effect The significance of this effect is that the actual cost curve function of a large steam plant is not continuous but more important it is non-linear. In reality, the generating units with multi-valve steam turbine have very different input–output curve compared with the smooth cost function. Therefore, the inclusion of the valve-point loading effects makes the representation of the incremental fuel cost function of the generating units more practical. The incremental fuel cost function of a generating unit with valve-point loadings is represented as follows: (3) Where eiand fiare the coefficients of generator i reflecting the valve-point effects. 2.2 Constraints 2.2.1 Equality Constraints for Active Power Balance The total power generated should be the same as the total load demand plus the total transmission losses. In this work, transmission power losses have not been considered and the active power balance can be expressed as: (4) where PD is the total power demand in MW.
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2.2.2 Inequality Constraints for Generation Capacity It is not always necessary that all the units of a plant are available to share a load. Some of the units may be taken off due to scheduled maintenance. Also it is not necessary that the less efficient units are switched off during off peak hours. There is a certain amount of shut down and startup costs associated with shutting down a unit during the off peak hours and servicing it back on-line during the peak hours. To complicate the problem further, it may take about eight hours or more to restore the boiler of a unit and synchronizing the unit with the bus. To meet the sudden change in the power demand, it may therefore be necessary to keep more units than it necessary to meet the load demand during that time. This safety margin in generation is called spinning reserve. The optimal load dispatch problem must then incorporate this startup and shut down cost for without endangering the system security. The power generation limit of each unit is then given by the inequality constraints (5) The maximum limit Pmax is the upper limit of power generation capacity of each unit. On the other hand, the lower limit Pmin pertains to the thermal consideration of operating a boiler in a thermal or nuclear generating station. An operational unit must produce a minimum amount of power such that the boiler thermal components are stabilized at the minimum design operating temperature.
III. SHUFFLED DIFFERENTIAL EVOLUTION OPTIMIZATION
In trying to address nonconvex ED problems the adoption of a hybrid solution technique based on a combination of differential evolution (DE) and shuffled frog leaping algorithm {SFLA) is proposed in this paper. In order to try and overcome the intrinsic limitations of DE and SFLA in solving nonconvex ED problems, an innovative technique called shuffled differential evolution (SDE) characterized by a novel mutation operator is proposed here. The proposed algorithm is based on the shuffling property of SFLA and DE algorithm. Similarly to other evolutionary algorithms, in SDE a population is initialized by randomly generating candidate solutions. The fitness of each candidate solution is then calculated and the population is sorted in descend solutions. The fitness of each candidate solution is then calculated and the population is sorted in descending order of their fitness and partitioned into memeplexes. 3.1. SDE parameters The effective application of the SDE algorithm requires a propersetting of its control parameters. They include the population size(P), the number of memeplexes (m), the number of frogs in amemeplex (n), the maximum number of internal evolution orinfection steps (IE) in a memeplex between two successive shuffling,the cross over rate (CR), and the scaling factor (F). Since thechoice of these parameters could sensibly affect the algorithm performances,some principles and guidelines aimed at supporting theanalyst are here discussed. The global optimum searching capabilityand the convergence speed are very sensitive to the choice of DEcontrol parameters such as scaling factor (F), and crossover rate(CR). Proper values of F and CR are chosen in between 0 and 1.An appropriate value for population size (P) is related to the complexityof the problem. 3.4 Pseudo code for Shuffled Differential Evolution Optimization The following is the pseudo code for implementing the SDE optimization. Begin; Initialize the SDE parameters Randomly generate a population of solutions (frogs); Foriis = 1 to SI (maximum no. of generations); For each individual (frog); calculate fitness of frogs; Sort the population in descending order of their fitness; Determine the global best frog; Divide population into m memeplexes; /*memeplex evolution step*/ Forim = 1 to m; Forie = 1 to IE (maximum no. of memetic evolutions) Determine the best frog; For each frog Generate new donor vector (frog) from mutation (using DE/memeplexbest/2) Apply crossover Evaluate the fitness of new frog; If new frog is better than old
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Replace the old with new one End if End for End for End for /*end of memeplex evolution step*/ Combine the evolved memeplex; Sort the population in descending order of their fitness; Update the global best frog; End for End 3.2. Constraint handling technique Equality constraint handling (i.e., power balance) represent one of the most complex issues to address in ED analysis. In this connection the application of penalty functions requires large penalty factors in order to make the ED problem feasible. These large values could distort the solution space leading the solution algorithm to diverge or to converge to a weak local optimum. In order to try or to overcome this limitations in this paper a novel technique for equality constraint handling is proposed.
IV. SIMULATION RESULTS
The proposed algorithm is implemented using MATLAB. In order to demonstrate the performance of the proposed SDE method, it was tested on two systems. In the next section, ED problem is solved with valve point loading effects considered 3 and 13-unit test systems are compared with well settled nature-inspired and bio-inspired optimization algorithms. 4.1. Three unit thermal system A system of three thermal units with the effects of valve-point loading was studied in this case. The expected load demand to be met by all the three generating units is 850 MW. The system data can be found from [7]. The convergence profile of the cost function is depicted in Fig. 1. The dispatch results using the proposed method and other algorithms are given in Table 1. The global optimal solution for this test system is 8241.5876 $/h. From Table 1 ,it is clear that the proposed method SDE reported the global optimum solution. The mean values also highlighted with red line in the fig 3. In the Table 1, SDE method is also compared with the GA [3] and MPSO [12] methods. The minimum cost for GA [3] and MPSO [12] is 8234.60 $/h and 8234.07 $/h respectively Fig. 5.2 shows the distribution of total costs of the SDE algorithm for a load demand of 850 MW for 100 different trials for 3-unit case study and observed that the maximum, minimum and average values are 8250.2047 $/h, is 8241.5876 $/h and 8240.9518 $/h respectively. The mean values also highlighted with red line in the fig.4. Table 1: Comparisons of Simulation results of different methods for 3-unit system
Unit
GA [3]
MPSO [12]
SDE
1
300.00
300.27
300.2669
2
400.00
400.00
400.0000
3
150.00
149.74
149.7331
Total power in MW
850.00
850.00
850.0000
Total cost in $/h
8234.60
8234.07
is 8241.5876
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Fig.3. Convergence profile of the total cost for 3-generating units. Fig.4 Distribution of total costs of the SDE algorithm for a load demand of 850 MW for 100 different trials for 3-unit case study 4.2. Thirteen unit thermal system The proposed hybrid algorithm is applied on 13-unit system with the effects of valve-point loading. Fig. 5 Convergence profile of the total cost for 13 generating units with PD = 1800 MW
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The problem is solved for two different power demands in order to show the effectiveness of the proposed method in producing quality solutions. In the first case, the expected load demand to be met by all the thirteen generating units is 1800 MW. The load demand is set at 2520 MW in second case. The data of the test system have been obtained by [7]. Table 2: Comparisons of simulation results of different methods for 13-unit case study system with PD = 1800 MW
Unit
IGA_MU [41]
HQPSO [42]
SDE
1
628.3151
628.3180
628.3185
2
148.1027
149.1094
222.7493
3
224.2713
223.3236
149.5995
4
109.8617
109.8650
60.0000
5
109.8637
109.8618
109.8665
6
109.8643
109.8656
109.8665
7
109.8550
109.7912
109.8665
8
109.8662
60.0000
109.8665
9
60.0000
109.8664
109.8665
10
40.0000
40.0000
40.0000
11
40.0000
40.0000
40.0000
12
55.0000
55.0000
55.0000
13
55.0000
55.0000
55.0000
Total power in MW
1800.0000
1800.0000
1800.0000
Total cost in $/h
17963.9848
17963.9571
17963.8293
Table 2 shows the best dispatch solutions obtained by the proposed method for the load demand of 1800 MW. The convergence profile for SDE method is presented in Fig. 5. The results obtained by the proposed methods are compared with those available in the literature as given in Table 2. Though the obtained best solution is not guaranteed to be the global solution, the SDE has shown the superiority to the existing methods. The minimum cost obtained by SDE method is 17963.8293 $/h, which is the best cost found so far and also compared the SDE method with the IGA_MU [41] and HQPSO [42] methods. The minimum cost for IGA_MU [41] and HQPSO [42] is 17963.9848 $/h and 17963.9571 $/h respectively. The results demonstrate that the proposed algorithm outperforms the other methods in terms of better optimal solution. Fig. 5.4 shows the variations of the fuel cost obtained by SDE for 100 different runs and convergence results for the algorithms are presented in Table 5.3 for 1800MW load. Fig. 6 Distribution of total costs of the SDE algorithm for a load demand of 1800 MW for 100 different trials for 13-unit case study Fig.6 shows the variations of the fuel cost obtained by SDE for 100 different runs and convergence results for the algorithms are presented in Table 5.3 for 1800MW load.
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Table 3: Convergence results (100 trial runs) for 13-unit test system with PD = 1800 MW
Method
Minimum cost ($/h)
Average cost ($/h)
Maximum cost ($/h)
IGA_MU [41]
17963.9848
NA
NA
HQPSO [42]
17963.9571
18273.8610
18633.0435
SDE
17963.8293
17972.8774
17975.3434
Table 3 shows the convergence results for 100 trials for 13-unit test system with load 1800 MW and compared the minimum, average and maximum cost for IGA_MU [41] and HQPSO [42] methods. It has been observed that minimum, average and maximum costs for SDE proposed method is 17963.8293 $/h, 17972.8774 $/h and 17975.3434 $/h respectively and also observed that the proposed method minimum, average and maximum cost values are low compared with the IGA_MU [41] and HQPSO [42] methods. Fig. 7 Convergence profile of the total cost for 13 generating units with PD = 2520 MW Table 4 shows the best dispatch solutions obtained by the proposed method for the load demand of 2520 MW. The convergence profile for SDE method is presented in Fig. 7. The results obtained by the proposed methods are compared with those available in the literature as given in Table 4. Though the obtained best solution is not guaranteed to be the global solution, the SDE has shown the superiority to the existing methods. The minimum cost obtained by SDE method is 24169.9177 $/h, which is the best cost found so far and also compared the SDE method with the GA_MU [48] and FAPSO-NM [20] methods. The minimum cost for GA_MU [48] and FAPSO-NM [20] is 24170.7550 $/h and 24169.92 $/h respectively. Table 4 Comparisons of simulation results of different methods for 13-unit case study system with PD = 2520 MW
Unit
GA_MU [48]
FAPSO-NM [20]
SDE
1
628.3179
628.32
628.3185
2
299.1198
299.20
299.1993
3
299.1746
299.98
299.1993
4
159.7269
159.73
159.7331
5
159.7269
159.73
159.7331
6
159.7269
159.73
159.7331
7
159.7302
159.73
159.7331
8
159.7320
159.73
159.7331
9
159.7287
159.73
159.7331
10
159.7073
77.40
77.3999
11
73.2978
77.40
77.3999
12
77.2327
87.69
92.3999
13
92.2598
92.40
87.6845
Total power in MW
2520.0000
2520.0000
2520.0000
Total cost in $/h
24170.7550
24169.92
24169.9177
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Fig. 8 Distribution of total costs of the SDE algorithm for a load demand of 2520 MW for 100 different trials for 13-unit case study The results demonstrate that the proposed algorithm outperforms the other methods in terms of better optimal solution. Fig. 8 shows the variations of the fuel cost obtained by SDE for 100 different runs and convergence results for the algorithms are presented in Table 5.3 for 2520 MW load. Table 5 Convergence results (100 trial runs) for 13-unit test system with PD = 2520 MW
Method
Minimum cost ($/h)
Average cost ($/h)
Maximum cost ($/h)
GA_MU [48]
24170.7550
24429.1202
24759.3120
FAPSO-NM [20]
24169.9200
24170.0017
24170.4402
SDE
24169.9176
24170.0960
24178.8346
Table 3 shows the convergence results for 100 trials for 13-unit test system with load 1800 MW and compared the minimum, average and maximum cost for GA_MU [48] and FAPSO-NM [20] methods. It has been observed that minimum, average and maximum costs for SDE proposed method is 17963.8293 $/h, 17972.8774 $/h and 17975.3434 $/h respectively. The results obtained by the proposed methods are compared with those available in the literature such as GA_MU [41], HQPSO [42], IGA_MU [48] and FAPSO-NM [20] as presented in Table 2 and Table 4. It can be seen from Table 3 and Table 5, the solution quality of SDE is better than those obtained by other methods. Use of memeplex/best mutation scheme often eliminate trapping of SDE algorithm into local minimum and provides global minimum. Analyzing the data it is worth noting as the identified solution satisfies all the system constraints. From the results obtained by SDE method, it is clear that the power balance constraint is satisfied even after considering the 4th decimal.
V. CONCLUSION
Economic Load Dispatch is one of the fundamental issues in power system operation. The problem of economic load dispatch with equality and inequality constraints has been investigated in this thesis. A novel hybrid heuristic method has been considered with simple active power balance, generation unit limits and valve point loading and successfully applied for nonconvex economic dispatch problems solution. The proposed approach is based on a hybrid shuffled differential evolution (SDE) algorithm which combines the benefits of shuffled frog leaping algorithm and differential evolution. The SDE algorithm integrates a novel differential mutation operator specifically designed for effectively addressed the problem. In order to validate the proposed methodology, detailed simulation results obtained on three standard test systems having 3, 13, and 40-units have been presented and discussed. The simulation results showed as the proposed method succeeded in achieving the goal of reduction generation costs. A comparative analysis with other settled nature-inspired solution algorithms demonstrated the superior performance of the proposed methodology in terms of both solution accuracy and convergence performances. Also it has better results compared to the other existing optimization techniques in terms of generation cost and constraints satisfactions and computation time. Therefore, the proposed method can greatly enhance the searching ability; ensure quality of average solutions, and also efficiently manages the system constraints.
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The following are future scope of work with respect to shuffled differential evolution optimization for economic load dispatch problem.
- Considering the spinning reserve capacity and ramp rate limits
- Considering the transmission losses and B co-efficient
- Develop the optimization for optimal power flow with the economic load dispatch
- Improving the shuffled differential evolution optimization for multi objective problem
- Investigating the other performance improvements for shuffled differential evolution
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Authors Profile
Miss. Chandrakala was born in 1990 in Tirumala, (Andhra Pradesh). She received
Bachelor of Technology from Yogananda Institute of Technology & Science, Tirupati
(Andhra Pradesh) in 2012. She is doing Master of Technology from Audisankara
Institute of Technology, Gudur (Andhra Pradesh). She is currently researching in the
field of Electrical Power Systems.
Mr.Ramakrishna obtained his B.Tech in Electrical and Electronics Engineering from
RGMCET-Nandyal, A.P., in 2009. After the completion of Masters Programme in
Power Systems operation& control at Sri Venkateswara University Tirupati in 2012,
presently he is working as Asst.Professor Dept.EEE, Audisankara Institute of
Technology-Gudur, Nellore, AP, India.
Mr. Jan Bhasha Shaik was born in Andhra Pradesh, India. He received the B.Tech
degree in Electrical and Electronics Engineering from JNT University, Hyderabad in
2004 and M.Tech degree in Power & Industrial Drives from JNT University Kakinada
in 2010. He is currently pursuing the Ph.D. degree at the JNT University, Anantapur,
Andhra Pradesh, India. He had worked as an Assistant Professor and IEEE student
Branch counselor at Hi-Tech College of Engineering, and worked as an Assistant
professor at KL University Guntur, AP. Currently He is working as an Associate
Professor at Audisankara Institute of Technology, Gudur, AP. He was the academic
project coordinator for Under-Graduate & Post Graduate students. His areas of
interest are HVDC, FACTS & SMART GRID.