The RenewElec Project - Exploring Challenges and ...

The RenewElec Project - Exploring Challenges and Opportunities for

Integrating Variable and Intermittent Renewable Resources.

Presented by Paulina Jaramillo, Executive Director

Eighth Annual Carnegie Mellon Conference On The Electricity Industry – March 13, 2012

33 States Currently Have RPS

•  Proponents of renewables argue that large amounts of variable and intermittent power can be easily accommodated in the present power system.

•  Others argue that even levels as low as

10% of generation by variable and intermittent power can cause serious disruptions to power system operation.

At the RenewElec project,

We believe a much-‐expanded role for variable and intermi0ent renewables is possible but only if we adopt a systems approach that considers and an8cipates the many changes in power system design and opera8on that may be needed, while doing so at an affordable price, and with acceptable levels of security and reliability.

Better Prediction of Variability

Novel Strategies to Reduce Variability

New Methods for Optimally Dispatching Power Plants and Reserves

Improved Strategies for Building, Monitoring and Controlling Transmission

Systems

Dispatching Power Plants to Changing Power Levels without Excessive Air

Emissions

Electric and Thermal Storage

Intelligent Distribution Systems and Customer Load Control

New Standards for Frequency and Voltage Control

Better Understanding of Offshore Wind Resources

Plug-in Electric Vehicles

New Regulatory and Rate Structures

Improved System-Wide Facilities Expansion Planning

The Role of Plug-in Vehicles in Supporting Wind Energy Integration in a Grid-to-Vehicle Configuration and Consequent Smart Grid

Support of Changing Demand

The Implications of Coal and Gas Plant Ramping as a Result of Wind Power

Balancing Area Consolidation and Interconnection Benefits

Estimating Regulating Reserves Requirements for Increasing Wind Deployment, without Gaussian Statistical Assumptions

Integrated Solar Combined Cycles (ISCC)

Quantifying the Hurricane Risk to Offshore Wind Turbines

The Expansion and Consolidation of Service Territories for Control and Balance Areas—Legal and Regulatory Implications

Comparative Analysis of Oil and Gas and Wind Project Decommissioning Regulations on Federal and State Land

The Costs of Wind Power Forecast Uncertainty

Ongoing Research and Stochastic Simulation Models

RenewElec Project Building Blocks

Reconfiguring Distribution Systems Dynamically to Accept more Variable Renewable Power with Low Loss.

How much can Demand Response Contribute to Buffering Variability of Wind and Solar Power?

Regulation and Public Engagement for Enhanced Geothermal Power to Minimize Induced Earthquakes.

Grid Stability Implica0ons of Large-‐Scale Wind Power.

Fossil Plan Mothball and Reactivation decisions with Increase Wind Power





Systems


Emissions
























Fossil Plan Mothball and Reactivation decisions with Increased Wind Power


Stephen Rose, Paulina Jaramillo, Jay Apt, Mitch Small, Iris Grossmann

Offshore Wind Poten8al in Atlan8c and Gulf Coasts

PA DE MD NJ

NH VA

ME RI GA MA

CT NY

SC

NC

LA

TX

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 25 50 75 100 125 150 175 200 225 250

Rate of H

urric

ane Occuren

ce λ [y

ear-‐1]

Offshore Wind Resource at Depth < 60 m [GW]

Wind Turbines are Vulnerable to Hurricanes Typhoon Maemi, Okinawa, 2003

Table 2 Damage of the Karimata Turbines

System specifications Damage

WT

No

. 3

Manufacturer: Micon Rated power: 400/100kW Regulation: Stall Hub height: 36m

!Collapsed due to the buckling of the tower near the entrance door

WT

No

. 4


!Nacelle cover drooped

!Nose cone lost

WT

No

. 5


!Collapsed due to the buckling of the tower near the entrance door

WT

No

. 6

Manufacturer: Enercon Rated power: 600kW Regulation: Pitch Hub height: 46m

!Blades broken !Nacelle cover

damaged

(a) WT No. 3 (b) WT No. 4

(c) WT No. 5 (d) WT No. 6

Fig. 7" Damaged wind turbines at Karimata

The Micon’s turbines were designed with the function

that the yaw should be locked with disk-brake after cut-out

wind speed. However, it was found that the yaws of WT

No.3, 4, 5 were moved clockwise from 94° to 156° when

the wind speed exceed 25 m/s as shown in Fig. 8. As a

result, they suffered larger wind load during the typhoon

than the designed wind load.

#

$#

%#

&#

'#

(#

$)*## $)*&# %#*## %#*&# %$*## %$*&# %%*## %%*&# %&*## %&*&#

+,- .

/,01"2.345,+67-89:

#

'#

;#

$%#

$<#

%##

1,=.5+,40"4>"0?5.33.

/ ,01"@.345,+6

1,=.5+,40"4>"0?5.33.

Fig. 8" Time series of yaw direction and the wind speed of

turbine No. 5 at Karimata on September 10, 2003

4. Mechanism of the damages of the turbines

To clarify the damage of wind turbines, investigation was

performed as shown in Fig. 9. First, an onsite investigation

was conducted to measure the direction of collapse,

entrance door and nacelle and the position of the blades.

Then material test was performed to find the property of the

steel tower and the concrete foundation using the specimen

extracted at the site. Maximum wind speed and the

maximum gust were estimated by wind tunnel test and

numerical simulation. Finally, FEM simulation and wind

response analysis were performed to evaluate the ultimate

bending moment and maximum bending moment acting on

the turbine towers and the foundation.

! 1. Onsite invstigation

ARecordBMeasurement

2. Wind velocity Estimation

Numerical Analysis

Evaluation of

wind load

Wind ResponseAnalysis

!Tower!Foundation

!Deformed Bar

Measurement

!Direction of collapse !Blade position !Entrance door direction !Nacelle Direction

3. Strength " factor

FEM Analysis

!Urban Model

Measure of

Wind Turbine

Wind Tunnel test

Analysis of Collapsed towers

Material Test"

Fig. 9 Flow chart of the investigation

4.1" Wind speed estimation

After the loss of grid connection, wind speed at the site

was not recorded for the evaluation of maximum wind load.

Although time series of wind speed and wind directions

was recorded at Miyakojima meteorological station, they

could not be directly used to estimate the maximum wind

load of the turbines because the station is located in urban

area and observed wind speed is strongly affected by

surrounding buildings.

In this study, a new hybrid method with a combination of

wind tunnel test and numerical simulation was proposed to

estimate the maximum wind speed and the turbulence at the

sites.

Generally, wind tunnel tests can accurately evaluate the

effect of buildings. But it is difficult to simulate the

roughness of the ocean. On the other hand, numerical

simulation (CFD) can easily simulate the roughness of

ocean and the effect of topography. However, it requires

numerous grids to estimate the flow around the building. In

this study, first, a wind tunnel test with urban model was

carried out to investigate the effect of urban roughness and

to obtain the time series of wind speed over flat terrain.

Next, the wind speed at the sites was estimated based on

those over flat terrain.

4.1.1" Wind tunnel tests

The observed wind speed at the meteorological station is

strongly affected by surrounding buildings. A wind tunnel

test with 1/1000 urban model was carried out to investigate

the effect of urban roughness and to estimate the wind

speed over flat terrain. Fig. 10 shows the urban model used

in wind tunnel test conducted at Wind Engineering

Laboratory, the University of Tokyo University.

Fig. 11 shows the time series of mean wind speed

recorded at the meteorological station and estimated one

over flat terrain. It was noticed that for northerly wind, the

wind speed decreases by the factor of 0.77 at the

meteorological station compared to flat terrain. Thus, the

maximum wind speed during typhoon is estimated to be

49.6m/s over flat terrain.

Takahara, et al (2004)

Turbines Destroyed in 20 Years 50-‐turbine wind farm

0 5 10 15 20 25 30 35 40 45 500.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Turbine Towers Buckled in 20 Years

Cum

ulat

ive

Prob

abilit

y

Galveston County, TXDare County, NCAtlantic County, NJDukes County, MA

Not yawing Yawing

0 5 10 15 20 25 30 35 40 45 500.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


Cum

ulat

ive

Prob

abilit

y


Not yawing Yawing

Turbines Destroyed in 20 Years 50-‐turbine wind farm

0 5 10 15 20 25 30 35 40 45 500.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


Cum

ulat

ive

Prob

abilit

y


Not yawing Yawing

0 5 10 15 20 25 30 35 40 45 500.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


Cum

ulat

ive

Prob

abilit

y


Not yawing Yawing

Probability That At Least 10% Of Turbines In A Wind Farm Will Be Destroyed By Hurricanes In 20 Years - No Yaw Scenario

Engineering Changes Can Reduce Risk •  Backup power for yaw system

– Survival depends on active system – Wind vane must survive – Turbine must yaw quickly

•  Stronger towers and blades – More steel in tower – More fiberglass in blades – 20 – 30% cost increase





Systems


Emissions

























The Effect Of Long-distance Interconnection On Wind Power Variability

Emily Fertig, Warren Katzenstein, Jay Apt, Paulina Jaramillo

Connecting wind plants within a region reduces high-frequency fluctuations compared to a single wind plant.

This reduces the need for quick-ramping resources such as batteries and peaker gas plants. Connecting all four regions provides negligible additional benefit compared with a single region (note log scale).

10−6 10−5 10−4

100

101

102

103

104

Frequency (Hz)

Pow

er S

pect

ral D

ensi

ty

Value expected for a single turbineAggregate wind output of all four regionsERCOT aggregate wind output

Interconnection substantially increases the percentage of firm wind capacity

12% of aggregate wind capacity of all four regions is available 90% of the time; only 1% to 6% of wind capacity of a single region is available 90% of the time

0 20 40 60 80 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Hours in 2009 (percent)

Norm

alize

d pow

er

BPACAISOMISOERCOTSum of all four

90%

Interconnection also reduces the per-unit standard deviation

Next Step: Benefit-Cost Analysis





Systems


Emissions

























Impacts of Large Scale Penetration of Wind on the Operations of Coal Power Plants

Source: (J Apt 2007) Source: CEMS 2008

Output from 6 Texas wind turbines

Days Hours

Output from a Texas Coal Plant

David Luke Oates, Paulina Jaramillo

Model Overview

System Data

•  Unit capacity, etc.

•  Hourly Demand

•  Hourly Wind

UCED Model

•  Optimization Model

•  Determine Schedule

Emissions Model

•  Many Regression Models

•  Determine emissions

“What are the capacities of each unit

and demand for electricity?”

“How much power does each unit produce every

hour?”

“How much CO2 and NOX are produced?”

•  Regressions models using CEMS data

•  Emissions rates vary with power level and ramp-‐rate

•  Capture emissions arising from cycling N

OX E

missions [lb/h]

Model uses power and ramp rate as explanatory variables

Model uses mean emissions produced during startup and shutdown

PJM Combined Cycle Unit

Emissions models capture changes in emissions rates during cycling

Sample Model Output at 10% Wind

08/07 08/08 08/09 08/10 08/11 08/12 08/130

10

20

30

40

50

60

70

80

90

Date in 2006

Powe

r Out

put [

GW

]

Energy Use Plot for Aug. 2006 in PJM 10% Wind

NUC BIT LIG PC SUB NGCC NGCT NGSTEAM WND WCNuclear is baseloaded

PJM uses a great deal of Bituminous coal

Wind curtailment

Effec8ve wind penetra8on 7.3%

Substan8al coal cycling

Energy Use Plot for Aug. 2006 in PJM – 10% Wind

Transi8on from 0.4% to 10% wind

11/06 11/07 11/08 11/09 11/10 11/11 11/120

50

100Energy Use Plot for Nov. 2006 in PJM

11/06 11/07 11/08 11/09 11/10 11/11 11/120

50

100

Powe

r Out

put [

GW

]

11/06 11/07 11/08 11/09 11/10 11/11 11/120

50

100

Power Outpu

t [GW

]

Observa0ons •  Increase in Coal

Cycling •  Wind offsets gas

and coal

Work is ongoing to: •  Refine UCDM •  Refine Emissions

Model •  Develop Scenarios

0.4% Wind

10% Wind

06/02 06/03 06/04 06/05 06/06 06/07 06/08150

155

160

165

170

175

180

185

190

195

200

Date in 2006

Powe

r Out

put [

GW

]

Energy Use Plot for Jun. 2006 in PJM 8 GW Wind No Coal Constraints

NUC BIT LIG PC SUB NGCC NGCT NGSTEAM WND WC

Thank you for your attention Questions? Visit us at www.renewelec.org

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