Energy storage flattens demand significantly throughout the year, and as shown in the histogram in the right panel, storage thus reduces the number of hours of peak generation and the magnitude of peak requirements while also increasing demand during the lowest few hours of the year. Average load and standard deviation for each of these cases are summarized in Table VI.
With the presence of CAES, the discrete scenario results show not only a concentration of load levels to be served, as in Figure 1, but also a small overall reduction in load.
With limited storage available, minimal reshaping of demand occurs, using storage to shift only the most expensive hours of the year, maximizing the benefit of what storage is available.
As in earlier results, the availability of energy storage improves dispatch of inexpensive generators by shaping renewables availability.
While there is no clear bias towards storage in any one period when energy storage is limited, when quantities are unlimited, storage is concentrated primarily in the winter and spring months, when stored energy is the cheapest. It is likely that the difference between generic and discrete storage behavior in the final months of the year is a consequence of limiting constraints in the discrete storage scenario.
Estimated capital costs for selected storage devices.16
For the purposes of this study, a small subset of storage types has been selected based on their suitability for daily storage.
Marginal costs for energy storage are also included in the discrete storage scenarios.
With low limits set for all available energy storage types, the optimal outcome still appears to be the maximum allowable storage.
Comparing capital costs to annual savings for each of the storage scenarios suggests the limited storage portfolio provides the best economic basis for implementation.
Comparing the effects of storage availability reveals that even limited storage can manage the highest cost hours of the year, though large quantities of seasonal storage has dramatic effects on dispatch throughout the year.
If possible, large quantities of energy storage will be allocated by the model, even when operating costs are included.
GAMS models are structured around controlling indices called “sets.”
Model parameters define the operating constraints of all generators in Table XIII as well as time-dependent functions.
Model variables are combined with parameters to form the objective function and constraint equations.
For the discrete storage scenarios, additional parameters are required to enable constraints on their assignment and operation.
Additional variables must be defined to constrain the selection and operation of energy storage in the discrete storage scenarios.
Austin Energy’s projected generating fleet in 2020 is comprised of a variety of thermal generating units as well as several types of renewables (PWR—pressurized water reactor; CC—combined-cycle; GT—gas turbine; NG—natural gas; and Pk—peaking).
Startup and marginal costs.
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