This paper proposes a systematic framework to assess the complementarity of renewable resources over arbitrary geographical scopes and temporal scales which is particularly well-suited to exploit very large data sets of climatological data. The concept of critical time windows is introduced, and a spatio-temporal criticality indicator is proposed, consisting in a parametrised family of scalar indicators quantifying the complementarity between renewable resources in both space and time. The criticality indicator is leveraged to devise a family of optimisation problems identifying sets of locations with maximum complementarity under arbitrary geographical deployment constraints. The applicability of the framework is shown in a case study investigating the complementarity between the wind regimes in continental western Europe and southern Greenland, and its usefulness in a power system planning context is demonstrated. Besides showing that the occurrence of low wind power production events can be significantly reduced on a regional scale by exploiting diversity in local wind patterns, results highlight the fact that aggregating wind power production sites located on different continents may result in a lower occurrence of system-wide low wind power production events and indicate potential benefits of intercontinental electrical interconnections.
We present the first frequency-dependent analyses of the geographic smoothing of wind power's variability, analyzing the interconnected measured output of 20 wind plants in Texas. Reductions in variability occur at frequencies corresponding to times shorter than 24Â h and are quantified by measuring the departure from a Kolmogorov spectrum. At a frequency of 2.8x10-4Â Hz (corresponding to 1Â h), an 87 reduction of the variability of a single wind plant is obtained by interconnecting 4 wind plants. Interconnecting the remaining 16 wind plants produces only an additional 8 reduction. We use step change analyses and correlation coefficients to compare our results with previous studies, finding that wind power ramps up faster than it ramps down for each of the step change intervals analyzed and that correlation between the power output of wind plants 200Â km away is half that of co-located wind plants. To examine variability at very low frequencies, we estimate yearly wind energy production in the Great Plains region of the United States from automated wind observations at airports covering 36 years. The estimated wind power has significant inter-annual variability and the severity of wind drought years is estimated to be about half that observed nationally for hydroelectric power. Energy in wind and incoming solar radiation has been observed to be complementary over time. That is, as one of these resources slackens, the other tends to increase. Considering the intermittency of wind and solar radiation, complementarity provides the potential to better exploit the two resources in combination. How well a hybrid system using both resources can improve the reliability of energy generation is partially a function of their complementarity. This study developed an approach to calculate the Complementarity Index of Wind and Solar Radiation (CIWS). Geographic analyses including principal component analysis and geographically weighted regression modeling were applied to investigate the impacts of diverse geographic factors on complementarity. Oklahoma was used as the case study area because of the availability of quality-controlled five-minute data from 127 Mesonet sites. The results indicate average CIWS (unitless) is 10.99, about 46 percent of the theoretical maximum value of 24; the stand... The power plants based on renewable sources face various acceptance difficulties mainly due to high initial costs and low overall efficiencies. A way to improve performance of these systems is to utilize more than one type of source of energy chosen to provide some degree of complementarity. An interesting, albeit improbable, combination is obtained using hydroelectric and photovoltaic energy, taking advantage of the complementarity between these two types of sources coupled to reservoir and or battery storage. This paper discusses energy complementarity in time and proposes a numerical dimensionless index, evaluating this energy complementarity between two types of energy sources, in the same or in different locations, or between two sources of the same type in different locations. In the end, the results of the application of this index to the solar and water availabilities over the state of Rio Grande do Sul, in southern Brazil, is presented in the form of maps. The power spectral density of the output of wind turbines provides information on the character of fluctuations in turbine output. Here both 1-second and 1-hour samples are used to estimate the power spectrum of several wind farms. The measured output power is found to follow a Kolmogorov spectrum over more than four orders of magnitude, from 30 s to 2.6 days. This result is in sharp contrast to the only previous study covering long time periods, published 50 years ago. The spectrum defines the character of fill-in power that must be provided to compensate for wind's fluctuations when wind is deployed at large scale. Installing enough linear ramp rate generation (such as a gas generator) to fill in fast fluctuations with amplitudes of 1 of the maximum fluctuation would oversize the fill-in generation capacity by a factor of two for slower fluctuations, greatly increasing capital costs. A wind system that incorporates batteries, fuel cells, supercapacitors, or other fast-ramp-rate energy storage systems would match fluctuations much better, and can provide an economic route for deployment of energy storage systems when renewable portfolio standards require large amounts of intermittent renewable generating sources. We examine the potential for geographic smoothing of solar photovoltaic (PV) electricity generation using 13 months of observed power production from utility-scale plants in Gujarat, India. To our knowledge, this is the first published analysis of geographic smoothing of solar PV using actual generation data at high time resolution from utility-scale solar PV plants. We use geographic correlation and Fourier transform estimates of the power spectral density (PSD) to characterize the observed variability of operating solar PV plants as a function of time scale. Most plants show a spectrum that is linear in the log–log domain at high frequencies f, ranging from to (slopes of −1.23 and −1.56), thus exhibiting more relative variability at high frequencies than exhibited by wind plants. PSDs for large PV plants have a steeper slope than those for small plants, hence more smoothing at short time scales. Interconnecting 20 Gujarat plants yields a spectrum, reducing fluctuations at frequencies corresponding to 6 h and 1 h by 23 and 45 , respectively. Half of this smoothing can be obtained through connecting 4–5 plants; reaching marginal improvement of 1 per added plant occurs at 12–14 plants. The largest plant (322 MW) showed an spectrum. This suggests that in Gujarat the potential for smoothing is limited to that obtained by one large plant.