carbon story carbon impact of smart distribution networks · 2019. 5. 20. · cost of electricity...

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Carbon Story – carbon impact of

smart distribution networks

Session 4b Marko Aunedi & Martin Wilcox

Imperial College London & UK Power Networks

In Partnership with:

2

Introduction

• Decarbonisation agenda in the UK based on large-scale deployment of intermittent RES and inflexible nuclear generation

• Challenges and costs associated with wind intermittency could offset a part of emission benefits of low-carbon generation

Objectives: • Analyse and quantify the implications of LCTs trialled in LCL (EVs,

HPs, I&C DSR, dToU) for the carbon emissions of the broader UK electricity system

• Evaluate carbon benefits of smart operation of LCTs • Assess system integration benefits of DSR through reducing the

overall system cost of intermittent RES

3

Wind, 56.9

PV, 15.8

Nuclear, 11

CCGT, 32.7

Coal, 3.3

Coal CCS, 3.5 OCGT, 15.4

Inflexible , 1.8Storage, 2.6 Interconnecto

r, 4

DSR case studies

No. Case

1 Non-smart

2 Smart EV

3 Smart EV / FR

4 Smart HP

5 Smart HP / FR

6 dToU

7 I&C DSR

8 Fully smart: balancing

9 Fully smart: balancing & FR

Wind, 82.4

PV, 14.1

Nuclear, 15

Gas CCS, 4.4

Coal CCS, 8.8

OCGT, 24.4

Inflexible , 1.8 Storage, 2.6Interconnecto

r, 4

2030 GW 2050 HR

Scenarios:

0

0.2

0.4

0.6

0.8

00:00 06:00 12:00 18:00 00:00

EV c

har

gin

g d

em

and

(kW

)

Time

Peak Average

0

1

2

3

00:00 06:00 12:00 18:00 00:00

HP

de

man

d p

er

ho

use

ho

ld (

kW)

Time

HP demand

EVs HPs

4

ASUC model

• Stochastic generation system scheduling taking into account RES uncertainty and impact on inertia • Based on rolling planning window • Optimises allocation of reserve between different technologies • Frequency response requirement driven by inertia of conventional generation • DSR models developed in LCL and included in ASUC model, using data from LCL trials

0

10000

20000

30000

40000

50000

60000

70000

1

11

21

31

41

51

61

71

81

91

10

1

11

1

12

1

13

1

14

1

15

1

16

1

Ge

ne

rati

on

(M

W)

Time (hr)

Wind

Storage

coal

CCGT

Nuclear

online capacity

Demand

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

00

:00

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:00

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16

:00

Ag

gre

ga

te w

ind

po

we

r (p

.u.)

Time (hrs)

Wind uncertainty model

Unavail-

able

λ Δt

μ Δt

Online

Outage model

Available

Storage Capacity: 18GWhRating: +/- 4GWRound-trip efficiency: 72%

-20000

-10000

0

10000

20000

30000

40000

50000

0 4 8 12 16 20 24

De

ma

nd

+ o

uta

ge

s n

et

win

d

(MW

)

Time horizon (hr)-20000

-10000

0

10000

20000

30000

40000

50000

0 4 8 12 16 20 24

De

ma

nd

+ o

uta

ge

s n

et

win

d

(MW

)

Time horizon (hr)

50th percentile

Reserve

Deterministic UC Stochastic UC

30,000 €/ MWh

VOLL

Scheduling model

0

10000

20000

30000

40000

50000

60000

1 9

17

25

33

41

49

57

65

73

81

89

97

10

5

11

3

12

1

12

9

13

7

14

5

15

3

16

1

De

ma

nd

ne

t w

ind

(M

W)

Time (hr)

wind, demand, outage realisation

Demand risk25000

30000

35000

40000

45000

50000

08

:00

09

:00

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00

:00

Ag

gre

ga

te d

em

an

d (

MW

)

Time (hrs)

Demand uncertainty model Dispatch,

operating costs, CO2 emissions...

Outage risk

Wind risk

startup

wind forecast

Deterministic security targetsResponseReserve

Stochastic security target

demand forecast

Wind statistics

5

Average system emissions and carbon benefits of smart demand

80

90

100

110

120

Smar

t

Smar

t/FR

Smar

t

Smar

t/FR

25

%

50

%

75

%

25

%

50

%

10

0%

Bal

anci

ng

Bal

. + F

req

.

Non-smart

EVs HPs dToU I&C DSR Fully smart

Syst

em

ave

rage

CO

2e

mis

sio

ns

(g/k

Wh

)

0

10

20

30

40

50

60

Smar

t

Smar

t/FR

Smar

t

Smar

t/FR

25

%

50

%

75

%

25

%

50

%

10

0%

Bal

anci

ng

Bal

. + F

req

.

Non-smart


Syst

em

ave

rage

CO

2e

mis

sio

ns

(g/k

Wh

)

20

30

GW

0

50

100

150

200

Smar

t

Smar

t/FR

Smar

t

Smar

t/FR

25

%

50

%

75

%

25

%

50

%

10

0%

Bal

anci

ng

Bal

. + F

req

.

EV HP dToU I&C DSR Fullysmart

Avo

ide

d e

mis

sio

ns

thro

ugh

DSR

(g

/kW

h)

0

50

100

150

200

Smar

t

Smar

t/FR

Smar

t

Smar

t/FR

25

%

50

%

75

%

25

%

50

%

10

0%

Bal

anci

ng

Bal

. + F

req

.

EV HP dToU I&C DSR Fullysmart

Avo

ide

d e

mis

sio

ns

thro

ugh

DSR

(g

/kW

h)

20

50

HR

6

System integration cost of intermittent RES

• Whole-System Cost (WSC) of intermittent RES consists of their Levelised Cost of Electricity (LCOE) and System Integration Cost (SIC)

• SIC is defined as additional infrastructure and/or operating costs as a result of integrating renewable power generation

• Benefits of LCT resources trialled in LCL are quantified in terms of reduced SIC of RES in 3 categories:

1. Reduced backup capacity cost (due to reduced peak net demand) 2. Reduced balancing operating cost (lower cost of system operation

including more efficient provision of reserve and response and reduced RES curtailment)

3. Reduced investment cost associated with balancing (reduced RES curtailment requires that less additional zero- or low-carbon generation capacity is built to meet the carbon target)

7

Reduced RES integration cost from deployment of smart LCTs

2030 GW 2050 HR

0

5

10

15

Smar

t

Smar

t/FR

Smar

t

Smar

t/FR

25

%

50

%

75

%

25

%

50

%

10

0%

Bal

anci

ng

Bal

. + F

req

.


Savi

ngs

in R

ES in

tegr

atio

n c

ost

(£

/MW

h)

Backup Balancing (OPEX) Balancing (CAPEX)

0

5

10

15

Smar

t

Smar

t/FR

Smar

t

Smar

t/FR

25

%

50

%

75

%

25

%

50

%

10

0%

Bal

anci

ng

Bal

. + F

req

.


Savi

ngs

in R

ES in

tegr

atio

n c

ost

(£

/MW

h)


0

5

10

15

20

25

30

35

Smart bal. Smart bal. +freq.


2030 GW 2050 HR

Ave

rage

RES

inte

grat

ion

be

ne

fits

of

smar

t LC

Ts (

£/M

Wh

)


0

5

10

15

20

25

30

35



2030 GW 2050 HR

Mar

gin

al R

ES in

tegr

atio

n b

en

efi

ts o

f sm

art

LCTs

(£

/MW

h)


Average Marginal

8

Key findings

• Carbon benefits of DSR primarily driven by the ability to: 1) support more efficient scheduling, and 2) provide frequency regulation

• Carbon savings per unit of smart demand are in the order of ~100 g/kWh, but vary depending on DSR flexibility

• Integration of electrified transport and heating demand is significantly less carbon intensive with smart operation

• DSR are capable to support cost-efficient decarbonisation of future electricity system by reducing RES integration cost

• Average RES integration benefits when all smart LCTs coexist in the system are £8-11/MWh of RES output; marginal benefit is 2-3 times higher than average

9

Report D6 Carbon impact of smart distribution networks

http://innovation.ukpowernetworks.co.uk/innovation/en/Projects/tier-2-

projects/Low-Carbon-London-(LCL)/Project-Documents/LCL Learning Report - D6 - Carbon impact of smart distribution networks.pdf

Marko Aunedi Fei Teng

Goran Strbac Imperial College London

UKPN LCL Industry Day, 9 February 2015


Grid Carbon Intensity Today


800kW Turndown Event


Electric Vehicle Charging Management

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14C

arb

on

Fo

otp

rin

t C

O2 (

kg)

Time

EV Trial - Carbon Footprint

Turndown Carbon Average Carbon