A simple approach to time series reduction Application in ... · Clemens Gerbaulet - 4 - Vienna,...

- 1 -Clemens Gerbaulet Vienna, 07. September 2017

SET-Nav Modelling Workshop in Vienna, Austria

Aggregating load profiles from power sector models towards use in large-

scale energy-system and integrated assessment models

07.09.2017

A simple approach to time series reduction

Application in the model dynELMOD

Clemens Gerbaulet


Determining cost-effective pathways in the electricity sector

dynELMOD:

Linear program to determine cost-effective development pathways in the European electricity sector

Model: 33 European countries31 conventional or renewable generation and storage technologies9 investment periods, five-year steps 2020 – 2050 Good storage representation (including reservoirs, DSM)Approximation of loop-flows in the HVAC electricity gridCCTS and CO2 storage constraints

1. Investment Investment into Conventional and renewable generation,

cross-border capacities Reduced time series used

2. Dispatch Investment result from step 1 fixed Time series with 8760 hours (validate result adequacy)

Outputs

➢ Investment into generation capacities, storage,

transmission capacities

➢ Generation and storage dispatch

➢ Emissions by fuel

➢ Flows, imports, exports

Boundary conditions

Investment

Full Dispatch

Time series calculation

PTDF calculation

Assumptions


Time-series reduction in dynELMOD

Time-series reduction is not a new topic

Approaches:

• k-means (Green et al. 2014, Munoz et al. 2016)

• Hierarchical clustering (Nahmmacher et al. 2016),

Després et al. 2017)

• MILP Optimization (van der Weijde and Hobbs 2012,

de Sisternes and Webster 2013, Poncelet et al. 2015)

• Load duration curve approximations (Ueckerdt et al.

2015) (Used in an integrated assessment model)

Requirements for dynELMOD:

• daily variation structure (wind, pv, load);

• seasonal structure (RoR, reservoirs);

• minimum and maximum values to capture full

amplitude of situations;

• Times series average, for renewables the estimated

full load hours;

• “smoothness” or hourly rate of change characteristic

(otherwise need for flexibility could be under- or

overestimated)

• continuous time series input into the model

• Fast and scalable, for testing on my on laptop


Time-series reduction steps at a glance

dynELMOD approach for time-series reduction

Step 1: Hour selection

• use every 25th (or 49th etc.) hour of the full time series, starting at 7th (or

any other) hour 351 hours

• In GAMS: Use a subset of the full time set and select only the required

time slices instant hour selection for all time-related data

• additionally include 24 consecutive hours with lowest renewable infeed

Step 2: Time series smoothing

• moving average window, size determined by hand

• The goal in trying to determine the window size is to keep the time-

dependent characteristic in place and meeting the time series’ variation

target

• In calculation with 8,760 hours no smoothing, except for data with a

monthly resolution no more “jumps”

Step 3: Time Series scaling

DNLP, optimizing Full-load hours (target), minimum value (mintarget),

maximum value (maxtarget)

All steps are directly implemented into the model code, and run at

every model run. This process takes only a few minutes.

Scaling equations


Timeseries German Load 2013 (normalized)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

12

59

51

77

75

10

33

12

91

15

49

18

07

20

65

23

23

25

81

28

39

30

97

33

55

36

13

38

71

41

29

43

87

46

45

49

03

51

61

54

19

56

77

59

35

61

93

64

51

67

09

69

67

72

25

74

83

77

41

79

99

82

57

85

15

Original timeseries

Original timeseries


Extract every 25th entry

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

12

59

51

77

75

10

33

12

91

15

49

18

07

20

65

23

23

25

81

28

39

30

97

33

55

36

13

38

71

41

29

43

87

46

45

49

03

51

61

54

19

56

77

59

35

61

93

64

51

67

09

69

67

72

25

74

83

77

41

79

99

82

57

85

15

Shortened Timeseries

Shortened Timeseries


Short comparison after shortening

Comparison of properties of the original and shortened timeseries

0

0,1

0,2

0,3

0,4

0,5

0,6

1

36

6

73

1

10

96

14

61

18

26

21

91

25

56

29

21

32

86

36

51

40

16

43

81

47

46

51

11

54

76

58

41

62

06

65

71

69

36

73

01

76

66

80

31

83

96

Rates of change

Original Shortened

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1

36

6

73

1

10

96

14

61

18

26

21

91

25

56

29

21

32

86

36

51

40

16

43

81

47

46

51

11

54

76

58

41

62

06

65

71

69

36

73

01

76

66

80

31

83

96

Duration Curves

Original Shortened

Initial impression

➢ Rate of change is too high

➢ Duration curve looks good

Next step

➢ Smooth the shortened timeseries using a simple moving average function. Windows size is picked by hand in trial and error.


Resulting smoothed timeseries

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

12

59

51

77

75

10

33

12

91

15

49

18

07

20

65

23

23

25

81

28

39

30

97

33

55

36

13

38

71

41

29

43

87

46

45

49

03

51

61

54

19

56

77

59

35

61

93

64

51

67

09

69

67

72

25

74

83

77

41

79

99

82

57

85

15

Smoothed timeseries

Smoothed timeseries


Short comparison after smoothing

Comparison of properties of the original, shortened and smoothed timeseries

Initial impression

➢ Rate of change is closer to original

➢ Duration curve is slightly too high

Next step

➢ Scale the timeseries to fit minimum, maximum, and sum of all values

0

0,1

0,2

0,3

0,4

0,5

0,6

1

62

7

12

53

18

79

25

05

31

31

37

57

43

83

50

09

56

35

62

61

68

87

75

13

81

39

Rates of change

Original Shortened Smoothed

0

0,5

1

1,5

2

1

58

5

11

69

17

53

23

37

29

21

35

05

40

89

46

73

52

57

58

41

64

25

70

09

75

93

81

77

Duration Curves

Original Shortened Smoothed


Timeseries scaling procedure

Goal:

optimization to meet Full-load hours (target), minimum value (mintarget), maximum value

(maxtarget)

Stst is the time series value in time t

1. Normalize stst to values between 0 and 1

2. First solve step: Find B and C, so that minimum and maximum are met

3. Second solve step: Find A to meet the timeseries‘ target sum

Normalization

First solve

step to find B

and C

Second solve

to find A


Resulting scaled and smoothed timeseries

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

12

59

51

77

75

10

33

12

91

15

49

18

07

20

65

23

23

25

81

28

39

30

97

33

55

36

13

38

71

41

29

43

87

46

45

49

03

51

61

54

19

56

77

59

35

61

93

64

51

67

09

69

67

72

25

74

83

77

41

79

99

82

57

85

15

Scaled and Smoothed timeseries

Scaled and Smoothed timeseries


Short comparison after scaling

Comparison of properties of the original, shortened and smoothed timeseries

Initial impression

➢ Rate of change is closer to original

➢ Duration curve fits

0

0,1

0,2

0,3

0,4

0,5

0,6

1

58

5

11

69

17

53

23

37

29

21

35

05

40

89

46

73

52

57

58

41

64

25

70

09

75

93

81

77

Rates of change

Original Shortened

Smoothed Scaled and Smoothed

0

0,5

1

1,5

2

15

49

10

97

16

45

21

93

27

41

32

89

38

37

43

85

49

33

54

81

60

29

65

77

71

25

76

73

82

21

Duration Curves

Original Shortened Smoothed Scaled and Smoothed


Timeseries after scaling

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

12

59

51

77

75

10

33

12

91

15

49

18

07

20

65

23

23

25

81

28

39

30

97

33

55

36

13

38

71

41

29

43

87

46

45

49

03

51

61

54

19

56

77

59

35

61

93

64

51

67

09

69

67

72

25

74

83

77

41

79

99

82

57

85

15

Original timeseries Scaled and Smoothed timeseries


Full time-series Germany 2013


Reduced time-series Germany 2013


Time-series rate of change

Solar PV Wind onshore


Original and processed load and infeed duration curves

Solar PV Wind onshore


Caveats

What we found during development

Problem SolutionAdd periods of low renewable availability

➢ Periods of low availability are added to the time

series after shortening

➢ Length of the period is chosen by hand (24

additional hours for calculations with 2 week

reduced time series)

➢ Location within the year is chosen automatically in a

preparatory step

Overestimation of renewables‘ firm capacity

➢ Times of low wind or low solar availability are not

long enough

➢ Investments into capacities and storages are too

low

➢ This leads to an inadequate electricity supply during

the runs with 8760 hours

Artificially decrease storage capacities

➢ The capacities of storages is reduced by a factor

➢ It depends on the number of model hours T/8760

Overestimation of storage capacities

➢ E.g. reservoir capacities are optimized over the

course of an entire year

➢ Time span on the model is too short to use their full

capacity

➢ Storage capacity is de facto infinite

➢ Storage investments are too low

➢ This also causes an inadequate electricity system


Testing the time series reduction procedure

Testing

• Comparison with full timeseries – is the investment outcome similar?

• Full model application not feasible with full time series

• Compare results for one country and one investment time step

• Differences relatively small, except for biomass: In calculation with full time-series, the model investments into biomass was 10% higher than with the reduced

timeseries – reasons are unclear

• Robustness of results – are the same effects visible across differing settings?

• Different reduction lengths, 25th, 49th, 73rd, 97th hour

• Multiple start hours for shortening step (covering all 24 options)

• Comparing calculation results for many scenarios shows robust results, when every 25th hour is used e.g. a time-series of two weeks

• With shorter time-series the results are less robust, especially regarding investments in neighboring countries

What about spatial correlation?

• Shortening process is identical for all countries, same hours are selected

• Separate smoothing for each country conducted

• Only short-term variation affected by smoothing

➢ Weekly, seasonal variation still kept intact

➢ Weekends?


Installed Capacity in Europe 2020 – 2050

Installed Capacity in Europe 2020 – 2050

0

500

1.000

1.500

2.000

2.500

3.000

2020 2025 2030 2035 2040 2045 2050

Ins

tall

ed

Cap

ac

ity i

n G

W

Storage

Other

Sun

Wind Offshore

Wind Onshore

Hydro

Biomass

Waste

Oil

Gas

Hard Coal

Lignite

Uranium


CO2 emissions in Europe 2020 – 2050

CO2 emissions in Europe 2020 – 2050

0

200

400

600

800

1.000

1.200

2020 2025 2030 2035 2040 2045 2050

CO

2E

mis

sio

ns

im

Mt

Biomass

Oil

Gas

Hard Coal

Lignite

Uranium


Electricity Generation in Europe 2020 – 2050

Electricity Generation in Europe 2020 – 2050

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

2020 2025 2030 2035 2040 2045 2050

Default scenario

Ele

ctr

icit

y G

en

era

tio

n in

TW

h

Other

Sun

Wind Offshore

Wind Onshore

Hydro

Biomass

Waste

Oil

Gas

Hard Coal

Lignite

Uranium


Dispatch Germany Summer 2050


Conclusion

The time-series reduction is usable

• Time-series reduction approach works for the

application in dynELMOD

• Fast, fairly robust results for application with every 25th

hour, less robust for shorter time-series

• Hand-picked smoothing parameter visual review

with Excel tool

• Hand-picked duration of low renewable infeed (but

automatic inclusion in model)

Requirements for dynELMOD:

• daily variation structure (wind, pv, load)

• seasonal structure (RoR, reservoirs)

• minimum and maximum values

• Times series average, for renewables full load hours

• “smoothness” or hourly rate of change characteristic

• continuous time series input into the model

• Fast and scalable, for testing on the laptop


Sources in the Presentation

Green, R., I. Staffell, and N. Vasilakos (2014). Divide and Conquer? k-Means Clustering of Demand Data Allows Rapid and Accurate Simulations of the British Electricity System. IEEE Transactions on Engineering Management 61(2), 251–260.

Munoz, F. D., B. F. Hobbs, and J. P. Watson (2016). New bounding and decomposition approaches forMILP investment problems: Multi-area transmission and generation planning under policyconstraints. European Journal of Operational Research 248(3), 888–898.

Nahmmacher, P., E. Schmid, and B. Knopf (2014). Documentation of LIMES-EU - A long-term electricitysystem model for Europe. url: https://www.pik-potsdam.de/members/paulnah/limes-eu-documentation-2014.pdf (visited on May 22, 2016).

Poncelet, K., H. Höschle, E. Delarue, and W. D’haeseleer (2015). Selecting representative days forinvestment planning models. TME Working Paper - Energy and Environment EN2015-10. url: https://www.mech.kuleuven.be/en/tme/research/energy_environment/Pdf/wpen201510.pdf (visited on April 18, 2016).

Ueckerdt, F., R. Brecha, G. Luderer, P. Sullivan, E. Schmid, N. Bauer, D. Böttger, and R. Pietzcker (2015). Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves. Energy 90, 1799–1814.

de Sisternes, F. J. and M. D. Webster (2013). Optimal selection of sample weeks for approximating the netload in generation planning problems. Massachusetts Institute of Technology Engineering Systems Division Working Paper Series ESD-WP-2013-03. Cambridge, MA, USA, 1–12. url: http://esd.mit.edu/WPS/2013/esd-wp-2013-03.pdf (visited on April 18, 2016).

van der Weijde, A. H. and B. F. Hobbs (2012). The economics of planning electricity transmission toaccommodate renewables: Using two-stage optimisation to evaluate flexibility and the cost ofdisregarding uncertainty. Energy Economics 34(6), 2089–2101.


Thanks!

Contact

Dr. Clemens Gerbaulet

[email protected]

+49 30-314-25377

A simple approach to time series reduction Application in ... · Clemens Gerbaulet - 4 - Vienna,...

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