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.
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 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.
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
The creation of wakes, with unique turbulence charac- teristics, downstream of turbines significantly increases the complexity of the boundary layer flow within a wind farm. In conventional wind farm design, analytical wake models are generally used to compute the wake-induced power losses, with different wake models yielding significantly different estimates. In this context, the wake behavior, and subsequently the farm power generation, can be expressed as functions of a series of key factors. A quantitative understanding of the relative impact of each of these factors is paramount to the development of more reliable power generation models; such an understanding is however missing in the current state of the art in wind farm design. In this paper, we quantitatively explore how the farm power generation, estimated using four different analytical wake models, is influenced by the following key factors: (i) incoming wind speed, (ii) land configuration, and (iii) ambient turbulence. The sensitivity of the maximum farm output potential to the input factors, when using different wake models, is also analyzed. The extended Fourier Amplitude Sensitivity Test (eFAST) method is used to perform the sensitivity analysis. The power generation model and the optimization strategy is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. In the case of an array-like turbine arrangement, both the first-order and the total-order sensitivity analysis indices of the power output with respect to the incoming wind speed were found to reach a value of 99%, irrespective of the choice of wake models. However, in the case of maximum power output, significant variation (around 30%) in the indices was observed across different wake models, especially when the incoming wind speed is close to the rated speed of the turbines.
http://windenergyresearch.org/?p=2836
A wind farm layout optimization framework based on a multi-fidelity model approach is applied to the offshore test case of Middelgrunden, Denmark. While aesthetic considerations have heavily influenced the famous curved design of Middelgrunden wind farm, this work focuses on testing a method that optimizes the profit of offshore wind farms based on a balance of the energy production, the electrical grid costs, the foundations cost, and the wake induced lifetime fatigue of different wind turbine components. The multi-fidelity concept uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU TopFarm project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production AEP, a foundations cost function based on the water depth, and an electrical grid cost function. The second level calculates the AEP and adds a wake induced fatigue cost function based on the interpolation of a database of simulations done for various wind speed and wake setups with the aero-elastic code HAWC2 and the Dynamic Wake Meandering (DWM) model. The third level includes directly the HAWC2 and DWM models in the optimization loop in order to estimate both the fatigue costs and the AEP.
The novelty of this work is the implementation of the multi-fidelity, the inclusion of the fatigue costs in the optimization framework, and its application on an existing offshore wind farm test case.
Paper presented at ISOPE 19-14 June 2011, Maui, Hawaii, USA
P.-E. Réthoré, P. Fuglsang, G.C. Larsen, T. Buhl, T.J. Larsen, H.A. Madsen. TopFarm: Multi-fidelity Optimization of Offshore Wind Farm<. (2011-TCP-1012). ISOPE conference. Maui, Hawaii, USA. 19-24 June 2011.
An integrated OPF dispatching model with wind power and demand response for d...IJECEIAES
In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resources are dispatched together with conventional generation. The uncertainty and volatility associated to renewable resources represents a new paradigm to be faced for power system operation. Moreover, in various electricity markets there are mechanisms to allow the demand participation through demand response (DR) strategies. Under operational and economic restrictions, the operator each day, or even in intra-day markets, dispatchs an optimal power flow to find a feasible state of operation. The operation decisions in power markets use an optimal power flow considering unit commitment to dispatch economically generation and DR resources under security restrictions. This paper constructs a model to include demand response in the optimal power flow under wind power uncertainty. The model is formulated as a mixed-integer linear quadratic problem and evaluated through Monte-Carlo simulations. A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting. The proposed integrated OPF model is tested on the standard IEEE 39-bus system.
PREDICTION OF REPAIR & MAINTENANCE COSTS OF DIESEL ENGINEijmech
Diesel engine is widely use for different applications, the failure frequency of diesel engine is more increase to increase the age & use of engine in order to take decision to replacement of engine on the basis of Repair & Maintenance cost (R&M) & predication of future Repair & Maintenance costs of diesel engine used in Borewell compressor. Present case study discusses prediction of accumulated R&M costs (Y) of Diesel engine against usage in hours (X). Recorded data from the company service station is used to determine regression models for predicting total R&M costs based on total usage hours. The statistical results of the study indicates that in order to predict total R&M costs is more useful for replacement
decisions than annual charge.
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 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.
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
The creation of wakes, with unique turbulence charac- teristics, downstream of turbines significantly increases the complexity of the boundary layer flow within a wind farm. In conventional wind farm design, analytical wake models are generally used to compute the wake-induced power losses, with different wake models yielding significantly different estimates. In this context, the wake behavior, and subsequently the farm power generation, can be expressed as functions of a series of key factors. A quantitative understanding of the relative impact of each of these factors is paramount to the development of more reliable power generation models; such an understanding is however missing in the current state of the art in wind farm design. In this paper, we quantitatively explore how the farm power generation, estimated using four different analytical wake models, is influenced by the following key factors: (i) incoming wind speed, (ii) land configuration, and (iii) ambient turbulence. The sensitivity of the maximum farm output potential to the input factors, when using different wake models, is also analyzed. The extended Fourier Amplitude Sensitivity Test (eFAST) method is used to perform the sensitivity analysis. The power generation model and the optimization strategy is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. In the case of an array-like turbine arrangement, both the first-order and the total-order sensitivity analysis indices of the power output with respect to the incoming wind speed were found to reach a value of 99%, irrespective of the choice of wake models. However, in the case of maximum power output, significant variation (around 30%) in the indices was observed across different wake models, especially when the incoming wind speed is close to the rated speed of the turbines.
http://windenergyresearch.org/?p=2836
A wind farm layout optimization framework based on a multi-fidelity model approach is applied to the offshore test case of Middelgrunden, Denmark. While aesthetic considerations have heavily influenced the famous curved design of Middelgrunden wind farm, this work focuses on testing a method that optimizes the profit of offshore wind farms based on a balance of the energy production, the electrical grid costs, the foundations cost, and the wake induced lifetime fatigue of different wind turbine components. The multi-fidelity concept uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU TopFarm project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production AEP, a foundations cost function based on the water depth, and an electrical grid cost function. The second level calculates the AEP and adds a wake induced fatigue cost function based on the interpolation of a database of simulations done for various wind speed and wake setups with the aero-elastic code HAWC2 and the Dynamic Wake Meandering (DWM) model. The third level includes directly the HAWC2 and DWM models in the optimization loop in order to estimate both the fatigue costs and the AEP.
The novelty of this work is the implementation of the multi-fidelity, the inclusion of the fatigue costs in the optimization framework, and its application on an existing offshore wind farm test case.
Paper presented at ISOPE 19-14 June 2011, Maui, Hawaii, USA
P.-E. Réthoré, P. Fuglsang, G.C. Larsen, T. Buhl, T.J. Larsen, H.A. Madsen. TopFarm: Multi-fidelity Optimization of Offshore Wind Farm<. (2011-TCP-1012). ISOPE conference. Maui, Hawaii, USA. 19-24 June 2011.
An integrated OPF dispatching model with wind power and demand response for d...IJECEIAES
In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resources are dispatched together with conventional generation. The uncertainty and volatility associated to renewable resources represents a new paradigm to be faced for power system operation. Moreover, in various electricity markets there are mechanisms to allow the demand participation through demand response (DR) strategies. Under operational and economic restrictions, the operator each day, or even in intra-day markets, dispatchs an optimal power flow to find a feasible state of operation. The operation decisions in power markets use an optimal power flow considering unit commitment to dispatch economically generation and DR resources under security restrictions. This paper constructs a model to include demand response in the optimal power flow under wind power uncertainty. The model is formulated as a mixed-integer linear quadratic problem and evaluated through Monte-Carlo simulations. A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting. The proposed integrated OPF model is tested on the standard IEEE 39-bus system.
PREDICTION OF REPAIR & MAINTENANCE COSTS OF DIESEL ENGINEijmech
Diesel engine is widely use for different applications, the failure frequency of diesel engine is more increase to increase the age & use of engine in order to take decision to replacement of engine on the basis of Repair & Maintenance cost (R&M) & predication of future Repair & Maintenance costs of diesel engine used in Borewell compressor. Present case study discusses prediction of accumulated R&M costs (Y) of Diesel engine against usage in hours (X). Recorded data from the company service station is used to determine regression models for predicting total R&M costs based on total usage hours. The statistical results of the study indicates that in order to predict total R&M costs is more useful for replacement
decisions than annual charge.
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.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
In developing complex engineering systems, model-based design approaches often face critical challenges due to pervasive uncertainties and high computational expense. These challenges could be alleviated to a significant extent though informed modeling decisions, such as model substitution, parameter estimation, localized re-sampling, or grid refine- ment. Informed modeling decisions therefore necessitate (currently lacking) design frame- works that effectively integrate design automation and human decision-making. In this paper, we seek to address this necessity in the context of designing wind farm layouts, by taking an information flow perspective of this typical model-based design process. Specif- ically, we develop a visual representation of the uncertainties inherited and generated by models and the inter-model sensitivities. This framework is called the Visually-Informed Decision-Making Platform (VIDMAP) for wind farm design. The eFAST method is used for sensitivity analysis, in order to determine both the first-order and the total-order in- dices. The uncertainties in the independent inputs are quantified based on their observed variance. The uncertainties generated by the upstream models are quantified through a Monte Carlo simulation followed by probabilistic modeling of (i) the error in the output of the models (if high-fidelity estimates are available), or (ii) the deviation in the outputs estimated by different alternatives/versions of the model. The GUI in VIDMAP is cre- ated using value-proportional colors for each model block and inter-model connector, to respectively represent the uncertainty in the model output and the impact (downstream) of the information being relayed by the connector. Wind farm layout optimization (WFLO) serves as an excellent platform to develop and explore VIDMAP, where WFLO is generally performed using low fidelity models, as high-fidelity models (e.g. LES) tend to be compu- tationally prohibitive in this context. The final VIDMAP obtained sheds new light into the sensitivity of wind farm energy estimation on the different models and their associated uncertainties.
Load types, estimation, grwoth, forecasting and duration curvesAzfar Rasool
It includes the detail analysis of the various types electrical load, how to estimatate the load, methods of load forecasting and explanation of the load duration curves.
Webinar: The cost effectiveness of natural gas combined cycle power plants wi...Global CCS Institute
This webinar will presented the findings of a study to assess the economic viability of natural gas combined-cycle power plants with CO2 capture and storage (NGCC-CCS) in climate change mitigation strategies, emphasising the use of renewable energy and natural gas for electric power generation. In this study, the cost of NGCC-CCS was compared on a level playing field to those of intermittent renewable energy systems (IRES) and energy storage technologies as a means of reducing power sector greenhouse gas emissions. Specifically, the levelised cost of electricity (LCOE) of NGCC-CCS was compared to that of offshore wind, photovoltaic systems, and concentrated solar power (CSP) together with pumped hydro storage (PHS), compressed air energy storage (CAES), and Li-ion, ZEBRA and Zn-Br battery storage systems. The cost of NGCC-CCS as a backup technology in conjunction with IRES also was assessed.
At this webinar, Machteld van den Broek, senior researcher at the Utrecht University, presented the findings of the study. Her expertise is energy systems modelling and CCS. Among others, she is involved in the CATO-2 programme, the second Dutch national research programme on CCS. During the webinar Niels Berghout, junior researcher at the Utrecht University and co-author of this study, assisted during the Q&A session. Professor Edward Rubin from Carnegie Mellon University also contributed to this study.
Webinar: The cost effectiveness of natural gas combined cycle power plants wi...Global CCS Institute
This second webinar was held on Friday 25th April, for anyone who wasn't able to join us for the previous webinar held on Thursday 20th March.
This webinar presented the findings of a study to assess the economic viability of natural gas combined-cycle power plants with CO2 capture and storage (NGCC-CCS) in climate change mitigation strategies, emphasising the use of renewable energy and natural gas for electric power generation. In this study, the cost of NGCC-CCS was compared on a level playing field to those of intermittent renewable energy systems (IRES) and energy storage technologies as a means of reducing power sector greenhouse gas emissions. Specifically, the levelised cost of electricity (LCOE) of NGCC-CCS was compared to that of offshore wind, photovoltaic systems, and concentrated solar power (CSP) together with pumped hydro storage (PHS), compressed air energy storage (CAES), and Li-ion, ZEBRA and Zn-Br battery storage systems. The cost of NGCC-CCS as a backup technology in conjunction with IRES also was assessed.
At this webinar, Machteld van den Broek, senior researcher at the Utrecht University, presented the findings of the study. Her expertise is energy systems modelling and CCS. Among others, she is involved in the CATO-2 programme, the second Dutch national research programme on CCS. During the webinar Professor Edward Rubin from Carnegie Mellon University and co-author of this study, will assist during the Q&A session. Niels Berghout, from Utrecht University also contributed to this study.
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.
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.
Quantitive Approaches and venues for Energy Trading & Risk ManagementManuele Monti
A presentation on Quantitative developments for the energy industry, comprising of two business cases in Renewable Energy and Power Asset Modelling and Optimization
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.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
In developing complex engineering systems, model-based design approaches often face critical challenges due to pervasive uncertainties and high computational expense. These challenges could be alleviated to a significant extent though informed modeling decisions, such as model substitution, parameter estimation, localized re-sampling, or grid refine- ment. Informed modeling decisions therefore necessitate (currently lacking) design frame- works that effectively integrate design automation and human decision-making. In this paper, we seek to address this necessity in the context of designing wind farm layouts, by taking an information flow perspective of this typical model-based design process. Specif- ically, we develop a visual representation of the uncertainties inherited and generated by models and the inter-model sensitivities. This framework is called the Visually-Informed Decision-Making Platform (VIDMAP) for wind farm design. The eFAST method is used for sensitivity analysis, in order to determine both the first-order and the total-order in- dices. The uncertainties in the independent inputs are quantified based on their observed variance. The uncertainties generated by the upstream models are quantified through a Monte Carlo simulation followed by probabilistic modeling of (i) the error in the output of the models (if high-fidelity estimates are available), or (ii) the deviation in the outputs estimated by different alternatives/versions of the model. The GUI in VIDMAP is cre- ated using value-proportional colors for each model block and inter-model connector, to respectively represent the uncertainty in the model output and the impact (downstream) of the information being relayed by the connector. Wind farm layout optimization (WFLO) serves as an excellent platform to develop and explore VIDMAP, where WFLO is generally performed using low fidelity models, as high-fidelity models (e.g. LES) tend to be compu- tationally prohibitive in this context. The final VIDMAP obtained sheds new light into the sensitivity of wind farm energy estimation on the different models and their associated uncertainties.
Load types, estimation, grwoth, forecasting and duration curvesAzfar Rasool
It includes the detail analysis of the various types electrical load, how to estimatate the load, methods of load forecasting and explanation of the load duration curves.
Webinar: The cost effectiveness of natural gas combined cycle power plants wi...Global CCS Institute
This webinar will presented the findings of a study to assess the economic viability of natural gas combined-cycle power plants with CO2 capture and storage (NGCC-CCS) in climate change mitigation strategies, emphasising the use of renewable energy and natural gas for electric power generation. In this study, the cost of NGCC-CCS was compared on a level playing field to those of intermittent renewable energy systems (IRES) and energy storage technologies as a means of reducing power sector greenhouse gas emissions. Specifically, the levelised cost of electricity (LCOE) of NGCC-CCS was compared to that of offshore wind, photovoltaic systems, and concentrated solar power (CSP) together with pumped hydro storage (PHS), compressed air energy storage (CAES), and Li-ion, ZEBRA and Zn-Br battery storage systems. The cost of NGCC-CCS as a backup technology in conjunction with IRES also was assessed.
At this webinar, Machteld van den Broek, senior researcher at the Utrecht University, presented the findings of the study. Her expertise is energy systems modelling and CCS. Among others, she is involved in the CATO-2 programme, the second Dutch national research programme on CCS. During the webinar Niels Berghout, junior researcher at the Utrecht University and co-author of this study, assisted during the Q&A session. Professor Edward Rubin from Carnegie Mellon University also contributed to this study.
Webinar: The cost effectiveness of natural gas combined cycle power plants wi...Global CCS Institute
This second webinar was held on Friday 25th April, for anyone who wasn't able to join us for the previous webinar held on Thursday 20th March.
This webinar presented the findings of a study to assess the economic viability of natural gas combined-cycle power plants with CO2 capture and storage (NGCC-CCS) in climate change mitigation strategies, emphasising the use of renewable energy and natural gas for electric power generation. In this study, the cost of NGCC-CCS was compared on a level playing field to those of intermittent renewable energy systems (IRES) and energy storage technologies as a means of reducing power sector greenhouse gas emissions. Specifically, the levelised cost of electricity (LCOE) of NGCC-CCS was compared to that of offshore wind, photovoltaic systems, and concentrated solar power (CSP) together with pumped hydro storage (PHS), compressed air energy storage (CAES), and Li-ion, ZEBRA and Zn-Br battery storage systems. The cost of NGCC-CCS as a backup technology in conjunction with IRES also was assessed.
At this webinar, Machteld van den Broek, senior researcher at the Utrecht University, presented the findings of the study. Her expertise is energy systems modelling and CCS. Among others, she is involved in the CATO-2 programme, the second Dutch national research programme on CCS. During the webinar Professor Edward Rubin from Carnegie Mellon University and co-author of this study, will assist during the Q&A session. Niels Berghout, from Utrecht University also contributed to this study.
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.
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.
Quantitive Approaches and venues for Energy Trading & Risk ManagementManuele Monti
A presentation on Quantitative developments for the energy industry, comprising of two business cases in Renewable Energy and Power Asset Modelling and Optimization
Synergy with Energy. India Energy Show 2009
Wind and Conventional Electricity -
Comparative Economics for Captive Power Plants
and Thermal Power Stations
Bharat J. Mehta
Vijayant Consultants
Ahmedabad, India
June 17, 2009
2BHK Apartments in Bangalore, bangalore5, 2bhk apartments for sale in Bangalore, 2bhk apartment in Bangalore, Bangalore property
More,
<a>Bangalore5</a>
Offshore Wind Energy – Potential for India
This presentation analyze energy demand scenario, especially that of almost unlimited wind energy and highlight vast potential of offshore wind energy for India in territorial water along its long coastline. Challenges to exploit this potential, financial viability of such offshore energy projects, social, environmental, and other related issues are discussed in Indian context to serve as a useful tool for policymakers to allocate resources for detailed studies for estimation and its ultimate utilization to add to growing pool of renewable energy
Opportunities & Challenges in Developing Wind Energy in India Devesh Gautam
This presentation will take you through the challenges and opportunities available for developing Wind Energy in India. The presentation has also covered various incentives and guidelines for availing same, tariff structure of various states. Various development models and financial models has also been covered. Finally way forward for any developer has also been touched upon.
This presentation focuses on risk assessment and financing options for renewable energy projects. Learn about carbon finance prospects for renewable energy projects.
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.
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 development of large scale wind farms that can produce energy at a cost comparable to that of other conventional energy resources presents significant challenges to today’s wind energy industry. The consideration of the key design and environmental factors that influence the performance of a wind farm is a crucial part of the solution to this challenge. In this paper, we develop a methodology to account for the configuration of the farm land (length-to-breadth ratio and North-South-East-West orientation) within the scope of wind farm optimization. This approach appropriately captures the correlation between the (i) land configuration, (ii) the farm layout, and (iii) the selection of turbines-types. Simultaneous optimization of the farm layout and turbine selection is performed to minimize the Cost of Energy (COE), for a set of sample land configurations. The optimized COE and farm efficiency are then represented as functions of the land aspect ratio and the land orientation. To this end, we apply a recently developed response surface method known as the Reliability-Based Hybrid Functions. The overall wind farm design methodology is applied to design a 25MW farm in North Dakota. This case study provides helpful insights into the influence of the land configuration on the optimum farm performance that can be obtained for a particular site.
#تواصل_تطوير
المحاضرة رقم 179
الدكتور / علي المراكبي
استشاري الهندسة الميكانيكية وخبير ترشيد الطاقة
بعنوان
"اقتصاديات إنتاج الكهرباء بالتكنولوجيات المختلفة"
يوم الإثنين 24 أكتوبر 2022
الثامنة مساء توقيت القاهرة
التاسعة مساء توقيت مكة المكرمة
و الحضور عبر تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUoc-quqTorGtB0hRPR6oljN7DKOanGdx1f
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة.
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.
Wind Farm Layout Optimization (WFLO) is a typical model-based complex system design process, where the popular use of low-medium fidelity models is one of the primary sources of uncertainties propagating into the esti- mated optimum cost of energy (COE). Therefore, the (currently lacking) understanding of the degree of uncertainty inherited and introduced by different models is absolutely critical (i) for making informed modeling decisions, and (ii) for being cognizant of the reliability of the obtained results. A framework called the Visually-Informed Decision-Making Platform (VIDMAP) was recently introduced to quantify and visualize the inter-model sensi- tivities and the model inherited/induced uncertainties in WFLO. Originally, VIDMAP quantified the uncertainties and sensitivities upstream of the energy production model. This paper advances VIDMAP to provide quantifica- tion/visualization of the uncertainties propagating through the entire optimization process, where optimization is performed to determine the micro-siting of 100 turbines with a minimum COE objective. Specifically, we deter- mine (i) the sensitivity of the minimum COE to the top-level system model (energy production model), (ii) the uncertainty introduced by the heuristic optimization algorithm (PSO), and (iii) the net uncertainty in the minimum COE estimate. In VIDMAP, the eFAST method is used for sensitivity analysis, and the model uncertainties are quantified through a combination of Monte Carlo simulation and probabilistic modeling. Based on the estimated sensitivity and uncertainty measures, a color-coded model-block flowchart is then created using the MATLAB GUI.
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.
In developing complex engineering systems, model-based design approaches often face critical challenges due to pervasive uncertainties and high computational expense. These challenges could be alleviated to a significant extent though informed modeling decisions, such as model substitution, parameter estimation, localized re-sampling, or grid refine- ment. Informed modeling decisions therefore necessitate (currently lacking) design frame- works that effectively integrate design automation and human decision-making. In this paper, we seek to address this necessity in the context of designing wind farm layouts, by taking an information flow perspective of this typical model-based design process. Specif- ically, we develop a visual representation of the uncertainties inherited and generated by models and the inter-model sensitivities. This framework is called the Visually-Informed Decision-Making Platform (VIDMAP) for wind farm design. The eFAST method is used for sensitivity analysis, in order to determine both the first-order and the total-order in- dices. The uncertainties in the independent inputs are quantified based on their observed variance. The uncertainties generated by the upstream models are quantified through a Monte Carlo simulation followed by probabilistic modeling of (i) the error in the output of the models (if high-fidelity estimates are available), or (ii) the deviation in the outputs estimated by different alternatives/versions of the model. The GUI in VIDMAP is cre- ated using value-proportional colors for each model block and inter-model connector, to respectively represent the uncertainty in the model output and the impact (downstream) of the information being relayed by the connector. Wind farm layout optimization (WFLO) serves as an excellent platform to develop and explore VIDMAP, where WFLO is generally performed using low fidelity models, as high-fidelity models (e.g. LES) tend to be compu- tationally prohibitive in this context. The final VIDMAP obtained sheds new light into the sensitivity of wind farm energy estimation on the different models and their associated uncertainties.
Cost development of renewable energy technologiesLeonardo ENERGY
This course covers the cost development of renewable energy technologies, which includes the analysis of technological change, in particular with regard to technological learning, the assessment of learning rates of renewable energy technologies available in literature and forecasting studies. For many (energy) technologies, a log-linear relation was found between the accumulated experience and the technical (e.g. efficiency) and economic performance (e.g. investment costs). The rate at which cost decline for each doubling of cumulative production is expressed by the progress ratio (PR). A progress ratio of 90% results in a learning Rate (LR) of 10% and similar cost reduction per doubling of cumulative production (IEA 2000; Junginger, Sark et al. 2010). Learning curves for the renewable energy technologies as well as levelised cost of electricity will be presented. The latter also include the impact of resource conditions (e.g. wind and solar yield) at different locations as well as operation and maintenance costs and fuel expenditures in the case of biomass technologies.
Electricity Demand Side Management and End-use efficiencyeecfncci
This presentation give an overview about demand side management and end-use efficiency for electricity supply systems. It was prepared for energy auditor training in Nepal in the context of GIZ/NEEP programme. For further information go to EEC webpage: http://eec-fncci.org/
Electricity Demand Side Management (DSM) and End-use Efficiencyeecfncci
This presentation explains the concept of Electical Demand Side Management and shows how to implement it in industries. It was prepared for energy auditor training in Nepal in the context of GIZ/NEEP programme. For further information go to EEC webpage: http://www.eec-fncci.org
Sizing of Hybrid PV/Battery Power System in Sohag cityiosrjce
This paper gives the feasibility analysis of PV- Battery system for an off-grid power station in Sohag
city. Hybrid PV-battery system was used for supplying a combined pumping and residential load. A simple cost
effective method for sizing stand-alone PV hybrid systems was introduced. The aim of sizing hybrid system is to
determine the cost effective PV configuration and to meet the estimated load at minimum cost. This requires
assessing the climate conditions which determine the temporal variation of the insolation in Sohag city. Sizing
of the hybrid system components was investigated using RETscreen and HOMER programs. The sizing software
tools require a set of data on energy resource demand and system specifications. The energy cost values of the
hybrid system agrees reasonably with those published before.
The analysis of complex system behavior often demands expensive experiments or computational simulations. Surrogates modeling techniques are often used to provide a tractable and inexpensive approximation of such complex system behavior. Owing to the lack of knowledge regarding the suitability of particular surrogate modeling techniques, model selection approach can be helpful to choose the best surrogate technique. Popular model selection approaches include: (i) split sample, (ii) cross-validation, (iii) bootstrapping, and (iv) Akaike's information criterion (AIC) (Queipo et al. 2005; Bozdogan et al. 2000). However, the effectiveness of these model selection methods is limited by the lack of accurate measures of local and global errors in surrogates.
This paper develops a novel and model-independent concept to quantify the local/global reliability of surrogates, to assist in model selection (in surrogate applications). This method is called the Generalized-Regional Error Estimation of Surrogate (G-REES). In this method, intermediate surrogates are iteratively constructed over heuristic subsets of the available sample points (i.e., intermediate training points), and tested over the remaining available sample points (i.e., intermediate test points). The fraction of sample points used as intermediate training points is fixed at each iteration, with the total number of iterations being pre-specified. The estimated median and maximum relative errors for the heuristic subsets at each iteration are used to fit a distribution of the median and maximum error, respectively. The statistical mode of the median and the maximum error distributions are then determined. These mode values are then represented as functions of the density of training points (at the corresponding iteration). Regression methods, called Variation of Error with Sample Density (VESD), are used for this purpose. The VESD models are then used to predict the expected median and maximum errors, when all the sample points are used as training points.
The effectiveness of the proposed model selection criterion is explored to find the best surrogate between candidates including: (i) Kriging, (ii) Radial Basis Functions (RBF), (iii) Extended Radial Basis Functions (ERBF), and (iv) Quadratic Response Surface (QRS), for standard test functions and a wind farm capacity factor function. The results will be compared with the relative accuracy of the surrogates evaluated on additional test points, and also with the prediction sum of square (PRESS) error given by leave-one-out cross-validation.
The application of G-REES to a standard test problem with two design variables (Branin-hoo function) show that the proposed method predicts the median and the maximum value of the global error with a higher level of confidence compared to PRESS. It also shows that model selection based on G-REES method is significantly more reliable than that currently performed using error measures such as PRESS. The
Effective and time-efficient decision-making in the early stages of wind farm planning can lay the foundation of a successful wind energy project. Undesirable concept-to-installation delays in wind farm development is often caused by conflicting decisions from the major parties involved (e.g., developer, investors, landowners, and local communities), which in turn can be (in a major part) attributed to the lack of an upfront understanding of the trade-offs between the technical, socio-economic, and environmental-impact aspects of the wind farm for the given site. This paper proposes a consolidated visualization platform for wind farm planning, which could facilitate informed and co-operative decision-making by the parties involved. This visualization platform offers a GUI-based land shape chart, which provides the following information: the variation of the energy production capacity and of the corresponding required optimal land shape with different land area and nameplate capacity decisions. In order to develop this chart, a bi-objective optimization problem is formulated (using the Unrestricted Wind Farm Layout Optimization framework) to max- imize the capacity factor and minimize the land usage, subject to different nameplate capacity decisions. The application of an Optimal Layout-based land usage estimate allows the wind farm layout optimization to run without pre-specifying any farm boundaries; the optimal land shape is instead determined as a post process, using convex hull and minimum bounding rectangle concept, based on the optimal arrangement of turbines. Three land shape charts are generated under three characteristic wind patterns - (i) single dominant wind direction, (ii) two opposite dominant wind directions, and (ii) two orthogonal domi- nant wind directions, all three patterns comprising the same wind speed distribution. The results indicate that the optimal land shape is highly sensitive to the variation in LAMI for small-capacity wind farms (few turbines) and to the variation in nameplate capacity for small allowed land area. For the same decided nameplate capacity and LAMI values, we observe reasonable similarity in the optimal land shapes and the maximum energy pro- duction potentials given the “single dominant direction” and the “two opposite dominant directions” wind patterns; the optimal land shapes and the maximum energy production potentials yielded by the “two orthogonal dominant directions” wind pattern is however observed to be relatively different from the other two cases.
Complex system design problems tend to be high dimen- sional and nonlinear, and also often involve multiple objectives and mixed-integer variables. Heuristic optimization algorithms have the potential to address the typical (if not most) charac- teristics of such complex problems. Among them, the Particle Swarm Optimization (PSO) algorithm has gained significant popularity due to its maturity and fast convergence abilities. This paper seeks to translate the unique benefits of PSO from solving typical continuous single-objective optimization problems to solving multi-objective mixed-discrete problems, which is a relatively new ground for PSO application. The previously de- veloped Mixed-Discrete Particle Swarm Optimization (MDPSO) algorithm, which includes an exclusive diversity preservation technique to prevent premature particle clustering, has been shown to be a powerful single-objective solver for highly con- strained MINLP problems. In this paper, we make fundamental advancements to the MDPSO algorithm, enabling it to solve challenging multi-objective problems with mixed-discrete design variables. In the velocity update equation, the explorative term is modified to point towards the non-dominated solution that is the closest to the corresponding particle (at any iteration). The fractional domain in the diversity preservation technique, which was previously defined in terms of a single global leader, is now applied to multiple global leaders in the intermediate Pareto front. The multi-objective MDPSO (MO-MDPSO) algorithm is tested using a suite of diverse benchmark problems and a disc-brake design problem. To illustrate the advantages of the new MO-MDPSO algorithm, the results are compared with those given by the popular Elitist Non-dominated Sorting Genetic Algorithm-II (NSGA-II).
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.
The power generation of a wind farm is significantly less than the summation of the power generated by each turbine when operating as a standalone entity. This power reduction can be attributed to the energy loss due to the wake effects − the resulting velocity deficit in the wind downstream of a turbine. In the case of wind farm design, the wake losses are generally quantified using wake models. The effectiveness of wind farm design (seeking to maximize the farm output) therefore depends on the accuracy and the reliability of the wake models. This paper compares the impact of the following four analytical wake
models on the wind farm power generation: (i) the Jensen model, (ii) the Larsen model, (iii) the Frandsen model, and (iv) the Ishihara model. The sensitivity of this impact to the Land Area per Turbine (LAT) and the incoming wind speed is also investigated. The wind farm power generation model used in this paper is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology. Single wake case studies show that the velocity deficit and the wake diameter estimated by the different analytical wake models can be significantly different. A maximum difference of 70% was also observed for the wind farm capacity factor values estimated using different wake models.
Development of a family of products that satisfies different sectors of the market introduces significant challenges to today’s manufacturing industries – from development time to aftermarket services. A product family with a common platform paradigm offers a powerful solution to these daunting challenges. The Comprehensive Product Platform Planning (CP3) framework formulates a flexible product family model that (i) seeks to eliminate traditional boundaries between modular and scalable families, (ii) allows the formation of sub-families of products, and (iii) yield the optimal depth and number of platforms. In this paper, the CP3 framework introduces a solution strategy that obviates common assumptions; namely (i) the identification of platform/non-platform design variables and the determination of variable values are separate processes, and (ii) the cost reduction of creating product platforms is independent of the total number of each product manufactured. A new Cost Decay Function (CDF) is developed to approximate the reduction in cost with increasing commonalities among products, for a specified capacity of production. The Mixed Integer Non-Liner Programming (MINLP) problem, presented by the CP3 model, is solved using a novel Platform Segregating Mapping Function (PSMF). The proposed CP3 framework is implemented on a family of universal electric motors.
This paper explores the adaptive optimal design of Active Thermally Insulated (ATI) windows to significantly improve energy efficiency. The ATI window design uses ther- mostats to actively control thermoelectric (TE) units and fans to regulate the overall ther- modynamic properties of the windows. The windows are used to maintain a comfortable indoor temperature. Since weather conditions vary with different geographical locations and with time, the thermodynamic properties of the windows should adapt accordingly. The electric power supplied to the TE units and the fans are dynamically controlled so as to provide an optimal operation under varying weather conditions. Optimization of the ATI window design is a multiobjective problem. The problem minimizes both the heat trans- ferred through the window and electric power consumption. The heat transfer through the ATI windows is analyzed using FLUENT; and the optimization is performed using MAT- LAB. Since the computational expense of optimization for numerous weather conditions is excessive, the power supplies are optimized under a reasonably small number of weather conditions. Based on the optimal results obtained for these conditions, a surrogate model is developed to represent the optimal results in a wide range of weather conditions. The surrogate model is used to evaluate optimal power supplies with respect to different val- ues of outside temperature, wind speed, and intensity of solar radiation. Thermometers, anemometers, and solar radiation sensors are used to sense these weather conditions. With the inputs from the sensors, the thermostats determine the operating conditions and cal- culate the corresponding optimal power supplies using the surrogate model. Since the ATI windows are operated with optimal power supplies, high energy efficiency is achieved.
The Comprehensive Product Platform Planning (CP3) framework presents a flexible mathematical model of the platform planning process, which allows (i) the formation of sub-families of products, and (ii) the simultaneous identification and quantification of plat- form/scaling design variables. The CP3 model is founded on a generalized commonality matrix that represents the product platform plan, and yields a mixed binary-integer non- linear programming problem. In this paper, we develop a methodology to reduce the high dimensional binary integer problem to a more tractable integer problem, where the com- monality matrix is represented by a set of integer variables. Subsequently, we determine the feasible set of values for the integer variables in the case of families with 3 − 7 kinds of products. The cardinality of the feasible set is found to be orders of magnitude smaller than the total number of unique combinations of the commonality variables. In addition, we also present the development of a generalized approach to Mixed-Discrete Non-Linear Optimization (MDNLO) that can be implemented through standard non-gradient based op- timization algorithms. This MDNLO technique is expected to provide a robust and compu- tationally inexpensive optimization framework for the reduced CP3 model. The generalized approach to MDNLO uses continuous optimization as the primary search strategy, how- ever, evaluates the system model only at the feasible locations in the discrete variable space.
This paper compares the performances of standard surrogate models in the development of an optimal control framework. The optimal control strategy is implemented on an Active Thermoelectric (ATE) window design. The ATE window design uses thermoelectric units to actively regulate the overall thermodynamic properties of the windows. The optimization of the design is a multiobjective problem, where both the heat transferred through the window and electric power consumption are minimized. The power supplies and the heat transfer are optimized under a reasonable number of randomly sampled environmental conditions. The subsequent optimal designs obtained are represented as functions of the corresponding environmental conditions using surrogate models. To this end, four types of surrogate models are used, namely, (i) Quadratic Response Surface Methodology (QRSM), (ii) Radial Basis Functions (RBF), (iii) Extended Radial Basis Functions (E-RBF), and (iv) Kriging. Their performances are compared using two accuracy measurement metrics: Root Mean Squared Error (RMSE) and Maximum Absolute Error (MAE). We found that any one of the surrogate modeling methods is not superior to the others over the whole domain for the optimal control of the ATE window.
In the past decade or so, there has been appreciable progress in developing renewable energy resources; among them, wind energy has taken a lead, and is currently contributing towards 2.5% of the global electricity consumption (WWEA, 2011). On the downside, the variability of this resource itself has been one of the major factors restricting its potential growth – wind speed and direction show strong temporal variations. In addition, the distribution of wind conditions varies significantly from year to year. The resulting ill-predictability of the annual distribution of wind conditions introduces significant uncertainties in the estimated resource potential and the predicted performance of the wind farm.
The planning of a wind farm, which minimizes the lifecycle project costs and maximizes the reliability of the expected power generation, presents significant challenges to today’s wind energy industry. An optimal wind farm planning strategy that simultaneously (i) accounts for the key engineering design factors, and (ii) addresses the major sources of un- certainty in a wind farm, can offer a powerful solution to these daunting challenges. In this paper, we develop a new methodology to characterize the long term uncertainties, partic- ularly those introduced by the ill-predictability of the annual variation in wind conditions (wind speed and direction, and air density). The annual variation in wind conditions is rep- resented using a non-parametric wind distribution model. The uncertainty in the predicted annual wind distribution is then characterized using a set of lognormal distributions. The uncertainties in the estimated (i) farm power generation and (ii) Cost of energy (COE) are represented as functions of the variances of these lognormal distributions. Subsequently, we minimize the uncertainty in the COE through wind farm optimization. To this end, we apply the Unrestricted Wind Farm Layout Optimization (UWFLO) framework, which provides a comprehensive platform for wind farm design. This methodology for robust wind farm optimization is applied to design a 25MW wind farm in N. Dakota. We found that layout optimization is appreciably sensitive to the uncertainties in wind conditions.
The determination of complex underlying relationships between system parameters from simulated and/or recorded data requires advanced interpolating functions, also known as surrogates. The development of surrogates for such complex relationships often requires the modeling of high dimensional and non-smooth functions using limited information. To this end, the hybrid surrogate modeling paradigm, where different surrogate models are aggregated, offers a robust solution. In this paper, we develop a new high fidelity surro- gate modeling technique that we call the Reliability Based Hybrid Functions (RBHF). The RBHF formulates a reliable Crowding Distance-Based Trust Region (CD-TR), and adap- tively combines the favorable characteristics of different surrogate models. The weight of each contributing surrogate model is determined based on the local reliability measure for that surrogate model in the pertinent trust region. Such an approach is intended to ex- ploit the advantages of each component surrogate. This approach seeks to simultaneously capture the global trend of the function and the local deviations. In this paper, the RBHF integrates four component surrogate models: (i) the Quadratic Response Surface Model (QRSM), (ii) the Radial Basis Functions (RBF), (iii) the Extended Radial Basis Functions (E-RBF), and (iv) the Kriging model. The RBHF is applied to standard test problems. Subsequent evaluations of the Root Mean Squared Error (RMSE) and the Maximum Ab- solute Error (MAE), illustrate the promising potential of this hybrid surrogate modeling approach.
This paper explores the effectiveness of the recently devel- oped surrogate modeling method, the Adaptive Hybrid Functions (AHF), through its application to complex engineered systems design. The AHF is a hybrid surrogate modeling method that seeks to exploit the advantages of each component surrogate. In this paper, the AHF integrates three component surrogate mod- els: (i) the Radial Basis Functions (RBF), (ii) the Extended Ra- dial Basis Functions (E-RBF), and (iii) the Kriging model, by characterizing and evaluating the local measure of accuracy of each model. The AHF is applied to model complex engineer- ing systems and an economic system, namely: (i) wind farm de- sign; (ii) product family design (for universal electric motors); (iii) three-pane window design; and (iv) onshore wind farm cost estimation. We use three differing sampling techniques to inves- tigate their influence on the quality of the resulting surrogates. These sampling techniques are (i) Latin Hypercube Sampling
∗Doctoral Student, Multidisciplinary Design and Optimization Laboratory, Department of Mechanical, Aerospace and Nuclear Engineering, ASME student member.
†Distinguished Professor and Department Chair. Department of Mechanical and Aerospace Engineering, ASME Lifetime Fellow. Corresponding author.
‡Associate Professor, Department of Mechanical Aerospace and Nuclear En- gineering, ASME member (LHS), (ii) Sobol’s quasirandom sequence, and (iii) Hammers- ley Sequence Sampling (HSS). Cross-validation is used to evalu- ate the accuracy of the resulting surrogate models. As expected, the accuracy of the surrogate model was found to improve with increase in the sample size. We also observed that, the Sobol’s and the LHS sampling techniques performed better in the case of high-dimensional problems, whereas the HSS sampling tech- nique performed better in the case of low-dimensional problems. Overall, the AHF method was observed to provide acceptable- to-high accuracy in representing complex design systems.
Currently, the quality of wind measure of a site is assessed using Wind Power Density (WPD). This paper proposes to use a more credible metric namely, one we call the Wind Power Potential (WPP). While the former only uses wind speed information, the latter exploits both wind speed and wind direction distributions, and yields more credible estimates. The new measure of quality of a wind resource, the Wind Power Potential Evaluation (WPPE) model, investigates the effect of wind velocity distribution on the optimal net power generation of a farm. Bivariate normal distribution is used to characterize the stochastic variation of wind conditions (speed and direction). The net power generation for a particular farm size and installed capacity are maximized for different distributions of wind speed and wind direction, using the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology. A response surface is constructed, using the recently developed Reliability Based Hybrid Functions (RBHF), to represent the computed maximum power generation as a function of the parameters of the wind velocity (speed and direction) distribution. To this end, for any farm site, we can (i) estimate the parameters of wind velocity distribution using recorded wind data, and (ii) predict the max- imum power generation for a specified farm size and capacity, using the developed response surface. The WPPE model is validated through recorded wind data at four differing stations obtained from the North Dakota Agricultural Weather Network (NDAWN). The results illustrate the variation of wind conditions and, subsequently, its influence on the quality of a wind resource.
This paper presents a new method to accurately char- acterize and predict the annual variation of wind conditions. Estimation of the distribution of wind conditions is necessary (i) to quantify the available energy (power density) at a site, and (ii) to design optimal wind farm configurations. We develop a smooth multivariate wind distribution model that captures the coupled variation of wind speed, wind direction, and air density. The wind distribution model developed in this paper also avoids the limiting assumption of unimodality of the distribution. This method, which we call the Multivariate and Multimodal Wind distribution (MMWD) model, is an evolution from existing wind distribution modeling techniques. Multivariate kernel density estimation, a standard non-parametric approach to estimate the probability density function of random variables, is adopted for this purpose. The MMWD technique is successfully applied to model (i) the distribution of wind speed (univariate); (ii) the distribution of wind speed and wind direction (bivariate); and (iii) the distribution of wind speed, wind direction, and air density (multivariate). The latter is a novel contribution of this paper, while the former offers opportunities for validation. Ten-year recorded wind data, obtained from the North Dakota Agricultural Weather Network (NDAWN), is used in this paper. We found the coupled distribution to be multimodal. A strong correlation among the wind condition parameters was also observed.
A wind farm planning strategy that simultaneously accounts for the key engineering design factors, and addresses the major sources of uncertainty in a wind farm, can offer a powerful impetus to the development of wind energy. The distribution of wind conditions, including wind speed, wind direction, and air density, vary significantly from year to year. The resulting ill-predictability of the annual distribution of wind conditions introduces significant uncertainties in the estimated resource potential as well as in the predicted performance of the wind farm. In this paper, a new methodology is developed (i) to char- acterize the uncertainties in the annual distribution of wind conditions, and (ii) to model the propagation of uncertainty into the local Wind Power Density (WPD) and the farm performance: Annual Energy Production (AEP) and Cost of Energy (COE). Both para- metric and non-parametric uncertainty models are formulated, which can be leveraged in conjunction with a wide variety of stochastic wind distribution models. The AEP and the COE are evaluated using advanced analytical models, adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The year-to-year variations in the wind distribution and the quantified uncertainties are illustrated using two case studies: (i) an onshore wind site at Baker, ND, and (ii) an offshore wind site near Boston, MA. Appreciable uncertainties are observed in the estimated yearly WPDs over the ten year period - approximately 11% for the onshore site, and 30% for the offshore site. Likewise, an appreciable uncertainty of 4% is observed in the performance of an optimized wind farm layout at the onshore site.
A product family with a common platform paradigm can increase the flexibility and responsiveness of the product- manufacturing process and help take away market share from competitors that develop one product at a time. The recently developed Comprehensive Product Platform Planning (CP3 ) method allows (i) the formation of sub-families of products, and (ii) the simultaneous identification and quantification of platform/scaling design variables. The CP3 model is founded on a generalized commonality matrix representation of the product-platform-plan. In this paper, a new commonality index is developed and introduced in CP3 to simultaneously account for the degree of inter-product commonalities and for the overlap between groups of products sharing different platform variables. To maximize both the performance of the product family and the new commonality measure, we develop and apply an advanced mixed-discrete Particle Swarm Optimization (MDPSO) algorithm. In the MDPSO algo- rithm, the discrete variables are updated using a deterministic nearest-feasible-vertex criterion after each iteration of the conventional PSO. Such an approach is expected to avoid the undesirable discrepancy in the rate of evolution of discrete and continuous variables. To prevent a premature stagnation of solutions (likely in conventional PSO), while solving the high dimensional MINLP problem presented by CP3, we introduce a new adaptive diversity-preservation technique. This technique first characterizes the population diversity and then applies a stochastic update of the discrete variables based on the estimated diversity measure. The potential of the new CP3 optimization methodology is illustrated through its application to design a family of universal electric motors. The optimized platform plans provide helpful insights into the importance of accounting for the overlap between different product platforms, when quantifying the effective commonality in the product family.
This paper develops a hybrid Measure-Correlate-Predict (MCP) strategy to predict the long term wind resource variations at a farm site. The hybrid MCP method uses the recorded data of multiple reference stations to estimate the long term wind condition at the target farm site. The weight of each reference station in the hybrid strategy is determined based on: (i) the distance and (ii) the elevation difference between the target farm site and each reference station. The applicability of the proposed hybrid strategy is investigated using four different MCP methods: (i) linear regression; (ii) variance ratio; (iii) Weibull scale; and (iv) Artificial Neural Networks (ANNs). To implement this method, we use the hourly averaged wind data recorded at six stations in North Dakota between the year 2008 and 2010. The station Pillsbury is selected as the target farm site. The recorded data at the other five stations
(Dazey, Galesbury, Hillsboro, Mayville and Prosper) is used as reference station data. Three sets of performance metrics are used to evaluate the hybrid MCP method. The first set of metrics analyze the statistical performance, including the mean wind speed, the wind speed variance, the root mean squared error, and the maximum absolute error. The second set of metrics evaluate the distribution of long term wind speed; to this end, the Weibull distribution and the Multivariate and Multimodal Wind Distribution (MMWD) models are adopted in this paper. The third set of metrics analyze the energy production capacity and the efficiency of the wind farm. The results illustrate that the many-to-one correlation in such a hybrid approach can provide more reliable prediction of the long term onsite wind variations, compared to one-to-one correlations.
Over the past decade or so, Particle Swarm Optimization (PSO) has emerged to be one of most useful methodologies to address complex high dimensional optimization problems - it’s popularity can be attributed to its ease of implementation, and fast convergence prop- erty (compared to other population based algorithms). However, a premature stagnation of candidate solutions has been long standing in the way of its wider application, particularly to constrained single-objective problems. This issue becomes all the more pronounced in the case of optimization problems that involve a mixture of continuous and discrete de- sign variables. In this paper, a modification of the standard Particle Swarm Optimization (PSO) algorithm is presented, which can adequately address system constraints and deal with mixed-discrete variables. Continuous optimization, as in conventional PSO, is imple- mented as the primary search strategy; subsequently, the discrete variables are updated using a deterministic nearest vertex approximation criterion. This approach is expected to avoid the undesirable discrepancy in the rate of evolution of discrete and continuous vari- ables. To address the issue of premature convergence, a new adaptive diversity-preservation technique is developed. This technique characterizes the population diversity at each it- eration. The estimated diversity measure is then used to apply (i) a dynamic repulsion towards the globally best solution in the case of continuous variables, and (ii) a stochas- tic update of the discrete variables. For performance validation, the Mixed-Discrete PSO algorithm is successfully applied to a wide variety of standard test problems: (i) a set of 9 unconstrained problems, and (ii) a comprehensive set of 98 Mixed-Integer Nonlinear Programming (MINLP) problems.
One of the primary drawbacks plaguing wider acceptance of surrogate models is their low fidelity (in general), which can be in a large part attributed to the lack of quantitative guidelines regarding the suitability of different models for diverse classes of problems. In this context, model selection techniques are immensely helpful in ensuring the selection and use of an optimal model for a particular design problem. A novel model selection technique was recently developed to perform optimal model search at three levels: (i) optimal model type (e.g., RBF), (ii) optimal kernel type (e.g., multiquadric), and (iii) optimal values of hyper-parameters (e.g., shape parameter) that are conventionally kept constant. The maximum and the median error measures to be minimized in this optimal model selection process are given by the REES error metrics, which have been shown to be significantly more accurate than typical cross-validation-based error metrics. Motivated by the promising results given by REES-based model selection, in this paper we develop a framework called Collaborative Surrogate Model Selection (COSMOS). The primary goal of COSMOS is to allow the selection and usage of globally competitive surrogate models. More specifically, this framework will offer an open online platform where users from within and beyond the Engineering Design (and MDO) community can submit training data to identify best surrogates for their problem, as well as contribute new and advanced surrogate models to the pool of models in this framework. This first-of-its-kind global platform will facilitate sharing of ideas in the area of surrogate modeling, benchmarking of existing surrogates, validation of new surrogates, and identification of the right surrogate for the right problem. In developing this framework, this paper makes three important fundamental advancements to the original REES-based model selection - (i) The optimization approach is modified through binary coding to allow surrogates with differing numbers of candidate kernels and kernels with differ- ing numbers of hyper-parameters. (ii) A robustness criterion, based on the variance of errors, is added to the existing criteria for model selection. (iii) Users are allowed to perform model selection for a specified region of the input space and not only for the entire input domain, subject to empirical constraints that are related to the relative sample strength of the region. The effectiveness of the COSMOS framework is demonstrated using a comprehensive pool of five major surrogate model-types (with up to five constitutive kernel types), which are tested on two standard test problems and an airfoil design problem.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
1. Economic Evaluation of Wind Farms
Based on
Cost of Energy Optimization
Jie Zhang*, Souma Chowdhury*, Achille Messac# and Luciano Castillo*
* Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace, and Nuclear Engineering
# Syracuse University, Department of Mechanical and Aerospace Engineering
13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference
September 13-15, 2010
Fort Worth, Texas
2. Outline
• Motivation
• Research Objectives
• Literature Review
• Response Surface Based Wind Farm Cost
(RS-WFC) Model
• Cost of Energy Optimization
• Concluding Remarks and Future Work
2
3. Motivation
The global installed wind capacity has been growing at an
average rate of 28% per year.
Wind energy is planed to account for 20% of the U.S. electricity
consumption by 2030.
Efficient planning and resource management is the key to the
success of an energy project.
Accurate (flexible to local market changes) cost models of wind
projects would allow investors to better plan their projects.
Investors can provide valuable insight into the areas that require
further development to improve the overall economics of wind
energy.
3
4. Motivation
International ranking of wind power
Country/Region
Total capacity
end 2009 [MW]
Added capacity
2009 [MW]
Growth rate
2009 [%]
USA 35,159 9,922 39.3
China 26,010 13,800 113.0
Germany 25,777 1,880 7.9
Spain 19,149 2,460 14.7
India 10,925 1,338 14.0
Italy 4,850 1,114 29.8
France 4,521 1,117 32.8
UK 4,092 897 28.1
Portugal 3,535 673 23.5
Denmark 3,497 334 10.6
4
Source: Berkely Lab estimates based on data from BTM Consult and elsewhere
U.S. is lagging behind other countries in wind energy as a percentage of electricity consumption
5. Current
Planned 2030
10 fold increase
Wind Energy
Wind Energy
Motivation
5
• Accurate cost models
6. Research Objectives
Develop an accurate cost model of wind farms
Construct a U.S. cost map for wind farm development
Investigate the optimal cost of a wind farm
6
7. Literature Review
• Existing Cost Models
• Short-cut Model
• Cost Model for the Greek Market
• OWECOP-Prob Cost Model
• JEDI-Wind
• Opti-OWECS Cost Model
• Existing O&M Cost Models
• The Operation & Maintenance Cost Estimator (OMCE)
• Cost of Grid Connection
7
9. Advantages of RS-WFC Model
• Evaluates the Cost of Energy (COE)
• Evaluates the power generation
• Includes life cycle cost
• Considers financial parameters
• Uses appropriate input and output parameters
• Provides analytical expression
9
10. Development of RS-WFC Model
• STEP I: This step formulates the wind farm cost model using
response surface method. The response surface is trained using the
data from the Energy Efficiency and Renewable Energy Program
at the U.S. Department of Energy.
• STEP II: This step incorporates a power generation model
(Unrestricted Wind Farm Layout Optimization (UWFLO)) with
the RS-WFC model.
• STEP III: The COE is evaluated and optimized in this step. COE is
defined as the total annual cost divided by the energy generated by
a wind farm in one year.
10
12. Response Surface Methods
Typical response surface methods include:
• Quadratic Response Surface Methodology (QRSM)
• Radial Basis Functions (RBFs)
• Extended Radial Basis Functions (E-RBF)
• Kriging
• Artificial Neural Networks (ANN), and
• Support Vector Regression (SVR)
12
Local accuracy
Nonlinear
Extended Radial Basis Functions (E-RBF) method is adopted to
develop the RS-WFC model in this paper. But the RS-WFC model
also has the flexibility to use other response surface methods.
13. E-RBF Method
13
Extended Radial Basis Functions (E-RBF) is a combination of Radial
Basis Functions (RBFs) and Non-Radial Basis Functions (N-RBFs).
Radial Basis Functions
The RBFs are expressed in terms of the Euclidean distance,
y (r) = r2 + c2
where c > 0 is a prescribed parameter.
The final approximation function is a linear combination of these basis
functions across all data points.
( )
% =å -
f x sy x x
1
( )
np
i
i
i
=
r = x - xi
One of the most effective forms is the multiquadric function:
14. 14
E-RBF Method
Non-Radial Basis Functions
N-RBFs are functions of individual coordinates of generic points x
relative to a given data point xi, in each dimension separately
It is composed of three distinct components
( i ) L L ( i ) R R ( i ) ( i )
ij j ij j ij j ij j
f x =a f x +a f x +b f b x
Extended Radial Basis Function (E-RBF)
The E-RBF approach incorporates both the RBFs and the N-RBFs
np np m
( )
% =å - i +ååéë L L i + R R i + i
ùû
f x sy x x a f x a f x b f b x
( ) ( ) ( ) ( )
i ij j ij j ij j
i i j
= = =
1 1 1
Methods: (i) linear programming, or (ii) pseudo inverse.
15. RS-WFC Model
• Installation cost,
• Annual O&M cost,
• Total annual cost,
in C
O&M C
t C
The number of coefficients of
the E-RBF cost model,
(3 1) u p n = m+ n
p ( n , Number of data points)
15
Function list of the RS-WFC model
Model Expression No. of
variables
Data
points
No. of
coefficients
Cin = f(CLC,CLT,CLM) 3 40 400
CO&M = f(N,CLT,CLM) 3 500 5,000
Ct = f(N,D) 2 101 707
Parameter Selection for RS-WFC Model
Parameter λ c t
Value 4.75 0.9 2
16. Total Annual Cost VS. The Input Factors
16
The total annual cost deceases from $131.3/KW
to $126.4/KW (approximately 3.73%) when the
rotor diameter of a wind turbine increases from
50m to 100m.
The total annual cost decreases slowly when the
rotor diameter is less than 70m.
The total annual cost begins to decrease sharply
when the rotor diameter changes from 70m to
85m.
If the rotor diameter continues to increase beyond
85m, the change in the total annual cost is
particularly limited.
The total annual cost based on the rotor
diameter of a wind turbine
17. 17
Total Annual Cost VS. The Input Factors
The total annual cost decreases from
$131.48/KW to $126.38/KW (approximately
3.88%) while the number of wind turbines
increases from 10 to 100.
The total annual cost does not change
significantly when the number of wind
turbines increases beyond 60.
The total annual cost based on the number of
wind turbines
20. Power Generation Model
• The Unrestricted Wind Farm Layout Optimization
(UWFLO) Power Generation Model
C 20 p: power coefficient curve, a: induction factor curve
22. Optimizing Cost of Energy (COE)
• COE is measured by Levelized Production Cost
(LPC)
COE C P N
= ´ ´ ´
AEP
• AEPnet: Net Annual Energy Production
22
0 1000 t
net
365 24 net farm AEP = ´ ´ P
23. Wind Farm Optimization Problem Formulation
Optimization Problem Constraints
Minimum clearance
Ensure the location of wind
turbines within the fixed size
23
24. Case Study
Wind farm parameters Wind conditions
Parameter Value
Number of wind turbines 25
Type of wind turbines ENERCON
E-82
Length of the wind farm 1,200m
Breadth of the wind farm 800m
Rated power (P0) 2MW
Rotor diameter (D) 82m
Hub height (H) 85m
Cut-in wind speed 2m/s
Cut-out wind speed 28m/s
Rated wind speed 13m/s
Parameter Value
Average wind speed 8.35m/s
Wind direction 00 with positive
x-axis
Air density 1.2kg/m3
24
28. Conclusion
• A Response Surface Based Wind Farm Cost (RS-WFC) model was
developed, which can estimate: (i) the installation cost, (ii) the annual
O&M cost, and (iii) the total annual cost of a wind farm.
• A power generation model was incorporated into the RS-WFC model.
• The Cost of Energy (COE) was minimized to improve the overall
economics of wind energy project.
• The preliminary cost map shows wide variation in wind farm cost in
the U.S..
• The resulting cost model could be a useful tool for wind farm
planning.
28
29. Future Work
Optimization of Operation and Maintenance (O&M)
strategy for offshore wind farms;
Pattern classification based market demand analysis
methodology: investigate the demand pattern and
identify markets niches for offshore wind turbines.
29
30. 30
Selected References
Zhang, J., Chowdhury, S., Messac, A., Castillo, L., and Lebron, J., “Response Surface Based Cost
Model for Onshore Wind Farms Using Extended Radial Basis Functions”. ASME 2010 International
Design Engineering Technical Conferences.
Chowdhury, S., Messac, A., Zhang, J., Castillo, L., and Lebron, J., “Optimizing the Unrestricted
Placement of Turbines of Differing Rotor Diameters in a Wind Farm for Maximum Power Generation”.
ASME 2010 International Design Engineering Technical Conferences (IDETC).
Mullur, A. A., and Messac, A., 2005. “Extended radial basis functions: More flexible and effective
metamodeling”. AIAA Journal, 43(6), pp. 1306–1315.
Mullur, A. A., and Messac, A., 2006. “Metamodeling using extended radial basis functions: a
comparative approach”. Engineering with Computers, 21(3), pp. 203–217.
Goldberg, M., 2009. Jobs and Economic Development Impact (JEDI) Model. National Renewable
Energy Laboratory, Golden, Colorado, US, October.
Sisbot, S., Turgut, O., Tunc, M., and Camdali, U., 2009. “Optimal positioning of wind turbines on
gokceada using multi-objective genetic algorithm”. Wind Energy.
Pallabazzer, R., 2004. “Effect of site wind properties on wind-electric conversion costs”. Wind
Engineering, 28(6), pp. 679–694.
Jin, R., Chen, W., and Simpson, T., 2001. “Comparative studies of metamodelling techniques under
multiple modelling criteria”. Structural and Multidisciplinary Optimization, 23(1), pp. 1–13.
Lindenberg, S., 2008. “20% wind energy by 2030: Increasing wind energy contribution to u.s.
electricity supply”. Tech. Rep. DOE/GO-102008-2567, U.S. Department of Energy: Energy Efficiency
& Renewable Energy, July.
Cockerill, T. T., Harrison, R., Kuhn, M., and Bussel, G. V., 1998. “Opti-owecs final report vol. 3:
Comparison of cost of offshore wind energy at European sites”. Tech. Rep. IW-98142R, Institute for
Wind Energy, Delft University of Technology, August.
These are four types of active windows. These are others efforts to decrease the energy lost through windows.
We sought to design a window system that will compensate for all of the heat gained through the glass and maintain a thermal balance. This is our window design that improves upon the current passive window model.
We chose thermoelectric units for our system because they are very small and are solid state.
