Smoothing effects of distributed wind turbines. Part 1. Coherence and smoothing effects at a wind farm

Abstract

Recently there has been a marked increase in wind power generation. From a power system point of view, because a wind turbine is an intermittent generator with large output fluctuation, any increase in the number of wind turbines gives rise to concerns about the adverse effects of wind turbines on power quality. The smoothing effects of wind turbine output fluctuation are of great importance in assessing the impacts of a large number of wind turbines. This article examines smoothing effects at a wind farm. First it presents a summary of wind measurements taken at two locations with six masts over a period of 1 year on both flat and complex terrain.Then the spatial coherence of wind speed is analysed, paying special attention to its dependence on the distance between observation points, wind direction, wind velocity and fluctuation frequency. Approximation equations for coherence of frequency and distance are obtained by applying Davenport's expression to the observed data. Second, coherence between turbine output at a wind farm is investigated; the results indicate that coherence for wind speed and turbine output shows a considerable resemblance. The article also examines smoothing effects at a wind farm using power spectral density through a theoretical approach. The study proves that smoothing effects can be approximated with a lowpass filter and that the effects at a wind farm should not be taken into account for periods of more than 10 min in case of assessing them on the safe side.

The character of power output from utility‐scale photovoltaic systems

“…The histogram of steps ( Figure 5) indicates that some damping of the higher magnitude fluctuations (above about 20% of nameplate capacity) occurs (Figure 5b), as previously observed for wind power output. 8,9 We caution that these data are 10-min samples, and that one array (Prescott) has twice the peak power output either of the other two. Samples at …”

“…The power spectrum for a single APS site is very similar to that from the three combined sites (Figure 9). For wind, Nanahara et al 8 report smoothing when combining output from six turbines relative to a single turbine as a change in the slope of the power spectrum; they report an attenuation of the magnitude of fluctuations of frequencies above $1Á0 Â 10 À3 Hz. The available data for the present study limit our observations to frequencies below 8Á3 Â 10 À4 Hz, but it appears from the spectral analysis that fluctuations slower than this frequency (20 min) and faster than $2 Â 10 À5 Hz (14 h) are not significantly diminished due to site diversity over several hundred km.…”

“…6 Comparison of the output from several distributed sites provides information about geographic smoothing, previously examined for distributed wind power. [6][7][8][9] The statistics of correlation between distributed sites in the time domain can also be used to assess geographic smoothing, an approach used previously to evaluate the impact of site diversity on wind power. 10 …”

Quantifying smoothing effects of wind power via Koopman mode decomposition: A numerical test with wind speed predictions in Japan

“…The authors of [9] demonstrated an 87% reduction in wind power variability by interconnecting four WFs, and that interconnecting 16 more WFs only gave 8% additional reduction. In [10,11], smoothing effects in a hypothetical WF and coherence between distant turbines were investigated via PSD, by using real measurements of wind speeds in Japan. A new index to quantify smoothing effects of wind power was proposed in [11], and the results from applying it indicated non-significant smoothing effects for periods longer than 100 min, for WFs located hundreds of kilometers apart.…”

“…spatial patterns oscillating with single frequencies for all observables (wind powers at hypothetical WFs in this paper). By utilizing the phase and amplitude information of each observable which characterize the participation in each mode, we propose an averaged index of wind power smoothing via KMD, which follows previous investigations of wind power smoothing in Japan [10,11] via PSDs. The performance of the proposed index is exemplified by incorporating large-scale weather simulation data from the Cloud Resolving Storm Simulator (CReSS) [18] to study practical smoothing effects of wind power in Japan.…”