www.ecn.nl

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Experiences of Modelling of

Intermittent Renewable Energy

Tom Kober (ECN)

JRC workshop on

Addressing Flexibility in Energy System Models Petten, 4 Dec 2014

Rationale

• Energy system models - strong tools for long-term energy analysis

• Renewable energy (RE) assessment requires modelling innovation

• No single model covers all facets of the integration of RE

o How can energy system models be improved to better represent intermittent RE?

Linkage with power models

Adopt model structure & data

Sensitivity analysis

Wind production Germany:

hourly profile vs. 12 time slices

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

DE onshore DE offshore times_de onshore times_de offshore

Winter Spring

Summer

Autumn Winter

Power systems models

Detailed representation of the electricity system

• What can energy system models learn?

• How can they be linked?

Two examples: – COMPETES (ECN)

– E2M2s (IER)

COMPETES electricity market

model (ECN)

• Optimization-based model (e.g. LP/MIP)

• Formulations for different goals: 1. OPF Static Economic dispatch

model with perfect competition (LP)

2. OPF Static Unit Commitment model with perfect competition (MIP)

3. Dynamic model (LP): • Two-period under perfect

competition • Investments in the first period

(generation + transmission) • Dispatch in the second period

Modelling intermittent RE in

COMPETES

• Deterministic approach using hourly power factors or capacity factors per country or node

• Capacity factors based on historic data: SODA Database, TradeWind Database, Websites European TSO’s

• Future wind and solar profiles are similar to historic data

• Future availability factors are scaled-up to reflect technological advancements (EWEA Pure Power report)

• Curtailment allowed

COMPETES

Unit Commitment Model

Objective: Minimize Total variable generation cost+ Min-Load costs+ Startup costs

+ load-shedding costs

subject to

− Power balance constraints: These constraints ensure demand and supply is balanced at each node at any time.

− Generation capacity constraints: These constraints limit the maximum available capacity of a generating unit.

− Cross-border transmission constraints: These limit the power flows between the countries for given NTC values.

− Ramping up and Down constraints : These limit the maximum increase/decrease in generation of a unit between two consecutive hours

− Minimum Load Constraints: These set the min generation level of a unit when it is committed (Relaxed for neighboring countries with aggregated capacities)

− Minimum up and down times (Only for NL)

Integer decisions

Minimum load and corresponding

costs for each unit in COMPETES

- Min Load Costs are incurred at Qmin - Relaxation on minimum load for neighboring

countries

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

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

LH

V e

ffic

ien

cy a

s

% o

f m

ax e

ffic

ien

cy

Production as % of max production

Part-load LHV efficiency curves

NuclearPCPC-CCSIGCCNGCCNGCC-CCSOCGT

COMPETES’ flexibility assumptions

Technology Decade of

commissioning

Minimum load (% of max capacity)

Ramp rate (% of max

capacity/hour)

Start-up costa (€/MWinstalled

per start) Min up time Min down time

Nuclear <2010 50 20 46 ±14 8 4

2010 50 20 46 ±14 8 4

>2010 50 20 46 ±14 8 4

Lignite and PC <2010 40 40 46 ±14 8 4

2010 35 50 46 ±14 8 4

>2010 30 50 46 ±14 8 4

IGCC <2010 45 30 46 ±14 8 4

2010 40 40 46 ±14 8 4

>2010 35 40 46 ±14 8 4

NGCC <2010 40 50 39 ±20 1 3

2010 30 60 39 ±20 1 3

>2010 30 80 39 ±20 1 3

OCGT <2010 10 100 16 ±8 1 1

2010 10 100 16 ±8 1 1

>2010 10 100 16 ±8 1 1

CHP <2010 10 90 16 ±8 1 1

2010 10 90 16 ±8 1 1

>2010 10 90 16 ±8 1 1

Sources [1-9] [1-8, 10] [11] [11] [11] Sources: [1] (Jeschke et al., 2012); [2] (Dijkema et al., 2009); [3] (OECD/IEA, 2012b); [4] (IEAGHG, 2012a); [5] (Klobasa et al., 2009); [6] (Balling, 2010); [7] (Hundt et al., 2010); [8] (Isles, 2012); [9] (Stevens et al., 2011); [10] (NETL, 2012b); [11] (Lew et al., 2012).

a) Warm start-up costs are assumed for all technologies but OCGT. For OCGT, a cold start is assumed.

Example: generation flexibility

in the Netherlands

-3000,0

-2000,0

-1000,0

0,0

1000,0

2000,0

3000,0

2012 2017 2023 2012 2017 2023

Supply of domestic flexibility per technology (GWh)

Decentralized CHP

Res-e

Nuclear

Gas Other

Gas GT

Gas CHP

Gas CCGT

Coal

Dem

and

to r

amp

up

(GW

h)

Dem

and

to r

amp

do

wn

(G

Wh

)

Source: ECN-E--14-039 (2014)

E2M2s (IER, Uni Stuttgart)

• Electricity market model for Germany

• All generation units

• Inter-temporal optimisation

• 144 time slices per year

• Stochastic electricity production for wind and solar technology

• Flexibility parameters for power plants – Ramp-up/down time & costs

– Minimum load

– Minimum down time

Link energy system model and

power market model

Power market model (E2M2s)

Long-term

144 timeslices Stochastics

European TIMES model (PanEU)

Long-term

12 timeslices LP

Electricity consumption CHP electricity generation Fuel prices

Capacity credit for wind and solar System reserve capacity Generation from flexible units

Power plant costs RE-generation (policy)

Example: wind capacity credit

Germany (power market model) C

apac

ity

cred

it [

%]

~150 TWh & ~60 GW

in 2030

Source: IER Energieprognose 2009

Adopting the energy system

structure in TIMES

• Energy system and technology parameters of intermittent RE depend on the technology’s market diffusion

• Unless RE deployment is exogenous to the model, introduce different model processes to control parameters

Parameter set x

Parameter set y

Parameter set z

Improved data for TIMES

energy system model

• Capacity credit NCAP_PKCNT

• System reserve capacity COM_PKRSV

• Generation from flexible units User constraints helps to model system flexibility that cannot be captured with low time resolution

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4200 4300 4400 4500 4600 4700

Positive reserve energy from

storages and flexible power plants

Negative balancing energy into

storages or flexible demand

User constraints for flexible

generation

• Determine energy production from flexible units as share (p,n) of production from intermittent RE (e.g. based on power model)

• per time slice

• per level of RE deployment (different technology processes)

• User constraint for positive energy:

ElcGen(storages, GT, IC) ≥ p% ElcGen(wind, pv)

• User constraint for negative energy:

ElcCons(storages, flex demand) ≥ n% ElcGen(wind, pv)

Storages in TIMES

● Pump storage

● Compressed air – Natural gas-CAES

– Adiabate CAES

● Stationary battery systems – Natrium-Sulfid

– Redox Flow

● Elektro mobility – E-vehicles loading from the grid only

– E-vehicles to grid (V2G)

● Hydrogen storage

● Power-to-gas + storage

CAES storages

source: Gillhaus 2007

• Base: natural gas caverns

• Existing storages: 36

• Cavern storage projects: 38

• Major storage regions: Germany, UK, Poland, France, Portugal, Spain

• Max CAES capacity estimated: 19 GW (of which 6 GW in Germany)

Electricity infrastructure

investments

• Implemented via grid processes and user constraints

• Grid processes = solar and wind sector fuel processes (TIMES)

• 6 stages with costs up to 400 Euro/kW refer to new installed capacity

0

50

100

150

200

250

300

350

400

450

0 100 200 300 400

Co

st f

or

tran

smis

sio

n s

yste

m e

xten

sio

n

[Eu

ro/k

Wn

ew c

apac

ity]

Installed capacity [GW]

Wind

Solar PV

Good proxy but no trade-off between infrastructure investments and flexible generation / demand

The ‘extreme’ timeslice

• Problem: hours of negative residual load level out when annual wind/solar power generation profiles are reduced to 12 time slices (no negative electricity prices in the model)

Introduce daynite timeslice per season that characterizes this condition (equivalent to peak time slice) and/or change distribution of annual profile to timeslices • Analysis of wind/solar peaks and the load during these hours

.000

.050

.100

.150

.200

.250

.300

RD RN RP SD SN SP FD FN FP WD WN WPAn

nu

al a

vaila

bili

ty

Conclusion

• Model coupling is valuable

• TIMES offers model framework to introduce flexibility mechanisms

• Model link enables improved parameters for the energy system model (data and model structure to be adopted)

• Challenge: incorporate trade-off between infrastructure investments and system flexibility

Thank you!

Tom Kober

Policy Studies | Global Sustainability

T: +31 88 515 4105 | F: +31 224 56 83 38 Radarweg 60, 1043 NT Amsterdam, The Netherlands

kober@ecn.nl

•Supplementary material

ECN’s experience on power markets in Europe

(National projects based on COMPETES)

2010-2014

Dutch consortium aiming to make

out a case for the role of the

Netherlands w.r.t. sustainable use

of energy resources. One of the

goals of this project is to explore

and understand the inter-market:

interaction between the gas and

electricity sector, via the technical

infrastructure, power and carbon

markets resulting from (changing)

institutions and regulation. ECN

has been developing a combined

gas and market model to analyze

the interactions between electricity

and gas markets.

2008

Future electricity prices

This study analyzed the impact

of structural changes (e.g., fuel

and CO2 prices, new

investments in generation and

transmission capacity) in the

Northwest European electricity

markets affecting the future

wholesale electricity prices and

exchanges between these

markets. The results of the

study supported Ministry’s

Energy Report in 2008.

2009-2012

Reference projections and

additional policies 2010-2020

A national baseline scenario was

developed for energy, greenhouse

gases and air pollutants. The aim

of the project was also to evaluate

the Clean and Efficient programme

of the Dutch Government. Three

variants op the projections include

without policies, with implemented

policies and with proposed policies.

On top of this, over 40 additional

policy options were separately

analyzed. In 2012, an update was

done up to 2030.

2009

Net benefits of a new Dutch

Congestion Management

System

This study analysed the new

connection policy that seeks to lift

restrictions on grid connection. A

scenario-based, quantitative

analysis of the net benefits of the

new connection policy was

presented by using COMPETES

model. Furthermore, pros and

cons of several alternative

designs for a congestion

management system were

identified and presented.

2012

This study developed a

A Social Cost Benefit Analysis

(SCBA) was developed to

secure optimal contribution of

the investments in

interconnection to the social

welfare of the involved countries.

With COMPETES a case study

was conducted of a ‘fictitious but

realistic’ investment project in

interconnection to illustrate how

certain social effects from the

developed SCBA framework can

be practically and concretely

established.

.

2012-2013

North Sea Translational Grid

The impact of wind offshore

generation on the benefits of the

major players in the electricity

sector are analyzed from a social

welfare perspective within a set

of North Sea Transnational Grid

scenarios. ECN uses

COMPETES model for the

economic analysis.

2012

Financing investments in new

generation capacity

Study on the incentives for

investments in new generation

capacity with an increasing

share of renewable energy in the

generation mix and the effects

of introducing a national capacity

market in Germany on the

electricity markets in neighboring

countries including the

Netherlands. This has been

examined with the European

electricity model COMPETES.

2014

The market value of large scale

storage options (forthcoming)

With COMPETES three types of

storage options operating in the

Dutch electricity system are

analyzed and compared w.r.t. their

utilization and (marginal) revenues,

namely; Compressed Air Energy

Storage (CAES), Power2Gas (P2G)

and an Energy Island with hydro

pumping.

2014

National Energy Outlook (2014)

Within the National Energy Outlook

Modelling System (NEOMS),

COMPETES covers the

developments in the Dutch electricity

system. Hence, projections on for

example the generation mix, e-

prices and trade flows are based on

calculations with the COMPETES

model.

ECN’s experience on power markets in

Europe (internat. projects of COMPETES)

2009-2012

IRENE-40

The project aimed to identify

strategies for investors and

regulators to build a more secure,

ecologically sustainable and

competitive European electricity

system. Main responsibilities of

ECN included the roadmap with

respect to electricity infrastructure

that specifies actions needed to

achieve public goals as well as the

construction of generation and

demand scenarios as a basis for

network analyses

2008/2009

A nodal pricing analysis of the

future German electricity market

Scenario-based analysis of the

impact of Germany's ambitious

renewable agenda, disputed

decommissioning of nuclear

facilities and unbundling of TSOs

as enforced by EU regulation on

the future German power market

while accounting for internal

congestion. The analysis was done

by using COMPETES model.

2007

Impact of the EU ETS on

electricity prices

The project analyzed the

implications of the EU ETS for the

power sector, in particular it

analyzed the pass through of the

(opportunity) costs of CO2

emissions trading to electricity

prices on spot and forward markets

in various EU countries.

2007-2010

Improgress

Improvement of the Social Optimal

Outcome of Market Integration of

DG/RES in European Electricity

Markets. The project analyzed the

interactions of DG/RES operators

with markets and networks,

developed DG/RES integration

scenarios for the EU-27, quantified

the market and network impact of

DG/RES integration in three case

study networks (in Spain, Germany

and the Netherlands)

2008-2011

SUSPLAN

Development of strategies,

recommendations and benchmarks

for the integration of RES by 2030-

2050 within an Europe-wide

context. Our work included reports

on trans-national infrastructure

developments on the electricity and

gas market (ECN being responsible

only for gas market modeling), and

socio-economic approaches for

integration of renewable energy

sources into grid infra-structures.

2012-2014

E-highways

The project aims to develop a top-

down planning methodology

providing a modular and robust

expansion of the Pan-European

Network from 2020 to 2050, in line

with the European energy policy

pillars. The contribution of ECN to

the project involves the scenario

development, regulatory

assessment, and economic

modeling of electricity markets.