www.ecn.nl
www.camecon.com
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
•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.