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Basics of energy modelling III: Time representation and considerations Basics of energy modelling III: Time representation and considerations

Basics of energy modelling III: Time representation and considerations - PowerPoint Presentation

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Basics of energy modelling III: Time representation and considerations - PPT Presentation

CLEWS Summer School ICTP Trieste Italy 13 June 2017 Time representation Longterm energy system models Span a large time horizon eg 2015 2050 Consider a large set of technologies ID: 757285

slice time hours season time slice season hours exercise definition demand slices annual months night day year 1667 model

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Slide1

Basics of energy modelling III: Time representation and considerations

CLEWS Summer School,

ICTP (Trieste), Italy,

13 June, 2017Slide2

Time representation

Long-term energy system models:

Span a large time horizon (e.g. 2015 – 2050)

Consider a large set of technologies

Span a large geographical region (e.g. USA, Africa, Europe, China)

Need to capture temporal variations:

Variability of supply (e.g. wind, solar, hydro)

Variability of demand (e.g. seasonal, monthly, weekly, daily)

Peak load requirements

Trade-off between level of detail and computational costSlide3

What are Time Slices?

Each year divided into representative time periods known as Time Slices:

(Summer-Winter-Intermediate) x (Day-Night) = 6 Time Slices

(Jan-Feb-Mar-…-Dec) x (Weekday-Weekend) x (Day-Night) = 48 Time Slices

(365 days) x (24 hours) = 8760 Time Slices

Number of Time Slices in OSeMOSYS is practically unlimitedTime Slice definition is consistently applied for the entire model

0

8760

Electricity

demand

Water resourcesSlide4

Example of Time Slice definition

2017

2050

Model horizon

Source: R

.

Loulou

, U.

Remne

, A.

Kanudia

, A.

Lehtila

, and G. Goldstein. Documentation for the TIMES model: Part

I

. ETSAP, April

2005

Annual

Seasonal

Weekly

DailySlide5

Time Slice-related parameters

Year Split

: Duration of each Time Slice as a fraction

of a

year

Capacity Factor: Fraction of a technology’s capacity that can be utilized in each Time SliceSpecified Annual Demand*: Fraction of annual demand for a commodity in a each Time Slice*Accumulated Annual Demand: Total annual demand for a commodity. Can be balanced in any Time Slice.Slide6

Time Slice Definition (

Example

of

Bolivia)Slide7

Time Slice Definition

(

Example

cont

.)Year divided in four Seasons

Season 1:

1

st January to 30th

AprilSeason 2: 1

st May to 31st AugustSeason 3: 1st September to 30th NovemberSeason 4: 1

st December to 31st DecemberSlide8

Time Slice Definition (

Example

cont

.

)Season 1

Season 2Slide9

Time Slice Definition (

Example

cont

.)

Season 3

Season 4Slide10

Time Slice Definition:

Exercise

1

01:00-07:00

07:00-18:00

18:00-22:00

22:00-01:00

Seasons

6 hours

11 hours

4 hours3 hoursSeason 1 (Jan-Apr)

????Season 2 (May-Aug)

?

?

?

?Season 3 (Sep-Nov)

?

??

?Season 4 (Dec)

??

??Slide11

Time Slice Definition:

Exercise

1

Time (duration)

01:00-07:00

07:00-18:00

18:00-22:00

22:00-01:00

6 hours

11 hours

4 hours

3 hoursTotal = 24 hoursSeason 1 (Jan-Apr)120 days0.082190.15068

0.05479

0.04110

Season 2 (May-Aug)

123 days0.08425

0.154450.056160.04212

Season 3 (Sep-Nov)91 days

0.062330.11427

0.041550.03116

Season 4 (Dec)31 days

0.021230.038930.01416

0.01062

Total = 365 days

Total = 8760 hoursSlide12

Exercise 2: Earlier example, with TimeSlices

YearSplit

Day

Night

6:00-18:00

18:00-6:00

12 hours

12 hours

Summer

4 months

0.1667

0.1667Winter4 months0.16670.1667Intermediate

4 months

0.1667

0.1667

SpecifiedDemandProfile

Day

Night

7:00-19:0019:00-7:00

12 hours

12 hoursSummer 4 months

0.150.05

Winter4 months0.5

0.1Intermediate4 months

0.150.05

NOTE: ‘SpecifiedAnnualDemand’ instead of ‘AccumulatedAnnualDemand’Slide13

Exercise 2: Earlier example, with TimeSlices Slide14

Exercise 2: YearSplitSlide15

Exercise 2: SpecifiedAnnualDemandSlide16

Exercise 2: SpecifiedDemandProfileSlide17

Exercise 2: DemandSlide18

Exercise 2: ResultsSlide19

Exercise 2: Results