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.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
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
Unit Commitment Problem in Electrical Power System: A Literature Review IJECEIAES
Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.
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.
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.
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.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
IRJET- Comparison between Ideal and Estimated PV Parameters using Evolutionar...IRJET Journal
This document discusses comparing the ideal and estimated parameters of photovoltaic (PV) panels using evolutionary algorithms. It begins by introducing microgrids and their importance in integrating renewable energy sources like solar PV. It then describes the ideal and practical electrical models of PV panels, noting that practical models account for additional factors. The document aims to estimate the parameters of single-diode and two-diode PV panel models using various optimization algorithms, compare the estimated models to experimental results, and compare the estimated models to the specifications provided by the panel manufacturer.
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.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
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
Unit Commitment Problem in Electrical Power System: A Literature Review IJECEIAES
Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.
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.
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.
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.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
IRJET- Comparison between Ideal and Estimated PV Parameters using Evolutionar...IRJET Journal
This document discusses comparing the ideal and estimated parameters of photovoltaic (PV) panels using evolutionary algorithms. It begins by introducing microgrids and their importance in integrating renewable energy sources like solar PV. It then describes the ideal and practical electrical models of PV panels, noting that practical models account for additional factors. The document aims to estimate the parameters of single-diode and two-diode PV panel models using various optimization algorithms, compare the estimated models to experimental results, and compare the estimated models to the specifications provided by the panel manufacturer.
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 an article about India's energy policy and the need to promote renewable energy sources. It discusses how India has vast renewable energy resources and the government has implemented various policies and incentives to promote greater renewable energy deployment. The key challenges are India's limited fossil fuel reserves, high fuel transportation costs, aging conventional power plants, need to rationalize power tariffs, and reduce transmission and distribution losses in the power sector. The government is aiming to source 10% of additional grid power from renewable sources by 2012 to help address these challenges in a sustainable manner.
Improved particle swarm optimization algorithms for economic load dispatch co...IJECEIAES
Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time.
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.
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
system into the integrated energy system that proves to be cost-effective, flexible, and carbon-free. The
proposed work uses EnergyPLAN to model electricity, district, and individual heating integrated energy
system of China for the year 2020. Furthermore, the addition of heat pumps, thermal storage, and demand
response is analyzed in different scenarios to minimize the annual costs, fuel consumption, and CO2
emissions. Technical simulation strategy is conducted for optimal operation of production components that
result in the reduction of the above-mentioned prominent factors while calculating the critical and exportable
excess electricity production. The simulation results demonstrate that demand response and thermal storage
significantly enhance the share of variable renewable energy sources. In addition, it substantially reduces the
annual costs and fuel consumption, while heat pump increases the system efficiency
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
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.
PV-solar / Wind Hybrid Energy System for GSM/CDMA Type Mobile Telephony Base ...IJERA Editor
This paper presents the design of optimized PV-Solar and Wind Hybrid Energy System for GSM/CDMA type mobile base station over conventional diesel generator for a particular site in south India (Chennai). For this hybrid system ,the meteorological data of Solar Insolation, hourly wind speed, are taken for Chennai (Longitude 80ο.16’and Latitude 13ο.5’ ) and the pattern of load consumption of mobile base station are studied and suitably modeled for optimization of the hybrid energy system using HOMER software. The simulation and optimization result gives the best optimized sizing of wind turbine and solar array with diesel generator for particular GSM/CDMA type mobile telephony base station. This system is more cost effective and environmental friendly over the conventional diesel generator. The presented system reduce approximate 70%-80% fuel cost over conventional diesel generator and also reduced the emission of CO2 and other harmful gasses in environments. It is expected that the proposed developed and installed system will provide very good opportunities for telecom sector in near future.
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.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
This document discusses strategies for conserving electrical energy in Gaza Strip through low-cost investments. It analyzes energy efficiency opportunities at two facilities - Gaza Training Center as a commercial sector case, and Palestine Factory as industrial. Energy audits found a potential 7.5% energy savings. Generalizing this could reduce Gaza's 30% electrical deficit by 3.3%. Recommended strategies include replacing inefficient light bulbs with LEDs, reducing air leaks, and improving cooling systems - low-cost approaches that provide individual paybacks.
IRJET-System Analysis and Optimization of Photovoltaic –Wind Hybrid System: R...IRJET Journal
This document reviews the use of artificial intelligence techniques to optimize photovoltaic-wind hybrid energy systems. It discusses various modeling approaches for system components like solar panels, wind turbines, batteries and loads. Traditional optimization methods like iterative, graphical and linear programming techniques are compared to newer artificial intelligence approaches like particle swarm optimization and genetic algorithms. The review concludes that artificial intelligence methods provide more accurate and faster optimization of hybrid systems compared to traditional techniques.
Inclusion of environmental constraints into siting and sizingIAEME Publication
This document summarizes a research paper that investigates including environmental constraints, specifically carbon dioxide (CO2) emissions, in the siting and sizing techniques used to determine optimal locations and capacities for localized gas turbine distributed generation units. The paper introduces a methodology to model CO2 emissions from both distributed generation units and centralized power stations based on their emission factors, power output, and other variables. The emissions constraint is incorporated into an existing distributed generation siting and sizing optimization model. The model is then applied to a case study of a real power distribution network to determine the optimal distributed generation configurations while accounting for environmental impacts.
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.
The document analyzes the optimal renewable fraction for a grid-connected photovoltaic (PV) system serving an office building in Indonesia. Simulations were conducted using HOMER software to determine the impact of renewable fraction on PV system size, electricity purchased from and sold to the grid, and net present cost (NPC). The results showed that a renewable fraction of 58% achieved the lowest total NPC, where 58% of electricity is supplied by the PV and 42% is purchased from the grid. Higher renewable fractions increased PV and inverter costs, outweighing revenue from electricity sales. Therefore, a renewable fraction of 58% represents the optimum design for minimizing total NPC and carbon dioxide emissions.
The document discusses environmental/economic scheduling of renewable energy resources in a micro-grid. It proposes a multi-objective framework to minimize the total operation cost and emission from generating units. Lexicographic optimization and a hybrid augmented-weighted epsilon-constraint method are used to solve the multi-objective optimization problem and generate Pareto optimal solutions. The decision making process uses a fuzzy technique. Case studies show the proposed method improves solutions for cost, emission, and execution time compared to other methods.
Analysis of Wind Diesel Hybrid System by Homer Softwareijtsrd
A hybrid power system is to avoid the use of depleting fossil fuels, improve the technical performance and reduce the greenhouse gases emission. Depending on the renewable energy sources, it is connected in the main grid or operates separately. Because of these reasons, operation, control and grid integration of renewable sources is a task of fundamental importance in modern power system. Hybrid power system modes must be studied.The simulation was carried out using various combinations of optimization and sensitivity variables developed in HOMER. The economic parameters play central role of deciding the dimension, feasibility and optimization of a proposed system. In order to achieve lowest Net Present Cost NPC , comparison of diesel generating system and wind diesel systems were compares for i economic ii technical and iii environmental parameters. Theingi Htun | Hnin Yu Wai | Myo Win Kyaw "Analysis of Wind-Diesel Hybrid System by Homer Software" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26729.pdfPaper URL: https://www.ijtsrd.com/engineering/electrical-engineering/26729/analysis-of-wind-diesel-hybrid-system-by-homer-software/theingi-htun
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.
Tutorial ini menjelaskan 4 langkah analisis data kualitatif, yaitu: 1) analisis domain untuk memperoleh gambaran umum data, 2) analisis taksonomi untuk memahami domain secara mendalam, 3) analisis komponensial untuk mengkontraskan unsur-unsur domain, dan 4) analisis tema kultural untuk menemukan tema dan hubungan antar domain. Analisis data kualitatif memerlukan kepekaan dan kecerdasan peneliti.
The document provides an overview of Shira Klasmer's skills and experience as a photographer and artist. She has over 10 years experience in professional photography, graphic design, and fine art. She is proficient in many software programs and can shoot with both digital and analogue cameras. She has worked on various freelance photography projects including portraits, dance, theater, art, furniture, and book design. She has also exhibited her artwork internationally and been published in several books and magazines.
Nativos e Inmigrantes : vedia, guiraldi, DiazPamela Vedia
El documento discute la diferencia entre nativos e inmigrantes digitales, señalando que los nativos han nacido en la era tecnológica mientras que los inmigrantes intentan introducirse a ella. También analiza las ventajas y desventajas del uso de las TIC en el aula desde la perspectiva de los docentes, destacando que puede motivar a los estudiantes pero también distraerlos. Finalmente, incluye cuatro referencias bibliográficas relacionadas al tema.
Tareq Alatrash has over 5 years of experience in PS and CS core networking engineering. He has worked on projects for Orange Jordan and Umniah involving hardware installation, software configuration, and testing of telecommunications systems. He is skilled in troubleshooting, performance monitoring, and coordination across engineering departments. Tareq holds a B.Sc. in Communication Engineering and industry certifications in networking, including CCNA and PMP.
This document summarizes an article about India's energy policy and the need to promote renewable energy sources. It discusses how India has vast renewable energy resources and the government has implemented various policies and incentives to promote greater renewable energy deployment. The key challenges are India's limited fossil fuel reserves, high fuel transportation costs, aging conventional power plants, need to rationalize power tariffs, and reduce transmission and distribution losses in the power sector. The government is aiming to source 10% of additional grid power from renewable sources by 2012 to help address these challenges in a sustainable manner.
Improved particle swarm optimization algorithms for economic load dispatch co...IJECEIAES
Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time.
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.
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
system into the integrated energy system that proves to be cost-effective, flexible, and carbon-free. The
proposed work uses EnergyPLAN to model electricity, district, and individual heating integrated energy
system of China for the year 2020. Furthermore, the addition of heat pumps, thermal storage, and demand
response is analyzed in different scenarios to minimize the annual costs, fuel consumption, and CO2
emissions. Technical simulation strategy is conducted for optimal operation of production components that
result in the reduction of the above-mentioned prominent factors while calculating the critical and exportable
excess electricity production. The simulation results demonstrate that demand response and thermal storage
significantly enhance the share of variable renewable energy sources. In addition, it substantially reduces the
annual costs and fuel consumption, while heat pump increases the system efficiency
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
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.
PV-solar / Wind Hybrid Energy System for GSM/CDMA Type Mobile Telephony Base ...IJERA Editor
This paper presents the design of optimized PV-Solar and Wind Hybrid Energy System for GSM/CDMA type mobile base station over conventional diesel generator for a particular site in south India (Chennai). For this hybrid system ,the meteorological data of Solar Insolation, hourly wind speed, are taken for Chennai (Longitude 80ο.16’and Latitude 13ο.5’ ) and the pattern of load consumption of mobile base station are studied and suitably modeled for optimization of the hybrid energy system using HOMER software. The simulation and optimization result gives the best optimized sizing of wind turbine and solar array with diesel generator for particular GSM/CDMA type mobile telephony base station. This system is more cost effective and environmental friendly over the conventional diesel generator. The presented system reduce approximate 70%-80% fuel cost over conventional diesel generator and also reduced the emission of CO2 and other harmful gasses in environments. It is expected that the proposed developed and installed system will provide very good opportunities for telecom sector in near future.
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.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
This document discusses strategies for conserving electrical energy in Gaza Strip through low-cost investments. It analyzes energy efficiency opportunities at two facilities - Gaza Training Center as a commercial sector case, and Palestine Factory as industrial. Energy audits found a potential 7.5% energy savings. Generalizing this could reduce Gaza's 30% electrical deficit by 3.3%. Recommended strategies include replacing inefficient light bulbs with LEDs, reducing air leaks, and improving cooling systems - low-cost approaches that provide individual paybacks.
IRJET-System Analysis and Optimization of Photovoltaic –Wind Hybrid System: R...IRJET Journal
This document reviews the use of artificial intelligence techniques to optimize photovoltaic-wind hybrid energy systems. It discusses various modeling approaches for system components like solar panels, wind turbines, batteries and loads. Traditional optimization methods like iterative, graphical and linear programming techniques are compared to newer artificial intelligence approaches like particle swarm optimization and genetic algorithms. The review concludes that artificial intelligence methods provide more accurate and faster optimization of hybrid systems compared to traditional techniques.
Inclusion of environmental constraints into siting and sizingIAEME Publication
This document summarizes a research paper that investigates including environmental constraints, specifically carbon dioxide (CO2) emissions, in the siting and sizing techniques used to determine optimal locations and capacities for localized gas turbine distributed generation units. The paper introduces a methodology to model CO2 emissions from both distributed generation units and centralized power stations based on their emission factors, power output, and other variables. The emissions constraint is incorporated into an existing distributed generation siting and sizing optimization model. The model is then applied to a case study of a real power distribution network to determine the optimal distributed generation configurations while accounting for environmental impacts.
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.
The document analyzes the optimal renewable fraction for a grid-connected photovoltaic (PV) system serving an office building in Indonesia. Simulations were conducted using HOMER software to determine the impact of renewable fraction on PV system size, electricity purchased from and sold to the grid, and net present cost (NPC). The results showed that a renewable fraction of 58% achieved the lowest total NPC, where 58% of electricity is supplied by the PV and 42% is purchased from the grid. Higher renewable fractions increased PV and inverter costs, outweighing revenue from electricity sales. Therefore, a renewable fraction of 58% represents the optimum design for minimizing total NPC and carbon dioxide emissions.
The document discusses environmental/economic scheduling of renewable energy resources in a micro-grid. It proposes a multi-objective framework to minimize the total operation cost and emission from generating units. Lexicographic optimization and a hybrid augmented-weighted epsilon-constraint method are used to solve the multi-objective optimization problem and generate Pareto optimal solutions. The decision making process uses a fuzzy technique. Case studies show the proposed method improves solutions for cost, emission, and execution time compared to other methods.
Analysis of Wind Diesel Hybrid System by Homer Softwareijtsrd
A hybrid power system is to avoid the use of depleting fossil fuels, improve the technical performance and reduce the greenhouse gases emission. Depending on the renewable energy sources, it is connected in the main grid or operates separately. Because of these reasons, operation, control and grid integration of renewable sources is a task of fundamental importance in modern power system. Hybrid power system modes must be studied.The simulation was carried out using various combinations of optimization and sensitivity variables developed in HOMER. The economic parameters play central role of deciding the dimension, feasibility and optimization of a proposed system. In order to achieve lowest Net Present Cost NPC , comparison of diesel generating system and wind diesel systems were compares for i economic ii technical and iii environmental parameters. Theingi Htun | Hnin Yu Wai | Myo Win Kyaw "Analysis of Wind-Diesel Hybrid System by Homer Software" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26729.pdfPaper URL: https://www.ijtsrd.com/engineering/electrical-engineering/26729/analysis-of-wind-diesel-hybrid-system-by-homer-software/theingi-htun
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.
Tutorial ini menjelaskan 4 langkah analisis data kualitatif, yaitu: 1) analisis domain untuk memperoleh gambaran umum data, 2) analisis taksonomi untuk memahami domain secara mendalam, 3) analisis komponensial untuk mengkontraskan unsur-unsur domain, dan 4) analisis tema kultural untuk menemukan tema dan hubungan antar domain. Analisis data kualitatif memerlukan kepekaan dan kecerdasan peneliti.
The document provides an overview of Shira Klasmer's skills and experience as a photographer and artist. She has over 10 years experience in professional photography, graphic design, and fine art. She is proficient in many software programs and can shoot with both digital and analogue cameras. She has worked on various freelance photography projects including portraits, dance, theater, art, furniture, and book design. She has also exhibited her artwork internationally and been published in several books and magazines.
Nativos e Inmigrantes : vedia, guiraldi, DiazPamela Vedia
El documento discute la diferencia entre nativos e inmigrantes digitales, señalando que los nativos han nacido en la era tecnológica mientras que los inmigrantes intentan introducirse a ella. También analiza las ventajas y desventajas del uso de las TIC en el aula desde la perspectiva de los docentes, destacando que puede motivar a los estudiantes pero también distraerlos. Finalmente, incluye cuatro referencias bibliográficas relacionadas al tema.
Tareq Alatrash has over 5 years of experience in PS and CS core networking engineering. He has worked on projects for Orange Jordan and Umniah involving hardware installation, software configuration, and testing of telecommunications systems. He is skilled in troubleshooting, performance monitoring, and coordination across engineering departments. Tareq holds a B.Sc. in Communication Engineering and industry certifications in networking, including CCNA and PMP.
El documento trata sobre los cuidados básicos de perros y gatos. Explica que los perros se han adaptado a ciertos alimentos como el almidón debido a la domesticación y que su buen cuidado depende de la prevención de problemas. También señala que los gatos han estado en compañía humana por unos 9500 años y provee información sobre sus cuidados básicos y comportamiento.
Este documento resume la historia del Real Colegio de Santa Isabel en Marchena, España desde su fundación en 1565 por los Duques de Arcos hasta 2015. Originalmente un colegio jesuita durante 202 años, luego sirvió como colegio de beatas terciarias franciscanas durante 111 años antes de ser tomado por las Franciscanas de los Sagrados Corazones en 1890, quienes continúan operando el colegio.
El mago Merlín educó al príncipe Arturo después de que su madre muriera y su padre no pudiera hacerse cargo de él. Años más tarde, cuando el rey murió, los nobles acudieron a Merlín para encontrar al nuevo monarca. Merlín colocó una espada mágica en una roca con la inscripción "Quien saque esta espada será el rey". Arturo fue el único capaz de sacar la espada, por lo que se convirtió en el nuevo rey de Inglaterra. Merlín continuó aconsejando a
Este documento presenta breves biografías de 7 importantes científicas: Marie Curie, Lise Meitner, Irene Joliot Curie, Dorothy Crowfoot Hodgkin, Gertrude Elion y Rosalind Franklin. Resalta sus logros científicos pioneros, como ser las primeras en obtener premios Nobel, descubrir nuevos elementos y moléculas como la insulina y la penicilina, y contribuir al entendimiento de la estructura del ADN.
El documento discute cómo la tecnología ha mejorado el fútbol a través de los años. Recientemente, la FIFA ha adoptado el sistema GoalControl para evitar goles fantasma como se vio en el gol de Benzema contra Honduras. Antes, se introdujeron la comunicación por radio entre árbitros y el uso de spray para marcar la distancia de barreras. La tecnología también ha beneficiado a los espectadores a través de tableros electrónicos, tarjetas de colores y transporte médico mejorado. La televisión también ha influido en camb
Siddharth Singh is seeking a position in information technology that offers professional growth. He has a B.Tech in Information Technology from Uttar Pradesh Technical University with first division honors. His technical skills include programming languages like C#, databases like MS SQL, and operating systems like Windows 7. He has over 2 years of experience as a database administrator working on projects for financial services companies in areas like backups, recovery, replication, and more. He is proficient in computer networking and hardware. Siddharth has participated in social services and debate competitions, and holds a certification in designing MS SQL server solutions.
Las Tecnologías de la Información y las Comunicaciones (TIC) permiten adquirir, producir, almacenar, procesar, comunicar, registrar y presentar información. Las TIC tienen características como herramientas interactivas que pueden transmitir información a varios medios y permiten la participación en proyectos con un solo fin. Entre los beneficios de las TIC se encuentran una mejor comunicación, omitir barreras de tiempo y espacio, respuestas innovadoras para el futuro y acceso igualitario al conocimiento.
This document is a research report on investigating the motivations of main sponsors of varsity rugby. It includes sections on the problem statement, research questions, goals and objectives, a literature review on motivations for sponsorship including strategy, corporate social responsibility, marketing, brand loyalty and personal motivations. It also discusses measuring sponsorship success, risks involved and the role of ethics. The literature review provides context on the varsity cup tournament structure and past sponsors Steinhoff and First National Bank. The methodology section outlines the research approach.
This document provides information about a freelance graphic designer named Simone Rudder. It summarizes her background, experience, areas of expertise, and portfolio of work. She has over 15 years of experience in graphic design and has created branding, marketing materials, and other visual assets for various corporate and resort clients throughout the Caribbean. Her work is influenced by minimalism, architecture, and nature. She works independently from her home studio in Trinidad and Tobago.
Feria de Ciencia y Tecnología en el Instituto Sarmiento de La Caleraln_idfs
El documento describe los esfuerzos de un instituto educativo para implementar un programa de ferias de ciencia que promueva el aprendizaje a través de la investigación. Comenzó participando en ferias de ciencia provinciales y ahora tiene un proceso establecido desde jardín de infantes hasta la educación superior, con alrededor de 20 proyectos presentados anualmente. Aún busca mejorar la participación a nivel secundario y con otras escuelas.
A multivariate approach for process variogramsQuentin Dehaine
This document summarizes a study that used multivariate variography to assess the representativeness of samples collected from a kaolin mining residue stream. Univariate variography, which looks at individual properties, may underestimate sampling variance. Multivariate variography captures variability across multiple properties. Principal component analysis identified three sources of variability: particle size, metal grade, and pulp density. Multivariograms showed higher sampling variance than univariate analysis. Considering the first few principal components in a multivariogram filters noise while retaining relevant variability information, providing a better estimate of overall sampling error than univariate or raw data approaches.
The document provides information about the annual St. Lucy's Christmas Boutique event, including details about shopping opportunities, children's activities, dining options, and opportunities for alumnae to get involved. It encourages donations from local businesses and alumnae to support the event. The Boutique will take place on November 8, 2015 from 10am to 3pm and all alumnae and their families are invited.
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 Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
The document presents an improved particle swarm optimization (IPSO) algorithm for solving the optimal unit commitment problem in power systems. The IPSO algorithm extends the standard PSO algorithm by using additional particle information to control mutation and mimic social behaviors. The algorithm was implemented on the IEEE 14 bus test system in MATLAB. Results showed the IPSO approach committed units to meet load demand over 24 hours while satisfying constraints, with bus voltages maintained between 1.0017 and 1.0751 per unit. Total costs including fuel, startup, and shutdown costs were minimized at each hour.
Optimal Operation of Wind-thermal generation using differential evolutionIOSR Journals
This document presents an optimal operation model for a wind-thermal power generation system using differential evolution (DE). DE is an evolutionary algorithm inspired by biological evolution that can solve complex constrained optimization problems. The paper formulates the economic dispatch problem to minimize total generation cost of the wind and thermal plants subject to various constraints like power balance, generator limits, ramp rates, and valve point loading effects. Five different DE mutation strategies are analyzed for solving the wind-thermal economic dispatch problem on a test system with 10 thermal units. The results show that the best mutation strategy and control parameter values (mutation rate and crossover rate) depend on the problem and can significantly impact the solution quality and consistency obtained by the DE algorithm.
A Decomposition Aggregation Method for Solving Electrical Power Dispatch Prob...raj20072
This document proposes a decomposition/aggregation method to solve large-scale economic dispatch problems with many generators. It decomposes a power system into areas, each containing generators and loads. An evolutionary programming technique optimizes dispatch in each area locally. The area solutions are then aggregated to solve the overall system problem while minimizing total cost. The method is demonstrated on 5-bus and 26-bus test systems decomposed into two areas each. Local area problems are solved as subproblems, while the overall system solution is the "master problem". Results are compared to a centralized approach. The decomposition/aggregation method shows promise in solving economic dispatch with large numbers of generators.
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 Review on Various Techniques Used for Economic Load Dispatch in Power Systemijtsrd
The document discusses various techniques used to solve the economic load dispatch (ELD) problem in power systems. The ELD problem involves determining the optimal power output of generators to minimize generation costs while meeting demand and operating constraints. The document reviews several methods that have been used to solve the ELD problem, including lambda iteration, gradient search, Newton's method, linear programming, dynamic programming, neural networks, evolutionary algorithms, particle swarm optimization, and other metaheuristic techniques. It provides details on how each method approaches solving the optimization problem posed by economic load dispatch.
Normally, the character of the wind energy as a renewable energy sources has uncertainty in generation. To resolve the Optimal Power Flow (OPF) drawback, this paper proposed a replacement Hybrid Multi Objective Artificial Physical Optimization (HMOAPO) algorithmic rule, which does not require any management parameters compared to different meta-heuristic algorithms within the literature. Artificial Physical Optimization (APO), a moderately new population-based intelligence algorithm, shows fine performance on improvement issues. Moreover, this paper presents hybrid variety of Animal Migration Optimization (AMO) algorithmic rule to express the convergence characteristic of APO. The OPF drawback is taken into account with six totally different check cases, the effectiveness of the proposed HMOAPO technique is tested on IEEE 30-bus, IEEE 118-bus and IEEE 300-bus check system. The obtained results from the HMOAPO algorithm is compared with the other improvement techniques within the literature. The obtained comparison results indicate that proposed technique is effective to succeed in best resolution for the OPF drawback.
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.
Multi objective economic load dispatch using hybrid fuzzy, bacterialIAEME Publication
The document summarizes a research paper that proposes a new approach for solving the economic load dispatch problem using a hybrid fuzzy, bacterial foraging-Nelder–Mead algorithm. The economic load dispatch problem minimizes generation costs while satisfying load demand under system constraints. The proposed approach considers generation costs, spinning reserve costs, and emission costs simultaneously. It also accounts for valve-point effects, prohibited operating zones, and other practical constraints. A hybrid bacterial foraging and Nelder–Mead algorithm combined with fuzzy logic is used to solve the optimization problem. Simulation results show the advantages of the proposed method in reducing total system costs.
Comparative study of the price penalty factors approaches for Bi-objective di...IJECEIAES
One of the main objectives of electricity dispatch centers is to schedule the operation of available generating units to meet the required load demand at minimum operating cost with minimum emission level caused by fossil-based power plants. Finding the right balance between the fuel cost the green gasemissionsis reffered as Combined Economic and Emission Dispatch (CEED) problem which is one of the important optimization problems related the operationmodern power systems. The Particle Swarm Optimization algorithm (PSO) is a stochastic optimization technique which is inspired from the social learning of birds or fishes. It is exploited to solve CEED problem. This paper examines the impact of six penalty factors like "Min-Max", "Max-Max", "Min-Min", "Max-Min", "Average" and "Common" price penalty factors for solving CEED problem. The Price Penalty Factor for the CEED is the ratio of fuel cost to emission value. This bi-objective dispatch problem is investigated in the Real West Algeria power network consisting of 22 buses with 7 generators. Results prove capability of PSO in solving CEED problem with various penalty factors and it proves that Min-Max price penalty factor provides the best compromise solution in comparison to the other penalty factors.
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.
IRJET- Particle Swarm Intelligence based Dynamics Economic Dispatch with Dail...IRJET Journal
This document discusses particle swarm intelligence techniques for solving economic load dispatch problems. It begins with an abstract that introduces economic load dispatch as a technique for allocating power generation levels among generating units to minimize costs while meeting demand and operational constraints. It then provides background on economic load dispatch and describes how particle swarm optimization can be applied to solve non-convex economic dispatch problems. Finally, it reviews several related works applying evolutionary algorithms like particle swarm optimization, genetic algorithms, and cuckoo search to economic load dispatch problems.
IRJET- A Comparative Study of Economic Load Dispatch Optimization MethodsIRJET Journal
This document presents a comparative study of different optimization methods for solving the economic load dispatch (ELD) problem in power systems. The ELD problem involves minimizing generation costs while meeting demand, and is formulated as a non-linear optimization problem with constraints. Various conventional and evolutionary algorithms have been used to solve ELD, but more recently bio-inspired algorithms like flower pollination algorithm and Jaya optimization have shown better performance. The paper evaluates these nature-inspired algorithms and compares their results for the ELD problem to demonstrate their effectiveness.
Statistical modeling and optimal energy distribution of cogeneration units b...IJECEIAES
Our main objective is to evaluate the performance of a new method to optimize the energy management of a production system composed of six cogeneration units using artificial intelligence. The optimization criterion is economic and environmental in order to minimize the total fuel cost, as well as the reduction of polluting gas emissions such as COx, NOx and SOx. First, a statistical model has been developed to determine the power that the cogeneration units can provide. Then, an economic model of operation was developed: fuel consumption and pollutant gas emissions as a function of the power produced. Finally, we studied the energy optimization of the system using genetic algorithms (GA), and contribute to the research on improving the efficiency of the studied power system. The GA has a better optimization performance, it can easily choose satisfactory solutions according to the optimization objectives, and compensate for these defects using its own characteristics. These characteristics make GA have outstanding advantages in iterative optimization. The robustness of the proposed algorithm is validated by testing six cogeneration units, and the obtained simulation results of the proposed system prove the value and effectiveness of GA for efficiency improvement as well as operating cost minimization.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
Combining both Plug-in Vehicles and Renewable Energy Resources for Unit Commi...IOSR Journals
This document presents a study that combines plug-in electric vehicles with vehicle-to-grid technology (V2G), renewable energy resources like wind and solar, and existing power plants, to optimize unit commitment in smart grids. The goal is to minimize total costs and emissions. A genetic algorithm is used to optimize scheduling of generation units, V2G vehicles providing spinning reserves, and time-varying renewable sources over a 24-hour period to meet load demand at lowest cost while satisfying constraints. Simulation results validate that integrating V2G and renewable energy sources can effectively reduce costs and emissions for the smart grid.
Network loss reduction and voltage improvement by optimal placement and sizin...nooriasukmaningtyas
This document presents a study on optimizing the placement and sizing of distributed generators (DGs) in radial distribution systems using a fine-tuned particle swarm optimization approach. Simulation results on the IEEE 33 bus, IEEE 69 bus, and a 54 bus Malaysian network show that integrating both active and reactive power injection (type II DGs) achieves greater reductions in network power losses and improvements in voltage profiles compared to only active power injection (type I DGs). The maximum power loss reductions achieved with three type II DGs are 89.54% for IEEE 33 bus, 94.95% for IEEE 69 bus, and 95.23% for the 54 bus Malaysian network.
Evolutionary algorithm solution for economic dispatch problemsIJECEIAES
A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints, these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed, the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.
The document describes a hybrid firefly-differential evolution algorithm for solving the economic load dispatch problem. The economic load dispatch problem involves allocating generation among power plants to minimize costs while satisfying constraints. The proposed hybrid algorithm combines the differential evolution and firefly algorithms. It was tested on a 3 unit power system and showed improved efficiency and robustness compared to other existing algorithms for solving the economic load dispatch problem.
Similar to Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using Particle Swarm Optimization (PSO) (20)
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using Particle Swarm Optimization (PSO)
1. The International Journal Of Engineering And Science (IJES)
|| Volume || 6 || Issue || 1 || Pages || PP 17-23 || 2017 ||
ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
www.theijes.com The IJES Page 17
Optimal Economic Load Dispatch of the Nigerian Thermal Power
Stations Using Particle Swarm Optimization (PSO)
Y. S. Haruna1
, Y. A. Yisah2
, G. A. Bakare1
, M. S. Haruna3
and S. O. Oodo3
1
Department of Electrical and Electronics Engineering, A. T. B. U Bauchi-Nigeria,
2
Power Equipment and Electrical Machinery Development Institute (PEEMADI), P.M.B. 1029, Okene, Kogi State.
3
Department of Electrical and Electronics Engineering, Nile University of Nigeria, Abuja.
--------------------------------------------------------ABSTRACT-------------------------------------------------------------
This paper deals with the optimization of economic load dispatch (ELD) problem; this is to find the optimal
combination of generators in order to minimize the operating costs of the system. This is done by using the
particle swarm optimization (PSO) algorithm. PSO is applied to search for the optimal schedule of all the
generator units that can supply the required demand at minimum fuel cost while satisfying all system
constraints. The PSO algorithm has been implemented using MATLAB optimization toolbox and was applied to
solve the ELD problem of the Nigeria thermal power stations. The results were compared with published results
obtained via micro-GA, conventional-GA and differential evolution (DE) techniques.
Keywords: Economic Load Dispatch, Particle Swarm Optimization and Nigeria Thermal Power Stations.
---------------------------------------------------------------------------------------------------------------------------------------
Date of Submission: 28 December 2016 Date of Accepted: 20 January 2017
--------------------------------------------------------------------------------------------------------------------------------------
I. INTRODUCTION
The modern power system around the world has grown in complexity of interconnection and power demand.
The focus has shifted towards the enhanced performance, increased customer focus, low cost, reliable and clean
power. In this changed perspective, scarcity of energy resources, increasing power generation cost,
environmental concern necessitates optimal economic load dispatch (ELD). In reality power stations neither are
at equal distances from load nor have similar fuel cost functions. Hence for providing cheaper power, load has
to be distributed among the various power stations in a way which results in lowest cost of generation [1].
The main aim of electric power utilities is to provide high-quality, reliable power supply to the consumers at the
lowest possible cost while operating to meet the limits and constraints imposed on the generating units. This
formulates the economic load dispatch (ELD) problem for finding the optimal combination of the output power
of all the online generating units that minimizes the total fuel cost, while satisfying an equality constraint and a
set of inequality constraints. As the cost of power generation is exorbitant, an optimum dispatch results in
economy [2].
Traditional algorithms like lambda iteration, base point participation factor, gradient method, and Newton
method can solve this ELD problems effectively if and only if the fuel-cost curves of the generating units are
piece-wise linear and monotonically increasing. Practically the input-output characteristics of the generating
units are highly non-linear, non-smooth and discrete in nature owing to prohibited operating zones, ramp rate
limits and multi fuel effects. Thus the resultant ELD becomes a challenging non-convex optimization problem,
which is difficult to solve using the traditional methods [3, 4].
Several heuristic approaches like evolutionary programming (EP), genetic algorithm (GA), ant colony search
(ACS), tabu search (TS), artificial neural network (ANN), bio-geography based optimization (BBO), differential
evolution (DE) and simulated annealing (SA) have been developed for solving both linear and non-linear ED
problems [5 - 8].
In this paper, particle swarm optimization (PSO) algorithm is proposed to solve the ELD problems in power
systems. The viability of the method is analyzed for its accuracy and rate of convergence on the Nigerian power
network (1999 model) and results were compared with other heuristics methods.
1.1 Nigerian Power System
The electricity demand in Nigeria far outstrips the supply and the supply is epileptic in nature. This is hindering
its development, notwithstanding the availability of vast natural resources in the country. Constant power supply
is the hallmark of a developed economy. Any nation whose energy need is epileptic in supply prolongs her
development and risks losing potential investors.
Reliable Power production is critical to the profitability of electricity utilities. This can only be realized when
the power generators are scheduled efficiently to meet electricity demand. Economic load dispatch have been
applied to obtain optimal fuel cost while satisfying systems system constraints and generator scheduling for
2. Optimal Economic Load Dispatch Of The Nigerian Thermal Power Stations Using Particle Swarm…
www.theijes.com The IJES Page 18
hourly anticipated load within a period of 24 hours. The efficiency of generating unit, the transmission looses
and the operating costs are important factors to be considered for the economic operation of the system.
In recent years, the Power Holding Company of Nigeria (PHCN) has been experiencing serious problem in
generation, transmission, distribution, maintenance, financial constraints and increase in power demand [9],
considering the generation/power demand problems, several units were on emergency/forced outages, which led
to system disturbance such as; partial and total system collapse. These problems were attributed to over stressing
the units to generate outside their normal operating conditions. This will thus lead to generating electric power
at loss.
In view of the above problem, it becomes necessary for one to study the cost functions of the available thermal
units, their power limits and the maximum power demand of the whole country at a particular time so as to carry
out the ELD problem.
The aim of this research is to apply a particle swarm optimization technique to solve the economic load dispatch
(ELD) problem; for the purpose of optimal allocation of the total power demand among the available generating
units that minimizes the total generation cost subject to specified system constraints.
1.2 Statement of the Problem
Consider a system consisting of N thermal units connected to a transmission network as shown in Figure1
below.
Figure 1: Interconnected Power System Network
Where, i
F fuel cost of unit i,
iG
P power delivered by unit i,
D
P total power demand and
L
P total power loss.
The quadratic cost function of unit i, is given by;
2
( )
i i Gi
i G i i G i
F P P P …(1)
Where, i
- Constant cost coefficient of unit i,
i
-Linear constant cost coefficient of unit i
i
-A quadratic constant cost coefficient of unit i.
Operating the system subject to generation constraints, this objective function can be expressed mathematically
as:
1
( )
i
N
T i G
i
F F P
i 1,2…….N … (2)
Where, T
F is the total cost of power generation and
( )
ii G
F P is the generation cost of unit i.
The ELD problem seeks to find the optimal combination of thermal power generations that minimized the total
cost while satisfying the total power demand and the systems constraints. The ELD problem is formulated as
minimization of Eqn. (2) subject to the following constraints:
1.2.1 Equality constraints
1
i
N
G D L
i
P P P
…(3)
Using the B-coefficient method, network losses are expressed as:
3. Optimal Economic Load Dispatch Of The Nigerian Thermal Power Stations Using Particle Swarm…
www.theijes.com The IJES Page 19
T
Li G i G i
P P B P …(4)
Where, B is the B matrix coefficient.
1.2.2 Inequality constraints
m in m a x
G i G i G i
P P P …(5)
Where,
m in
G i
P is the minimum power limits of the unit i
m a x
G i
P is the maximum power limits of unit i.
Finally, PSO was applied to the coordination of the Nigerian thermal power plants. This research on economic
load dispatch is modeled using the quadratic cost function. The issues of environmental constraints, generation
ramp-rate limits, valve-point effect and piecewise linear cost function are out of scope of this paper.
II. LITERATURE REVIEW
The ELD problem has been widely studied and reported by different authors. The techniques used in the
literature ranges from the classical optimization techniques to the recent meta- heuristic optimization techniques
such as evolutionary algorithms (EA), genetic algorithm (GA), simulated annealing (SA) and particle swarm
optimization (PSO).
[3] developed a Pareto frontier Differential Evolution (PDE) technique to solve MOED problem. The proposed
method was implemented on the standard IEEE-30 bus system having six generating units including valve point
effects to evaluate its performance and applicability. From the results obtained, the proposed method
demonstrated its effectiveness by solving the Multi Objective economic dispatch problem considering security
constraints.
[4] proposed and developed evolutionary algorithms to solve the economic dispatch problem for the optimum
performance of a nonlinear and complex systems. They considered all the constraints of power dispatch for
economic operation of the power system.
[5] presented the economic power dispatch problems using ant colony optimization (ACO) technique which is a
meta-heuristic approach for solving hard combinatorial optimization problems. This technique was tested
using the standard IEEE 26-Bus RTS and the results revealed that the proposed technique has the merit in
achieving optimal solution for addressing the problems. Comparative studies with artificial immune system
(AIS) were also conducted in order to highlight the strength of the proposed technique.
In the year 2012, [6] presented an effective and reliable particle swarm optimization (PSO) technique for the
economic load dispatch problem using the standard 3-generator and 6-generator systems with and without
consideration of transmission losses. The final results obtained using PSO are compared with conventional
quadratic programming and found to be encouraging.
[7] proposed a method for solving economic dispatch problem using Particle Swarm Optimization (PSO)
Algorithm and Simulated Annealing (SA) for the three generating units as a case study. PSO and SA were
applied to find out the minimum cost for different power demand. They compared their results with the
traditional technique, where PSO displayed better result and better convergence characteristic.
[8] presented an improved exponential harmony search algorithm for improving the HS algorithm to solve the
SELD problem considering the valve-point effect. The numerical results show that the proposed method has
better convergence and also lower production costs than the conventional HS and particle swarm optimization
methods.
[10] presented the overview of different methods for solving economic load dispatch (ELD) problem using
MATLAB. They concluded that lambda iteration method converges rapidly but complexities increases as
system size increase. Gradient and newton methods can only be applied where cost function is much more
complex while for the non convex input-output curves, dynamic programming method can be used to solve the
economic load dispatch problem.
[11] presented lambda iteration method to solve the ELD problem using MATLAB for the three and six
generating units with and without transmission losses.
[12] presented an application of the GAMS method to power economic dispatch (PED) problem with Power
loss for 3 and 6 generator test case systems. The simulation results show that the proposed GAMS Method
outperforms previous optimization methods.
2.1 Heuristic Optimization Techniques
As an alternative to the conventional mathematical approaches, the heuristic optimization techniques such as
genetic algorithms, Tabu search, simulated annealing, and recently introduced particle swarm optimization
4. Optimal Economic Load Dispatch Of The Nigerian Thermal Power Stations Using Particle Swarm…
www.theijes.com The IJES Page 20
(PSO) are considered as realistic and powerful solution schemes to obtain the global optimums in power system
optimization problems.
2.2 Particle Swarm Optimization
Particle swarm optimization was first introduced by Kennedy and Eberhart in the year 1995. It is an exciting
new methodology in evolutionary computation and a population-based optimization tool. PSO is motivated from
the simulation of the behaviour of social systems such as fish schooling and birds flocking. The PSO algorithm
requires less computation time and less memory because of its inherent simplicity [7].
The basic assumption behind the PSO algorithm is that birds find food by flocking and not individually. This
leads to the assumption that information is owned jointly in the flocking. The swarm initially has a population of
random solutions. Each potential solution, called a particle (agent), is given a random velocity and is flown
through the problem space.
All the particles have memory and each particle keeps track of its previous best position (pbest) and the
corresponding fitness value. The swarm has another value called gbest, which is the best value of all the pbest.
Particle swarm optimization has been found to be extremely effective in solving a wide range of engineering
problems and solves them very quickly.
III. APPLICATION OF PSO IN ELD
The aim of this research is to distribute the total power demand among the available thermal generating stations
to minimizing the total fuel cost subject to both equality and inequality constraints as earlier stated in Eqns. 1-3.
Particle Swarm Optimization was used to achieve this desired goal.
The solution of the ELD problem using the classical approach presents some limitation in its implementation.
One of such limitation is that the Lamda-iteration method assumes the cost coefficient to be a continuous
function. The method breaks down when it is applied to a discontinuous function with prohibited zones or larger
steam turbine generating units [13]. Also, there is a large tendency for this approach to converge at a local
minimum when the power system operating status is far outside the normal situation, for instance during and
after large disturbances. For this purpose, the Particle Swarm Optimization technique was applied in this paper
to solve an ELD problem in order to eliminate the limitation of the Lamda-iterations enumerated above.
In a PSO system, population of particles exists in the n-dimensional search space. Each particle has certain
amount of knowledge and will move about the search space on the basis of this knowledge. The particle has
some inertia attributed to it and hence will continue to have a component of motion in the direction it is moving.
The Particle knows its location in the search space and will encounter with the best solution. The particle will
then modify its direction such that it has additional components towards its own best position, pbest and towards
the overall best position, gbest. The particle updates its velocity and position using the following Eqn. (6) and
(7).
𝑉𝑖
𝑘+1
= 𝑤𝑉𝑖
𝑘
+ 𝑐1 𝑅𝑎𝑛𝑑1() × 𝑝𝑏𝑒𝑠𝑡𝑖 − 𝑆𝑖
𝑘
+ 𝑐2 𝑅𝑎𝑛𝑑2() × 𝑔𝑏𝑒𝑠𝑡𝑖 − 𝑆𝑖
𝑘
… (6)
( 1 ) ( 1 )k K k
i i i
S S V
…(7)
Where, k
i
V is the velocity of individual i at iteration k,
k is pointer of iterations,
W is the weighing factor,
C1,C2 are the acceleration coefficients,
Rand1( ), Rand2( ) are the random numbers between and 1,
Sk
is the current position of individual i at iteration k,
pbesti is the best position of individual i and
gbest is the best position of the group.
The coefficients C1 and C2 pull each particle towards pbest and gbest positions. Low values of acceleration
coefficients allow particles to roam far from the target regions, before being tugged back. Hence, the
acceleration coefficients C1 and C2 are often set to be 2 according to past experiences. The term C1Rand1 () x
(pbesti -Si
k
) is called particle memory influence or cognition part which represents the private thinking of the
itself and the term C2 Rand2( ) x (gbest - Si
k
) is called swarm influence or the social part which represents the
collaboration among the particles.
In the procedure of the particle swarm paradigm, the value of maximum allowed particle velocity Vmax
determines the resolution, or fitness, with which regions are to be searched between the present position and the
target position. If Vmax
is too high, particles may fly past good solutions. If Vmax
is too small, particles may not
explore sufficiently beyond local solutions. Thus, the system parameter Vmax
has the beneficial effect of
5. Optimal Economic Load Dispatch Of The Nigerian Thermal Power Stations Using Particle Swarm…
www.theijes.com The IJES Page 21
preventing explosion and scales the exploration of the particle search. The choice of a value for Vmax
is often set
at 10-20% of the dynamic range of the variable for each problem.
Suitable selection of inertia weight W provides a balance between global and local explorations, thus requiring
less iteration on an average to find a sufficiently optimal solution. Since W decreases linearly from about 0.9 to
0.4 quite often during a run, the following weighing function of Eqn. (8) is used in Eqn. (6):
m a x m in
m a x
m a x
W W
W W ite r
ite r
…(8)
Where, Wmax is the initial weight,
Wmin is the final weight,
Itermax is the maximum iteration number,
iter is the current iteration number.
Eqn. (6) is used to calculate the particle's new velocity according to its previous velocity and the distances of its
current position from its own best experience (position) and the group's best experience. Then the particle flies
towards a new position according to Eqn. (7). The performance of each particle is measured according to a
predefined fitness function, which is related to the problem to be solved.
3.1 Implementation of PSO for ELD
The main objective of ELD is to obtain the amount of real power to be generated by each committed generator,
while achieving a minimum generation cost within the constraints. The evaluation function for evaluating the
minimum generation cost of each individual in the population is adopted as follows:
Minimize
1
( )
d
T i i
i
F F P
…(9)
The search procedure for calculating the optimal generation quantity of each unit is summarized as follows:
i) In the ELD problems the number of online generating units is the 'dimension' of this problem. The particles
are randomly generated between the maximum and the minimum operating limits of the generators and
represented using equation (9).
ii) To each individual of the population calculate the dependent unit output Pdu from the power balance
equation and employ the B-coefficient loss formula to calculate the transmission loss PL using constraint
satisfaction technique.
iii) Calculate the evaluation value of each individual Pgi in the population using the evaluation function f, given
by equation (10).
iv) Compare each individual's evaluation value with its pbest. The best evaluation value among the pbest is
identified as gbest.
v) Modify the member velocity V of each individual Pgi according to the following equation:
𝑉𝑖𝑑
𝑡+1
= 𝑤𝑉𝑖𝑑
𝑡
+ 𝑐1 𝑅𝑎𝑛𝑑1() × 𝑝𝑏𝑒𝑠𝑡𝑖𝑑 − 𝑃𝑔𝑖𝑑
𝑡
+𝑐2 𝑅𝑎𝑛𝑑2() × 𝑔𝑏𝑒𝑠𝑡 𝑑 − 𝑃𝑔𝑖𝑑
𝑡
i =1,2 ,. n, d =1,2, .. m … (10)
Where, n is the population size, m is the generator units.
vi) Check the velocity constraints of the members of each individual from the following conditions:
( 1) m ax ( 1) m ax
( 1) m ax ( 1) m in
m in m in m ax m ax
, ,
, ,
0.5 , 0.5
t t
id d id d
t t
id d id d
d d d d
ifV V thenV V
ifV V thenV V
w hereV P V P
…(11)
vii) Modify the member position of each individual Pg1 according to Eqn.(12):
( 1) ( ) ( 1)
id
t t t
g gid id
P P V
…(12)
( 1)
id
t
g
P
must satisfy the constraints, namely the generating limits, described by Eqn. (5). If
( 1)
id
t
g
P
violates the
constraints, then
( 1)
id
t
g
P
must be modified towards the nearest margin of the feasible solution.
viii)If the evaluation value of each individual is better than previous pbest, the current value is set to be pbest. If
the best pbest is better than gbest, the best pbest is set to be gbest.
ix) If the number of iterations reaches the maximum, then go to step (x). Otherwise, go to step (ii).
x) The individual that generates the latest gbest is the optimal generation power of each unit with the
minimum total generation cost.
6. Optimal Economic Load Dispatch Of The Nigerian Thermal Power Stations Using Particle Swarm…
www.theijes.com The IJES Page 22
IV. RESULTS AND DISCUSSION
The procedure described in chapter three for the Particle Swarm Optimization based for solving ELD problems
for the thermal power plant has been implemented using the developed PSO software on Matlab 7.1 for
windows. The feasibility and the effectiveness of the method have been tested on the Nigerian thermal power
plant. This was executed on hp laptop computer with the specification as follows, Processor: Intel® Celeron®
CPU N2830 @ 2.16GHz, installed Memory (RAM): 2GB, system type: 64-bit operating system, hard disc:
500GB and operating system: windows 8.1. The results of the stimulation studies are presented.
[1] used the two approaches to solve this problem; micro genetic algorithm (MGA) and conventional genetic
algorithm (CGA). [2] used differential evolution (DE) to solve the same problem. They all used three sets of
power demand PD: 340MW, 850MW and 1150MW.
PSO was applied to the above system for obtaining economic load dispatch of similar load requirements. PSO
was implemented according to the flow chart shown. For each sample load, under the same objective function
and individual definition, 20 trials were performed to observe the evolutionary process and to compare their
solution quality and convergence characteristics.
4.1 Simulation Results of the Nigerian Power System
The developed PSO software for ELD problem was applied to the Nigerian power system whose single line
diagram is shown in Figure 2. The Nigerian power system grid is essentially a 31-bus, 330-kV network
interconnecting four thermal generating stations and three hydro stations to the various load points. The
network data (Bus data, Generator data and Branch) were obtained from [1, 2]. Table 1 presents the cost
coefficients of the four Nigerian thermal power stations and their minimum and maximum loading limits.
Figure 2: Nigerian 330 kV, 31- Bus Grid System
Table 1: Nigerian Thermal Power Plants Characteristics [1]
Station Α Α Α PG
min
(MW) PG
max
(MW)
Sapele 6929.0 7.84 0.13 137.5 550.0
Delta 525.74 -6.13 1.20 75.0 300.0
Afam 1998.0 56.0 0.092 135.0 540.0
Egbin 12787.0 13.1 0.031 275.0 1100.0
7. Optimal Economic Load Dispatch Of The Nigerian Thermal Power Stations Using Particle Swarm…
www.theijes.com The IJES Page 23
Table 2: Results Comparison between MGA. CGA, DE and the Proposed PSO Techniques
Power Station MGA CGA DE Proposed Method
Sapele PG1(MW) 365.35 482.79 390.931 525.0564
Delta PG2(MW) 78.48 79.51 80.008 76.5315
Afam PG3(MW) 340.46 255.82 343.363 281.1126
Egbin PG4(MW) 878.39 844.56 848.078 839.5335
Kainji PG5(MW) 350 350 350 350
Shiroro PG6(MW) 490 490 490 490
Jebba PG7(MW) 450 450 450 450
Total Power Generated(MW) 2952.80 2952.68 2952.38 3012.242
Total Power Demanded(MW) 2823.10 2823.10 2823.10 2975.203
Total Power Loss (MW) 130.28 130.28 129.28 37.04
Total Cost(N/hr) 114,521.33 116,946.55 107,430.00 97,321.00
Figure 3: Convergence Characteristics of PSO
V. CONCLUSION
PSO method was successfully employed to solve the ELD problem. The comparison of results for the 31-bus
Nigerian grid system clearly shows that the proposed PSO method was indeed capable of obtaining high quality
solution efficiently for ELD problems. Figure 3 shows the convergence characteristics of the proposed method
at the normal demand. The convergence is good since the algorithm takes few numbers of iterations to converge
hence less computation time. From the results obtained, the proposed PSO technique minimizes the total
production cost and transmission losses better than MGA and CGA, except in some cases where the DE also
performed equally good.
REFERENCES
[1]. Haruna, Y. S. (2004). Comparison of Economic Load Dispatch using Genetic Algorithm and Classical Optimization Method.
Unpublished M.Eng. Thesis, Abubakar Tafawa Balewa University, Bauchi.
[2]. Awodiji, O.O., Bakare, G. A., Aliyu, U. O. (2014). Short Term Economic Load Dispatch of Nigerian Thermal Power Plants Based
on Differential Evolution Approach. IJSER. Vol 5 Issue 3.
[3]. Jagadeesh, G. (2011). Multi Objective Economic Dispatch Using Pareto Frontier Differential Evolution. International Journal of
Engineering Science and Technology (IJEST). ISSN : 0975-5462 Vol. 3 No. 10.
[4]. Faheemullah, S., Pervez, H., Shaikh, M., Mirani, M. and Aslam, U. (2012). Multi Criteria Optimization Algorithm for Economic
Dispatch Complications for Sustainable Interconnected Power System. International Journal of Computer Applications (0975 –
8887), Volume 50 – No.4.
[5]. Ismail, M., Nur, H. F. I., Mohd, R. K., Muhammad, K. I., Titik. K. A., and Mohd, R. A. (2008). Ant Colony Optimization (ACO)
Technique in Economic Power Dispatch Problems. Proceedings of the International Multi Conference of Engineers and Computer
Scientists 2008. Vol II, pp 19-21.
[6]. Hardiansyah, Junaidi and Yohannes, M. S. (2012). Solving Economic Load Dispatch Problem Using Particle Swarm Optimization
Technique, I.J. Intelligent Systems and Applications, Vol. 12, pp 12-18.
[7]. Senthilkumar, S. and Vijayalakshmi, V. J. (2013). A New Approach to the Solution of Economic Dispatch Using Particle Swarm
Optimization with Simulated Annealing. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.3.
[8]. Damoon, R. D., Asef, G. and Seyyed, M. H. (2016). Solving Static Economic Load Dispatch Using Improved Exponential Harmony
Search Optimization. Australian Journal of Electric and Electronics Engineering, Vol. 13, Issue 2.
[9]. Makoju, J. (2003). Resuscitating the Nigerian Power Sector: The Resuscitating and Privatisation Reforms. COREN Assembly,
Abuja.
[10]. Rahul, D., Nikita, G. and Harsha, S. (2014). Economic Load Dispatch Problem and MATLAB Programming of Different Methods.
International Conference of Advance Research and Innovation (ICARI-2014).
[11]. Susheel, K. D., Achala, J. and Huddar, A. P. (2015). IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), Vol. 10,
Issue 2 Ver. III, PP 27-32.
[12]. Sonal, A. and Devendra, D. (2016). Power Economic Dispatch of Thermal Power Plant Using Classical Traditional Method.
International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol. 4 Issue II, pp2321-9653.
[13]. Bakare, G. A. (2001). Removal of overloads and Voltage problems in Electric Power Systems using Genetic Algorithm/Expert
System Approaches, Shaker Verlag, Aachen Germany.