This document summarizes a study analyzing the potential cost reductions and economic viability of offshore wind energy development in the United States from 2015-2030. The study uses modeling to estimate levelized costs of energy (LCOE) under various technology scenarios and finds that with continued innovation, average LCOE could drop from $130-450/MWh in 2015 to $80-220/MWh by 2027. Certain coastal regions like the Northeast Atlantic may reach economic viability by 2030 without subsidies. Costs are projected to converge for fixed-bottom and floating technologies, with an economic breakpoint of 45-60 meters water depth.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
Fatigue Analysis of a Pressurized Aircraft Fuselage Modification using Hyperw...Altair
Fatigue Analyses of modifications on pressurized aircraft fuselages are both necessary and tedious. Using the Hyperworks software suite and StressCheck, RUAG has developed a fatigue analysis method which streamlines the process from the creation of the spectrum up to the detailed analysis of selected fastener holes and delivers results quickly and efficiently.
This method was then used to certify the installation of two large windows in the floor of a single engine turboprop A/C for aerial survey applications.
Speakers
David Schmid, Manager Structural Analysis, RUAG Schweiz AG
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
Fatigue Analysis of a Pressurized Aircraft Fuselage Modification using Hyperw...Altair
Fatigue Analyses of modifications on pressurized aircraft fuselages are both necessary and tedious. Using the Hyperworks software suite and StressCheck, RUAG has developed a fatigue analysis method which streamlines the process from the creation of the spectrum up to the detailed analysis of selected fastener holes and delivers results quickly and efficiently.
This method was then used to certify the installation of two large windows in the floor of a single engine turboprop A/C for aerial survey applications.
Speakers
David Schmid, Manager Structural Analysis, RUAG Schweiz AG
Wind farm development is an extremely complex process, most often driven by three im- portant performance criteria: (i) annual energy production, (ii) lifetime costs, and (iii) net impact on surroundings. Generally, planning a commercial scale wind farm takes several years. Undesirable concept-to-installation delays are primarily attributed to the lack of an upfront understanding of how different factors collectively affect the overall performance of a wind farm. More specifically, it is necessary to understand the balance between the socio-economic, engineering, and environmental objectives at an early stage in the design process. This paper proposes a Wind Farm Tradeoff Visualization (WiFToV) framework that aims to develop first-of-its-kind generalized guidelines for the conceptual design of wind farms, especially at early stages of wind farm development. Two major performance objectives are considered in this work: (i) cost of energy (COE) and (ii) land area per MW installed (LAMI). The COE is estimated using the Wind Turbine Design Cost and Scaling Model (WTDCS) and the Annual Energy Production (AEP) model incorporated by the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The LAMI is esti- mated using an optimal-layout based land usage model, which is treated as a post-process of the wind farm layout optimization. A Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm is used to perform the bi-objective optimization, which simultaneously optimizes the location and types of turbines. Together with a novel Pareto translation technique, the proposed WiFToV framework allows the exploration of the trade-off between COE and LAMI, and their variations with respect to multiple values of nameplate capacity.
Diesel backup generators are commonly installed in hospitals, data centers, universities, hotels, and other businesses for use in the event of power disruptions. These engines have quick response times that provide an unmatched reliable source of emergency backup power. Facilities that have these backup engines can also benefit from enrolling in demand response (DR) programs that offer economic incentives to participants who volunteer the use of their backup generators to supply electricity to the grid during certain periods of high electricity demand. In recent years, there has been an increase in the number of backup engines that have enrolled in DR programs in exchange for economic incentives. DR programs provide grid reliability, especially during periods of high electricity demand. Therefore, this is a win-win situation for backup engine owners and power utility companies offering these incentives. Generally, a backup generator with a capacity of 500 kilowatt (kW) or more is necessary to participate in DR programs. Participants in these DR programs agree with the local power company to use their backup engines when directed; usually during periods of peak electricity demand or power disruption. However, recent air quality regulations that apply to backup generators can be challenging to meet when participating in a DR program. That is the case because the applicable requirements for backup engine depend on whether the use is strictly for emergency purposes or for DR (considered non-emergency). Purely emergency use engines are subject to work practice standards while non-emergency engines are subject to emission limits that may require emission controls. Additionally, non-emergency engines may be subject to dispersion modeling requirements to show compliance with the national ambient air quality standards (NAAQS). At the moment the dispersion model used in permitting evaluations is extremely conservative and can show compliance issues. In conclusion, DR programs can be a profitable way to get additional cash for owners and operators of backup engines. However, the permitting implications should be considered thoroughly before enrolling in such a program to avoid any unintended adverse consequences.
Evaluating AERMOD and Wind Tunnel Derived Equivalent Building DimensionsSergio A. Guerra
While the current EBD method is the best available option to determine correct building dimensions in the model, a different method was suggested by EPA in the 2011 Memo: Model Clearinghouse Review of EBD for AERMOD.9 Attachment B to the 2011 Memo includes an assessment of the Alcoa Davenport Works EBD Study. In this evaluation EPA compared wind tunnel observations with AERMOD derived concentrations. However, this evaluation has important shortcomings. First, to carry out this comparison between wind tunnel and AERMOD concentrations, it is necessary to collect velocity profiles that include longitudinal and vertical turbulent intensity measurements upwind of the stack. These data were not available for the EPA evaluation of the Alcoa Davenport Works EBD Study. Second, the wind tunnel model operating conditions were converted to full scale conditions by using exact similarity. However, exact similarity is not used to specify model operating conditions since only momentum ratios are matched but not buoyancy ones. Whereas EPA did not provide important details on how this study was performed, this paper outlines how to properly carry out this new method where AERMOD is used to determine equivalent building dimensions. The viability of this new method was also evaluated and discussed.
Probabilistic & Source Characterization Techniques in AERMOD ComplianceSergio A. Guerra
The short term NAAQS are more stringent and traditional techniques are not suitable anymore. The probabilistic nature of these standards also opens the door to modeling techniques based on probability. Source characterization studies can also be used to refine AERMOD’s inputs to be more accurate and achieve reductions of more than half. This presentation will cover these compliance methods.
Currently, it is assumed that a given emission unit is in operation at its maximum capacity every hour of the year. However, assuming constant maximum emissions is overly conservative for facilities such as power plants that are not in operation all the time at full load. A better approach is the use of the Monte Carlo technique to account for emission variability. Another conservative assumption in NAAQS modeling relates to combining predicted concentrations from AERMOD with maximum or design concentrations from the monitor. A more reasonable approach is to combine the 50th percentile background concentration with AERMOD values.
The inputs to AERMOD can be obtained by more accurate source characterization studies. Such is the case of building dimensions commonly calculated with BPIP. These dimensions tend to overstate the wake effects and produce significantly higher concentrations especially for lattice structures, elongated buildings, and streamlined structures. An Equivalent Building Dimensions (EBD) study can be used to inform AERMOD with more accurate downwash characteristics.
Evaluation of the Theoretical Problems with Building Downwash Using A New Met...Sergio A. Guerra
While the current EBD method is the best available option to determine correct building dimensions in the model, a different method was suggested by EPA in the 2011 Memo: Model Clearinghouse Review of EBD for AERMOD. Attachment B to the 2011 Memo includes an assessment of the Alcoa Davenport Works EBD Study. In this evaluation EPA compared wind tunnel observations with AERMOD derived concentrations. However, this evaluation has important shortcomings. First, to carry out this comparison between wind tunnel and AERMOD concentrations, it is necessary to collect velocity profiles that include longitudinal and vertical turbulent intensity measurements upwind of the stack. These data were not available for the EPA evaluation of the Alcoa Davenport Works EBD Study. Second, the wind tunnel model operating conditions were converted to full scale conditions by using exact similarity. However, exact similarity is not used to specify model operating conditions since only momentum ratios are matched but not buoyancy ones. Whereas EPA did not provide important details on how this study was performed, this paper outlines how to properly carry out this new method where AERMOD is used to determine equivalent building dimensions. The viability of this new method was also evaluated and discussed.
Using Physical Modeling to Refine Downwash Inputs to AERMOD at a Food Process...Sergio A. Guerra
Demonstrating compliance with air quality standards using dispersion modeling is increasingly difficulty because of significant tightening National Ambient Air Quality Standards (NAAQS) that has occurred in the last decade. Compliance with these standards is usually demonstrated using AERMOD, EPA’s standard model for assessing air quality impacts from industrial sources. However, AERMOD often produces higher predictions of air quality impacts due to the inherent conservative (high) assumptions and simplifications in its formulation. A specific situation involves the calculations used to assess the impacts of air flow downwash around buildings. Although the theory used to estimate these effects was developed for a limited set of building types, these formulae are applied indiscriminately to all types of buildings in a conservative fashion, often leading to significant overpredictions of downwash effects.
This presentation covers the basics of wind tunnel modeling and how it can be used to correct downwash induced overpredictions to achieve compliance. The presentation will also describe the setup and execution of wind tunnel modeling at a food processing facility to develop improved downwash parameters and increase the accuracy of dispersion modeling results.
An approach to evaluate the heat exchanger retrofit for installed industrial ...eSAT Journals
Abstract This is part 2 of the conducted study to develop a new systematic method to evaluate the heat exchanger retrofit on existing industrial gas turbines. A new approach has been introduced and validated in the first part comparing with the measured data. This method was used to optimize the obtained cycle performance characteristics and the generated heat exchanger design options based on technical prospective to attain the highest possible improvement on the simple cycle performance. However, it is essential to consider the economic viability of using the recuperative cycle which will be investigated in thispaper. Although that there are several tools which can be used to achieve that objective, this study uses the Net Present Value (NPV) method due to its simplicity and accuracy. The established technique has been applied for the same described gas turbine cycles in the previous part. Based on the stated assumptions, it was found that by applying the recuperation in the first engine,W6BRC, and at full load and 100% utilization factor conditions, the payback period has increased by one year by applying over that of simple cycle. Moreover, at the end of the project life, the recuperative cycle of this engine is expected to achieve an increase of $11M in the NPV over that of simple cycle. This difference between the two cycles becomes greater in the case of the second engine, W7FA, which is ranging between $33.9M and $46.8M.However, the drop in the availability of the overall recuperative gas turbine by about 18% over the simple cycle gas turbine causes the NPV of both cycles to be equal. Moreover, this paper includes a sensitivity study to investigate the effects of utilization factor and recuperator effectiveness and pressure drop on the cumulative discounted cash flow. KeyWords:new systematic method,economic viability, Net Present Value, utilization factor,availability
Multi-objective Genetic Algorithm Applied to Conceptual Design of Single-stag...Masahiro Kanazaki
"Multi-objective Genetic Algorithm Applied to Conceptual Design of Single-stage Rocket Using Hybrid Propulsion System" presented at The Eighth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems (CJK-OSM).
Aircraft Finite Element Modelling for structure analysis using Altair ProductsAltair
The Airbus airframe design process has considerably evolved since 20 years with the constant improvement of numerical simulation capability and the computational means capacity. Today the size of Finite Element Models for aircraft structural behaviour study is exceeding the boundary of airframe components (fuselage section, wing); for the A350, a very large scale non-linear model of more than 60 million degrees of freedom has been developed to secure the static test campaign. This communication will illustrate the partnership with Altair and the use of Altair products for the creation and verification of very large models at Airbus. It will deal with: - Geometry preparation - Meshing - Property assignment - Assembly - Checking More generally, numerical simulation will play more and more a major role in the aircraft process, from the development of new concepts / derivatives to the support of the in-service fleet. Then, this presentation will also state the coming needs regarding model creation tools to cope with Airbus strategy.
Speakers
Marion Touboul, Ingénieur en Simulation Numérique - Calcul Structure, Airbus Opérations SAS
Modular Multi-Objective Genetic Algorithm for Large Scale Bi-level ProblemsStefano Costanzo
A genetic algorithm is used to solve the Centralised Peak-Load Pricing model on the European Air Traffic Management system. The Stackelberg equilibrium is obtained by means of an optimisation problem formulated as a bilevel linear programming model where the Central Planner sets one peak and one off-peak en-route charge and the Airspace Users choose the route among the available alternatives.
A Response Surface Based Wind Farm Cost (RS-WFC) model, is developed to evaluate the economics of wind farms. The RS-WFC model is developed using Extended Radial Basis Functions (E-RBF) for onshore wind farms in the U.S.. This model is then used to explore the in uence of di erent design and economic parameters, including number of turbines, rotor diameter and labor cost, on the cost of a wind farm. The RS-WFC model is composed of three parts that estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The accuracy of the cost model is favorably established through comparison with pertinent commercial data. Moreover, the RS-WFC model is integrated with an analytical power generation model of a wind farm. A recently developed Unrestricted Wind Farm Layout Optimization (UWFLO) model is used to determine the power generated by a farm. The ratio of the total annual cost and the energy generated by the wind farm in one year (commonly known as the Cost of Energy, COE) is minimized in this paper. The results show that the COE could decreasesigni cantlythroughlayoutoptimization,toobtainmillionsofannualcostsavings.
Wind farm development is an extremely complex process, most often driven by three im- portant performance criteria: (i) annual energy production, (ii) lifetime costs, and (iii) net impact on surroundings. Generally, planning a commercial scale wind farm takes several years. Undesirable concept-to-installation delays are primarily attributed to the lack of an upfront understanding of how different factors collectively affect the overall performance of a wind farm. More specifically, it is necessary to understand the balance between the socio-economic, engineering, and environmental objectives at an early stage in the design process. This paper proposes a Wind Farm Tradeoff Visualization (WiFToV) framework that aims to develop first-of-its-kind generalized guidelines for the conceptual design of wind farms, especially at early stages of wind farm development. Two major performance objectives are considered in this work: (i) cost of energy (COE) and (ii) land area per MW installed (LAMI). The COE is estimated using the Wind Turbine Design Cost and Scaling Model (WTDCS) and the Annual Energy Production (AEP) model incorporated by the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The LAMI is esti- mated using an optimal-layout based land usage model, which is treated as a post-process of the wind farm layout optimization. A Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm is used to perform the bi-objective optimization, which simultaneously optimizes the location and types of turbines. Together with a novel Pareto translation technique, the proposed WiFToV framework allows the exploration of the trade-off between COE and LAMI, and their variations with respect to multiple values of nameplate capacity.
Diesel backup generators are commonly installed in hospitals, data centers, universities, hotels, and other businesses for use in the event of power disruptions. These engines have quick response times that provide an unmatched reliable source of emergency backup power. Facilities that have these backup engines can also benefit from enrolling in demand response (DR) programs that offer economic incentives to participants who volunteer the use of their backup generators to supply electricity to the grid during certain periods of high electricity demand. In recent years, there has been an increase in the number of backup engines that have enrolled in DR programs in exchange for economic incentives. DR programs provide grid reliability, especially during periods of high electricity demand. Therefore, this is a win-win situation for backup engine owners and power utility companies offering these incentives. Generally, a backup generator with a capacity of 500 kilowatt (kW) or more is necessary to participate in DR programs. Participants in these DR programs agree with the local power company to use their backup engines when directed; usually during periods of peak electricity demand or power disruption. However, recent air quality regulations that apply to backup generators can be challenging to meet when participating in a DR program. That is the case because the applicable requirements for backup engine depend on whether the use is strictly for emergency purposes or for DR (considered non-emergency). Purely emergency use engines are subject to work practice standards while non-emergency engines are subject to emission limits that may require emission controls. Additionally, non-emergency engines may be subject to dispersion modeling requirements to show compliance with the national ambient air quality standards (NAAQS). At the moment the dispersion model used in permitting evaluations is extremely conservative and can show compliance issues. In conclusion, DR programs can be a profitable way to get additional cash for owners and operators of backup engines. However, the permitting implications should be considered thoroughly before enrolling in such a program to avoid any unintended adverse consequences.
Evaluating AERMOD and Wind Tunnel Derived Equivalent Building DimensionsSergio A. Guerra
While the current EBD method is the best available option to determine correct building dimensions in the model, a different method was suggested by EPA in the 2011 Memo: Model Clearinghouse Review of EBD for AERMOD.9 Attachment B to the 2011 Memo includes an assessment of the Alcoa Davenport Works EBD Study. In this evaluation EPA compared wind tunnel observations with AERMOD derived concentrations. However, this evaluation has important shortcomings. First, to carry out this comparison between wind tunnel and AERMOD concentrations, it is necessary to collect velocity profiles that include longitudinal and vertical turbulent intensity measurements upwind of the stack. These data were not available for the EPA evaluation of the Alcoa Davenport Works EBD Study. Second, the wind tunnel model operating conditions were converted to full scale conditions by using exact similarity. However, exact similarity is not used to specify model operating conditions since only momentum ratios are matched but not buoyancy ones. Whereas EPA did not provide important details on how this study was performed, this paper outlines how to properly carry out this new method where AERMOD is used to determine equivalent building dimensions. The viability of this new method was also evaluated and discussed.
Probabilistic & Source Characterization Techniques in AERMOD ComplianceSergio A. Guerra
The short term NAAQS are more stringent and traditional techniques are not suitable anymore. The probabilistic nature of these standards also opens the door to modeling techniques based on probability. Source characterization studies can also be used to refine AERMOD’s inputs to be more accurate and achieve reductions of more than half. This presentation will cover these compliance methods.
Currently, it is assumed that a given emission unit is in operation at its maximum capacity every hour of the year. However, assuming constant maximum emissions is overly conservative for facilities such as power plants that are not in operation all the time at full load. A better approach is the use of the Monte Carlo technique to account for emission variability. Another conservative assumption in NAAQS modeling relates to combining predicted concentrations from AERMOD with maximum or design concentrations from the monitor. A more reasonable approach is to combine the 50th percentile background concentration with AERMOD values.
The inputs to AERMOD can be obtained by more accurate source characterization studies. Such is the case of building dimensions commonly calculated with BPIP. These dimensions tend to overstate the wake effects and produce significantly higher concentrations especially for lattice structures, elongated buildings, and streamlined structures. An Equivalent Building Dimensions (EBD) study can be used to inform AERMOD with more accurate downwash characteristics.
Evaluation of the Theoretical Problems with Building Downwash Using A New Met...Sergio A. Guerra
While the current EBD method is the best available option to determine correct building dimensions in the model, a different method was suggested by EPA in the 2011 Memo: Model Clearinghouse Review of EBD for AERMOD. Attachment B to the 2011 Memo includes an assessment of the Alcoa Davenport Works EBD Study. In this evaluation EPA compared wind tunnel observations with AERMOD derived concentrations. However, this evaluation has important shortcomings. First, to carry out this comparison between wind tunnel and AERMOD concentrations, it is necessary to collect velocity profiles that include longitudinal and vertical turbulent intensity measurements upwind of the stack. These data were not available for the EPA evaluation of the Alcoa Davenport Works EBD Study. Second, the wind tunnel model operating conditions were converted to full scale conditions by using exact similarity. However, exact similarity is not used to specify model operating conditions since only momentum ratios are matched but not buoyancy ones. Whereas EPA did not provide important details on how this study was performed, this paper outlines how to properly carry out this new method where AERMOD is used to determine equivalent building dimensions. The viability of this new method was also evaluated and discussed.
Using Physical Modeling to Refine Downwash Inputs to AERMOD at a Food Process...Sergio A. Guerra
Demonstrating compliance with air quality standards using dispersion modeling is increasingly difficulty because of significant tightening National Ambient Air Quality Standards (NAAQS) that has occurred in the last decade. Compliance with these standards is usually demonstrated using AERMOD, EPA’s standard model for assessing air quality impacts from industrial sources. However, AERMOD often produces higher predictions of air quality impacts due to the inherent conservative (high) assumptions and simplifications in its formulation. A specific situation involves the calculations used to assess the impacts of air flow downwash around buildings. Although the theory used to estimate these effects was developed for a limited set of building types, these formulae are applied indiscriminately to all types of buildings in a conservative fashion, often leading to significant overpredictions of downwash effects.
This presentation covers the basics of wind tunnel modeling and how it can be used to correct downwash induced overpredictions to achieve compliance. The presentation will also describe the setup and execution of wind tunnel modeling at a food processing facility to develop improved downwash parameters and increase the accuracy of dispersion modeling results.
An approach to evaluate the heat exchanger retrofit for installed industrial ...eSAT Journals
Abstract This is part 2 of the conducted study to develop a new systematic method to evaluate the heat exchanger retrofit on existing industrial gas turbines. A new approach has been introduced and validated in the first part comparing with the measured data. This method was used to optimize the obtained cycle performance characteristics and the generated heat exchanger design options based on technical prospective to attain the highest possible improvement on the simple cycle performance. However, it is essential to consider the economic viability of using the recuperative cycle which will be investigated in thispaper. Although that there are several tools which can be used to achieve that objective, this study uses the Net Present Value (NPV) method due to its simplicity and accuracy. The established technique has been applied for the same described gas turbine cycles in the previous part. Based on the stated assumptions, it was found that by applying the recuperation in the first engine,W6BRC, and at full load and 100% utilization factor conditions, the payback period has increased by one year by applying over that of simple cycle. Moreover, at the end of the project life, the recuperative cycle of this engine is expected to achieve an increase of $11M in the NPV over that of simple cycle. This difference between the two cycles becomes greater in the case of the second engine, W7FA, which is ranging between $33.9M and $46.8M.However, the drop in the availability of the overall recuperative gas turbine by about 18% over the simple cycle gas turbine causes the NPV of both cycles to be equal. Moreover, this paper includes a sensitivity study to investigate the effects of utilization factor and recuperator effectiveness and pressure drop on the cumulative discounted cash flow. KeyWords:new systematic method,economic viability, Net Present Value, utilization factor,availability
Multi-objective Genetic Algorithm Applied to Conceptual Design of Single-stag...Masahiro Kanazaki
"Multi-objective Genetic Algorithm Applied to Conceptual Design of Single-stage Rocket Using Hybrid Propulsion System" presented at The Eighth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems (CJK-OSM).
Aircraft Finite Element Modelling for structure analysis using Altair ProductsAltair
The Airbus airframe design process has considerably evolved since 20 years with the constant improvement of numerical simulation capability and the computational means capacity. Today the size of Finite Element Models for aircraft structural behaviour study is exceeding the boundary of airframe components (fuselage section, wing); for the A350, a very large scale non-linear model of more than 60 million degrees of freedom has been developed to secure the static test campaign. This communication will illustrate the partnership with Altair and the use of Altair products for the creation and verification of very large models at Airbus. It will deal with: - Geometry preparation - Meshing - Property assignment - Assembly - Checking More generally, numerical simulation will play more and more a major role in the aircraft process, from the development of new concepts / derivatives to the support of the in-service fleet. Then, this presentation will also state the coming needs regarding model creation tools to cope with Airbus strategy.
Speakers
Marion Touboul, Ingénieur en Simulation Numérique - Calcul Structure, Airbus Opérations SAS
Modular Multi-Objective Genetic Algorithm for Large Scale Bi-level ProblemsStefano Costanzo
A genetic algorithm is used to solve the Centralised Peak-Load Pricing model on the European Air Traffic Management system. The Stackelberg equilibrium is obtained by means of an optimisation problem formulated as a bilevel linear programming model where the Central Planner sets one peak and one off-peak en-route charge and the Airspace Users choose the route among the available alternatives.
A Response Surface Based Wind Farm Cost (RS-WFC) model, is developed to evaluate the economics of wind farms. The RS-WFC model is developed using Extended Radial Basis Functions (E-RBF) for onshore wind farms in the U.S.. This model is then used to explore the in uence of di erent design and economic parameters, including number of turbines, rotor diameter and labor cost, on the cost of a wind farm. The RS-WFC model is composed of three parts that estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The accuracy of the cost model is favorably established through comparison with pertinent commercial data. Moreover, the RS-WFC model is integrated with an analytical power generation model of a wind farm. A recently developed Unrestricted Wind Farm Layout Optimization (UWFLO) model is used to determine the power generated by a farm. The ratio of the total annual cost and the energy generated by the wind farm in one year (commonly known as the Cost of Energy, COE) is minimized in this paper. The results show that the COE could decreasesigni cantlythroughlayoutoptimization,toobtainmillionsofannualcostsavings.
The planning of a wind farm, which minimizes the project costs and maximizes the power generation capacity, presents significant challenges to today’s wind energy industry. An optimal wind farm planning strategy that accounts for the key factors (that can be designed) influencing the net power generation offers a powerful solution to these daunting challenges. This paper explores the influences of (i) the number of turbines, (ii) the farm size, and (iii) the use of a combination of turbines with differing rotor diameters, on the optimal power generated by a wind farm. We use a recently developed method of arranging turbines in a wind farm (the Unrestricted Wind Farm Layout Optimization (UWFLO)) to maximize the farm efficiency. Response surface based cost models are used to estimate the cost of the wind farm as a function of the the turbine rotor diameters and number of tur- bines. Optimization is performed using a Particle Swarm Optimization (PSO) algorithm. A robust mixed-discrete version of the PSO algorithm is implemented to appropriately account for the discrete choice of feasible rotor diameters. The use of an optimal combi- nation of turbines with differing rotor diameters was observed to significantly improve the net power generation. Exploration of the influences of (i) the number of turbines, and (ii) the farm size, on the cost per KW of power produced, provided interesting observations.
The maintenance cost of wind farms is one of the major factors influencing the prof- itability of wind projects. During preventive maintenance, the shutdown of wind turbines results in downtime wind energy losses. Appropriate determination of when to perform maintenance and which turbine(s) to maintain can reduce the overall downtime losses sig- nificantly. This paper uses a wind farm power generation model to evaluate downtime energy losses during preventive maintenance for a given group of wind turbines in the en- tire array. Wakes effects are taken into account to accurately estimate energy production over a specified time period. In addition to wind condition, the influence of wake effects is a critical factor in determining the selection of turbine(s) under maintenance. To min- imize the overall downtime loss of an offshore wind farm due to preventive maintenance, an optimal scheduling problem is formulated that selects the maintenance time of each turbine. Weather conditions are imposed as constraints to ensure the safety of mainte- nance personnel, transportation, and tooling infrastructure. A genetic algorithm is used to solve the optimal scheduling problem. The maintenance scheduling is optimized for a utility-scale offshore wind farm with 25 turbines. The optimized schedule not only reduces the overall downtime loss by selecting the maintenance dates when wind speed is low, but also considers the wake effects among turbines. Under given wind direction, the turbines under maintenance are usually the ones that can generate strong wake effects on others during certain wind conditions, or the ones that generate relatively less power being under excessive wake effects.
In this paper, we develop a flexible design platform to ac- count for the influences of key factors in optimal planning of commercial scale wind farms. The Unrestricted Wind Farm Lay- out Optimization (UWFLO) methodology, which avoids limit- ing assumptions regarding the farm layout and the selection of turbines, is used to develop this design platform. This paper presents critical advancements to the UWFLO methodology to allow the synergistic consideration of (i) the farm layout, (ii) the types of commercial turbines to be installed, and (iii) the ex- pected annual distribution of wind conditions at a particular site. We use a recently developed Kernel Density Estimation (KDE) based method to characterize the multivariate distribution of wind speed and wind direction. Optimization is performed using an advanced mixed discrete Particle Swarm Optimization algo- rithm. We also implement a high fidelity wind farm cost model that is developed using a Radial Basis Function (RBF) based response surface. The new optimal farm planning platform is applied to design a 25-turbine wind farm at a North Dakota site. We found that the optimal layout is significantly sensitive to the annual variation in wind conditions. Allowing the turbine-types to be selected during optimization was observed to improve the annual energy production by 49% compared to layout optimiza- tion alone.
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
This paper develops a cost model for onshore wind farms in the U.S.. This model is then used to analyze the influence of different designs and economic parameters on the cost of a wind farm. A response surface based cost model is developed using Extended Radial Basis Functions (E-RBF). The E-RBF ap- proach, a combination of radial and non-radial basis functions, can provide the designer with significant flexibility and freedom in the metamodeling process. The E-RBF based cost model is composed of three parts that can estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The input param- eters for the E-RBF based cost model include the rotor diameter of a wind turbine,the number of wind turbines in a wind farm, the construction labor cost, the management labor cost and the technician labor cost. The accuracy of the model is favorably explored through comparison with pertinent real world data. It is found that the cost of a wind farm is appreciably sensitive to
the rotor diameter and the number of wind turbines for a given desirable total power output.
The development of utility-scale wind farms that can produce energy at a cost comparable to that of conventional energy resources presents significant challenges to today’s wind energy industry. The consideration of the combined impact of key design and environmental factors on the performance of a wind farm is a crucial part of the solution to this challenge. The state of the art in optimal wind project planning includes wind farm layout design and more recently turbine selection. The scope of farm layout optimization and the predicted wind project performance however depends on several other critical site-scale factors, which are often not explicitly accounted for in the wind farm planning literature. These factors include: (i) the land area per MW installed (LAMI), and (ii) the nameplate capacity (in MW) of the farm. In this paper, we develop a framework to quantify and analyze the roles of these crucial design factors in optimal wind farm planning. A set of sample values of LAMI and installed farm capacities is first defined. For each sample farm definition, simultaneous optimization of the farm layout and turbine selection is performed to maximize the farm capacity factor (CF). To this end, we apply the recently de- veloped Unrestricted Wind Farm Layout Optimization (UWFLO) method. The CF of the optimized farm is then represented as a function of the nameplate capacity and the LAMI, using response surface methodologies. The variation of the optimized CF with these site-scale factors is investigated for a representative wind site in North Dakota. It was found that, a desirable CF value corresponds to a cutoff “LAMI vs nameplate capacity” curve – the identification of this cutoff curve is critical to the development of an economically viable wind energy project.
Introduction
Data aquisition
Generation & demand
RES potential
Transmission network
Corridor selection
Costs assumption
Methodology and modeling
Results of the simulation
Conclusions and recommendations
This paper presents a new method (the Unrestricted Wind Farm Layout Optimization (UWFLO)) of arranging turbines in a wind farm to achieve maximum farm efficiency. The powers generated by individual turbines in a wind farm are dependent on each other, due to velocity deficits created by the wake effect. A standard analytical wake model has been used to account for the mutual influences of the turbines in a wind farm. A variable induction factor, dependent on the approaching wind velocity, estimates the velocity deficit across each turbine. Optimization is performed using a constrained Particle Swarm Optimization (PSO) algorithm. The model is validated against experimental data from a wind tunnel experiment on a scaled down wind farm. Reasonable agreement between the model and experimental results is obtained. A preliminary wind farm cost analysis is also performed to explore the effect of using turbines with different rotor diameters on the total power generation. The use of differing rotor diameters is observed to play an important role in improving the overall efficiency of a wind farm.
“Random variables in the Offshore Wind Turbine fatigue reliability design wit...TRUSS ITN
Abstract: The fatigue design of Offshore Wind Turbines (OWT) is one of the most resource demanding tasks in the OWT design process. Techniques have been developed recently to simplify the amount of effort needed to design to structural fatigue. This is the example of the usage of Kriging surrogate models. These may be used in OWTs design not only, to reduce the computational effort needed to analyse an OWT, but also to allow their design to be robust. Due to the stress variability and its non-linear character, the short-term fatigue damage variability is high, and converging the stochastic field approached by the surrogate model in relation to the real observations is challenging. A thorough analysis of the different components that load an OWT and are more critical for the tower component fatigue life is required, and therefore, presented and discussed in the current paper. The tower, jointly with the foundation, are particular components of the OWT regarding the fatigue analysis process. Statistical assessments of the extrapolation of fatigue loads for the tower and the influence of the environmental parameters in the short-term damage are presented in this paper. This sets a support analysis for the creation of the Kriging response surfaces for fatigue analysis. NREL’s 5 MW monopile turbine is used due to its state of the art character. Five environmental variables are considered in the analysis. A sensitivity analysis is conducted to identify which variables are most prominent in the quantification of the short-term damage uncertainty in the tower. The decoupling of the different external contributions for the fatigue life is a major contribution of the work presented. Preliminary guidelines are drawn for the creation of surrogate models to analyse fatigue of OWT towers and the most relevant conclusions are presented in an industry-oriented design outline regarding the most critical random variables that influence OWT short-term fatigue calculation.
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
"Structural probabilistic assessment of offshore wind turbine operation fatig...TRUSS ITN
Abstract: The probabilistic analysis of Offshore Wind Turbines (OWT) is not a new practice. The standards for designing OWT (IEC 61400 class) emphasizes that assessing uncertainty is of major importance inside the design chain. Still, major challenges related to the uncertainty and the probabilistic assessment pose to the sector and its development. The analysis of operational loads is one them. The problem of analyzing extreme responses or cumulated damage in operation during the design phase is significantly related to its high computational cost. As we progressively add complexity to the system to account for its uncertainties, the computational effort increases and a perceptive design becomes a heavy task. If an optimization process is then sought, the designing effort grows even further. In the particular case of fatigue analysis, it is frequent to not be able to cover a full lifetime of simulations due to computational cost restrictions. The mentioned difficulties fomented the utilization of surrogate models in the reliability analysis of OWT. From these surrogate approximations the ones based on Kriging models gained a special emphasis recently for structural reliability. It was shown that, for several applications, these models can be efficient and accurate to approximate the response of the system or the limit state surfaces. The presented paper tackles some of the issues related to their applicability to OWT, in a case specific scenario of the tower component subjected to operational fatigue loads. A methodology to assess the reliability of the tower component to fatigue damage is presented. This methodology combines a Kriging model with the theory of extreme values. A one-dimensional Kriging case using the state of art NREL’s monopile turbine is presented. The reliability of the OWT tower is calculated for 20 years. The results show that the usage of a Kriging model to calculate the long term damage variation shows a high potential to assess the reliability of OWT towers to fatigue failure.
Giles Hundleby's presenation on Wave and Tidal Cost reduction delivered at ICOE 2016, Edinburgh, February 2016. He outlines that the industry needs to take a radical new approach to be successful.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
1. NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
A Spatial-Economic Cost-Reduction Pathway
Analysis for U.S. Offshore Wind Energy
Development from 2015–2030
Philipp Beiter and Tyler Stehly
2016 International Offshore Wind Partnering Forum
Life-Cycle Cost for Offshore Wind Workshop
Newport, Rhode Island
October 5, 2016
NREL/PR-6A20-67204
2. 2
Objectives
Quantify the impact from a variety of spatial characteristics on the levelized cost of energy
(LCOE) in the United States at specific points in time
Fixed-bottom foundations (e.g., monopile, jacket)
Floating foundations (e.g., spar, semisubmersible)
Model the impact from technology innovation and market maturity during the time frame
from 2015–2027 (commercial operation date [COD])* on LCOE
Provide a framework to quantify economic
viability for offshore wind in the United States
Determine the cost-optimal choice between
fixed-bottom and floating offshore wind
technologies under various site conditions.
* The modeled LCOE from 2015‒2027 (COD) was extrapolated until 2030 (COD).
Offshore wind substructure types for varying water depths.
Illustration by Josh Bauer, National Renewable Energy Laboratory
3. 3
General Methodology
• The general methodology consists of a combination of geographic information system
(GIS) data layers, performance modeling, and cost modeling.
DELPHOS: “a series of cost models and basic data sets to improve the analysis of the impact of innovations on (future offshore wind) costs” developed in the
United Kingdom by BVG Consulting and KIC InnoEnergy (KIC InnoEnergy 2016)
4. 4
General Assumptions
• Domestic deployment and supply chain maturity
• Technology assumptions
• Focus on fundamental differences between technologies
• Technology availability to meet industry needs
• All costs reported in real 2015 dollars.
Key Assumptions
Financial Close (FC) 2013 2020 2025
Commercial Operations Date (COD) 2015 2022 2027
Turbine Rated Power (megawatts [MW]) 3.4 6 10
Plant Size (MW) 600 600 600
Turbine Hub Height (meters [m]) 85 100 125
Turbine Rotor Diameter (m) 115 155 205
Turbine Specific Power (watts [W]/m2) 327 318 303
5. 5
Several Methodological Simplifications
The following several spatial variables were not considered:
• Extreme design conditions
• Surface ice exposure
• Hurricane exposure
• Soil conditions
The following modeling generalizations were used:
• Generic project layout
• Focus on 6-MW turbines.
6. 6
Wind Project Layout and Performance Modeling
Coverage includes:
• Major offshore areas except for Alaska
• Depths restricted up to 1,000 m to reflect limits
of current technology
Wind project layout includes:
• One cell comprising 100 turbines
• Spacing based on 6-MW turbines in a 10-by-10
grid, spaced at 7 rotor diameters
Each project layout considered independently
includes:
• 7,159 distinct wind power plant layouts*
• No gaps between adjacent layouts
• No wake interaction between layouts.
* A potential wind farm was considered to qualify if at least 50% of the turbines met the depth
restriction criteria.
Using Openwind, 7,159-unit wind power plants were modeled throughout the
resource area of the continental United States from 0 nautical miles (nm) to 50 nm
Conceptual project layout with 100 generic 6-MW turbines
7. 7
Cost Reduction Pathways – DELPHOS Tool
• The DELPHOS tool (BVG Consulting/KIC InnoEnergy) is a “series of cost models and basic
data sets to improve the analysis of the impact of innovations on [offshore wind] costs”*
– Method: Involves a comprehensive bottom-up assessment of the potential to reduce cost from
elements in the cost breakdown structure and by improving system reliability and performance;
aggregates 58 potential technology innovations and supply chain effects and estimates the
resulting LCOE at for two future focus years: 2022 (COD) and 2027 (COD), projected from the base
year set at 2015 (COD)
– Data: Obtained from the Crown Estate’s 2012 study based on expert elicitations from 54 entities
involved in the offshore wind industry and projected the Crown Estate Financial Close (FC) year
2020 cost targets out to FC 2025
– Findings: Discovered that small but significant improvements in cost from each subassembly in
the offshore wind system can lead to LCOE reductions of sufficient magnitude to achieve
economic competitiveness
• The DELPHOS tool only considers fixed-bottom technology
• NREL complemented the DELPHOS tool with a preliminary assessment of floating
technology cost reductions for focus years 2022 (COD) and 2027 (COD).
*DELPHOS (KIC Innoenergy 2016)
8. 8
Spatio-Economic Analysis Combines a Number of Models and Data
Sources to Estimate LCOE
NREL
Offshore
Wind Cost
Model
Spatial-economic processing framework
9. 9
The Spatio-Economic Analysis Combines a Number of
Models and Data Sources to Estimate LCOE
LCOE calculation framework and modeling assumptions
10. 10
Substructure Parameter Study
Case study: Monopile for 3-MW turbine
Substructure Unit
CAPEX
Substructure Unit
CAPEX
Reference system, load locations, and
definitions of subcomponents for a
monopile substructure. Image modified
from an illustration by Josh Bauer, NREL
11. 11
Substructure Parameter Study
For each combination of turbine rating (3, 6, and 10 MW) and water
depth we assessed:
• Fixed-bottom substructures, including:
o A monopile (depths of 5 to 100 m) using the TowerSE model to optimize the
pile, transition piece, and tower
o A jacket (depths of 5 to 100 m) using the JacketSE model to optimize the pin-
piles, trusses, transition piece, and tower
• Floating substructures, including:
o A semisubmersible (depths of 40 to 1,000 m) using the Floating Sizing Tool to
optimize the semisubmersible’s platform and mooring system
o A spar (depths of 100 to 1,000 m) using the Floating Sizing Tool to optimize
the spar’s platform and mooring system.
Key variables: Water depth and turbine rating
Substructure Unit
CAPEX
12. 12
Substructure Parameter Study
• Fabrication cost for fixed
based on European
market data and recent
industry studies (e.g., cost
reduction pathways,
Great Lakes Wind
Network subcontract, and
so on)
• 100-unit order quantity
Component unit cost estimates
Mass results in metric tons for 3-MW monopile-based systems and comparison to industry data
Substructure Unit
CAPEX
• Scaling equations are developed for each substructure type and application of fabrication
and transportation costs are used to estimate the delivered cost at the staging port.
13. 13
Electrical Parameter Study
Map showing the boundaries among electrical infrastructure categories
Case study:
Fixed-bottom
substructure
export system
14. 14
Electrical Parameter Study
• Capital expenditure (CAPEX) curves estimated using the NREL Offshore Balance of System
model and a variety of other sources
• Transmission system losses estimated through analysis in PSCAD, lost revenue is valued at
$200/megawatt-hour (MWh) (based on industry input).
Summary of export system parameter study results for fixed-bottom technology
Minimum cost by distance
15. 15
Installation Parameter Study
Case study: Installation of a
3-MW turbine on a
monopile substructure
Pacific Orca installation vessel. Photo from Lars Blicher, Swire Blue
Ocean
16. 16
Installation Parameter Study
The installation parameter study used the NREL Offshore Balance of System model to
estimate the costs of installing each of the four substructure technologies (monopile, jacket,
semisubmersible, and spar) over a range of location-specific conditions for three turbine
sizes: 3, 6, and 10 MW.
Key variables: Distance from project site to staging port, turbine size, and water depth
Key parameter ranges for installation
19. 19
Operation and Maintenance (O&M)
Parameter Study
Case study: O&M for a fixed-bottom
substructure
Illustration of the UMOE Mandel AS Wave Craft.
Image from Are Søreng, UMOE
The analysis considers three corrective maintenance strategies to represent the five substructure scenarios:
• In-situ (monopile, jacket), in which maintenance is performed at the project location by a jack-up crane
vessel
• Tow-to-Port (semisubmersible, spar horizontal tow), in which the substructure-turbine unit is
disconnected from moorings and towed to port for repair by a standard crawler crane
• Tow-to-Assembly-Area (spar vertical tow), in which the substructure-turbine unit is disconnected from
the moorings and towed to the inshore assembly site. Requires mobilization of installation equipment
spread (e.g., barges, cranes).
Key variables: Distance from project to operations port and meteorological ocean (metocean) conditions
20. 20
O&M Parameter Study
Model Outputs:
• The Energy Research Centre of the Netherlands (ECN) O&M Tool outputs are
operational expenditures (OPEX), availability, and total O&M cost (OPEX +
revenue loss)
• Parameterized curves fit to the ‘least cost O&M strategy’ at each distance
(defined as O&M costs + lost revenue) for inclusion in the spatio-economic
LCOE model.
Depiction of O&M optimization criteria
21. 21
O&M Parameter Study
Three sites were selected to
represent the range of
metocean conditions across the
U.S. offshore wind resource
(model requires 10 years of
correlated wind and wave data)
• ECN O&M Tool set up for
each site (i.e., mild,
moderate, and severe)
• Results are applied across
the Outer Continental Shelf
by using average significant
wave height as an indicator
of severity of site-specific
metocean conditions.
Representative wave information system stations for O&M
analysis
22. 22
O&M Parameter Study
• Access strategies (e.g., for getting
personnel on to the wind turbine) will
likely be similar for across technologies
• For each site and each corrective
maintenance approach, the parameter
study considers a range of different
access strategies, ranging from basic to
innovative.
Matrix of operational expenditure modeling parameters
23. 23
O&M Parameter Study
Moderate site total O&M costs for the fixed-bottom substructure
Minimum cost by distance
• Identifies economic breakpoints between O&M strategies for each of the three
representative sites.
24. 24
O&M Parameter Study
Develop OpEx (OPEX in the figure) and availability equations for each technology
• Analysts determine how OpEx and availability might change with distance to port
assuming adoption of the optimal O&M strategy at each distance
• Curves are then fitted to the OpEx and availability result data to describe the relationship
between OpEx and availability.
OpEx results for the fixed-bottom substructure
25. 25
General Limitations
General limitations of this initial assessment include the following:
An assumption of continued investments in technology innovation, developments, and
market visibility of a robust domestic supply chain
The need for domestic cost reductions to require additional activities to reduce risk and
uncertainty of early projects, including addressing U.S.-specific challenges (e.g., hurricanes,
deeper water, Jones Act requirements) and incentivizing markets
Model simplifications, such as:
o Models—parameter studies were conducted with first-order tools
o Cost data— validation of assumptions
o Suitability/availability of technology
o Macroeconomic factors (e.g., exchange rates, commodity prices)
Analysis does not consider several significant design variables that may contribute to
variability among regions
Preliminary assessment of the levelized avoided cost of energy LACE limited by available
data and a set of simplifying assumptions.
32. 32
Results: Economic Viability
Net value ($/MWh) = LACE – LCOE
LACE: levelized avoided cost of energy (proxy for available revenue to a
project; a combination of wholesale electricity prices and capacity value)
Economic potential (unsubsidized) of U.S. offshore wind sites in 2027 (COD)
33. 33
Conclusions
In 2015, offshore wind costs span an estimated range from $130/MWh–$450/MWh
Cost-reduction pathway modeling and analysis of future conditions show that cost ranges
are reduced by 2022 to a range from $95/MWh–$300/MWh, and they are further reduced
by 2027 to a range from $80 MWh–$220/MWh among U.S. coastal sites
By 2030, offshore wind may become economically viable in some parts of the United States,
particularly in parts of the northeastern Atlantic Ocean and in a small number of locations
along the mid-Atlantic Coast (without consideration for direct policy support)
During the time period considered, the costs of the two technologies are found to converge
under the cost-reduction pathway scenarios modeled
Analyses comparing fixed and floating technology using four typical substructure types show
economic break points in water depths between 45 m and 60 m.
34. 34
References
Beiter, P., W. Musial, A. Smith, L. Kilcher, R. Damiani, M. Maness, S. Sirnivas, T. Stehly, V.
Gevorgian, M. Mooney, G. Scott. 2016. A Spatial-Economic Cost-Reduction Pathway for
U.S. Offshore Wind Energy Development from 2015‒2030 (Technical Report), NREL/TP-
6A20-66579, National Renewable Energy Laboratory (NREL), Golden, CO (US).
http://www.nrel.gov/docs/fy16osti/66579.pdf.
Gilman, P., B. Maurer, L. Feinberg, A. Duerr, L. Peterson, W. Musial, P. Beiter, J. Golladay,
J. Stromberg, I. Johnson, D. Boren, A. Moore. 2016. National Offshore Wind Strategy:
Facilitating the Development of the Offshore Wind Industry in the United States.
DOE/GO-102016-4866. U.S. Department of Energy, Washington, D.C. (US).
http://energy.gov/sites/prod/files/2016/09/f33/National-Offshore-Wind-Strategy-
report-09082016.pdf.
KIC InnoEnergy. 2016. “DELPHOS. Tracking the impact of innovation on the levelised
cost of energy.” Accessed August 2016. http://www.kic-innoenergy.com/delphos/.