SOLAR TODAY - September/October 2011

getting to large-scale solar integration

pollutants and even surface reflectivity. The industry needs analyses of existing high-quality data to evaluate current forecasting skill. The ARM Southern Great Plains program has deployed a vast suite of meteorological instruments that can help directly address how the various environmental factors can affect solar radiation available for electricity production. Analyzing data from the ARM SGP site will allow solar forecasters to quantify the quality of forecasts and identify the parameters that are contributing most significantly to observed forecast errors. Such analysis should enable us to improve the models’ physics and, consequently, decrease forecast errors of irradiance.

An improved version of LAPS, called Space-Time Multiscale Analysis System (STMAS) will use variational methods to improve the assimilation of clouds and radar data. That helps to make the model fields more consistent with the obser-vational data and the atmospheric equations of motion. This consistency is important so that the clouds are more accurately forecast beyond the first hour or so of the forecast period.

The LAPS system as shown in figure 3 (page 55) is an example of how solar forecasts can be made. Clouds are initialized at time zero of the model forecast and continue to progress through the first few hours of the forecast. Forecast solar radiation varies with the cloud motion and the time of day. The right panel shows a Weather Research and Forecast (WRF) model solar radiation forecast that was initialized using the LAPS cloud analysis. Two hours into the forecast, the

Public-Private Partnership Is Essential

Obtaining the meteorological measurements and NWP model improvements needed for a national, operational solar forecast with the spectral, spatial and temporal accuracy needed for efficient large-scale solar power integration will require close collaboration among multiple federal agencies, the private sector and academia. The public-sector role is to provide an adequate foundational weather forecast that the private sector can then use to provide value-added forecasts and tailored products for the energy industry, including forecasts of power production. Because this effort is in a very early stage, details of what this arrangement should be are yet to be defined. As solar energy development expands, continual communication about respective roles will be crucial among federal agencies, such as the DOE, NOAA, the National Renewable Energy Laboratory and the National Science Foundation; private entities, such as solar power plant developers, owners, and operators, private forecast vendors, utilities, balancing authorities and independent system operators; and academic institutions. Flexibility to accommodate changes in the energy industry’s needs and advances in technology will be essential as these partnerships grow.

The meteorological research community is well poised to address the issues critical to the success of solar energy. However, a number of questions need to be addressed to support solar energy development. For instance, at what spatial density are observations of the DNI or global solar irradiance necessary, and in which regions of the nation, to obtain the needed improvements in NWP forecasts of solar irradiance? Specialized studies to examine the quantitative impact of water vapor, aerosols and clouds will be needed to improve forecasts on temporal and spatial scales important for grid balancing. This work will require a combination of improvements in weather forecast models, high-quality measurements and radiative transfer models. Because the forecasting requirements depend on the technology, the required research needs to be flexible and adaptive. The academic and governmental institutions can make strong contributions to the fundamental research, while private industry can apply the research to specific business needs and focused forecasts. The work of the two is intertwined; the success of solar energy development will require the work of both to succeed. ST

solar radiation field closely matches the analyzed cloud-cover locations from the same time on the left panel. This shows a fairly accurate short-term forecast as a storm moved across California earlier this year.

In addition to forecasting clouds, we can better analyze and forecast aerosols and water vapor using models such as the WRF, particularly if atmospheric chemistry components of the model are activated. NOAA is developing a version of the HRRR model that includes explicit forecasts of aerosols. Improved networks of solar radiation observations can be used to help verify model forecasts.

The most pressing need for the solar industry is for advances in the hour-ahead forecast, which, despite its name, refers to the time two to four hours ahead. For such short-term prediction needs, NWP forecasts can be supplemented by additional approaches. As Ihnen (2009) explains, one strategy is to use near-real-time satellite images of the solar plant area with ground-based observations and other information to derive horizontal wind speeds and other meteorological variables such as humidity structure. Then a statistical technique called a Markov grid is applied to derive cloud fraction and opacity in the time period of interest: two to four hours out. For minute-ahead forecasts, a tailored all-sky camera can be deployed at the solar plant, and observations from it are used to provide probabilities of power ramp events, for instance, from single clouds, in each 10-minute period for the next hour.

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