Short-term uncertainty in long-term energy models

1. v
v
Short-term uncertainty in
long-term energy models
71TH SEMI-ANNUAL ETSAP MEETING
Maryland, USA 10.07.2017
Pernille Seljom (pernille.seljom@ife.no) &
Asgeir Tomasgard (asgeir.tomasgard@ntnu.no)

2. v
v
PhD thesis:
Stochastic modelling of short-term uncertainty in long-term energy
models - Applied to TIMES models of Scandinavia
• Seljom, P., Tomasgard, A., 2015. Short-term uncertainty in long-term energy system
models — A case study of wind power in Denmark. Energy Economics 49, 157-167.
• Seljom, P., Tomasgard, A., 2017. The impact of policy actions and future energy prices
on the cost-optimal development of the energy system in Norway and Sweden. Energy
Policy 106, 85-102.
• Seljom, P., Lindberg, K.B., Tomasgard, A., Doorman, G., Sartori, I., 2017. The impact of
Zero Energy Buildings on the Scandinavian energy system. Energy 118, 284-296.
• Seljom, P., Tomasgard, A. Sample Average Approximation and stability tests applied to
energy system design. Submitted to an international peer-reviewed journal.
10.07.2017
Background
2

3. v
v
• The thesis uses Stochastic Programming to consider short-term
uncertainty in TIMES models
• Methodology is applicable to long-term energy models
• Endogenous investments
• Energy system and electricity models
• First time this methodology is used in
• energy system models
• Scandinavian models
• Denmark, Norway & Sweden
10.07.2017
Background
3

4. v
v
• The future climate is uncertain
• Temperature, solar irradiation, wind speed & precipitation
• Short-term & long-term uncertainty
• Short-term uncertainty = periodically recurring
• Short-term climate uncertainty →
• Uncertain renewable electricity generation & building heat demand
• A higher share of renewables in the electricity generation mix →
• More short-term uncertainty in long-term energy models
• To value flexibility in energy models it is important to consider of short-term
uncertainty
10.07.2017
Motivation
4

5. v
v
Motivation
Hourly wind power availability in Denmark West
Availability = hourly generation/ capacity
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 20 40 60 80 100 120 140 160
Hourlywindavailability,
week1DK-W
2008 2009 2010 2011
10.07.2017 5

6. v
v
Motivation
Hourly heat demand for non-residential buildings in the spot price area
of Oslo in winter
10.07.2017
0
100
200
300
400
0 2 4 6 8 10 12 14 16 18 20 22 24
WinterheatdemandNO12050,GWh
Hour
6

7. v
v
• Methods to consider short-term uncertainty in long-term energy
models
1. Ignore the uncertainty and use the expected value
2. Add constraints to ensure investments in flexible technologies
3. Link with other models
4. Run the model with different outcomes of the uncertainty
5. Use Stochastic Programming
• A mathematical framework to consider uncertainty and to value flexibility in
optimisation models
10.07.2017
Methodology
7

8. v
v
• Two-stage stochastic model
• S scenarios = possible outcomes of the short-term uncertainty
• Uncertain parameters ds with a probability of ps to occur
• First stage decisions, x, investments in new capacity
• Second stage decisions, ys, operation of the energy sector
10.07.2017
Methodology
min
.
( ) 0
(x, ) 0
s s s
s S
s
v c x p d y
s t
f x
g y s S


 
  
 

 

8

9. v
v
• The investments
• consider the outcomes of short-term uncertainty
• are feasible for all scenarios
• minimise the expected cost
10.07.2017
Methodology
2010 2050
2010 2050
Stage 1
Stage 2
Investment decisions
Operational decisions
9

10. v
v
• The computational effort increases with number of scenarios
• In most cases, the true distribution of the short-term uncertainty
cannot be used
• Using a subset of the true distribution gives an estimate of the optimal
value and solution
• Poor scenarios can give inadequate model results and misleading
model insights
• It is important to evaluate the quality of the stochastic model solution!
10.07.2017
Methodology
10

11. v
v
• A solution evaluation requires
• scenarios
• an optimised model
• optimal value and solution
• Satisfactory solution criteria →
Model analysis
• Unsatisfactory solution criteria →
new scenarios & model run
• New random scenarios
• Different scenario generation method
• Higher number of scenarios
10.07.2017
Methodology Data
Scenario
generation
Optimisation
Solution
evaluation
Value, v
Solution, x
Scenarios, N
Model analysis
No
Yes
Satisfactory
solution criteria
11

12. v
v
• Scenario generation methods
• Random sampling
• Statistical methods: Moment matching & Distance measures
• It is more important with a good quality solution than to use scenarios
that accurately replicate the uncertainty
• Solution evaluation methods
• General: In-sample and out-of-sample stability tests
• Linked to scenario generation method
• Sample Average Approximation (SAA) - Random Sampling
• Optimality gap of clustered scenarios - Distance measures
10.07.2017
Methodology
12

13. v
v
Scenario generation
• Random sampling
• Moment matching
Solution evaluation
• Stability tests
• SAA
• Used short-term uncertainty
• Wind power production
• Hydropower production
• PV production
• Building heat demand
• Electricity trade prices
10.07.2017 13
Methodology
Random sampling
Stability
tests
Scenario generation Solution evaluation
Moment matching
Distance measures
SAA
Optimality
gap
Grey - thesis

14. v
v
• Examples from the thesis publications, Paper I - Paper IV
• The papers use different model assumptions and various uncertain
parameters
• Focus on the difference between stochastic and deterministic model
results & solution quality
• Deterministic model
• One scenario
• Assume all input parameters are certain
• Traditional energy system approach
10.07.2017
Results
14

15. v
v
• Electricity generation capacity in Denmark - 2050
• Uncertain wind power production
→ 43 % lower wind power capacity with a stochastic approach
10.07.2017
Result – Paper I
15

16. v
v
• Difference in heat capacity (deterministic – stochastic) in buildings in Norway
and Sweden - 2030
• Uncertain hydro production, wind production & electricity trade prices
→ NNU: 24 % higher low-cost electricity capacity with a stochastic approach
10.07.2017
Result – Paper II
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
REF NNU HYD LTD
Heatcapacity,GW
Gas
Heat pump
Electricity
Biomass
16
Policy assumptions:
REF, NNU, HYD, LTD

17. v
v
• Difference in Scandinavian heat capacity in buildings
• Uncertain hydro production, wind production, electricity trade prices, PV production &
heat demand
→ REF 2050: 71 % higher low-cost electricity capacity with a stochastic approach
10.07.2017
Result – Paper III
-10
-8
-6
-4
-2
0
2
REF PBU SUN ZEB ZEB* REF PBU SUN ZEB ZEB*
2030 2050
Deterministic-Stochasticheat
capacity,GW
Bio Gas Heat Pump Electricity
17
Model instances:
REF, PBU, SUN, ZEB, ZEB*

18. v
v
• Difference in Scandinavian electricity generation capacity
• Uncertain hydro production, wind production, electricity trade prices, PV production & heat
demand
→ ZEB* 2030: 24 % lower wind power capacity with a stochastic approach
10.07.2017
Result – Paper III
-1
0
1
2
3
4
REF PBU SUN ZEB ZEB* REF PBU SUN ZEB ZEB*
2030 2050
Deterministic-Stochastic
electricitycapacity,GW
Wind Non-flexible hydro CHP
18
Model instances:
REF, PBU, SUN, ZEB, ZEB*

19. v
v
• Wind capacity in Denmark – 3 random samples scenarios
• Uncertain wind power production
→ Few scenarios can give poor solutions!
10.07.2017
Result – Paper IV
0
2,000
4,000
6,000
8,000
10,000
12,000
2020 2025 2030 2035 2040 2045 2050
Windcapacity,MW
Det
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
19
M = candidate samples

20. v
v
• Wind capacity in Denmark – 60 random samples scenarios
• Uncertain wind power production
• → SAA identify M5 to be the best solution
10.07.2017
Result – Paper IV
20
M = candidate samples
0
2,000
4,000
6,000
8,000
10,000
12,000
2020 2025 2030 2035 2040 2045 2050
Windcapacity,MW
Det
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10

21. v
v
• Stochastic Programming is a suitable tool to explicitly consider short-
term uncertainty in long-term energy models
• It is important to use a scenario representation that gives a good
quality of the stochastic solution
• For our analyses, a stochastic approach lowers investments in
intermittent electricity generation & increases investments in low-cost
electricity heating compared to a deterministic approach
• We recommend using a stochastic representation of short-term
uncertainty in long-term energy models for more solid model insights
10.07.2017
Conclusions
21

22. v
v
Thank you for the attention
pernille.seljom@ife.no
10.07.2017 22