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Smart Grid Gotland -­‐ Wind Power Integra4on
Reference Group Mee4ng, Visby, 5th March 2014, Daniel A. Brodén
Smart Grid Gotland
SGG > Wind Power Integra4on
Market test
Power quality
with distributed
generation
Wind power
integration
Information and
Communication
Technology (ICT)
Market
installations
Smart
SCADA
Smart
substations
and rural grid
Energy
storage
Smart
Meters
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Situa4on on Gotland
~170 MW wind power
HVDC cables
-­‐ Wind power capacity ~170 MW
-­‐ Max grid capacity 195 MW
-­‐ HVDC capacity 2x130 MW
-­‐ ~21,000 detached houses
-­‐ 3 major industries
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Situa4on on Gotland
195 MW wind power
HVDC cables
2012 Produc4on & Consump4on
195 MW installed capacity
170 MW installed capacity
90 MW export
70 MW export
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Situa4on on Gotland
200 MW wind power
HVDC cables
+5 MW 2012 was risk free!
However, the risk s4ll exists!
Max prod -­‐ Min cons = 200 -­‐ 65 = 135 135 MW > 130 MW!
2012 Produc4on & Consump4on
200 MW installed capacity
95 MW export
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Situa4on on Gotland
200 MW wind power
HVDC cables
+5 MW Demand-­‐Response
Demand-­‐Response
Demand-­‐Response Management System (DRMS)
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Demand-­‐Response can help increase the hos4ng capacity of wind power on Gotland and solve conges4on problems in the network
Gotland Challenges
Produc4on Prognosis on Short-­‐Term (hour-­‐ahead)
Actual
Prognosis
Produc4on Prognosis on Long-­‐Term (day-­‐ahead)
Uncertainty in produc4on prognosis makes it difficult to rely on Demand-­‐Response for conges4on management
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Research
Is it technically feasible to balance 5 MW addi4onal wind power capacity in the exis4ng distribu4on network with an Ancillary Service Toolbox?
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Research > AS Toolbox
Data Inputs (2012)
Wind data
Network Simulator
Load data
Long-­‐Term DR
Short-­‐Term DR
Bacery
Flexibility tools
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Wind Curtail.
Research > Forming Clusters
200 MW wind power
HVDC cables
+5 MW Long-­‐Term Cluster
Op4mized consump4on schedule set 24 hours ahead
ST+LT prognosis
Peak hours
Prod
Cons
Load shift
Short-­‐Term Cluster
LT prognosis
Peak hours
Prod
Cons
Load shift
The cluster op4miza4on is executed sequen4ally where the ST cluster minimizes the prognosis errors from the LT cluster
Op4mized consump4on schedule set hourly
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Research > Forming Clusters
200 MW wind power
HVDC cables
+5 MW Long-­‐Term Cluster
Bacery Energy Storage System
Absorbs the prognosis errors from the ST cluster. Short-­‐Term Cluster
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Research > Detached House Model
75 % of all electricity in a detached house is consumed by space hea4ng and domes4c hot water
Mathema4cal Modeling
Space hea4ng
Domes4c hot water
Consump4on es4mates of detached houses
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Research > Detached House Model
Consump4on Model based on “Forecasting household consumer electricity
load profiles with a combined physical and
behavioral approach”
by Claes Sandels, ICS, KTH
Model validated with the consump4on of 41 Swedish residents living in detached houses
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Research > Industry Model
Industrial consump4on shares
Cementa (86%), Nordkalk (6% )
Arla (5%), Others (3%)
A poten4al DR ac4vity for Cementa modeled
MWh
2.8
Addi4onal produc4on ac4vity during weekdays & dayshijs
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4me (h)
Research > Simula4on Setup
-­‐ 3 day periods simulated -­‐ Considering seasonal varia4on
-­‐ 2012 data in hourly granularity
-­‐ Data adjusted to provoke export problem
17%
23%
20%
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Research > Results & Findings
Required cluster size (w/o Cementa)
Total power to balance per day and scenario
40,00
35,00
x 1900
day 1
Minimum cluster size/day
day 2
day 3
x 1600 (LT)
x 300 (ST)
Power [MW] h
30,00
17%
25,00
20,00
1500
900
1700
1300
1100
15,00
1700
900
23%
20%
10,00
1500
1000
900
1000
1000
5,00
0,00
Scenario 1: Winter days
Scenario 2:Spring days
Scenario 3: Summer days
Scenario 4: Autumn days
Scenarios
The minimum number of par4cipants required to Total number of hourly export problems per day and scenario
solve all export problems for all scenarios
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Scenario 1: Winter
Scenario 2: Spring
Scenario 3: Summer
Scenario 4: Autumn
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6
16
16
0
12
24
36
Time [hours]
48
60
72
Research > Results & Findings
Indoor temperature
Indoor temperature change for a LT household participant
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Winter
Spring
Summer
Autumn
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Less than +/-­‐ 1°C varia4on for all seasonal scenarios. Comfort level is kept!
17%
23%
20%
Temperature [degees C]
23
22
21
20
19
18
17
16
0
12
24
17
36
Time [hours]
48
60
72
16
0
12
24
36
Time [hours]
48
60
72
Research > Results & Findings
Tank temperature change for LT household participant
Tank temperature
130
Winter
Spring
Summer
Autumn
120
Varia4ons are within the boundaries for all scenarios. Comfort level is kept!
17%
23%
20%
Tank temperature [degees C]
110
100
90
80
70
60
50
40
0
24
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Figure 12:
18
36
Time [hours]
48
60
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Research > Results & Findings
300
BESS opera4on
(winter scenario)
BESS level
Wind curtailment
Max BESS capacity
250
No wind curtailment needed in this scenario
Power [kW]h
200
17%
23%
150
100
20%
50
0
0
12
24
36
Time [hours]
48
60
BESS charges to account for prognosis errors not accounted by the DR par4cipants
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Research > Results & Findings
Industry par4cipa4on
The modeled DR ac4vity for Cementa significantly reduced cluster size!
+100
-­‐700
17%
-­‐700
23%
20%
DR dynamics changes when cluster size is reduced. This explains the increase on Saturday when Cementa is no longer par4cipa4ng
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Research > Validity & Reliability?
17%
23%
20%
-­‐ Worst case condi4ons reflected
-­‐ Uniform household consump4on model
-­‐ DR par4cipant can not override the consump4on
-­‐ Network simulator not included in study
-­‐ Implementa4on difficul4es not considered
-­‐ Economical constraints not considered
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Research > Benefits for SGG project
Opera4on Strategies
Simula4on results
17%
Household modeling
23%
20%
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Research > From Model to Reality?
17%
23%
20%
Ongoing collabora4on with VENTYX on development & implementa4on
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Thank you for your acen4on
Daniel A. Brodén, +46 762185980
[email protected]
Smart Grid Gotland
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