The document discusses voltage stability enhancement in radial distribution systems through optimal placement of distributed generators (DGs) and capacitors. It proposes using the Moth Flame Optimization algorithm to determine the optimal location and sizing of DGs and capacitors to minimize power losses and maximize net savings. The methodology considers constraints like voltage limits and power flow limits. Test systems like IEEE 12 and 33 node systems are used to demonstrate the effectiveness of the proposed approach.
This document describes research on optimizing distribution systems using the Moth Flame Optimization algorithm. The research aims to minimize power losses and improve voltage stability by optimally placing distributed generators and capacitors on the network. It formulates the optimization problem and describes the Moth Flame Optimization technique. The research is expected to contribute by demonstrating the viability of using the Moth Flame Optimization algorithm to solve the distribution system optimization problem.
stability of power flow analysis of different resources both on and off gridrehman1oo
This document presents a power flow optimization strategy model for a distribution network that considers source, load, and storage. The model aims to minimize total cost, voltage deviation, and power losses over time periods determined through k-means clustering of an equivalent load curve. A particle swarm optimization algorithm is used to solve the multi-objective optimization model subject to power flow, voltage, and other constraints. The model is tested on an IEEE 33-node system and is shown to improve economic and reliability performance compared to a fixed weighting approach.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
Performance enhancement of maximum power point tracking for grid-connected ph...TELKOMNIKA JOURNAL
This paper presents a new variant of smart adaptive algorithm of Maximum Power Point Tracking (MPPT) in the photovoltaic (PV) system. The algorithm was adopted from Modified Perturb and Observe (MP&O). The smart adaptive MPPT is used to search Maximum Power Point (MPP) of the PV system under various irradiance changes. This algorithm incorporates information of current change (ΔI), maximum operating point margin and dynamic perturbation step to prevent MPPT diverging away from the MPP and minimize the steady state oscillation. The smart adaptive MPPT algorithm performance is compared with the dI-P&O and conventional P&O to prove its effectiveness. The comparison is performed under the various gradient of irradiance change. It was found that, for all the tests, the smart adaptive algorithm scheme improve the tracking efficiency under various gradients of irradiance changes and increase the efficiency of extraction power from PV system.
Comparative study of methods for optimal reactive power dispatchelelijjournal
Reactive power dispatch plays a main role in order to provide good facility secure and economic operation
in the power system. In a power system optimal reactive power dispatch is supported to improve the voltage
profile, to reduce losses, to improve voltage stability, to reduce cost etc. This paper presents a brief literature survey of reactive power dispatch and also discusses a comparative study of conventional and evolutionary computation techniques applied for reactive power dispatch. The paper is useful for researchers for further research and study so that it can apply in the various areas of power system
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
Network loss reduction and voltage improvement by optimal placement and sizin...nooriasukmaningtyas
This document presents a study on optimizing the placement and sizing of distributed generators (DGs) in radial distribution systems using a fine-tuned particle swarm optimization approach. Simulation results on the IEEE 33 bus, IEEE 69 bus, and a 54 bus Malaysian network show that integrating both active and reactive power injection (type II DGs) achieves greater reductions in network power losses and improvements in voltage profiles compared to only active power injection (type I DGs). The maximum power loss reductions achieved with three type II DGs are 89.54% for IEEE 33 bus, 94.95% for IEEE 69 bus, and 95.23% for the 54 bus Malaysian network.
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.
This document describes research on optimizing distribution systems using the Moth Flame Optimization algorithm. The research aims to minimize power losses and improve voltage stability by optimally placing distributed generators and capacitors on the network. It formulates the optimization problem and describes the Moth Flame Optimization technique. The research is expected to contribute by demonstrating the viability of using the Moth Flame Optimization algorithm to solve the distribution system optimization problem.
stability of power flow analysis of different resources both on and off gridrehman1oo
This document presents a power flow optimization strategy model for a distribution network that considers source, load, and storage. The model aims to minimize total cost, voltage deviation, and power losses over time periods determined through k-means clustering of an equivalent load curve. A particle swarm optimization algorithm is used to solve the multi-objective optimization model subject to power flow, voltage, and other constraints. The model is tested on an IEEE 33-node system and is shown to improve economic and reliability performance compared to a fixed weighting approach.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
Performance enhancement of maximum power point tracking for grid-connected ph...TELKOMNIKA JOURNAL
This paper presents a new variant of smart adaptive algorithm of Maximum Power Point Tracking (MPPT) in the photovoltaic (PV) system. The algorithm was adopted from Modified Perturb and Observe (MP&O). The smart adaptive MPPT is used to search Maximum Power Point (MPP) of the PV system under various irradiance changes. This algorithm incorporates information of current change (ΔI), maximum operating point margin and dynamic perturbation step to prevent MPPT diverging away from the MPP and minimize the steady state oscillation. The smart adaptive MPPT algorithm performance is compared with the dI-P&O and conventional P&O to prove its effectiveness. The comparison is performed under the various gradient of irradiance change. It was found that, for all the tests, the smart adaptive algorithm scheme improve the tracking efficiency under various gradients of irradiance changes and increase the efficiency of extraction power from PV system.
Comparative study of methods for optimal reactive power dispatchelelijjournal
Reactive power dispatch plays a main role in order to provide good facility secure and economic operation
in the power system. In a power system optimal reactive power dispatch is supported to improve the voltage
profile, to reduce losses, to improve voltage stability, to reduce cost etc. This paper presents a brief literature survey of reactive power dispatch and also discusses a comparative study of conventional and evolutionary computation techniques applied for reactive power dispatch. The paper is useful for researchers for further research and study so that it can apply in the various areas of power system
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
Network loss reduction and voltage improvement by optimal placement and sizin...nooriasukmaningtyas
This document presents a study on optimizing the placement and sizing of distributed generators (DGs) in radial distribution systems using a fine-tuned particle swarm optimization approach. Simulation results on the IEEE 33 bus, IEEE 69 bus, and a 54 bus Malaysian network show that integrating both active and reactive power injection (type II DGs) achieves greater reductions in network power losses and improvements in voltage profiles compared to only active power injection (type I DGs). The maximum power loss reductions achieved with three type II DGs are 89.54% for IEEE 33 bus, 94.95% for IEEE 69 bus, and 95.23% for the 54 bus Malaysian network.
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.
Atmosphere Clouds Model Algorithm for Solving Optimal Reactive Power Dispatch...ijeei-iaes
In this paper, a new method, called Atmosphere Clouds Model (ACM) algorithm, used for solving optimal reactive power dispatch problem. ACM stochastic optimization algorithm stimulated from the behavior of cloud in the natural earth. ACM replicate the generation behavior, shift behavior and extend behavior of cloud. The projected (ACM) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed algorithm in plummeting the real power loss.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Several algorithms have been offered to track the Maximum Power Point when we have one maximum power point. Moreover, fuzzy control and neural was utilized to track the Maximum Power Point when we have multi-peaks power points. In this paper, we will propose an improved Maximum Power Point tracking method for the photovoltaic system utilizing a modified PSO algorithm. The main advantage of the method is the decreasing of the steady state oscillation (to practically zero) once the Maximum Power Point is located. moreover, the proposed method has the ability to track the Maximum Power Point for the extreme environmental condition that cause the presence of maximum multi-power points, for example, partial shading condition and large fluctuations of insolation. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging circumstance, namely step changes in irradiance, step changes in load, and partial shading of the Photovoltaic array. Finally, its performance is compared with the perturbation and observation” and fuzzy logic results for the single peak, and the neural-fuzzy control results for the multi-peaks.
Parametric estimation in photovoltaic modules using the crow search algorithmIJECEIAES
The problem of parametric estimation in photovoltaic (PV) modules considering man- ufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (R p and R )), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 10 s 28 and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Keywords:BP neural network, Immune Genetic Particle Swarm Optimization algorithm, Optimal Reactive Power, Transmission loss.
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
Identification study of solar cell/module using recent optimization techniquesIJECEIAES
This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.
Multi-objective whale optimization based minimization of loss, maximization o...IJECEIAES
Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.
Genetic algorithm based Different Re-dispatching Scheduling of Generator Unit...IDES Editor
Proper pricing of active power is an important issue
in deregulated power environment. This paper presents a
flexible formulation for determining short run marginal cost
of synchronous generators using genetic algorithm based
different re-dispatching scheduling considering economic load
dispatch as well as optimized loss condition. By integrating
genetic algorithm based solution, problem formulation became
easier. The solution obtained from this methodology is quite
encouraging and useful in the economic point of view and it
has been observed that for calculating short run marginal
cost, generator re-dispatching solution is better than classical
method solution. The proposed approach is efficient in the
real time application and allows for carrying out active power
pricing independently. The paper includes test result of IEEE
30 bus standard test system.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
A Reliable Tool Based on the Fuzzy Logic Control Method Applying to the DC/DC...phthanh04
Solar energy performs an important role in electric energy based on renewable energy generation systems when referring to
clear energy. Systems for harvesting renewable energy frequently use DC/DC converters, especially solar photovoltaic systems. The
DC/DC boost converter has been used for converting the output voltage from the solar PV system to the required voltage rating of the
utility grid under the disturbance in the photovoltaic temperature and irradiation level. Because of that, a new maximum power point
tracking based on the fuzzy logic controller (MPPT-FLC) algorithm applying the DC/DC boost converter is developed. The proposed
approach aims toward improving the PV system's performance and tracking effectiveness. This aim can be achieved by adjusting the
DC/DC boost converter's duty cycle to ensure that the PV system operates close to its MPP under varying environmental conditions. The
effectiveness of the proposed method is verified in the off-grid PV system under conditions of the change of irradiation and temperature,
and the comparison of between the proposed method, the incremental conductance (INC), perturb and observe (P&O), and modified P&O
methods is also made. The obtained simulation results show that the MPPT capability significantly improved and achieved the highest
MPPT efficiency of 99.999% and an average efficiency of 99.98% in total when applying the proposed method.
The use of a new control method for grid-connected inverters for reducing the output current harmonic distortion in a wide range of grid-connected distributed generation (DG) applications, including wind turbine (WT) and fuel cell (FC) inverters is proposed in this paper. The control method designed to eliminate main harmonics in a microgrid (MG) and between MG and point of common coupling (PCC) and responsible for the correction of the system unbalance. Another advantage of the proposed control method is that it can be easily adopted into the DG control system without the installation of extra hardware. The proposed control method is comprised of the synchronous reference frame method (SRF). Results from the proposed control method are provided to show the feasibility of the proposed approach.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
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.
IRJET- Optimal Placement and Size of DG and DER for Minimizing Power Loss and...IRJET Journal
This document summarizes research on optimizing the placement and sizing of distributed generation (DG) and distributed energy resources (DER) in a 33-bus distribution system to minimize power losses. Two optimization techniques are evaluated: Grasshopper Optimization Algorithm (GOA) and Moth Flame Optimization (MFO). MFO shows better results, identifying bus 13, 24 and 30 as optimal locations for DG, reducing losses from 0.2027 MW to 0.0715 MW at normal load. For DER, optimal locations are DG at buses 13, 25, 30 and capacitors at buses 7, 13, 30, further reducing losses to 0.0144 MW. Graphs and tables show MFO placement
Energy Management System in Smart Microgrid Using Multi Objective Grey Wolf O...IRJET Journal
This document proposes an energy management system for a smart microgrid using a multi-objective grey wolf optimization algorithm. The goals are to maximize the use of local renewable energy generation, minimize consumer energy costs, and reduce greenhouse gas emissions. It describes energy controllers that would manage energy sharing between providers and customers. The multi-objective grey wolf optimization technique is said to provide faster optimization than other methods. Simulation results reportedly show reductions in both pollution and energy consumption costs with this approach.
In photovoltaic (PV) systems, maximum power point tracking (MPPT) techniques are used to track the maximum power from the PV array under the change in irradiance and temperature conditions. The perturb and observe (P&O) is one of the most widely used MPPT techniques in recent times due to its simple implementation and improved performance. However, the P&O has limitations such as oscillation around the MPP during which time the P&O algorithm will become confused due to rapidly changing atmospheric conditions. To overcome the above limitation, this paper uses the fuzzy logic controller (FLC) to track the maximum power from the PV system under different irradiance, integrates it with a DC-DC boost converter as a tracker. The result of the FLC performance is compared with the traditional P&O method and shows the MPPT algorithm based on FLC ensures continuous tracking of the maximum power within a short period compared with the traditional P&O method. Besides that, the proposed method (FLC) has a faster dynamic response and low oscillations at the operating point around the MPP under steady-state conditions and dynamic change in irradiance.
Improvement of voltage profile for large scale power system using soft comput...TELKOMNIKA JOURNAL
In modern power system operation, control, and planning, reactive power as part of power system component is very important in order to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to improve the voltage profile and decrease the total power loss of transmission line system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. The effectiveness of the proposed technique is examined by utilizing Java-Madura-Bali (JAMALI) 500 kV power system grid as the test system. From the simulation results, the PSO and ABC algorithms are providing satisfactory results in obtaining the capacitor size and can reduce the total power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u.
Determining optimal location and size of capacitors in radial distribution ne...IJECEIAES
In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.
This paper recommends the use of grasshopper optimization algorithm (GOA), a nature-inspired optimization algorithm, for optimizing switching-angle applied to cascaded H-bridge multilevel inverter (CHBMLI). Switching angles are selected based on the minimum value of the objective function formulated using the concept of selective harmonic minimization pulse width modulation (SHMPWM) technique. MATLAB/Simulink-PSIM dynamic co-simulation conducted on a 3-phase 9-level CHBMLI shows that the CHBMLI controlled using GOA derived switching-angle is able to respond to varying modulation index demand and synthesize an AC staircase output voltage waveform with the desired fundamental harmonic and minimized selected low-order harmonics. Compared to Newton Raphson (NR) technique, GOA is able to find optimum switching-angle solutions over a wider modulation index range. Compared to Genetic Algorithm (GA), GOA is able to find global minima with higher probability. The simulation results validate the performance of GOA for switching-angle calculation based on the concept of SHMPWM.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Atmosphere Clouds Model Algorithm for Solving Optimal Reactive Power Dispatch...ijeei-iaes
In this paper, a new method, called Atmosphere Clouds Model (ACM) algorithm, used for solving optimal reactive power dispatch problem. ACM stochastic optimization algorithm stimulated from the behavior of cloud in the natural earth. ACM replicate the generation behavior, shift behavior and extend behavior of cloud. The projected (ACM) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed algorithm in plummeting the real power loss.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Several algorithms have been offered to track the Maximum Power Point when we have one maximum power point. Moreover, fuzzy control and neural was utilized to track the Maximum Power Point when we have multi-peaks power points. In this paper, we will propose an improved Maximum Power Point tracking method for the photovoltaic system utilizing a modified PSO algorithm. The main advantage of the method is the decreasing of the steady state oscillation (to practically zero) once the Maximum Power Point is located. moreover, the proposed method has the ability to track the Maximum Power Point for the extreme environmental condition that cause the presence of maximum multi-power points, for example, partial shading condition and large fluctuations of insolation. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging circumstance, namely step changes in irradiance, step changes in load, and partial shading of the Photovoltaic array. Finally, its performance is compared with the perturbation and observation” and fuzzy logic results for the single peak, and the neural-fuzzy control results for the multi-peaks.
Parametric estimation in photovoltaic modules using the crow search algorithmIJECEIAES
The problem of parametric estimation in photovoltaic (PV) modules considering man- ufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (R p and R )), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 10 s 28 and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Keywords:BP neural network, Immune Genetic Particle Swarm Optimization algorithm, Optimal Reactive Power, Transmission loss.
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
Identification study of solar cell/module using recent optimization techniquesIJECEIAES
This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.
Multi-objective whale optimization based minimization of loss, maximization o...IJECEIAES
Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.
Genetic algorithm based Different Re-dispatching Scheduling of Generator Unit...IDES Editor
Proper pricing of active power is an important issue
in deregulated power environment. This paper presents a
flexible formulation for determining short run marginal cost
of synchronous generators using genetic algorithm based
different re-dispatching scheduling considering economic load
dispatch as well as optimized loss condition. By integrating
genetic algorithm based solution, problem formulation became
easier. The solution obtained from this methodology is quite
encouraging and useful in the economic point of view and it
has been observed that for calculating short run marginal
cost, generator re-dispatching solution is better than classical
method solution. The proposed approach is efficient in the
real time application and allows for carrying out active power
pricing independently. The paper includes test result of IEEE
30 bus standard test system.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
A Reliable Tool Based on the Fuzzy Logic Control Method Applying to the DC/DC...phthanh04
Solar energy performs an important role in electric energy based on renewable energy generation systems when referring to
clear energy. Systems for harvesting renewable energy frequently use DC/DC converters, especially solar photovoltaic systems. The
DC/DC boost converter has been used for converting the output voltage from the solar PV system to the required voltage rating of the
utility grid under the disturbance in the photovoltaic temperature and irradiation level. Because of that, a new maximum power point
tracking based on the fuzzy logic controller (MPPT-FLC) algorithm applying the DC/DC boost converter is developed. The proposed
approach aims toward improving the PV system's performance and tracking effectiveness. This aim can be achieved by adjusting the
DC/DC boost converter's duty cycle to ensure that the PV system operates close to its MPP under varying environmental conditions. The
effectiveness of the proposed method is verified in the off-grid PV system under conditions of the change of irradiation and temperature,
and the comparison of between the proposed method, the incremental conductance (INC), perturb and observe (P&O), and modified P&O
methods is also made. The obtained simulation results show that the MPPT capability significantly improved and achieved the highest
MPPT efficiency of 99.999% and an average efficiency of 99.98% in total when applying the proposed method.
The use of a new control method for grid-connected inverters for reducing the output current harmonic distortion in a wide range of grid-connected distributed generation (DG) applications, including wind turbine (WT) and fuel cell (FC) inverters is proposed in this paper. The control method designed to eliminate main harmonics in a microgrid (MG) and between MG and point of common coupling (PCC) and responsible for the correction of the system unbalance. Another advantage of the proposed control method is that it can be easily adopted into the DG control system without the installation of extra hardware. The proposed control method is comprised of the synchronous reference frame method (SRF). Results from the proposed control method are provided to show the feasibility of the proposed approach.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
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.
IRJET- Optimal Placement and Size of DG and DER for Minimizing Power Loss and...IRJET Journal
This document summarizes research on optimizing the placement and sizing of distributed generation (DG) and distributed energy resources (DER) in a 33-bus distribution system to minimize power losses. Two optimization techniques are evaluated: Grasshopper Optimization Algorithm (GOA) and Moth Flame Optimization (MFO). MFO shows better results, identifying bus 13, 24 and 30 as optimal locations for DG, reducing losses from 0.2027 MW to 0.0715 MW at normal load. For DER, optimal locations are DG at buses 13, 25, 30 and capacitors at buses 7, 13, 30, further reducing losses to 0.0144 MW. Graphs and tables show MFO placement
Energy Management System in Smart Microgrid Using Multi Objective Grey Wolf O...IRJET Journal
This document proposes an energy management system for a smart microgrid using a multi-objective grey wolf optimization algorithm. The goals are to maximize the use of local renewable energy generation, minimize consumer energy costs, and reduce greenhouse gas emissions. It describes energy controllers that would manage energy sharing between providers and customers. The multi-objective grey wolf optimization technique is said to provide faster optimization than other methods. Simulation results reportedly show reductions in both pollution and energy consumption costs with this approach.
In photovoltaic (PV) systems, maximum power point tracking (MPPT) techniques are used to track the maximum power from the PV array under the change in irradiance and temperature conditions. The perturb and observe (P&O) is one of the most widely used MPPT techniques in recent times due to its simple implementation and improved performance. However, the P&O has limitations such as oscillation around the MPP during which time the P&O algorithm will become confused due to rapidly changing atmospheric conditions. To overcome the above limitation, this paper uses the fuzzy logic controller (FLC) to track the maximum power from the PV system under different irradiance, integrates it with a DC-DC boost converter as a tracker. The result of the FLC performance is compared with the traditional P&O method and shows the MPPT algorithm based on FLC ensures continuous tracking of the maximum power within a short period compared with the traditional P&O method. Besides that, the proposed method (FLC) has a faster dynamic response and low oscillations at the operating point around the MPP under steady-state conditions and dynamic change in irradiance.
Improvement of voltage profile for large scale power system using soft comput...TELKOMNIKA JOURNAL
In modern power system operation, control, and planning, reactive power as part of power system component is very important in order to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to improve the voltage profile and decrease the total power loss of transmission line system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. The effectiveness of the proposed technique is examined by utilizing Java-Madura-Bali (JAMALI) 500 kV power system grid as the test system. From the simulation results, the PSO and ABC algorithms are providing satisfactory results in obtaining the capacitor size and can reduce the total power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u.
Determining optimal location and size of capacitors in radial distribution ne...IJECEIAES
In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.
This paper recommends the use of grasshopper optimization algorithm (GOA), a nature-inspired optimization algorithm, for optimizing switching-angle applied to cascaded H-bridge multilevel inverter (CHBMLI). Switching angles are selected based on the minimum value of the objective function formulated using the concept of selective harmonic minimization pulse width modulation (SHMPWM) technique. MATLAB/Simulink-PSIM dynamic co-simulation conducted on a 3-phase 9-level CHBMLI shows that the CHBMLI controlled using GOA derived switching-angle is able to respond to varying modulation index demand and synthesize an AC staircase output voltage waveform with the desired fundamental harmonic and minimized selected low-order harmonics. Compared to Newton Raphson (NR) technique, GOA is able to find optimum switching-angle solutions over a wider modulation index range. Compared to Genetic Algorithm (GA), GOA is able to find global minima with higher probability. The simulation results validate the performance of GOA for switching-angle calculation based on the concept of SHMPWM.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
2. INTRODUCTION
OBJECTIVE OF THE THESIS
VOLTAGE STABILITY ENHANCEMENT IN RDS
COST BENEFIT ANALYSIS OF DISCO’S AND DG OWNERS
SOLUTION METHODOLOGY
RESULTS AND DISCUSSIONS
CONCLUSION AND REFERENCES
LIST OF PUBLICATIONS
2
Overview of presentation
4. 4
In the regulated power industry, Distribution systems hold a
very significant position in the power system since it is the
main point of link between bulk power and consumers.
Effective planning of radial distribution network is required to
meet the present growing domestic, industrial and commercial
load day by day.
Distribution networks commonly employed in radial structure.
Due to inappropriate design and planning of distribution
networks, the power system would face various problems. It
includes decreasing reliability, increasing power loss,
reducing voltage stability and other safety issues.
Among these problems, Voltage stability enhancement and
power loss minimization is the more important concern of
distribution system operators.
5. 5
Solutions addressed in literature to solve the above problems
are
Capacitor placement
Installation of DGs
Using FACTS devices and
Distribution Network Reconfiguration (DNR)
In the present research, the following methods are adopted to
improvise the benefits for distribution system
Distribution Generations (DGs)
Capacitors placements and
Network reconfiguration.
6. 6
Proper placement and optimal sizing of DGs and capacitors
to effectively regulate the voltage profile, reliability and
power quality, reduce the network losses, and maximize
operational benefits of DISCOs and DG owners.
This research proposes optimization algorithm to address
the problems of distribution system by optimal placement
of DGs, Capacitors, and Network reconfiguration to
enhance the voltage profile, minimize the power loss and
improve the net saving cost of RDS.
Research Gaps from Literature
8. 8
Deregulation of electric power systems have created a
competitive open market environment.
A deregulated system allows the power supply to function
competitively, as well as allows the consumers to choose
suppliers of electric energy.
Deregulation involves unbundling, represents
disaggregation into the basic parts of generation,
transmission and distribution
Liberalization – Introduction of a less restrictive regulatory
framework for companies within a power sector.
Modification of existing regulation.
Privatization – sale of government assets to the private
sector
11. 11
To propose a simple and efficient optimization approach of
Moth Flame Optimization (MFO) algorithm for the solution
of voltage stability problem by optimal placement and sizing
of combined DGs and capacitors.
To evolve a solution Methodology based on network
reconfiguration and optimal allocation and sizing of DGs and
Capacitors with a view of improving the voltage stability and
net saving cost of radial distribution system.
To implement and investigate the performance of proposed
MFO approach for optimal allocation of DG units in DISCOs
so as to enhance the voltage profile, minimize the power loss,
and maximize benefit of the system.
To suggest a practical approach of combined capacitor and
DG units with network reconfiguration to maximize the
Profit of DISCOs in a Competitive electricity market
12. 12
EXISTING METH0DOLOGY IN LITERATURE
The classical methods such as
• Dynamic programming (DP)
• Lagrangian relaxation (LR)
• Mixed-integer programming (MIP)
• Benders decomposition
• Network flow with Newton’s method
• Linear programming and
• Nonlinear programming
14. 14
•These all have been successfully employed to solve the
voltage stability problem, However each algorithm has its
own merits & demerits
•This research presents a simple and parameter less algorithm
of MFO to solve the various voltage stability problem under
regulated and deregulated power system
16. 16
Moth – Flame Optimization (MFO)
Technique
Moth fly optimization algorithm which was introduced by
Mirjalili during 2015.
It has received wider attention among the researchers and has
been applied to solve multi objective optimization algorithms.
It exhibits a competitive performance over other algorithms
because of its good convergence attitude.
This technique is formulated on the basis of biological
behaviour of moth fighting flames in field.
The MFO technique uses a community of moths in order to do
the optimization process and each and every moth is needs to
upgrade their position with reference to the flame.
17. 17
It protects the moth to evade from the entrapment of local
optima and to regain its inspection process in the search space.
More specifically, its performance is on the virtue of the
transverse orientation process.
The navigating nature of the moth has inspired the researchers
to carry out this kind of optimization problem.
It is illustrated in the below figure, where the light is the prime
source and convergence of moths be exercised by preserving a
fixed angle.
Fig.: Artificial light
source for moths in
spiral flying path
18. 18
MFO technique is basically swarm based optimization
module, the population of moth can be expressed as
While ‘n’ indicates amount of count of moths and ‘d’ refers
dimension size of the optimization problem in this solution
space.
Further, it is also pretended that there is a proportionate series
of fitness vectors, and it can be described as
1,1 1,2 1,
2,1 2,2 2,
,1 ,2 ,
d
d
n n n d
m m m
m m m
M
m m m
1
2
n
OM
OM
OM
OM
19. 19
It is necessary for every moth to renew its place with respect
to the flame relevant over it, in order to avert the technique
slipping in the local optimal point.
This processes support the technique moving towards the
global searching mode.
Here the location of the moth and flame is the search area and
become variable matrices of the equal dimension.
The fitness value vectors are assumed as,
1
2
n
OF
OF
OF
OF
20. 20
The suggested MFO implements three types of operations for
achieving the global best optimal values and the tasks are
outlined as,
The random distribution of moth is formulated as
Hence, the logarithmic spiral of the MFO algorithm may be
formulated as
The space among the i-th moth and the j-th flame represented
in the form of
( , , )
MFO I P T
, * ( )
M i j ub i lb j rand lb i
, coscos(2 )
bt
S Mi Fj Die t Fj
Di Fi Mi
21. 21
Updating the count of Flames
During the course of iteration process, the number of flames
gets decreased so as to keep the equilibrium in exploration and
exploitation process.
This balanced decrement in the count of flames stabilises the
activities in the exploration space.
For the best moth position, the best flame has to be identified
from the previous iteration and the best objective function value
is obtained using equation.
N l
Flame no round N l
t
23. 23
In this Research a simple methodology has been proposed for
optimally placing and sizing of the combined DG and capacitor
units in RDS.
The work is focused to improve the characteristic nature of
voltage profile and to minimise the network losses.
A metaheuristic algorithm namely Moth Flame Optimization
algorithm is employed to figure out this problem.
24. 24
The algorithm works on the basis of natural behavior of moths
against lights and it has two essential components of moth and
flames.
The viability of the proposed method has been demonstrated
by the test case analysis on IEEE 12 and 33 node systems and
the observations are correlated with that of different methods
reported in literatures.
25. 25
The mathematical equation for computing the VSI is
formulated as The mathematical equation for computing the
VSI is formulated as
(1)
Objective function
The prime intention of the proposed exercise is to minimize the
total active power loss at a point of full load condition of the
distribution system as suggested by the following equation
(2)
Where,
PL = Power Loss
Ipq= Current passing through the section connected across p and
q is specified as
2
4 2
4 4
F F F F
p q pq q pq q pq q pq p
VSI q V P X Q R P R Q X V
2
, | , B
L pq pq
p q p q S
MinP I R
26. 26
2 2
2
pq pq
pq
p
P Q
I
V
Rpq = Series resistance
1. Equality and Inequality Constraints
The various equality and inequality constraints are presented
from equations (2) – (9).
While implementing the DGs, the voltage of different buses
and current through the lines are to be kept at safer limits for
the stable functioning of distribution system.
27. 27
2. Real and reactive power flow limit
The mathematical representation of active and reactive power
flow over the line m is defined using equation (3) and (4)
(3)
(4)
3. Magnitude of the Voltage
The sending and receiving point voltage magnitude of the RDS
must satisfy equation (5)
2 2
2
pq
F L DG
pq q q q pq pq
p
R
P P P P P Q
V
2 2
2
pq
F L DG C
pq q q q q pq pq
p
X
Q Q Q Q Q P Q
V
2 2
2 2 2 2
2
2
pq pq
q p pq pq pq pq pq pq
p
R X
V V P R Q X P Q
V
(5)
28. 28
4. Voltage profile
Bus voltage of each bus must lies between minimum and
maximum limits of the tolerable limits.
(6)
5. Line current
The line current in each branch must lie within the thermal limit.
(7)
6. Capacity of DG unit
The capacity of DG unit should be less than or equal to some
percentage of total feeder load.
max
qq
min
q B
V q S
rated
pq pq B
I I pandq S
2 2 2 2
0.5
B B
DG DG L L
q q q q
q S q S
P Q P Q
(8)
29. 29
7. Size of the Capacitor
Size of the capacitor must be within the sum of reactive power
load of the system.
1.0
B B
c L
q q
q S q S
Q Q
(9)
30. 30
The MFO algorithm has been applied for the computation of best
solution by using the following steps
STEP 1: Read the system information
STEP 2: Execute distribution load flow for base case.
STEP 3: Fix number of DG and Capacitor are to be used to in RDS.
STEP 4: Initialize count of moths (Population), maximum no of
iterations, dimension, lower bound and upper bound (node and size of
DG and Capacitor respectively)
STEP 5: Set iteration=1.
31. 31
STEP 6: Calculate fitness (i.e. loss in network) for each moth by
placing DG and Capacitor at their respective buses using eqn. (15).
STEP 7: Update the position of flame and save the best fitness values
in an array corresponding to eqn. (16)
STEP 8: Update the record of flames and the flames are arranged
using eqn. (17) based on their fitness values
STEP 9: Compute the present position of moths.
STEP 10: Check the all constrains are satisfied, if yes move to next
step, else go to step 6.
STEP 11: Check If the number of iteration process is equal to
maximum number of iterations, go to step 12. Otherwise go to step 5.
STEP 12: Display the optimal solution and STOP the program.
32. Fig. 5 Flow chart of the proposed MFO technique
33. 33
CASE STUDY AND RESULTS
In the present investigation, two standard test systems such as
IEEE12 and 33 nodes are taken in to account to illustrate the
validity of devised algorithm.
The simulations are performed on MATLAB 14.0 platform.
The solution has been obtained with different test cases.
Test system 1: IEEE12 node RDS
In this test case, total capacity of the system is 11 KV, it contains
12 node and 11 lines with total real and reactive load of 435 kW and
395 KVAr.
The proposed MFO is a parameter less algorithm and it has only
common control parameters.
It includes agents or number of moth = 30, Most extreme number
of iterations = 100, Number of variables = 11. The following three
different test cases are analysed by MFO approach
34. 34
Optimal allocation of capacitor alone with its best size and
placement
Optimal allocation of simply DGs operating at unity PF at
best location
Optimal allocation both capacitor and DGs operated at
unity PF with best size.
Distribution power flow method is proposed to do the base case
power flow.
The voltage profile of the IEEE12 bus system with dissimilar
cases are reported in Table 1 and graphically represented in fig.
6.
From the table, the voltage profiles are highly improved by
optimal placement of combined DG and capacitor compared
with base case, single capacitor and sing DG.
The VSI with various cases are presented in table 2 and also
graphically displayed in figure 7.
42. 42
Table 4. Optimal location, size and minimum voltage for proposed
with existing method
Particulars
Conventional Method [4] MFO (Proposed)
Location and size
Min voltage
(p.u.)
Location and
size
Min voltage
(p.u.)
Base case -
0.94414 at bus
12
- -
Capacitor
0.16 MVAr
capacitor at bus
12
0.95596 at bus
11
0.2 MVAr
capacitor at bus
9
0.9556at bus
12
DG at UPF 0.2MW DG bus 12
0.98032 at bus
8
0.2355 MW DG
bus 9
0.9830 at bus
12
Both DG and Capacitor
0.12MW DG at bus
12 and 0.24 MVAr
capacitor at bus
12
0.9815 at bus
8
0.23296 MW DG
at bus 9 and
0.25 MVAr
capacitor at bus
8
0.9919at bus 6
Table 5. Network losses and loss reduction for proposed with
existing method
Particulars
Conventional Method MFO (Proposed)
Network
loss (kW)
% Loss
reduction
Network
loss (kW)
% Loss
reduction
Base case 198.9 - - -
Capacitor 134.3 32.47 126.028 36.6375
DG at UPF 109.2 45.09 107.744 45.8301
DG and
Capacitor
71.93 63.8 31.693 84.0659
44. 44
The numerical results are clearly reported in Table 3, it
includes optimal location, size and network losses of the RDS
with different cases.
The convergence characteristics of three different cases are
displayed in fig. 8, fig. 9 and fig. 10.
The power loss of the three different cases is graphically
displayed in fig. 11.
The comparative review has also been done to assess the
applicability and superiority of planned MFO.
Comparison of optimal location, size and minimum voltage for
proposed with prevailing practices are presented in Table 4.
The system losses and curtailment of losses are also compared
with accepted methods are recorded in Table 5.
From the table 1 and 5, it is established that the proposed MFO
enhances the voltage profile besides reducing the network losses
of the system.
45. Test system 2: IEEE33 Bus RDS
45
In second case, the large scale system of IEEE33 node system
is taken into account in order to demonstrate the efficacy of the
devised MFO methodology.
The voltage rating is 12.66 KV with a absolute load of 3.72
MW and 2.3 MVAR are considered in this test system.
The MFO algorithmic specification includes count of search
operators or count of moth = 40, Maximum iterations = 100,
total variables = 11.
The proposed system has been analysed on the following five
different test cases.
46. 46
Optimal allocation of capacitor alone with its best size and
placement
Optimal allocation of simply DGs operating at unity PF at
best location
Optimal allocation of both capacitor and DGs operated at
unity PF with best size
Optimal allocation of both DGs and capacitor at 0.9 Pf lag
with economical size
Optimal allocation of both DGs and capacitor at 0.85 Pf lag
with economical size
51. 51
Fig. 12. Voltage profile
for IEEE 33 node RDS
(Case1, 2 and 3)
Fig. 13. Voltage profile for IEEE
33 node RDS (Case 4 and 5)
52. 52
Fig. 14. VSI for IEEE 33
node RDS (Case 1, 2 and
3)
Fig. 15. VSI for IEEE 33 node RDS
(Case 4 and 5)
53. 53
Table 8. Numerical results for IEEE 33 node RDS
Components
Optimal
location
Optimal
Size
Power
Loss (kW)
Capacitor 29 1.7 157.6864
DG at UPF 12 1 129.9648
DG and Capacitor 12, 30 1,1. 2 75.0069
Fig. 16. Convergence curve for Single
Capacitor placement
54. 54
Fig.17. Convergence curve for
Single DG placement
Fig. 18. Convergence curve for
combined DG (UPF) with Capacitor
placement
55. 55
Table 9. Numerical results of IEEE 33
node RDS with low power factor
Cases
Optimal
location
Optimal
Size
Power Loss
(kW)
0.85pf
Capacitor 29 1
84.3753
DG 30 1.8
0.9pf
Capacitor 29 1
83.5144
DG 30 1.7
56. 56
Table 10. Comparison of optimal location, size and minimum voltage for
proposed with existing method
Particulars
Conventional Method [1] MFO (proposed)
Location and
size
Min voltage
(p.u.)
Location
and size
Min voltage
(p.u.)
Base case - 0.9065 at 18 - -
Capacitor
1.0 MVAr at
33
0.91654 at 18
1.7 MVAr at
29
0.9499 at
18
DG at UPF 1.0 MW, at 18 0.9311 at 33 1MW at 12
0.9199 at
18
DG and
Capacitor
1.0 MW at 18
and 1.0 MVAr
at 33
0.96003 at 30
1.0 MW at
12 and
1.2 MVAr at
30
0.9617 at
18
0.9 PF lag
1.0 MW at 18
and 1.0 MVAr
at 33
0.9646 at 30
1.8 MW at
30 and 1.0
MVAr at 29
0.9312 at
18
0.85 PF lag
0.8 MW at 18
and 0.8 MVAr
at 33
0.9566 at 30
1.7 MW at
30 and 1.0
MVAr at 29
0.9309at 18
57. 57
Table 11. Comparison of power loss and loss reduction
for proposed with existing method
Particulars
Conventional Method [1] MFO (proposed)
Network loss
(kW)
% Loss
reduction
Total real
power loss
(kW)
% Loss
reduction
Base case 213.3 - - -
Capacitor 164.6 22.83 157.6864 26.0729
DG at UPF 142.34 33.29 129.9648 39.0695
DG and
Capacitor
96.70 54.66 75.0069 64.8350
0.9 PF lag 90.9 57.38 83.5144 60.8465
0.85 PF lag 89.72 57.94 84.3753 60.4429
58. SUMMARY
58
In this work has been subjected to various case studies with
different configurations under two bench mark test systems to
substantiate the excellence of the projected algorithm.
The outcome of the problem has been compared with the other
conventional approach in order to validate the results.
The solution of the case studies demonstrates the strength of
this algorithm in distribution systems.
59. Optimal Network Reconfiguration and Capacitor
Placement for improving Voltage Stability and
Net Savings in Radial Distributed Systems
59
In this case study the combined methodology of Capacitor
placement and Network reconfiguration is properly applied to
maximize the net saving cost, minimize the power loss and improve
the voltage profile.
The size and location of capacitors and tie-line switches of nodes are
optimally allocated by the effectual Moth-Flame Optimization (MFO)
algorithm.
The MFO is an effective nature-inspired algorithm based on the
chemical effect of light on moths as an animal with bilateral
symmetry.
This algorithm provides a better solution with less computational
time by two searching operators of Moth and Flame.
The Performance of the MOF is analyzed by a standard test system
of IEEE 33 and 69-node RDS.
60. 60
The best simulation results of loss reduction, voltage
enhancement, and cost-saving are numerically and graphically
reported.
The experimental outcomes obtained from this method are
compared with that of other soft computing techniques available
in the literature.
61. PROBLEM FORMULATION
61
Voltage stability Indices (VSI)
The VSI for the node can be mathematically represented using
the following equation.
Objective function
The prime objective of the proposed work is to maximize the net
saving cost of the RDS. The savings of the RDS mainly depends
on the reconfiguration process, power loss, and optimal
allocation and value of capacitors.
The MFO algorithm-based net saving maximization is
mathematically formulated as,
2
2
4
1
2
2
0
.
4
2
2
0
.
4
1
2
m
V
jj
x
m
Q
jj
r
m
P
jj
r
m
Q
jj
x
m
P
m
V
m
VSI
B
o
N
i
ci
P
B
I
LA
LB
E N
C
Q
C
N
C
T
P
P
C
f
B
1
max
62. 62
Equality and inequality Constraints
Real and Reactive power limits
The active and reactive power of RDS are mathematically represented as
follows
Reactive Power Compensation Limits
The reactive power delivered by each switched capacitor is limited by its
lower and upper limits as,
The acceptable capacitor range 0 to 1500 KVAr with step of 50 KVAr.
)
(
)
(
1
2
j
P
i
P
P
NL
j
loss
NB
i
D
SS
)
(
)
(
)
(
1
1
2
k
Q
j
Q
i
Q
Q
NC
k
C
NL
j
loss
NB
i
D
SS
max
0 Ci
Ci Q
Q
63. 63
Voltage Profile Limits
The voltage magnitude of each node in the radial distribution system is
strictly maintained as,
Line thermal Limits
The current flows in the branches should not go beyond the thermal
capacity of the line.
max
min
i
i
i V
V
V
max
)
,
(
, j
i
j
i I
I
64. 64
IMPLEMENTATION OF MFO ALGORITHM
The following steps are used for optimal allocation and sizing of capacitor
with network configuration to enhance the voltage profile using MFO
algorithm.
1. Read the line, bus, and load data of RDS,
2. Run the distribution power flow and calculate the loss using the exact
loss formula for the base case.
3. Fix a number of Capacitors that are to be used in the Radial Distribution
System.
4. Initialize the parameters of the MFO algorithm such as Population,
dimension, maximum no of iteration number, lower bound, and upper
bound (node and size of Capacitor respectively).
5. Set iteration=1
6. Calculate fitness (i.e. loss in a network) for each moth by placing DG and
Capacitor at their respective buses using Eqn. (15).
7. Evaluate the objective functions of each moth and determine the net
savings of RDS...
65. 65
8. Update the position of flame and save the best fitness values in an array
corresponding to Eqn. (16)
9. Update the record of flames and the flames are arranged using Eqn. (17)
based on their fitness values
10. Compute the present position of moths.
11. Check that all constraints are satisfied, if yes move to the next step, else
go to step 6.
12. Check If the number of iteration processes is equal to a maximum
number of iterations, go to step 13. Otherwise, go to step 5.
13. Display the global best solution of net saving cost and voltage profile
and STOP the program.
66. RESULTS AND DISCUSSIONS
66
In this study two standard test systems of IEEE 33 and 69
node RDS are considered to determine the superior
performance of the proposed MFO algorithm.
The one-line diagram of Network Reconfiguration with
capacitors for IEEE 33-node RDS is shown in fig. 1.
The enhanced voltage for each node is compared to base case
voltage and graphically displayed in fig 2 and obtained VSI
also compared with fig. 3.
67. 67
Fig 1. Network Reconfiguration with capacitors for IEEE33-node
RDS
68. 68
Fig.2. Comparison of Voltage
profile of base case and
proposed MFO
Fig.3. Comparison of VSI of
base case and proposed MFO
72. 72
Test case 2 : IEEE 69 node RDS
In the second case, the large-scale system of 69 node is
considered to find the ability of the projected MFO algorithm.
The reconfiguration process and capacitor allocation are
implemented in this test system.
73. 73
Fig 5. Network Reconfiguration with capacitors for IEEE 69-node RDS
74. 74
Fig. 6 Voltage profile for base
case and Reconfiguration with
placement of capacitor in IEEE
69-bus test system
Fig. 7 VSI for base case and
Reconfiguration with placement
of capacitor in
IEEE 69-bus test system
75. 75
Table 3: Comparisons of numerical results in various methods for IEEE 69-
node system
Method Switches
opened
Ploss (KW) VSImin Vmin (p.u.) Node no. Capacitor
size
(KVAr)
Loss
reduction
(%)
Base case 69,70,71,
72,73
224.97 0.6833 0.9090 - - -
MFPA[12] 10,68,60,
44,15
153.93 0.7494 0.9305 64
63
62
350
600
250
31.58
MFO
(Proposed
)
26, 45,
58, 16, 10
85.793 0.82688 0.95359 61
64
21
900
200
250
61.86
Fig. 8: Convergence
characteristics of IEEE 69-
node test system
76. SUMMARY
76
In this study IEEE 33 and 69 node test systems are taken into
account to test the performance of the MFO.
This algorithm effectively maximizes the net saving cost,
minimizes power loss, and improves the stability of the system.
From the results, it can be concluded that it is a most excellent
and robust algorithm for solving all engineering optimization
problems.
77. 77
•In recent years, Distributed Generation (DG) has been utilized in
electric power networks increasingly. DG units can affect the
system operational conditions in different ways such as voltage
profile improvement, amending voltage stability, reliability
enhancement, securing power market, etc.
•The Distribution Companies (DISCOs) are continually trying to
supply reliable and economical electric power to consumers. The
design, operation and maintenance of the DISCOs are framed on
the lowest cost and for the highest benefit.
•The voltage profile enhancement and power loss reduction are
two important tasks in the DISCOs for achieving maximum profit.
Many technical ideas and creative programs are being developed
by the DISCOs to improve the performance.
78. 78
An innovative parameter less algorithm of MFO is suggested
to optimize the best position and accurate value of DG units.
The uncertainty of load demand, power generation, electricity
price and reliability are considered in this work.
The validation of this method is tested on standard IEEE 33
and IEEE 69 node system to illustrate the superior performance
of MFO algorithm.
The simulation results of voltage profile, power loss, location
and size of DG, cost-benefit of DISCOs and DG owners are
numerically and graphically presented.
The comparative study also has been made to prove the
success of the devised method.
79. 79
PROBLEM FORMULATION
The prime objective of this study is to maximize the profit of DG owners and
minimize the various cost of DISCOs. Profit of DG owners is mathematically
represented as follows.
where t = 1, 2,3,…,N; NPV = net present value, IF = inflation rate, and IR =
interest rate
The present cost value of CDG,Gen is estimated using Eq. (8).
Benefit Evaluation
This cost actually includes the sum of energy loss reduction cost, which is
taken as US$ 0.05 per kWh and the cost of DG generated power at US$ 300
per kW.
MPF
F
)
max(
Expenses
Benefit
MPF
N
t IR
IF
NPVFactor
1 1
1
,
80. 80
Cost of Energy Loss Reduction
Initially, the load flow solution for the test system is solved without DG to
read real power losses, and again the process is repeated with the presence of
DG. The difference in losses represents the net loss reduction given through
Eq. (4).
(4)
The obtained loss reduction with DG is converted into cost value using Eq.
(5)
(5)
The present cost value of CNLR is calculated using Eq. (6).
(6)
where t = 1, 2,3,…,N; CNLR = cost of net loss reduction.
DG
loss
loss P
P
NLR ,
8760
)
/
(cos
)
(
KWh
aving
tofenergys
NLR
C NLR
N
t
t
NLR
NLR C
C
NPV
1
)
(
81. 81
Cost of DG Power Generation
The type of DG considered for this study is a solar PV system. The data for
this cost is taken from [6] and is calculated using Eq. (7).
(7)
The present cost value of CDG,Gen is estimated using Eq. (8).
(8)
where t = 1, 2,3,…,N; CDG,Gen = cost of DG power generation.
Expenses Cost
This cost includes the sum of DG investment cost and cumulative of the
operation and maintenance cost of DG over the planning period.
yr
KW
RCOST
DGGENERATO
DGSIZE
C GEN
DG
/
(
)
(
)
,
(
N
t
t
GEN
DG
GEN
DG C
C
NPV
1
,
, )
(
82. 82
Operation and Maintenance
Cost This cost includes the operation and maintenance O&M cost of DG
connected to the grid and is calculated using Eq. (9). The O&M cost details
of DG placement were taken from
(9)
The present cost value of CDG,O&M is calculated using Eq. (10).
(10)
where t = 1, 2,3,…,N; CDG,O&M = cost of DG operation and maintenance.
Investment Cost
The invested cost of optimally placed solar PV-type DG is calculated using
Eq. (11).
(11)
The economic validation of the above discussed objective function depends
on the optimal location and rating of DG.
yr
KW
M
DGO
DGSIZE
C M
O
DG
/
&
(
)
(
)
&
,
(
N
t
t
M
O
DG
M
O
DG C
C
NPV
1
&
,
&
, )
(
KW
t
ent
DGinvestim
DGSIZE
C INV
DG /
cos
(
)
(
)
,
(
83. 83
1. Read the line, bus and load data of RDS, Technical and Commercial
Information of DISCOs and DGs owner, Market price and used market
parameters.
2. Initialize the parameters of MFO algorithm such as Population size, elite
size and maximum no of iteration number.
3. Randomly generate the populations (P) of MFO using a Heuristic
algorithm subjected to system constrains
4. Evaluate the objective functions of each population and determine the
DG owner’s profit and DISCO’s cost
5. Set iteration=1.
6. Calculate fitness (i.e. voltage profile, VSI, power loss) for each moth by
placing DG at their respective buses using eqn. (15).
7. Evaluate the objective functions of each moth and determine the profit of
DG owners and various cost of DISCOs..
84. 84
8. Update the position of flame and save the best fitness values in an array
corresponding to eqn. (16)
9. Update the record of flames and the flames are arranged using eqn. (17)
based on their fitness values
10. Compute the present position of moths.
11. Check the all constrains are satisfied, if yes move to next step, else go to
step 6.
12. Check If the number of iteration process is equal to maximum number of
iterations, go to step 13. Otherwise go to step 5.
13. Display the global best solution of various cost of DISCOs and profit of
DG owners STOP the program.
85. RESULTS AND DISCUSSION
85
Table1 Voltage profile for base case placement of DG in IEEE 33-bus test
system
Bus
No.
Base Case
voltage
MFO Voltage (Proposed)
1 1.0000 1.0000
2 0.9970 0.9978
3 0.9829 0.9888
4 0.9754 0.9845
5 0.9680 0.9803
6 0.9495 0.9688
7 0.9460 0.9649
8 0.9323 0.9496
9 0.9260 0.9423
10 0.9201 0.9366
11 0.9192 0.9357
12 0.9177 0.9342
13 0.9115 0.9282
14 0.9092 0.926
15 0.9078 0.9246
16 0.9064 0.9233
87. 87
Fig. 1 Voltage profile for
base case placement of
DG in IEEE 33-bus test
system
Fig. 2 VSI for base case
placement of DG in IEEE
33-bus test system
88. 88
Table 2 VSI for base case placement of DG in IEEE 33-bus test system
Bus
No.
Base case VSI MFO VSI (Proposed)
1 1.0000 1.0000
2 0.9978 0.9978
3 0.9888 0.9888
4 0.9845 0.9845
5 0.9803 0.9803
6 0.9688 0.9688
7 0.9649 0.9649
8 0.9496 0.9496
9 0.9423 0.9423
10 0.9366 0.9366
11 0.9357 0.9357
12 0.9342 0.9342
13 0.9282 0.9282
14 0.926 0.926
15 0.9246 0.9246
16 0.9233 0.9233
17 0.9213 0.9213
18 0.9207 0.9207
19 0.9973 0.9973
20 0.9936 0.9936
90. 90
Various cost of DISCOs Fuzzy with DE Algorithm MFO
(Proposed algorithm)
Cost of Energy Loss
Reduction (K$/year)
39.89
36.43
Cost of DG Power Generation
(K$/year)
341.16
407.77
Operation and Maintenance
Cost of DG
(K$/year)
18.19
21.748
Investment Cost of DG
(K$/year)
2302.86
2752.46
Table 3 simulation results of IEEE 33 bus system after 1 year of planning
period
91. 91
Various cost of
DISCOs
Fuzzy with DE Algorithm
MFO
(Proposed algorithm)
Cost of Energy Loss
Reduction (M$/year)
1.94 1.77
Cost of DG Power
Generation (M$/year)
16.58 19.82
Operation and Maintenance
Cost of DG
(M$/year)
0.88 1.06
Investment Cost of DG
(M$/year)
2.30 2.75
Benefit of DISCOs (M$/year) 18.53 21.59
Expenses of DISCOs
(M$/year)
3.19 3.81
Total profit of DISCOs
(M$/year)
15.34 17.78
Table 4 simulation results of IEEE 33 bus system after
planning period
92. 92
Fig. 3 Comparison of
cost, benefit and profit
of DISCOs for IEEE 33-
bus test system
Fig. 4 Convergence curve
of IEEE 33-bus test
system
93. 93
Results for IEEE69 node test system
Table 5 Voltage profile for base case and placement of DG in IEEE 69-bus test
system
Bus
No.
Base Case voltage MFO voltage (Proposed)
1 1.0000 1.0000
2 1.0000 1.0000
3 0.9999 1.0000
4 0.9998 0.9999
5 0.9990 0.9994
6 0.9901 0.9954
7 0.9808 0.9913
8 0.9786 0.9903
9 0.9774 0.9899
10 0.9724 0.9849
11 0.9713 0.9838
12 0.9682 0.9807
13 0.9652 0.9778
14 0.9623 0.9749
15 0.9595 0.9721
16 0.9589 0.9716
101. 101
Fig. 5 Voltage profile for
base case and placement
of DG in IEEE 69-bus test
system
Fig. 6 VSI for base case
and placement of DG in
IEEE 69-bus test system
102. 102
Table 5 simulation results of IEEE 69 bus system after 1 year of planning
period
Various cost of DISCOs
MFO
(Proposed algorithm)
Cost of Energy Loss Reduction
(K$/year)
61.84
Cost of DG Power Generation
(K$/year)
600
Operation and Maintenance
Cost of DG
(K$/year)
32
Investment Cost of DG
(K$/year)
4050
103. 103
Table 6 simulation results of IEEE 69 bus system after planning period
Various cost of
DISCOs
MFO
(Proposed algorithm)
Cost of Energy Loss Reduction
(M$/year)
3.01
Cost of DG Power Generation
(M$/year)
29.17
Operation and Maintenance Cost
of DG
(M$/year)
1.56
Investment Cost of DG
(M$/year)
4.05
Benefit of DISCOs (M$/year) 32.18
Expenses of DISCOs (M$/year) 5.61
Total profit of DISCOs (M$/year) 26.57
105. SUMMARY
105
In this work solves the voltage stability problem under
deregulated environment using MFO.
The proposed methodology effectively maximize the
profit of DG owners and minimize the various costs of
DISCO’s. it also improve the voltage profile and reduce the
network losses in IEEE 33 bus and 69 bus node test system.
Moreover the comparison study made with fuzzy and
Differential Evolution (DE) algorithm. To evaluate the
performance of proposed (MFO) algorithm.
106. Network Reconfiguration with Optimal allocation
of Capacitors and DG units for Maximizing DISCOs
Profit in a Restructured Power Market
106
In this research, recently developed and comprehensive moth flame
optimization algorithm is presented for maximizing the profit of the DISCOs
under competitive environment.
The execution process of network reconfiguration and proper placement and
sizing of capacitors and DG units are taken care of by MFO algorithm.
Reconfiguration of network is a mechanism of shuffling existing pattern of
feeders duly changing ON and OFF status of tie-line switches to improve the
performance of the DISCOs.
The algorithm also minimizes the various types of cost such as investment,
maintenance and operational cost.
The proposed MFO algorithm is implemented on IEEE 33 node and 69
node systems to evaluate its performance.
Evaluation of solution in MATLAB software demonstrates the skills of
MFO in DISCOs.
107. 107
PROBLEM FORMULATION
The cost evaluation and benefit evaluation of allocation of Capacitors and
DG units in a network is represented as below:
Cost evaluation of DG units and Capacitors allocation
Investment cost
(1)
Operating Costs
(2)
Present worth Factor (3)
For an operating cost in a planning year, present worth value is given by
the equation (4)
(4)
NCap
i
j
capj
NDG
i
i
DGi IC
K
IC
K
C
1
1
1
NDG
i
i
DGi T
OC
K
C
1
2
t
n
t
t
IR
IF
1 1
1
t
NDG
i
i
DGi T
OC
K
C
PWV
1
2
108. 108
Maintenance Costs
(5)
The annual cost present worth value during the period of planning is
estimated as in equation (6)
(6)
Benefit evaluation of DG units and Capacitors allocation
a. Distribution line active power demand reduction
Energy delivered to Grid in a segment over time is given in equation (7),
(7)
The present worth value of power generated by distribution company is
given by the equation (8)
(8)
NCap
i
capj
j
capj
NDG
i
DGi
i
DGi MC
IC
K
MC
IC
K
C
1
1
3
t
NCap
i
capj
j
capj
NDG
i
DGi
i
DGi MC
IC
K
MC
IC
K
C
PWV
1
1
3
NDG
i
G
DGi T
EP
K
B
1
1
t
NDG
i
G
DGi T
EP
K
B
PWV
1
1
109. 109
b. Revenue from loss reduction
(9)
The present worth value for loss minimization revenue in a period of planning
is given by equation (10),
(10)
Objective function for DISCOs profit
Maximum profit of DISCOs is given by the equation (11) and (12),
(11)
NDG
i
NCap
j
G
ij T
EP
LOSS
B
1 1
2
t
NDG
i
NCap
j
G
ij T
EP
LOSS
B
PWV
1 1
2
s
Investment
Benefits
Z
Max
1 1
1 1
.
1 . 1 .
NCap
NDG
t
DGi G ij G DGi i
i j
NCap
NDG
t t
DGi i DGi Cap j capj
i j
MaxZ K EP LOSS EP K OC T
K IC MC K IC MC
(12)
110. 110
System Constraints
a. Constraints for Power balance
Constraints for active and reactive power are given in equation (13) and (14),
(13)
(14)
b. Voltage constraints
Each bus must satisfy the following voltage constraint as in equation (15),
(15)
c. Current limit
Current flowing in the distribution segment should not exceed from their
maximum ratings as given in equation (16),
1
cos sin , 1,2,3,......
N
i i j ij i j ij i j
j
P VV G B i N
1
sin cos , 1,2,3,......
N
i i j ij i j ij i j
j
Q VV G B i N
min max
i i i
V V V
i N
,
,
i iRated Br
I I i N
(16)
111. 111
d. Constraints for size of capacitors and DGs
al
T
DG S
S Re
30
.
0
.
Re
30
.
0
. act
T
Cap S
S
(17)
112. IMPLEMENTATION
112
The following procedural steps are followed for optimal allocation
and sizing of combined DG and capacitor with network configuration
to evaluate the profit of DISCOs in a competitive electricity market
using MFO algorithm.
1. Read the line data, bus data and load data of RDS, cost of
installation, running cost and maintenance cost of the capacitor
and DG, Interest rate, Inflation rate, Market price and Planning
period.
2. Run the distribution power flow and calculate the loss using exact
loss formula for base case.
3. Choose the number of Capacitors and DG to be used to in Radial
Distribution System.
4. Set the parameters of MFO algorithm such as Population,
dimension, maximum number of iterations, lower and upper bound
(size and node Capacitor and DG respectively).
5. Fix iteration=1
113. 113
6. Compute fitness (i.e. loss in network) for each and every moth by
installing DG and Capacitor at their respective buses using eqn. (15).
7. Evaluate the objective functions of each moth and determine the profit
of DISCOs.
8. Update the position of flame and save the best fitness values in an array
corresponding to eqn. (16)
9. Update the record of flames and the flames are arranged using eqn. (17)
based on their fitness values
10.Estimate the present position of moths.
11.If all constraints are satisfied move to next step, if all constraints not
satisfied go to step 6.
12.Go to step 13, if maximum number of iterations is attained. else go to
step 5.
13.Display the global best solution of various cost and DISCOs profit and
STOP the program.
114. RESULT AND DISCUSSION
114
With a view of assessing the superiority of the
proposed MFO, a test has been carried out on IEEE 33
bus and 69 node redial distribution network under
deregulated environment.
Obtaining global solution of deregulated system is
more complex and competitive than conventional
RDS.
The proposed methodology has more ability to
achieve the best numerical solutions.
115. 115
Fig. 1. Network Reconfiguration by
installation of DGs and Capacitor in IEEE
33-node test system
116. 116
Test system 1: IEEE 33-node RDS
In this test case, capacitors and DG placement are carried out in the
33-node RDS with planning period of 10 years.
Network reconfiguration is also made to improve the voltage profile
and profit of DISCOs.
The projected MFO is properly optimized the location and size of the
DG and capacitor.
The best placement and tuned value of DG and capacitor are 30, 18
and 1.5 MW, 0.9 MVAr respectively.
The tie-line switches 33, 34, 35, 36 are used for network
reconfiguration.
The switches 7, 28, 12, 15, 21 are opened for enhancing the voltage
stability by proposed MFO method.
The one line diagram of network Reconfiguration with positioning of
DG and Capacitor in 33-bus test system by proposed method is
displayed in figure1.
117. 117
Table 1. IEEE 33-node test system
Parameters Optimal Value
Open switches 7, 28, 12, 15, 21
Tie-line switches 33, 34, 35, 36
DG Optimal placement 30
Capacitor Optimal placement 18
Capacitor Optimal sizing 0.9 MVAr
DG Optimal sizing 1.5 MW
Voltage stability index (p.u) 0.91256
Minimum Voltage (p.u) 0.97740
Fig. 2. Voltage profile
for base case and
Reconfiguration by the
installation of DG units
and capacitors for IEEE
33 node test system
118. 118
Fig. 3. VSI for base case and
Reconfiguration with
installation of DG and
capacitor in IEEE 33node test
system
Table 2. Cost-Benefit analysis of DISCOs for IEEE
33 node test system
Costs, Benefits and Profits of DISCOs Values (Rs)
Capacitor Installation cost 9 x 104
DG Installation cost 375 x 105
Reduction in purchased energy benefits 4.99 x 108
Loss reduction benefits 5.4979x 107
DG Maintenance cost 6.342 x 107
DG Operational costs 2.495 x 108
Capacitor Maintenance cost 1.8988 x 105
Planning period 10 year
Total profit of DISCOs 2032.8 x 105
119. 119
Table 3. Comparison of Various Cost and
Benefits of DISCOs for IEEE 33-nodetest
system
Parameters
Value of Various costs,
Benefits and Profit of
DISCOs
Value of Various
costs, Benefits and
Profit of DISCOs
PSO [21]
MFO
(Proposed)
Location DG and Capacitor
8
30
30
18
Size DG and Capacitor
1.5 (MW)
0.9 (MVAr)
1.5 MW
0.9 MVAr
Network Reconfiguration No Yes
Open switches - 7,28,12,15,21
Tie-line switches - 33, 34, 35,36
Planning period 10 year 10 year
Installation cost of DG (Rs) 375 x 105 375 x 105
Installation cost of Capacitor (Rs) 9 x 104 9 x 104
Benefits of loss reduction (Rs) 4.35 x 107 5.4979x 107
Benefits of reduction in purchased
energy (Rs)
4.99 x 108 4.99 x 108
Operational costs of DG (Rs) 2.49 x 108 2.495 x 108
Maintenance cost of DG (Rs) 6.34 x 107 6.342 x 107
Maintenance cost of Capacitor (Rs) 1.94 x 105 1.8988 x 105
Total profit of DISCOs (Rs) 1937.94 x 105 2032.8 x 105
121. 121
Fig. 5. Network Reconfiguration with placement of DG and Capacitor in
IEEE 69 node test system
122. 122
Test case 2: IEEE 69-node test system
In order to validate the applicability of the MFO algorithm, it is tested on a of 69 bus
test system to obtain maximum profit of DISCOs.
Planning period of Ten years is considered for this process.
The MFO algorithm computes the required configuration with best location and size
of the DG and capacitor.
The one line diagram of network reconfiguration with placement of DG and
Capacitor in 69 bus test system is shown in figure 5.
123. 123
Fig. 6.Voltage profile for base
case and Reconfiguration with
placement of DG and
capacitor in IEEE 69-bus test
system
Fig. 7. VSI for base case and
Reconfiguration with
placement of DG and capacitor
in IEEE 69-bus test system
124. 124
Table 4. Optimal location and sizing of DG and capacitor
for IEEE 69- node DISCOs considering network
reconfiguration
Parameters Optimal Value
Open switches 14, 17, 55, 12, 41
Tie-line switches 69, 70, 71, 72, 73
DG Optimal location 61
Capacitor Optimal location 27
DG Optimal sizing 1.5 MW
Capacitor Optimal sizing 0.8 MVAr
Minimum Voltage (p.u) 0.97985
Voltage stability index (p.u) 0.91896
Power loss (KW) 24.05
125. 125
Table 5. Cost-Benefit analysis of DISCOs for IEEE 69-nodetest
system
Costs, Benefits and Profits of
DISCOs
Values (Rs)
DG Installation cost 375 x 105
Capacitor Installation cost 10.5 x 104
Loss reduction benefits 6.684x 107
Reduction in purchased
energy benefits
4.99x 108
Operational charges of DG 2.495 x 108
Maintenance charges of DG 6.342 x 107
Maintenance charges of
Capacitor
1.8988x 105
Planning period 10 year
Total profit of DISCOs 2151.4x 105
126. 126
Table 6. Comparison of Various Cost and Benefits of DISCOs for
IEEE 69-nodetest system
Parameters Value of Various costs, Benefits and
Profit of DISCOs
PSO [56]
MFO
(Proposed)
61 61
Capacitor Location 61 27
DG Size 1.5 MW 1.5 MW
Capacitor Size 1.2 MVAr 1.05 MVAr
Network Reconfiguration No Yes
Open switches - 14, 17, 55, 12, 41
Tie-line switches - 69, 70, 71, 72, 73
Planning period 10 year 10 year
Installation cost of DG (Rs) 375 x 105 375 x 105
Installation cost of Capacitor
(Rs)
1.2 x 105 10.5 x 104
Benefits of loss reduction (Rs) 6.52 x 107 6.684x 107
Benefits of reduction in
purchased energy (Rs)
4.99 x 108 4.99x 108
Operational costs of DG (Rs) 2.49 x 108 2.495 x 108
Maintenance cost of DG (Rs) 6.34 x 107 6.342 x 107
Maintenance cost of Capacitor
(Rs)
2.45 x 105 1.8988x 105
Total profit of DISCOs (Rs) 2137.19 x 105
2151.40 x 105
128. SUMMARY
128
The proposed methodologies effectively reduce the line losses
and thereby boost the benefits of the DGs owners.
Moreover, it also saves various types of cost such as
investment and operational cost by selling the power recovered
from the minimized line losses.
The test studies were carried out on standard IEEE 33 and 69-
node systems.
The experimental results show that the proposed MFO
algorithm is a promising approach for improving the profit
margin of distribution companies.
129. CONCLUSION
129
In this research work, an intelligent computational optimization
algorithm of moth–flame optimization (MFO) is proposed to solve
the various voltage stability problems in regulated and deregulated
power system.
MFO is considered one of the promising meta-heuristic
algorithms and successfully applied and improve the voltage
stability of radial distribution systems.
It has two essential components of moths and flames. Moreover,
both the moths and the flames have been considered as a solution.
The two searching operators of MFO has effectively improve
the voltage profile, maximize the net saving cost and reduce the
network losses of RDS by proper allocation and sizing of various
capacitors, DG units and network reconfiguration process.
130. 130
Also, a devised MFO algorithm has been applied to improve the
benefits of DISCOs and DG owners under deregulated environment.
Here, the various cost and revenue of DISCOs and DG owners are
calculated.
It includes investment cost of capacitors and DGs, operating and
maintenance cost of capacitors and DGs, customer interruption cost,
substation cost, Revenue and profit of DG owners..Numerical
example with IEEE standard RDS test systems has been considered
to prove the performance of MFO.
The result shows that the proposed algorithm offers an increase in
profit with less computational time compared to other competitive
algorithms. Therefore, it can be concluded that the proposed MFO
approach paves the best way for solving the power system
optimization problems under deregulated environment.
131. 131
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