This document summarizes a research paper that applies a Thunderstorm Algorithm to determine the optimal committed power output of a power system while considering cloud charge parameters. The algorithm mimics the natural processes of thunderstorms through cloud, streamer, and avalanche phases to search for solutions. It was tested on the IEEE 62-bus system to optimize the total cost of fuel consumption and emissions under technical constraints. The results showed that incorporating cloud charge information into the algorithm improved its statistical and computational performance for finding the optimal committed power output solution.
This document summarizes a research paper that proposes a new approach for solving the economic dispatch problem in power systems using a hybrid particle swarm optimization and simulated annealing algorithm. The paper introduces economic dispatch and describes previous solution methods. It then presents the new hybrid algorithm, which combines the global search capabilities of particle swarm optimization with the probabilistic jumping of simulated annealing to find high-quality solutions faster. The paper applies the method to test cases and finds it performs better than traditional and other computational techniques at determining low-cost generation schedules that satisfy operational constraints.
A new approach to the solution of economic dispatch using particle Swarm opt...ijcsa
This document presents a new approach to solving the economic dispatch problem using particle swarm optimization combined with simulated annealing (PSO-SA). The economic dispatch problem aims to minimize the total generation cost while satisfying constraints like power demand and generator limits. Previous solutions had limitations. The authors propose using PSO-SA to find high quality solutions more efficiently. PSO is able to find global optima but can get trapped in local optima. SA helps avoid this through probabilistic jumping. The authors combine PSO and SA techniques to leverage their benefits while overcoming individual limitations. They test the PSO-SA method on three generator systems and find it provides better results than traditional and other computational methods.
Advanced SOM & K Mean Method for Load Curve Clustering IJECEIAES
From the load curve classification for one customer, the main features such as the seasonal factors, the weekday factors influencing on the electricity consumption may be extracted. By this way some utilities can make decision on the tariff by seasons or by day in week. The popular clustering techniques are the SOM & K-mean or Fuzzy K-mean. SOM &Kmean is a prominent approach for clustering with a two-level approach: first, the data set will be clustered using the SOM and in the second level, the SOM will be clustered by K-mean. In the first level, two training algorithms were examined: sequential and batch training. For the second level, the K-mean has the results that are strongly depended on the initial values of the centers. To overcome this, this paper used the subtractive clustering approach proposed by Chiu in 1994 to determine the centers. Because the effective radius in Chiu’s method has some influence on the number of centers, the paper applied the PSO technique to find the optimum radius. To valid the proposed approach, the test on well-known data samples is carried out. The applications for daily load curves of one Southern utility are presented.
PuShort Term Hydrothermal Scheduling using Evolutionary Programmingblished pa...Satyendra Singh
In this paper, Evolutionary Programming method
is used for short term hydrothermal scheduling which minimize
the total fuel cost while satisfying the constraints. This paper
developed and studies the performance of evolutionary programs
in solving hydrothermal scheduling problem. The effectiveness of
the developed program is tested for the system having one hydro
and one thermal unit for 24 hour load demand. Numerical results
show that highly near-optimal solutions can be obtained by
Evolutionary Programming.
Insights into the Efficiencies of On-Shore Wind Turbines: A Data-Centric Anal...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/insights-into-the-efficiencies-of-on-shore-wind-turbines-a-data-centric-analysis/
Literature on renewable energy alternative of wind turbines does not include a multidimensional benchmarking studythat can help investment decisions as well as design processes. This paper presents a data-centric analysis of commercial on-shore wind turbines and provides actionable insights through analytical benchmarking through Data Envelopment Analysis (DEA), visual data analysis, and statistical hypothesis testing. The paper also introduces a novel visualization approach for the understanding and the interpretation of reference sets, the set of efficient wind turbines that should be taken as benchmark by inefficient ones.
Achieving Energy Proportionality In Server ClustersCSCJournals
a great amount of interests in the past few years. Energy proportionality is a principal to ensure that energy consumption is proportional to the system workload. Energy proportional design can effectively improve energy efficiency of computing systems. In this paper, an energy proportional model is proposed based on queuing theory and service differentiation in server clusters, which can provide controllable and predictable quantitative control over power consumption with theoretically guaranteed service performance. Futher study for the transition overhead is carried out corresponding strategy is proposed to compensate the performance degradation caused by transition overhead. The model is evaluated via extensive simulations and is justified by the real workload data trace. The results show that our model can achieve satisfied service performance while still preserving energy efficiency in the system.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
This document summarizes a research paper that proposes a new approach for solving the economic dispatch problem in power systems using a hybrid particle swarm optimization and simulated annealing algorithm. The paper introduces economic dispatch and describes previous solution methods. It then presents the new hybrid algorithm, which combines the global search capabilities of particle swarm optimization with the probabilistic jumping of simulated annealing to find high-quality solutions faster. The paper applies the method to test cases and finds it performs better than traditional and other computational techniques at determining low-cost generation schedules that satisfy operational constraints.
A new approach to the solution of economic dispatch using particle Swarm opt...ijcsa
This document presents a new approach to solving the economic dispatch problem using particle swarm optimization combined with simulated annealing (PSO-SA). The economic dispatch problem aims to minimize the total generation cost while satisfying constraints like power demand and generator limits. Previous solutions had limitations. The authors propose using PSO-SA to find high quality solutions more efficiently. PSO is able to find global optima but can get trapped in local optima. SA helps avoid this through probabilistic jumping. The authors combine PSO and SA techniques to leverage their benefits while overcoming individual limitations. They test the PSO-SA method on three generator systems and find it provides better results than traditional and other computational methods.
Advanced SOM & K Mean Method for Load Curve Clustering IJECEIAES
From the load curve classification for one customer, the main features such as the seasonal factors, the weekday factors influencing on the electricity consumption may be extracted. By this way some utilities can make decision on the tariff by seasons or by day in week. The popular clustering techniques are the SOM & K-mean or Fuzzy K-mean. SOM &Kmean is a prominent approach for clustering with a two-level approach: first, the data set will be clustered using the SOM and in the second level, the SOM will be clustered by K-mean. In the first level, two training algorithms were examined: sequential and batch training. For the second level, the K-mean has the results that are strongly depended on the initial values of the centers. To overcome this, this paper used the subtractive clustering approach proposed by Chiu in 1994 to determine the centers. Because the effective radius in Chiu’s method has some influence on the number of centers, the paper applied the PSO technique to find the optimum radius. To valid the proposed approach, the test on well-known data samples is carried out. The applications for daily load curves of one Southern utility are presented.
PuShort Term Hydrothermal Scheduling using Evolutionary Programmingblished pa...Satyendra Singh
In this paper, Evolutionary Programming method
is used for short term hydrothermal scheduling which minimize
the total fuel cost while satisfying the constraints. This paper
developed and studies the performance of evolutionary programs
in solving hydrothermal scheduling problem. The effectiveness of
the developed program is tested for the system having one hydro
and one thermal unit for 24 hour load demand. Numerical results
show that highly near-optimal solutions can be obtained by
Evolutionary Programming.
Insights into the Efficiencies of On-Shore Wind Turbines: A Data-Centric Anal...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/insights-into-the-efficiencies-of-on-shore-wind-turbines-a-data-centric-analysis/
Literature on renewable energy alternative of wind turbines does not include a multidimensional benchmarking studythat can help investment decisions as well as design processes. This paper presents a data-centric analysis of commercial on-shore wind turbines and provides actionable insights through analytical benchmarking through Data Envelopment Analysis (DEA), visual data analysis, and statistical hypothesis testing. The paper also introduces a novel visualization approach for the understanding and the interpretation of reference sets, the set of efficient wind turbines that should be taken as benchmark by inefficient ones.
Achieving Energy Proportionality In Server ClustersCSCJournals
a great amount of interests in the past few years. Energy proportionality is a principal to ensure that energy consumption is proportional to the system workload. Energy proportional design can effectively improve energy efficiency of computing systems. In this paper, an energy proportional model is proposed based on queuing theory and service differentiation in server clusters, which can provide controllable and predictable quantitative control over power consumption with theoretically guaranteed service performance. Futher study for the transition overhead is carried out corresponding strategy is proposed to compensate the performance degradation caused by transition overhead. The model is evaluated via extensive simulations and is justified by the real workload data trace. The results show that our model can achieve satisfied service performance while still preserving energy efficiency in the system.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
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.
Performance analysis based on probabilistic modelling of Quaid-e-Azam Solar P...Power System Operation
The solar photovoltaic (PV) technology has gained global importance to overcome the global warming and meet
future energy needs. The performance of a solar PV plant depends on many factors such as solar irradiance,
weather conditions, various types of energy losses and system degradation over time. Although the deterministic
models nicely predict the PV performance at a single instant in time, however, they fail to account for the uncertainty
and randomness in the input parameters. Probabilistic models, in contrast, are more useful to predict
the system performance over a time span under real conditions. In this study, a probabilistic model has been
developed for the performance analysis of a recently commissioned 100 MW power plant at Bahawalpur,
Pakistan. The model is based on Monte-Carlo simulation method and uses the probable range of input data from
the site of the power plant. The performance of the power plant is presented in terms of monthly and seasonal
electricity generation. The associated energy losses are discussed in detailed. Furthermore, a comprehensive cost
analysis of the power plant has been provided. According to results from the model, the power produced in the
first year of operation of the plant is 136,700 MWh and the projected cumulative energy produced during a plant
lifetime of 25 years is 3,108,450 MWh. The levelized cost of energy (LCOE) estimated by the model is 0.0795
$/kWh, which is quite reasonable in comparison to the average 0.1 $/kWh cost of electricity to a domestic
customer in Pakistan.
Wind Farm Layout Optimization (WFLO) is a typical model-based complex system design process, where the popular use of low-medium fidelity models is one of the primary sources of uncertainties propagating into the esti- mated optimum cost of energy (COE). Therefore, the (currently lacking) understanding of the degree of uncertainty inherited and introduced by different models is absolutely critical (i) for making informed modeling decisions, and (ii) for being cognizant of the reliability of the obtained results. A framework called the Visually-Informed Decision-Making Platform (VIDMAP) was recently introduced to quantify and visualize the inter-model sensi- tivities and the model inherited/induced uncertainties in WFLO. Originally, VIDMAP quantified the uncertainties and sensitivities upstream of the energy production model. This paper advances VIDMAP to provide quantifica- tion/visualization of the uncertainties propagating through the entire optimization process, where optimization is performed to determine the micro-siting of 100 turbines with a minimum COE objective. Specifically, we deter- mine (i) the sensitivity of the minimum COE to the top-level system model (energy production model), (ii) the uncertainty introduced by the heuristic optimization algorithm (PSO), and (iii) the net uncertainty in the minimum COE estimate. In VIDMAP, the eFAST method is used for sensitivity analysis, and the model uncertainties are quantified through a combination of Monte Carlo simulation and probabilistic modeling. Based on the estimated sensitivity and uncertainty measures, a color-coded model-block flowchart is then created using the MATLAB GUI.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve
scheduling. In this paper, a combination of imperialist competition algorithm (ICA) and gravitational attraction is used for to address the
problem of independent task scheduling in a grid environment, with the aim of reducing the makespan and energy. Experimental results
compare ICA with other algorithms and illustrate that ICA finds a shorter makespan and energy relative to the others. Moreover, it
converges quickly, finding its optimum solution in less time than the other algorithms.
Reinforcement Learning for Building Energy Optimization Through Controlling o...Power System Operation
This paper presents a novel methodology to control HVAC system and minimize energy cost
on the premise of satisfying power system constraints. A multi-agent architecture based on game theory and
reinforcement learning is developed so as to reduce the cost and computational complexity of the microgrid.
The multi-agent architecture comprising agents, state variables, action variables, reward function and cost
game is formulated. The paper lls the gap between multi-agent HVAC systems control and power system
optimization and planning. The results and analysis indicate that the proposed algorithm is benecial to deal
with the problem of ``curse of dimensionality'' for multi-agent microgrid HVAC system control and speed
up learning of unknown power system conditions.
Load shedding in power system using the AHP algorithm and Artificial Neural N...IJAEMSJORNAL
This paper proposes the load shedding method based on considering the load importance factor, primary frequency adjustment, secondary frequency adjustment and neuron network. Consideration the process of primary frequency control, secondary frequency control helps to reduce the amount of load shedding power and restore the system’s frequency to the permissible range. The amount of shedding power of each load bus is distributed based on the load importance factor. Neuron network is applied to distribute load shedding strategies in the power system at different load levels. The experimental and simulated results on the IEEE 37- bus system present the frequency can restore to allowed range and reduce the damage compared to the traditional load shedding method using under frequency relay- UFLS.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...ijccsa
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
KEYWORDS
Energy Consumption, Virtual Machine Placement, Harmony Search Algorithm, Server Consolidati
Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasti...IJECEIAES
The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ( [ 1,2,7,16,18,35,46 ] , 1, [ 1,3,13,21,27,46 ] )( 1,1,1 ) 48 ( 0,0,1 ) 336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
Wind farm layout optimization (WFLO) is the process of optimizing the location of turbines in a wind farm site, with the possible objective of maximizing the energy production or minimizing the average cost of energy. Conventional WFLO methods not only limit themselves to prescribing the site boundaries, they are also generally applicable to designing only small-to-medium scale wind farms (<100 turbines). Large-scale wind farms entail greater wake-induced turbine interactions, thereby increasing the computa- tional complexity and expense by orders of magnitude. In this paper, we further advance the Unrestricted WFLO framework by designing the layout of large-scale wind farms with 500 turbines (where energy pro- duction is maximized). First, the high-dimensional layout optimization problem (involving 2N variables for a N turbine wind farm) is reduced to a 6-variable problem through a novel mapping strategy, which allows for both global siting (overall land configuration) and local exploration (turbine micrositing). Sec- ondly, a surrogate model is used to substitute the expensive analytical WF energy production model; the high computational expense of the latter is attributed to the factorial increase in the number of calls to the wake model for evaluating every candidate wind farm layout that involves a large number of turbines. The powerful Concurrent Surrogate Model Selection (COSMOS) framework is applied to identify the best surrogate model to represent the wind farm energy production as a function of the reduced variable vector. To accomplish a reliable optimum solution, the surrogate-based optimization (SBO) is performed by implementing the Adaptive Model Refinement (AMR) technique within Particle Swarm Optimization (PSO). In AMR, both local exploitation and global exploration aspects are considered within a single optimization run of PSO, unlike other SBO methods that often either require multiple (potentially mis- leading) optimizations or are model-dependent. By using the AMR approach in conjunction with PSO and COSMOS, the computational cost of designing very large wind farms is reduced by a remarkable factor of 26, while preserving the reliability of this WFLO within 0.05% of the WFLO performed using the original energy production model.
Unit Commitment Problem in Electrical Power System: A Literature Review IJECEIAES
Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.
Random Forest Ensemble of Support Vector Regression for Solar Power ForecastingMohamed Abuella
This document describes a methodology for generating combined solar power forecasts using an ensemble of support vector regression models. The methodology includes:
1. Developing 24 individual SVR forecasting models from two different solar power datasets.
2. Using random forest regression to combine the forecasts from the 24 SVR models.
3. Evaluating the combined forecasts against individual model forecasts and a simple average combination, finding the ensemble approach improved accuracy in most months.
A Research on Optimal Power Flow Solutions For Variable LoaIJERA Editor
This document discusses research on using optimization techniques to solve the optimal power flow problem under variable load conditions. It proposes combining the continuation method with an interior point algorithm to track optimal power flow solutions as the load parameter is increased. This would allow analyzing system behavior near the maximum loadability limit. The research aims to study optimal power flow behavior near limits, evaluate the proposed methodology's efficiency, and analyze critical bus indices and sensitivity of maximum load to reactive power injections. Results show the proposed approach can track solutions continuously for load increases where no new operational limits become active.
MFBLP Method Forecast for Regional Load Demand SystemCSCJournals
Load forecast plays an important role in planning and operation of a power system. The accuracy of the forecast value is necessary for economically efficient operation and also for effective control. This paper describes a method of modified forward backward linear predictor (MFBLP) for solving the regional load demand of New South Wales (NSW), Australia. The method is designed and simulated based on the actual load data of New South Wales, Australia. The accuracy of discussed method is obtained and comparison with previous methods is also reported.
AN INTEGER-LINEAR ALGORITHM FOR OPTIMIZING ENERGY EFFICIENCY IN DATA CENTERSijfcstjournal
Nowadays, to meet the enormous computational requests, energy consumption, the largest part which is
related to idle resources, is strictly increased as a great part of a data center's budget. So, minimizing
energy consumption is one of the most important issues in the field of green computing. In this paper, we
present a mathematical model formed as integer-linear programming which minimizes energy consumption
and maximizes user’s satisfaction, simultaneously. However, migration variables, as principal decision
variables of the model, can be relaxed to continuous activities in some practical problems. This constraint
relaxation helps a decision maker to find faster solutions that are usually good approximations for
optimum. Near feasible solutions (infeasible solutions that are desirably close to the feasible region) have
been investigated as another relaxation considering the kind of solutions. For this purpose, we initially
present a measure to evaluate the amount of infeasibility of solutions and then let the model consider an
extended region including solutions with remissible infeasibility, if necessary.
This document summarizes the application of computational intelligence techniques like genetic algorithms and particle swarm optimization for solving economic load dispatch problems. It first applies a real-coded genetic algorithm to minimize generation costs for a 6-generator test system with continuous fuel cost equations, showing superiority over quadratic programming. It then uses particle swarm optimization to minimize costs for a 10-generator system with each generator having discontinuous fuel options, showing better results than other published methods. The document provides background on economic load dispatch problems and optimization techniques like quadratic programming, genetic algorithms, and particle swarm optimization.
Determination of wind energy potential of campus area of siirt universitymehmet şahin
In this study, wind energy potential of Siirt
University campus area is statistically examined by using the mean hourly wind speed data between 2014
and 2015 years which are measured by Vantage Pro2 device, located at the roof of the Engineering Faculty building with 6 m altitude. Weibull distribution
function and Rayleigh distribution function are used
as statistical approach to evaluate the wind data. Weibull distribution function is examined by using two different methods that are maximum likelihood estimation and Rayleigh method. The determination
coefficient (R2) and Root Mean Square Error (RMSE) values of these methods are compared. According the error analysis, it is indicated that the Rayleigh method
gives better results. Wind speed and wind power density are calculated in pursuance of Weibull distribution parameters. The results are evaluated as
monthly and annually. Hence, this preliminary study is made to determine the wind energy potential of Siirt University campus area.
Short-term Load Forecasting using traditional demand forecastingIOSR Journals
This document discusses and compares five traditional methods for short-term load forecasting (STLF): simple moving average, weighted moving average, exponential moving average, auto regressive, and multiple linear regression. It presents the mathematical formulas for each method and discusses how each was used to generate hourly load forecasts for a case study using historical load data from an electricity market in India. The results show that time series forecasting using traditional techniques can produce reasonably accurate hourly load forecasts.
Dropbox es un servicio gratuito de almacenamiento en la nube que permite a los usuarios acceder y compartir fácilmente archivos entre dispositivos a través de una carpeta sincronizada. Al instalar Dropbox, se crea una carpeta en el equipo que se sincroniza automáticamente con todos los demás dispositivos del usuario y con la cuenta en línea de Dropbox. Los archivos arrastrados a esta carpeta se comparten instantáneamente a través de todas las plataformas asociadas con la cuenta del usuario.
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.
Performance analysis based on probabilistic modelling of Quaid-e-Azam Solar P...Power System Operation
The solar photovoltaic (PV) technology has gained global importance to overcome the global warming and meet
future energy needs. The performance of a solar PV plant depends on many factors such as solar irradiance,
weather conditions, various types of energy losses and system degradation over time. Although the deterministic
models nicely predict the PV performance at a single instant in time, however, they fail to account for the uncertainty
and randomness in the input parameters. Probabilistic models, in contrast, are more useful to predict
the system performance over a time span under real conditions. In this study, a probabilistic model has been
developed for the performance analysis of a recently commissioned 100 MW power plant at Bahawalpur,
Pakistan. The model is based on Monte-Carlo simulation method and uses the probable range of input data from
the site of the power plant. The performance of the power plant is presented in terms of monthly and seasonal
electricity generation. The associated energy losses are discussed in detailed. Furthermore, a comprehensive cost
analysis of the power plant has been provided. According to results from the model, the power produced in the
first year of operation of the plant is 136,700 MWh and the projected cumulative energy produced during a plant
lifetime of 25 years is 3,108,450 MWh. The levelized cost of energy (LCOE) estimated by the model is 0.0795
$/kWh, which is quite reasonable in comparison to the average 0.1 $/kWh cost of electricity to a domestic
customer in Pakistan.
Wind Farm Layout Optimization (WFLO) is a typical model-based complex system design process, where the popular use of low-medium fidelity models is one of the primary sources of uncertainties propagating into the esti- mated optimum cost of energy (COE). Therefore, the (currently lacking) understanding of the degree of uncertainty inherited and introduced by different models is absolutely critical (i) for making informed modeling decisions, and (ii) for being cognizant of the reliability of the obtained results. A framework called the Visually-Informed Decision-Making Platform (VIDMAP) was recently introduced to quantify and visualize the inter-model sensi- tivities and the model inherited/induced uncertainties in WFLO. Originally, VIDMAP quantified the uncertainties and sensitivities upstream of the energy production model. This paper advances VIDMAP to provide quantifica- tion/visualization of the uncertainties propagating through the entire optimization process, where optimization is performed to determine the micro-siting of 100 turbines with a minimum COE objective. Specifically, we deter- mine (i) the sensitivity of the minimum COE to the top-level system model (energy production model), (ii) the uncertainty introduced by the heuristic optimization algorithm (PSO), and (iii) the net uncertainty in the minimum COE estimate. In VIDMAP, the eFAST method is used for sensitivity analysis, and the model uncertainties are quantified through a combination of Monte Carlo simulation and probabilistic modeling. Based on the estimated sensitivity and uncertainty measures, a color-coded model-block flowchart is then created using the MATLAB GUI.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve
scheduling. In this paper, a combination of imperialist competition algorithm (ICA) and gravitational attraction is used for to address the
problem of independent task scheduling in a grid environment, with the aim of reducing the makespan and energy. Experimental results
compare ICA with other algorithms and illustrate that ICA finds a shorter makespan and energy relative to the others. Moreover, it
converges quickly, finding its optimum solution in less time than the other algorithms.
Reinforcement Learning for Building Energy Optimization Through Controlling o...Power System Operation
This paper presents a novel methodology to control HVAC system and minimize energy cost
on the premise of satisfying power system constraints. A multi-agent architecture based on game theory and
reinforcement learning is developed so as to reduce the cost and computational complexity of the microgrid.
The multi-agent architecture comprising agents, state variables, action variables, reward function and cost
game is formulated. The paper lls the gap between multi-agent HVAC systems control and power system
optimization and planning. The results and analysis indicate that the proposed algorithm is benecial to deal
with the problem of ``curse of dimensionality'' for multi-agent microgrid HVAC system control and speed
up learning of unknown power system conditions.
Load shedding in power system using the AHP algorithm and Artificial Neural N...IJAEMSJORNAL
This paper proposes the load shedding method based on considering the load importance factor, primary frequency adjustment, secondary frequency adjustment and neuron network. Consideration the process of primary frequency control, secondary frequency control helps to reduce the amount of load shedding power and restore the system’s frequency to the permissible range. The amount of shedding power of each load bus is distributed based on the load importance factor. Neuron network is applied to distribute load shedding strategies in the power system at different load levels. The experimental and simulated results on the IEEE 37- bus system present the frequency can restore to allowed range and reduce the damage compared to the traditional load shedding method using under frequency relay- UFLS.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...ijccsa
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
KEYWORDS
Energy Consumption, Virtual Machine Placement, Harmony Search Algorithm, Server Consolidati
Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasti...IJECEIAES
The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ( [ 1,2,7,16,18,35,46 ] , 1, [ 1,3,13,21,27,46 ] )( 1,1,1 ) 48 ( 0,0,1 ) 336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
Wind farm layout optimization (WFLO) is the process of optimizing the location of turbines in a wind farm site, with the possible objective of maximizing the energy production or minimizing the average cost of energy. Conventional WFLO methods not only limit themselves to prescribing the site boundaries, they are also generally applicable to designing only small-to-medium scale wind farms (<100 turbines). Large-scale wind farms entail greater wake-induced turbine interactions, thereby increasing the computa- tional complexity and expense by orders of magnitude. In this paper, we further advance the Unrestricted WFLO framework by designing the layout of large-scale wind farms with 500 turbines (where energy pro- duction is maximized). First, the high-dimensional layout optimization problem (involving 2N variables for a N turbine wind farm) is reduced to a 6-variable problem through a novel mapping strategy, which allows for both global siting (overall land configuration) and local exploration (turbine micrositing). Sec- ondly, a surrogate model is used to substitute the expensive analytical WF energy production model; the high computational expense of the latter is attributed to the factorial increase in the number of calls to the wake model for evaluating every candidate wind farm layout that involves a large number of turbines. The powerful Concurrent Surrogate Model Selection (COSMOS) framework is applied to identify the best surrogate model to represent the wind farm energy production as a function of the reduced variable vector. To accomplish a reliable optimum solution, the surrogate-based optimization (SBO) is performed by implementing the Adaptive Model Refinement (AMR) technique within Particle Swarm Optimization (PSO). In AMR, both local exploitation and global exploration aspects are considered within a single optimization run of PSO, unlike other SBO methods that often either require multiple (potentially mis- leading) optimizations or are model-dependent. By using the AMR approach in conjunction with PSO and COSMOS, the computational cost of designing very large wind farms is reduced by a remarkable factor of 26, while preserving the reliability of this WFLO within 0.05% of the WFLO performed using the original energy production model.
Unit Commitment Problem in Electrical Power System: A Literature Review IJECEIAES
Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.
Random Forest Ensemble of Support Vector Regression for Solar Power ForecastingMohamed Abuella
This document describes a methodology for generating combined solar power forecasts using an ensemble of support vector regression models. The methodology includes:
1. Developing 24 individual SVR forecasting models from two different solar power datasets.
2. Using random forest regression to combine the forecasts from the 24 SVR models.
3. Evaluating the combined forecasts against individual model forecasts and a simple average combination, finding the ensemble approach improved accuracy in most months.
A Research on Optimal Power Flow Solutions For Variable LoaIJERA Editor
This document discusses research on using optimization techniques to solve the optimal power flow problem under variable load conditions. It proposes combining the continuation method with an interior point algorithm to track optimal power flow solutions as the load parameter is increased. This would allow analyzing system behavior near the maximum loadability limit. The research aims to study optimal power flow behavior near limits, evaluate the proposed methodology's efficiency, and analyze critical bus indices and sensitivity of maximum load to reactive power injections. Results show the proposed approach can track solutions continuously for load increases where no new operational limits become active.
MFBLP Method Forecast for Regional Load Demand SystemCSCJournals
Load forecast plays an important role in planning and operation of a power system. The accuracy of the forecast value is necessary for economically efficient operation and also for effective control. This paper describes a method of modified forward backward linear predictor (MFBLP) for solving the regional load demand of New South Wales (NSW), Australia. The method is designed and simulated based on the actual load data of New South Wales, Australia. The accuracy of discussed method is obtained and comparison with previous methods is also reported.
AN INTEGER-LINEAR ALGORITHM FOR OPTIMIZING ENERGY EFFICIENCY IN DATA CENTERSijfcstjournal
Nowadays, to meet the enormous computational requests, energy consumption, the largest part which is
related to idle resources, is strictly increased as a great part of a data center's budget. So, minimizing
energy consumption is one of the most important issues in the field of green computing. In this paper, we
present a mathematical model formed as integer-linear programming which minimizes energy consumption
and maximizes user’s satisfaction, simultaneously. However, migration variables, as principal decision
variables of the model, can be relaxed to continuous activities in some practical problems. This constraint
relaxation helps a decision maker to find faster solutions that are usually good approximations for
optimum. Near feasible solutions (infeasible solutions that are desirably close to the feasible region) have
been investigated as another relaxation considering the kind of solutions. For this purpose, we initially
present a measure to evaluate the amount of infeasibility of solutions and then let the model consider an
extended region including solutions with remissible infeasibility, if necessary.
This document summarizes the application of computational intelligence techniques like genetic algorithms and particle swarm optimization for solving economic load dispatch problems. It first applies a real-coded genetic algorithm to minimize generation costs for a 6-generator test system with continuous fuel cost equations, showing superiority over quadratic programming. It then uses particle swarm optimization to minimize costs for a 10-generator system with each generator having discontinuous fuel options, showing better results than other published methods. The document provides background on economic load dispatch problems and optimization techniques like quadratic programming, genetic algorithms, and particle swarm optimization.
Determination of wind energy potential of campus area of siirt universitymehmet şahin
In this study, wind energy potential of Siirt
University campus area is statistically examined by using the mean hourly wind speed data between 2014
and 2015 years which are measured by Vantage Pro2 device, located at the roof of the Engineering Faculty building with 6 m altitude. Weibull distribution
function and Rayleigh distribution function are used
as statistical approach to evaluate the wind data. Weibull distribution function is examined by using two different methods that are maximum likelihood estimation and Rayleigh method. The determination
coefficient (R2) and Root Mean Square Error (RMSE) values of these methods are compared. According the error analysis, it is indicated that the Rayleigh method
gives better results. Wind speed and wind power density are calculated in pursuance of Weibull distribution parameters. The results are evaluated as
monthly and annually. Hence, this preliminary study is made to determine the wind energy potential of Siirt University campus area.
Short-term Load Forecasting using traditional demand forecastingIOSR Journals
This document discusses and compares five traditional methods for short-term load forecasting (STLF): simple moving average, weighted moving average, exponential moving average, auto regressive, and multiple linear regression. It presents the mathematical formulas for each method and discusses how each was used to generate hourly load forecasts for a case study using historical load data from an electricity market in India. The results show that time series forecasting using traditional techniques can produce reasonably accurate hourly load forecasts.
Dropbox es un servicio gratuito de almacenamiento en la nube que permite a los usuarios acceder y compartir fácilmente archivos entre dispositivos a través de una carpeta sincronizada. Al instalar Dropbox, se crea una carpeta en el equipo que se sincroniza automáticamente con todos los demás dispositivos del usuario y con la cuenta en línea de Dropbox. Los archivos arrastrados a esta carpeta se comparten instantáneamente a través de todas las plataformas asociadas con la cuenta del usuario.
DarQRoom lauréat du Réseau Entreprendre AtlantiqueDarQroom
DarQroom est lauréat 2008 du Réseau Entreprendre Atlantique, association de chefs d'entreprise dont l'objectif est de contribuer au développement économique local en accompagnant les créateurs et repreneurs de futures PMEs.
Plus d'infos sur : http://www.reseau-entreprendre-atlantique.fr/reseau-entreprendre-atlantique/fr/s01_home/s01p01_home.php ou sur le Twitter de l'association @REpaysdelaloire
DarQroom is one of the 2008 graduates of the Réseau Entreprendre Atlantique (Atlantic Entrepreneurship Network), which is a Western-France based association of entrepreneurship helping other entrepreneur foster their business.
Im Alter selbständig in den eigenen vier Wänden lebenTECLA e.V.
Vortrag von Gabriele Schwentek, Geschäftsführerin der Diakonie Halberstadt zur Frage "Was beeinflusst ein langes selbstbestimmtes Leben in den eigenen vier Wänden?" bei der TECLA-Fachtagung 2013 am 15.10.2013 in Halberstadt.
This weekly report summarizes the progress of a student design group working on a food spoilage detection machine. In the third week, the student was moved to a new group called ID Company focused on how human instincts aid survival. The group agreed to design a machine using sensors to detect food spoilage by measuring changes in pH levels and microorganisms. The student's tasks for the next week are to research sensors and present information to the project architect.
Este documento proporciona 7 pasos para crear un blog en Blogger: 1) Entrar a la página de Blogger, 2) Hacer clic en "Crear cuenta", 3) Ir al siguiente paso, 4) Hacer clic en "Nuevo Blogger", 5) Crear un nuevo blog, 6) Hacer clic en "Crear blog", 7) Ver el nuevo blog con su nombre.
La globalización neoliberal no ha resuelto los problemas de pobreza y desigualdad en el mundo, sino que los ha empeorado. Las grandes corporaciones multinacionales controlan los mercados y presionan a los gobiernos para servir a sus propios intereses de lucro. Estas empresas evaden impuestos a gran escala a través de paraísos fiscales, mientras que los ciudadanos tienen que pagar más o renunciar a servicios públicos. Muchos servicios públicos esenciales ahora se privatizan y monopolizan con el único propósito de
Barcelona, June 2009 - Daniel Nevers, 7th artist-in-residence for homesession, is an American artist based
in San Francisco. His work is a platform from which he hopes to transform the audience’s perceptions and
perspectives.
Using home-improvement materials in nonconventional ways, Nevers proposes that “do-it-yourself is the new
self-help.” (He begins his artist statement with this slogan, which sheds light on his creative stance toward
punk ideology and pop psychology.) He advances a system of artistic production based in contradictions
dealing with attraction and repulsion, creation and consumption, and function and form.
El documento habla sobre la crisis de deuda europea. Felipe Calderón, presidente temporal del G-20, insta a Europa a sacar la "bazuca" para resolver la crisis lo antes posible y evitar que se propague a otras economías. Calderón advierte que el fracaso en resolver el problema podría resultar en una implosión del euro y una crisis económica global devastadora. También habla sobre la necesidad de restaurar la confianza y realizar diagnósticos honestos sobre el costo de las medidas.
This summary provides the stock prices and changes for 6 companies - ExxonMobil, Dell, Hewlett-Packard, Intel, MetLife, and PepsiCo. It lists the closing price from the previous day, the high and low prices for the current day, the daily volume and price change in both amount and percentage for each stock.
El documento presenta una lección sobre Don Quijote y Sancho que incluye diferentes actividades como la lectura de un texto teatral, la dramatización de una escena, juegos lingüísticos, definición de adjetivos, signos de puntuación y repaso del verbo y unidades anteriores. La situación comunicativa se centra en temas de caballería.
An overview of electricity demand forecasting techniquesAlexander Decker
This document provides an overview of different techniques for electricity demand forecasting. It begins by explaining the importance of accurate electricity demand forecasting for utility companies and market participants. It then divides forecasting into three categories based on timeframe: short-term (1 hour to 1 week), medium-term (1 week to 1 year), and long-term (over 1 year). The document goes on to group forecasting techniques into three major categories: traditional, modified traditional, and soft computing techniques. Traditional techniques discussed include regression, multiple regression, and exponential smoothing. The document provides mathematical equations to describe some of these traditional forecasting models.
Summary of Modern power system planning part one
"The Forecasting of Growth of Demand for Electrical Energy"
the main topic of this chapter is the analysis of the various techniques required for utility planning engineers to optimally plan the expansion of the electrical power system.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Forecasting of electric consumption in a semiconductor plant using time serie...Alexander Decker
This document summarizes a study that used time series methods to forecast electricity consumption in a semiconductor plant. The study analyzed 36 months of historical electricity consumption data from 2010-2012 to select the best forecasting model. Single exponential smoothing was found to have the lowest Mean Absolute Percentage Error (MAPE) of 5.60% and was determined to be the best forecasting method. The selected model will be used to forecast future electricity consumption for the plant.
The document proposes a new approach for solving the economic dispatch problem in power systems using a hybrid particle swarm optimization and simulated annealing algorithm. It begins with introductions to economic dispatch and optimization techniques like particle swarm optimization and simulated annealing. It then describes the economic dispatch problem formulation, including the objective of minimizing generation cost while satisfying constraints. The document proposes a novel hybrid algorithm that combines the salient features of particle swarm optimization and simulated annealing to generate high-quality solutions efficiently. It presents the particle swarm optimization, simulated annealing and hybrid algorithms in detail. The effectiveness of the proposed approach is demonstrated through case studies on different power systems.
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.
Optimization Methods and Algorithms for Solving Of Hydro- Thermal Scheduling ...IOSR Journals
This document reviews various optimization methods and algorithms that have been used to solve hydrothermal scheduling problems. It finds that while older methods have limitations, newer methods improve on them by providing more efficiency, faster convergence, robustness and adaptability. Specifically, it discusses optimization techniques like Lagrangian relaxation, augmented Lagrangian methods, differential evolution, genetic algorithms and particle swarm optimization that have been applied to solve the hydrothermal scheduling problem. It also compares the performance of these various algorithms on test problems and finds that methods like enhanced cultural algorithm and fuzzy adaptive particle swarm optimization generate better solutions than other approaches.
The document summarizes electricity load forecasting techniques for power system planning. It discusses using curve fitting algorithms to forecast electricity load based on analyzing past load data from 2012. Specifically, it proposes using a Fourier series curve fitting model to predict future load based on factors like temperature, humidity, and time of day or year. The document also briefly describes other common load forecasting techniques including multiple regression, exponential smoothing, and neural networks.
This document discusses designing an interregional transmission overlay for the United States power grid to facilitate high levels of renewable energy. It introduces a 4-step study framework: 1) generating a 40-year generation forecast; 2) selecting transmission candidates; 3) optimizing network expansion using mixed-integer linear programming; and 4) evaluating benefits compared to a benchmark case. The framework is applied to the U.S. grid to design an overlay under a high renewable scenario. Results suggest such an overlay provides social, economic and environmental benefits over the benchmark case with limited interregional transmission.
An interactive approach for solar energy system:design and manufacturing IJECEIAES
The energy production in the word is a very complex problem with decreasing the pollution. Therefore, the aim is to find an optimal solution, this research focuses on the development and the optimization of parabolic concentrator using an interactivity approach and virtual design tools. Recently, several works have been developed in this area. In this study, a new conception, design Optimization approach has been involved in system energy design including new concept. The design strategy has been successfully applied to design problems. The optimizer tool developed for based on Heuristic: Gravitational Search Algorithm. The results of the presented in this paper are significant in the system energy design, which presents an effective approach of development by reducing the cost of installation, the time of analysis by increasing the radiation and solar flux concentrated within the parabola generating an increase in accumulated energy.
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.
Energy management system for distribution networks integrating photovoltaic ...IJECEIAES
The concept of the energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of purchased energy, power losses and voltage drops. In order to achieve these objectives, the non-dominated sorting genetic algorithm II (NSGA-II) was modified and applied to an IEEE 33-bus test network containing 10 photovoltaic power plants and 4 battery energy storage systems placed at optimal points in the network. To evaluate the system performance, the resolution was performed under several test conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS) and entropy-TOPSIS. The simulation results obtained by NSGA-II and classified using entropy-TOPSIS showed a significant and considerable reduction in terms of purchased energy cost, power losses and voltage drops while successfully meeting all constraints. In addition, the diversity of the results proved once again the robustness and effectiveness of the algorithm. A graphical interface was also developed to display all the decisions made by the algorithm, and all other information such as the states of power systems, voltage profiles, alarms, and history.
This document summarizes recent advances in economic dispatch from papers published between 1977-1988. It identifies four important areas of economic dispatch: (1) optimal power flow, (2) economic dispatch in relation to automatic generation control, (3) dynamic dispatch, and (4) economic dispatch with non-conventional generation sources. For optimal power flow, it discusses various mathematical techniques used to solve the optimal power flow problem such as linear programming and non-linear programming. It also summarizes several papers that applied these techniques.
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 presents a novel approach for static transmission expansion planning and
allocation of the associated expansion costs to individual market entities in a restructured power
system. The approach seeks the optimal addition of transmission lines among the possible candidate
transmission lines minimizing the overall system costs and at the same time satisfying the system
operational and security constraints. Novelty of the approach lies in applying a widely known
technique used for overload security analysis to an area such as Transmission expansion planning.
Transmission expansion costs are allocated using distribution factors to the individual entities in a
fair and transparent manner. The results for modified Garver Test system demonstrate that the
approach with the advantage of its simplicity can be applied to transmission expansion planning and
cost allocation in restructured power system
This document summarizes an article from the International Journal of Electrical Engineering and Technology (IJEET) that presents a novel approach for transmission expansion planning and cost allocation in deregulated power systems. The approach seeks to optimally add transmission lines to minimize costs while satisfying operational and security constraints. It applies an overload security analysis technique to transmission expansion planning. Transmission expansion costs are allocated to individual market participants using distribution factors in a fair manner. The approach is demonstrated on the modified Garver test system and is shown to be effective for transmission expansion planning and cost allocation in restructured power systems.
Short-term load forecasting with using multiple linear regression IJECEIAES
This document discusses short-term load forecasting using multiple linear regression. It summarizes the research method used, which involves developing a multiple linear regression model to predict electrical load based on variables like temperature, humidity, day of week, and previous load data. The model is trained on historical load and weather data from New York City over 9 years. Testing shows the model can predict load a day ahead with 5.15% mean absolute percentage error. Regression coefficients, t-statistics, and p-values indicate the trained model explains about 90% of the variation in load and the predictors are statistically significant. An example day-ahead hourly load forecast is provided for June 25, 2019.
Load Shifting Technique on 24Hour Basis for a Smart-Grid to Reduce Cost and P...IRJET Journal
This document summarizes a research paper that proposes a load shifting technique using particle swarm optimization to reduce costs and peak demand in a smart grid. The technique shifts loads from peak hours to off-peak hours on a daily basis. Simulation results show that applying the load shifting technique to residential, commercial, and industrial loads in a smart grid reduces both the overall operational cost and peak load demand. The particle swarm optimization algorithm performs better than genetic algorithms at minimizing costs and shifting loads to reduce peaks.
Bi-objective Optimization Apply to Environment a land Economic Dispatch Probl...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Similar to Application of Thunderstorm Algorithm for Defining the Committed Power Output Considered Cloud Charges (20)
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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.
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.
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.
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Application of Thunderstorm Algorithm for Defining the Committed Power Output Considered Cloud Charges
1. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-5, May- 2016]
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Application of Thunderstorm Algorithm for
Defining the Committed Power Output
Considered Cloud Charges
A.N. Afandi
Electrical Engineering, Universitas Negeri Malang, Indonesia
Abstract— This paper presents an application of
Thunderstorm Algorithm for determining a committed
power output considered cloud charges with various
technical constraints and an environmental requirement.
These works also implemented on IEEE-62 bus system
throughout an operational economic dispatch covered for
economic and emission aspects. The results obtained
show that statistical and numerical performances are
associated with charges. It also presents fast and stable
characteristics for the searching speeds. By considering
the cloud charge parameter, it contributes to
performances and results of Thunderstorm Algorithm. In
addition, the introduced algorithm seems strongly to be a
new promising approach for defining the committed
power output problem.
Keywords—Cloud charge, economic dispatch,
intelligent computation, power system, thunderstorm
algorithm.
I. INTRODUCTION
Presently, technical problems are more complicated than
previous cases included numerous variables for
representing physical systems in suitable models as
closed as its functions in nature with natural
characteristics and behaviours. Many problems have
become crucial topics to solve correctly in feasible
ranges within high qualities under numerous constraints
and environmental requirements for searching the desired
performances. To cover these conditions, the problems
adopted many parameters are expressed in optimization
functions considered potential variables and limitations
in order to obtain better solutions within a period time
operation. Moreover, these functions are conducted to
designed models for presenting real cases in
mathematical statements as the objective function
constrined by technical conditions and environmental
requirements.
By considering mathematical expressions, real problems
are solvable easily using various methods of
computations associated with its defined functions
through traditional or evolutionary approaches. Both
methods are commonly used to carry out the problem and
applied to evaluate its performances. Actually, these
approaches has different characteristics while searching
the optimal solution. In detail, traditional methods use
mathematical programs given in various versions as the
proposed names at the early introduction. As long as the
period implementation, popular classical methods are
linear programming, lambda iteration, quadratic
programming, gradient search, Newton’s method,
dynamic programming, and Lagrangian relaxation [1],
[2], [3], [4]. On the other hand, evolutionary methods use
optimization techniques, such as genetic algorithm,
neural network, simulated annealing, evolutionary
programming, ant colony algorithm, particle swarm
optimization, and harvest season artificial bee colony
algorthm [5], [6], [7], [8], [9]. These methods have been
proposed for replacing classical approaches on the base
of its weaknesses considered many phenomena and
behaviours in nature with mimicking its mechanisms.
Nowadays, evolutionary methods are frequently used to
solve optimization problems, not only for real cases but
also for designed themes [10]. These methods are useful
for breaking out large systems and multi dimensions
constructued using multiple variables and constraints. In
particular, many types have been proposed at different
times as an introduction early based on its inspirations.
Since the first time of the evolutionary idea became a
new computation era out of the classical period, many
works have been done for developing and improving its
performances with modified techniques and phases.
Moreover, these developments are also subjected to
expand computational performances for increasing
abilities to carry out numerous problems with many
proposed procedures.
In this paper, a new intelligent computation application is
introduced to solve the power system operation problem
(PSOP) and it is used to define the balanced power
production. In addition, this paper presents its powerful
for searching the optimal solution of the PSOP associated
with cloud charges.
2. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-5, May- 2016]
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II. THUNDERSTORM ALGORITHM
At present, the lightning is considered as an atmospheric
discharge during thunderstorms or other possibility
factors produced by several steps in terms of Charge
separation; Leader formation; and Discharge channel.
Moreover, the lightning process is defined as an electric
discharge in the form of a spark in a charged cloud that
the negative and positive charges are deployed at different
positions [11], [12], [13]. In addition, a seat of electrical
processes can be produced by a thunderstorm and it is
rapidly advanced during the continuous lightning in the
thunderstorm. In this phenomenon, the defining
atmospheric material for the thunderstorm is very
important things and urgently observations covered in
moisture; unstable air; and lift.
Many studies have been done to observe these
phenomena with numerous discussions for searching
suitable models and understanding its mechanisms.
Various characteristics have been tested and reported for
analyzing these curious issues in many studies in order to
recognize natural behaviours [14], [15], [16], [11], [12],
[17], [18], [19], [20]. In general, the introduced algorithm
entitled Thunderstorm Algorithm (TA) has adopted a
phenomenon in nature for pretending natural processes
performed using several stages to explain the adoption of
the inspiration [21]. Furthermore, this inspiration is
associated with a natural mechanism conducted to define
multiple natural lightning in the computation.
Fig. 1: Thunderstorm Algorithm’s Phases
By considering this phenomenon, its mechanisms are
transferred into certain procedures as the sequencing
computation presented in pseudo-codes in terms of Cloud
Phase; Streamer Phase; and Avalanche Phase [21]. Cloud
Phase is used to produce cloud charges and to evaluate
the clouds before defining the pilot leader. Another step,
Streamer Phase, is supposed to define the prior streamer
and to guide striking directions included the path
evaluation for defining the streaming track. The final
process is Avalanche Phase, which is used to evaluate
channels, replace the streaming track for keeping the
streamer. In detail, these phases are depicted in Fig. 1.
In these phases, the searching mechanism is conducted to
striking processes and channeling avalanches to deploy
the cloud charges, which is populated using (1).
Moreover, TA is also consisted of various distances of the
striking direction related to the hazardous factor for each
position of the striking targets as presented in (2). Each
solution is located randomly based on the generating
random directions of multiple striking targets. In
principle, the sequencing computation of TA is given in
several procedures as presented in following
mathematical main functions.
Cloud charge: Q = (1 + k. c). Q , (1)
Striking path: D = (Q ).b.k, (2)
Charge’s probability: probQsj
Qsj
m
∑ Qs
m for m
Qsj
n
∑ Qs
n for n
, (3)
where Qsj is the current charge, Qmidj is the middle
charges, s is the streaming flow, Dsj is the striking
charge’s position, Qsdep is the deployed distance, n is the
striking direction of the hth
, k is the random number with
[-1 and 1], c is the random within [1 and h], h is the
hazardous factor, b is the random within (1-a), n is the
striking direction, j ∈ (1,2,..,a), a is the number of
variables, m ∈ (1,2,..,h).
III. COMMITTED POWER OUTPUT
The power system operation is able to measure using a
financial aspect for defining the whole operation, such as
fixed cost; maintenance cost; and production cost, in
order to the PSOP can be conditioned in an economic
portion with the suitable budget. Since the operation is
concerned in the technical cost of products and services,
the optimal operation and planning are very important
things for deciding in the balanced power production.
Economically, these problems become urgently issues to
decrease running charges of the electric energy while
supplying load demands at different places. It also needs
to manage using an economic strategy for selecting the
optimal operating cost.
To cover these issues, the committed power output (CPO)
is more complicated problems included all generating
units under technical constraints and environmental
requirements. In this problem, the CPO is focused on the
generating unit participation for supporting power
productions associated with the given load demand. In
addition, the CPO is measured in the optimal total cost for
the fuel consumption and the pollutant compensation [1],
[4], [22], [23], [24], [25]. In detail, this problem is
optimized using an integrated economic dispatch (IED)
with considering the load dispatch (LD) and the emission
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dispatch (ED) [23], [24], [26]. Moreover, The IED is
formulated by equation (4) and each fuel cost
participation is expressed in (5) for defining the LD as
given in (6). In particular, the individual pollutant
discharge of generating unit is formed in (7) and the ED’s
function is presented in (8) for all participants in the CPO.
In general, the CPO is commonly approached using main
mathematical functions as follows:
IED: Φ = w. F + (1 − w). h. E , (4)
Fi(Pi) =ci+biPi +aiPi
2
, (5)
LD: F = ∑ (c + b . P + a . P!
),"
#$ (6)
E (P) = γ + β . P + α . P!
, (7)
ED: E = ∑ &γ + β . P + α . P!
'
"
#$ , (8)
where Φ is the IED ($/h), w is a compromised factor, h is
a penalty factor, Ftc is the total fuel cost ($/h), Et is the
total emission (kg/h), Fi is the fuel cost of the ith
generating unit ($/h), Pi is a power output of the ith
generating unit, ai; bi; ci are fuel cost coefficients of the ith
generating unit, ng is the number of generating unit, Ei is
an emission of the ith
generating unit (kg/h), αi; βi; γi are
emission coefficients of the ith
generating unit.
IV. APPLICATION’S PROCEDURES
In these studies, simulations adopt a standard model of
the power system for demonstrating the impact of the
cloud charges related to the CPO with various technical
constraints. The use of the standard model is commonly
approached by researchers for performing own problems,
even practical systems are also able to apply for the same
problem. In these works, the IEEE-62 bus system is
selected as the sample system, which is consisted of 19
generators; 62 buses; and 89 lines as discussed
completely in [24]. Technically, it data are presented in
Table I; Table II; and Table III for coefficients and power
limits which are given in individual generating units.
Table I. Fuel Cost Coefficients
Gen α β γ Gen α β γ
G1 0.0070 6.80 95 G11 0.00450 1.60 65
G2 0.0055 4.00 30 G12 0.00250 0.85 78
G3 0.0055 4.00 45 G13 0.00500 1.80 75
G4 0.0025 0.85 10 G14 0.00450 1.60 85
G5 0.0060 4.60 20 G15 0.00650 4.70 80
G6 0.0055 4.00 90 G16 0.00450 1.40 90
G7 0.0065 4.70 42 G17 0.00250 0.85 10
G8 0.0075 5.00 46 G18 0.00450 1.60 25
G9 0.0085 6.00 55 G19 0.00800 5.50 90
G10 0.0020 0.50 58 a ($/MWh2
), b ($/MWh)
Table II. Emission Coefficients
Ge
n
a b c
Ge
n
a b c
G1
0.01
8
-
1.8
1
24.3
0
G1
1
0.01
4
-
1.2
5
23.0
1
G2
0.03
3
-
2.5
0
27.0
2
G1
2
0.01
2
-
1.2
7
21.0
9
G3
0.03
3
-
2.5
0
27.0
2
G1
3
0.01
8
-
1.8
1
24.3
0
G4
0.01
4
-
1.3
0
22.0
7
G1
4
0.01
4
-
1.2
0
23.0
6
G5
0.01
8
-
1.8
1
24.3
0
G1
5
0.03
6
-
3.0
0
29.0
0
G6
0.03
3
-
2.5
0
27.0
2
G1
6
0.01
4
-
1.2
5
23.0
1
G7
0.01
3
-
1.3
6
23.0
4
G1
7
0.01
4
-
1.3
0
22.0
7
G8
0.03
6
-
3.0
0
29.0
3
G1
8
0.01
8
-
1.8
1
24.3
0
G9
0.04
0
-
3.2
0
27.0
5
G1
9
0.04
0
-
3.0
0
27.0
1
G1
0
0.01
4
-
1.3
0
22.0
7
α (kg/MWh2
), β
(kg/MWh)
Table III. Power Limits of Generators
Gen
Pmin
(MW)
Pmax
(MW)
Qmin
(MVar)
Qmax
(MVar)
G1 50 300 0 450
G2 50 450 0 500
G3 50 450 -50 500
G4 0 100 0 150
G5 50 300 -50 300
G6 50 450 -50 500
G7 50 200 -50 250
G8 50 500 -100 600
G9 0 600 -100 550
G10 0 100 0 150
G11 50 150 -50 200
G12 0 50 0 75
G13 50 300 -50 300
G14 0 150 -50 200
G15 0 500 -50 550
G16 50 150 -50 200
G17 0 100 0 150
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G18 50 300 -50
G19 100 600 -100
These applications are applied to IEEE-62 bus system as
the power system model using several programs, which
are compiled together in the sequencing processes based
on the pseudo-codes covered the cloud phase; streamer
phase; and avalanche phase. Each phase follows its
mechanism for involving all parameters of TA in the
processes while searching the optimal solution with
various charges in the cloud charge phase.
In particular, these processes are run in designed
programs in terms of main program; evaluate program;
cloud charge program; streamer program; avalanche
program; and dead track program. N addition, TA
performed using 1 of the avalanche; 100 of the streaming
flows; and 4 of the hazardous factor. Moreover, the tested
system feeds the power production for 2,766.7 MW and
1,206.1 MVar of load demands constrained by 10% of the
total loss; 0.5 of the weighting factor; 0.85 kg/h of the
standard emission; ± 5% of voltage violations at each bus;
and 95% of the power transfer capability for the line.
V. RESULTS AND DISCUSSIONS
As given in the previous section, these works consider
2,766.7 MW for the load constrained by various technical
limitations. By considering 10% of the total loss; 0.85
kg/h of the standard emission; the equilibrium
demand and the power production, the cloud charge
distributions are illustrated in following
figures are presented for each cloud size
and 100 charges, which are deployed at different positions
randomly in Fig. 2; Fig. 3; Fig. 4; and Fig.
to these figures, charges affect to the cloud’s
characteristics and charged density within
desired locations. In detail, the highest size has the
highest density for the charge.
Fig. 2: Cloud’s profile with 25 charges
International Journal of Advanced Engineering, Management and Science (IJAEMS)
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400
600
62 bus system as
the power system model using several programs, which
are compiled together in the sequencing processes based
codes covered the cloud phase; streamer
phase; and avalanche phase. Each phase follows its
for involving all parameters of TA in the
processes while searching the optimal solution with
hese processes are run in designed
programs in terms of main program; evaluate program;
rogram; streamer program; avalanche
N addition, TA is
performed using 1 of the avalanche; 100 of the streaming
flows; and 4 of the hazardous factor. Moreover, the tested
system feeds the power production for 2,766.7 MW and
1,206.1 MVar of load demands constrained by 10% of the
total loss; 0.5 of the weighting factor; 0.85 kg/h of the
5% of voltage violations at each bus;
and 95% of the power transfer capability for the line.
DISCUSSIONS
As given in the previous section, these works consider
2,766.7 MW for the load constrained by various technical
10% of the total loss; 0.85
equilibrium of the load
on, the cloud charge
distributions are illustrated in following figures. These
s are presented for each cloud size for 25; 50; 75;
and 100 charges, which are deployed at different positions
Fig. 5. According
s, charges affect to the cloud’s
within different
desired locations. In detail, the highest size has the
Cloud’s profile with 25 charges
Fig. 3: Cloud’s profile with 50 charges
Fig. 4: Cloud’s profile with 75 charges
Fig. 5: Cloud’s profile with 100 charges
Table IV. Statistical Results Based on
N
o
Parameters
25
1
Max point
($/h)
17,15
1
2
Min point
($/h)
16,72
0
3 Range ($/h) 431
4 Mean ($/h)
16,75
1
5 Median ($/h)
16,72
0
[Vol-2, Issue-5, May- 2016]
ISSN : 2454-1311
Page | 296
Cloud’s profile with 50 charges
Cloud’s profile with 75 charges
Cloud’s profile with 100 charges
Table IV. Statistical Results Based on the Charges
Cloud charges
50 75 100
17,15 16,63
3
17,66
6
16,53
9
16,72 16,12
0
16,45
5
15,84
1
431 513 1,211 698
16,75 16,18
7
16,61
3
15,91
5
16,72 16,12
0
16,45
5
15,84
1
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6 Streaming 14 19
7 Opt. time (s) 2.6 3.5
8 Run time (s) 16.9 17.2
Graphically, TA’s abilities are give in Fig.
for streaming flows and time consumptions associated
with cloud charges. Fig. 6 presents convergence speeds of
computations while finishing all processes for
determining optimal solutions in 100 streaming flows
with its individual time usage for each process as
illustrated in Fig. 7. Moreover, the processes have
different started points for searching solutions of the
as similar as the obtained streaming flows of the optimal
points remained in different speeds. For 25 charges, the
computation is started at 17,151 $/h before declining to
16,720 for the optimal position obtained in
consuming 2.6 s of the running time. This execution
needs around 16.9 s for completing 100 of the streaming
flow. In general, the solution is searched in smooth and
fast even the cloud charges used different amounts. In
detail, its statistical performances are listed in Table IV
covered in maximum points; minimum points; range; and
median.
Furthermore, various time consumptions are depicted in
Fig. 7 related to cloud charges. This figure
random time consumptions, which are used to search the
optimal solutions and to complete the processes of the
IED problem considered LD and ED. By considering
these compilations, all results are also provided in Table
IV for the optimal time usage and the running time for
streaming flows. According to these results, the higher
cloud size has longer time consumptions, which are 6.2 s
for obtaining the solution and 18.8 s for completing the
computation associated with 100 of charges.
Fig. 6: Convergences considered the charges
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25 30
5.6 6.2
18.5 18.8
Fig. 6 and Fig. 7
for streaming flows and time consumptions associated
6 presents convergence speeds of
computations while finishing all processes for
determining optimal solutions in 100 streaming flows
with its individual time usage for each process as
7. Moreover, the processes have
oints for searching solutions of the IED
as similar as the obtained streaming flows of the optimal
points remained in different speeds. For 25 charges, the
computation is started at 17,151 $/h before declining to
16,720 for the optimal position obtained in 14 steps with
consuming 2.6 s of the running time. This execution also
needs around 16.9 s for completing 100 of the streaming
flow. In general, the solution is searched in smooth and
fast even the cloud charges used different amounts. In
tistical performances are listed in Table IV
maximum points; minimum points; range; and
, various time consumptions are depicted in
figure illustrates the
re used to search the
optimal solutions and to complete the processes of the
problem considered LD and ED. By considering
these compilations, all results are also provided in Table
IV for the optimal time usage and the running time for
According to these results, the higher
cloud size has longer time consumptions, which are 6.2 s
for obtaining the solution and 18.8 s for completing the
computation associated with 100 of charges.
considered the charges
Fig. 7: Time consumptions considered the charges
Table V. Power Productions Based on the Charges
Gen
Power outputs (MW)
25 50
G1 105.7 105.7
G2 200.0 265.7
G3 227.2 78.4
G4 99.6 91.9
G5 294.2 105.7
G6) 395.9 395.2
G7 108.6 108.6
G8 234.9 227.7
G9 87.9 273.6
G10 91.9 91.9
G11 80.1 147.2
G12 105.3 105.3
G13 149.3 287.8
G14 137.0 150.0
G15 90.2 90.2
G16 149.6 104.6
G17 91.9 91.9
G18 105.8 200.8
G19 240.7 100.0
Total 2,995.8 3,022.1
Load 2,766.7 2,766.7
Loss 229.1 255.4
Refer to multiple directions as presented as the hazardous
factor in TA’s processes, all numerous statistical results
are provided in Table IV associated with cloud charges as
depicted in Fig. 2 to Fig. 5 for the cloud charge’s profiles.
In addition, Table IV has been performed by each
procedure of TA while determining optimal solutions to
meet 2,776.7 MW of the load. This table shows that the
cloud charges give impacts on various aspects, such as,
maximum points; optimal points; and times
[Vol-2, Issue-5, May- 2016]
ISSN : 2454-1311
Page | 297
Time consumptions considered the charges
Table V. Power Productions Based on the Charges
Power outputs (MW)
50 75 100
105.7 105.7 105.7
265.7 376.6 343.4
78.4 132.1 78.4
91.9 93.1 91.9
105.7 190.7 174.6
395.2 291.0 186.1
108.6 166.3 108.6
227.7 278.8 266.2
273.6 87.9 239.0
91.9 91.9 91.9
147.2 149.1 83.5
105.3 105.3 105.3
287.8 105.7 252.7
150.0 70.8 146.9
90.2 99.6 90.2
104.6 104.6 150.0
91.9 91.9 91.9
200.8 270.3 292.8
100.0 210.5 100.0
3,022.1 3,021.9 2,999.0
2,766.7 2,766.7 2,766.7
255.4 255.2 232.3
Refer to multiple directions as presented as the hazardous
factor in TA’s processes, all numerous statistical results
are provided in Table IV associated with cloud charges as
5 for the cloud charge’s profiles.
IV has been performed by each
procedure of TA while determining optimal solutions to
meet 2,776.7 MW of the load. This table shows that the
cloud charges give impacts on various aspects, such as,
s; optimal points; and times.
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Table VI. Emissions Based on the Charges
Gen
Pollution productions (kg/h)
25 50 75 100
G1 34.1 34.1 34.1 34.1
G2 847.2 1,692.3 3,765.9 3,059.3
G3 1,162.8 33.9 272.6 33.9
G4 31.5 20.8 22.4 20.8
G5 1,049.9 34.1 333.5 257.0
G6) 4,209.8 4,193.0 2,093.8 704.3
G7 28.7 28.7 156.5 28.7
G8 1,310.2 1,211.9 1,991.3 1,782.1
G9 54.8 2,146.3 54.8 1,547.6
G10 20.8 20.8 20.8 20.8
G11 12.7 142.4 147.7 16.2
G12 20.4 20.4 20.4 20.4
G13 155.1 994.4 34.1 716.1
G14 121.5 158.1 8.3 148.9
G15 51.3 51.3 87.4 51.3
G16 149.2 45.4 45.4 150.5
G17 20.8 20.8 20.8 20.8
G18 34.3 386.4 850.4 1,037.2
G19 1,622.8 127.0 1,167.5 127.0
Total 10,938.0 11,362.2 11,127.8 9,777.0
Table VII. Operational Fees Based on the Charges
Gen
Operating costs (kg/h)
25 50 75 100
G1 909.0 909.0 909.0 909.0
G2 1,473.7 2,327.2 4,199.4 3,581.6
G3 1,819.3 409.3 805.6 409.3
G4 135.3 119.6 122.0 119.6
G5 2,417.7 590.3 1,281.9 1,134.6
G6) 4,640.6 4,626.3 2,766.6 1,376.8
G7 643.4 643.4 1,081.8 643.4
G8 2,289.1 2,179.0 3,018.9 2,799.9
G9 675.5 3,406.3 675.5 2,748.6
G10 131.3 131.3 131.3 131.3
G11 228.4 469.3 477.3 238.0
G12 205.4 205.4 205.4 205.4
G13 532.6 1,504.5 338.2 1,207.0
G14 449.4 505.3 225.0 491.6
G15 582.5 582.5 656.3 582.5
G16 474.6 308.3 308.4 476.5
G17 119.6 119.6 119.6 119.6
G18 261.8 720.8 1,211.5 1,397.8
G19 2,689.0 783.5 2,185.7 783.5
Total 20,678.3 20,540.9 20,719.6 19,356.1
Final results of the PSOP based on the CPO are presented
in the IED as provided in Table V covered cloud charges
for the individual power production. This table also
provides the committed power output and the total loss to
meet the load. According to this table, it is known that
generating units contribute to the power procurement with
different capacities as own scheduled power productions.
Its pollutant productions are listed in Table VI for 19
generating units. Specifically G10 feeds to the power to
the system in the constant amount of 91.9 MW. This
condition is also given by G1 and G17 produced in 105.7
MW and 91.9 MW. In total, generating units deliver the
power to the load center from 2,995.8 MW to 3,022.1
MW with various amounts of the power loss related to the
each cloud charge as given in Table V. As the impact of
the environmental requirement, these power productions
also discharge pollutants around 9,777.0 kg/h to 11,362.2
kg/h corresponded to cloud charges with individual
contributions for the emissions as given in Table VI. In
detail, the higher pollutant contributors are G2; G3; G5;
G6; G8; and G19.
By considering the whole selections for determining the
optimal solutions of the IED problem, the cheapest
operation is determined using the higher cloud charge as
provided in Table VII presented totally for fuel costs and
emission cost compensations. This operation needs
around 19,356.1 $ for existing generating units during
producing power outputs to meet the load demand. In
accordance to individual power productions, several
generators spent the budget in high procurement.
Practically, power outputs of generating units are
associated with the load to set fixed power outputs. The
least operating cost becomes a very crucial decision for
operating the system in the cheapest budget. In this case,
the expensive operations are belonged to several
generating units while producing powers, such as, G2;
G3; G5; G6; and G19, even these payments are depended
on cloud charges. For all compositions of cloud charges,
the cheapest operation is existed by G17 with spent in
119.6 $/h.
VI. CONCLUSIONS
This paper evaluates cloud charge impacts of
Thunderstorm Algorithm on the power system operation
problem presented in the operational economic dispatch
based on load and emission dispatches. By considering
technical constraints and the cloud charges, the results
demonstrated successful application this algorithm for
solving the problem using the IEEE-62 bus system. The
performances indicate that the small size of the cloud
charge has faster iteration and shorter time consumption.
Moreover, cloud charges influenced to the committed
power output combination for 19 generating units.
Finally, from these works, implementations on real and
larger systems are subjected to the future studies.
7. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-5, May- 2016]
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www.ijaems.com Page | 299
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