Great conference last week in Bremen: 6th International Conference Drivetrain Concepts for Wind Turbines. Mikel Zabaleta, Ingeteam Wind Energy Product Manager, presented “Optimizing energy yield in multi-MW power converters for wind”.
This document provides a summary of a lecture on economic dispatch in power systems. Economic dispatch aims to minimize the total operating cost of generators while meeting the total demand plus losses. It is formulated as a constrained optimization problem solved using Lagrange multipliers or lambda iteration. Lambda iteration works by iteratively finding the optimal value of lambda, which determines the generation dispatch. Generator costs are represented by incremental cost curves and generators have minimum and maximum output limits that must be considered.
This document discusses economic dispatch in power systems. It begins by announcing homework and exam due dates. It then discusses how generation costs, including fuel and capital costs, contribute to retail electricity prices. Different generation technologies have varying costs. The document focuses on minimizing variable operating costs, primarily fuel costs, to meet demand by optimizing the generation dispatch. It provides details on modeling generation costs through input/output curves, fuel cost curves, heat rate curves and incremental cost curves. Examples are given to illustrate coal usage and incremental generation costs.
Solution of Combined Heat and Power Economic Dispatch Problem Using Different...Arkadev Ghosh
This document presents a study that uses the Mine Blast Algorithm (MBA) and Bare Bones Teaching Learning Based Optimization (BBTLBO) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The CHPED problem involves determining the optimal power and heat allocation among generation units to minimize costs while considering constraints. The document describes the mathematical formulation of the CHPED problem and provides an example simulation on a 7 generator system. The results show that both MBA and BBTLBO algorithms find low-cost solutions for the CHPED problem and outperform other algorithms in terms of solution quality and convergence speed.
This document discusses economic dispatch in power systems. It begins with an introduction that defines economic dispatch and optimal power flow problems. It then discusses various constraints in economic dispatch problems, including generator limits, transmission line limits, and reserve requirements. Different economic dispatch problems are examined, including ones that neglect transmission losses and include losses. The document also discusses unit commitment problems and provides an example of calculating the optimal dispatch to minimize total generation costs.
This document discusses hydrothermal scheduling, which involves optimally scheduling hydroelectric and thermal power plants together to minimize generation costs. Hydrothermal scheduling is classified as either long-range (months or years) or short-range (days or weeks). The key aspects are using low-cost hydroelectric generation where possible to reduce reliance on more expensive thermal plants. Mathematical optimization techniques are used to determine the optimal dispatch of hydro and thermal plants while meeting demand and respecting water availability constraints. While hydrothermal coordination can lower costs, the variable nature of hydro inflows makes the optimization problem complex.
This document summarizes a lecture on economic dispatch in power systems. It begins with announcements about homework assignments and readings. It then discusses the formulation of economic dispatch as minimizing generation costs subject to meeting demand. The document uses an example two generator system to illustrate solving the optimization using Lagrange multipliers. It describes the lambda iteration method for solving economic dispatch with multiple generators. Finally, it discusses including transmission losses in the economic dispatch formulation.
This document provides a summary of a lecture on economic dispatch in power systems. Economic dispatch aims to minimize the total operating cost of generators while meeting the total demand plus losses. It is formulated as a constrained optimization problem solved using Lagrange multipliers or lambda iteration. Lambda iteration works by iteratively finding the optimal value of lambda, which determines the generation dispatch. Generator costs are represented by incremental cost curves and generators have minimum and maximum output limits that must be considered.
This document discusses economic dispatch in power systems. It begins by announcing homework and exam due dates. It then discusses how generation costs, including fuel and capital costs, contribute to retail electricity prices. Different generation technologies have varying costs. The document focuses on minimizing variable operating costs, primarily fuel costs, to meet demand by optimizing the generation dispatch. It provides details on modeling generation costs through input/output curves, fuel cost curves, heat rate curves and incremental cost curves. Examples are given to illustrate coal usage and incremental generation costs.
Solution of Combined Heat and Power Economic Dispatch Problem Using Different...Arkadev Ghosh
This document presents a study that uses the Mine Blast Algorithm (MBA) and Bare Bones Teaching Learning Based Optimization (BBTLBO) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The CHPED problem involves determining the optimal power and heat allocation among generation units to minimize costs while considering constraints. The document describes the mathematical formulation of the CHPED problem and provides an example simulation on a 7 generator system. The results show that both MBA and BBTLBO algorithms find low-cost solutions for the CHPED problem and outperform other algorithms in terms of solution quality and convergence speed.
This document discusses economic dispatch in power systems. It begins with an introduction that defines economic dispatch and optimal power flow problems. It then discusses various constraints in economic dispatch problems, including generator limits, transmission line limits, and reserve requirements. Different economic dispatch problems are examined, including ones that neglect transmission losses and include losses. The document also discusses unit commitment problems and provides an example of calculating the optimal dispatch to minimize total generation costs.
This document discusses hydrothermal scheduling, which involves optimally scheduling hydroelectric and thermal power plants together to minimize generation costs. Hydrothermal scheduling is classified as either long-range (months or years) or short-range (days or weeks). The key aspects are using low-cost hydroelectric generation where possible to reduce reliance on more expensive thermal plants. Mathematical optimization techniques are used to determine the optimal dispatch of hydro and thermal plants while meeting demand and respecting water availability constraints. While hydrothermal coordination can lower costs, the variable nature of hydro inflows makes the optimization problem complex.
This document summarizes a lecture on economic dispatch in power systems. It begins with announcements about homework assignments and readings. It then discusses the formulation of economic dispatch as minimizing generation costs subject to meeting demand. The document uses an example two generator system to illustrate solving the optimization using Lagrange multipliers. It describes the lambda iteration method for solving economic dispatch with multiple generators. Finally, it discusses including transmission losses in the economic dispatch formulation.
A presentation on economic load dispatchsouravsahoo28
This document contains a presentation on economic load dispatch by Sourav Sahoo. It discusses distributing load between generating units and plants to minimize costs. It introduces the lambda iteration method for solving economic dispatch problems and considers transmission losses. In summary, it outlines that economic dispatch determines the lowest cost generation allocation, lambda iteration efficiently solves this, and transmission losses are accounted for with penalty factors.
1. Unit commitment involves determining the optimal mix of generators to meet expected demand while satisfying operational constraints like minimum up and down times. It aims to minimize total costs which include start-up costs and variable running costs.
2. The example problem determines the lowest cost combination of 3 generators to produce 550MW of power. Various constraints like minimum generation levels and ramp rates must be considered.
3. Key constraints in unit commitment include minimum and maximum generation limits, minimum up and down times, and ramp rates for changing output. System constraints require matching generation to load while maintaining sufficient operating reserves. Environmental and network limits also factor into the optimization.
This document discusses economic load dispatch, which is the process of allocating generation levels to generating units so that system load is supplied entirely and most economically. The objective is to calculate the output power of each generating unit to meet all demands at minimum cost, while satisfying technical constraints of the network and generators. Economic dispatch is one part of scheduling operations over different timeframes to maintain supply-demand balance from minutes to hours. The summary discusses the system constraints, including load balance, reserve capacity, emissions limits, and network constraints.
Economic load dispatch(with and without losses)Asha Anu Kurian
The document discusses unit commitment in power systems. Unit commitment involves determining the optimal schedule for starting up and shutting down generators to meet changing load at minimum cost while satisfying operational constraints. These constraints include minimum up and down times for generators, crew constraints, transition costs, and constraints related to different generator types like hydro, nuclear, and generators requiring minimum output. The objective is to determine the combination and scheduling of generators that supplies the load as economically as possible over a given period.
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
The document discusses unit commitment in power systems. Unit commitment involves determining which generating units to operate and when to operate them in order to meet the changing electricity demand at the lowest possible production cost while satisfying operational constraints. It describes the unit commitment problem and various constraints like minimum up/down times, ramp rates, reserve requirements, and start-up costs that make it more complex than economic dispatch. It provides a simple example to illustrate the concepts.
Input output , heat rate characteristics and Incremental costEklavya Sharma
This document discusses the input-output, heat rate, and incremental cost characteristics of thermal power plants. It defines input-output characteristics as a plot of fuel input versus power output. Heat rate is the ratio of fuel input to energy output and is the slope of the input-output curve. An incremental fuel rate curve plots the incremental fuel rate, or change in input divided by change in output, versus output. The incremental cost curve multiplies incremental fuel rate by fuel cost to determine incremental cost in monetary terms per unit of output. Economic dispatch of power plants aims to minimize total incremental costs while meeting demand.
1. The document describes models for predicting the spectral shift of cadmium telluride (CdTe) and monocrystalline silicon (multi-Si) photovoltaic modules under varying atmospheric conditions.
2. For CdTe modules, the spectral shift is modeled as a function of precipitable water content. For multi-Si modules, it is modeled as a function of absolute air mass.
3. The document proposes a new two-variable model for spectral shift that considers both absolute air mass and precipitable water content. Validation using field data from three locations in the US shows the new two-variable model improves accuracy compared to the single-variable models.
Economic Load Dispatch Optimization of Six Interconnected Generating Units Us...IOSR Journals
This document describes using particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem of optimizing the operation of six interconnected generating units. ELD aims to minimize total generation costs while satisfying constraints. PSO is applied to find optimal unit outputs that minimize cost, accounting for transmission losses. The proposed PSO approach is compared to genetic algorithms and conventional methods on a test system, showing PSO provides better solutions faster. Key steps of the PSO algorithm for ELD are initializing particles, evaluating fitness at each iteration, and updating personal and global best positions to iteratively improve solutions.
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
This document discusses optimization of power system operation through techniques like unit commitment and economic dispatch. It begins with an introduction to meeting varying electricity demand. It then outlines the steps of optimization including long-term planning, unit commitment for hourly/monthly decisions, and economic dispatch for instantaneous dispatch. Unit commitment deals with deciding which generation units to operate to satisfy demand while considering constraints. Common solution methods for unit commitment include priority lists, heuristics, mixed integer programming and dynamic programming. The document provides examples of advances in these areas and practical software used for optimization.
This document presents an overview of economic load dispatch (ELD). ELD is the process of determining the most cost-effective way to schedule power plant generations to meet load demand, while satisfying constraints. It formulates the ELD problem using a Lagrange function to minimize generation costs subject to load equalities. Transmission losses are then incorporated by expanding the function. The impact of losses is that generators appear more/less expensive depending on loss factors. Solution methods like lambda iterations are described. Finally, it distinguishes types of ELD problems and summarizes the key aspects.
Profit based unit commitment for GENCOs using Parallel PSO in a distributed c...IDES Editor
In the deregulated electricity market, each
generating company has to maximize its own profit by
committing suitable generation schedule termed as profit
based unit commitment (PBUC). This article proposes a
Parallel Particle Swarm Optimization (PPSO) solution to the
PBUC problem. This method has better convergence
characteristics in obtaining optimum solution. The proposed
approach uses a cluster of computers performing parallel
operations in a distributed environment for obtaining the
PBUC solution. The time complexity and the solution quality
with respect to the number of processors in the cluster are
thoroughly tested. The method has been applied to 10 unit
system and the results show that the proposed PPSO in a
distributed cluster constantly outperforms the other methods
which are available in the literature.
Novel Adaptive Controller for PMSG Driven Wind Turbine To Improve Power Syste...IJMERJOURNAL
1) The document describes a novel adaptive controller for a permanent magnet synchronous generator (PMSG) driven wind turbine system to improve power stability.
2) The proposed system uses a PMSG generator coupled with a pulse-width modulated current source converter. An adaptive PI controller is designed to track the current reference and integrate it into the grid.
3) The adaptive controller uses an online identifier to estimate the system parameters in real-time and automatically tune the PI parameters accordingly based on a linear approximation model to achieve the desired closed-loop response.
This document discusses unit commitment in power systems. Unit commitment aims to schedule generating units to meet forecasted load at minimum cost while maintaining reliability. It considers startup costs, operating costs, and shutdown costs over a daily load cycle. Dynamic programming is used to solve the unit commitment problem by evaluating combinations of generating units at each time interval and carrying minimum costs backward from the final interval to find the overall lowest-cost solution. The objective is to determine the optimal set of units to operate at each time period to supply predicted load economically.
A MIP Approach to the Yearly Scheduling Problem of a Mixed Hydrothermal Syste...Costas Baslis
This document presents a mixed integer programming approach for the yearly scheduling of a mixed hydrothermal power system. The model schedules thermal generating units on an hourly basis while considering reservoir levels and pumping operations for hydro units on both an hourly and monthly basis. The objective is to minimize total annual thermal generation costs given load predictions and constraints for the power balance, reserve requirements, and hydro plant and reservoir operations. The model is tested on a power system based on Greek electricity data from 2004 consisting of 29 thermal units totaling 6.9 GW of capacity and 13 hydro plants with 3 GW of capacity.
input output characteristics of thermal plantmathamramesh
This document discusses key characteristics of thermal power plants, including:
1. Input-output characteristics, which is a fundamental curve that plots the plant's fuel input in Btu/hour versus power output in MW.
2. Heat rate characteristics, which is the ratio of fuel input to energy output measured in Btu/KWh, and is the slope of the input-output curve. A lower heat rate means higher fuel efficiency.
3. Incremental fuel rate and cost curves, where incremental fuel rate is the change in fuel input divided by the change in output, and incremental cost is the product of incremental fuel rate and fuel cost per unit.
This document summarizes and compares economic dispatch solutions with and without transmission losses. Economic dispatch is described as an optimization problem that determines generator outputs to minimize total production costs while satisfying demand. Without losses, the problem is solved using Lagrange multipliers to minimize costs subject to the demand constraint. With losses modeled as a quadratic function of outputs, the same approach is used but includes transmission losses in the objective function and calculations. The conclusion is that accounting for transmission losses is important to obtain the most economic dispatch solution.
IJERD(www.ijerd.com)International Journal of Engineering Research and Develop...IJERD Editor
This document presents a fuzzy-logic based approach to solve the unit commitment problem in power generation systems. The unit commitment problem aims to determine the optimal on/off schedule of generating units to minimize operating costs while meeting demand and constraints. The proposed approach models key factors like generator load capacity, fuel costs, and startup costs as fuzzy variables. It then uses fuzzy logic techniques to determine a commitment schedule. The approach is demonstrated on a case study of a 4-unit thermal power plant in Turkey. Results are compared to dynamic programming to show the fuzzy logic approach provides preferable solutions with less computational time.
This document summarizes Ambuja Cement's best practices in energy efficiency. It discusses optimizations made to cement mills, clinker quality, fly ash absorption, installation of efficient turbo blowers, compressor optimization, cooler fan modifications, and maintenance practices. These measures have led to reductions in specific electrical energy consumption and increases in production rates. The document also outlines energy efficiency improvements at Ambuja's power plant, including turbine retrofitting and reductions to auxiliary power consumption. Finally, it discusses benefits achieved from ISO 50001 certification, such as increased energy savings, improved corporate image, and a systematic approach to continuous energy efficiency improvements.
Grameen Phone operates a co-generation power system at their corporate headquarters (GP House) in Dhaka, Bangladesh to generate electricity and chilled water for air conditioning. The system uses two 1.4MW natural gas turbines that produce waste heat to power three 827RT absorption chillers. This co-generation system saves 2MW of power compared to separate power and cooling systems. Co-generation improves efficiency by capturing waste heat to produce useful outputs.
A presentation on economic load dispatchsouravsahoo28
This document contains a presentation on economic load dispatch by Sourav Sahoo. It discusses distributing load between generating units and plants to minimize costs. It introduces the lambda iteration method for solving economic dispatch problems and considers transmission losses. In summary, it outlines that economic dispatch determines the lowest cost generation allocation, lambda iteration efficiently solves this, and transmission losses are accounted for with penalty factors.
1. Unit commitment involves determining the optimal mix of generators to meet expected demand while satisfying operational constraints like minimum up and down times. It aims to minimize total costs which include start-up costs and variable running costs.
2. The example problem determines the lowest cost combination of 3 generators to produce 550MW of power. Various constraints like minimum generation levels and ramp rates must be considered.
3. Key constraints in unit commitment include minimum and maximum generation limits, minimum up and down times, and ramp rates for changing output. System constraints require matching generation to load while maintaining sufficient operating reserves. Environmental and network limits also factor into the optimization.
This document discusses economic load dispatch, which is the process of allocating generation levels to generating units so that system load is supplied entirely and most economically. The objective is to calculate the output power of each generating unit to meet all demands at minimum cost, while satisfying technical constraints of the network and generators. Economic dispatch is one part of scheduling operations over different timeframes to maintain supply-demand balance from minutes to hours. The summary discusses the system constraints, including load balance, reserve capacity, emissions limits, and network constraints.
Economic load dispatch(with and without losses)Asha Anu Kurian
The document discusses unit commitment in power systems. Unit commitment involves determining the optimal schedule for starting up and shutting down generators to meet changing load at minimum cost while satisfying operational constraints. These constraints include minimum up and down times for generators, crew constraints, transition costs, and constraints related to different generator types like hydro, nuclear, and generators requiring minimum output. The objective is to determine the combination and scheduling of generators that supplies the load as economically as possible over a given period.
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
The document discusses unit commitment in power systems. Unit commitment involves determining which generating units to operate and when to operate them in order to meet the changing electricity demand at the lowest possible production cost while satisfying operational constraints. It describes the unit commitment problem and various constraints like minimum up/down times, ramp rates, reserve requirements, and start-up costs that make it more complex than economic dispatch. It provides a simple example to illustrate the concepts.
Input output , heat rate characteristics and Incremental costEklavya Sharma
This document discusses the input-output, heat rate, and incremental cost characteristics of thermal power plants. It defines input-output characteristics as a plot of fuel input versus power output. Heat rate is the ratio of fuel input to energy output and is the slope of the input-output curve. An incremental fuel rate curve plots the incremental fuel rate, or change in input divided by change in output, versus output. The incremental cost curve multiplies incremental fuel rate by fuel cost to determine incremental cost in monetary terms per unit of output. Economic dispatch of power plants aims to minimize total incremental costs while meeting demand.
1. The document describes models for predicting the spectral shift of cadmium telluride (CdTe) and monocrystalline silicon (multi-Si) photovoltaic modules under varying atmospheric conditions.
2. For CdTe modules, the spectral shift is modeled as a function of precipitable water content. For multi-Si modules, it is modeled as a function of absolute air mass.
3. The document proposes a new two-variable model for spectral shift that considers both absolute air mass and precipitable water content. Validation using field data from three locations in the US shows the new two-variable model improves accuracy compared to the single-variable models.
Economic Load Dispatch Optimization of Six Interconnected Generating Units Us...IOSR Journals
This document describes using particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem of optimizing the operation of six interconnected generating units. ELD aims to minimize total generation costs while satisfying constraints. PSO is applied to find optimal unit outputs that minimize cost, accounting for transmission losses. The proposed PSO approach is compared to genetic algorithms and conventional methods on a test system, showing PSO provides better solutions faster. Key steps of the PSO algorithm for ELD are initializing particles, evaluating fitness at each iteration, and updating personal and global best positions to iteratively improve solutions.
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
This document discusses optimization of power system operation through techniques like unit commitment and economic dispatch. It begins with an introduction to meeting varying electricity demand. It then outlines the steps of optimization including long-term planning, unit commitment for hourly/monthly decisions, and economic dispatch for instantaneous dispatch. Unit commitment deals with deciding which generation units to operate to satisfy demand while considering constraints. Common solution methods for unit commitment include priority lists, heuristics, mixed integer programming and dynamic programming. The document provides examples of advances in these areas and practical software used for optimization.
This document presents an overview of economic load dispatch (ELD). ELD is the process of determining the most cost-effective way to schedule power plant generations to meet load demand, while satisfying constraints. It formulates the ELD problem using a Lagrange function to minimize generation costs subject to load equalities. Transmission losses are then incorporated by expanding the function. The impact of losses is that generators appear more/less expensive depending on loss factors. Solution methods like lambda iterations are described. Finally, it distinguishes types of ELD problems and summarizes the key aspects.
Profit based unit commitment for GENCOs using Parallel PSO in a distributed c...IDES Editor
In the deregulated electricity market, each
generating company has to maximize its own profit by
committing suitable generation schedule termed as profit
based unit commitment (PBUC). This article proposes a
Parallel Particle Swarm Optimization (PPSO) solution to the
PBUC problem. This method has better convergence
characteristics in obtaining optimum solution. The proposed
approach uses a cluster of computers performing parallel
operations in a distributed environment for obtaining the
PBUC solution. The time complexity and the solution quality
with respect to the number of processors in the cluster are
thoroughly tested. The method has been applied to 10 unit
system and the results show that the proposed PPSO in a
distributed cluster constantly outperforms the other methods
which are available in the literature.
Novel Adaptive Controller for PMSG Driven Wind Turbine To Improve Power Syste...IJMERJOURNAL
1) The document describes a novel adaptive controller for a permanent magnet synchronous generator (PMSG) driven wind turbine system to improve power stability.
2) The proposed system uses a PMSG generator coupled with a pulse-width modulated current source converter. An adaptive PI controller is designed to track the current reference and integrate it into the grid.
3) The adaptive controller uses an online identifier to estimate the system parameters in real-time and automatically tune the PI parameters accordingly based on a linear approximation model to achieve the desired closed-loop response.
This document discusses unit commitment in power systems. Unit commitment aims to schedule generating units to meet forecasted load at minimum cost while maintaining reliability. It considers startup costs, operating costs, and shutdown costs over a daily load cycle. Dynamic programming is used to solve the unit commitment problem by evaluating combinations of generating units at each time interval and carrying minimum costs backward from the final interval to find the overall lowest-cost solution. The objective is to determine the optimal set of units to operate at each time period to supply predicted load economically.
A MIP Approach to the Yearly Scheduling Problem of a Mixed Hydrothermal Syste...Costas Baslis
This document presents a mixed integer programming approach for the yearly scheduling of a mixed hydrothermal power system. The model schedules thermal generating units on an hourly basis while considering reservoir levels and pumping operations for hydro units on both an hourly and monthly basis. The objective is to minimize total annual thermal generation costs given load predictions and constraints for the power balance, reserve requirements, and hydro plant and reservoir operations. The model is tested on a power system based on Greek electricity data from 2004 consisting of 29 thermal units totaling 6.9 GW of capacity and 13 hydro plants with 3 GW of capacity.
input output characteristics of thermal plantmathamramesh
This document discusses key characteristics of thermal power plants, including:
1. Input-output characteristics, which is a fundamental curve that plots the plant's fuel input in Btu/hour versus power output in MW.
2. Heat rate characteristics, which is the ratio of fuel input to energy output measured in Btu/KWh, and is the slope of the input-output curve. A lower heat rate means higher fuel efficiency.
3. Incremental fuel rate and cost curves, where incremental fuel rate is the change in fuel input divided by the change in output, and incremental cost is the product of incremental fuel rate and fuel cost per unit.
This document summarizes and compares economic dispatch solutions with and without transmission losses. Economic dispatch is described as an optimization problem that determines generator outputs to minimize total production costs while satisfying demand. Without losses, the problem is solved using Lagrange multipliers to minimize costs subject to the demand constraint. With losses modeled as a quadratic function of outputs, the same approach is used but includes transmission losses in the objective function and calculations. The conclusion is that accounting for transmission losses is important to obtain the most economic dispatch solution.
IJERD(www.ijerd.com)International Journal of Engineering Research and Develop...IJERD Editor
This document presents a fuzzy-logic based approach to solve the unit commitment problem in power generation systems. The unit commitment problem aims to determine the optimal on/off schedule of generating units to minimize operating costs while meeting demand and constraints. The proposed approach models key factors like generator load capacity, fuel costs, and startup costs as fuzzy variables. It then uses fuzzy logic techniques to determine a commitment schedule. The approach is demonstrated on a case study of a 4-unit thermal power plant in Turkey. Results are compared to dynamic programming to show the fuzzy logic approach provides preferable solutions with less computational time.
This document summarizes Ambuja Cement's best practices in energy efficiency. It discusses optimizations made to cement mills, clinker quality, fly ash absorption, installation of efficient turbo blowers, compressor optimization, cooler fan modifications, and maintenance practices. These measures have led to reductions in specific electrical energy consumption and increases in production rates. The document also outlines energy efficiency improvements at Ambuja's power plant, including turbine retrofitting and reductions to auxiliary power consumption. Finally, it discusses benefits achieved from ISO 50001 certification, such as increased energy savings, improved corporate image, and a systematic approach to continuous energy efficiency improvements.
Grameen Phone operates a co-generation power system at their corporate headquarters (GP House) in Dhaka, Bangladesh to generate electricity and chilled water for air conditioning. The system uses two 1.4MW natural gas turbines that produce waste heat to power three 827RT absorption chillers. This co-generation system saves 2MW of power compared to separate power and cooling systems. Co-generation improves efficiency by capturing waste heat to produce useful outputs.
KK Wind Solutions presentation on Control System RetrofitRené Balle
1) A presentation was given on retrofitting control systems in wind turbines. Retrofitting control systems can improve power output, availability, and reduce operation and maintenance costs.
2) A case study was presented on retrofitting the control systems on 50 Bonus 1.3MW turbines. The retrofit improved the power curve, replaced pitch and main controllers, and added remote monitoring capabilities.
3) The retrofit paid for itself in less than 3 years through increased revenue from higher output and reduced operation costs from improvements like fewer yaw motor replacements. Retrofitting control systems was shown to be more profitable than other wind turbine upgrades.
Developing a new generation of energy efficiency products for reciprocating e...Bowman Power
Learn how a new energy efficiency product gets made, from opportunity to concept, design, validation and production, with this free presentation from the 73rd Indonesia National Electricity Day & POWER-GEN Asia. #PGASIA
The transformation of a fixed speed wind turbine to a variable
speed topology through the installation of an autonomous
conversion system enables to maximize the Return
on Investment (RoI) of the wind turbine.
This document compares the efficiency of four power electronic converters used in grid-connected permanent magnet wind turbine systems: intermediate boost converter, intermediate buck-boost converter, back-to-back converter, and matrix converter. It develops a model to calculate power generation from the wind turbine at different wind speeds. It then establishes a relationship between wind speed and power losses in the semiconductor devices of each converter to evaluate efficiency over the wind speed range. Simulation results show the power loss characteristics with varying wind speed and determine the most efficient converter is the intermediate boost converter for typical wind conditions at the kW level.
- A wind farm study analyzed 1 year of wind data from a site including average wind speed, direction, and other parameters.
- Based on the data, a 100m diameter 2MW wind turbine was selected as the optimal design with the lowest LCOE and highest annual energy output.
- Performance simulations were conducted on the turbine, showing how its pitch, speed, power, and forces vary with wind speed.
- For integrating wind power onto the existing grid, the maximum penetration was found to be 10MW of wind (5 turbines), providing around 20% of annual electricity with 30.8% curtailment and a 26.88% capacity factor.
H&M Power Conversion Segmented Inverter 2010 R1Sammy Germany
The document discusses a proposed modular power conversion system for wind turbines to improve reliability and availability. It notes that lost revenue from non-operating turbines increases energy costs at large wind farms. The proposed system uses independent and redundant power modules to maximize uptime even if one module fails. Analysis shows that for a 30MW wind farm experiencing typical failure rates, the system could save over $165,000 per year by preventing lost energy capture from turbine downtime. With multiple annual failures averted over the farm's 20-year lifespan, the financial benefits outweigh the additional $42,000 cost per turbine for the modular system.
"How Gas Engines can help with the Challenges of a Changing Energy System"Adam Wray-Summerson
Presented by Adam Wray-Summerson, Project & Market Development Manager for Clarke Energy, at the IDGTE Annual Seminar & Luncheon, Thursday 2nd May 2019
Why Efficiency Matters. See how just a 1% gain saves you millions in TCO.GE Energy Connections
The document discusses how small gains in energy efficiency for data center equipment like UPS systems can result in millions of dollars saved over the lifespan of the equipment when considering total cost of ownership (TCO). It provides an example where a 1% improvement in UPS efficiency from 93% to 94% saves $1.4 million over 10 years of operation. Newer UPS technologies can achieve as high as 96.5% efficiency, saving almost $3.4 million compared to 93% efficient systems. Looking at TCO factors like capital expenditures and operational expenditures is important for understanding the long-term costs of data center design and equipment selection.
In urban area, sitting renewable energy (RE) can be a challenging issue because only few spacious land is available but the demand of the energy is high. Hence the proper selection of RE technology is important to ensure plenty of energy are delivered from limited site area. This paper present how does the local climate condition in typical urban area, Auckland Central Business District, affect annual electricity production and energy production of PV or Wind Power system. The analysis is then extended to find the energy density for respective RE system.The result are strategic to advise which renewable energy system can actually optimize energy production in the small land area.
The document discusses SMEDA's energy efficiency program which includes international collaborations and aims to minimize energy usage and develop efficient energy management systems. It has benefited 62 industrial units through various projects including establishing demonstration projects, implementing energy management systems, and training energy managers. Key projects outlined include detailed energy audits conducted, training energy service companies, and developing an energy efficiency management program targeting specific sectors. The document then provides case studies on improving energy efficiency through measures such as optimizing power distribution systems, installing variable frequency drives, improving electric motors and compressed air systems, balancing air flow, and upgrading welding plants.
The document discusses innovations in 3-phase UPS technology and energy storage. It describes new inverter topologies like 4-level inverters and hybrid inverters that improve efficiency. It also discusses the new ECOnversion mode that provides near double conversion performance at near ECO mode efficiency of 98-99%. The document then covers the use of lithium-ion battery technology which reduces footprint and weight while increasing available energy storage and battery life. These innovations help reduce operating expenses through higher efficiencies and less maintenance needs.
Controllers are used in renewable energy systems like electric vehicles, wind turbines, and solar power plants to regulate various functions. Modern controllers for electric vehicles use pulse width modulation to smoothly control motor speed and acceleration. Advanced controllers for wind turbines and solar plants employ strategies like variable pitch control, maximum power point tracking, and fuzzy logic to optimize power capture despite changing environmental conditions. Controllers are critical for integrating renewable sources into smart grids and ensuring stable, efficient system operation as use of intermittent renewables increases.
Compensator Based Performance Enhancement Strategy for a SIQO Buck ConverterIAES-IJPEDS
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Optimizing energy yield in multi-MW power converters for wind” by Mikel zabaleta_Ingeteam Wind Energy 2015
1. OPTIMIZING ENERGY YIELD IN MULTI-MW
POWER CONVERTERS FOR WIND
Bremen, 30th November 2015
Speaker: Mikel Zabaleta
MV-Wind Product Manager
mikel.zabaleta@ingeteam.com
2. INDEX
• Scenario
• Introduction
• Conclusions
• Converter arrangements for multi-MW turbines
• Ingeteam’s solution
• Efficiency effect on energy yield
• Modularity effect on energy yield
3. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Introduction
The following terms and acronyms have been used along the presentation:
• Conversion line: is the association of inverters in a back to back topology
characterized by the fact that a failure in any of its semiconductors, lead to
the total shutdown of it.
4. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Introduction
• Conversion stage: is the association of conversion lines in parallel to form
a higher output power unit.
• Corrective period: is the elapsed time between the occurrence of an
event and the maintenance actions performed to restore it. If a failure
occurs on Monday but the service personnel can not access to fix it until
Friday, the corrective period would be 5 days..
5. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Introduction
• PTF (Probability To Fail): indicates the probability that has a component
or subsystem to fail during one year of operation. This indicator is inherent
to the component itself and to the application requirements.
• PTS (Probability To Stop): indicates the probability that has a component
or subsystem to cause a full shutdown of the conversion line (or stage),
that includes it, during one year of operation. In this indicator, it affects not
only the component, but also the level of redundancy. If there is no
redundancy, PTS=PTF.
• MAEP (Mean Annual Energy Production): is the ratio between the actual
energy produced by the conversion stage and the maximum that have
been available in the site.
6. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Description of the Scenario
The study considers a 8 MW power stage for a windturbine with the rated
power curve shown on the next figure. It has a cut in and cut out speed of 3
and 26 m/s respectively.
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Power[kW]
Wind speed [m/s]
Power curve
7. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Description of the Scenario
The base site mean wind-speed is considered 7 m/s (3863 equivalent hours).
The wind speed distribution is fitted to a Weibull distribution with form factor of
2.
0,00%
2,00%
4,00%
6,00%
8,00%
10,00%
12,00%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Wind speed [m/s]
Wind speed probability
8. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Description of the Scenario
Other considerations are:
• The standard corrective period is considered to be 1 month.
• The windturbine lifetime is 30 years.
• The price for the MWh has been considered 200 euros for the sake of
simplicity
• The PTF of each conversion line is calculated to be 18%
• The PTF of the converter controls is calculated to be 4%
• The PTF of the converter cooling is calculated to be 2%
• The cost of a maintenance access is estimated to be 15 k€
The analysis will cover the following parameter deviations:
• Efficiency: from 97 to 98%
• Corrective period: from 1 to 8 [Days], 1 to 4 [Weeks], 1 to 12 [Months]
• Mean Wind Speed: from 5 to 8 m/s
9. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Efficiency effect on energy yield
A lot of importance has been assigned to the efficiency of the conversion
stage in the past. This obviously affects the energy harvested by a
windturbine, but its effect is not straightforward.
The efficiency of the conversion stage only affects during partial loads
operation (during the maximum power tracking MPT);
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0,00%
2,00%
4,00%
6,00%
8,00%
10,00%
12,00%
14,00%
16,00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Wind speed [m/s]
5 m/s 6 m/s 7 m/s 8 m/s Power curve
Mean Wind Speed Probability in MPT
5 m/s 71%
6 m/s 71%
7 m/s 66%
8 m/s 60%
9 m/s 54%
10 m/s 48%
10. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Efficiency effect on energy yield
Generally, as the mean wind speed of the site increases, the effect of the
efficiency of the conversion stage becomes less important as less partial load
hours are probable.
0,00%
0,10%
0,20%
0,30%
0,40%
0,50%
0,60%
0,70%
0,80%
0,90%
1,00%
5 5,5 6 6,5 7 7,5 8 8,5 9 9,5 10
MAEP
Mean wind speed [m/s]
MAEP improvement for a 1% efficiency step
One conversion line Two conversion line
11. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Modularity effect on energy yield
The modularity also affects the MAEP because when a failure occurs in the
conversion stage, it may stop it shutting down the entire production (in the
case of a single conversion line) or it may reduce the available output power
(in the case of two or more conversion lines).
Here is where the corrective period plays its role.
1 2 3 4 5 6 7 8
One conversion line 99,94% 99,87% 99,81% 99,74% 99,68% 99,61% 99,55% 99,48%
Two conversion line 99,95% 99,90% 99,85% 99,79% 99,74% 99,69% 99,64% 99,59%
Three conversion line 99,95% 99,91% 99,86% 99,81% 99,76% 99,72% 99,67% 99,62%
Four conversion line 99,96% 99,91% 99,87% 99,82% 99,78% 99,73% 99,69% 99,64%
99,40%
99,50%
99,60%
99,70%
99,80%
99,90%
100,00%
MAEP
MaintenanceAccess [Days]
One conversion line Two conversion line
Three conversion line Four conversion line
12. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Modularity effect on energy yield
If the corrective period is in the weeks timeframe,
1 2 3 4
One conversion line 99,51% 99,03% 98,54% 98,06%
Two conversion line 99,61% 99,23% 98,84% 98,45%
Three conversion line 99,65% 99,29% 98,94% 98,59%
Four conversion line 99,66% 99,33% 98,93% 98,57%
97,0%
97,5%
98,0%
98,5%
99,0%
99,5%
100,0%
MAEP
MaintenanceAccess [Weeks]
One conversion line Two conversion line
Three conversion line Four conversion line
13. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Modularity effect on energy yield
If the corrective period is in the months timeframe,
1 2 3 4 5 6 7 8 9 10 11 12
One conversion line 98,06% 96,11% 94,17% 92,22% 90,28% 88,33% 86,39% 84,44% 82,50% 80,56% 78,61% 76,67%
Two conversion line 98,45% 96,91% 95,36% 93,82% 90,89% 89,06% 87,24% 85,42% 83,59% 81,77% 76,89% 74,79%
Three conversion line 98,59% 96,90% 95,35% 93,25% 91,56% 89,05% 87,22% 84,29% 82,33% 76,21% 73,83% 69,79%
Four conversion line 98,57% 96,96% 95,16% 93,19% 90,22% 87,73% 85,05% 80,89% 77,69% 72,68% 67,16% 59,76%
60,00%
65,00%
70,00%
75,00%
80,00%
85,00%
90,00%
95,00%
100,00%
105,00%
MAEP
MaintenanceAccess [Months]
One conversion line Two conversion line
Three conversion line Four conversion line
14. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Conclusions
• The effect of the efficiency (only at partial loads!!) on the MAEP
decreases with the mean wind-speed of the site.
• The modularity, in conjunction with the corrective period, has the strongest
influence on the MAEP. Here, the cost of the maintenance access should
also be taken into account (15 k€).
NOL 75% NOL 66% NOL 50% NOL 33% NOL 25% NOL 0%
Annual
Energy
[MWh] MAEP
Extra Annual
Income (discounted
maintenance) [€]
One CL 0 0 0 0 0 7 30304 98.056% 0
Two CL 0 0 10 0 0 2 30455 98.545% 26640
Three CL 0 16 0 0 0 2 30468 98.588% 25166
Four CL 22 0 1 0 0 2 30462 98.568% 19674
• The extra annual income (for 2 CL) is even higher when including
redundancies in the control system (in CPU, in power supplies) reaching
above 44k€.
15. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Conclusions
• Comparing the four conversion stages regarding some design drivers, it
can easily be seen how a two conversion line solution is optimal.Energyharvest
Initialcost
Footprintand
weight
Unscheduled
maintenance
accesses
Multiplesources
forcomponents
One CL LOW HIGH HIGH LOW LOW
Two CL HIGH MEDIUM MEDIUM MEDIUM HIGH
Three CL HIGH HIGH HIGH HIGH HIGH
Four CL HIGH HIGH HIGH HIGH HIGH
16. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Power converter arrangements for multi-MW windturbines
In the multi-MW range, several different converter arrangements can be
found:
• In low voltage (LV), three or more 2L conversion lines are usually needed
to reach output power.
• In medium voltage (MV), basically two different trends appear:
• Press-pack based converters which reach the output power with only
one 3L conversion line,
• HV-IGBT based converters which usually require the parallelization of
two (or three) 3L conversion lines.
As it has been seen before, a two conversion lines stage is optimum from the
energy harvest point of view and the operational costs.
Some internal and external [1] studies have highlighted that a reduction in the
CoE between 2-4% can be achieved with MV power stages in the multi-MW
range.
[1] W. Erdman and M. Behnke, Low Wind Speed Turbine Project Phase II: The application of Medium-Voltage Electrical
Apparatus to the Class of Variable Speed Multi-Megawatt Low Wind Speed Turbines. Golden, CO, USA: National Renewable
Energy Lab. (N.R.E.L.), 2012
17. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Ingeteam’s solution for multi-MW windturbines
The MV product family has been designed with the following drivers
Maximize energy harvest,
Minimize initial cost,
Minimize footprint and weight,
Scheduled maintenance intervals greater than one year,
Minimize unscheduled maintenance accesses,
Reduced MTTR,
Multiple sources for components
Harsh environment withstand
These has led to the following solution
HV-IGBT based converter (reduced MTTR, multiple sources, reduced initial cost, footprint and
weight)
Two conversion lines (High energy harvest, low initial cost)
Oversized key components and redundancies in control and cooling systems (higher energy
harvest, increases maintenance intervals and minimizes unscheduled maintenance)
Fully closed (IP54+) water-cooled cabinets (high immunity against aggressive atmospheres)
Key design drivers
18. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Ingeteam’s solution for multi-MW windturbines
Three products are available depending on the required output:
INGECON WIND MV100 3400 -> 635 Arms with overload 700 Arms
INGECON WIND MV100 4600 -> 850 Arms with overload 950 Arms
INGECON WIND MV100 5600 -> 1050 Arms with overload 1150 Arms
Arranging two conversion lines of each type, the following output
power can be reached:
2 CONVERSION LINES ARRANGEMENT
GRID MACHINE
Target WT
power [kW]
simultaneity
0,9/0,9
Target WT
power [kW]
simultaneity
0,9/0,95
Target WT
power [kW]
simultaneity
0,95/0,95
Target WT
power [kW]
Vn@PF=1
Target WT
Power [kW]
Vn@PF=0,95
INGECON WIND MV100 5600
9100 9600 10100 11200 11400
10000 10600 11200 12400 12500
INGECON WIND MV100 4600
7500 7900 8300 9200 9300
8300 8700 9200 10200 10300
INGECON WIND MV100 3400
5500 5800 6100 6800 6800
6200 6500 6900 7600 7600
PERMANENT POWER
OVERLOAD
19. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Ingeteam’s solution for multi-MW windturbines
The power stack is based on small “bricks” called Basic Power Modules
(BPMs).
These modules contain the semiconductor devices (HV-IGBTs) and its
ancillary systems (drivers, cooling, temperature probes, etc.) of a 3L-NPC
phase leg.
The BPMs are assembled on sliding guides helping to obtain and extremely
reduced MTTR (less than 30 minutes).
Two BPM modules (sharing the electromechanical design) cover all the range
of powers required by means of changing the semiconductor devices
(matching its ratings).
20. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Ingeteam’s solution for multi-MW windturbines
The engineering cabinet includes:
The grid side and machine side reactors
The internal cooling system
The dVdt filter (for 1,5 kV/us)
The precharge and discharge system
The grid side and machine side contactors
The machine side voltage measurements
The machine side manual disconnector and grounding
The DC-bus grounding disconnector
Grid side and machine side surge protections
Three different engineering cabinets are available to optimally fit all the
targeted output powers. These share the basic design (the components
layout) and differs in the size of certain components.
Engineering
21. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Ingeteam’s solution for multi-MW windturbines
Control electronics accessible from outside
Inspection windows
Engineering
22. OPTIMIZING ENERGY YIELD IN MULTI-MW POWER
CONVERTERS FOR WIND
Introduction
Scenario
Modularity
Conclusions
Converter
arrangements
Ingeteam’s
solution
Efficiency
Ingeteam’s solution for multi-MW windturbines
WCU BPMs 1 ENG 1 BPMs 2 ENG 2 GSF