Electricity market impacts of increased demand flexibility enabled by

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Electricity market impacts of
increased demand flexibility
enabled by smart grid
Åsa Grytli Tveten, Iliana Ilieva, and Torjus Folsland Bolkesjø
Presenting: Iliana Ilieva, Business PhD Candidate
Norwegian University of Life Sciences/Brady Energy Norway AS
FERC Workshop/Trans-Atlantic Infraday (TAI) , 6-7 November 2014
1
Demand flexibility as a resource
What is demand flexibility?
Adjusting the consumption pattern to variations in
supply on a short-term basis.
Balancing
Technology
Investments
Contracts
Efficiency
Acceptance
“The power system flexibility option with the highest benefit cost ratio”
(IEA)
2
Purpose of the scientific work
Analyze how increased demand flexibility will affect the
power system in Northern Europe in terms of:
- technology mix/need for peak power
- electricity prices
- system costs
- producer revenues
3
Method
The analysis is made by applying a comprehensive power
market model – Balmorel
• Simulates generation, transmission and consumption of
electricity
• Hourly resolution
• Input data
• Four scenarios with flexibility as % electricity
consumption moved within a day:
“Empowering
Customer Choice in
Electricity Markets”
“Impact of Smart Grid
Technologies on
Peak Load to 2050”
(IEA 2011)
4
Regions in the
Balmorel model
About the Balmorel model
• Linear partial equilibrium model
• Calculates the electricity production per technology, time
unit and region
𝑚𝑎𝑥
𝐾𝐷
𝑠∈𝑆
𝑡∈𝑇
𝑟∈𝑅 𝐶
𝐷𝑟,𝑡,𝑠 𝑑𝑟,𝑡,𝑠 −
𝑃
𝑖∈𝐼 𝐾𝑖
𝑔𝑟,𝑖,𝑡,𝑠 +
𝑇
𝐴∈𝑅,𝐴≠𝑟 𝐾𝐴,𝑟
𝑋𝑡
𝐴,𝑟
+
𝑖∈𝐼 𝑔𝑟,𝑖,𝑡,𝑠
• A set of linear constraints:
Energy balance
𝑖 𝑔𝑟,𝑖,𝑡
+
𝐴∈𝑅,𝐴≠𝑟
𝑋𝑡
𝐴,𝑟
− 𝑋𝑡
𝑟,𝐴
= 𝑑𝑟,𝑡 , ∀𝑖 ∈ 𝐼
Transmission capacity 𝑋𝑡 𝐴,𝐵 ≤ 𝑋𝑡 𝐴,𝐵 , ∀𝐴, 𝐵 ∈ 𝑅, 𝐴 ≠ 𝐵
Maximum capacity per generation unit 𝑔𝑟,𝑖 ≤ 𝑔𝑟,𝑖
Ramping
Min and max production levels
Hydro reservoir storage level and more
6
Demand flexibility in the
model
Energy balance:
𝑔𝑟,𝑖,𝑡 +
𝑖
𝐴∈𝑅,𝐴≠𝑟
20
𝑋𝑡
𝐴,𝑟
− 𝑋𝑡
𝑟,𝐴
Daily max
= 𝑑𝑟,𝑡 + ∆𝑑𝑟,𝑡 ,
19
Limitations on maximum allowed shift in
demand in hour t and day n:
∆𝑑ℎ,𝑛 ≤ 𝑑𝑛𝑚𝑎𝑥 − 𝑑𝑛 ∙ 𝛾
𝑑𝑛 =
1
𝐻
𝐻
ℎ 𝑑ℎ,𝑛
Average demand (GW)
∀𝑖 ∈ 𝐼
Mean
18
Peak demand=
daily max – daily
mean.
Share (𝛾) may be
shifted on diurnal
17
, ℎ = 1,2, … , 𝐻 , 𝐻 = 24
16
Total daily consumption is fixed:
15
𝑢𝑝
𝐻 ∆𝑑ℎ,𝑛 = 0 or, analogously: 𝐻 ∆dℎ,𝑛 = −
ℎ = 1,2, … , 𝐻 , 𝐻 = 24
𝑑𝑜𝑤𝑛
𝐻 ∆dℎ,𝑛
01
03
05
07
09 11 13 15 17
Hour of the day
Norwegian University of Life Sciences
19
21
23
7
Results Part I:
20
Changes in the hourly
demand profile
Norway WF
19
Norway WH
Norway WB
18
17
16
15
01
03
05
07
09
11
13
15
Hour of the day
17
19
21
23
01
03
05
07
09
11
13
15
17
19
21
23
11
13
15
Hour of the day
17
19
21
23
80
Germany WM
Norway vs Germany
Winter weeks
Average demand (GW)
Average demand (GW)
Norway WM
Germany WF
75
Germany WH
70
Germany WB
65
60
55
50
01
03
05
07
09
Federal Energy Regulatory Commission, Washington DC
8
Results Part I:
12
01
03
05
07
09
11
13
15
17
19
21
23
demand profile
Norway SF
11
Norway SH
Norway SB
10
9
8
01
03
05
07
09
11
13
15
Hour of the day
17
19
21
23
70
01
03
05
07
09
11
13
15
17
19
21
23
11
13
15
Hour of the day
17
19
21
23
Germany SM
Norway vs Germany
Summer weeks
Average demand (GW)
Average demand (GW)
Norway SM
65
Germany SF
Germany SH
60
Germany SB
55
50
45
40
01
03
05
07
09
9
Results Part II - Production mix
when flexibility increases
Change in the hourly Northern European production mix caused by the increase in demand
response, Full flexibility scenario (all model countries, all-year average)
Change in average power production (GW)
3
2
1
Solar
Natural gas
Reservoir hydro
Solids
ROR hydro
Wind
Nuclear
CHP and biomass
0
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Reservoir hydro:
Volumes moved from
day to night
Nat. gas
-1
IRE generation
-2
Solids (coal&lignite)
-3
GHG
-4
Hour of the day
10
Results Part III: Price Effects
11
Results Part III:
Average el. price (€/MWh)
01
03
05
07
49
Norway WM
47
Norway WF
09
11
13
15
17
19
21
23
electricity prices
Norway WH
45
Norway WB
43
41
39
37
35
01
03
05
07
09
11
13
15
Hour of the day
17
19
21
Norway vs Germany
Winter weeks
Average el. price (€/MWh)
55
01
23
03
05
07
09
11
13
15
17
19
21
23
11
13
15
Hour of the day
17
19
21
23
Germany WM
50
Germany WF
45
Germany WH
Germany WB
40
35
30
25
01
03
05
07
09
12
Results Part IV: Change in
consumers’ costs (M€)
Full flexibility scenario
Baseline
scenario
Change
%-change
in costs
All countries
582
-5.1
-1,0
Norway
102
-1.5
-1,5
Denmark
14
-0.2
-1,4
Germany
136
-2.9
-2,9
Netherlands
44
-0.7
-1,6
UK
133
-0.2
-0,2
Annual system
cost reduction of
9 billion €
13
Results Part V:Producers’
M€
revenues
100
50
0
CHP and Nuclear
biomass
-50
Natural
gas
Wind
Coal/
lignite
ROR
hydro
power
Reservoir
hydro
Solar
power
-100
-150
-200
-250
-300
14
Market clearing conditions: The case
for Germany, A winter week
80
75
Wind power production
70
Demand_B Germany
50
65
Demand_F Germany
40
60
60
30
55
20
50
10
45
40
Hourly variation in
power demand for the
Baseline and Full
flexibility scenarios
0
000
012
024
036
048
060
072 084 096 108
Hour of the week
60
120
132
144
156
Solar power production
Wind power production
Price_B Germany
Price_F Germany
50
40
Power price (€/MWh)
Power production (GW)
70
Solar power production
70
60
50
40
30
30
20
20
10
10
0
Power production (GW)
Power demand (GW)
85
Hourly variation in
power prices for the
Baseline and Full
flexibility scenarios
0
000
012
024
036
048
060
072 084 096 108
Hour of the week
120
132
144
156
15
Summary
-Lower avg prices
-Reduced variability
Price
effects
System
stability
-Less hours with
peak power
-Reduced max
residual demand
-Facilitate IRE
Demand
flexibility
Environ
mental
effects
Lower GHG
emissions?
Revenu
es/Cost
effects
-Different revenues
per technology
-Consumer costs
decrease slightly
-Reduced system
costs
16
Symbol
Definition
s, S
Season of the year, 𝑠 = 1,2, … , 52 , 𝑆 = 52 (total weeks of the year)
t, T
Hour of the week, 𝑡 = 1,2, … , 𝑇 , 𝑇 = 168 (total hours of the week)
h, H
Hour of the day, ℎ = 1,2, … , 𝐻 , 𝐻 = 24 (total hours of the day)
c, C
Country, 𝑐 = 𝐷𝐾, 𝐹𝐼, 𝐺𝐸, 𝑁𝐸, 𝑁𝑂, 𝑆𝐸, 𝑈𝐾 , 𝐶 = 𝐴𝑙𝑙 𝑚𝑜𝑑𝑒𝑙 𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠
r, R
Region, r = 𝐷𝑒𝑛𝑚𝑎𝑟𝑘1, 𝐷𝑒𝑛𝑚𝑎𝑟𝑘2, … , 𝑈𝐾 , 𝑅 = 𝐴𝑙𝑙 𝑚𝑜𝑑𝑒𝑙 𝑟𝑒𝑔𝑖𝑜𝑛𝑠
A
Alias for r (Region, a = 𝐷𝑒𝑛𝑚𝑎𝑟𝑘1, 𝐷𝑒𝑛𝑚𝑎𝑟𝑘2, … , 𝑈𝐾 )
D(d)
Consumer’s utility function
D
Electricity demand (MWh)
I
Power generation technology type, i = 𝑖1, 𝑖2, … , 𝑖𝐼 , 𝐼 = 𝐴𝑙𝑙 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑖𝑒𝑠
G
Electricity generation (MWh)
𝑋
𝑎,𝑟
𝐾𝑃, 𝐾𝑇 , 𝐾𝐷
𝑔, 𝑔
Electricity transmission from region a to region r (MWh)
Electricity production, transmission and distribution cost (€/MWh)
Maximum and minimum power generation level
𝑣𝑠
Water amount in reservoir at end of time period s (MWh)
𝜔𝑠
Water inflow in time period s (MWh)
𝑖𝐻𝑌
Reservoir hydro power generation units
𝑖𝐼𝑀
Intermittent renewable power generation units
𝑣, 𝑣
Maximum and minimum level of hydro reservoir (MWh)
𝛾
Potential for demand shifting (percentage)
17

Electricity market impacts of increased demand flexibility enabled by

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