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 – 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

𝐽 𝑡0 = 1

2𝑆𝑞 𝑄𝑓𝑤 𝑇 − 𝑄0

2+

1

2 𝒂 𝑄𝑓𝑤 𝑡 − 𝑄0

2+ 𝒃𝑃𝑔

2 𝑡 𝑑𝑡

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

𝑑𝑄𝑓𝑤

𝑑𝑡= 𝑃𝑔 − 𝐷𝐿 −

1

𝜏𝑓𝑤𝑄𝑓𝑤

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:

• Diurnal storage require flywheels sized to 0.5–0.2 C-rate capabilities

• Power smoothing require flywheel designs with increase power rate

capabilities of 1–3C

• C-rate requirements decrease as the flywheel is moved to higher

locations in the grid

• 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.