1) The document discusses a research project analyzing how distributed demand response (DR) resources on the island of Gotland could help integrate additional wind power capacity into the local distribution network. 2) The research used real wind power and load data from 2012 to model different DR clusters and scenarios, including detached houses, industries and an energy storage system. 3) The results found that a minimum of 1300-1700 DR participants could help balance up to 5 MW of additional wind power across different seasons and scenarios while maintaining customer comfort levels. Participation from large industries further reduced the required cluster size. 4) While the study had some limitations, it demonstrated the potential benefits of DR for the Smart Grid Gotland project's objectives

1. Smart Grid Gotland
Smart Grid Gotland -­‐ Wind Power Integra4on
Reference Group Mee4ng, Visby, 5th March 2014, Daniel A. Brodén

2. SGG > Wind Power Integra4on
Power quality
with distributed
generation
Smart
SCADA
Smart
Meters
Energy
storage
Market
installations
Information and
Communication
Technology (ICT)
Wind power
integration
Market test
Smart
substations
and rural grid
2

3. Situa4on on Gotland
-­‐ Wind power capacity ~170 MW
-­‐ Max grid capacity 195 MW
-­‐ HVDC capacity 2x130 MW
-­‐ ~21,000 detached houses
-­‐ 3 major industries
HVDC cables
3
~170 MW wind power

4. Situa4on on Gotland
2012 Produc4on & Consump4on
HVDC cables
170 MW installed capacity195 MW installed capacity
70 MW export
90 MW export
195 MW wind power
4

5. Situa4on on Gotland
HVDC cables
+5 MW
2012 Produc4on & Consump4on
200 MW installed capacity
95 MW export
However, the risk s4ll exists!
Max prod -­‐ Min cons =
200 -­‐ 65 = 135
135 MW > 130 MW!
2012 was risk free!
5
200 MW wind power

6. Situa4on on Gotland
Demand-­‐Response
HVDC cables
+5 MW
Demand-­‐Response
Demand-­‐Response can help
increase the hos4ng capacity of
wind power on Gotland and
solve conges4on problems in
the network
Demand-­‐Response Management System (DRMS)
200 MW wind power
6

7. Gotland Challenges Produc4on Prognosis on
Short-­‐Term (hour-­‐ahead)
Produc4on Prognosis on
Long-­‐Term (day-­‐ahead)
Actual
Prognosis
Uncertainty in produc4on prognosis makes
it difficult to rely on Demand-­‐Response for
conges4on management
7

8. Research
Is it technically feasible to balance 5 MW
addi4onal wind power capacity in the
exis4ng distribu4on network with an
Ancillary Service Toolbox?
8

9. Data Inputs (2012)
Research > AS Toolbox
Wind data
Load data
Network Simulator
Flexibility tools
Long-­‐Term DR Short-­‐Term DR Bacery Wind Curtail.
9

10. Research > Forming Clusters
HVDC cables
The cluster op4miza4on is executed
sequen4ally where the ST cluster minimizes
the prognosis errors from the LT cluster
Long-­‐Term Cluster
Op4mized
consump4on schedule
set 24 hours ahead
Op4mized consump4on
schedule set hourly
+5 MW
Short-­‐Term Cluster
10
Prod
Cons
Peak hours
Load shift
Prod
Cons
Load shift
LT prognosis
Peak hours
ST+LT prognosis
200 MW wind power

11. Research > Forming Clusters
HVDC cables
Long-­‐Term Cluster
Short-­‐Term Cluster
+5 MW
Bacery Energy Storage System
Absorbs the prognosis errors
from the ST cluster.
11
200 MW wind power

12. Research > Detached House Model
12
Mathema4cal Modeling
Domes4c hot waterSpace hea4ng
75 % of all electricity in a detached
house is consumed by space hea4ng
and domes4c hot water
Consump4on es4mates of
detached houses

13. 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
13

14. Research > Industry Model
4me (h)12 24
MWh
2.8
14
Industrial consump4on shares
Cementa (86%), Nordkalk (6% )
Arla (5%), Others (3%)
A poten4al DR ac4vity for
Cementa modeled
Addi4onal produc4on
ac4vity during weekdays
& dayshijs

15. Research > Simula4on Setup
17%
20%
23%
-­‐ 3 day periods simulated
-­‐ Considering seasonal varia4on
-­‐ 2012 data in hourly granularity
-­‐ Data adjusted to provoke export problem
15

16. Required cluster
size (w/o Cementa)
x 1900
x 1600 (LT)
x 300 (ST)
Research > Results & Findings
Total power to balance per day and scenario
0,00
5,00
10,00
15,00
20,00
25,00
30,00
35,00
40,00
Scenario 1: Winter days Scenario 2:Spring days Scenario 3: Summer days Scenario 4: Autumn days
Scenarios
Power[MW]
day 1
day 2
day 3
Total number of hourly export problems per day and scenario
6
7
8
Scenario 1: Winter
Scenario 2: Spring
Scenario 3: Summer
Scenario 4: Autumn
17%
20%
23%
16
The minimum number of par4cipants required to
solve all export problems for all scenarios
1300
1500
1700
1100
1000
900
900
1000
1500
1000
900
1700
Minimum
cluster size/day
h

17. 0 12 24 36 48 60 72
16
Time [hours]
Indoor temperature
Less than +/-­‐ 1°C
varia4on for all
seasonal scenarios.
Comfort level is kept!
Research > Results & Findings
17%
20%
23%
17
0 12 24 36 48 60 72
16
17
18
19
20
21
22
23
24
25
Time [hours]
Temperature[degeesC]
Indoor temperature change for a LT household participant
Winter
Spring
Summer
Autumn

18. 0 12 24 36 48 60 72
16
Time [hours]
Tank temperature
Varia4ons are
within the
boundaries
for all scenarios.
Comfort level is kept!
Research > Results & Findings
18
Autumn
Summer
Spring
Winter
Tank temperature change for LT household participant
Tanktemperature[degeesC]
Time [hours]
0 12 24 36 48 60 72
40
50
60
70
80
90
100
110
120
130
Figure 12:
17%
20%
23%

19. BESS opera4on
(winter scenario)
No wind curtailment
needed in this
scenario
Research > Results & Findings
17%
20%
23%
19
Max BESS capacity
Wind curtailment
BESS level
Power[kW]
Time [hours]
0 12 24 36 48 60 72
0
50
100
150
200
250
300
h
BESS charges to account for prognosis errors not
accounted by the DR par4cipants

20. Industry
par4cipa4on
The modeled DR
ac4vity for Cementa
significantly reduced
cluster size!
Research > Results & Findings
17%
20%
23%
-­‐700
+100
20
-­‐700
DR dynamics changes when cluster size
is reduced. This explains the increase on
Saturday when Cementa is no longer
par4cipa4ng

21. Research > Validity & Reliability?
21
-­‐ 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 considered17%
20%
23%

22. Research > Benefits for SGG project
22
17%
20%
23%
Opera4on
Strategies
Simula4on
results
Household
modeling

23. Research > From Model to Reality?
23
17%
20%
23%
Ongoing collabora4on with VENTYX on
development & implementa4on

24. Smart Grid Gotland
Thank you for your acen4on
Daniel A. Brodén, +46 762185980
daniel.broden1@vacenfall.com
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