A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation

Appropriate wind speed modeling for generating synthetic wind speed data is crucial in microgrid reliability evaluation studies. This paper proposes a 2-D wind speed statistical model based on historical wind speed data. The first dimension of the proposed wind model focuses on the probability distribution on the time duration of different wind speed scales, while the second dimension focuses on the probability distribution of wind speed in each wind speed scale. Unlike traditional wind speed models, the proposed model can simultaneously deal with the probabilistic characteristics of wind speed and wind time duration. This paper also presents a 2-D wind model-based data sampling method, and incorporates it into the microgrid reliability assessment algorithm. Using historical wind speed data in Tianjin, China, it shows that the proposed 2-D wind speed statistical model helps to fully simulate the volatility of wind energy. Furthermore, the proposed wind speed model is applied on the microgrid reliability evaluation study, which shows that the proposed model can be effectively utilized by planners to conduct reliability evaluation for microgrid.

In this paper, offshore wind resources within the Japan's EEZ (Exclusive Economic Zone) are assessed using wind speed data from the microwave scatterometer SeaWinds onboard QuikSCAT. At first, from the 10m-height wind speed from QuikSCAT, 60 m-height wind speed is estimated by using an empirical equation for height correction. Based on the 60 m-height wind speeds, annual energy Production is calculated under an assumption of installing 2 MW wind turbines every <TEX>$0.64km^2$</TEX>. The annual energy production is then accumulated for the entire Japan's territorial waters and EEZ (<TEX>$4.47{\times}10^6km^2$</TEX>). As a result, it is shown that the total energy Production is estimated to be <TEX>$4.86{\times}10^4$</TEX> TWh/yr. This offshore wind energy Potential within the EEZ is approximately 50 times higher than the actual annual electricity production in Japan.

Wind Shear Coefficients and their Effect on Energy Production

<strong class="journal-contentHeaderColor">Abstract.</strong> The main goals of a wind resource assessment (WRA) at a given site are to estimate the wind speed and annual energy production (AEP) of the planned wind turbines. Several steps are involved in going from initial wind speed estimations of specific locations to a comprehensive full-scale AEP assessment. These steps differ significantly between the chosen tool and the individuals performing the examination. The goal of this work is to compare different WRA simulation tools at the Perdig&atilde;o site in Portugal, for which a large amount of wind measurement data is available, in terms of both accuracy and costs. Results from nine different simulations from five different modellers were obtained via the "IEA Wind Task 31 Comparison metrics simulation challenge for wind resource assessment in complex terrain", consisting of a range of linear models, Reynolds-Averaged Navier-Stokes (RANS) computational fluid dynamics models and Large Eddy Simulations (LES). The wind speed and AEP prediction errors for three different met mast positions across the site were investigated and further translated into relative &ldquo;skill&rdquo; and &ldquo;cost&rdquo; scores, using a method previously developed by the authors. This allowed the most optimal simulation tool in terms of accuracy and cost to be chosen for this site. It was found that the RANS simulations achieved very high prediction accuracy at relatively low costs for both wind speed and AEP estimations. The LES simulations achieved great wind speed prediction for certain conditions, but at a much higher cost, which in turn also reduced the number of possible simulations, leading to a decrease in AEP prediction accuracy. For some of the simulations, the forest canopy was explicitly modelled, which was proven to be beneficial for wind speed predictions at lower heights above the ground, but lead to under-estimations of wind speeds at upper heights, decreasing the AEP prediction accuracy. Lastly, low correlation qualities between wind speed and AEP prediction error were found for each position, showing that accurate wind modelling is not necessarily the only important variable in the WRA process, and that all the steps must be considered.

Assessing complementarity of wind and solar resources for energy production in Italy. A Monte Carlo approach

Impact of urbanization on groundwater recharge rates in Dübendorf, Switzerland

Multiple wind farms (WFs) model consists of wind speed model and single WF model. In the multiple WFs integrated power system, the wind speeds at different WFs are correlated to some degree if the distance between WFs is not very large. Hence, a novel MC-Nataf-CD method combining partitions Monte Carlo (MC) sampling, Nataf transformation and Cholesky Decomposition (CD) is proposed to simulate correlated wind speeds with desired cross-correlation coefficients. The wind speeds generated by the proposed wind speed model is closer to both the historical data and the Weibull fitting curve. For the single WF model, wake effects, the forced outage rates (FORs) of wind turbine generators (WTGs) and the reliability of the connected grids inside a WF need to be taken into consideration. Four different single WF models are proposed to reveal the impact of these relevant factors. A statistical method based on state-sampling MC simulation is implemented for the reliability estimation of Roy Billinton Test System (RBTS) with multiple WFs, using both historical and simulated wind speed data. The simulation results and the calculated reliability indices demonstrate that the proposed techniques are effective and accurate in the modeling of correlated multiple WFs.

This Paper signifies techno-economic assessments of predicted wind energy generation from 50 MW wind farm at Gawadar city, Baluchistan -Pakistan. Key purpose of the current study is to find wind power potential at low, medium and higher heights. To explore the best power potential, four years' wind speed data is forecasted using statistical model. ARIMA models present a better understanding of the change and estimate of the wind speed. Based on this forecasted wind speed, 317 turbines of different manufacturers were examined at different heights (60m, 80m and 100m) and of maximum possible rating up to 5 MW. The results show the best turbine selection at particular location with maximum possible capacity factor achievable through various numerical as well as simulation methods. Moreover this study further investigates the economic feasibility of 50 MW wind farm through numerical methods at the potential site using Minitab, Stats Graphics, and EViews. The results obtained from this software were validated by RETScreen Software. The results show that the most suitable hub height for wind turbines in Gawadar-Pakistan is 100m and capacity factor found at this height is 13.32 percent with annual wind energy production of 58 GWh/year.

The technical feasibility of off-shore wind energy in the Philippines is assessed. Geographic information system is utilized to integrate the different technical data into a single model. Off-shore wind speed data for five years at elevations 10m, 20m, 80m, and 100m from a local database was used as reference for the wind resource study. Two wind turbines were considered for the energy conversion component, Siemens SWT-3.6-120 and Senvion 6.2 M126. The wind speed data was interpolated to 90m and 95m using standard power law to match the hub heights of the turbines studied. The wind power density, wind power, and annual energy production were calculated from the interpolated wind speeds. Areas in the Philippines with capacity factor greater than 30% and performance greater than 10% were considered technically viable. Exclusion criteria were applied to narrow down the potential siting for offshore wind farms, namely, active submerged cables, local ferry routes, marine protected areas, reefs, oil and gas extraction areas, bathymetry, distance to grid, typhoons, and earthquakes. Several sites were determined to be viable with north of Cagayan having the highest capacity factor. The highest wind capacity factor for the offshore wind farms are located in north of Ilocos Norte (SWT-3.6-120: 54.48%–62.60%; 6.2M126: 54.04%–64.79%), north of Occidental Mindoro (SWT-3.6-120: 46.81%–60.92%; 6.2M126: 45.30%–62.60%) and southeast of Oriental Mindoro (SWT-3.6-120: 45.60%–59.52%; 6.2M126: 45.30%–62.60%). However, these sites are not acceptable due to technical, social, and political constraints. The constraints considered in the study are active submerged cables with a buffer of 5 km, local ferry routes with a buffer of 3km, marine protected areas with a buffer 3 km, reefs with a buffer of 3 km, oil and gas extraction areas with a buffer of 5 km, bathymetry less than 50m, distance to grid of within 120 km, historical typhoon tracks with greater than 250 kph and 50 km buffer, and historical earthquakes with greater than 6.5 magnitude with a buffer of 15 km. Upon application of these exclusion criteria, the potential sites for offshore wind farms are north of Cagayan, west of Rizal, north of Camarines Sur, north of Samar, southwest of Masbate, Dinagat Island, Guimaras, and northeast of Palawan.

Annual energy production (AEP) is often the objective function in wind plant layout optimization studies. The conventional method to compute AEP for a wind farm is to first evaluate power production for each wind direction and speed using either computational fluid dynamics simulations or engineering wake models. The AEP is then calculated by weighted-averaging (based on the wind rose at the wind farm site) the power produced across all wind directions. We propose a novel formulation for time-averaged wake velocity that incorporates an analytical integral of a wake deficit model across every wind direction. This approach computes the average flow field more efficiently, and layout optimization is an obvious application to exploit this benefit. The clear advantage of this new approach is that the layout optimization produces solutions with comparable AEP performance yet is completed about 700 times faster. The analytical integral and the use of a Fourier expansion to express the wind speed and wind direction frequency create a more smooth solution space for the gradient-based optimizer to excel compared with the discrete nature of the existing weighted-averaging power calculation.

Influence of ambient conditions on wind speed measurement: Impact on the annual energy production assessment

Assessing probabilistic wind load effects via a multivariate extreme wind speed model: A unified framework to consider directionality and uncertainty

The magnitude of wind speed as well as the amount of energy production increases with the height at which a wind turbine generator (WTG) is installed. This paper investigates on the optimum height at which a WTG should be installed at a location. Annual Levelized Cost of Energy (ALCoE) is chosen as the index to optimize the height. Six Indian wind sites have been chosen for the study. With time series wind speed data collected for the reference height, the wind regimes at different hub heights at each of the sites have been modeled and then used for estimation of annual energy production (AEP) using WAsP. AEP has been modified to account for uncertainties and then ALCoE has been computed. It is found that the cost of tower and foundation increases with the height, and therefore there is an optimum hub height at which ALCoE is the minimum.

According to the Chinese Load Code, the wind load for the structural design is based on the T-year return period of the annual maximum 10-min average wind speed. Therefore, determining the accurate regional annual maximum 10-min average wind speed, distribution is extremely important and essential. In this article, a new approach named generalised unified probability plotting (GUPP) method is proposed to parametrically analyse the regional wind speed distribution. The historical data of wind speed from 1953 to 2015 at southeast coastal city Xiamen, the distribution and parameters of the average 10-min annual maximum wind speeds are obtained. From the analysis results, the annual maximum 10-min average wind speed can be best modelled by the Pearson-III distribution among several distributions examined in the study, including the Gumbel distribution which underlies the Chinese Load Code. To further determine the significance of the updated wind speed distribution (namely the Pearson III), the wind-induced pressures at different story-levels of a high-rise building in Xiamen is examined relative to the Code-recommended distribution. Results show that the wind pressures corresponding to the Chinese Load Code is much higher than those of the Pearson-III. The difference is significant and may induce unreasonably high wind loads for design.AbbreviationsGuppGeneralised Unified Probability Plot; CDF: Cumulative Distribution Function; PDF: Probability Density Function; QQ Plot: Quantile-Quantile Plot; RMS: Root Mean Square; K–S: Kolmogorov–Smirnov

Sensitivity analysis of the system of wind speed distributions