presentation by

Yannick Degeilh

ECE 590 I SeminarUniversity of Illinois at Champaign-Urbana

November 26th, 2012 1

STOCHASTIC SIMULATION OF POWER

SYSTEMS WITH INTEGRATED

RENEWABLE AND STORAGE

RESOURCES

KEY OBJECTIVES

Construct a comprehensive simulation approach to emulate the behavior of power systems with integrated storage and renewable energy resources in a competitive environment

Develop models for the resources and the loads that capture their salient characteristics –variability and uncertainty, with spatial and temporal dependencies – as well as their interactions over the grid and in the markets

Demonstrate the simulation approach capabilities with a number of case studies that assess the impacts of storage and renewable resource integration on the power system variable effects

2

3

US RENEWABLE PORTFOLIO STANDARDS

Source: www.dsireusa.org, November 2012

29 states,+ Washington DC and 2 territories,have

Renewable Portfolio Standards

(8 states and 2 territories have

renewable portfolio goals).

THE CHANGING ENVIRONMENT

There is a growing worldwide interest in integrating renewable resources, as well as storage resources, into the grid to displace costly and polluting fossil-fuel-fired generation

The objective of such integration is to push the creation of sustainable paths to meet the nation’s energy needs and to veer it towards energy independence

The context within which this progress unrolls is in the restructured, competitive electricity industry and the advances in the implementation of the Smart Grid

4

SALIENT CHARACTERISTICS OF SOLAR AND WIND POWER OUTPUTS

Highly time-dependent nature:

variability characteristics

intermittency effects

seasonal dependence

Spatially correlated

Inherently uncertain and difficult to characterize

analytically

Predictable with limited accuracy

Come with limited controllability/dispatchability 5

6

PV POWER OUTPUT OF 1 - MW CdTe ARRAY IN GERMANY

kW

1000

0

samples collected on a 5 – minute basis

5:00

6:00

7:00 8:

009:

0010

:00

11:0

012

:00

13:0

014

:00

15:0

016

:00

17:0

018

:00

19:0

020

:00

900

800

700

600

500

400

300

200

100

PV OUTPUT AND LOAD

Sources: ERCOT and NREL

10

20

30

40

50

load

(G

W )

24 48 72 96 120 144 1680

100

200

300

PV

ou

tpu

t (M

W )

time (h)

500

400

7

CAISO DAILY WIND POWER PATTERNS IN MARCH 2005

Source: CAISO

MW700

600

500

400

300

200

100

0

hour8

Source: CAISO

ONTARIO DAILY WIND POWER OUTPUT

MW700

600

500

400

300

200

100

0

hour9Source: IESO

MISALIGNMENT OF WIND POWER OUTPUT AND LOAD

0

50

100

150

200

250

win

d po

wer

ou

tpu

t (M

W)

24 48 72 96 120 144 168 3000

4000

5000

6000

7000

8000

load

(M

W)

hour

wind power

load

10

ISSUES IN INTEGRATING WIND AND SOLAR RESOURCES

Misalignment of the wind power outputs with the

load implies that the wind speeds are rarely ade-

quate at times when needed to supply the loads

While morning and mid-day solar power outputs

are aligned with the loads, their quick decline after

sunset occurs when the loads are still high

The risk of spilling wind due to inadequate loads at

night and the challenges of managing base-loaded

unit shut down for short periods during low-load

hours is a major problem11

TAKING ADVANTAGE OF INTEGRATED STORAGE RESOURCES

In order to take advantage of the increased flexi-

bility imparted by the grid-integrated storage

devices, we developed appropriate models,

methodologies and tools

The resulting approach has applications to:

planning and investment analysis;

policy analysis;

operations; and

market performance12

UTILITY - SCALE STORAGE CHARACTERISTICS

A storage unit may act as either a generating unit; or a load; or is idle – neither as a load nor as a generator

Storage unit operations are driven by the other

resources and so storage is a highly time-dependent resource storage operations are uncertain due to its

dependence on other uncertain resources Exploitation of arbitrage opportunities in the

determination of storage operations is critical 13

14

UTILITY - SCALE STORAGE APPLICATION

MW

storage resource discharging

during peak hours

storage resource charging

during low-load hours

0 12 24

15

WIND/STORAGE SYMBIOTIC INTERACTIONS

MW

hours

unit i - 1

unit i

unit i - 2

…

base-loaded units

unit i + 1unit i + 2unit i + 3

units are

loaded in

order of

increasing

prices of unit

production

energy discharged during peak hours

energy charged during low-load hours

daily load shape

modified daily load shape by wind

0 12 24

chargingunits

displaced units

16

THE NEED FOR A NEW SIMULATION APPROACH

The detailed simulation of integrated storage, wind,

solar and active demand response resources re-

quires the representation of the time–dependent

operational actions in line with the market results

Uncertainties in the loads, wind and solar outputs

and conventional unit available capacities requires

their explicit representation together with their

time–varying nature

17

NEED TO EXPLICITLY REPRESENT

The loads and their associated uncertainty

The resources and their associated uncertainty:

conventional generators

utility-scale storage units

renewable resources

The spatial and temporal correlations among the

resources at the various sites and the loads

The impacts of the grid constraints

The hourly day-ahead markets (DAMs)

18

THRUST OF THE APPROACH

We develop a comprehensive, computationally

efficient Monte Carlo simulation approach to emulate

the behavior of the power system with integrated

storage and renewable energy resources We model the system load and the resources by

discrete-time stochastic processes We deploy the storage scheduler to utilize arbitrage

opportunities in the storage unit operations We emulate the transmission-constrained hourly day-

ahead markets (DAMs) to determine the power

system operations in a competitive environment

19

THRUST OF THE APPROACH

We construct appropriate c.d.f. approximations to

evaluate the expected system variable effects

Metrics we evaluate include:

nodal electricity prices (LMPs)

generation by resource and revenues

congestion rents

CO 2 emissions

LOLP and EUE system reliability indices

20

KEY CONTRIBUTIONS

Development of a new simulation tool appropriate

to address today’s power industry challenges

Salient features include:

quantification of the power system expected

variable effects – economics, reliability and

environmental impacts – in each sub-period

computationally tractable for practical systems

21

STUDY CONTRIBUTIONS

detailed stochastic models of the time–varying

resources and loads allow the representation of

spatial and temporal correlations

storage scheduler for optimized storage

operation to exploit arbitrage opportunities

representation of the transmission–constrained

market outcomes

flexibility in the representation of the market

environment / policies

22

THE TIME DIMENSION IN SIMULATION STUDIES

We decompose a multi-year study period into T

non-overlapping simulation periods

We specify the simulation periods in such a way

that no changes in the resource mix, unit commit-

ment, the transmission grid and the policy environ-

ment occur during each simulation period; such

changes may occur in subsequent periods

. . .time

study period

period tperiod 1 period T. . . . . .

23

THE SUBPERIODS: THE SMALLEST INDECOMPOSABLE UNITS OF TIME

We introduce sub-periods to explicitly represent

the time dependence of the side-by-side power

system and market operations

We use hourly sub-periods in the simulation and no

phenomena of shorter duration are represented

. . .period 1 period T. . . . . .period t

sub-period 1 sub-period Hsub-period h. . . . . .time

study period

24

THE SIMULATION APPROACH: KEY ASSUMPTIONS

The system is in the “steady state” for each sub-

period and we ignore shorter duration phenomena

The behavior of each market participant is

independent of that of the other participants and

no participant engages in strategic behavior

The grid is lossless and the DC power flow conditions

hold over the entire study period

25

PROPOSED SIMULATION APPROACH: CONCEPTUAL STRUCTURE

“dri

ver”

sto

chas

tic

pro

cess

es

renewable power outputs

conventional generator available capacities

loads

storage schedule

market

clearing

procedure

(DCOPF)

congestion rents

CO 2

emissions

storage operations

. . .

“ou

tcom

e” s

toch

asti

c pr

oces

ses

LMPs

26

CONCEPTUAL REPRESENTATION OF A SINGLE SIMULATION RUN

27

QUANTIFICATION OF SYSTEM VARIABLE EFFECTS

We use the approximations to the joint c.d.f.s of

the market outcome stochastic processes to compute

the expected values of the system variable effects

in each sub-period

In this way, we evaluate all the figures of merit of

interest, including expected electricity payments,

expected energy supplied by each resource,

reliability indices and expected emissions

28

TO ENSURE NUMERICAL TRACTABILITY

We introduce various schemes that reduce the

computing burden and provide tractability

Key implementational aspects:

selection of a group of representative weeks

variance reduction techniques: stratification,

control variates and common random numbers

warm-start of the linear program solver

parallelization of simulation runs

29

TYPICAL APPLICATIONS

Resource planning studies

Production costing issues

Transmission utilization issues

Environmental assessments

Reliability analysis

Investment analysis

30

CASE STUDIES

29

31

MOTIVATION

We present case studies aimed at illustrating the

various capabilities of the simulation approach

and relevant to the integration of storage and

renewable resources

We perform sensitivity studies to investigate

several aspects of storage integration into the

grid, notably its impact in a system with

deepening wind penetration, its siting, and to

what extent storage and renewable resources

may replace conventional generation

32

CASE STUDIES

We present 3 sets of representative case studies:

case study set I: impacts of an integrated

utility-scale storage unit under a deepening

wind penetration scenario

case study set II: siting of 4 energy storage

units and the impacts on transmission usage

case study set III: substitution of conventional

generation resources by a combination of

storage and wind energy resources

33

CASE STUDY SET I: DEEPENING WIND PENETRATION

The objective of this study is to perform a wind

penetration sensitivity analysis and to quantify the

enhanced ability to harness wind resources with

the addition of a storage energy resource

We evaluate the key metrics for variable effect

assessment, including wholesale purchase

payments, reliability indices and CO 2 emissions

34

THE STUDY TEST SYSTEM: A MODIFIED IEEE 118-BUS SYSTEM

Annual peak load: 8,090.3 MW

Conventional generation resource mix: 9,714 MW

4 wind farms located in the Midwest with total

nameplate capacity in multiples of 680 MW

A storage unit with 400 MW capacity, 5,000 MWh

storage capability and 89 % round-trip efficiency

Unit commitment uses a 15 % reserves margin provi-

ded by conventional units and the storage resources

35

casetotal installed wind

nameplate capacity in MW

base  0

A  680

B 1,360

C 2,040

D 2,720

SENSITIVITY CASES IN STUDY SET I

36

CASE D: AVERAGE HOURLY STORAGE UTILIZATION

hou

rly

sto

rage

ch

arge

/dis

char

ge

capa

city

in

MW

load

0 24 48 72 96 120 144 168- 400

- 300

- 200

- 100

0

100

200

300

400

hour

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

load

in

GW

37

NODE 80 AVERAGE HOURLY LMPs

LM

P i

n $

/MW

h

hour

base case

case A

case B

case D

case C

0 24 48 72 96 120 144 1680

5

10

15

20

25

30

35

40

45

50with storage without storage

38

0 20 40 60 80 1000

5

10

15

20

25

30

35

40

45

50

NODE 80 AVERAGE HOURLY LMP DURATION CURVE

LM

P i

n $

/MW

h

% of hours in the week

with storage without storage

base case

case A

case B

case D

case C

39

EXPECTED WHOLESALE PURCHASE PAYMENTS

thou

san

d $

base

case ca

se A

case

Bca

se C

case

D100

110

120

130

140

150

160

170

180

- 5.

1 %

- 9.

1 %

- 17

.9 %

- 25

.2 %

- 31

.1 %

- 15

.4 %

- 24

.4 %

- 31

.2 %

- 36

.9 %

with storagewithout storage

40

EXPECTED CO 2 EMISSIONS

base

case

case

Aca

se B

case

C

- 5.

6 %

- 10

.5 %

- 14

.5 %

- 17

.5 %

- 6.

0 %

- 11

.8 %

- 16

.4 %

- 19

.8 %

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

+ 0

.3 %

thou

san

d m

etri

c to

ns

case

D

with storagewithout storage

41

ANNUAL RELIABILITY INDICES

EUE

base

case

case

Aca

se B

case

Cca

se D

6.24

0

1.04

2.08

3.12

4.16

5.20

base

case ca

se A

case

Bca

se C

case

D0

2

4

6

8

10

12

x 10-4

- 37

.5 %

- 50

.0 %

- 65

.6 %

- 80

.1 %

- 43

.8 %

- 56

.3 %

- 67

.1 %

- 71

.9 %

- 87

.5 %

- 42

.5 %

- 73

.9 %

- 80

.2 %

- 64

.1 %

- 55

.1 %

- 90

.0 %

- 82

.4 %

- 73

.5 %

- 94

.3 %

14

LOLP

with storagewithout storage with storagewithout storage

MWh

42

CASE STUDY SET II: STORAGE UNIT SITING

The objective of this study is to perform a sensiti-

vity analysis on the siting of 4 storage units in the

system and assess its impacts on transmission

usage and on the economics at the most heavily

loaded bus in the network

We quantify the expected LMPs at the load center

at node 59 and the total congestion rents

43

TEST SYSTEM OF THE STUDY: A MODIFIED IEEE 118-BUS SYSTEM

Annual peak load: 8,090.3 MW

Conventional generation resource mix: 9,714 MW

4 wind farms located in the Midwest with total

nameplate capacity 2,720 MW

4 identical utility-scale storage units, each having

200 MW capacity, 5,000 MWh storage capability and

89% round-trip efficiency

Reserves margin is set at 15 % and is provided by

conventional and storage resources

44

STORAGE SITING ON THE MODIFIEDIEEE 118 – BUS TEST SYSTEM

15

14

13

12

117

16

1

2

3

4

11

5 6

7

8

9

10

17

30

113

31

29

28

27

32

114 115

18 19

33

34

37

20

21

22

23

25

26

35

36

38

39

40 41 42

43

44

45

4647

48

49

52

53

54

50 51

57 58

56

55

60

59

61

62

64

63

65

66

67

6869

24

72

70

71

73

74

75 118

76

77

78

116

79

81

80

82

83

84

85

86

97

96

98

88

95 94

89 92

90

91

93

99

102 101

100

103

104

105

106

107

108

109

110

111 112

87

0.95 pu

1.00 pu1.00 pu

1.00 pu

1.00 pu

1.00 pu 0.99 pu

1.00 pu

1.00 pu

1.01 pu

1.00 pu

1.00 pu

1.00 pu

1.00 pu

1.00 pu

1.05 pu

1.00 pu0.97 pu

0.99 pu

1.00 pu

1.00 pu 0.96 pu

0.97 pu

1.00 pu1.00 pu

1.01 pu

1.05 pu

1.00 pu

1.00 pu

1.00 pu

1.00 pu

1.00 pu

0.97 pu0.96 pu

1.00 pu

0.97 pu

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0.98 pu 1.00 pu

0.98 pu

0.99 pu 0.99 pu

0.98 pu

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0.96 pu

0.98 pu

1.03 pu

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1.00 pu

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0.94 pu

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1.02 pu

1.00 pu

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0.98 pu1.00 pu0.97 pu

0.95 pu 0.95 pu

1.00 pu

1.00 pu

1.00 pu

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1.05 pu

1.00 pu

1.00 pu

1.00 pu

1.00 pu

1.00 pu

1.00 pu 1.00 pu 1.01 pu

1.00 pu

1.00 pu 1.00 pu

1.00 pu

0.99 pu

0.99 pu

0.98 pu

1.00 pu 1.00 pu1.01 pu

0.97 pu

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1.00 pu

1.00 pu

1.00 pu

0.98 pu

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0.98 pu0.98 pu

0.97 pu

1.00 pu

1.00 pu

0.96 pu

1.00 pu

0.95 pu

51 MW

27 Mvar

39 MW

10 Mvar

30 MW

12 Mvar

-9 MW

0 Mvar

70.00 MW

23.00 Mvar

0 MW

0 Mvar

52 MW

22 Mvar

85 MW

0 Mvar

47 MW

10 Mvar

19 MW

2 Mvar

-28 MW

0 Mvar

25 MW

10 Mvar

34 MW

16 Mvar

14 MW

1 Mvar

20 MW

8 Mvar 9 Mvar

20 MW

450 MW

0 Mvar

24 MW

4 Mvar

43 MW

27 Mvar

7 MW

0 Mvar

-6 MW

0 Mvar

3 Mvar

11 MW

7 Mvar

17 MW

23 Mvar

59 MW

0 MW

0 Mvar

62 MW

13 Mvar

-9 MW

0 Mvar

3 Mvar

8 MW

7 Mvar

22 MW

314 MW

0 Mvar

0 Mvar

220 MW

10 MW

5 Mvar

8 Mvar

14 MW

18 MW

3 Mvar

0 MW

0 Mvar

60 MW

34 Mvar

0 MW

0 Mvar

45 MW

25 Mvar

33 MW

9 Mvar

0 MW

0 Mvar 30 Mvar

90 MW

23 MW

9 Mvar

27 MW

11 Mvar

-25 Mvar

59 MW

26 Mvar

0 MW

0 Mvar

13 Mvar

0 Mvar

0 MW

31 MW

17 Mvar

0 Mvar

-13 MW

0 Mvar

-6 MW

-12 MW

0 Mvar

47 MW

11 Mvar

68 MW

27 Mvar

0 MW

0 Mvar

11 Mvar 66 MW

20 Mvar

0 Mvar

0 MW

34 MW

0 Mvar

10 Mvar

19 MW

0 Mvar

28 MW

10 Mvar

22 Mvar

53 MW

10 Mvar

15 Mvar 11 Mvar

20 MW

7 Mvar

18 MW

10 Mvar 8 Mvar

16 MW

15 Mvar

33 MW

36 Mvar

68 MW

0 Mvar

0 MW

4 MW

0 Mvar

10 Mvar

21 MW

0 Mvar

0 MW

15 Mvar

24 MW

10 Mvar

48 MW

0 Mvar

607 MW

7 Mvar

11 MW

10 Mvar

20 MW

10 Mvar

0 Mvar

0 MW

20 Mvar

27 Mvar

54 MW

0 MW

0 Mvar

61 MW

28 Mvar

-184 MW

0 Mvar

0 Mvar

391 MW

87 MW

30 Mvar

204 MW

0 Mvar

5 Mvar

18 MW

11 Mvar

23 MW

23 Mvar

37 MW

0 Mvar

-59 MW

10 Mvar

37 MW

23 Mvar

20 MW

0 Mvar

0 Mvar

48 MW

32 Mvar

113 MW

0 Mvar

0 MW

18 Mvar

84 MW

12 MW

3 Mvar

12 MW

3 Mvar

17 MW

4 Mvar

17 MW

8 Mvar

18 Mvar

39 MW

392 MW

0 Mvar

7 Mvar

28 MW

32 Mvar

39 MW

20 Mvar

71 MW

26 Mvar

26 Mvar

130 MW

0 Mvar

477 MW

15 MW

9 Mvar

34 MW

8 Mvar

-42 MW

0 Mvar

15 Mvar

38 MW

42 MW

31 Mvar

30 MW

16 Mvar

12 MW

7 Mvar

78 MW

42 Mvar

-85 MW

0 Mvar

-10 MW

0 Mvar

65 MW

10 Mvar

5 MW

3 Mvar

22 MW

15 Mvar

23 MW

16 Mvar

40 MW

0 Mvar

0 Mvar

0 MW

25 Mvar

38 MW 0 Mvar

252 MW

37 MW

18 Mvar

77 MW

14 Mvar

0 MW

0 Mvar

160 MW

0 Mvar

78 MW

3 Mvar

155 MW

113 Mvar

277 MW

113 Mvar

0 Mvar

0 MW 63 MW

22 Mvar

13 Mvar

25 MW

-43 MW

0 Mvar

36 MW

0 Mvar

30 Mvar

39 MW

6 Mvar

8 MW

3 Mvar

2 MW

1 Mvar

19 Mvar 0 Mvar

0 MW

26 Mvar

31 MW

-22 MW

0 Mvar

12 Mvar

28 MW

5 Mvar

16 Mvar

43 MW

-40 Mvar

0 Mvar

W

W

W

W

storage siting region

most heavily loaded bus at node 59

43

45

case siting of the storage units

base no storage units 

S0  at the principal load center

S1 1 node away

S2 2 nodes away

S3 3 nodes away

SENSITIVITY CASES IN STUDY SET II

each case has 2,720 MW nameplate wind capacity

46

STORAGE SITING REGION

38

39

40 41 42

43

44

45

4647

48

49

52

5354

50 51

57 58

56

55

60

59

61

62

64

63

65

66

67

6869

72

70

71

73

78

116

79

1.00 pu

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

0.99 pu

0.98 pu

1.00 pu

A

MVA

0.98 pu

1.03 pu

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

A

MVA

1.00 pu

1.00 pu

1.00 pu

A

MVA

A

MVA

1.00 pu

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

1.00 pu

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

1.02 pu

A

MVA

A

MVA

1.00 pu

A

MVA

A

MVA

1.00 pu

0.98 pu

A

MVA

A

MVA

A

MVA

1.00 pu0.97 pu

A

MVA

A

MVA

0.95 pu

A

MVA

0.95 pu

A

MVA

A

MVA

A

MVA

A

MVA

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

1.05 pu

A

MVA

1.00 pu

A

MVA

A

MVA

1.00 pu

1.00 pu

A

MVA

A

MVA

0.99 pu

1.00 pu

A

MVA

A

MVA

1.00 pu

A

MVA

1.00 pu

A

MVA

A

MVA

A

MVA

1.00 pu

0.98 pu

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

0.95 pu

A

MVA

A

MVA

A

MVA

27 MW

11 Mvar

-25 Mvar

0 Mvar

-6 MW

-12 MW

0 Mvar

68 MW

27 Mvar 11 Mvar 66 MW

20 Mvar

0 Mvar

0 MW

34 MW

0 Mvar

10 Mvar

19 MW

0 Mvar

28 MW

10 Mvar

22 Mvar

53 MW

10 Mvar

15 Mvar 11 Mvar

20 MW

7 Mvar

18 MW

10 Mvar 8 Mvar

16 MW

68 MW 0 MW

-184 MW

0 Mvar

0 Mvar

391 MW

87 MW

30 Mvar

204 MW

0 Mvar

5 Mvar

18 MW

11 Mvar

23 MW

23 Mvar

37 MW

0 Mvar

-59 MW

10 Mvar

37 MW

23 Mvar

20 MW

0 Mvar

0 Mvar

48 MW

32 Mvar

113 MW

0 Mvar

0 MW

18 Mvar

84 MW

12 MW

3 Mvar

12 MW

3 Mvar

17 MW

4 Mvar

17 MW

8 Mvar

18 Mvar

39 MW

392 MW

0 Mvar

7 Mvar

28 MW

32 Mvar

39 MW

20 Mvar 77 MW

14 Mvar

0 MW

0 Mvar

160 MW

0 Mvar

78 MW

3 Mvar

155 MW

113 Mvar

277 MW

113 Mvar

0 Mvar

0 MW 63 MW

22 Mvar

S0 S0

S0 S0

S1S1

S1

S1

S2

S2

S2

S2

S3S3

S3

S3

45

47

NODE 59 EXPECTED HOURLY LMPs

0

20

40

60

0 24 48 72 96 120 144 1684

5

6

7

8

9

10

LM

P i

n $

/MW

h

hour

load

in G

W

0 24 48 72 96 120 144 1680

10

20

30

40

50

60

LM

P in

$/M

Wh

number of hours in the week

base case

case S0

case S1

case S2

case S3base case

case S0

case S1

case S2 case S3

48

EXPECTED HOURLY CONGESTION RENTS

0

10

20

30

40

50

60

0 24 48 72 96 120 144 1684

5

6

7

8

9

10

con

gest

ion

ren

ts in

thou

san

d $

hour

load

in

GW

0 24 48 72 96 120 144 1680

10

20

30

40

50

60

con

gest

ion

ren

ts in

thou

san

d $

number of hours in the week

base case

case S0

case S1

case S2

case S3

base case

case S0

case S1

case S2

case S3

49

TRANSMISSION PATH CONGESTION AND ITS REENFORCEMENT

38

39

40 41 42

43

44

45

4647

48

49

52

5354

50 51

57 58

56

55

60

59

61

62

64

63

65

66

67

6869

72

70

71

73

78

116

79

1.00 pu

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

0.99 pu

0.98 pu

1.00 pu

A

MVA

0.98 pu

1.03 pu

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

A

MVA

1.00 pu

1.00 pu

1.00 pu

A

MVA

A

MVA

1.00 pu

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

1.00 pu

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

1.02 pu

A

MVA

A

MVA

1.00 pu

A

MVA

A

MVA

1.00 pu

0.98 pu

A

MVA

A

MVA

A

MVA

1.00 pu0.97 pu

A

MVA

A

MVA

0.95 pu

A

MVA

0.95 pu

A

MVA

A

MVA

A

MVA

A

MVA

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

1.00 pu

1.00 pu

A

MVA

A

MVA

A

MVA

1.05 pu

A

MVA

1.00 pu

A

MVA

A

MVA

1.00 pu

1.00 pu

A

MVA

A

MVA

0.99 pu

1.00 pu

A

MVA

A

MVA

1.00 pu

A

MVA

1.00 pu

A

MVA

A

MVA

A

MVA

1.00 pu

0.98 pu

A

MVA

A

MVA

A

MVA

A

MVA

A

MVA

0.95 pu

A

MVA

A

MVA

A

MVA

27 MW

11 Mvar

-25 Mvar

0 Mvar

-6 MW

-12 MW

0 Mvar

68 MW

27 Mvar 11 Mvar 66 MW

20 Mvar

0 Mvar

0 MW

34 MW

0 Mvar

10 Mvar

19 MW

0 Mvar

28 MW

10 Mvar

22 Mvar

53 MW

10 Mvar

15 Mvar 11 Mvar

20 MW

7 Mvar

18 MW

10 Mvar 8 Mvar

16 MW

68 MW 0 MW

-184 MW

0 Mvar

0 Mvar

391 MW

87 MW

30 Mvar

204 MW

0 Mvar

5 Mvar

18 MW

11 Mvar

23 MW

23 Mvar

37 MW

0 Mvar

-59 MW

10 Mvar

37 MW

23 Mvar

20 MW

0 Mvar

0 Mvar

48 MW

32 Mvar

113 MW

0 Mvar

0 MW

18 Mvar

84 MW

12 MW

3 Mvar

12 MW

3 Mvar

17 MW

4 Mvar

17 MW

8 Mvar

18 Mvar

39 MW

392 MW

0 Mvar

7 Mvar

28 MW

32 Mvar

39 MW

20 Mvar 77 MW

14 Mvar

0 MW

0 Mvar

160 MW

0 Mvar

78 MW

3 Mvar

155 MW

113 Mvar

277 MW

113 Mvar

0 Mvar

0 MW 63 MW

22 Mvar

S0 S0

S0 S0

S2

S2

2 x 400MW nuclear plants

most heavily loaded bus at node 59

48

50

PRE – PATH – REENFORCEMENT NODE 59 AVERAGE HOURLY LMPs

0

20

40

60

0 24 48 72 96 120 144 1684

5

6

7

8

9

10

LM

P i

n $

/MW

h

hour

load

in G

W

0 24 48 72 96 120 144 1680

10

20

30

40

50

60

LM

P in

$/M

Wh

number of hours in the week

base case

case S0

case S1

case S2

case S3base case

case S0

case S1

case S2case S3

51

POST – PATH – REENFORCEMENT NODE 59 AVERAGE HOURLY LMPs

0 24 48 72 96 120 144 1680

10

20

30

40

50

4

5

6

7

8

9

hour

load

in G

W

LM

P in

$/M

Wh

0 24 48 72 96 120 144 1680

10

20

30

40

50

60

LM

P in

$/M

Wh

number of hours in the week

base case

case S0

case S1

case S2

case S3

60base case

case S0

case S1

case S2

case S3

52

PRE – PATH – REENFORCEMENT AVERAGE HOURLY CONGESTION RENTS

0

10

20

30

40

50

60

0 24 48 72 96 120 144 1684

5

6

7

8

9

10

con

gest

ion

ren

ts in

thou

san

d $

hour

load

in

GW

0 24 48 72 96 120 144 1680

10

20

30

40

50

60

con

gest

ion

ren

ts in

thou

san

d $

number of hours in the week

base case

case S0

case S1

case S2

case S3

base case

case S0

case S1

case S2

case S3

53

POST – PATH – REENFORCEMENT AVERAGE HOURLY CONGESTION RENTS

24 48 72 96 120 144 1680

500

1,000

1,500

2,000

2,500

3,000

3,500

con

gest

ion

ren

ts i

n $

load

in

GW

con

gest

ion

ren

ts i

n $

hour number of hours in the week

base case

case S0

case S1

case S2

case S3

24 48 72 96 120 144 1680

500

1,000

1,500

2,000

2,500

3,000

3,500

5000

6000

7000

8000

0

base case

case S0

case S1

case S2

case S3

54

STUDY SET III: SUBSTITUTION FOR THE CONVENTIONAL RESOURCES

The aim of this study is to quantify the extent, from

a purely reliability perspective, wind resources can

substitute for conventional generation capacity in a

power system with integrated storage resources

We deem storage units to be firm capacity and use

them to meet the desired reserves margin

As the wind resources are integrated, we decrease

progressively the system reserves margin, retire

conventional unit capacity and assess the impacts

55

THE STUDY TEST SYSTEM: A MODIFIED IEEE 118-BUS SYSTEM

Annual peak load: 8,090.3 MW

Conventional generation resource mix: 9,714 MW

4 wind farms located in the Midwest with total

nameplate capacity of 2,720 MW

4 units: each has a 100 MW capacity, 1,000 MWh

storage capability and 89 % round-trip efficiency

The unit commitment is performed to ensure the

desired reserves margin is attained from the

conventional and storage resources

56

SET IV SENSITIVITY CASES

case reserves margin in %

base (no wind, no storage resources) 15

R0 15

R1 14

R2 13

R3 12

R4 11

R5 10

R6 9

R7 8

R8 7

57

WEEKLY RELIABILITY INDICES vs. RESERVES MARGINS

reserves margin in %

EUE

7 8 9 10 11 12 13 14 150

0.1

0.2

0.3

0.4

0.5

0.6

0.7

wee

kly

LO

LP

con

trib

uti

on

LOLP

reserves margin in %7 8 9 10 12 14

0

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

11

wee

kly

EU

E c

ontr

ibu

tion

in M

Wh

base case base case

1513

58

MEAN HOURLY WHOLESALE PURCHASE PAYMENT IMPACTS

24 48 72 96 120 144 1680

50

100

150

200

250

300

350

400

base case

15%

14%

13%

12%

11%

10%

9%

8%

7%

wh

oles

ale

purc

has

e pa

ymen

ts in

thou

san

d $

hour

59

KEY FINDINGS OF THE ILLUSTRATIVE STUDIES

Deeper penetration of wind resources reduces

DAM LMPs, wholesale purchase payments, CO 2

emissions and improves system reliability

Storage works in synergy with wind to drive

wholesale purchase payments further down and

improve system reliability

Overall, CO 2 emissions are not significantly

affected by the integration of a storage unit

60

KEY FINDINGS OF THE CASE STUDIES

Storage siting significantly impacts the congestion

rents and the LMPs at certain nodes

In a system whose storage resources are used to

substitute for conventional generation to meet the

desired reserves margin requirements, large

amounts of wind capacity are required to replace

the retired conventional generation capacity: in the

case studies presented the 2,720 MW of wind can

substitute for about 300 MW of retired conventional

generation capacity – about 3.7 % of peak load

61

KEY FINDINGS OF THE CASE STUDIES

Absent storage units, with all other conditions

remaining unchanged, the 2,720 MW wind can

replace only about 220 MW of retired conventional

generation capacity – about 2.7 % of peak load

We attain significant reductions in wholesale

purchase payments – about 25 % – when storage

and wind resources substitute for conventional

resource capacity, at the same reliability level

62

SALIENT SIMULATION APPROACH CHARACTERISTICS

A practically-oriented approach to simulate large-

scale systems over longer-term periods

Comprehensive and versatile approach to quantify

the impacts of the integration of storage devices

into power systems with deepening penetration of

renewable resources

Demonstration of the capabilities of the proposed

approach to a broad range of planning, investment,

transmission utilization and policy analysis studies

63

CONCLUDING REMARKS

Storage and wind resources consistently pair

well together: they reduce wholesale purchase

dollars and improve system reliability; storage

seems to attenuate the “diminishing returns”

trend seen with deeper wind power penetration The location of a storage unit can have large local

impacts; siting requires case-by-case studies Wind resources can substitute for conventional

resources to a very limited extent, even in a

system with integrated storage resources