Background of Model
Qualitative details on wind variability
Wind power, because the resource is variable and unpredictable and neither the resource nor the resulting electricity can readily be stored, is complicated to model. ReEDS, in an attempt to capture the peculiarities of wind power, has a detailed, statistical treatment of wind power that is unique among power sources. CSP, were it not assumed in ReEDS to have six hours of thermal storage, would have similar issues; as it is, for now, only wind has such involved variability calculations. (NREL is in the process of modifying ReEDS to accommodate photovoltaics and CSP without storage.) The variability of the wind resource can impact the electric grid in several ways. One useful way to examine these impacts is to categorize them in terms of time, ranging from multiyear planning issues to small instantaneous fluctuations in output.
At the longest time interval, a utility's capacity-expansion plans may call for the construction of more nameplate generation capacity. To meet this need, the planners can plan to build dispatchable capacity or wind. The variability of wind precludes the planners from considering a MW of nameplate of wind capacity to be the same as a MW of nameplate of dispatchable capacity: wind capacity can not be counted on to be available when peak demand for electricity occurs. Actually, conventional capacity also can not be considered 100% reliable. The difference is in the degree of reliability and the correlation in that reliability between sites/plants; conventional generators experience forced outages on the order of 2%-20% of the time, while wind energy is available at varying levels that average about 30%-45% of the time depending on the quality of the wind site. For planning purposes, this lack of reliability is handled in the same way—a statistical treatment that calculates how much more load can be added to the system for each MW of additional nameplate wind capacity, or Effective Load Carrying Capability (ELCC).
Effective Load Carrying Capability is less for wind than for conventional capacity; first, because the wind availability is less than that of conventional generators. And second, because at any given instant, the generation from a new wind farm can be heavily correlated with the output from the existing wind farms—if the wind isn't blowing at one wind site, there is a reasonable chance it is also not blowing at another nearby site. On the other hand, there is essentially no correlation between the outputs of any two conventional generation plants.
Fortunately, there are ways to partly mitigate both the low availability of the wind resource and its correlation between sites. In the past 20 years, there has been considerable improvement in wind capacity factor (the ratio of actual output over a period of time to its output had it operated at full capacity over that same interval) of new wind installations. This is attributable to both better site exploration/characterization and to improvements in the wind turbines themselves (largely higher towers).
The correlation in wind output between sites also can be reduced. Increasing the distance between sites and the terrain features that separate them reduces the chances that two sites will experience the same winds at the same time. Correlation coefficients between wind sites (and the load) have been calculated based on wind hourly resource data for thousands of sites around the country. These correlations are used in the calculation of the ELCC as next described.
Between each 2-year-period optimization and for each demand region, ReEDS updates its estimate of the marginal ELCC associated with adding wind of each resource class in each wind supply region to meet demand within a region. This marginal ELCC is a strong function of the wind capacity factor and the correlations with the existing wind systems to the new wind site for which the ELCC is being calculated. It is also a weak function of the demand region's load-duration curve and the size and forced outage rates of the conventional capacity. This marginal ELCC is assumed to be the capacity value of each MW of that wind class added in the next period in that wind supply region to serve the RTO's demand.
Everything else being equal, when expanding wind capacity, ReEDS will select the next site in a region that is least correlated with existing sites to ensure the highest ELCC for the next wind site. (More practically, everything else is never "equal," and ReEDS considers the tradeoffs between ELCC and wind site quality, transmission availability/cost, and local siting costs.)
Generally, for the first wind site supplying a demand region, these capacity values (ELCC) are almost equal to the capacity factor. However, as the wind penetrates to higher levels, the ELCC can decline to zero in an individual wind supply region.
No matter the market structure, however, the imbalances must be offset with adequate operating reserves. Therefore, to capture the essence of the unit-commitment issue, ReEDS estimates the impact of wind variability on the need for operating reserves (includes quick-start and spinning reserves) that can rapidly respond to changes in wind output. The operating reserves are assumed to be a linear function of the variance in the sum of generation (both wind and conventional) minus load. Because the variability of wind is statistically independent of the load variability and forced outages, the total variance with wind can be calculated as the sum of the variance associated with the normal (i.e., no wind) operating reserve and the total (over all the wind supply regions) variance in the wind output over the reconciliation period. Before each 2-year optimization, ReEDS calculates the marginal operating reserve additions required by the next unit of wind (added in a particular wind supply region from a particular wind class) as the difference between the operating reserve required by the system with and without that marginal unit of wind. This value is then used throughout the next 2-year linear program optimization as the marginal operating reserve requirement induced by the next MW of wind addition in that region of that wind resource class.
At the shortest time interval, instantaneous changes in wind output must be compensated for by regulation reserves. Regulation reserves are normally provided by automatic generation control of conventional generators whose output can be automatically adjusted to compensate for small changes in voltage on the grid. Fortunately, these instantaneous changes in wind output do not all occur at the same time, even from wind turbines within the same wind farm. This lack of correlation over time and the ease with which conventional generators can respond allows us to reasonably ignore this second order cost.
ReEDS assumes that any wind generation delivered to a specific demand region in a specific time-slice that exceeds the total electric load in that region/time-slice will be lost. In addition, as mentioned above, ReEDS also statistically accounts for surplus wind lost within a time-slice due to variations in load and wind within the time-slice.
ReEDS has three endogenous options for mitigating the impact of variability. The first is to add conventional generators that can provide spinning reserve (e.g. gas combined-cycle) and quick-start capabilities (combustion turbines).The second, and usually least costly, is to allow the dispersion of new wind installations reducing the correlation of the outputs from the different wind sites. The third, and usually most costly form of operating reserves, is to allow for storage of electricity. Storage options available for satisfying operating reserves in ReEDS are pumped hydro, compressed air, and batteries.
This section includes:
Qualitative model description
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