Application of Monte Carlo and Fuzzy Analytic Hierarchy Processes for ranking floating wind farm locations

2022 ◽  
Vol 245 ◽  
pp. 110453
Author(s):  
H. Díaz ◽  
A.P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Monte Carlo ◽  
Wind Farm ◽  
Analytic Hierarchy

Integrated AHP-BPNN Model for Wind Farm Investment Evaluation

2014 ◽  
Vol 541-542 ◽  
pp. 966-971
Author(s):  
Xiang Feng Zhang ◽  
Tian Yu Liu ◽  
Bin Jiao
Keyword(s):  
Wind Farm ◽  
Evaluation Method ◽  
Back Propagation ◽  
Wind Farms ◽  
Back Propagation Neural Network ◽  
Analytic Hierarchy ◽  
Farm Investment ◽  
Hierarchy Process ◽  
Power Project ◽  
Bpnn Model

The construction of wind farms grows quickly in China. It is necessary for stakeholders to estimate investment costs and to make good decisions about a wind power project by making a budget for the investment. This paper proposed an evaluation method by integrating the analytic hierarchy process (AHP) with back-propagation neural network (BPNN) to evaluate wind farm investment. In the AHP-BPNN model, the AHP method is used to determine the factors of wind farm investment. The factors with high importance are reserved while those with low importance are eliminated, which can decrease the number of inputs of the BPNN. The experiment results show that the integrated model is feasible and effective.

Reliability Modeling of Wind Farm Considering the Outages of Connection Cables

2013 ◽  
Vol 291-294 ◽  
pp. 461-466
Author(s):  
Guo Bing Qiu ◽  
Wen Xia Liu ◽  
Jian Hua Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power Output ◽  
Complex Terrain ◽  
Wind Farm ◽  
Sequential Monte Carlo ◽  
Reliability Model ◽  
Wake Effect ◽  
Reliability Modeling ◽  
Wake Model

Considering the randomness of wind speed and wind direction, the partial wake effect between wind turbines (WTs) in complex terrain was analyzed and a multiple wake model in complex terrain was established. Taking the power output characteristic of WT into consideration, a wind farm reliability model which considered the outages of connection cables was presented. The model is implemented in MATLAB using sequential Monte Carlo simulation and the results show that this model corrects the power output of wind farm, while improving the accuracy of wind farm reliability model.

Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu
Keyword(s):  
Monte Carlo ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Assessment Methods ◽  
Probabilistic Methods ◽  
Model Parameters ◽  
Probability Assessment ◽  
Reliability Models ◽  
Reliability Parameters

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

scholarly journals Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 325
Author(s):  
Emad Mohamed ◽  
Parinaz Jafari ◽  
Simaan AbouRizk
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Wind Farm ◽  
Historical Data ◽  
Expert Knowledge ◽  
Bivariate Distribution ◽  
Expert Elicitation ◽  
Illustrative Case ◽  
Input Modeling

Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).

scholarly journals Use of Analytic Hierarchy Process for Wind Farm Installation Region Prioritization–Case Study

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2284 ◽  
Author(s):  
Rômulo Lemos Bulhões ◽  
Eudemário Souza de Santana ◽  
Alex Álisson Bandeira Santos
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Wind Farm ◽  
Greenhouse Gas Emission ◽  
Wind Farms ◽  
Clean Energy ◽  
Poor Quality ◽  
Analytic Hierarchy ◽  
Hierarchy Process

Electricity generation via renewable sources is emerging as a possible solution to meet the growing demand for electricity worldwide. Additionally, the need to produce clean energy, with little or no pollutants or greenhouse gas emission is paramount. Due to these factors, wind farms are noticeably increasing in number, especially in Brazil. However, the vast size of the country and the poor quality of its infrastructure are among several factors that make it difficult for effective decision-making to accelerate the growth of this segment in Brazil. With the purpose of assisting government agencies, regulatory agencies and other institutions in this area, the use of a multi-criteria selection method called the analytic hierarchy process is proposed here to assist in decision-making and to select priority regions for implementing wind farms. This work focuses on a case study of the state of Bahia, in which 27 territories were selected for an installation priority evaluation. Computational tools were used to hierarchize these chosen territories, including Matlab, for the construction of the computational algorithm. The results indicate the priority pf the regions according to the established criteria, which allows installation locations to be mapped—these could serve as a basis for regional investment.

Monte Carlo methods to include the effect of asymmetrical uncertainty sources in wind farm yield assessment

2018 ◽  
Vol 42 (6) ◽  
pp. 624-632 ◽  
Author(s):  
Alexander Gleim ◽  
Rolf-Erik Keck ◽  
John Amund Lund
Keyword(s):  
Monte Carlo ◽  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Sensitivity Study ◽  
Uncertainty Sources ◽  
Industry Standard ◽  
Wind Speed Distribution ◽  
Mean Wind Speed ◽  
A Site

This article presents a method for incorporating the effect on expected annual energy production of a wind farm caused by asymmetric uncertainty distributions of the applied losses and the nonlinear response in turbine production. The necessity for such a correction is best illustrated by considering the effect of uncertainty in the oncoming wind speed distribution on the production of a wind turbine. Due to the shape of the power curve, variations in wind speed will result in a skewed response in annual energy production. For a site where the mean wind speed is higher than 50% of the rated wind speed of the turbine (in practice all sites with sufficiently high wind speed to motivate the establishment of a wind farm), a reduction in mean wind will cause a larger reduction in annual energy production than a corresponding increase in mean wind would increase the annual energy production. Consequently, the expected annual energy production response when considering the uncertainty of the wind will be lower than the expected annual energy production based on the most probable incoming wind. This difference is due to a statistical bias in the industry standard methods to calculate expected annual energy production of a wind farm, as implemented in tools in common use in the industry. A method based on a general Monte Carlo approach is proposed to calculate and correct for this bias. A sensitivity study shows that the bias due to wind speed uncertainty and nonlinear turbine response will be on the order of 0.5% – 1.5% of expected annual energy production. Furthermore, the effect on expected annual energy production due to asymmetrical distributions of site specific losses, for example, loss of production due to ice, can constitute additional losses of several percent.

Combining AHP and TOPSIS Methods to Evaluate Investment for Wind Farm Construction

2013 ◽  
Vol 860-863 ◽  
pp. 280-286 ◽  
Author(s):  
Xiang Feng Zhang
Keyword(s):  
Alternative Energy ◽  
Wind Farm ◽  
Evaluation Method ◽  
Evaluation Criteria ◽  
Wind Farms ◽  
Investment Strategy ◽  
Analytic Hierarchy ◽  
Multi Criteria Evaluation ◽  
Order Preference ◽  
Rational Investment

Wind is one of the most promising sources of alternative energy. The construction of wind farms grows quickly in China. It is necessary for stakeholders to estimate investment costs and make good decisions on a wind power project by making a budget for the investment. However, the identification of rational investment practices is technically challenging because of the lack of scientific tools to evaluate optimal decisions. A multi-criteria evaluation method was proposed to select rational investment strategy for wind farm construction. The method is based on the analytic hierarchy process (AHP) together with a technique for order preference by similarity to ideal solution (TOPSIS). A decision problem hierarchy with three layers were investigated. The top layer is an objective layer for evaluating the investment rationality. The intermediate layer includes three evaluation criteria, that is, configuration of wind turbine generator systems, physical environment and social environment. Some relative and important indicators for each criterion are in the low layer. The evaluation results illustrate that the proposed method is practical and helpful to indentify the investment rationality for wind farms.

scholarly journals Wind Energy Development in India and a Methodology for Evaluating Performance of Wind Farm Clusters

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Sanjeev H. Kulkarni ◽  
Tumkur Ramakrishnarao Anil ◽  
Rajakumar Dyamenally Gowdar
Keyword(s):  
Wind Energy ◽  
Power Plants ◽  
Wind Farm ◽  
Pairwise Comparison ◽  
Wind Farms ◽  
Analytic Hierarchy ◽  
Healthy Environment ◽  
Indian State ◽  
Wind Energy Development ◽  
Hierarchy Process

With maturity of advanced technologies and urgent requirement for maintaining a healthy environment with reasonable price, India is moving towards a trend of generating electricity from renewable resources. Wind energy production, with its relatively safer and positive environmental characteristics, has evolved from a marginal activity into a multibillion dollar industry today. Wind energy power plants, also known as wind farms, comprise multiple wind turbines. Though there are several wind-mill clusters producing energy in different geographical locations across the world, evaluating their performance is a complex task and is an important focus for stakeholders. In this work an attempt is made to estimate the performance of wind clusters employing a multicriteria approach. Multiple factors that affect wind farm operations are analyzed by taking experts opinions, and a performance ranking of the wind farms is generated. The weights of the selection criteria are determined by pairwise comparison matrices of the Analytic Hierarchy Process (AHP). The proposed methodology evaluates wind farm performance based on technical, economic, environmental, and sociological indicators. Both qualitative and quantitative parameters were considered. Empirical data were collected through questionnaire from the selected wind farms of Belagavi district in the Indian State of Karnataka. This proposed methodology is a useful tool for cluster analysis.

scholarly journals Wind Turbine Placement Optimization by means of the Monte Carlo Simulation Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
S. Brusca ◽  
R. Lanzafame ◽  
M. Messina
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Wind Farm ◽  
Simulation Method ◽  
Optimal Placement ◽  
Monte Carlo Simulation Method ◽  
Energy Evaluation

This paper defines a new procedure for optimising wind farm turbine placement by means of Monte Carlo simulation method. To verify the algorithm’s accuracy, an experimental wind farm was tested in a wind tunnel. On the basis of experimental measurements, the error on wind farm power output was less than 4%. The optimization maximises the energy production criterion; wind turbines’ ground positions were used as independent variables. Moreover, the mathematical model takes into account annual wind intensities and directions and wind turbine interaction. The optimization of a wind farm on a real site was carried out using measured wind data, dominant wind direction, and intensity data as inputs to run the Monte Carlo simulations. There were 30 turbines in the wind park, each rated at 20 kW. This choice was based on wind farm economics. The site was proportionally divided into 100 square cells, taking into account a minimum windward and crosswind distance between the turbines. The results highlight that the dominant wind intensity factor tends to overestimate the annual energy production by about 8%. Thus, the proposed method leads to a more precise annual energy evaluation and to a more optimal placement of the wind turbines.

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