Maritime Regional Wind Energy Resources:Determining preferred regions for additional onshore and offshore wind energy development

2021-02

Nathaniel Pearre (PhD) and Lukas Swan (PhD, PEng)

Renewable Energy Storage Laboratory

Dalhousie University

Halifax, Nova Scotia, B3J 0H6, Canada

W: http://resl.me.dal.ca

E: Lukas.Swan@Dal.Ca

E: Nathaniel.Pearre@Dal.Ca

About RESL

• Research

– Our research focuses on advanced energy storage solutions to allow for increased penetration of renewable electricity generators

– Physical battery lab for testing and understanding performance & degradation

– Energy system modelling group

• Team

– Composed of post-graduate students (PhD, MASc), research assistants, undergraduate students, visitors, and associates.

– Each has independent projects and is providing unique perspectives and results.

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 2

Acknowledgements• This report is funded by the Atlantic Canada Opportunities Agency (ACOA) under the

Atlantic Policy Research Initiative (Project #213972), which provides a vehicle for the analysis of key socioeconomic policy issues in Atlantic Canada. Additional funding came from the Nova Scotia Department of Energy and Mines (NS-DEM).

• Special thanks go to our research partners for sharing their data– Thanks to the provincial utilities NSP, NBP, & PEIE – Wind farm developer / operator Katalyst Wind & WEICan– Canadian governmental organizations Environment Canada & the DFOC– US National Oceanic and Atmospheric Administration (NOAA), and the Northeastern Regional

Association of Coastal Ocean Observing Systems (NERACOOS) – Iowa State University for maintaining their weather network data portal

• We appreciate the efforts of the Nova Scotia Offshore Energy Research Association (OERA) in organizing a public webinar on this research

• The authors are responsible for the accuracy, reliability and currency of the information.2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 3

MOTIVATION, INTRODUCTION, AND DATA SOURCES

Motivation and objective

• Historic wind energy development– Windy sites– Simple grid connections

• Outcomes– A lot of wind farms in few locations– Correlated power output– Synchronized wind lulls

• Present study– Metrics other than annual capacity factor– Locations that are more ‘harmonious’ to

electricity grid needs– Data driven (removes assumptions) – Graphical outputs (enhances accessibility)

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 5

Approach

• Wind data from Maritimes (NS, NB, PE) and adjoining jurisdictions

– Meteorological stations

– Wind farms

– Offshore buoys

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 6

• Quality control and processing

– Harmonize time zone, time-step

– Scan for errors & bad data

– Engineering judgement

Map of data sources

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 7

Data pre-processing• Use observed wind farm power curve to convert wind speed to power

• Applied a mean capacity factor (cf) of 37%

• Load and transmission data from provincial electricity utilities

• Output

– Each MET location has power output timeseries

– All locations time-synchronized for wind farm power and electrical load

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 8

Transmission for access and congestionTransmission system Power constraints

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 9

Bathymetry for offshore wind

2021-02-11 PROJECT TITLE HERE - Dalhousie University RESL Slide 10

CORRELATION METRIC AND RESULTS

Correlation• How similarly a site varies to a reference timeseries

– High correlation (r > 0.5); high when reference is high, low when it is low.

– Correlation near zero indicates that a site varies independently from reference

– High negative correlation (r < -0.5); low when ref is high, high when ref is low

• Correlation to aggregate existing wind– Lower correlation is better

• Correlation to regional load– Higher correlation is better

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 12

Correlation to Maritime regional wind

• Lower correlations (lighter colors) are better

• Best areas:

– NW NB

– NE Cape Breton

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 13

Correlation to Maritime load

• Higher correlations (lighter colors) are better

• Best areas:

– SW NB

– Far W NS

– Mouth of Bay of Fundy

– Sable Island

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 14

TIME SHIFT METRIC AND RESULTS

Timeshift• If, on average, wind events arrive at one location before or after they

arrive at the reference, and by how much.

– Test different timeshifts between site and reference

– Find timeshift that produces highest correlation

• Timeshifted resources reduce aggregate power ramp rates

– Reduce strain on dispatchable resources

– Reduce integration costs

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 16

Timeshift vs Maritime regional wind

• Higher absolute values are better (hours)

• Avoid:

– Central NS

– All PE

– E NB

• Best areas:

– W NB (esp. NB Panhandle)

– Cape Breton NS

– Mouth of Bay of Fundy2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 17

CAPACITY VALUE METRIC AND RESULTS

Capacity value• How much reliable wind power is there when load is high

• Examine only times of highest load defined as top 10%

• Evaluate distribution of observed wind power production at each site during those points in time

• Report fraction of that distribution as ‘reliable capacity’ based on 15th

percentile, 85% reliable

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 19

The 15th percentile power in top 10% loads

• Higher values (lighter colors) are better

• Best areas:– Mouth of Bay of Fundy– NS Atlantic coast– North CB

• Other capacity percentiles and load percentiles were evaluated– Numerical results changed– Spatial patterns were consistent

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 20

TRANSMISSION METRIC AND RESULTS

Maritime Aggregate Import (-Export)

• Import Bias

– Net import

– Higher peak import

• Seasonality

– Winter peaking

– Summer dip

• More wind will almost always mean less import

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 22

Mean wind during Maritime Peak Import

• Higher production (lighter colors) are better

• Best areas:

– Mouth of Bay of Fundy

– Sable Island

– Northern ME

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 23

CLOSING

Multiple metrics• Select metrics of interest

– Correlation

– Timeshift

– Capacity value

• Apply threshold criteria

– r value

– Hours

– Power

• Overlay results on a diagram

• Can be focused to specific stakeholder interests

• Can be focused to specific provinces

2021-02-11 Maritime Regional Wind Energy Resources: Determining preferred regions for additional development - Dalhousie University RESL Slide 25

Nathaniel Pearre (PhD) and Lukas Swan (PhD, PEng)

W: http://resl.me.dal.ca, E: Nathaniel.Pearre@Dal.Ca, E: Lukas.Swan@Dal.Ca