Post on 05-Aug-2020
Sizing Advanced Flywheel Energy Storage Clay Hearn1, Sid Pratap1, Michael Lewis1, Robert Hebner1, Dongmei Chen2,
and Raul Longoria2
Introduction
Energy storage needs to be designed to account for operational performance, not just peak power or
energy. Consequently an improved design approach using an optimal control technique has been
developed. This method allows the loss dynamics of the flywheel system to be incorporated into the
sizing procedure, and allows trade studies to be performed with different flywheel sizes to minimize
peak grid power use. The effectiveness of the sizing methodology is illustrated through a case study
based on home consumption and solar generation data collected from one of the premier Smart Grid
programs in the world, the Pecan Street Project in Austin, Texas.
Data Driven Flywheel Sizing Analysis
1 The Center for Electromechanics at The University of Texas at Austin 2 The Department of Mechanical Engineering at The University of Texas at Austin
To understand how location affects energy storage sizing, home and solar generation data provided by Pecan Street, Inc. was used to study
flywheel sizing at different levels throughout a power distribution system. Pecan Street Inc. is an active R&D effort in Austin TX to implement and
evaluate smart grid technology in a functioning community. There are 741 homes in this community, and 25% of these homes have solar
installations, which translates into approximately 1 MW of generation capacity. Flywheel energy storage was sized at the individual homes, local
transformers, and community level.
Publications 1. C.S. Hearn, M.C. Lewis, S.B. Pratap, R.E. Hebner, F.M. Uriate, D. Chen, R.G. Longoria.
Utilization of Optimal Control Law to Size Grid-Level Flywheel Energy Storage.
Submitted to IEEE Transactions on Sustainable Energy, July 9, 2012.
Load profile for individual home in Mueller community. Power spikes due to A/C Loads. Data
provided by Pecan Street Inc.
Aggregate load profile for all 741 homes in the Mueller community. Natural smoothing of peak loads through
aggregation. Data provided by Pecan Street Inc.
Trade-offs between flywheel storage capacity and reduced peak grid power demand at the home level. Study shows a 2 kWh / 6.2 kW flywheel could reduce peak power draw for most homes by 30 โ 60%.
Location Peak Demand Power
Diurnal Storage Power Smoothing
Singe Home 5.7 KW
(average) 15 kWh /
6 kW
2 kWh / 6.2 kW
Transformer (8 homes)
30.7 kW 70 kWh /
16.3 kW
6 kWh /
12 kW
Community
(741 homes) 2500 kW 5.9 MWh /
1200 kW
450 kWh /
300 kW
Trade-offs between flywheel storage capacity and peak grid power demand for the 741 home community. Larger flywheel storage for diurnal applications will require flywheels with low losses and higher spin-down time constants
0.1
1
10
100
10.0 15.0 20.0 25.0 30.0
FW
Del
iver
ed E
ner
gy [
kW
h]
Peak Grid Power [kW]
Transformer Energy Storage vs. Peak Grid Power
Time Constant 200 Hrs
Time Constant 50 Hrs1
10
100
1000
10000
1000 1500 2000 2500 3000FW
Del
iver
ed E
ner
gy [
kW
h]
Peak Grid Power [kW]
Community Energy Storage vs. Peak Power
Time Constant 200 Hrs
Time Constant 50 Hrs
Aggregate load profile for transformer (~8 homes) in Mueller community. Data provided
by Pecan Street Inc.
0
5
10
15
20
25
0% 20% 40% 60% 80% 100%
Fly
wh
eel E
ner
gy
Del
iver
ed [k
Wh
]
Percent Decrease in Peak Grid Power
Flywheel Energy Storage Sizing for Individual Homes
Home 1412
Home 1420
Home 1421
Home 1422
Home 1423
Home 1424
Home 1425
Home 1426
Home 1427
Home 1439
Home 1446
Home 1458
Home 1463
๐ฝ ๐ก0 = 1
2๐๐ ๐๐๐ค ๐ โ ๐0
2+
1
2 ๐ ๐๐๐ค ๐ก โ ๐0
2+ ๐๐๐
2 ๐ก ๐๐ก
Flywheels are electro-mechanical devices which kinetically store energy via a high speed rotating
mass. Motor-generators are used to transfer energy to and from the spinning mass, which allows
flywheels to have improved power performance over the most advanced battery systems. A key
aspect of flywheel energy storage, which separates it from other devices such as batteries or
ultracapacitors, is that energy transfer, to charge and discharge a flywheel, is provided by motor-
generators. Therefore, energy storage capacity and power capability can be tailored to meet specific
grid requirements. Designers must understand power and energy storage requirements at different
locations within the utility grid, since grid design does not promote a single optimal storage approach
for all locations.
To properly size energy storage for a given load demand, or power generation source, a controller
should be selected which will determine the real time grid power requirements to maintain the stored
energy of the storage device. For flywheel energy storage, the change in stored energy with respect
to time will equal the grid power into the flywheel minus the load demand and minus losses which
may come from windage or bearings. For flywheels, the losses can be estimated by using a linear
time constant. By selecting an optimal control law for the controller, parametric studies can be
performed to evaluate performances of energy storage versus power output.
Sizing Flywheel Energy Storage
Optimal Controller Cost Function
The following cost function is used to develop the optimal control law. The first term of the cost
function is an end constraint which requires the flywheel stored energy at the end of the simulation
to equal the initial amount of stored energy. The integral portion of the cost function seeks to
minimize the grid power, Pg, and deviation of flywheel stored energy, Qfw, from the initial stored
energy, Q0. Parametric studies on changing the values of a and b can be performed to study
tradeoffs between grid power requirements and energy storage sizing.
Controller S
๐๐๐๐ค
๐๐ก= ๐๐ โ ๐ท๐ฟ โ
1
๐๐๐ค๐๐๐ค
Wind
Solar
Usage
Total Demand: DL
Grid : Pg
Flywheel Energy: Qfw Conclusions
Flywheel energy storage specifications can be derived at various
locations within a system using the proposed methodology with real-
world data. For the demonstrated system, our study found:
โข Diurnal storage require flywheels sized to 0.5โ0.2 C-rate capabilities
โข Power smoothing require flywheel designs with increase power rate
capabilities of 1โ3C
โข C-rate requirements decrease as the flywheel is moved to higher
locations in the grid
โข Reduction of flywheel losses is critical for effective use of diurnal
energy storage
Trade-offs between flywheel storage capacity and peak grid power demand for flywheel location at local transformers.