To examine the potential benefits of energy storage in the electric grid, a generalized unit commitment model of thermal generating units and energy storage facilities is developed. Three different storage scenarios were tested—two without limits to total storage assignment and one with a constrained maximum storage portfolio. Given a generation fleet based on the City of Austin’s renewable energy deployment plans, results from the unlimited energy storage deployment scenarios studied show that if capital costs are ignored, large quantities of seasonal storage are preferred. This operational approach enables storage of plentiful wind generation during winter months that can then be dispatched during high cost peak periods in the summer. These two scenarios yielded $70 million and $94 million in yearly operational cost savings but would cost hundreds of billions to implement. Conversely, yearly cost reductions of $40 million can be achieved with one compressed air energy storage facility and a small set of electrochemical storage devices totaling 13 GWh of capacity. Similarly sized storage fleets with capital costs, service lifetimes, and financing consistent with these operational cost savings can yield significant operational benefit by avoiding dispatch of expensive peaking generators and improving utilization of renewable generation throughout the year. Further study using a modified unit commitment model can help to clarify optimal storage portfolios, reveal appropriate market participation approaches, and determine the optimal siting of storage within the grid.

1.
K.
Forsten
,
R.
Lordan
, and
N.
Jones
, “
Green transmission efficiency initiative: Series of regional workshops
,” Tech. Rep. 1019531 (Electric Power Research Institute, Palo Alto,
2009
).
2.
DOE, “Bottling electricity: Storage as a strategic tool for managing variability and capacity concerns in the modern grid” (DOE Electricity Advisory Committee,
2008
), http://energy.gov/sites/prod/files/oeprod/DocumentsandMedia/final-energy-storage_12-16-08.pdf.
3.
J. M.
Eyer
,
J. J.
Iannucci
, and
G. P.
Corey
, “
Energy storage benefits and market analysis handbook: A study for the DOE energy storage systems program
,” Tech. Rep. SAND2004-6177 (Sandia National Laboratories, Albuquerque,
2004
).
4.
P.
Sullivan
,
W.
Short
, and
N.
Blair
,
Wind Eng.
32
,
603
(
2008
).
5.
R.
Sioshansi
,
P.
Denholm
,
T.
Jenkin
, and
J.
Weiss
,
Energy Economics
31
,
269
(
2009
).
6.
R.
Walawalkar
,
J.
Apt
, and
R.
Mancini
,
Energy Policy
35
,
2558
(
2007
).
7.
L. E.
Benitez
,
P. C.
Benitez
, and
G. C.
van Kooten
,
Energy Econ.
30
,
1973
(
2008
).
8.
A.
Tuohy
and
M.
O’Malley
,
Energy Policy
39
,
1965
(
2011
).
9.
J. B.
Greenblatt
,
S.
Succar
,
D. C.
Denkenberger
,
R. H.
Williams
, and
R. H.
Socolow
,
Energy Policy
35
,
1474
(
2007
).
10.
E.
Fertig
and
J.
Apt
,
Energy Policy
39
,
2330
(
2011
).
11.
N.
Lu
,
J.
Chow
, and
A. A.
Desrochers
,
IEEE Trans. Power Syst.
19
,
834
(
2004
).
12.
H.
Lund
,
G.
Salgi
,
B.
Elmegaard
, and
A.
Andersen
,
Appl. Therm. Eng.
29
,
799
(
2009
).
13.
J.
Barton
and
D.
Infield
,
IEEE Trans. Energy Convers.
19
,
441
(
2004
).
14.
N. P.
Padhy
,
IEEE Trans. Power Syst.
19
,
1196
(
2004
).
15.
Austin
Energy
, “
Resource guide: Planning for Austin’s future energy resources
” (Austin Energy, Austin,
2008
), http://www.austinenergy.com/About%20Us/Newsroom/Reports/taskForce/docAustinEnergyResourceGuide.pdf.
16.
EPRI, “
Energy storage for grid connected wind generation applications
,” Tech. Rep. 3 (Electric Power Research Institute, Palo Alto,
2007
).
17.
R.
Baldick
,
IEEE Trans. Power Syst.
10
,
465
(
1995
).
18.
A.
Tuohy
,
P.
Meibom
,
E.
Denny
, and
M.
O’Malley
,
IEEE Trans. Power Syst.
24
,
592
(
2009
).
19.
M. C.
Lott
,
C. W.
King
, and
M. E.
Webber
, in
3rd International Conference on Energy Sustainability
(
American Society of Mechanical Engineers
,
Phoenix
,
2009
), pp.
1
9
.
20.
Energy Information Administration, “U.S. natural gas prices,” http://tonto.eia.doe.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm (
2010
).
21.
Energy Information Administration, “EIA—Average price of coal by state and mine type,” http://www.eia.doe.gov/cneaf/coal/page/acr/table28.html (
2010
).
22.
I.
Borg
and
C.
Briggs
, “U.S. energy flow–1988,” UCID-19227-88 (Lawrence Livermore National Laboratory, Livermore, CA, on June 1,
1989
).
23.
Environmental Protection Agency, “eGRID: Clean energy: US EPA,” http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html (
2007
).
24.
National Renewable Energy Laboratory, “NSRDB: 1991-2005 update,” http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/ (
2005
).
25.
D. M.
Wogan
,
M.
Webber
, and
A. K.
da Silva
,
J. Renewable and Sustainable Energy
2
,
1
(
2010
).
26.
T.
Markel
,
K.
Smith
, and A. Pesaran, “
PHEV energy storage performance/life/cost trade-off analysis
,” Tech. Rep. 43159 (National Renewable Energy Laboratory, Golden,
2008
).
27.
N.
Desai
,
S.
Gonzales
,
D. J.
Pemberton
, and
T. W.
Rathjen
, “The economic impact of CAES on wind in TX, OK, and NM” (Ridge Energy Storage & Grid Services LP,
2005
), http://www.seco.cpa.state.tx.us/zzz_re/re_wind_projects-compressed2005.pdf.
28.
The model case with discrete energy storage is not strictly “unlimited,” as there are integer limits on the total storage permitted for each type, but these limits are quite high.
29.
M.
Carrión
and
J.
Arroyo
,
IEEE Trans. Power Syst.
21
,
1371
(
2006
).
30.
S.
Shahidehpour
and
C.
Wang
,
IEEE Trans. Power Syst.
8
,
1341
(
1993
).
You do not currently have access to this content.