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© OECD/IEA 2013

Energy Analysis and Modelling: Power Sector Modelling with TIMES

Energy Training Week 2014

8-9 April, 2014

Uwe Remme, Luis Munuera

© OECD/IEA 2013

Module overview

8 April: Power Sector Modelling with TIMES (1)

Introduction to energy systems modelling and TIMES

Getting started in running a power sector model in TIMES (using

ANSWER-TIMES and a simplified Excel interface)

9 April: Power Sector Modelling with TIMES (2)

Refining the power sector analysis:

Treatment of load curves

Modelling storage technologies

© OECD/IEA 2013

Introduction to Energy Systems Modelling and

TIMES


8-9 April, 2014


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Interdependencies in the energy system

Global energy flows in 2009

© OECD/IEA 2013

Electricity generation mixes in 2011 in participants‘ countries

44%

1%14%

23%

3%6%

9%

Coal Oil Natural gas Nuclear Hydro Wind Other renewables

5%3%

81%

6%

3%

90%

4% 2%

12%

10%

15%59%

79%

2%2% 15%

100%

8%

24%

68%

44%

23%

20%

7%5%

59%

7%

33%

81%

9%

9%

100%

12%

16%

53%

3%

12%

3%

47%

26%

16%

8%

3%

1%

98%

16%

63%

21% 16%

3%

49%

16%

16%

94%

5% 9%

50%

40%

13%

87%

99%

1%

44%

1%14%

23%

3%6%

9%

Coal Oil Natural gas Nuclear Hydro Wind Other renewables21%

5%

44%

30%

Can you spot your country?

A B C D E F

G H I J K L

M N O P Q R

S T

© OECD/IEA 2013

Electricity generation mixes in 2011 in participants‘ countries

44%

1%14%

23%

3%6%

9%

Coal Oil Natural gas Nuclear Hydro Wind Other renewables

5%3%

81%

6%

3%

90%

4% 2%

12%

10%

15%59%

79%

2%2% 15%

100%

8%

24%

68%

44%

23%

20%

7%5%

59%

7%

33%

81%

9%

9%

100%

12%

16%

53%

3%

12%

3%

47%

26%

16%

8%

3%

1%

98%

16%

63%

21% 16%

3%

49%

16%

16%

94%

5% 9%

50%

40%

13%

87%

99%

1%

Current situation, future challenges and opportunities are quite diverse around the world.

Brazil Cambodia Canada China Gambia Ghana

Indonesia Israel Kazakhstan Laos Mexico Morocco

Namibia Nigeria Russia South Africa

Sri Lanka

Swaziland

Tunisia

44%

1%14%

23%

3%6%

9%

Coal Oil Natural gas Nuclear Hydro Wind Other renewables21%

5%

44%

30%

Vietnam

© OECD/IEA 2013

Why energy planning?

Integrated analysis is key for energy sustainable development

(including linkages to land and water use)

Economic way of covering energy needs

Efficient use of domestic resources

Enhancing energy security

Access to energy

Reducing environmental impacts

Essential for sound decision-making and policy design

Developing long-term strategies (avoiding stop-gap policies)

Exploring and testing policy options

Identifying the investment and financing requirements

© OECD/IEA 2013

Why energy modelling?

Technology/Energy carrier level

Long-term nature of planning decisions (e.g. lifetime of power plants)

Future development of technologies

Comparative assessment of technologies

System level

Infrastructure for energy (e.g. T&D for electricity)

Interdependencies of technologies and sectors (e.g. electric heat pump or EVs with

power sector)

Integration of variable renewables

Stakeholder level

Tool to develop strategies involving wide range of actors in energy sector (from

households, industry to government)

Communication

Policy level

How to reach policy goals

Effectiveness and impacts of individual policy measures

Energy system does not allow for real-world experiments

Scenarios: Exploring the future

Models: Developing consistent scenarios

© OECD/IEA 2013

Type of energy models

Energy system models

Energy models

Economic models

• Focus on the energy system alone

• Description of large number of

single energy technologies

• Capturing dynamics of the system,

such as substitution of energy

carriers, energy savings or

technology deployment

• Typical results: energy

consumption, energy technology

mix and capacities, emissions

• Relationship of the energy sector to

the general economy central

element

• Energy system described in a

highly aggregated way

• Technologies embedded in

production functions

• Typical results: energy

consumption, emissions, GDP,

employment, trade balance

© OECD/IEA 2013

Optimisation

Simulation

Top-down Bottom-up

E3MG

Econometric models

POLES

MARKAL/TIMES

MESSAGE

PRIMES

Technology-rich

least-cost models

OSeMOSYS

DEEP

CGE models

OECD ENV-Linkages

LEAP

Technology-rich

simulation models

MoMo

(Source: van Ruijven and van Vuuren, 2009)

Universe of energy models

© OECD/IEA 2013

Top down economic models

Economic models

Econometric models

…

…

…

…

…

…

from

secto

r

to sector Demand Gross production value

Primary

inputs

Gross

production

value

Input-Output-Table

Computational General

Equilibrium models

Producers Households

Maximise profits Maximise utility

Perfect markets

in equilibrium

(simulation)

Government

interventions

Demand for

goods Production

of goods

Factor inputs

(labor, energy)

Labor

supply

Markets for

goods Factor

marktes

Taxes

Taxes,

transfers

Agent

behaviour

(optimisation)

© OECD/IEA 2013

Energy system models: Technology-rich representation

Cost and emissions balance

GDP

Process energy

Heating area

Population

Light

Communication

Power

Passenger

kilometers

Freight

kilometers

Demand services

Coal processing

Refineries

Power plants

and

Transportation

CHP plants

and district

heat networks

Gas network

Industry

Commercial and

tertiary sector

Households

Transportation

Final energy Primary energy

Domestic

sources

Imports

En

erg

y p

rices,

Reso

urc

e a

vailab

ilit

y

Dem

an

ds

ElectricityCoal

Supercrititcal

coal plant (SCP)

CO2

Process

Commodities

FlowsFlows

2.2 kWh

1 kWh0.27 kgSKE

0.736 kg

Technology

© OECD/IEA 2013

Reference energy system

Gas heating

Oil heating

Elc. heating

Local heat grid

Gas water boiler

Elc. water boiler

Room heat boilers

Process heat boilers

Industrial boilers

LH2 car

Busses

Gasoline car

Gasoil car

Elc. heat pump

Warm water boilers

Trucks

Coal processing

Refinery

Gas processing

Coal cond. PP

Lignite cond. PP

Coal IGCC PP

Gas CC PP

Wind converter

Coal CHP

Coal CHP

Gas CC CHP

Biomass CC CHP

Electrolysis

Gasification Liquefication

Area PV,

Solarthermal,

Energy crops

Area Wind

Residual wood

Gas import

Gas resources

Coal import

Coal resources

Oil resources

Oil import

Lignite resources

Industry

Transport

Commercial

Residential

Elec sector

CHP sector

Primary

energy

supply

Conversion

sector

End-use

sectors

© OECD/IEA 2013

Technology representation

Electricity Coal

Supercrititcal

coal plant (SCP)

CO2

Process

Commodities

Flows Flows

Coal PC Supercrit. Unit 2005 2010 2020 2030

Size MWel 600 600 600 600

Construction time Years 3 3 3 3

Lifetime Years 35 35 35 35

Efficiency (LHV) % 46 47 48 50

Max. availability h/a 7500 7500 7500 7500

Spec. Investment

costs (overnight) €/kWel 1175 1175 1140 1140

Fixed O&M €/(kW a) 40.5 40.5 40.5 40.5

Var. O&M €/MWhel 2.6 2.6 2.6 2.6

2.2 kWh

1 kWh 0.27 kgSKE

0.736 kg

ELCSCPCOALSCPSPC FLOFLO ,,

2,,2,, COSCPCOALSCPCOCOALSCP FLOFLO

ELCSCPSCPELCSCP CAPACT ,,

ELCSCPSCP FLOACT ,

Efficiency eqn

Emission eqn

Activity definition

Utilization eqn

SPC Plant efficiency

O2SCP,COAL,Cε CO2 Emission factor

SCP Annual availability

Input parameter

© OECD/IEA 2013

Horizontal dimension of the RES: technology chains

Supercritical

coal plant

Coal

transport

Grid HV &

Transf.

Lighting

Urban trains

Industry

Domestic

mining

Coal

imports Grid MV &

Transf.

ELCSCPCOLSCPSPC FLOFLO ,,

2,,, COSCPCOLSCPCOLSCP FLOFLO


ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,




ELCSCPSCP FLOACT ,

Appliances

ELCDEMELCSCP FLOFLO ,, ELCDEMELCSCP FLOFLO ,, ELCDEMELCSCP FLOFLO ,, ELCDEMELCSCP FLOFLO ,, ELCDEMELCSCP FLOFLO ,,

Backward Loops

© OECD/IEA 2013

Vertical dimension of the RES: competition

Supercritical

coal plant (exst) Coal

transport

Grid HV &

Transf.

Lighting

Urban trains

Industry

Domestic

mining

Coal

imports

Grid MV &

Transf.

Appliances

Ultrasupercritical

coal plant (new)

Natural gas

GT (exist)

Natural gas

CC (new)

Supercritical

lignite plant (exst)

Supercritical

lignite plant (new)

Competing options to produce

electricity:

• between technologies

• between old and new plants

Influenced by:

• Technology costs

• Efficiencies

• Emission factors

• Fuel prices…

© OECD/IEA 2013

System aspects: Limited resources

Gas heating

Biomass heating

Elc. heating

Local heat grid

Gas water boiler

Elc. water boiler

Room heat boilers


Industrial boilers

LH2 car

Busses

Gasoline car

Gasoil car

Elc. heat pump

Warm water boilers

Trucks

Coal processing

Refinery

Gas processing

Coal cond. PP

Lignite cond. PP

Biomass IGCC PP

Gas CC PP

Wind converter

Coal CHP

Coal CHP

Gas CC CHP

Biomass CC CHP

Electrolysis


Area PV,

Solarthermal,

Energy crops

Area Wind

Residual wood

Gas import

Gas resources

Coal import

Coal resources

Oil resources

Oil import

Lignite resources

Industry

Transport

Commercial

Residential

Elec sector

CHP sector

Primary

energy

supply

Conversion

sector

End-use

sectors

© OECD/IEA 2013

System aspects: substitution

Gas heating

Oil heating

Elc. heating

Local heat grid

Gas water boiler

Elc. water boiler

Room heat boilers


Industrial boilers

LH2 car

Busses

Gasoline car

Gasoil car

Elc. heat pump

Warm water boilers

Trucks

Coal processing

Refinery

Gas processing

Coal cond. PP

Lignite cond. PP

Coal IGCC PP

Gas CC PP

Wind converter

Coal CHP

Coal CHP

Gas CC CHP

Biomass CC CHP

Electrolysis


Area PV,

Solarthermal,

Energy crops

Area Wind

Residual wood

Gas import

Gas resources

Coal import

Coal resources

Oil resources

Oil import

Lignite resources

Industry

Transport

Commercial

Residential

Elec sector

CHP sector

Primary

energy

supply

Conversion

sector

End-use

sectors

Substitution

options

© OECD/IEA 2013

Gas heating

Oil heating

Elc. heating

Local heat grid

Gas water boiler

Elc. water boiler

Room heat boilers


Industrial boilers

LH2 car

Busses

Gasoline car

Gasoil car

Elc. heat pump

Warm water boilers

Trucks

Coal processing

Refinery

Gas processing

Coal cond. PP

Lignite cond. PP

Coal IGCC PP

Gas CC PP

Wind converter

Coal CHP

Coal CHP

Gas CC CHP

Biomass CC CHP

Electrolysis


Area PV,

Solarthermal,

Energy crops

Area Wind

Residual wood

Gas import

Gas resources

Coal import

Coal resources

Oil resources

Oil import

Lignite resources

Industry

Transport

Commercial

Residential

Elec sector

CHP sector

Primary

energy

supply

Conversion

sector

End-use

sectors

Partial equilibrium

(Source: GianCarlo Tosato)

© OECD/IEA 2013

Interdependencies

Gas heating

Oil heating

Elc. heating

Local heat grid

Gas water boiler

Elc. water boiler

Room heat boilers


Industrial boilers

LH2 car

Busses

Gasoline car

Gasoil car

Elc. heat pump

Warm water boilers

Trucks

Coal processing

Refinery

Gas processing

Coal cond. PP

Lignite cond. PP

Coal IGCC PP

Gas CC PP

Wind converter

Coal CHP

Coal CHP

Gas CC CHP

Biomass CC CHP

Electrolysis


Area PV,

Solarthermal,

Energy crops

Area Wind

Residual wood

Gas import

Gas resources

Coal import

Coal resources

Oil resources

Oil import

Lignite resources

Industry

Transport

Commercial

Residential

Elec sector

CHP sector

Primary

energy

supply

Conversion

sector

End-use

sectors

At optimal solution:

• Equilibrium between electricity supply and demand

Changes in the system (e.g. introduction CO2 price)

yield new equilibrium e.g.:

1) Shift to low-carbon technologies

2) Increase in electricity price

3) Substitution and saving of electricity in the end-use sectors

© OECD/IEA 2013

Mathematical formulation

Minimizing discounted system costs

=

Sum of

• Import/Extraction costs,

• variable and fixed O&M costs,

• Investment costs,

• …

Transformation relationship

( e.g. efficiency relationship for power plant)

Energy and emission balances

Capacity-activity constraint

( e. g. available capacity limits elec gen of power plant)

Peaking constraint

( Ensuring reserve capacity at peak load)

Scenario specific constraints

( e. g. bound on CO2 emissions, quota for renewables)

Inter-temporal constraints on new capacity additions

( e.g. early investments needed for using technology on

larger scale in later periods)

Storage equations (e.g. pumped storage)

Cumulated constraints over time

( e. g. available fossil resources)

Objective

function:

Model equations:

Cost data

Efficiencies

Full load hours

Emission factors

Demands

…

Input data

Energy/Emission flows

Process activities

New capacities

Fundamental prices

Decision/Output

variables

Load curve equations

© OECD/IEA 2013

Energy system model

Constraints

Energy security

Existing policy Behaviour

Resources

Statistics

Expert knowledge

Reports

Calibration of energy & emissions

Government Statistics

International statistics

Scenarios

Reference

Policy

Technology database

Experts

Literature review

Stakeholder workshop

Other energy system models

Demand projections

Outputs from macro and demand models

Assumptions

Source: William Usher, 2010

Not only about modeling, but also data analysis

© OECD/IEA 2013

Energy Technology Data Source of ETSAP implementing agreement

Consistent data set for more than 60 energy supply and demand technologies

Free access on www.iea-etsap.org

http://www.iea-etsap.org/



© OECD/IEA 2013

Getting a free evaluation license for ETSAP tools

To test ETSAP’s modelling tools,

visit http://iea-etsap.org/web/AcquiringETSAP_Tools.asp

and get a free 60-day evaluation license.

http://iea-etsap.org/web/AcquiringETSAP_Tools.asp




© OECD/IEA 2013

Advanced Features/Variants

● Multi-regional

● Inter-temporal

● Elastic demands

● Endogeneous learning

● Discrete capacity expansion

● Early retirement of capacity

● Macroeconomic linkage

● Climate extension

● Stochastic programming

● Alternative objective functions

● Multi-criteria optimization

Methodology • Bottom-up Model

• Perfect competition

• Perfect foresight (or myopic)

• Optimization (LP/MIP/NLP)

Min/Max Objective function s.t. Equations, Constraints Decision Variables <=> Solution Input parameters

Development • By ETSAP (Energy Technology

Systems Analysis Program; www.iea-etsap.org)

• Implementation in GAMS

• Model generator

TIMES (The Integrated MARKAL

EFOM System)

Example of an energy system model: TIMES

© OECD/IEA 2013

Applications of MARKAL/TIMES around the world

Used by more than 150 institutions in 63 countries

Contracting parties

Model users

© OECD/IEA 2013

ETP-TIMES model for supply side supplemented with spreadsheet-based end-use sector models

Model horizon: 2009-2050 (2075) in 5 year periods

28-36 world regions/countries depending on sector

Primary

energy

Conversion

sectors Final energy End-use

sectors

End-use service

demands

Electricity

production

Fossil

Renewables

Nuclear

Refineries

Synfuel

plants

CHP and

heat plants

etc.

Electricity

Gasoline

Diesel

Natural

gas

Heat

etc.

Industry

Buildings

Transport

Material

demands

Heating

Cooling

Passenger

travel

Freight

etc.

ETP-TIMES model

MoMo model

Energy costs

Energy demand

ETP modelling framework

© OECD/IEA 2013

ETP model regions

© OECD/IEA 2013

Applications: Island of Réunion (TIMES)

(Source: Maïzi, N. et al., Flexibility and reliability in long-term planning

exercises dedicated to the electricity sector, XXI World Energy Congress,

Montreal, September 12-16, 2010)

© OECD/IEA 2013

Experience with energy models in your organisation?

Which energy models are used in your country or

region?

Discussion

© OECD/IEA 2013

Summary

Energy models provide a consistent analysis framework

Energy models are simplified representation of the real-world

system

Level of detail depends on questions to be addressed and available

data

Choice of model type depending on analysis question

Energy system models: focus on role of technologies and

interactions within the energy sector

Economic models: focus on interdependencies of the energy sector

with the remaining economy

Energy modelling is a long-term and continuous process,

involving experts from different disciplines

Limitations of individual model approaches important when

interpreting model results (no analysis is perfect)

Energy planning not about predicting the future, but supporting

decision making under inherent uncertainties

© OECD/IEA 2013

Getting Started with TIMES:

Running a Power Sector Investment Model


8-9 April, 2014


© OECD/IEA 2013

0

1000

2000

3000

4000

5000

6000

2011 2015 2020 2025 2030 2035 2040 2045 2050

GW

Geothermal

Wind

Solar

Biomass and waste

Nuclear

Oil

Natural gas

Coal

Hydro

Key challenges

• Aging power capacity has to be

replaced

• Investment characterized by

long lifetime and high upfront

capital demand

• Decisions have to take into

account long time horizon with

uncertain or unknown

conditions:

• Long-term energy prices?

• Economic development?

• Market conditions?

• Technology development?

• Climate policies?

• Operational aspects

(variable renewables,

electrification)?

Demand?

How to replace?

Decommissioning scenario for existing global

capacity

© OECD/IEA 2013

Typical structure of a power sector model

Wind turbine, onshore

Wind turbine, offshore

Hydro, run-of-river

Gas turbine

NGCC

Coal plant, supercritical

Coal plant, subcritical Coal supply

Gas supply

Wind potential, onshore

Wind potential, offshore

Hydro potential, run-of-river

Transmission and distribution

…

Industry demand

Transport demand

Residential demand

Commercial demand

Fuel supply Generation T&D Demand

Approach: Optimisation vs. simulation

Foresight: Perfect foresight vs. myopic

Horizon: Short-term (dispatch planning) vs. long-term (investment planning)

© OECD/IEA 2013

Least-cost optimisation

Objective function:

Minimising total discounted costs of power system including investment, operating and

fuel costs

Constraints

Coverage of given electricity demand

Technical characteristics of power technologies

Capacity constraints

Resource constraints (domestic, imports)

Renewable potentials

Decision variables

Dispatch of power plants

Operation of storage processes

Construction of new power plants

© OECD/IEA 2013

Basic model constraints based on fundamental principles

Energy conversion Transformation equation

Generation of a technology limited

by available capacity

Capacity-utilisation constraint

Energy conservation Commodity balance

TIMES equivalent Principle

Flow variables

Decision variables

New capacity variables

Generation variables

Objective function: Minimise system

costs Cost coefficients of

decision variables

© OECD/IEA 2013

Factors influencing power technology choice

Fuel input

Efficiency

Fuel prices Fuel availability

Emission factors

Emissions

Taxes Sectoral bounds

(annual or cumulative)

Full load hours

Total capacity Bounds

FOM costs

Electricity generation Var. OM costs

Inv. costs Tech. lifetime

Econ. lifetime

Bounds

New capacity Existing capacity

Model variables

Inputs parameters Load

curves

Capacity factor

Firm capacity during peak load

Reserve margin

© OECD/IEA 2013

Exercise: Reference energy system

ECOLSC0

EGASTUR

EGASCC0

EWINON0

Electricity

REGION1

DEM_PRC

Electricity demand

DEM_ELCTR IMPCOL0

IMPGAS0

RENWIN0

Coal USC plant

Gas turbine

Gas combined-cycle

Wind onshore

COL

GAS

WIN

ELECTR

IMPREG

RNWREG

Reference energy system (RES), i.e. network of commodities and technologies, used to describe real-world system

RES can be adapted to considered case study

RES contains existing and future technologies

NUC

IMPNUC0

ENUCLWR

Nuclear LWR

© OECD/IEA 2013

Model interface: ANSWER-TIMES

© OECD/IEA 2013

ANSWER-TIMES: Technology representation

Parameter TIMES handle Units Value

Efficiency ACT_EFF - 0.44 (2010) 0.50 (2050)

CO2 emissions factor

FLO_EMIS ktCO2/PJ 95

Maximum availability

NCAP_AFA - 0.85

Capital cost NCAP_COST Billion USD/GW (USD/kW)

2800

Fixed O&M costs NCAP_FOM Billion USD/GW (USD/kW)

56

Construction time NCAP_ILED Years 4

Technical lifetime NCAP_TLIFE Years 40

Existing capacity PRC_RESID GW 300

© OECD/IEA 2013

Hands-on exercises (TIMES_DEMO_V1.xlsm)

Question 1: Which gas price decline by 2050 is needed to see a switch from coal to gas in the new capacity additions in 2050?

Question 2: Introduce the CO2 constraint, what is the impact on the electricity mix?

Question 3: What are the cost reductions needed to make wind more cost effective than nuclear under a CO2 target?

© OECD/IEA 2013

Overview

Power sector analysis:

Off-model approaches for assessing cost-competitiveness of power

technologies: LCOE & Screening curves + load duration curves

Hands-on exercise:

Load curves and storage in TIMES

© OECD/IEA 2013

Levelised costs of electricity (LCOE)

0

50

100

150

200

250

300

350

USC Gas turbine

NGCC Onshore wind

PV,utility

PV,rooftop

CSP LWR

LCO

E (U

SD/M

Wh

)

Fuel costs

Capital costs

6.31000

8760

eff

fuelprice

af

fomidccrfinvLCOE

USD/MWh Availability factor

as fraction

Efficiency as

fraction

Fuel price

(USD/GJ)

Fixed operating and

maintenance costs

(USD/kW) Specific investments

costs (USD/kW)

11

1

n

n

i

iicrf

Capital recovery factor

Interest during

construction factor

© OECD/IEA 2013

Technology assumptions for LCOE

Technology Overnight

investment

costs

(USD/kW)

Fixed O&M

costs

(USD/kW))

Technical

lifetime

(a)

Construction

time

(a)

Load factor

(%)

Efficiency

(%)

Fuel costs

(USD/GJ)

USC coal 2300 46 35 4 85 41 2.1

Gas turbine 500 10 30 1 10 38 6.2

NGCC 1000 20 30 3 85 57 6.2

Onshore wind 1800 36 25 1 26 100 0

PV,

utility 4000 40 25 0 19 100 0

PV,

rooftop 4900 49 25 0 17 100 0

CSP 6500 65 30 2 45 40 0

LWR 4600 115 50 5 90 36 0.7

© OECD/IEA 2013

Screening curves - Concept

To

tal a

nn

ua

l p

ow

er

pla

nt

co

sts

(U

SD

/MW

)

Hours of operation during the year

Fi

teff

fuelpricefomidccrfinv 6.3costplant annual Total

Annual fixed costs Fi Variabe costs Vi

© OECD/IEA 2013

0

100

200

300

400

500

600

0% 20% 40% 60% 80% 100%

USD

/(M

Wh

/yr)

Load factor

USC

Gas turbine

NGCC

Screening curves - Example

Gas turbine NGCC USC coal

Operating ranges

US

D/M

W

© OECD/IEA 2013

Gas

Oil

Wind

Nuclear

Coal

Hydro

Variability of the electricity system

Demand load curves influence technology choice

Need for flexible generation

Storage to balance between peak and base load demand

Demand side response

Fluctuations also on supply side: some renewables (wind, PV) and CHP

plants (if heat-driven)

Reserve margin

2 4 6 8 10 12 14 16 18 20 22 24

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

110,000

0

Load

Firm capacity during peak time

© OECD/IEA 2013

Load duration curve

0

5

10

15

20

25

30

0 1000 2000 3000 4000 5000 6000 7000 8000

GW

hours

Chronological load curve over a year

0

5

10

15

20

25

30

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

GW

hours

Load duration curve

Reordering by load

8760

8760

© OECD/IEA 2013

0

5

10

15

20

25

30

0.00

010.

0219

0.04

370.

0655

0.08

730.

1091

0.13

090.

1527

0.17

450.

1963

0.21

820.

2400

0.26

180.

2836

0.30

540.

3272

0.34

900.

3708

0.39

260.

4144

0.43

620.

4580

0.47

980.

5016

0.52

340.

5452

0.56

700.

5888

0.61

060.

6324

0.65

420.

6760

0.69

780.

7196

0.74

140.

7632

0.78

500.

8068

0.82

870.

8505

0.87

230.

8941

0.91

590.

9377

0.95

950.

9813

GW

Load factor

Combining screening and load duration curves

0

100

200

300

400

500

600

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

USD

/(M

W/y

r)

Load factor

USC

Gas turbine

NGCC

0

5

10

15

20

25

30

0.00

010.

0219

0.04

370.

0655

0.08

730.

1091

0.13

090.

1527

0.17

450.

1963

0.21

820.

2400

0.26

180.

2836

0.30

540.

3272

0.34

900.

3708

0.39

260.

4144

0.43

620.

4580

0.47

980.

5016

0.52

340.

5452

0.56

700.

5888

0.61

060.

6324

0.65

420.

6760

0.69

780.

7196

0.74

140.

7632

0.78

500.

8068

0.82

870.

8505

0.87

230.

8941

0.91

590.

9377

0.95

950.

9813

GW

Load factor

0

5

10

15

20

25

30

0.00

010.

0219

0.04

370.

0655

0.08

730.

1091

0.13

090.

1527

0.17

450.

1963

0.21

820.

2400

0.26

180.

2836

0.30

540.

3272

0.34

900.

3708

0.39

260.

4144

0.43

620.

4580

0.47

980.

5016

0.52

340.

5452

0.56

700.

5888

0.61

060.

6324

0.65

420.

6760

0.69

780.

7196

0.74

140.

7632

0.78

500.

8068

0.82

870.

8505

0.87

230.

8941

0.91

590.

9377

0.95

950.

9813

GW

Load factor

0

5

10

15

20

25

30

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

GW

Load factor

Load duration curve

13 GW

4 GW

8 GW

USC

NGCC

Gas turbine

© OECD/IEA 2013

Hands-on exercise: Load curves and storage

ECOLSC0

EGASTUR

EGASCC0

EWINON0

Electricity

REGION1

DEM_PRC

Electricity demand

DEM_ELCTR IMPCOL0

IMPGAS0

RENWIN0

Coal USC plant

Gas turbine

Gas combined-cycle

Wind onshore

COL

GAS

WIN

ELECTR

IMPREG

RNWREG

NUC

IMPNUC0

ENUCLWR

Nuclear LWR

ESTGPMPIN

ESTGPMPOUT

ELECTR_STG

Electricity stored

ESTGPMP000

Turbine

Pump

Storage reservoir

TIMES_DEMO_V2.xlsm

© OECD/IEA 2013

Hands-on exercise model: Load curves for typical days

Example: Hourly load profile for four typical days

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

1.8%

0 4 8 12 16 20 24

Pe

rce

nta

ge o

f an

nu

al d

em

and

Hours

Winter day

Fall day

Spring day

Summer day

Period 4Period 3Period 2Period 1

Model horizon

Seasons

Weekday (level left out in exercise)

Hourly

WISU FA

Representative year(milestoneyear)

Spring

SP

_01

SP

_02

SP

_03

SP

_04

SP

_05

SP

_06

SP

_07

SP

_08

SP

_10

SP

_11

SP

_12

SP

_13

SP

_14

SP

_15

SP

_16

SP

_17

SP

_19

SP

_20

SP

_21

SP

_22

SP

_23

SP

_24

SP

© OECD/IEA 2013

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Cap

acit

y fa

cto

r (-)

Wind speed (m/s)

Hands-on exercise model: Background on wind assumptions

0

5

10

15

20

0 24 48 72 96 120 144 168

Win

d s

pe

ed

(m

/s)

Hours

0

200

400

600

800

1000

1200

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Fre

qu

en

cy

Wind speed (m/s)

Wind speed distribution

0

50

100

150

200

250

300

350

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Ge

ne

rati

on

(M

Wh

)

Wind speed (m/s)

Distribution of wind generation for 1 MW

Assumed normalised operational curve for wind turbine

+

© OECD/IEA 2013

Hands-on exercises (TIMES_DEMO_V2.xlsm)

Question 1: Run the model without a CO2 constraint, i.e. 5,000,000 kt upper bound. Save the run as BASE scenario. How sensitive is the model to a 40% increase/decrease to the gas price in 2050 (cells N46, N73)?

Question 2: Reset the gas price to 10 USD/GJ Add a CO2 constraint to the model (copy cells F10:N10 into F9:N9). How does the solution change?

Question 3:How does the solution change when allowing storage to be added (delete new capacity bound of zero in row 174)?

© OECD/IEA 2013

Key features of power sector analysis in energy system models

Strengths

Detailed technology representation of power sector

Integrated optimisation of investment planning and system operation

Deriving least-cost strategies to cover given electricity demand taking into

account possible additional policy goals (how-to)

Analysing impact of individual power technologies on costs, energy use and

emissions (what-if)

Analysing interdepedencies between different policy goals/instruments, e.g.

CO2 pricing and renewable support

Weaknesses

Operational aspects represented in an approximated fashion:

No detailed representation of electricity grid infrastructure

Ramp-up, ramp-down constraints, start-up costs, minimum operation time, part-load efficiency

generally not considered

Uncertainty can only be included to some extent (computational limitations)

Perfect competition on electricity markets, i.e. no market power of individual

players

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