One of the main objectives of electricity dispatch centers is to schedule the operation of available generating units to meet the required load demand at minimum operating cost with minimum emission level caused by fossil-based power plants. Finding the right balance between the fuel cost the green gasemissionsis reffered as Combined Economic and Emission Dispatch (CEED) problem which is one of the important optimization problems related the operationmodern power systems. The Particle Swarm Optimization algorithm (PSO) is a stochastic optimization technique which is inspired from the social learning of birds or fishes. It is exploited to solve CEED problem. This paper examines the impact of six penalty factors like "Min-Max", "Max-Max", "Min-Min", "Max-Min", "Average" and "Common" price penalty factors for solving CEED problem. The Price Penalty Factor for the CEED is the ratio of fuel cost to emission value. This bi-objective dispatch problem is investigated in the Real West Algeria power network consisting of 22 buses with 7 generators. Results prove capability of PSO in solving CEED problem with various penalty factors and it proves that Min-Max price penalty factor provides the best compromise solution in comparison to the other penalty factors.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
The document presents an improved particle swarm optimization (IPSO) algorithm for solving the optimal unit commitment problem in power systems. The IPSO algorithm extends the standard PSO algorithm by using additional particle information to control mutation and mimic social behaviors. The algorithm was implemented on the IEEE 14 bus test system in MATLAB. Results showed the IPSO approach committed units to meet load demand over 24 hours while satisfying constraints, with bus voltages maintained between 1.0017 and 1.0751 per unit. Total costs including fuel, startup, and shutdown costs were minimized at each hour.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
Multi objective economic load dispatch using hybrid fuzzy, bacterialIAEME Publication
The document summarizes a research paper that proposes a new approach for solving the economic load dispatch problem using a hybrid fuzzy, bacterial foraging-Nelder–Mead algorithm. The economic load dispatch problem minimizes generation costs while satisfying load demand under system constraints. The proposed approach considers generation costs, spinning reserve costs, and emission costs simultaneously. It also accounts for valve-point effects, prohibited operating zones, and other practical constraints. A hybrid bacterial foraging and Nelder–Mead algorithm combined with fuzzy logic is used to solve the optimization problem. Simulation results show the advantages of the proposed method in reducing total system costs.
Improved particle swarm optimization algorithms for economic load dispatch co...IJECEIAES
Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time.
A novel approach for solving Optimal Economic load dispatch problem in power ...IRJET Journal
This document presents a novel approach for solving the optimal economic load dispatch problem in power systems using the Lion Optimization Algorithm (LOA), a meta-heuristic algorithm. LOA simulates the behavior of lion prides to find optimal solutions. The economic load dispatch problem aims to minimize total fuel costs while satisfying constraints like power balance and generator limits. Environmental emissions are also considered to minimize pollution. LOA is applied to solve the economic and emission dispatch problem and results show it performs better than other algorithms like PSO and GA, finding lower-cost solutions that satisfy constraints.
A Simple Approach for Optimal Generation Scheduling to Maximize GENCOs Profit...IJAPEJOURNAL
In this paper an attempt has been made to solve the profit based unit commitment problem (PBUC) using pre-prepared power demand (PPD) table with an artificial bee colony (ABC) algorithm. The PPD-ABC algorithm appears to be a robust and reliable optimization algorithm for the solution of PBUC problem. The profit based unit commitment problem is considered as a stochastic optimization problem in which the objective is to maximize their own profit and the decisions are needed to satisfy the standard operating constraints. The PBUC problem is solved by the proposed methodology in two stages. In the first step, the unit commitment scheduling is performed by considering the pre-prepared power demand (PPD) table and then the problem of fuel cost and revenue function is solved using ABC Algorithm. The PPD table suggests the operator to decide the units to be put into generation there by reducing the complexity of the problem. The proposed approach is demonstrated on 10 units 24 hour and 50 units 24 hour test systems and numerical results are tabulated. Simulation result shows that this approach effectively maximizes the GENCO’s profit than those obtained by other optimizing methods.
Optimal tuning of a wind plant energy production based on improved grey wolf ...journalBEEI
The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
The document presents an improved particle swarm optimization (IPSO) algorithm for solving the optimal unit commitment problem in power systems. The IPSO algorithm extends the standard PSO algorithm by using additional particle information to control mutation and mimic social behaviors. The algorithm was implemented on the IEEE 14 bus test system in MATLAB. Results showed the IPSO approach committed units to meet load demand over 24 hours while satisfying constraints, with bus voltages maintained between 1.0017 and 1.0751 per unit. Total costs including fuel, startup, and shutdown costs were minimized at each hour.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
Multi objective economic load dispatch using hybrid fuzzy, bacterialIAEME Publication
The document summarizes a research paper that proposes a new approach for solving the economic load dispatch problem using a hybrid fuzzy, bacterial foraging-Nelder–Mead algorithm. The economic load dispatch problem minimizes generation costs while satisfying load demand under system constraints. The proposed approach considers generation costs, spinning reserve costs, and emission costs simultaneously. It also accounts for valve-point effects, prohibited operating zones, and other practical constraints. A hybrid bacterial foraging and Nelder–Mead algorithm combined with fuzzy logic is used to solve the optimization problem. Simulation results show the advantages of the proposed method in reducing total system costs.
Improved particle swarm optimization algorithms for economic load dispatch co...IJECEIAES
Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time.
A novel approach for solving Optimal Economic load dispatch problem in power ...IRJET Journal
This document presents a novel approach for solving the optimal economic load dispatch problem in power systems using the Lion Optimization Algorithm (LOA), a meta-heuristic algorithm. LOA simulates the behavior of lion prides to find optimal solutions. The economic load dispatch problem aims to minimize total fuel costs while satisfying constraints like power balance and generator limits. Environmental emissions are also considered to minimize pollution. LOA is applied to solve the economic and emission dispatch problem and results show it performs better than other algorithms like PSO and GA, finding lower-cost solutions that satisfy constraints.
A Simple Approach for Optimal Generation Scheduling to Maximize GENCOs Profit...IJAPEJOURNAL
In this paper an attempt has been made to solve the profit based unit commitment problem (PBUC) using pre-prepared power demand (PPD) table with an artificial bee colony (ABC) algorithm. The PPD-ABC algorithm appears to be a robust and reliable optimization algorithm for the solution of PBUC problem. The profit based unit commitment problem is considered as a stochastic optimization problem in which the objective is to maximize their own profit and the decisions are needed to satisfy the standard operating constraints. The PBUC problem is solved by the proposed methodology in two stages. In the first step, the unit commitment scheduling is performed by considering the pre-prepared power demand (PPD) table and then the problem of fuel cost and revenue function is solved using ABC Algorithm. The PPD table suggests the operator to decide the units to be put into generation there by reducing the complexity of the problem. The proposed approach is demonstrated on 10 units 24 hour and 50 units 24 hour test systems and numerical results are tabulated. Simulation result shows that this approach effectively maximizes the GENCO’s profit than those obtained by other optimizing methods.
Optimal tuning of a wind plant energy production based on improved grey wolf ...journalBEEI
The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources are intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit commitment scheduling can improve the total operating cost significantly.
Inclusion of environmental constraints into siting and sizingIAEME Publication
This document summarizes a research paper that investigates including environmental constraints, specifically carbon dioxide (CO2) emissions, in the siting and sizing techniques used to determine optimal locations and capacities for localized gas turbine distributed generation units. The paper introduces a methodology to model CO2 emissions from both distributed generation units and centralized power stations based on their emission factors, power output, and other variables. The emissions constraint is incorporated into an existing distributed generation siting and sizing optimization model. The model is then applied to a case study of a real power distribution network to determine the optimal distributed generation configurations while accounting for environmental impacts.
Optimal Expenditure and Benefit Cost Based Location, Size and Type of DGs in ...TELKOMNIKA JOURNAL
The economic issue is an essential element to determine whether DG should be installed or not. This work presents the economical approach for multi-type DGs placement in microgrid systems with more comprehensive overview from DG’s owner perspective. Adaptive Real Coded GA (ARC-GA) with replacement process is developed to determine the location, type, and rating of DGs so as the maximum profit is achieved. The objectives of this paper are maximizing benefit cost and minimizing expenditure cost. All objectives are optimized while maintaining the bus voltage at the acceptable range and the DGs penetration levels are below of the DGs capacities.The proposed method is applied on the 33 bus microgrids systems using conventional and renewable DG technology, namely Photovoltaic (PV), Wind Turbine (WT), Micro Turbine (MT) and Gas Turbine (GT). The simulation results show the effectiveness of the proposed approach.
The document discusses energy consumption and renewable energy potential in India. It notes that a 6% increase in India's GDP would impose a 9% increased demand on the energy sector. India has significant potential to harness solar energy, with a total potential of 178 billion MW. However, large scale utilization of solar energy is still limited by production efficiency and costs. The document then discusses TATA BP Solar India Limited, which manufactures solar cells at 32 MW per year. It aims to capitalize on the potential of solar energy in India.
Techno Economic Modeling for Replacement of Diesel Power PlantSyamsirAbduh2
Several problems occur in an old diesel power plant such as derating, low efficiency, high emission and noise decrease the performances of the systems. Besides, most of the old diesel power plants in Indonesia is still use High-Speed Diesel (HSD). In order to decide if the old diesel power plant is still feasible from the technical and economical point of view, a detailed analysis should be done. This paper proposes a model management tools to determine its techno-economic feasibility analysis from some factor such as cost, reliability, availability and economic life. This paper also propose the modeling calculation of Cost of Electricity (COE), Life Cycle Cost (LCC) and Equivalent Uniform Annual Cost (EUAC) methods to determine in techno-economic. A simple case study is discussed. The result recommends for asset retirement without abandonment for the old diesel power plant and replacement with the new Power Plant using a dual fuel engine (Gas Fuel and Marine Fuel Oil (MFO)). From the new power plant, it also can be estimated the replacement should be carried out in 14th year for the future. Finally, model management tools can be used to facilitate decision making in similar cases in the diesel power plant.
A Genetic Algorithm Approach to Solve Unit Commitment ProblemIOSR Journals
This document describes a study that uses a genetic algorithm approach to solve the unit commitment problem of scheduling generation units in a power system over an 8-hour period. The genetic algorithm approach is able to find near-optimal solutions to the unit commitment problem and results in lower total operating costs than the traditional dynamic programming approach. The genetic algorithm approach encodes potential solutions as strings that are evaluated and evolved over generations to find low-cost solutions that satisfy constraints. The results show the genetic algorithm approach finds schedules with total costs that are $255 lower than those found by dynamic programming for the test power system.
This paper presents a new method using quadratic programming to solve economic dispatch problems that minimize fuel costs and emission dispatch problems that minimize pollutant emissions from power plants, while meeting demand. The method transforms variables to linearize constraints and applies quadratic programming recursively until convergence. It is shown to find the global minimum for economic load dispatch, minimum emission dispatch, combined economic and emission dispatch, and emission-constrained economic dispatch problems, and performs better than genetic algorithms. The algorithm is tested on a system and results demonstrate the effectiveness of the proposed quadratic programming method.
A Generalized Multistage Economic Planning Model for Distribution System Cont...IJERD Editor
This document presents a generalized multistage economic planning model for distribution systems containing distributed generation (DG) units. The model minimizes total investment and operation costs over a planning horizon divided into multiple periods, taking into account load growth, equipment capacities and voltages limits. Constraints include power flow equations and logical constraints relating planning periods. The model is applied to a sample 11kV distribution network with one substation, 23 load buses and 32 feeders over 4 annual periods. The mixed integer nonlinear optimization problem is solved using LINGO software to obtain the least-cost expansion plan.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
GC energy & environmental newsletter April 2012generalcarbon
The Perform-Achieve-Trade (PAT) scheme was launched by the Bureau of Energy Efficiency to improve energy efficiency in large industries, with 478 companies designated to reduce energy consumption by certain percentages between 2012-2015; accelerated depreciation benefits for wind power projects were removed, which could impact investment; and lower emissions in the EU may reduce demand and prices for carbon credits if no market interventions occur.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
IRJET-Comparative Analysis of Unit Commitment Problem of Electric Power Syste...IRJET Journal
The document presents a comparative analysis of solving the unit commitment problem (UCP) of electric power systems using dynamic programming technique. It formulates the UCP considering fuel costs, voltage stability, and imbalance limits. Dynamic programming is described as a conventional algorithm for solving deterministic problems optimally through sequential decision making. The developed algorithm is implemented on 4-unit and 10-unit power systems. Results found the dynamic programming approach provides satisfactory solutions by minimizing total generation costs while meeting operational constraints.
IRJET- A Comparative Study of Economic Load Dispatch Optimization MethodsIRJET Journal
This document presents a comparative study of different optimization methods for solving the economic load dispatch (ELD) problem in power systems. The ELD problem involves minimizing generation costs while meeting demand, and is formulated as a non-linear optimization problem with constraints. Various conventional and evolutionary algorithms have been used to solve ELD, but more recently bio-inspired algorithms like flower pollination algorithm and Jaya optimization have shown better performance. The paper evaluates these nature-inspired algorithms and compares their results for the ELD problem to demonstrate their effectiveness.
This document provides an overview and instructions for using the Energy Saving Scheme - Energy Management and Benchmark Tool (ESS-EMBT). The ESS-EMBT is an Excel-based tool that allows companies to track energy consumption, calculate energy indicators, analyze consumption trends, and benchmark performance. The summary includes:
1) The ESS-EMBT collects production and energy consumption data from companies. It then calculates indicators like specific energy consumption and compares performance to benchmarks.
2) Setup requires enabling macros and inputting company/segment data, energy invoices, and production figures. Results include consumption graphs, regression analysis, and benchmarking.
3) Benchmarking compares company indicators to averages from 27 EU companies in 42
IRJET- A Review on Computational Determination of Global Maximum Power Point ...IRJET Journal
This document reviews computational techniques for determining the global maximum power point (GMPP) for photovoltaic (PV) arrays under partial shading conditions. Partial shading causes the PV array characteristics to exhibit multiple local maxima, making it difficult for conventional maximum power point tracking techniques to identify the true GMPP. The document categorizes and discusses analytical, meta-heuristic, and fuzzy-based computational approaches that have been proposed to address this issue, including methods using the Lambert W function, particle swarm optimization, simulated annealing, and fuzzy logic. It proposes using the Das-Saetre model of PV characteristics along with meta-heuristic techniques to more accurately compute the GMPP while reducing computational complexity compared to previous approaches.
Introducing Electricity Dispatchability Features in TIMES modelling FrameworkIEA-ETSAP
This document provides an update on the status of a project to improve the dispatch modeling of power plants in the TIMES energy systems modeling framework. It describes the implementation of a unit commitment (UC) problem into TIMES, which will allow the model to consider start-up costs and minimum run times of power plants when determining the optimal dispatch schedule. The document outlines the key features and constraints of the UC problem being modeled, provides an overview of the current implementation progress and tasks completed, and describes two different approaches - using binary variables or continuous variables - for formulating the UC problem in TIMES. Examples are also presented to demonstrate the UC modeling capabilities.
Stochastic fractal search based method for economic load dispatchTELKOMNIKA JOURNAL
This paper presents a nature-inspired meta-heuristic, called a stochastic fractal search based
method (SFS) for coping with complex economic load dispatch (ELD) problem. Two SFS methods are
introduced in the paper by employing two different random walk generators for diffusion process in which
SFS with Gaussian random walk is called SFS-Gauss and SFS with Levy Flight random walk is called
SFS-Levy. The performance of the two applied methods is investigated comparing results obtained from
three test system. These systems with 6, 10, and 20 units with different objective function forms and
different constraints are inspected. Numerical result comparison can confirm that the applied approach has
better solution quality and fast convergence time when compared with some recently published standard,
modified, and hybrid methods. This elucidates that the two SFS methods are very favorable for solving
the ELD problem.
Optimal power generation for wind-hydro-thermal system using meta-heuristic a...IJECEIAES
In this paper, cuckoo search algorithm (CSA) is suggested for determining optimal operation parameters of the combined wind turbine and hydrothermal system (CWHTS) in order to minimize total fuel cost of all operating thermal power plants while all constraints of plants and system are exactly satisfied. In addition to CSA, Particle swarm optimization (PSO), PSO with constriction factor and inertia weight factor (FCIW-PSO) and social ski-driver (SSD) are also implemented for comparisons. The CWHTS is optimally scheduled over twenty-four one-hour interval and total cost of producing power energy is employed for comparison. Via numerical results and graphical results, it indicates CSA can reach much better results than other ones in terms of lower total cost, higher success rate and faster search process. Consequently, the conclusion is confirmed that CSA is a very efficient method for the problem of determining optimal operation parameters of CWHTS.
The study of reducing the cost of investment in wind energy based on the cat ...TELKOMNIKA JOURNAL
Wind and solar are the most important source of renewable energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. We apply the cat swarm meta-heuristic optimization method to solve the problem of wind power system design optimization. The electrical power components of the system are characterized by their cost, capacity and reliability. This study seeks to optimize the design of parallel power systems in which multiple choices of generators wind, transformers and lines. Our plan has the advantage of allowing electrical components with different parameters to be customized in electrical power systems. The UMGF method is applied to allow rapid reliability estimation. A computer program is developed for the UMGF application and CS algorithm. An example is provided to explain.
ECONOMIC LOAD DISPATCH USING GENETIC ALGORITHMIJARIIT
This paper present the application of Genetic Algorithm (GA) to Economic Load Dispatch problem of the power system. Economic Load Dispatch is one of the major optimization problems dealing with the modern power systems.ELD determines the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfactory the load demand. The objective is to minimize the total generation fuel cost and maintain the power flow within safety limits. The introduced algorithm has been demonstrated for the given test systems considering the transmission line losses.
Optimal Operation of Wind-thermal generation using differential evolutionIOSR Journals
This document presents an optimal operation model for a wind-thermal power generation system using differential evolution (DE). DE is an evolutionary algorithm inspired by biological evolution that can solve complex constrained optimization problems. The paper formulates the economic dispatch problem to minimize total generation cost of the wind and thermal plants subject to various constraints like power balance, generator limits, ramp rates, and valve point loading effects. Five different DE mutation strategies are analyzed for solving the wind-thermal economic dispatch problem on a test system with 10 thermal units. The results show that the best mutation strategy and control parameter values (mutation rate and crossover rate) depend on the problem and can significantly impact the solution quality and consistency obtained by the DE algorithm.
This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources are intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit commitment scheduling can improve the total operating cost significantly.
Inclusion of environmental constraints into siting and sizingIAEME Publication
This document summarizes a research paper that investigates including environmental constraints, specifically carbon dioxide (CO2) emissions, in the siting and sizing techniques used to determine optimal locations and capacities for localized gas turbine distributed generation units. The paper introduces a methodology to model CO2 emissions from both distributed generation units and centralized power stations based on their emission factors, power output, and other variables. The emissions constraint is incorporated into an existing distributed generation siting and sizing optimization model. The model is then applied to a case study of a real power distribution network to determine the optimal distributed generation configurations while accounting for environmental impacts.
Optimal Expenditure and Benefit Cost Based Location, Size and Type of DGs in ...TELKOMNIKA JOURNAL
The economic issue is an essential element to determine whether DG should be installed or not. This work presents the economical approach for multi-type DGs placement in microgrid systems with more comprehensive overview from DG’s owner perspective. Adaptive Real Coded GA (ARC-GA) with replacement process is developed to determine the location, type, and rating of DGs so as the maximum profit is achieved. The objectives of this paper are maximizing benefit cost and minimizing expenditure cost. All objectives are optimized while maintaining the bus voltage at the acceptable range and the DGs penetration levels are below of the DGs capacities.The proposed method is applied on the 33 bus microgrids systems using conventional and renewable DG technology, namely Photovoltaic (PV), Wind Turbine (WT), Micro Turbine (MT) and Gas Turbine (GT). The simulation results show the effectiveness of the proposed approach.
The document discusses energy consumption and renewable energy potential in India. It notes that a 6% increase in India's GDP would impose a 9% increased demand on the energy sector. India has significant potential to harness solar energy, with a total potential of 178 billion MW. However, large scale utilization of solar energy is still limited by production efficiency and costs. The document then discusses TATA BP Solar India Limited, which manufactures solar cells at 32 MW per year. It aims to capitalize on the potential of solar energy in India.
Techno Economic Modeling for Replacement of Diesel Power PlantSyamsirAbduh2
Several problems occur in an old diesel power plant such as derating, low efficiency, high emission and noise decrease the performances of the systems. Besides, most of the old diesel power plants in Indonesia is still use High-Speed Diesel (HSD). In order to decide if the old diesel power plant is still feasible from the technical and economical point of view, a detailed analysis should be done. This paper proposes a model management tools to determine its techno-economic feasibility analysis from some factor such as cost, reliability, availability and economic life. This paper also propose the modeling calculation of Cost of Electricity (COE), Life Cycle Cost (LCC) and Equivalent Uniform Annual Cost (EUAC) methods to determine in techno-economic. A simple case study is discussed. The result recommends for asset retirement without abandonment for the old diesel power plant and replacement with the new Power Plant using a dual fuel engine (Gas Fuel and Marine Fuel Oil (MFO)). From the new power plant, it also can be estimated the replacement should be carried out in 14th year for the future. Finally, model management tools can be used to facilitate decision making in similar cases in the diesel power plant.
A Genetic Algorithm Approach to Solve Unit Commitment ProblemIOSR Journals
This document describes a study that uses a genetic algorithm approach to solve the unit commitment problem of scheduling generation units in a power system over an 8-hour period. The genetic algorithm approach is able to find near-optimal solutions to the unit commitment problem and results in lower total operating costs than the traditional dynamic programming approach. The genetic algorithm approach encodes potential solutions as strings that are evaluated and evolved over generations to find low-cost solutions that satisfy constraints. The results show the genetic algorithm approach finds schedules with total costs that are $255 lower than those found by dynamic programming for the test power system.
This paper presents a new method using quadratic programming to solve economic dispatch problems that minimize fuel costs and emission dispatch problems that minimize pollutant emissions from power plants, while meeting demand. The method transforms variables to linearize constraints and applies quadratic programming recursively until convergence. It is shown to find the global minimum for economic load dispatch, minimum emission dispatch, combined economic and emission dispatch, and emission-constrained economic dispatch problems, and performs better than genetic algorithms. The algorithm is tested on a system and results demonstrate the effectiveness of the proposed quadratic programming method.
A Generalized Multistage Economic Planning Model for Distribution System Cont...IJERD Editor
This document presents a generalized multistage economic planning model for distribution systems containing distributed generation (DG) units. The model minimizes total investment and operation costs over a planning horizon divided into multiple periods, taking into account load growth, equipment capacities and voltages limits. Constraints include power flow equations and logical constraints relating planning periods. The model is applied to a sample 11kV distribution network with one substation, 23 load buses and 32 feeders over 4 annual periods. The mixed integer nonlinear optimization problem is solved using LINGO software to obtain the least-cost expansion plan.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
GC energy & environmental newsletter April 2012generalcarbon
The Perform-Achieve-Trade (PAT) scheme was launched by the Bureau of Energy Efficiency to improve energy efficiency in large industries, with 478 companies designated to reduce energy consumption by certain percentages between 2012-2015; accelerated depreciation benefits for wind power projects were removed, which could impact investment; and lower emissions in the EU may reduce demand and prices for carbon credits if no market interventions occur.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
IRJET-Comparative Analysis of Unit Commitment Problem of Electric Power Syste...IRJET Journal
The document presents a comparative analysis of solving the unit commitment problem (UCP) of electric power systems using dynamic programming technique. It formulates the UCP considering fuel costs, voltage stability, and imbalance limits. Dynamic programming is described as a conventional algorithm for solving deterministic problems optimally through sequential decision making. The developed algorithm is implemented on 4-unit and 10-unit power systems. Results found the dynamic programming approach provides satisfactory solutions by minimizing total generation costs while meeting operational constraints.
IRJET- A Comparative Study of Economic Load Dispatch Optimization MethodsIRJET Journal
This document presents a comparative study of different optimization methods for solving the economic load dispatch (ELD) problem in power systems. The ELD problem involves minimizing generation costs while meeting demand, and is formulated as a non-linear optimization problem with constraints. Various conventional and evolutionary algorithms have been used to solve ELD, but more recently bio-inspired algorithms like flower pollination algorithm and Jaya optimization have shown better performance. The paper evaluates these nature-inspired algorithms and compares their results for the ELD problem to demonstrate their effectiveness.
This document provides an overview and instructions for using the Energy Saving Scheme - Energy Management and Benchmark Tool (ESS-EMBT). The ESS-EMBT is an Excel-based tool that allows companies to track energy consumption, calculate energy indicators, analyze consumption trends, and benchmark performance. The summary includes:
1) The ESS-EMBT collects production and energy consumption data from companies. It then calculates indicators like specific energy consumption and compares performance to benchmarks.
2) Setup requires enabling macros and inputting company/segment data, energy invoices, and production figures. Results include consumption graphs, regression analysis, and benchmarking.
3) Benchmarking compares company indicators to averages from 27 EU companies in 42
IRJET- A Review on Computational Determination of Global Maximum Power Point ...IRJET Journal
This document reviews computational techniques for determining the global maximum power point (GMPP) for photovoltaic (PV) arrays under partial shading conditions. Partial shading causes the PV array characteristics to exhibit multiple local maxima, making it difficult for conventional maximum power point tracking techniques to identify the true GMPP. The document categorizes and discusses analytical, meta-heuristic, and fuzzy-based computational approaches that have been proposed to address this issue, including methods using the Lambert W function, particle swarm optimization, simulated annealing, and fuzzy logic. It proposes using the Das-Saetre model of PV characteristics along with meta-heuristic techniques to more accurately compute the GMPP while reducing computational complexity compared to previous approaches.
Introducing Electricity Dispatchability Features in TIMES modelling FrameworkIEA-ETSAP
This document provides an update on the status of a project to improve the dispatch modeling of power plants in the TIMES energy systems modeling framework. It describes the implementation of a unit commitment (UC) problem into TIMES, which will allow the model to consider start-up costs and minimum run times of power plants when determining the optimal dispatch schedule. The document outlines the key features and constraints of the UC problem being modeled, provides an overview of the current implementation progress and tasks completed, and describes two different approaches - using binary variables or continuous variables - for formulating the UC problem in TIMES. Examples are also presented to demonstrate the UC modeling capabilities.
Stochastic fractal search based method for economic load dispatchTELKOMNIKA JOURNAL
This paper presents a nature-inspired meta-heuristic, called a stochastic fractal search based
method (SFS) for coping with complex economic load dispatch (ELD) problem. Two SFS methods are
introduced in the paper by employing two different random walk generators for diffusion process in which
SFS with Gaussian random walk is called SFS-Gauss and SFS with Levy Flight random walk is called
SFS-Levy. The performance of the two applied methods is investigated comparing results obtained from
three test system. These systems with 6, 10, and 20 units with different objective function forms and
different constraints are inspected. Numerical result comparison can confirm that the applied approach has
better solution quality and fast convergence time when compared with some recently published standard,
modified, and hybrid methods. This elucidates that the two SFS methods are very favorable for solving
the ELD problem.
Optimal power generation for wind-hydro-thermal system using meta-heuristic a...IJECEIAES
In this paper, cuckoo search algorithm (CSA) is suggested for determining optimal operation parameters of the combined wind turbine and hydrothermal system (CWHTS) in order to minimize total fuel cost of all operating thermal power plants while all constraints of plants and system are exactly satisfied. In addition to CSA, Particle swarm optimization (PSO), PSO with constriction factor and inertia weight factor (FCIW-PSO) and social ski-driver (SSD) are also implemented for comparisons. The CWHTS is optimally scheduled over twenty-four one-hour interval and total cost of producing power energy is employed for comparison. Via numerical results and graphical results, it indicates CSA can reach much better results than other ones in terms of lower total cost, higher success rate and faster search process. Consequently, the conclusion is confirmed that CSA is a very efficient method for the problem of determining optimal operation parameters of CWHTS.
The study of reducing the cost of investment in wind energy based on the cat ...TELKOMNIKA JOURNAL
Wind and solar are the most important source of renewable energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. We apply the cat swarm meta-heuristic optimization method to solve the problem of wind power system design optimization. The electrical power components of the system are characterized by their cost, capacity and reliability. This study seeks to optimize the design of parallel power systems in which multiple choices of generators wind, transformers and lines. Our plan has the advantage of allowing electrical components with different parameters to be customized in electrical power systems. The UMGF method is applied to allow rapid reliability estimation. A computer program is developed for the UMGF application and CS algorithm. An example is provided to explain.
ECONOMIC LOAD DISPATCH USING GENETIC ALGORITHMIJARIIT
This paper present the application of Genetic Algorithm (GA) to Economic Load Dispatch problem of the power system. Economic Load Dispatch is one of the major optimization problems dealing with the modern power systems.ELD determines the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfactory the load demand. The objective is to minimize the total generation fuel cost and maintain the power flow within safety limits. The introduced algorithm has been demonstrated for the given test systems considering the transmission line losses.
Optimal Operation of Wind-thermal generation using differential evolutionIOSR Journals
This document presents an optimal operation model for a wind-thermal power generation system using differential evolution (DE). DE is an evolutionary algorithm inspired by biological evolution that can solve complex constrained optimization problems. The paper formulates the economic dispatch problem to minimize total generation cost of the wind and thermal plants subject to various constraints like power balance, generator limits, ramp rates, and valve point loading effects. Five different DE mutation strategies are analyzed for solving the wind-thermal economic dispatch problem on a test system with 10 thermal units. The results show that the best mutation strategy and control parameter values (mutation rate and crossover rate) depend on the problem and can significantly impact the solution quality and consistency obtained by the DE algorithm.
The problem of power system optimization has become a deciding factor in electrical power system engineering practice with emphasis on cost and emission reduction. The economic emission dispatch (EED) problem has been addressed in this paper using a Biogeography-based optimization (BBO). The BBO is inspired by geographical distribution of species within islands. This optimization algorithm works on the basis of two concepts-migration and mutation. In this paper a non-uniform mutation operator has been employed. The proposed technique shows better diversified search process and hence finds solutions more accurately with high convergence rate. The BBO with new mutation operator is tested on ten unit system. The comparison which is based on efficiency, reliability and accuracy shows that proposed mutation operator is competitive to the present one.
Dynamic economic emission dispatch using ant lion optimizationjournalBEEI
The document describes using the Ant Lion Optimization (ALO) algorithm to solve the dynamic economic emission dispatch (DEED) problem. The DEED problem involves scheduling power generation outputs from multiple units over time to minimize operating costs and emissions, while satisfying constraints. The ALO algorithm is inspired by how ant lions hunt prey. It was applied to a 5-unit power system over 24 hours and outperformed other algorithms in terms of solution quality. Key constraints of the DEED problem include ramp rate limits, prohibited operating zones, valve-point effects, and transmission losses.
A Decomposition Aggregation Method for Solving Electrical Power Dispatch Prob...raj20072
This document proposes a decomposition/aggregation method to solve large-scale economic dispatch problems with many generators. It decomposes a power system into areas, each containing generators and loads. An evolutionary programming technique optimizes dispatch in each area locally. The area solutions are then aggregated to solve the overall system problem while minimizing total cost. The method is demonstrated on 5-bus and 26-bus test systems decomposed into two areas each. Local area problems are solved as subproblems, while the overall system solution is the "master problem". Results are compared to a centralized approach. The decomposition/aggregation method shows promise in solving economic dispatch with large numbers of generators.
Hybrid method for solving the non smooth cost function economic dispatch prob...IJECEIAES
This article is focused on hybrid method for solving the non-smooth cost function economic dispatch problem. The techniques were divided into two parts according to: the incremental cost rates are used to find the initial solution and bee colony optimization is used to find the optimal solution. The constraints of economic dispatch are power losses, load demand and practical operation constraints of generators. To verify the performance of the proposed algorithm, it is operated by the simulation on the MATLAB program and tests three case studies; three, six and thirteen generator units which compared to particle swarm optimization, cuckoo search algorithm, bat algorithm, firefly algorithm and bee colony optimization. The results show that the proposed algorithm is able to obtain higher quality solution efficiently than the others methods.
Economic dispatch by optimization techniquesIJECEIAES
The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
This document presents a traditional approach called the lambda iteration method to solve the economic load dispatch (ELD) problem considering generator constraints. The ELD problem aims to minimize the total fuel cost while meeting demand and generator constraints. The lambda iteration method is implemented on a three-unit and six-unit system, with and without transmission losses, in MATLAB. The results show that considering transmission losses provides a more accurate solution to the ELD problem compared to ignoring losses. The lambda iteration method provides an effective traditional technique for solving the ELD problem.
Economic and Emission Dispatch using Whale Optimization Algorithm (WOA) IJECEIAES
This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system.
Statistical modeling and optimal energy distribution of cogeneration units b...IJECEIAES
Our main objective is to evaluate the performance of a new method to optimize the energy management of a production system composed of six cogeneration units using artificial intelligence. The optimization criterion is economic and environmental in order to minimize the total fuel cost, as well as the reduction of polluting gas emissions such as COx, NOx and SOx. First, a statistical model has been developed to determine the power that the cogeneration units can provide. Then, an economic model of operation was developed: fuel consumption and pollutant gas emissions as a function of the power produced. Finally, we studied the energy optimization of the system using genetic algorithms (GA), and contribute to the research on improving the efficiency of the studied power system. The GA has a better optimization performance, it can easily choose satisfactory solutions according to the optimization objectives, and compensate for these defects using its own characteristics. These characteristics make GA have outstanding advantages in iterative optimization. The robustness of the proposed algorithm is validated by testing six cogeneration units, and the obtained simulation results of the proposed system prove the value and effectiveness of GA for efficiency improvement as well as operating cost minimization.
Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using P...theijes
This document summarizes the application of particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem for Nigeria's thermal power stations. PSO is used to determine the optimal allocation of total power demand among generating units to minimize total fuel costs while satisfying constraints. The PSO algorithm is applied to a 1999 model of Nigeria's power network and results are compared to other heuristic methods. PSO efficiently distributes load to minimize costs and overcomes limitations of traditional optimization techniques for non-linear power system problems.
A Review on Various Techniques Used for Economic Load Dispatch in Power Systemijtsrd
The document discusses various techniques used to solve the economic load dispatch (ELD) problem in power systems. The ELD problem involves determining the optimal power output of generators to minimize generation costs while meeting demand and operating constraints. The document reviews several methods that have been used to solve the ELD problem, including lambda iteration, gradient search, Newton's method, linear programming, dynamic programming, neural networks, evolutionary algorithms, particle swarm optimization, and other metaheuristic techniques. It provides details on how each method approaches solving the optimization problem posed by economic load dispatch.
This document presents a multi-objective optimization method for economic emission load dispatch (EELD) that considers economy, emissions, and transmission line security as objectives. The problem is formulated to minimize total fuel costs and emissions while maximizing line security for a power system. The multi-objective problem is converted to a single objective using goal attainment and then solved using simulated annealing. Results are presented for a 30-bus and 57-bus IEEE test case system to demonstrate the proposed method.
This document proposes a multi-objective framework for short-term scheduling of a microgrid considering cost minimization and emission minimization objectives. It formulates the problem as a mixed integer nonlinear program with constraints including power balance and unit generation limits. The Normal Boundary Intersection method is employed to solve the multi-objective problem and generate a Pareto front of optimal solutions. Simulation results are presented comparing the proposed approach to other methods.
Optimization of Economic Load Dispatch with Unit Commitment on Multi MachineIJAPEJOURNAL
Economic load dispatch (ELD) and Unit Commitment (UC) are significant research applications in power systems that optimize the total production cost of the predicted load demand. The UC problem determines a turn-on and turn-off schedule for a given combination of generating units, thus satisfying a set of dynamic operational constraints. ELD optimizes the operation cost for all scheduled generating units with respect to the load demands of customers. The first phase in this project is to economically schedule the distribution of generating units using Gauss seidal and the second phase is to determine optimal load distribution for the scheduled units using dynamic programming method is applied to select and choose the combination of generating units that commit and de-commit during each hour. These precommitted schedules are optimized by dynamic programming method thus producing a global optimum solution with feasible and effective solution quality, minimal cost and time and higher precision. The effectiveness of the proposed techniques is investigated on two test systems consisting of five generating units and the experiments are carried out using MATLAB R2010b software. Experimental results prove that the proposed method is capable of yielding higher quality solution including mathematical simplicity, fast convergence, diversity maintenance, robustness and scalability for the complex ELD-UC problem.
International Journal of Engineering Research and DevelopmentIJERD Editor
This document presents an efficient approach for solving the dynamic economic load dispatch problem with transmission losses using multi-objective particle swarm optimization. The objective is to determine the most economic dispatch of generating units to meet load demand over time at minimum operating cost while satisfying constraints. The proposed MOPSO algorithm evaluates Pareto optimal solutions and preserves diversity better than standard PSO. It is tested on 6-unit and 15-unit systems and shows improved total fuel cost savings compared to the Brent method. The results demonstrate the effectiveness and superiority of the MOPSO approach for dynamic economic dispatch problems.
The document describes the economic environmental dispatch (EED) problem, which aims to minimize both the fuel cost and emissions of fossil fuel power plants simultaneously while satisfying operational constraints. The EED problem is formulated as a multi-objective optimization problem with conflicting cost and emission objectives and equality and inequality constraints. Multi-objective differential evolution is proposed to solve the EED problem and find the Pareto optimal solutions. Test results show the proposed approach performs comparably or better than other multi-objective evolutionary algorithms for the EED problem.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Solving combined economic emission dispatch problem in wind integrated power ...nooriasukmaningtyas
A meta-heuristic based optimization method for solving combined economic
emission dispatch (CEED) problem for the power system with thermal and
wind energy generating units is proposed in this paper. Wind energy is
environmentally friendly and abundantly available, but the intermittency and
variability of wind power affects the system operation. Therefore, the system
operator (SO) must aware of wind forecast uncertainty and dispatch the wind
power accordingly. Here, the CEED problem is solved by including the
nonlinear characteristics of thermal generators, and the stochastic behavior of
wind generators. The stochastic nature of wind generators is handled by
using probability distribution analysis. The purpose of this CEED problem is
to optimize fuel cost and emission levels simultaneously. The proposed
problem is changed into a single objective optimization problem by using
weighted sum approach. The proposed problem is solved by using particle
swarm optimization (PSO) algorithm. The feasibility of proposed
methodology is demonstrated on six generator power system, and the
obtained results using the PSO approach are compared with results obtained
from genetic algorithm (GA) and enhanced genetic algorithms (EGA).
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
Similar to Comparative study of the price penalty factors approaches for Bi-objective dispatch problem via PSO (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
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
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
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challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
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Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Home security is of paramount importance in today's world, where we rely more on technology, home
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AI based model home security system. The system has a user-friendly interface, allowing users to start
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and saves images of friends and family members. The system scans this folder to recognize familiar faces
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Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
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Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
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a registered donor, with some of the formalities with the organization it can be done.
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application to the mobile.
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needed blood group during the time of the emergency.
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SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
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Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
2. ISSN: 2088-8708
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3344
thermal power plants [4], the environmental issues has become a big concern which has to be addressedto
mitigate the effects of pollution and hence rectify problem of global warming. Therefore, production of
electricity with an optimized costat a lower green gas emmissionsacts as two vital parts of economic dispatch
problem. Production at the minimum cost result in a relatively high amount of emissions. Similarly, ensuring
minimum gas emissions limits the production of utilities running on fossil fuels. In order to find a right
balance in the present tradeoff, this optimization problem can be modelled asa multi-objective function
(Economic/Emission) which involves minimization of the cost function of producing electrical energy and
minimization of the gas emission function, by satisfying the constraints of both functions.
In the modeling of the bi-objective economic dispatch problem, the presentcomparative study
examines different types of the constraints and various types of price penalty factors. The following
parameters are considered:
a. Fuel cost and emission functions are modelled as second order polynomial function for both.
b. The following types of price penalty factors are used for the multi-objective dispatch problem:
- Min-Max price penalty factor
- Max-Max price penalty factor
- Min-Min price penalty factor
- Max-Min price penalty factor
- Average price penalty factor
- Common price penalty factor
c. Type of constraints to be satisfied are:
- Load/supply balance
- Minimum/maximum limits of the energy produced by the generators
- Transmission line losses
In order to overcome the above illustrated drawbacks, heuristic methodologies have been under
research for solving CEED problem. In the past the traditional methods used to solve this economic load
dispatch problem are the Lambda iteration method, Gradient, Newton, linear programming and interior point
method. Recently, meta-heuristic techniques such as Simulated Annealing, Genetic Algorithm (GA), Particle
Swarm Optimization (PSO), and Tabu search algorithm are used to solve this problem [5]. In this paper,
the Particle Swarm Optimization based-approach is proposed to solve the CEED problem. In order to
facilitate the search for the optimized solution, the price penalty factor is used to convert the bi-objective
CEED problem into a single objective function. The proposed method has been examined and tested on a real
grid in west Algeria which consists of a 22-bus system of 220 Kvvoltage level. Satisfactory simulation results
show the effectiveness of the proposed algorithm.
2. MATHEMATICAAL FORMULATION OF CEED PROBLEM
The bi-objective function for CEED problem [6-12] is given as follows:
𝐶𝐸𝐸𝐷 = 𝑀𝑖𝑛(𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡) + 𝑝𝑒𝑛𝑎𝑙𝑡𝑦 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑀𝑖𝑛(𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑣𝑎𝑙𝑢𝑒)
𝐹𝑐 = 𝑀𝑖𝑛 ∑ 𝐹𝑖(𝑃𝐺𝑖)𝑛𝐺
𝑖=1 = 𝑀𝑖𝑛 ∑ (𝑎𝑖 𝑃𝐺𝑖
2
+ 𝑏𝑖 𝑃𝐺𝑖 + 𝑐𝑖)𝑛𝐺
𝑖=1 (1)
where Fc is the total fuel cost of the system is, ng is the number of generators, PGi is real power generation of
a generator unit i, and ai,biand ciare the cost coefficients of the ith
generating unit.
𝐸 𝑇 = 𝑀𝑖𝑛 ∑ (𝛼𝑖 𝑃𝐺𝑖
2
+ 𝛽𝑖 𝑃𝐺𝑖 + 𝛾𝑖)𝑛𝐺
𝑖=1 (2)
where, 𝐸 𝑇 is total emission; 𝛼𝑖, 𝛽𝑖,𝛾𝑖are emission coefficients of generating unit iin [kg/MW2
h], [kg/MWh]
and [kg/h] respectively. Price penalty factorℎ𝑖 is used to convert the bi-objective CEED optimization problem
into a single objective [6-13] problem:
𝐹𝑇 = ∑ [((𝑎𝑖 𝑃𝐺𝑖
2
+ 𝑏𝑖 𝑃𝐺𝑖 + 𝑐𝑖)) + ℎ𝑖((𝛼𝑖 𝑃𝐺𝑖
2
+ 𝛽𝑖 𝑃𝐺𝑖 + 𝛾𝑖))]𝐺𝑔
𝑖=1 (3)
where, FT is total CEEDfuel cost; hi is price penalty factor.
3. PRICE PENALTY FACTORS (PPF)
The PPF [6, 11, 13-23] for CEED problem is formulated taking the ratio fuel cost and emission
value of the corresponding generators as follows:
3. Int J Elec & Comp Eng ISSN: 2088-8708
Comparative study of the price penalty factors approaches for Bi-objective dispatch … (Youssef Mouloudi)
3345
- Min-Max price penalty factor is described as:
ℎ𝑖 =
aiPGi,min
2
+biPGi,min+ci
αiPGi,max
2 +βiPGi,max+𝛾i
(4)
- Max-Max price penalty factor is described as:
ℎ𝑖 =
aiPGi,max
2
+biPGi,max+ci
diPGi,max
2 +eiPGi,max+𝛾i
(5)
- Min-Min price penalty factor is described as:
ℎ𝑖 =
aiPGi,min
2
+biPGi,min+ci
αiPGi,min
2 +βiPGi,min+𝛾i
(6)
- Max-Min price penalty factor is described as:
ℎ𝑖 =
aiPGi,max
2
+biPGi,max+ci
αiPGi,min
2 +βiPGi,min+𝛾i
(7)
- Average price penalty factor is formulated as:
ℎ 𝐴𝑉𝐸𝑅𝐴𝐺𝐸 𝑖 =
∑ ℎ 𝑖
4
1
4
(8)
- Common price penalty factor is formulated as:
ℎ 𝐶𝑂𝑀𝑀𝑂𝑁 𝑖 =
ℎ 𝐴𝑉𝐸𝑅𝐴𝐺𝐸 𝑖
4𝑛
(9)
where: n is operational generating unit.
4. CONSTRAINTS
4.1. Power balance constraints [24]
Where, PG, PDemand and PLossare the total generated power, load demand and transmission line loss
of the system respectively. Transmission line loss constraint can be given as, [25]:
PG = ∑ Pi = PDemand + PLoss
n
i=1 (10)
where, Pi, and Pj is the active power of unit iih
and jih
respectively. Bij, B0i and B00 is the transmission loss
coefficients.
PL = ∑ ∑ PiBijPj
n
i=j + ∑ B0iPi
n
i=1 + B00
n
i=1 (11)
4.2. Generator limits
The power output of each generator is restricted by minimum and maximum power limits,
is given as:
PGi min ≤ PGi ≤ PGi max (12)
5. PARTICAL SWARM OPTIMIZATION ALGORITHM
Particle swarm optimization PSO is a population-based optimization technique which was first
introduced by Kennedy and Eberhart in 1995 [26], inspired by social behavior of bird flocking or fish
schooling in search of food.The most important prominent features of PSO, compared to other existing
heuristic optimization strategies such as genetic algorithm, are its easy implementation, there are few
parameters to adjust and computation efficiency. In a PSO system, particles fly around in a multidimensional
search space. During flight, each particle adjusts its trajectory towards its own previous best position this
value is called (Pbest), and towards the best previous position attained by any member of its neighborhood or
4. ISSN: 2088-8708
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3346
globally, the whole swarm this value is called (Gbest) [27-32]. The two equations which are used in PSO are
velocity update equation (13) and position update equations (14). These are to be modified at each time step,
of PSO algorithm to converge the optimum solution.
Vi(t + 1) = ωVi(t) + c1r1[Pbesti(t) − Xi(t)] + c2r2[Gbesti(t) − Xi(t)] (13)
𝑋𝑖(𝑡 + 1)𝑋𝑖(𝑡) + 𝑉𝑖(𝑡 + 1) (14)
Where,i is the particle index; is the inertia coefficient; are acceleration coefficients 22,10 cc ; rr 2,1 are
random values, rr 2,10 regenerated every velocity cc 2,1 update; Vi is the particles velocity at time t ; X i
is the particles position at time t; Pbestis the particles individual best solution as of time t; Gbest is
the swarms best solution as of time t.
6. SIMULATION RESULTS AND ANALYSIS
The west algerian power network is a 22 bus system with 7production units. This latter is considered
in an attempt to solve the CEED problem using “Min-Max“, “Max-Max“, “Min-Min“, “Max-Min“,
”Average” and “Common” price penalty factors. The test system consists of 7 thermal units, 15 load buses
and 31 transmission lines, 03compensator VARSTATIC SVC [3* (+40Mvar and )10Mvar)]. The total system
demand is 856 MW. The data for the considering test system is shown in Table 1. The real power limits of
the generators, fuel cost coefficients are also given in the Table 1. Programming of the CEED using the PSO
method has been applied by using MATLAB software, tested on a CORE i5, personal computer with 2.20
GHz and 4 GO RAM. Table 2 show solution of CEED problem with different price penalty factors such as
“Min-Max”, “Max-Max”, “Min-Min”, “Max-Min”, Average and Common. Table 3 compares the results
obtained with all six penalty factors. As illustrated in Table 2 the results show an acceptable improvement in
the fuel cost, and total fuel cost CEED of the system when using the Min-Max price penalty factor compared
to other penalty factors. The emission value is less when using Max-Max price penalty factor in comparison
with the other penalty factors. The Max-Min price penalty factor is better in terms of the lowest transmission
loss compared to other penalty factors.
Table 1. 22 bus system data
Generator
Numbers
Generator limits [MW] Fuel cost coefficients
𝑃 𝑚𝑖𝑛 [𝑀𝑊] 𝑃𝑚𝑎𝑥 [𝑀𝑊] 𝑎𝑖[$/MW2
h] 𝑏𝑖[$/MWh] 𝑐𝑖 [$/h]
1 100 500 0.007 7.5 240
2 50 200 0.008 7 200
3 80 300 0.0085 7.5 220
4 50 150 0.009 7 200
5 50 200 0.009 9 220
6 50 120 0.0075 10 190
7 10 80 0.009 6.3 180
Table 2. Solution of CEED problem using PSO with various price penalty factors
Price Penalty Factors Data
From
SONELG
AZ [30]
Min-Max Max-Max Min-Min Max-Min Average Common
𝑃1 [MW] 200 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
𝑃2 [MW] 200 206.2277 186.6597 246.9342 210.3191 164.5518 185.9998
𝑃3 [MW] 300 188.3073 219.3888 224.8282 191.1794 257.3631 236.7597
𝑃4 [MW] 80 130.3337 96.3901 138.6912 141.6869 56.5279 60.8637
𝑃5 [MW] 100 124.7016 124.0204 65.8667 60.0516 135.4592 105.7232
𝑃6 [MW] 100 88.4415 86.7610 63.0696 109.6222 73.1928 104.4470
𝑃7 [MW] 10 19.5432 50.2276 27.0108 43.4842 73.6464 83.2097
Power Loss [MW] 21.4 20.882 20.175 20.087 17.409 21.550 19.049
Total output [MW] 990 857.5555 863.4476 866.4007 856.3434 860.7412 877.0031
Power demand [MW] 856 856 856 856 856 856 856
Generation cost[$/h] 9104.44 8892.0 8899.4 9089.8 8904.5 8909.5 9040
Emission [Kg/h] * 1096.1 1078.1 1228.9 1196.5 1101.4 1225.5
Total cost[$/h] * 10903 14406 18985 36863 32346 40640
Temps [S] * 0.095112 0.106198 0.080914 0.084423 0.096332 0.096956
5. Int J Elec & Comp Eng ISSN: 2088-8708
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3347
Table 3. Comparison of simulation results obtained from “Min-Max”, “Max-Max”, “Min-Min”, “Max-Min”,
“Average”,“Common” price penalty factors
Criterion Min-Max
price penalty
factor
Max-Max
price penalty
factor
Min-Min
price penalty
factor
Max-Min
price penalty
factor
Average
price penalty
factor
Common
price penalty
factor
Power Loss [MW] 100% 96.61% 96.19% 83.37% 103.19% 93.03%
Generation cost[$/h] 100% 100.10% 102.22% 100.14% 100.19% 100.23%
Emission[Kg/h] 100% 98.36% 112.12% 109.16% 100.48% 103.52%
Total cost[$/h] 100% 132.13% 174.12% 338.09 296.67% 652.79%
Figure 1 show clearly that the convergence profile obtained by PSO algorithm of functions such as
CEED total cost, generation cost, emission cost and transmission loss when using Min-Max, Max-Max,
Min-Min, Max-Min, average and common price penalty factors is faster and more effective, which proves
that the proposed algorithm has more ability to find the optimal points in a search space compared with data
provided by SONELGAZ, the company which is in charge of operating the above mentioned grid of west of
Algeria [30].
From Figure 1(a), the variation of CEED fuel cost values of the bi-objective dispatch problem using
Min-Max price penalty factor are the lowest compared to other penalty factors. Similarly, the variation of
fuel cost values of the bi-objective dispatch problem using Min-Max price penalty factor are the lowest
compared to other penalty factors, see Figure 1(b). Likewise, according to Figure 1(c) the variation of
emission values of the bi-objective dispatch problem using Max-Max price penalty factor has minimum
pollution control compared to other pnalty factors. Finally, From Figure 1(d) the variation of power
lossvalues of the bi-objective dispatch problem using Max-Min price penalty factor has lowest transmission
power loss compared to other penalty factors.
(a) (b)
(c) (d)
Figure 1. Convergence curve for functions such as, (a) CEED (comparison of CEED total cost using various
price penalty factors), (b) fuel cost (comparison of generation cost usingvarious price penalty facteur),
(c) emission value (comparison of emission value using various price penalty facteur), (d) power loss
(comparison of power loss using variousprice penalty facteur)
1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
x 10
4
Iterations
CEEDTotalCost[$/h]
Min-MaxPrice penaltyfactor
Max-MaxPrice penaltyfactor
Min-MinPrice penaltyfactor
Max-MinPrice penaltyfactor
Average Price penaltyfactor
CommonPrice penaltyfactor
1 2 3 4 5 6 7 8 9 10
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
Iterations
GenerationCost[$/h]
Min-MaxPrice penaltyfactor
Max-MaxPrice penaltyfactor
Min-MinPrice penaltyfactor
Max-MinPrice penaltyfactor
Average Price penaltyfactor
CommonPrice penaltyfactor
1 2 3 4 5 6 7 8 9 10
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
Iterations
Emission[Kg/h]
Min-MaxPrice penaltyfactor
Max-MaxPrice penaltyfactor
Min-MinPrice penaltyfactor
Max-MinPrice penaltyfactor
Average Price penaltyfactor
CommonPrice penaltyfactor
1 2 3 4 5 6 7 8 9 10
16
18
20
22
24
26
28
30
32
34
36
Iterations
PowerLoss[MW]
Min-MaxPrice penaltyfactor
Max-MaxPrice penaltyfactor
Min-MinPrice penaltyfactor
Max-MinPrice penaltyfactor
Average Price penaltyfactor
CommonPrice penaltyfactor
6. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3343 - 3349
3348
7. CONCLUSION
In this paper, the impact of price penalty factors on the solution of the bi-objective power system
economic dispatch optimization problem is examined on electric grid of west algeria which consists of
22-Bus system. The Particle Swarm Optimization algorithm is proposed for solving the combined economic
emission dispatch problem. On the basis of results obtained some conclusions are made: the simulation
results show that Min-Max price penalty factor yields a minimum generation cost for bi-objective power
dispatch problem. The results show that theminimum emission values are less in Max-Max price penalty
factor compared to other penalty factors. The Max-Min price penalty factor is better in terms of the lowest
transmission loss compared to other penalty factrors.
In Summary, it has been shown that the minimum overall cost for the bi-objective power system
dispatch optimization problem can be obtained using Min-Max Price penalty factor. From Table 2 the CEED
fuel cost values are significantly lower with Min-Max price penalty factor by 32.13% in comparison to
the solution using Max-Max price penalty factor. The results also show that the emission values are less in
Max-Max price penalty factor by 1.68% when compared to Min-Max price penalty factor.
ACKNOWLEDGEMENTS
Authors would like to thank the heads of Laboratory of Analysis, Control andOptimization of
Electro-Energetic Systems (CAOSEE) and Laboratory of Smart Grids & Renewable Energies
(ENERGARID) at the university TAHRI Mohammed, Béchar (Algeria).
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