alexandre oudalov, abb switzerland ltd., 10th...

Microgrid Storage IntegrationBattery modeling and advanced control

Alexandre Oudalov, ABB Switzerland Ltd., 10th Microgrid Symposium, Beijing, November 13-14, 2014

Microgrid Storage IntegrationOutline

Introduction

Energy storage technologies and applications

LIB aging model

Semi-empirical model overview

Advanced BESS control strategies

Application and SoC control

Conclusions

LIB Lithium Ion Battery

BESS Battery Energy Storage System

SOC State of Charge

Energy Storage Applications

ESS

Integration of

renewables

1-100 MW,1-10hPeak shaving

0.5-10 MW, 1h

220 kV

110 kV

20 kV ring

20 kV

Conventional

Central

Generation

Variable

Renewable

Generation

220 KV

Load leveling

for generation utilization

10-1000 MW, 1-8h

ESS

110 kV Industry/

Large Commercial

Load Center

20 kV

ESS

Spinning reserve

In case of line loss

10-500 MW, 0.25-1 h

Load leveling

for postponement of grid upgrade

1-10 MW, 1-6h

ESS

ESS

Frequency Regulation

1-50 MW, 0.25-1h

220 kV

110 kV

ESS

Solar PV time shift

1-100 kW, 2-6h

ESS

0.4 kV

Residential/Small Commercial/Nano-gridsESS

Micro-grid

0.1-5 MW, 5 min (stabilization)

0.1-50 MW, 1-10h (self-

consumption of local

renewables)

Microgrid Storage IntegrationEnergy vs Power Applications in Microgrids

Grid ON Grid OFFWeak grid ON

Energ

y

P

ow

er

Energy applications Power applications

Energy Time Shift

Capacity firming

Ramp rate ctrl Ramp rate ctrl

Spinning reserve

Energy Time Shift

Line load levelling

Frequency ctrl

Spinning reserve

Energy Time Shift

Microgrid Storage IntegrationKey Technologies

Batteries Hydrogen

Super-

conducting

(SMES)

Capacitors

Supercaps

Pumped

hydro

Compr. Air

(CAES)

Flywheel Vanadium

ZnBr

PSBr

Electro-

lyser &

Fuel Cell

Lead acid

NiCd

NaS

Lithium

Ni-MH

Flow cells

Adiabatic

CAES

Pressure

Pressure

Heat

Gravitation

Kinetic Electric

Magnetic

Mechanical Thermo-

dynamic

Electro-

magnetic

Electrochemical

Thermo

electric

Heat

Projected Cost Reductions of LIB ($/kWh)

0

200

400

600

800

1000

1200

1400

1600

2010 2015 2020 2025 2030

Bloomberg New Energy Finance Navigant Research Deutsche Bank IHS (iSuppli)

*) final prices include margins most probably in the range of 30-40%

Battery Aging ModelOverview

Battery capacity fading is a limiting

factor for BESS performance

Customers usually expect a certain

battery life in years or number of

cycles for a given application

Battery manufacturers usually over-

dimension the battery to reduce the

risk of earlier system depletion

We recommend to include battery

aging models in the design of and

operation strategies for BESS

Source:batteryuniversity.com

Source: skywriting-net

Battery Aging ModelLIB Model

We propose a semi-empirical model

Capacity fades due to battery

cycling and time elapsed

𝑙𝑟𝑒𝑚𝑎𝑖𝑛= 𝑝𝑆𝐸𝐼𝑒

−𝑟𝑆𝐸𝐼𝑙𝑓𝑎𝑑𝑒𝑑 + 1 − 𝑝𝑆𝐸𝐼 𝑒−𝑙𝑓𝑎𝑑𝑒𝑑

𝑙𝑓𝑎𝑑𝑒𝑑 = 𝑙𝑐𝑦𝑐𝑙𝑖𝑛𝑔 + 𝑙𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟

Cycling aging is fully dependent on

battery usage and is modeled as a

sum of aging during each cycle

Calendar aging is independent of

battery usage and is modeled as a

linear function of time, average SoC

and temperature

𝑙𝑐𝑦𝑐𝑙𝑖𝑛𝑔

=

𝑖=1

𝑁

𝜎𝐷𝑜𝐷 𝐷𝑜𝐷𝒊 𝜎𝑆𝑜𝐶 𝑆𝑜𝐶𝒊 𝜎𝐼(𝐼𝒊) 𝜎𝑇(𝑇𝒊)

𝑙𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟

= 𝑘𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟 𝜎𝑆𝑜𝐶

𝑖=1

𝑁𝑆𝑜𝐶𝑖𝑁𝜎𝑇

𝑖=1

𝑁𝑇𝑖𝑁𝑖

𝑡

Battery Aging ModelLIB Test Data vs. Model Reconstruction

© ABB Group

December 9, 2014 | Slide 10

Grid

Su

b-S

tatio

nC

on

ve

rte

rS

tora

ge

Converter

control

System

level

control

Switching

commands

V, I, T

on/off

P, Q

status

Secondary side

V, I, f, P, Q

P, Q, status,

application

SoC, SoH, Ramp limit, faultson/off

BMS

HVAC control

speed

Status

Status, availability (SoC)

HVAC control

on/off

on/off

T

Modbus TCP/RTU

Primary side Functionality

Local applications

and ESS

protection

Charge and health

monitoring

Protection

Switching of power

electronics

BESSGeneral system architecture

Other

controllers

Microgrid Plus SystemEfficient and reliable power flow management

PV plant

Diesel Generator

Grid Stabilising

System

Distribution feeder

Power

Micro-Grid

Wind Turbines

MGC600-W M+ Operations

Local and remoteMGC600-P

MGC600-G

MGC600-E

MGC600-F

Communication

Network

Distribution feederMGC600-F

Grid connection

MGC600-N

Residential

Industrial & commercial

Advanced Battery Control StrategiesCombination of application and SoC control

Due to a battery inefficiency and in some

applications due to a none zero mean control

signal, the BESS can be totally discharged or

charged in a short time interval

It limits a use of BESS until its SoC will be

back into an acceptable range

An optimal BESS control strategy must cover

both:

Application control

State of charge control

Consideration of a battery aging model can

provide additional information in order to take

pro-active measures to fulfill the lifetime

targets

BESS PCS

Application

control

State of charge

control

SetpointsActual state

Constraints

& setpoints

Setpoints

0 2000 4000 6000 8000 10000

-1

-0.5

0

0.5

1

x 105

Time (seconds)

Pow

er

(W)

0 2000 4000 6000 8000 10000

0.45

0.5

0.55

0.6

0.65

SoC

(-)

P offset

SoC

SoCmin

SoCmax

0 2000 4000 6000 8000 10000-0.1

-0.05

0

0.05

0.1

Time (seconds)

Fre

qu

ency

De

via

tion

(H

z)

modified frequency deviation

Advanced Battery Control StrategiesFrequency Control in Microgrids

State of charge (SoC) control according to one of the

following strategies

Strategy 1:

Active when SoC exceeds adjustable thresholds

and frequency is within a regulation dead-band

Off-set value is 0-100% *𝑃𝑛𝑜𝑚 at any time step

(preferably small values)

Strategy 2:

Is continuously activated and adjusts a power

set-point using an average over the previous

usage, i.e. ctrl signal is zero-mean

Variable off-set value is taken from a secondary

reserve

Strategy 3:

Active when SoC exceeds adjustable thresholds

Fixed off-set value is taken via a ramp of fixed

slope for a fixed duration from a secondary

reserve or an intraday market

0 2000 4000 6000 8000 10000

-1

-0.5

0

0.5

1

x 105

Time (seconds)

Pow

er

(W)

0 2000 4000 6000 8000 10000

0.45

0.5

0.55

0.6

0.65

SoC

(-)

P offset

SoC

SoCmin

SoCmax

0 2000 4000 6000 8000 10000-0.1

-0.05

0

0.05

0.1

Time (seconds)

Fre

qu

ency

De

via

tion

(H

z)

modified frequency deviation

0 2000 4000 6000 8000 10000

-1

-0.5

0

0.5

x 105

Time (seconds)

Pow

er

(W)

0 2000 4000 6000 8000 100000.4

0.5

0.6

SoC

(-)

0 2000 4000 6000 8000 10000-0.1

-0.05

0

0.05

0.1

Time (seconds)

Fre

qu

ency

De

via

tion

(H

z)

modified frequency deviation

P offset

SoC

0 2000 4000 6000 8000 10000

-1

-0.5

0

0.5

1

x 105

Time (seconds)

Pow

er

(W)

0 2000 4000 6000 8000 100000.45

0.5

0.55

0.6

0.65

SoC

(-)

P offset

SoC

SoCmin

SoCmax

0 2000 4000 6000 8000 10000-0.1

-0.05

0

0.05

0.1

Time (seconds)

Fre

qu

ency

De

via

tion

(H

z)

modified frequency deviation

SoC control strategy 1Offset and SoC variations

• For Poffset = 5% of rated power, the annual capacity fading ≈ 6%

• For Poffset = 10% of rated power, the annual capacity fading ≈ 3.7%

0 2000 4000 6000 8000 10000-3

-2

-1

0

1

2

3

4x 10

5

Time (seconds)

Pow

er

(W)

P AS

P BESS

P Offset

SoC control strategy 1Variations of different power signals

• Offset is active when system frequency is within a deadband

• BESS follows exactly the requested ancillary service power when Δf≠0

Charge

Discharge

0 2000 4000 6000 8000 10000

-1

-0.5

0

0.5

x 105

Time (seconds)

Pow

er

(W)

0 2000 4000 6000 8000 100000.4

0.5

0.6

SoC

(-)

0 2000 4000 6000 8000 10000-0.1

-0.05

0

0.05

0.1

Time (seconds)

Fre

qu

ency

De

via

tion

(H

z)

modified frequency deviation

P offset

SoC

SoC control strategy 2Offset and SoC variations

• For an averaging period of 1 hour and a dispatch delay of 15 min,

the annual capacity fading ≈ 3.2%

0 2000 4000 6000 8000 10000 12000-4

-3

-2

-1

0

1

2

3

4x 10

5

Time (seconds)

Pow

er

(W)

P AS

P BESS

P Offset

SoC control strategy 2Variations of different power signals

• Offset mechanism ‘forces’ the BESS power signal to be zero-mean

• Deviations between PAS and PBESS

Charge

Discharge

0 2000 4000 6000 8000 10000

-1

-0.5

0

0.5

1

x 105

Time (seconds)

Pow

er

(W)

0 2000 4000 6000 8000 100000.45

0.5

0.55

0.6

0.65

SoC

(-)

P offset

SoC

SoCmin

SoCmax

0 2000 4000 6000 8000 10000-0.1

-0.05

0

0.05

0.1

Time (seconds)

Fre

qu

ency

De

via

tion

(H

z)

modified frequency deviation

SoC control strategy 3Offset and SoC variations

• For SoC upper threshold = 53%, SoC lower threshold = 47%, off-set level = 10% of rated power,

ramp up/down in 5 min and min offset duration = 15 min, the annual capacity fading ≈ 3%

0 2000 4000 6000 8000 10000-4

-3

-2

-1

0

1

2

3

4x 10

5

Time (seconds)

Pow

er

(W)

P AS

P BESS

P Offset

SoC control strategy 3Variations of different power signal

• Offset active when SoC hits the thresholds

• Deviations between PAS and PBESS

Charge

Discharge

Comparison of three SoC control strategies

0 2000 4000 6000 8000 100000.44

0.46

0.48

0.5

0.52

0.54

0.56

0.58

0.6

0.62

0.64

Time (sec)

SoC

(-)

SoC evolution according to the three strategies

SoC 1

SoC 2

SoC 3

Offset 1 depends on Δf → frequent threshold violations with limited control

Offset 2 depends on past values of PAS → forces BESS to reach SoCnom

Offset 3 is activated once thresholds are hit → less sensitive to SoC variation

Reduced peak

for strategy 3

Offset 3 is activated

once thresholds are hit

Less degradation

for strategy 3

Continuous adjustment of

SoC for strategy 2

(even between thresholds)

Preferred SoC Control StrategiesNeed for an adaptive approach

Depending on the operation mode grid-tied or off-grid we switch between

strategies

Based on the actual generation mix we can tune the parameters of each

strategy

Operation

modeReference Advantages

Off-grid Strategy 1• The offset does not directly cancel

the control signal

Grid-tied Strategy 3

• Less energy is cycled through

the offset than in strategy 2

• Less battery capacity fading

Conclusions

Battery based energy storage plays an important role in

microgrids with a large amount of RES

A battery model allows to quantify capacity fading and to

take corrective measures in case of deviations from the

initially planned lifetime trajectory

There are several strategies to control SoC and a preferred

strategy depends on:

status of the microgrid (grid-tied vs isolated)

available options for the off-setting part (available

generation mix, accessibility to power markets, etc.)

Availability of forecast information (RES, load, scheduled

islanding operation, etc.) can help to predict future SoC

and parameterize the control system accordingly

Battery Aging ModelStress Factors

0 10 20 3040 50 6070 80 901000

2

4

6

DoD[%]

f DoD(

10

-5)

DoD Stress Model

0 10 20 3040 50 6070 80 901000.5

1

1.5

2

SoC[%]

f SoC

SoC Stress Model

0 1 2 3 40.5

1

1.5

2

2.5

C-rate

f C

C-rate Stress Model

15 20 25 30 35 40 45 50 55

1

3

5

7

T[C]

f T

Temperature Stress Model

0 0.5 1 1.5 2 2.5 3

x 106

0

10

20

30

40

50

60

70

80

90

100SoC evolution - Model 1

Time (seconds)

SoC

(%

)

SoC evolution

SoC limit violations

SoC limits

SoC Control StrategiesModel 1 – SoC evolution for one month

December 9, 2014 | Slide 25

© ABB

BESS degradation after 1 year: = 3.67 %

𝑃𝑜𝑓𝑓𝑠𝑒𝑡 = 10% 𝑃𝑛𝑜𝑚

0 0.5 1 1.5 2 2.5 3

x 106

0

10

20

30

40

50

60

70

80

90

100SoC evolution - Model 2

Time (seconds)

SoC

(%

)

SoC evolution

SoC limit violations

SoC limits

SoC Control StrategiesModel 2 – SoC evolution for one month

December 9, 2014 | Slide 26

© ABB

BESS degradation after 1 year: = 3.22 %

Averaging period:

1 hour

Dispatch delay:

15 min

0 0.5 1 1.5 2 2.5 3

x 106

0

10

20

30

40

50

60

70

80

90

100SoC evolution - Model 3

Time (seconds)

SoC

(%

)

SoC evolution

SoC limit violations

SoC limits

SoC Control StrategiesModel 3 – SoC evolution for one month

BESS degradation after 1 year: = 3.02 %

SoC upper threshold:

53 %

SoC lower threshold:

47 %

Ramp duration:

5 min

Min offset time:

15 min

References – Ancillary power system servicesSP AusNet Grid Energy Storage System

Project name

SP AusNet GESSCountry

Victoria, AustraliaCustomer

SP AusNetCompletion date

Due to be completed in 2014

ABB solution

Design, engineering, installation and testing of PowerStore-Battery, transformer and diesel generator

Microgrid Plus System for overall system management

Based on transportable containerized solution

Customer benefits

Active and reactive power support during high demand periods

Transition into isolated/Off-grid operation on command or in emergency cases without supply interruption

Delay of power line investments

First grid-tied microgrid with Battery Energy Storage for

distribution network support in Australia

About the project