This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using P...theijes
This document summarizes the application of particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem for Nigeria's thermal power stations. PSO is used to determine the optimal allocation of total power demand among generating units to minimize total fuel costs while satisfying constraints. The PSO algorithm is applied to a 1999 model of Nigeria's power network and results are compared to other heuristic methods. PSO efficiently distributes load to minimize costs and overcomes limitations of traditional optimization techniques for non-linear power system problems.
Islanded microgrid congestion control by load prioritization and shedding usi...IJECEIAES
The document discusses congestion management in an islanded microgrid supplied by renewable energy sources using an artificial bee colony algorithm. It formulates the congestion management problem as an optimization problem that aims to minimize overloads and power losses by optimally shedding loads based on priority indices. The artificial bee colony algorithm is applied to determine the optimal amount and location of loads to shed. It is tested on a modified IEEE 30-bus distribution system in MATLAB. The results are compared to other algorithms to demonstrate the effectiveness of this approach for congestion control in islanded microgrids with intermittent renewable generation.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
Optimal Generation Scheduling of Power System for Maximum Renewable Energy...IJECEIAES
This paper proposes an optimal generation scheduling method for a power system integrated with renewable energy sources (RES) based distributed generations (DG) and energy storage systems (ESS) considering maximum harvesting of RES outputs and minimum power system operating losses. The main contribution aims at economically employing RES in a power system. In particular, maximum harvesting of renewable energy is achieved by the mean of ESS management. In addition, minimum power system operating losses can be obtained by properly scheduling operating of ESS and controllable generations. Particle Swam Optimization (PSO) algorithm is applied to search for a near global optimal solutions. The optimization problem is formulated and evaluated taking into account power system operating constraints. The different operation scenarios have been used to investigate the effective of the proposed method via DIgSILENT PowerFactory software. The proposed method is examined with IEEE standard 14-bus and 30-bus test systems.
In this paper a load flow based method using MATLAB Software is used to determine the optimum location and optimum size of DG in a 43-bus distribution system for voltage profile improvement and loss reduction. This paper proposes analytical expressions for finding optimal size of three types of distributed generation (DG) units. DG units are sized to achieve the highest loss reduction in distribution networks. Single DG installation case was studied and compared to a case without DG, and 43-bus distribution system is used to demonstrate the effectiveness of the proposed method. The proposed analytical expressions are based on an improvement to the method that was limited to DG type, which is capable of injecting real power only, DG capable of injecting reactive power only and DG capable of injecting both real and reactive power can also be identified with their optimal size and location using the proposed method. This paper has been analysed with varying DG size and complexity and validated using analytical method for Summer case and Winter case in 43-bus distribution system in Myanmar.
Keywords- analytical method,distributed generation,power loss reduction,voltage profile improvement.
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using P...theijes
This document summarizes the application of particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem for Nigeria's thermal power stations. PSO is used to determine the optimal allocation of total power demand among generating units to minimize total fuel costs while satisfying constraints. The PSO algorithm is applied to a 1999 model of Nigeria's power network and results are compared to other heuristic methods. PSO efficiently distributes load to minimize costs and overcomes limitations of traditional optimization techniques for non-linear power system problems.
Islanded microgrid congestion control by load prioritization and shedding usi...IJECEIAES
The document discusses congestion management in an islanded microgrid supplied by renewable energy sources using an artificial bee colony algorithm. It formulates the congestion management problem as an optimization problem that aims to minimize overloads and power losses by optimally shedding loads based on priority indices. The artificial bee colony algorithm is applied to determine the optimal amount and location of loads to shed. It is tested on a modified IEEE 30-bus distribution system in MATLAB. The results are compared to other algorithms to demonstrate the effectiveness of this approach for congestion control in islanded microgrids with intermittent renewable generation.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
Optimal Generation Scheduling of Power System for Maximum Renewable Energy...IJECEIAES
This paper proposes an optimal generation scheduling method for a power system integrated with renewable energy sources (RES) based distributed generations (DG) and energy storage systems (ESS) considering maximum harvesting of RES outputs and minimum power system operating losses. The main contribution aims at economically employing RES in a power system. In particular, maximum harvesting of renewable energy is achieved by the mean of ESS management. In addition, minimum power system operating losses can be obtained by properly scheduling operating of ESS and controllable generations. Particle Swam Optimization (PSO) algorithm is applied to search for a near global optimal solutions. The optimization problem is formulated and evaluated taking into account power system operating constraints. The different operation scenarios have been used to investigate the effective of the proposed method via DIgSILENT PowerFactory software. The proposed method is examined with IEEE standard 14-bus and 30-bus test systems.
In this paper a load flow based method using MATLAB Software is used to determine the optimum location and optimum size of DG in a 43-bus distribution system for voltage profile improvement and loss reduction. This paper proposes analytical expressions for finding optimal size of three types of distributed generation (DG) units. DG units are sized to achieve the highest loss reduction in distribution networks. Single DG installation case was studied and compared to a case without DG, and 43-bus distribution system is used to demonstrate the effectiveness of the proposed method. The proposed analytical expressions are based on an improvement to the method that was limited to DG type, which is capable of injecting real power only, DG capable of injecting reactive power only and DG capable of injecting both real and reactive power can also be identified with their optimal size and location using the proposed method. This paper has been analysed with varying DG size and complexity and validated using analytical method for Summer case and Winter case in 43-bus distribution system in Myanmar.
Keywords- analytical method,distributed generation,power loss reduction,voltage profile improvement.
IRJET- A Review on Computational Determination of Global Maximum Power Point ...IRJET Journal
This document reviews computational techniques for determining the global maximum power point (GMPP) for photovoltaic (PV) arrays under partial shading conditions. Partial shading causes the PV array characteristics to exhibit multiple local maxima, making it difficult for conventional maximum power point tracking techniques to identify the true GMPP. The document categorizes and discusses analytical, meta-heuristic, and fuzzy-based computational approaches that have been proposed to address this issue, including methods using the Lambert W function, particle swarm optimization, simulated annealing, and fuzzy logic. It proposes using the Das-Saetre model of PV characteristics along with meta-heuristic techniques to more accurately compute the GMPP while reducing computational complexity compared to previous approaches.
Group Search Optimization technique is used to minimize reactive power generation in a power system. The objective is to control generator voltages to reduce reactive power production while meeting system constraints. IEEE 14-bus test system is used with 4 generator buses. Generator voltages are optimized using Group Search Optimization, which finds the minimum reactive power of 5.26 MVAR after 26 iterations. Reactive power and line losses are reduced compared to the base case, showing the effectiveness of the technique in minimizing reactive power generation.
The document summarizes a study comparing time series and artificial neural network (ANN) methods for short-term load forecasting of Covenant University, Nigeria. Load data from October 15-16, 2012 was used to develop forecasting models using moving average, exponential smoothing (time series methods) and ANN. The ANN model with inputs of previous load, time of day, day of week and weekday/weekend proved most accurate with a mean absolute deviation of 0.225, mean squared error of 0.095 and mean absolute percent error of 8.25, making it the best forecasting method according to the error measurements.
Modeling of off grid hybrid power system under jordanian climateAlexander Decker
This document summarizes a study that models an off-grid hybrid power system for a village in Jordan using Matlab Simulink. The hybrid system combines photovoltaic (PV) panels and wind turbines. The study models the PV system, wind turbines, batteries, and load to simulate the hybrid system's output under different conditions. It is found that Matlab Simulink with a graphical user interface can effectively plan and analyze hybrid power systems for off-grid villages.
FEASIBILITY ANALYSIS OF GRID/WIND/PV HYBRID SYSTEMS FOR INDUSTRIAL APPLICATIONWayan Santika
The present study offers technical and economical analyses of grid-connected hybrid power systems for a large scale production industry located in Bali. The peak load of observed system can reach 970.630 kW consuming on average 16 MWh of electricity a day. Software HOMER was utilized as the optimization tool. The proposed hybrid renewable energy systems consist of wind turbines, a PV system, a converter, and batteries. The system is connected to the grid. Optimization results show that the best configuration is the Grid/Wind hybrid system with the predicted net present cost of
-884,896 USD. The negative sign indicates that revenues (mostly from selling power to the grid) exceed costs. The levelized cost of electricity of the system is predicted to be -0.013 USD/kWh. The present study also conducts sensitivity analysis of some scenarios i.e. 50% and 100% increases in grid electricity prices, 50% reduction of PV and WECS prices, and 10 USD and 50 USD carbon taxes per ton CO2 emission. Implications of the findings are discussed.
This document summarizes a research paper that proposes using a genetic algorithm to optimize the placement of FACTS devices (TCSC and SVC) to maximize available transfer capability (ATC) and minimize contingencies in a power system. It first provides background on ATC and FACTS devices. It then describes modeling TCSC and SVC and constructing the genetic algorithm. The algorithm is tested on a two-area 11 bus power system model. Results show that optimally placing TCSC and SVC using the genetic algorithm can increase ATC and reduce contingencies compared to having no FACTS devices.
This document discusses using predictive analysis to optimize energy management systems. It proposes integrating predictive analytics with energy management systems (EMS) to improve optimization of energy source selection and usage. Currently, EMS systems select energy sources like grid, diesel, solar, batteries based on simple priority rules. Integrating predictive analytics can help EMS systems better forecast power outages and optimize cost and emissions by deciding which sources to use and in what proportion, based on machine learning of past and present energy and environmental data to predict the future. This could increase optimization of source selection from the current 40-50% with traditional EMS to 80-90%. The document uses telecom tower energy usage as a case study.
A Solution to Optimal Power Flow Problem using Artificial Bee Colony Algorith...IOSR Journals
This document presents an artificial bee colony (ABC) algorithm approach to solve the optimal power flow (OPF) problem incorporating a flexible AC transmission system (FACTS) device, specifically a static synchronous series compensator (SSSC). The ABC algorithm is tested on the IEEE 14-bus test system both with and without the SSSC. Results show that the ABC algorithm gives a better solution when incorporating the SSSC, improving the system performance in terms of lower total cost, lower power losses, and better voltage profile compared to the case without SSSC.
17 9740 development paper id 0014 (edit a)IAESIJEECS
This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.
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 Demand Analysis of Telecom Towers of Nepal with Strategic Scenario Dev...IJRES Journal
Telecom towers, technically known as BTS (Base Transceiver Stations) are the most energy intensive part of cellular network architecture and contribute up to 60 to 80% of total cellular power consumption and varies in response to the real traffic demand throughout the day and night. But, thelack of grid availability highlightsa potential barrier to telecom industry growth in Nepal. Nepal has approximately 5,222 telecom towers of which about 22% do operate on diesel generators (DGs) while the remaining by grid electricity with some shares of renewable energy technologies (RETs: solar and/or wind). Despite the large carbon imprint, the uncertainty in power availability has compelled telecom operators to use DGs to ensure continuous supply of power for the better network availability, which translates huge operating costs along with adverse environmental impact. So, it becomes an imperative solution for telecom operators to evaluate all alternatives in order to increase network reliability with reduced energy cost. This study report intentionally focus on current energy consumptionof such telecom towers and forecast thefuture energydemand with reference to growing subscriber trend up to 2025 using LEAP (Long Range Energy Alternative Planning System)withBusiness As Usual (BAU) scenario. A clean energy technology (CET) scenario with possible RET options is also developed and compared with base case scenario through some policy mechanics on behalf of environmental benefits and sustainable cellular communication. Furthermore, this study concludes a potential energy cum cost saving with RET adoption with basic cost economics analysis.
Performance analysis of grid-tied photovoltaic system under varying weather c...IJECEIAES
Model and simulation of the impact of the distribution grid-tied photovoltaic (PV) system feeding a variable load with its control system have been investigated in this study. Incremental Conductance (IncCond) algorithm based on maximum power point tracking (MPPT) was implemented for the PV system to extract maximum power under different weather conditions when solar irradiation varies between 250 W/m 2 and 1000 W/m 2 . The proposed system is modelled and simulated with MATLAB/Simulink tools. Under different weather conditions, the dynamic performance of the PV system is evaluated. The results obtained show the efficacy of the proposed MPPT method in response to rapid daytime weather variations. The results also show that the surplus power generated is injected into the grid when the injected power from the PV system is higher than the load demand; otherwise, the grid supplies the load.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
This document presents a method for optimizing the placement and sizing of multiple distributed generation (DG) units in a transmission system to minimize power losses and improve voltage. Fuzzy logic is used to determine the optimal locations for DG units based on power loss index and voltage. Particle swarm optimization is then used to determine the optimal size of DG units at the identified locations. The method is tested on the IEEE 14-bus system, showing that placing DG units at buses 3, 4 and 5 can reduce power losses by up to 94.47% and improve voltages compared to using a single DG unit.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
The document describes a two-stage method for optimal allocation of capacitors in a radial distribution system. In the first stage, loss sensitivity factors are used to calculate candidate locations for capacitors. In the second stage, a harmony search algorithm is used to minimize total costs, including capacitor costs and power loss costs, by determining the optimal capacitor sizes and numbers placed at the candidate locations. The method is tested on 33-bus and 69-bus test systems and results in reduced power losses and costs compared to the base case without capacitors.
Capacitor Placement and Reconfiguration of Distribution System with hybrid Fu...IOSR Journals
The document describes a hybrid fuzzy-opposition based differential evolution algorithm for capacitor placement and distribution system reconfiguration to minimize transmission losses and costs. The algorithm considers constraints like voltage limits and current limits while optimizing the objective function of total annual cost, which includes energy loss costs and capacitor costs. It was tested on the IEEE 33-bus distribution test system and able to reduce losses and satisfy power flow constraints.
This document proposes a universal algorithm for stage switching in hypercube interconnection networks used in multi-core systems. It analyzes a 4-stage 16x16 hypercube network and derives a switching algorithm where the selection bit sequence changes at each stage in a predefined manner. This algorithm is then generalized for an n-stage hypercube network to establish relationships between the selection bit patterns at different stages. The proposed universal algorithm could be used for linear switching in hypercube networks of any size to efficiently design higher order interconnection blocks for multi-core systems.
Power losses reduction of power transmission network using optimal location o...IJECEIAES
Due to the growth of demand for electric power, electric power loss reduction takes great attention for the power utility. In this paper, a low-level generation or distributed generation (DG) has been used for transmission power losses reduction. Karbala city transmission network (which is the case study) has been represented by using MATLAB m-file to study the load flow and the power loss for it. The paper proposed the particle swarm optimization (PSO) technique in order to find the optimal number and allocation of DG with the objective to decrease power losses as possible. The results show the effect of the optimal allocation of DG on power loss reduction.
This document discusses ENEA, the Italian Energy, New Technologies and Environment Agency. ENEA's mission is to support Italy's competitiveness and sustainable development. The document discusses ENEA's focus areas including environment, biotechnology, nuclear energy, new materials, and energy efficiency/renewables. It then discusses using soft computing approaches for modeling ambient temperature and humidity, optimizing eco-building design, and forecasting regional energy consumption in Italy. Neural networks, genetic algorithms, and hybrid models are evaluated for developing accurate models with limited historical data.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
A hybrid artificial neural network-genetic algorithm for load shedding IJECEIAES
This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method.
IRJET- A Review on Computational Determination of Global Maximum Power Point ...IRJET Journal
This document reviews computational techniques for determining the global maximum power point (GMPP) for photovoltaic (PV) arrays under partial shading conditions. Partial shading causes the PV array characteristics to exhibit multiple local maxima, making it difficult for conventional maximum power point tracking techniques to identify the true GMPP. The document categorizes and discusses analytical, meta-heuristic, and fuzzy-based computational approaches that have been proposed to address this issue, including methods using the Lambert W function, particle swarm optimization, simulated annealing, and fuzzy logic. It proposes using the Das-Saetre model of PV characteristics along with meta-heuristic techniques to more accurately compute the GMPP while reducing computational complexity compared to previous approaches.
Group Search Optimization technique is used to minimize reactive power generation in a power system. The objective is to control generator voltages to reduce reactive power production while meeting system constraints. IEEE 14-bus test system is used with 4 generator buses. Generator voltages are optimized using Group Search Optimization, which finds the minimum reactive power of 5.26 MVAR after 26 iterations. Reactive power and line losses are reduced compared to the base case, showing the effectiveness of the technique in minimizing reactive power generation.
The document summarizes a study comparing time series and artificial neural network (ANN) methods for short-term load forecasting of Covenant University, Nigeria. Load data from October 15-16, 2012 was used to develop forecasting models using moving average, exponential smoothing (time series methods) and ANN. The ANN model with inputs of previous load, time of day, day of week and weekday/weekend proved most accurate with a mean absolute deviation of 0.225, mean squared error of 0.095 and mean absolute percent error of 8.25, making it the best forecasting method according to the error measurements.
Modeling of off grid hybrid power system under jordanian climateAlexander Decker
This document summarizes a study that models an off-grid hybrid power system for a village in Jordan using Matlab Simulink. The hybrid system combines photovoltaic (PV) panels and wind turbines. The study models the PV system, wind turbines, batteries, and load to simulate the hybrid system's output under different conditions. It is found that Matlab Simulink with a graphical user interface can effectively plan and analyze hybrid power systems for off-grid villages.
FEASIBILITY ANALYSIS OF GRID/WIND/PV HYBRID SYSTEMS FOR INDUSTRIAL APPLICATIONWayan Santika
The present study offers technical and economical analyses of grid-connected hybrid power systems for a large scale production industry located in Bali. The peak load of observed system can reach 970.630 kW consuming on average 16 MWh of electricity a day. Software HOMER was utilized as the optimization tool. The proposed hybrid renewable energy systems consist of wind turbines, a PV system, a converter, and batteries. The system is connected to the grid. Optimization results show that the best configuration is the Grid/Wind hybrid system with the predicted net present cost of
-884,896 USD. The negative sign indicates that revenues (mostly from selling power to the grid) exceed costs. The levelized cost of electricity of the system is predicted to be -0.013 USD/kWh. The present study also conducts sensitivity analysis of some scenarios i.e. 50% and 100% increases in grid electricity prices, 50% reduction of PV and WECS prices, and 10 USD and 50 USD carbon taxes per ton CO2 emission. Implications of the findings are discussed.
This document summarizes a research paper that proposes using a genetic algorithm to optimize the placement of FACTS devices (TCSC and SVC) to maximize available transfer capability (ATC) and minimize contingencies in a power system. It first provides background on ATC and FACTS devices. It then describes modeling TCSC and SVC and constructing the genetic algorithm. The algorithm is tested on a two-area 11 bus power system model. Results show that optimally placing TCSC and SVC using the genetic algorithm can increase ATC and reduce contingencies compared to having no FACTS devices.
This document discusses using predictive analysis to optimize energy management systems. It proposes integrating predictive analytics with energy management systems (EMS) to improve optimization of energy source selection and usage. Currently, EMS systems select energy sources like grid, diesel, solar, batteries based on simple priority rules. Integrating predictive analytics can help EMS systems better forecast power outages and optimize cost and emissions by deciding which sources to use and in what proportion, based on machine learning of past and present energy and environmental data to predict the future. This could increase optimization of source selection from the current 40-50% with traditional EMS to 80-90%. The document uses telecom tower energy usage as a case study.
A Solution to Optimal Power Flow Problem using Artificial Bee Colony Algorith...IOSR Journals
This document presents an artificial bee colony (ABC) algorithm approach to solve the optimal power flow (OPF) problem incorporating a flexible AC transmission system (FACTS) device, specifically a static synchronous series compensator (SSSC). The ABC algorithm is tested on the IEEE 14-bus test system both with and without the SSSC. Results show that the ABC algorithm gives a better solution when incorporating the SSSC, improving the system performance in terms of lower total cost, lower power losses, and better voltage profile compared to the case without SSSC.
17 9740 development paper id 0014 (edit a)IAESIJEECS
This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.
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 Demand Analysis of Telecom Towers of Nepal with Strategic Scenario Dev...IJRES Journal
Telecom towers, technically known as BTS (Base Transceiver Stations) are the most energy intensive part of cellular network architecture and contribute up to 60 to 80% of total cellular power consumption and varies in response to the real traffic demand throughout the day and night. But, thelack of grid availability highlightsa potential barrier to telecom industry growth in Nepal. Nepal has approximately 5,222 telecom towers of which about 22% do operate on diesel generators (DGs) while the remaining by grid electricity with some shares of renewable energy technologies (RETs: solar and/or wind). Despite the large carbon imprint, the uncertainty in power availability has compelled telecom operators to use DGs to ensure continuous supply of power for the better network availability, which translates huge operating costs along with adverse environmental impact. So, it becomes an imperative solution for telecom operators to evaluate all alternatives in order to increase network reliability with reduced energy cost. This study report intentionally focus on current energy consumptionof such telecom towers and forecast thefuture energydemand with reference to growing subscriber trend up to 2025 using LEAP (Long Range Energy Alternative Planning System)withBusiness As Usual (BAU) scenario. A clean energy technology (CET) scenario with possible RET options is also developed and compared with base case scenario through some policy mechanics on behalf of environmental benefits and sustainable cellular communication. Furthermore, this study concludes a potential energy cum cost saving with RET adoption with basic cost economics analysis.
Performance analysis of grid-tied photovoltaic system under varying weather c...IJECEIAES
Model and simulation of the impact of the distribution grid-tied photovoltaic (PV) system feeding a variable load with its control system have been investigated in this study. Incremental Conductance (IncCond) algorithm based on maximum power point tracking (MPPT) was implemented for the PV system to extract maximum power under different weather conditions when solar irradiation varies between 250 W/m 2 and 1000 W/m 2 . The proposed system is modelled and simulated with MATLAB/Simulink tools. Under different weather conditions, the dynamic performance of the PV system is evaluated. The results obtained show the efficacy of the proposed MPPT method in response to rapid daytime weather variations. The results also show that the surplus power generated is injected into the grid when the injected power from the PV system is higher than the load demand; otherwise, the grid supplies the load.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
This document presents a method for optimizing the placement and sizing of multiple distributed generation (DG) units in a transmission system to minimize power losses and improve voltage. Fuzzy logic is used to determine the optimal locations for DG units based on power loss index and voltage. Particle swarm optimization is then used to determine the optimal size of DG units at the identified locations. The method is tested on the IEEE 14-bus system, showing that placing DG units at buses 3, 4 and 5 can reduce power losses by up to 94.47% and improve voltages compared to using a single DG unit.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
The document describes a two-stage method for optimal allocation of capacitors in a radial distribution system. In the first stage, loss sensitivity factors are used to calculate candidate locations for capacitors. In the second stage, a harmony search algorithm is used to minimize total costs, including capacitor costs and power loss costs, by determining the optimal capacitor sizes and numbers placed at the candidate locations. The method is tested on 33-bus and 69-bus test systems and results in reduced power losses and costs compared to the base case without capacitors.
Capacitor Placement and Reconfiguration of Distribution System with hybrid Fu...IOSR Journals
The document describes a hybrid fuzzy-opposition based differential evolution algorithm for capacitor placement and distribution system reconfiguration to minimize transmission losses and costs. The algorithm considers constraints like voltage limits and current limits while optimizing the objective function of total annual cost, which includes energy loss costs and capacitor costs. It was tested on the IEEE 33-bus distribution test system and able to reduce losses and satisfy power flow constraints.
This document proposes a universal algorithm for stage switching in hypercube interconnection networks used in multi-core systems. It analyzes a 4-stage 16x16 hypercube network and derives a switching algorithm where the selection bit sequence changes at each stage in a predefined manner. This algorithm is then generalized for an n-stage hypercube network to establish relationships between the selection bit patterns at different stages. The proposed universal algorithm could be used for linear switching in hypercube networks of any size to efficiently design higher order interconnection blocks for multi-core systems.
Power losses reduction of power transmission network using optimal location o...IJECEIAES
Due to the growth of demand for electric power, electric power loss reduction takes great attention for the power utility. In this paper, a low-level generation or distributed generation (DG) has been used for transmission power losses reduction. Karbala city transmission network (which is the case study) has been represented by using MATLAB m-file to study the load flow and the power loss for it. The paper proposed the particle swarm optimization (PSO) technique in order to find the optimal number and allocation of DG with the objective to decrease power losses as possible. The results show the effect of the optimal allocation of DG on power loss reduction.
This document discusses ENEA, the Italian Energy, New Technologies and Environment Agency. ENEA's mission is to support Italy's competitiveness and sustainable development. The document discusses ENEA's focus areas including environment, biotechnology, nuclear energy, new materials, and energy efficiency/renewables. It then discusses using soft computing approaches for modeling ambient temperature and humidity, optimizing eco-building design, and forecasting regional energy consumption in Italy. Neural networks, genetic algorithms, and hybrid models are evaluated for developing accurate models with limited historical data.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
A hybrid artificial neural network-genetic algorithm for load shedding IJECEIAES
This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method.
Optimal design and_model_predictive_control_of_stPremkumar K
This document summarizes a research paper that analyzes the optimal design and model predictive control of a standalone hybrid renewable energy system (HRES) for residential demand side management in Pakistan. It describes using HOMER Pro software to simulate 9 scenarios of PV-wind-diesel-battery-converter systems to determine the optimal system design with the lowest cost. The optimal design was found to be a PV-wind-battery-converter system with a net present cost of $47,398 and levelized cost of energy of $0.309/kWh, providing a 100% reduction in emissions. A MATLAB/Simulink model of the optimal HRES was developed and validated. Model predictive control algorithms were also applied to regulate
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Keywords:BP neural network, Immune Genetic Particle Swarm Optimization algorithm, Optimal Reactive Power, Transmission loss.
stability of power flow analysis of different resources both on and off gridrehman1oo
This document presents a power flow optimization strategy model for a distribution network that considers source, load, and storage. The model aims to minimize total cost, voltage deviation, and power losses over time periods determined through k-means clustering of an equivalent load curve. A particle swarm optimization algorithm is used to solve the multi-objective optimization model subject to power flow, voltage, and other constraints. The model is tested on an IEEE 33-node system and is shown to improve economic and reliability performance compared to a fixed weighting approach.
Quantum behaved artificial bee colony based conventional controller for opti...IJECEIAES
Since a multi area system (MAS) is characterized by momentary overshoot, undershoot and intolerable settling time so, neutral copper conductors are replaced by multilayer zigzag graphene nano ribbon (MLGNR) interconnects that are tremendously advantageous to copper interconnects for the future transmission line conductors necessitated for economic and emission dispatch (EED) of electric supply system giving rise to reduced overshoots and settling time and greenhouse effect as well. The recent work includes combinatorial algorithm involving proportional integral and derivative controller and heuristic swarm optimization; we say it as Hybridparticle swarm optimization (PSO) controller. The modeling of two multi area systems meant for EED is carried out by controlling the conventional proportional integral and derivative (PID) controller regulated and monitored by quantum behaved artificial bee colony (ABC) optimization based PID (QABCOPID) controller in MATLAB/Simulink platform. After the modelling and simulation of QABCOPID controller it is realized that QABCOPID is better as compared to multi span double display (MM), neural network based PID (NNPID), multi objective constriction PSO (MOCPSO) and multi objective PSO (MOPSO). The real power generation fixed by QABCOPID controller is used to estimate the combined cost and emission objectives yielding optimal solution, minimum losses and maximum efficiency of transmission line.
Performance assessment of an optimization strategy proposed for power systemsTELKOMNIKA JOURNAL
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
The document presents the optimal sizing of a hybrid photovoltaic-wind power system for rural electrification in India. It involves modeling the system components, including photovoltaic modules, wind turbines and batteries. The optimization aims to minimize the levelized cost of energy while meeting the desired reliability, represented by the loss of power supply probability. The modeling is implemented using MATLAB software. Simulation results for a sample location in Andhra Pradesh, India show that a hybrid solar-wind system can generate sufficient power for a rural village by optimizing the component sizes.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
The document presents the optimal sizing of a hybrid photovoltaic-wind power system for rural electrification in India. It involves modeling the system components, optimizing the system size according to loss of power supply probability and levelized cost of energy, and applying the optimization model to a location in India. The optimal configuration is determined using MATLAB simulations to minimize costs while meeting reliability requirements. Simulation results show the hybrid system can generate enough power for some villages in rural areas using solar and wind energy resources.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a study on optimally sizing a hybrid photovoltaic-wind power system for rural electrification in India. The study involves modeling the system components, optimizing the system size based on loss of power supply probability and levelized cost of energy, and simulating the optimal system configuration using MATLAB. The proposed system combines solar panels, wind turbines, and batteries. Simulation results for a specific rural location in India show the optimally sized system meets reliability requirements at lowest cost.
A NOVEL SYSTEM OPTIMIZATION OF A GRID INDEPENDENT HYBRID RENEWABLE ENERGY SYS...ijscmcj
Hybrid renewable energy based off-grid or distribute power supply has customarily thought to be a solitary
innovation based restricted level of supply to meet the essential needs, without considering dependable
energy procurement to rural or remote commercial enterprises. The aim of the paper is to propose a design
idea off-grid hybrid system to fulfil the load demand of the telecom base station by using renewable energy
resources for rural regions. HOMER software tool is used for simulation and optimization and it also
analysis the total net present cost (TNPC) $100,757, carbon emission is zero percent, initial cost $70,920,
operating cost $2,334, Capacity Shortage 0.17% and the cost of energy (COE) $0.502. The HOMER
simulation outcome gives the most feasible hybrid system configuration for electric power supply to the
remote location telecom base station.
This document presents an immunized-evolutionary algorithm technique for loss control in transmission systems with multiple load increments. The technique uses an immune evolutionary programming (IEP) approach to optimize the size and location of photovoltaic (PV) systems injected into the transmission network. IEP combines classical evolutionary programming with an immune algorithm to reduce computational burden and improve optimization performance. The algorithm is tested on IEEE 12-bus and 14-bus systems. Results show that IEP is able to determine the optimal PV configuration to control losses in the transmission system as load increases, demonstrating its effectiveness and potential for practical implementation.
Multi-objective optimal placement of distributed generations for dynamic loadsIJECEIAES
Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High R/X ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
This document proposes methods to improve the dynamic response of a grid-connected hybrid power system comprising wind and photovoltaic generation. It implements an artificial neural network trained by genetic algorithm (ANN-GA) to maximize solar array output under varying irradiation and temperature conditions. It also uses a fuzzy logic controller for wind turbine pitch angle control in high wind speeds, compared to a conventional PI controller. Simulation results show the ANN-GA method achieves maximum power point tracking for the solar arrays more reliably than conventional perturb and observe or incremental conductance methods. The fuzzy logic controller also provides faster response and smoother power curves for the wind turbine, improving overall dynamic system performance.
New typical power curves generation approach for accurate renewable distribut...IJECEIAES
This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration.
Economic dispatch by optimization techniquesIJECEIAES
The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
Residential Community Load Management based on Optimal Design of Standalone H...Asoka Technologies
This document presents a study on designing a standalone hybrid renewable energy system (HRES) for a residential community in Pakistan using PV, wind, diesel, and battery. Nine system configurations were analyzed using HOMER software to determine the optimal and most economical design. HOMER results showed a PV-wind-battery system with 13.4 kW PV, 4 kW wind, and 20 battery units was optimal, with a net present cost of $28,620 and cost of energy of $0.311/kWh. MATLAB/Simulink modeling validated this design could maintain voltages and safe battery SOC while meeting load, even with generation and demand fluctuations. The HRES design and control strategy presented provides a tool for planning
Improvement of voltage profile for large scale power system using soft comput...TELKOMNIKA JOURNAL
In modern power system operation, control, and planning, reactive power as part of power system component is very important in order to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to improve the voltage profile and decrease the total power loss of transmission line system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. The effectiveness of the proposed technique is examined by utilizing Java-Madura-Bali (JAMALI) 500 kV power system grid as the test system. From the simulation results, the PSO and ABC algorithms are providing satisfactory results in obtaining the capacitor size and can reduce the total power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u.
Similar to Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles (20)
ASSESSMENT OF VOLTAGE FLUCTUATION AND REACTIVE POWER CONTROL WITH SVC USING PSOKashif Mehmood
Smart grid novel growth in the arena of power, this is a new scheme and apparatus for producing and distributing
electricity. Smart grids are very important part of the electrical circuit. Distribution Management System (DMS) used by the utilities for the
state Estimation (SE). Basic application distribution control system, evaluation (SE) and the control reactive power). There SE principally
used to monitor and to control the entire distributed network. In the distribution network has a problem voltage profile. It is controlled by
distributed generators (DG), which are located in diverse positions in the system to maintain the voltage within certain limits. Validation
through the implementation on the IEEE 14-bus radial transmission system indicated that PSO is reasonable to achieve the
task. MATLAB software is used for results simulation
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...Kashif Mehmood
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays
the vital role to address challenges such as optimal generation, economic scheduling, dispatching and
contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network
(ANN) technique to perform STFL but long training time and convergence issues caused by bias,
variance and less generalization ability, unable this algorithm to accurately predict future loads. This
issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint
partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and
increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process
of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of
this method by taking mean improves the overall performance. This method of combining several
predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging
method by further increasing the generalization ability and STLF accuracy.
Optimizing Size of Variable Renewable Energy Sources by Incorporating Energy ...Kashif Mehmood
The electricity sector contributes to most of the global warming emissions generated from
fossil fuel resources which are becoming rare and expensive due to geological extinction and climate
change. It urges the need for less carbon-intensive, inexhaustible Renewable Energy Sources (RES) that
are economically sound, easy to access and improve public health. The carbon-free salient feature is the
driving motive that propels widespread utilization of wind and solar RES in comparisons to rest of RES.
However, stochastic nature makes these sources, variable renewable energy sources (VRES) because it brings
uncertainty and variability that disrupt power system stability. This problem is mitigated by adding energy
storage (ES) or introducing the demand response (DR) in the system. In this paper, an electricity generation
network of China by the year 2017 is modeled using EnergyPLAN software to determine annual costs,
primary energy supply (PES) and CO2 emissions. The VRES size is optimized by adding ES and DR (daily,
weekly, or monthly) while maintaining critical excess electricity production (CEEP) to zero. The results
substantiate that ES and DR increase wind and solar share up to 1000 and 874 GW. In addition, it also
reduces annual costs and emissions up to 4.36 % and 45.17 %
Optimal Load Shedding Using an Ensemble of Artifcial Neural NetworksKashif Mehmood
This document describes using an ensemble of artificial neural networks for optimal load shedding in Pakistan's power system. It aims to maintain power system frequency stability by shedding an accurate amount of load. Real load and generation data from Pakistan's system is used to train individual neural networks. The outputs of the neural networks are then combined using a bagging algorithm with disjoint partitioning to form an ensemble. This ensemble approach helps reduce errors from the individual networks, improving load shedding accuracy and aiding power system stability.
Modelling and Implementation of Microprocessor Based Numerical Relay for Prot...Kashif Mehmood
This paper includes the design and implementation of Numerical Relay that can protect the equipment against over-voltage, over-current and under voltage. Although, every power system is subjected to faults and these faults can severe damage to the power system. Therefore, it is necessary
to observe and resolve in time to avoid a large damage such as blackouts. For this purpose, there
should be some sensing devices, which give signals to the circuit breakers for preventing of power
system damages. The multipurpose relays have much importance role in power system for sensing
and measuring the amplitude of faults. Numerical relay provides settings of over-current, overvoltage and under voltage values. Simulations have been carried out using Proteus software along
with tested on hardware with Arduino Uno Microcontroller that proves the working and operation of
numerical relay.
Comparative Performance Analysis of RPL for Low Power and Lossy Networks base...Kashif Mehmood
The Internet of Things (IoT) is an extensive
network between people-people, people-things and things-things.
With the overgrown opportunities, then it also comes with a lot of
challenges proportional to the number of connected things to the
network. The IPv6 allows us to connect a huge number of things.
For resource-constrained IoT devices, the routing issues are very
thought-provoking and for this purpose an IPv6 Routing
Protocol for Low-Power and Lossy Networks (RPL) is proposed.
There are multi-HOP paths connecting nodes to the root node.
Destination Oriented Directed Acyclic Graph (DODAG) is
created taking into account different parameters such as link
costs, nodes attribute and objective functions. RPL is flexible
and it can be tuned as per application demands, therefore, the
network can be optimized by using different objective functions.
This paper presents a novel energy efficient analysis of RPL by
performing a set of simulations in COOJA simulator. The
performance evaluation of RPL is compared by introducing
different Objective functions (OF) with multiple metrics for the
network.
Improved Virtual Synchronous Generator Control to Analyse and Enhance the Tra...Kashif Mehmood
In recent years, the integration of renewable energy resource (RES) into the power system is growing rapidly, and
it is necessary to analyse and evaluate the effect of RES on transient stability of the power system. In this paper, centre of
inertia (COI) concept is implemented to analyse and evaluate the integration effects of an auxiliary damping control (ADC) based
virtual synchronous generator (VSG) consisting an improved governor. The impact of VSG integration is divided into synchronous
generator (SG) linked parts and COI associated parts. Due to VSG integration into the power system, the significant elements
which disturb the COI dynamic motion and rotor dynamics of SG are examined in detail. Different cases are considered to evaluate
the effectiveness of the proposed method, i.e., VSG’s different integrating location and different power capacities. It is observed
in simulation results that COI dynamic motion and rotor dynamics of SG are positively affected by VSG integration and transient
stability improves significantly
Integrated Energy System Modeling of China for 2020 by Incorporating Demand R...Kashif Mehmood
Electricity and heat energy carriers are mostly produced by the fossil fuel sources that are
conventionally operated independently, but these carriers have low efficiency due to heat losses. Moreover,
a high share of variable renewable energy sources disrupts the power system reliability and flexibility.
Therefore, the coupling of multiple energy carriers is underlined to address the above-mentioned issues that
are supported by the latest technologies, such as combined heat and power, heat pumps, demand response,
and energy storages. These coupling nodes in energy hubs stimulate the conversion of the electric power
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Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
1. Received August 9, 2019, accepted September 28, 2019, date of publication October 11, 2019, date of current version November 6, 2019.
Digital Object Identifier 10.1109/ACCESS.2019.2946640
Optimal Power Generation in Energy-Deficient
Scenarios Using Bagging Ensembles
KASHIF MEHMOOD 1,2, HAFIZ TEHZEEB UL HASSAN3, ALI RAZA 2,
ALI ALTALBE4, AND HAROON FAROOQ 5
1School of Electrical Engineering, Southeast University, Nanjing 210096, China
2Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan
3Department of Electrical Engineering, University of the Punjab, Lahore 54000, Pakistan
4Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
5Department of Electrical Engineering, Rachna College of Engineering and Technology, Gujranwala campus, University of Engineering and Technology, Lahore
54000, Pakistan
Corresponding author: Kashif Mehmood (kashifzealot@gmail.com)
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant No.
(DF-656-611-1441). The authors, therefore, gratefully acknowledge DSR technical and financial support.
ABSTRACT This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR.
INDEX TERMS Artificial neural networks, bootstrap aggregation, bagging algorithm, disjoint partition,
economic dispatch, optimal power generation.
I. INTRODUCTION
Interconnected power systems are foundations on which
modern civilization rests. The economy of any developing
country, like Pakistan, is based on the provision of cheap and
abundant sources of electrical energy. Thus, optimal power
generation using better scheduling of available generating
units is essential to supply economical energy to consumers
and thus enhance the sustainability of power systems.
The foremost economic influences on any modern power
system are the cost of generating active power and reduc-
ing system reactive power flow. In practical power systems,
hundreds of generating units run in parallel to meet the load
demand. So, it is essential to run power plants economically.
The associate editor coordinating the review of this manuscript and
approving it for publication was Nan Liu .
The economic operation of a power plant is a sub-problem
of the unit commitment (UC) [1]. The generation schedul-
ing problem for energy deficient scenarios or large UC has
become the subject of considerable discussion during the last
few years [2]–[6]. Mathematical programming and heuristic
methods have been extensively used to define UC for power
plants [2], [5].
Economic dispatch (ED) problems have been investigated
through various heuristic, intelligent and hybrid techniques
such as; evolutionary programming (EP) [7], artificial bee
colony algorithm (ABC) [8], particle swarm optimization
(PSO) [9], hopfield neural network with quadratic program-
ming (HNN-QP) [10] and some others are summarized in
the Table 1. Although the discussed methods are useful in
optimizing the production cost for ED problems, but does
not converge rapidly [11], [12]; particularly in the case when
generation schedule is on an hourly basis to overcome the
VOLUME 7, 2019
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
155917
2. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
TABLE 1. Economic dispatch investigation via heuristic, intelligent and
hybrid techniques.
power shedding in an under-developed country like Pakistan.
Artificial neural networks (ANN) attracted attention to solv-
ing ED problems optimally as it has high computational
speed, a high convergence rate even for short intervals and
self-error correction ability, unlike other intelligence tech-
niques [19], [20].
In [19], a dynamic neural network is employed to solve
the combined economic and emission dispatch problem with
fast convergence. Fukuyama et al. employed neural network
techniques to solve the ED problem considering security con-
straints [21]. Liang applied neural network based re-dispatch
method for solving the spinning reserve constrained ED prob-
lem [18]. Chan et al. presented an artificial neural network
and genetic algorithm to optimize the load distribution for a
chiller plant [20]. However, ANN alone may undergo pre-
mature convergence and a tendency to be trapped at a local
optimum [22], which results in a significant error in learning.
The error in ANN learning is reduced by using bootstrap
aggregation [23], which gives better precision, improved gen-
eralization, and prediction along with reduced machine learn-
ing errors like bias and variance. Thus, a research objective
is to overcome the mentioned shortcomings and to explore
economic-dispatch optimally using Lagrange relaxation with
an enhanced neural network (NN) by adopting a bootstrap
aggregation algorithm for energy deficient scenarios. Such an
enhanced neural network is termed an ensemble of artificial
neural networks (EANN).
The proposed neural network ensemble approach is applied
to the IEEE 30-bus system and evaluated for a day on an
hourly basis. There are six generators and twenty load buses.
The constraints considered are generator limits, transmission
line losses, and the power demand. The input data for the
neural network is described by a 20 × 24 matrix which
gives the power demand distribution among 20 buses during
24 hours. The target data for the neural network is via a
6 × 24 matrix that describes the generation of six gener-
ators for a day. Multiple neural networks are incorporated
by applying a Bagging algorithm to create an ensemble of
neural networks. The predictions from the individual neural
networks are aggregated by an algebraic method to get the
ensembled output. Finally, a comparison between ANNs and
EANNs show the predominance of the proposed method.
The remainder of the paper is organized as follows:
problem formulation of economic dispatch is described in
section II. In section III, system description and power
dispatch is discussed. In sections IV and V, the artificial
neural network with feed-forward back-propagation model
and ensemble of artificial neural network with Bagging are
presented, respectively. Simulation results are presented in
section VI, with conclusions then drawn.
II. ECONOMIC DISPATCH PROBLEM FORMULATION
Thermal power plants operate in parallel to fulfill the demand
of different load centers. Typically, cost of power generation
is not the same for each generating unit as this depends
upon the operating efficiency and fuel cost of a unit, and
transmission losses. For optimal generation, minimization
of the cost of real power generated from all units must be
achieved. The amount of fuel consumed by a generating unit,
and in turn the fuel cost, depends upon the produced power
at a specific operating point as shown in Fig. 1 [1].
FIGURE 1. (a) Heat-rate curve (b) Fuel-cost curve.
A generating unit is assumed to have a quadratic cost
function in terms of its produced active power [1]:
CGi = αi + βiPGi + γiP2
Gi (1)
where CGi is the cost of the ith power generating unit, PGi is
the real power generated by the ith generating unit, and αi, βi
and γi are the cost coefficients.
The constraint equations for optimal generation are given
by (2), (3) and (4):
n
i=1
PGi = PD + PL (2)
where ‘n’ is the total number of generating plants, PD is the
load demand, and PL is the total transmission loss.
PL =
n
i=1
n
j=1
PGiBijPGj +
n
i=1
B0iPGi + B00 (3)
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3. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
Transmission losses as a function of generator powers are
expressed through B-coefficients and given by Kron’s loss
formula is described in [24]:
Where PGi and PGj are real power generation at the ith
and jth generating units, respectively. Bij and B0i are the loss
coefficients (constant for certain conditions), and B00 is a loss
constant. However, the generation limits of units are given by:
PGi(min) ≤ PGi ≤ Pi(max) (4)
From the Lagrange method, the overall objective func-
tion, including constraint limits and transmission line losses,
becomes:
F = Ct+λ(PD+PL−
n
i=1
PGi) +
n
i=1
µi(max)(PGi − PGi(max))
+
n
i=1
µi(min)(PGi − PGi(min)) (5)
The constant µi(max) = 0 when Pi < Pi(max) and µi(min) =
0 when Pi > Pi(min). These constants play an active role when
constraints are violated, specifically Pi > Pi(max) and Pi <
Pi(min). Ct and λare the total cost and incremental cost of all
generating units, respectively, and Ct is defined as:
Ct =
n
i=1
CGi (6)
The following conditions hold if the function ‘F’ is to be a
minimum:
∂F
∂PGi
= 0
∂F
∂λ
= 0 (7)
∂F
∂µi(max)
= PGi − PGi(max) = 0 (8)
∂F
∂µi(min)
= PGi − PGi(min) = 0 (9)
If PGi is within its limits, then µi(min) and µi(max) are zero
and the Lagrange (λ) of (7) is:
∂Ct
∂PG
+ λ(0 +
∂PL
∂PGi
− 1) = 0 (10)
The incremental transmission line losses are incorporated
as the partial derivative of (3) with respect to the real power
generation PGi:
∂PL
∂PGi
= 2
n
j=1
BijPGj + B0i (11)
Substituting (1) and (11) into (6) and simplifying gives:
γGi
λ
+ Bii PGi +
n
j=1
j=1
BijPGj =
1
2
1 − B0i −
βGi
λ
(12)
Equation (12) for all units, results in matrix form,
equation (13), for all linear equations.
Solving (13) gives ED for an estimated value of λ. An itera-
tive approach is employed to find optimal generation, until all
FIGURE 2. Single line diagram of the IEEE 30-bus test system.
TABLE 2. Cost function parameters of the generators.
constraints are satisfied, the PG matrix from (13) is calculated
and the total cost of generation is obtained from (6). More
details of the Lagrange method can be found in [1].
γG1
λ
+ B11 B12 · · · B1n
B21
...
γG2
λ
+ B22 · · · B2n
Bn1
...
Bn22
...
...
· · ·
γGn
λ
+ Bnn
PG1
PG2
...
PGn
=
1
2
1 − B01 −
βG1
λ
1 − B02 −
βG2
λ
...
1 − B0n −
βGn
λ
(13)
III. SYSTEM DESCRIPTION AND POWER DISPATCH
Lagrange relaxation and the ensembled artificial neural net-
work (EANN) are applied on the IEEE 30-bus system,
as shown in Fig. 2, to not only solve the ED problem optimally
but also to boost and enhance system learning. Generation
scheduling is done on an hourly basis to overcome the power
shedding. EANN is explained in detail in section 5.
The beta loss coefficients matrix ‘Bij’ of order 6×6 for the
IEEE 30-bus system with 6 generators is obtained from the
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4. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
TABLE 3. Optimal power generated by employing lagrange relaxation method for supervised learning of the ANN.
power flow calculations using the Newton-Raphson method
and is given by (14), as shown at the bottom of this page, [23].
Other loss coefficients, B0i and B00, are also presented.
The cost function parameters of the six generators in the
IEEE 30-bus system are given in Table 2. [25].
α, β and γ are the cost coefficients in Table 2. The col-
umn ‘Gen. Bus’ give the bus to which a generator is con-
nected. The maximum generation from all the six generators
is 435 MW while the minimum generation is 117 MW. The
daily load data is obtained from the National Power Control
Centre (NPCC) of Pakistan and is shown in Fig. 3, and is used
for optimal generation.
Optimal generation for the IEEE 30-bus system is obtained
by applying the Lagrange method for supervised learning of
the ANN, as given in Table 3.
IV. FEED-FORWARD BACK-PROPAGATION
NEURAL NETWORK
The artificial neural network is derived from the biological
neural network (BNN). ANNs are not as complex but the data
handling method is similar to the BNN. The organization of
FIGURE 3. Daily load curve obtained from NPCC-Pakistan.
an ANN is shown in Fig. 4. It is an interconnected system that
is capable of quickly solving highly non-linear issues.
A multilayer feed-forward back-propagation neural net-
work is employed here instead of a single layer neural
network.
Each layer is associated with the neighboring layer which
means all the neurons in each layer are connected to all
the neurons in a neighboring layer, as shown in Fig. 5 for
a three-layer feed forward back-propagation NN. The first
Bij =
2.18E − 04 1.03E − 04 9.00E − 06 −1.00E − 05 2.00E − 06 2.70E − 05
1.03E − 04 1.81E − 04 4.00E − 06 −1.50E − 05 2.00E − 06 3.00E − 05
9.00E − 06 4.00E − 06 4.17E − 04 −1.31E − 04 −1.53E − 04 −1.07E − 04
−1.40E − 04 −1.50E − 05 −1.31E − 04 2.21E − 04 9.40E − 05 5.00E − 05
2.00E − 06 2.00E − 06 −1.53E − 04 9.40E − 05 2.43E − 04 0.00E + 00
2.70E − 05 3.00E − 05 −5.00E − 05 5.00E − 04 0.00E + 00 3.58E − 04
B0i = [ 1.40E − 05 ]
B00 = [ −3.00E − 06 2.10E − 05 − 5.60E − 05 3.40E − 05 1.50E − 05 7.80E − 05 ] (14)
155920 VOLUME 7, 2019
5. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
FIGURE 4. Basic construction of an ANN.
FIGURE 5. A three-layer feed-forward back-propagation neural network.
layer is the input layer, the last layer is the output layer
and the intermediate layer is termed a hidden layer. The
number of hidden layers and selection of neurons affects the
results [26]. Because of this composition, a multilayer feed
forward neural network is trained (by back-propagation) to
learn non-linear patterns in linear space. Back-propagation is
a form of supervised training, where a network is provided
with both sample inputs and target values.
The input values are fed directly to the output layer via a
weight matrix as in Fig. 4. Inputs (x1, x2..., xN) and their
respective weights (w1j, w2j, . . ., wnj) are sent to the transfer
functions (TF) of the hidden layers. Activation function Oj is
added to TF to check whether or not the output is produced
through comparison with the threshold value θj. TF governs
the threshold values. The difference between the ANN and
desired values gives the error, which controls ANN training.
Thus, the error is fed-back to re-set the weights, until the
ANN outputs resemble the desired values. Neural network
training is a repetitive process. Repetition continues until
the stopping criteria is met, namely a specified mean square
error (MSE).
In this study, the developed supervised neural network is
trained using the parameters in Table 4. The input to the
neural network is power demand which is divided among
20 load busses while the output of the neural network is the
power generated on 6 busses, obtained from the Lagrange
TABLE 4. Training parameter of a single neural network.
relaxation method. This generated power fulfills the system
constraints and hence optimizes generation cost.
V. ENSEMBLE OF ARTIFICIAL NEURAL NETWORK
A. BOOTSTRAP AGGREGATION
Artificial neural network performance is satisfactory for spec-
ified values but when the data values constantly vary, as in
this study, in hours, then the ANN experiences under or
over fitting because of reduced generalization ability. Differ-
ence between the anticipated and actual values occurs even
after several ANN times training periods. This error occurs
because of the flawed learning process. The three main error
factors in learning are: variance, noise and bias [26].
Error = Variance + Noise + Bias (15)
Large variance is the reason of over fitting and large bias
causes under fitting of the data. Generalization of the ANN
to adjust with the new values increases variance and bias are
reduced to a minimum, and then the data-set will not undergo
under and over fitting. Additionally, the percentage change in
input and output values during the learning process decreases,
hence prediction improves.
Thus, bootstrap aggregating is employed to refine and
enhance the precision and stability of the machine learning
algorithm, ANN. It reduces variance and bias which helps
avoid over and under fitting, respectively [26], [27]. The
Bagging structure is shown in Fig. 6. Bootstrap aggregating
is also called bagging and is classified as follows:
1. Disjoint partitions
2. Small bags
3. No replication small bags
4. Disjoint bags
Disjoint partitions bagging is implemented here, by using
MATLAB, as it is more effective and thus outperform other
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6. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
FIGURE 6. A typical idea for the ensemble of neural networks.
FIGURE 7. Original dataset D.
FIGURE 8. Disjoint partitions (3 bootstraps).
techniques [28]–[30]. To understanding disjoint partitions,
assume the data set D as shown in Fig. 7. A subset is formed
by random selection of elements from the defined data set
without replication. For this, each certain element is taken
only once as shown in Fig. 8. These subsets are called Boot-
straps.
B. PROPOSED METHOD
The proposed method for optimal generation uses the follow-
ing steps:
1. Real data set generation
2. ANN design
3. Bootstrap design
STEP 1: The employed dataset (power demand against each
hour) is from the National Power Control Centre (NPCC)
in-charge of controlling and monitoring electric power flow
in Pakistan’s power system, shown in Fig. 3. This dataset is
tested on the IEEE 30-bus system. The worst-case scenario
of zero spinning reserve is considered for optimal power
generation.
STEP 2: The initial step for the formation of the ANN
classifier, is the selection of the target and input datasets. The
input data for the neural network is power demand, a 20 ×
24 matrix, among 20 buses during 24 hours. The target values
for the NN are a 6 × 24 matrix that describes the generation
of six generators for a day. The target data is obtained from
the Lagrange method for supervised learning. The biases
and weights of each neuron are randomly generated. The
specifications in Table 4 are used for ANN classifiers.
The training of the classifiers employs the Levenberg–
Marquardt Back Propagation algorithm, for fast convergence
with reduced error. Gradient descent is employed to reset the
bias and weights in the adaption learning process since the
convergence of ‘batch gradient descent with momentum’ is
fast enough for feed-forward networks. Gradient descent also
reduces the mean square error (MSE) [31]:
MSE =
1
n
n
I=1
e(i)2
=
1
n
n
i=1
(xi − zi)2
(16)
where n is the number of samples, xi is the prediction and is
the target value.
The data is divided as: 70% is used for training, 15% is for
validation to reduce over-fitting, and the remaining 15% is
used to predict the ANN final value.
STEP 3: Several ANN training sessions reveal a devia-
tion from the target value; mainly when the input dataset
varies abruptly. So Hypothetical 30 bootstraps are created
with different Network topology and Training Algorithms.
Bootstraps re-sample the initial data before ANN training
to improve the generalization ability. The Bootstrap ANN
(EANN) result is closer to the target with reduced percentage
error. EANN and ANN outputs are compared and presented
in the result section.
The proposed methodology is described in the algorithm
as:
1. Initialization of the original training data set D for
i = 1, 2, 3 . . . n.
2. Formation of a fresh data set Di called bootstrap of
the same size D by arbitrary choosing some data of
training samples from the set D (several samples can
be selected recurrently, and some of the samples may
not be selected at all, i.e. with or without replacement).
3. Training and learning of a specific classifier Ni of Di
by some machine learning procedures which depend on
the actual training set Di.
4. Combining the predictions of n classifiers by taking an
average.
The flow chart of optimal power generation, using a Bag-
ging/Bootstrap algorithm, is shown in Fig. 9 and further
explained via following algorithm:
1. START
2. Define system for economic dispatch:
a. IEEE 30 bus system which includes 6 generator
buses, 20 load busses
3. Obtained the data for economic dispatch:
a. Sum of power demands at 20 load busses at a par-
ticular hour from daily load curve
b. Beta loss coefficients matrix (Transmission losses)
obtained after Load Flow Analysis
c. Generator maximum and minimum power gener-
ated limits in MW including cost coefficients
4. Find out the dispatch using Lagrange Relaxation (LR)
method in MATLAB:
a. Active power generation in MW at 6th generator
busses to fulfil the daily load demand
b. The cost in $/hr to fulfil power demands
5. Prepared the N number of Neural Networks in MAT-
LAB:
155922 VOLUME 7, 2019
7. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
FIGURE 9. Flowchart of ensemble neural network using bootstrap
aggregating.
a. For each Neural Network, selection of the input data
set i.e. bootstraps of order 20∗24
b. Selection of target data set of order 6∗24 obtained
from LR
6. Setting the parameters of Neural Network:
a. Data division, hidden layers, number of neurons in
the hidden layers, training function, performance
function etc.
7. Separate training, testing and validation data for Neural
Network
8. Run ANN
9. Check ANN converge. If YES, go to step 10. Otherwise
go to step 6
10. Create N Bootstraps by applying bagging algorithm on
input data
11. Create N classifiers similar to ANN
12. Aggregating the Ensemble Neural Network to get final
prediction
13. Check EANN converge. If YES, go to step 14. Other-
wise, go to step 10
14. Print the output results
15. END
VI. SIMULATION RESULTS
The performance of ANN with regression analysis, compar-
ison of ANN prediction with target data, the formation of an
ensemble of neural networks with bootstrap aggregation, and
its influence on the predicted values along with regression
analysis and error histograms, are discussed in this section.
The optimal solution for the IEEE 30-bus system in Table 3,
is obtained by applying the Lagrange method.
The training window is presented in appendix, Fig. 10,
representing the mapping of IEEE 30-bus system into the
neural network.
FIGURE 10. Training window of the artificial neural network.
The data division is ‘Random’ and Levenberg-Marquardt is
selected as a training method which is further employed with
MSE performance functions. This is the most effective train-
ing method for feed-forward neural networks with respect to
the training precision [32].
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8. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
FIGURE 11. Regression plots obtained during the training of artificial
neural network.
FIGURE 12. Comparison between target data and ANN prediction.
The relation between the anticipated value (target data) and
the actual value (ANN output) is shown in the regression plots
of Fig. 11. The regression plots (R) gives the relation between
the desired and actual output for training, testing, validation,
and overall data. Ideally, data division (circles) should be on
the line for good generalization, representing y = x, which
shows output is equal to the target value and thus ‘R’ is unity.
The ANN value at R = 0.86 does not match with the target
value because of the load change each hour which indicates
the lack of good generalization ability of ANN.
Comparison of the target value (real power generation) and
the ANN prediction is shown in the Fig. 12. The compar-
ative analysis shows that the ANN forecast does not match
precisely with the actual power demand resulting in the
sub-optimal and uneconomical dispatch. The closer differ-
ence is represented by the error histogram of ANN as shown
in the Fig. 13.
The error histogram in Fig. 13 shows the percentage error
between the target power generation and the EANN forecast
against each hour of the day. The maximum percentage error
FIGURE 13. Error histogram of target and ANN predictions.
FIGURE 14. Regression plots obtained during the training of ensembled
artificial neural network.
is at the 8th hour that is 0.747% and the minimum percent-
age change is at 21st hour, which is 0.247%. Although the
percentage error is small against each hour, but the appli-
cation of EANN reflects the significant improvement in the
percentage error, better generalization ability and faster con-
vergence with the reduction in bias and variance under the
same training parameters and operating conditions. The ANN
has trained again for the re-sampled data to enhance its ability
and to adapt to new values speedily, after creating the Boot-
strap data by using the Bagging algorithm for the formation
of EANN. The Bagging algorithm to produce Bootstraps is
implemented in MATLAB by following the steps described
in section V.
The trials are done 50 times, and average value is taken to
analyze the results. It is shown in the results that the neural
network ensembles make fewer errors than the simple ANN
alone. The regression analysis of EANN is provided in the
Fig. 14. R = 0.98 shows the improved performance of the
classifiers. The data division is so aligned that y = x and have
a very little deviation in training, validation and test data sets
as compared to ANN.
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9. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
TABLE 5. Accuracy percentage of EANN.
FIGURE 15. Comparison between target data and EANN prediction.
FIGURE 16. Error histogram of target and EANN predictions.
Comparison of the target value and the EANN prediction
is shown in the Fig. 15. The EANN prediction is more close
to the target value (power generation) incorporating better
generalization ability as than ANN. The percentage change
of the EANN is shown via error histogram of Fig. 16.
FIGURE 17. Comparison of percentage errors between ANN and EANN.
It is noticed that the maximum percentage error is at the 9th
hour that is 0.199% and the minimum percentage error is
at the 13th hour, which is 0.031%. The significant reduction
in the percentage error shows the superiority of EANN over
ANN with reduced bias and variance due to the inherent
property of the Bootstrap aggregation.
Fig. 17 shows the comparison of percentage errors between
ANN and the proposed EANN. The comparison graphically
portrays the dominance of the Bootstraps algorithm over the
conventional ANN.
To summarize the results of ANN and EANN, the accuracy
percentage is measured and is shown in the Table 5.
A. VALIDATION OF PROPOSED ALGORITHM
As the load data could change day by day, so the pro-
posed algorithm is implemented on seven days’ data sets.
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10. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
FIGURE 18. Weekly load curve obtained from NPCC-Pakistan.
FIGURE 19. Error histogram of ANN predictions for a week.
FIGURE 20. Error histogram of EANN predictions for a week.
The weekly load curve is shown in Fig. 18 for twenty-four
hours.
The error analysis of ANN forecast for a full week is shown
in the Fig. 19. It is noted that the error is 1% percent, resulting
in the sub-optimal and uneconomical dispatch.
The error analysis of EANN forecast for a full week is
shown in the Fig. 20 of appendix and noted that the error
ranges from 0.0%-0.3%, which is far less than the errors
produced by ANN. Thus, the proposed algorithm is valid for
the variation in datasets.
The regression analysis of EANN during a week is pro-
vided in the Fig. 21. R = 0.99 shows the significant improve-
ment in the proposed algorithm when the dataset is varied.
B. COMPARATIVE STUDY
To check the accuracy of the proposed method, we uti-
lized four standard performance measures: Mean Square
Error (MSE), Mean Absolute Percentage Error (MAPE) and
Root Mean Square Error (RMSE) and Mean Error Devia-
tion (MAD) on various persistence models. The MSE is dis-
cussed before while others performance measures are defined
FIGURE 21. Regression plots obtained during the training of ensembled
artificial neural network for a week.
as:
MAD =
1
n
n
i=1
(xi − zi) (17)
RMSE =
1
n
n
i=1
(xi − zi)2
(18)
MAPE =
100%
n
n
i=1
(xi − zi)
xi
(19)
The errors have been computed independently for training,
testing and the Validation data. Their values are important
indicators of the practical usefulness of the forecasting frame-
work. The performance of EANN is compared with other
state-of-the-art machine learning algorithms namely Arti-
ficial Neural Network-Multilayer perceptron (ANN-MLP),
Support vector machines (SVM), Radial basis function
(RBF). Table 6 shows the performance metric of these
models.
The results show that using an EANN resulted in higher
accuracy than using other classifiers, for all four performance
metric. The improvements in terms of MAPE for the val-
idation data, averaged over all case studies are 0.14, 0.46,
0.95 and 0.87 for EANN, ANN, SVM, RBF respectively. The
computational cost of training an ensemble is higher than for
a single ANN but suitable for both offline and online practical
applications.
C. COMPUTATIONAL COMPLEXITY ANALYSIS
EANN is a bootstrapping technique, mainly influenced by
the learning phase. The resampling step, along with training
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11. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
TABLE 6. Models performance metric.
TABLE 7. Computational complexity analysis.
of the classifier for a certain number of bags leads to an
expensive approach than a simple ANN and SVM. Three
phases are considered, distinguished as training, search of
the optimal ensemble and inference, to show computational
complexity of ensemble techniques. Let N represents the
number of classifiers, R represents number of replacements
and B represents number of bags. If σ represents the time
complexity in terms of learning a computational complexity
of EANN, ANN and SVM is presented in the Table 7.
In terms of optimal searching, the SVM is the most time
intense approach characterized by an exponential time com-
plexity of σ(2N) compared to the linear ANN and Bagging.
However, EANN (bagging) shows a bit computational com-
plexity due to number of bags (B) and replacements(R),
defined by the term σ(B (N + R)) which is by definition
greater than one. However, this can be tolerated as compared
with SVM and ANN.
To measure the time complexity of the model with experi-
mental dataset at a particular load, comparisons of 50 individ-
ual trials have been done and the values are averaged. RMSE
(for training, testing and validation), and prediction accuracy
(under the tolerance of 15%) are used as indicators to evaluate
the models. The time complexity of SVM, ANN and EANN
(15 bootstraps) ANN and SVM is given in Table 8.
In general, the forecast results of any classifiers are accept-
able that have lower RMSEs. Table 8 show that the EANN
have lowest RMSEs (1.28) and having the prediction accu-
racy of 99.50% with the tolerance of 15%.
TABLE 8. Time complexity analysis.
FIGURE 22. EANN performance over the number of bootstraps.
However, with increase in number of classifiers the error
of ensemble learning is greatly reduced but the total time
including (training, testing and validation) is increased subse-
quently. To show the effectiveness of the proposed approach,
further EANN performance over the number of bootstraps
is analyzed. In Fig. 22, EANN performance varies over the
number of bootstraps employed in the model. Higher the
bootstraps in the model higher the accuracy and time it
takes. EANN is bit computationally intensive because of the
complex network architecture and greater spatial dimensions.
However, the runtime still allows the use of the model for a
real-time estimation with the advantage of a better and robust
performance.
VII. CONCLUSION
A solution for the optimal power generation problem using
an ensemble of artificial neural networks through bootstrap
aggregation is proposed in this paper. Load demand data for
Pakistan’s power system has been considered for a day and
then validated for a week. Lagrange Relaxation method is
developed and used to train the several conventional ANNs.
The proposed algorithm EANN is then applied to analyze the
results.
Results show that the feed forward back propagation of
the ANN model alone is not suitable for power scheduling
as it produces significant errors for the simulated and actual
generations. It is shown that when the proposed EANN is
used to deal with ED problem, a great deal of improvement is
witnessed compared with the ANN. EANN approach shows
better generalization ability, faster convergence with reduced
bias and variance due to the inherent property of the Bootstrap
aggregation, under the same operating conditions.
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12. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
The ANN is accurate for the given set of data during
different scenarios of overloading and contingencies in the
Pakistan’s power system but when load changes are rapid
or change in the following hour, the ANN suffers under or
overfitting. These ANN limitations lead to inaccuracy dur-
ing load shedding and disturb power system stability. The
computational complexity analysis for the proposed EANN
shows real-time estimation with better and robust perfor-
mance. Thus, proposed EANN algorithm is preferred for
scenarios where the load sheds/changes rapidly, solving the
shortcomings of an ANN.
APPENDIX
See Figure. 10.
ACKNOWLEDGMENT
This project was funded by the Deanship of Scientific
Research (DSR), King Abdulaziz University, Jeddah, under
Grant No. (DF-656-611-1441). The authors, therefore, grate-
fully acknowledge DSR technical and financial support.
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KASHIF MEHMOOD received the B.Sc. and
M.Sc. degrees (Hons.) in electrical engineering
from The University of Lahore, Lahore, Pakistan,
in 2011 and 2015, respectively. He is currently pur-
suing the Ph.D. degree with Southeast University
Nanjing China. In 2012, he joined The University
of Lahore, where he is currently with the Depart-
ment of Electrical Engineering. He has supervised
several projects, and authored and coauthored in
several research articles. His current research inter-
ests include power system operation and control, flexible AC transmission
and distribution systems (FACTS), the application of metaheuristic opti-
mization (artificial intelligence) techniques in power system’s problem, and
the UHV magnetically saturable controllable reactor for reactive power
compensation.
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13. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles
HAFIZ TEHZEEB UL HASSAN received the
bachelor’s degree in electrical engineering and the
master’s degree in power engineering from
the University of Engineering and Technology,
Lahore, Lahore, Pakistan, in 1975 and 1981,
respectively. He is currently an Associate Profes-
sor with the University of the Punjab, Pakistan.
He has more than 30 years of experience in
research and teaching with dozens of project
supervisions at both graduate and post graduate
level. He has authored more than 30 scholarly articles published in interna-
tionally recognized journals.
ALI RAZA received the B.S. and M.Sc. degrees
in electrical engineering from the University of
Engineering and Technology, Lahore, Pakistan,
in 2010 and 2013, respectively, and the Ph.D.
degree in electrical engineering from the Harbin
Institute of Technology, Harbin, China, in 2016.
He is currently with the Department of Electrical
Engineering, The University of Lahore, Pakistan.
He has authored and coauthored several technical
journal articles and technical conference proceed-
ings. His current research interests include operation and the control of
M-VSC-HVDC including its effects on power systems, protection, optimiza-
tion, and the topological evaluation of MT-HVDC transmission systems for
large offshore wind power plants.
Dr. Ali has been a TPC Member of the International Symposium on
Wireless Systems and Networks (ISWSN), since 2017. He received the first
Best Paper Award 2014 IEEE International Conference on Control Science
and Systems Engineering. He is the Co-Chair of Asia Pacific International
Conference on Electrical Engineering 2019 (APICEE 2019).
ALI ALTALBE received the M.Sc. degree in
information technology from Flinders Univer-
sity, Australia, in 2011, and the Ph.D. degree in
information technology from The University of
Queensland, Australia, in 2018. He is currently
an Assistant Professor with the Department of IT,
King Abdulaziz University, Jeddah, Saudi Arabia.
His current research interest includes protection of
multiterminal HVDC grids using machine learning
approaches.
HAROON FAROOQ received the Ph.D. degree
in electrical engineering from Glasgow Caledo-
nian University, U.K., in 2012. He is currently an
Assistant Professor with the Electrical Engineer-
ing Department, Rachna College of Engineering
and Technology, Gujranwala campus, University
of Engineering and Technology, Lahore, Pakistan.
His current research interests include power qual-
ity, renewable energy systems, electric vehicles,
and demand side management.
VOLUME 7, 2019 155929