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A unit commitment study of the application of energy storage toward the integration of renewable generation
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10.1063/1.3683529
/content/aip/journal/jrse/4/1/10.1063/1.3683529
http://aip.metastore.ingenta.com/content/aip/journal/jrse/4/1/10.1063/1.3683529

Figures

Image of FIG. 1.
FIG. 1.

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.

Image of FIG. 2.
FIG. 2.

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.

Image of FIG. 3.
FIG. 3.

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.

Image of FIG. 4.
FIG. 4.

As in earlier results, the availability of energy storage improves dispatch of inexpensive generators by shaping renewables availability.

Image of FIG. 5.
FIG. 5.

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.

Tables

Generic image for table
Table I.

Estimated capital costs for selected storage devices.16

Generic image for table
Table II.

For the purposes of this study, a small subset of storage types has been selected based on their suitability for daily storage.

Generic image for table
Table III.

Marginal costs for energy storage are also included in the discrete storage scenarios.

Generic image for table
Table IV.

With low limits set for all available energy storage types, the optimal outcome still appears to be the maximum allowable storage.

Generic image for table
Table V.

Comparing capital costs to annual savings for each of the storage scenarios suggests the limited storage portfolio provides the best economic basis for implementation.

Generic image for table
Table VI.

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.

Generic image for table
Table VII.

If possible, large quantities of energy storage will be allocated by the model, even when operating costs are included.

Generic image for table
Table VIII.

GAMS models are structured around controlling indices called “sets.”

Generic image for table
Table IX.

Model parameters define the operating constraints of all generators in Table XIII as well as time-dependent functions.

Generic image for table
Table X.

Model variables are combined with parameters to form the objective function and constraint equations.

Generic image for table
Table XI.

For the discrete storage scenarios, additional parameters are required to enable constraints on their assignment and operation.

Generic image for table
Table XII.

Additional variables must be defined to constrain the selection and operation of energy storage in the discrete storage scenarios.

Generic image for table
Table XIII.

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).

Generic image for table
Table XIV.

Startup and marginal costs.

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/content/aip/journal/jrse/4/1/10.1063/1.3683529
2012-02-27
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: A unit commitment study of the application of energy storage toward the integration of renewable generation
http://aip.metastore.ingenta.com/content/aip/journal/jrse/4/1/10.1063/1.3683529
10.1063/1.3683529
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