slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
presentation by Yannick Degeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana PowerPoint Presentation
Download Presentation
presentation by Yannick Degeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana

Loading in 2 Seconds...

play fullscreen
1 / 63

presentation by Yannick Degeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana - PowerPoint PPT Presentation


  • 111 Views
  • Uploaded on

STOCHASTIC SIMULATION OF POWER SYSTEMS WITH INTEGRATED RENEWABLE AND STORAGE RESOURCES. presentation by Yannick Degeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana November 26th , 2012. 1. KEY OBJECTIVES.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'presentation by Yannick Degeilh ECE 590 I Seminar University of Illinois at Champaign-Urbana' - conan


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

STOCHASTIC SIMULATION OF POWER SYSTEMS WITH INTEGRATED RENEWABLE AND STORAGE RESOURCES

presentation by

YannickDegeilh

ECE 590 I Seminar

University of Illinois at Champaign-Urbana

November 26th, 2012

1

key objectives
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 studiesthatassess the impacts of storage and renewableresourceintegration on the power system variable effects

2

us renewable portfolio standards
US RENEWABLE PORTFOLIO STANDARDS

29 states,+ Washington DC and 2 territories,have Renewable Portfolio Standards

(8 states and 2 territories have renewable portfolio goals).

Source: www.dsireusa.org, November 2012

the changing environment
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
SALIENT CHARACTERISTICS OF SOLAR AND WIND POWER OUTPUTS
  • Highly time-dependentnature:
    • 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

slide6

PV POWER OUTPUT OF 1-MWCdTeARRAY IN GERMANY

1000

900

800

700

600

500

kW

400

300

200

100

0

5:00

6:00

8:00

9:00

7:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

19:00

20:00

samples collected on a 5 – minute basis

pv output and load
PV OUTPUT AND LOAD

50

500

40

400

PV output (MW )

load (GW )

300

30

200

20

100

0

10

24

48

72

96

120

144

168

time (h)

7

Sources: ERCOT and NREL

caiso daily wind power patterns in march 2005
CAISO DAILY WIND POWER PATTERNS IN MARCH 2005

MW

Source: CAISO

700

600

500

400

300

200

100

0

hour

8

Source: CAISO

ontario daily wind power output
ONTARIO DAILY WIND POWER OUTPUT

MW

700

600

500

400

300

200

100

0

hour

Source: IESO

9

misalignment of wind power output and load
MISALIGNMENT OF WIND POWER OUTPUT AND LOAD

8000

250

7000

load

200

6000

150

load (MW)

wind power output (MW)

5000

100

4000

50

wind power

0

3000

48

72

96

120

144

168

24

hour

10

issues in integrating wind and solar resources
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 spillingwinddue to inadequateloadsat night and the challenges of managing base-loadedunit shut down for short periodsduringlow-loadhoursis a major problem

11

taking advantage of integrated storage resources
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 performance

12

utility scale storage characteristics
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

utility scale storage application
UTILITY - SCALE STORAGE APPLICATION

MW

storageresource discharging during peak hours

storage resource charging during low-load hours

0

12

24

wind storage symbiotic interactions
WIND/STORAGE SYMBIOTIC INTERACTIONS

energy discharged during peak hours

MW

unit i + 3

units are loaded in order of increasing prices of unit production

displaced units

unit i + 2

daily load shape

unit i + 1

unit i

unit i - 1

unit i - 2

charging

units

modified daily load shape by wind

base-loaded units

hours

0

12

24

energy charged during low-load hours

the need for a new simulation approach
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
need to explicitly represent
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)
thrust of the approach
THRUST OF THE APPROACH
  • We develop a comprehensive, computationally efficient Monte Carlo simulationapproach 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 stochasticprocesses
  • Wedeploy the storageschedulerto utilize arbitrage opportunities in the storageunit operations
  • We emulate the transmission-constrained hourly day-ahead markets(DAMs)to determine the power system operations in a competitive environment
thrust of the approach1
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
    • CO2 emissions
    • LOLPand EUE system reliability indices
key contributions
KEY CONTRIBUTIONS
  • Development of a new simulation toolappropriate to addresstoday’s power industry challenges
  • Salientfeaturesinclude:
    • quantification of the power system expected variable effects – economics, reliabilityand environmental impacts – in eachsub-period
    • computationally tractable for practicalsystems
study contributions
STUDY CONTRIBUTIONS
  • detailedstochasticmodels of the time–varying resources and loadsallow the representation of spatial and temporal correlations
  • storageschedulerfor optimizedstorageoperation to exploit arbitrage opportunities
  • representation of the transmission–constrainedmarketoutcomes
  • flexibility in the representation of the marketenvironment / policies
the time dimension in simulation studies
THE TIME DIMENSION IN SIMULATION STUDIES

. . .

. . .

study period

time

. . .

period 1

period T

period t

  • 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
the subperiods the smallest indecomposable units of time
THE SUBPERIODS: THE SMALLEST INDECOMPOSABLE UNITS OF TIME

. . .

. . .

sub-period 1

sub-period h

sub-period H

study period

. . .

. . .

. . .

period 1

period T

period t

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
the simulation approach key assumptions
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 gridislosslessand the DC power flow conditionsholdover the entirestudyperiod
proposed simulation approach conceptual structure
PROPOSED SIMULATION APPROACH: CONCEPTUAL STRUCTURE

loads

LMPs

congestion rents

conventional generator available capacities

market clearing procedure

(DCOPF)

“outcome” stochastic processes

“driver” stochastic processes

renewable power outputs

  • CO 2emissions

. . .

storage schedule

storage operations

quantification of system variable effects
QUANTIFICATION OF SYSTEM VARIABLE EFFECTS
  • We use the approximations to the joint c.d.f.s of the market outcome stochastic processesto 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
to ensure numerical tractability
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 variatesand common random numbers
    • warm-start of the linear program solver
    • parallelization of simulation runs
typical applications
TYPICAL APPLICATIONS
  • Resource planning studies
  • Production costing issues
  • Transmission utilization issues
  • Environmentalassessments
  • Reliability analysis
  • Investment analysis
motivation
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
case studies1
CASE STUDIES

We present 3sets 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 4energy 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
case study set i deepening wind penetration
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 withthe addition of a storage energy resource
  • We evaluate the key metrics for variable effect assessment, including wholesale purchase payments, reliability indices and CO2 emissions
the study test system a modified ieee 118 bus system
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 MWhstorage capabilityand89 % round-trip efficiency
  • Unit commitment uses a 15 % reserves margin provi-ded by conventional units and the storage resources
case d average hourly storage utilization
CASE D: AVERAGE HOURLY STORAGE UTILIZATION

load

8.5

400

8.0

300

7.5

200

7.0

100

hourly storage charge/dischargecapacity inMW

load in GW

6.5

0

6.0

- 100

5.5

- 200

5.0

- 300

4.5

- 400

0

24

48

72

96

120

144

168

hour

node 80 average hourly lmp s
NODE 80 AVERAGE HOURLY LMPs

with storage

without storage

50

base case

45

case A

40

case B

35

case C

30

case D

25

LMP in $/MWh

20

15

10

5

0

0

24

48

72

96

120

144

168

hour

node 80 average hourly lmp duration curve
NODE 80AVERAGE HOURLY LMPDURATION CURVE

50

45

40

35

30

25

20

15

10

5

0

0

20

40

60

80

100

with storage

without storage

base case

case A

case B

case C

case D

LMP in $/MWh

% of hours in the week

expected wholesale purchase payments
EXPECTED WHOLESALE PURCHASE PAYMENTS

180

170

160

150

140

130

120

110

100

with storage

without storage

- 5.1 %

- 9.1%

- 15.4%

- 17.9%

- 24.4%

thousand $

- 25.2%

- 31.1%

- 31.2%

- 36.9%

case A

base case

case B

case C

case D

expected co 2 emissions
EXPECTED CO2 EMISSIONS

1.05

+ 0.3 %

with storage

without storage

1.00

- 6.0%

- 5.6%

0.95

- 10.5%

- 11.8%

- 14.5%

0.90

thousand metric tons

- 16.4%

- 17.5%

0.85

- 19.8%

0.80

0.75

0.70

case A

case B

base case

case C

case D

annual reliability indices
ANNUAL RELIABILITY INDICES

6.24

5.20

4.16

3.12

2.08

1.04

0

EUE

LOLP

MWh

x 10-4

without storage

with storage

without storage

with storage

14

12

- 37.5%

- 43.8 %

- 50.0%

-55.1 %

- 42.5%

-56.3 %

10

-73.5 %

-67.1 %

- 65.6%

-71.9 %

- 64.1%

-82.4 %

- 73.9%

-90.0 %

- 80.2%

8

- 80.1%

-94.3 %

-87.5 %

6

4

2

case A

0

base case

base case

case B

case B

case D

case C

case D

case C

case A

case study set ii storage unit siting
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
test system of the study a modified ieee 118 bus system
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 MWhstorage capabilityand89% round-trip efficiency
  • Reserves margin is set at 15 %and is provided by conventional and storage resources
storage siting on the modified ieee 118 bus test system
STORAGE SITING ON THE MODIFIEDIEEE 118 – BUS TEST SYSTEM

W

W

most heavily loaded bus at node 59

W

W

storage siting region

43

sensitivity cases in study set ii
SENSITIVITY CASES IN STUDY SET II

each case has 2,720MW nameplate wind capacity

storage siting region
STORAGE SITING REGION

S1

S1

S1

S2

S0

S0

S2

S0

S0

S3

S1

S3

S3

S2

S3

S2

45

node 59 expected hourly lmp s
NODE 59 EXPECTED HOURLY LMPs

60

10

60

50

9

40

8

40

30

7

6

20

20

5

10

4

0

0

0

24

48

72

96

120

144

168

0

24

48

72

96

120

144

168

case S0

case S0

case S1

load in GW

LMP in $/MWh

LMP in $/MWh

case S2

case S1

case S3

case S2

case S3

base case

base case

number of hours in the week

hour

expected hourly congestion rents
EXPECTED HOURLY CONGESTION RENTS

10

case S0

60

60

9

case S0

50

50

8

40

case S1

40

congestion rents in thousand $

load in GW

congestion rents in thousand $

7

30

30

case S2

6

20

20

case

S2

base case

10

case

S1

5

10

case S3

0

0

4

0

24

48

72

96

120

144

168

0

24

48

72

96

120

144

168

hour

number of hours in the week

case S3

base case

transmission path congestion and its reenforcement
TRANSMISSION PATH CONGESTION AND ITS REENFORCEMENT

most heavily loaded bus at node 59

S2

S0

S0

S2

S0

S0

2x400MW

nuclear plants

48

pre path reenforcement node 59 average hourly lmp s
PRE – PATH – REENFORCEMENT NODE 59 AVERAGE HOURLY LMPs

60

60

10

50

9

40

40

8

30

7

20

20

6

10

5

4

0

0

0

24

48

72

96

120

144

168

0

24

48

72

96

120

144

168

case S0

case S0

case S1

load in GW

LMP in $/MWh

LMP in $/MWh

case S2

case S1

case S3

base case

case S3

base case

case S2

number of hours in the week

hour

post path reenforcement node 59 average hourly lmp s
POST – PATH – REENFORCEMENT NODE 59 AVERAGE HOURLY LMPs

60

9

50

50

8

40

40

7

30

30

6

20

20

5

10

10

4

0

0

0

24

48

72

96

120

144

168

0

24

48

72

96

120

144

168

case S0

60

base case

case S1

case S2

case S3

base case

load in GW

LMP in $/MWh

LMP in $/MWh

case S0

case S2

case S1

case S3

hour

number of hours in the week

pre path reenforcement average hourly congestion rents
PRE – PATH – REENFORCEMENT AVERAGE HOURLY CONGESTION RENTS

10

60

case S0

60

9

case S0

50

50

8

40

case S1

40

congestion rents in thousand $

load in GW

congestion rents in thousand $

30

7

30

case S2

20

6

case

S2

20

case

S1

base case

10

5

10

case S3

0

0

4

0

24

48

72

96

120

144

168

0

24

48

72

96

120

144

168

hour

number of hours in the week

case S3

base case

post path reenforcement average hourly congestion rents
POST – PATH – REENFORCEMENT AVERAGE HOURLY CONGESTION RENTS

3,500

3,000

2,500

2,000

1,500

1,000

500

0

24

48

72

96

120

144

168

case S2

case S0

case S0

3,500

case S1

case S2

8000

3,000

base case

case S1

2,500

7000

congestion rents in $

load in GW

2,000

congestion rents in $

1,500

6000

case S3

1,000

500

5000

base case

0

0

24

48

72

96

120

144

168

hour

number of hours in the week

case S3

study set iii substitution for the conventional resources
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
the study test system a modified ieee 118 bus system1
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 MWhstorage 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
weekly reliability indices vs reserves margins
WEEKLY RELIABILITY INDICES vs. RESERVES MARGINS

0.0040

0.0035

0.0030

0.0025

0.0020

0.0015

0.0010

0.0005

0

7

8

9

10

12

14

LOLP

EUE

0.7

0.6

0.5

0.4

weekly EUE contribution in MWh

weeklyLOLP contribution

0.3

base case

base case

0.2

0.1

0

11

15

7

8

9

10

11

12

13

14

15

13

reserves margin in %

reserves margin in %

mean hourly wholesale purchase payment impacts
MEAN HOURLY WHOLESALE PURCHASE PAYMENT IMPACTS

base case

400

15%

14%

350

13%

12%

11%

300

10%

9%

8%

250

7%

200

150

100

50

0

24

48

72

96

120

144

168

wholesale purchase payments in thousand $

hour

key findings of the illustrative studies
KEY FINDINGS OF THE ILLUSTRATIVE STUDIES
  • Deeper penetration of wind resources reduces DAM LMPs, wholesale purchase payments, CO2 emissions and improves system reliability
  • Storage works in synergy with wind to drive wholesale purchase payments further down and improve system reliability
  • Overall,CO2 emissions are not significantly affected by the integration of a storage unit
key findings of the case studies
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 300MW of retired conventional generation capacity – about 3.7% of peak load
key findings of the case studies1
KEY FINDINGS OF THE CASE STUDIES
  • Absent storage units, with all other conditions remaining unchanged, the 2,720 MWwind can replace only about 220MW 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
salient simulation approach characteristics
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

concluding remarks
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 resourcescan substitute for conventionalresources to a verylimitedextent, even in a system withintegratedstorageresources