PRIMES Model Presentation for
Peer Review – Part 1
E3MLab
Brussels 26 September 2011
PRIMES Model
 Geographical coverage
• Each EU-27 member-state taken individually
• Also, candidate MS and neighbors, such as Norway, Switzerland,
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Turkey, South East Europe
Time frame: 2000 to 2050 by five-years periods
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Model results fully calibrated to Eurostat data for the period 1990
to 2005. Projections start from 2010
Core of the model: market linked sub-models for demand sectors
(industry, services households, etc), power/steam generation, fuel
supply
Satellite models: Biomass supply, refineries, detailed transport sector
model, gas supply (Eurasian), H2 supply
Model Running:
• Country-by-country
• Multiple countries with endogenous electricity trade
DATA SOURCES
 EUROSTAT
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Energy Balance sheets
Energy prices (complemented by other
sources)
Macroeconomic and sectoral activity data
Population data and projection
IEA - ENERDATA
 Technology databases mostly
developed under EC programs
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MURE, ICARUS, ODYSEE – demand
sectors
VGB, SAPIENTIA, TECHPOL – supply
sector technologies
NEMS model database, US DOE
IEA Technology data
Activity data from Industry associations
 Various surveys (e.g. CHP) and Platts
database on power plants
 Specifically commissioned studies
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DLR, ECN and Observer’s databases on
RES potential
TNO study on CO2 storage potential
Wuppertal and Fraunhofer databases on
energy efficiency
Specific database on biomass resources
and possibilities
Macroeconomic/sectoral
activity GEM-E3 model
Energy
demand-supply
Costs, prices,
emissions and
investment
PRIMES
model
Transport activity
and flows
SCENES or
TRANSTOO
LS
World energy
oil, gas, coal
prices
POLES or
Prometheus
model
power plants inventory:
Platts, ESAP,
Technology
costs/performance:
(TechPol,VGB)
EU refineries - IFP
Air Quality and
non CO2 GHG
emissions – IIASA
- GAINS model
Renewables potential
DLR, ECN, Observer, ...
Energy efficiency
Fraunhofer, Wuppertal,
ODYSEE, MURE
databases
What PRIMES can do
 The distinctive feature of PRIMES is the combination of micro-economic
foundations with engineering at a fairly high level of detail, compatible with a
long-term time scale and sectoral detail of available statistics for Europe
 Designed to provide long term energy system projections and system
restructuring up to 2050, both in the demand and the supply sides. Projections
include detailed energy balances, structure of demand by sector, structure of
power system and other fuel supplies, investment and technology uptake, costs
per sector, overall costs, consumer prices and certificate prices (incl. ETS) if
applicable, emissions, overall system costs and investment.
 Impact assessment of specific energy and environment policies, applied at
Member State or EU level, including
 Price signals, such as taxation, subsidies, ETS
 Technology promoting policies
 RES supporting policies
 Efficiency promoting policies
 Environmental policies
 The linked model system PRIMES, GEM-E3 and IIASA’s GAINS (for non-CO2
gases and air quality) perform energy-economy-environment policy analysis in a
closed-loop
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What PRIMES cannot do
 Cannot deliver short-term forecasts as it is not an econometric model (so
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projections are not statistically based on past observations, which in PRIMES are
only used for parameter calibration)
Cannot perform closed-loop energy-economy equilibrium analysis, unless linked
with a macroeconomic model such as GEM-E3
Cannot perform detailed short-term engineering analysis of electricity system or
gas system operation, as specialised models do (e.g. for an hourly operation for a
single year)
Although rich in sectoral disaggregation, PRIMES is limited by the concept of
representative consumer per sector, not capturing differences due to heterogeneity
of consumer types and sizes
PRIMES lacks spatial information and representation (at a level below that of
countries) and so lacks details about distribution and transport infrastructure and
flows that depend on detailed spatial information (except electricity and gas flows
over a country-to-country based grid infrastructure, which is represented in
PRIMES)
PRIMES is an empirical numerical model with emphasis on sectoral and country
specific detail; it has a very large size and so some compromises were necessary
to limit computer time at reasonable levels; compact small models may have a
more sound theoretical foundation but lack the level of detail and the richness of
PRIMES in representing technologies and policy instruments.
Final energy demand and representation of
behaviour of agents
 Separately by sector and sub-sector
 Firstly, demand for useful energy is a non-linear positive function of a
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macro-economic driver (with saturation effects) and a negative non linear
function of total average cost of useful energy (with floor)
Demand is further decomposed by process and energy use types as a
result of cost minimisation by agent, the processes/uses being partly
substitutable or complementary with each other
Cost of processing (capital, variable) and energy conversion efficiency is
associated to processes/uses; choice of processing technology (vintages)
is endogenous, more efficient technologies being more costly.
At the bottom of the nesting the model calculates demand to be supplied
by fuels and fuel using equipment; technology choice follows vintages
with different costs.
Utility maximisation is combined with total cost minimisation per agent by
concatenating first order conditions (mixed complementarity problem); the
derivation takes into account gradients of technology and possibility loci
for energy efficiency (energy savings from thermal integrity, control
systems, etc.).
The concept of perceived costs
 The concept of perceived cost relate to technology choices by
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final consumers
Purchasers of energy technology perceive costs differently
according to degree of innovation
Different vintages of technology are associated with different
perceived costs
Consumers compare technologies considering costs generally
different (mostly higher) than engineering estimates of the same
costs
Consumers consider (“perceive”)
 uncertainty about performance and maintenance of new technologies
 Convenience and comfort of technology use (and of insulation)
 Hidden costs (e.g. storage, volume of equipment, easiness of
installation)
 and apply technology-related risk premium
 Specific policies (labeling, campaigns, technology supply support,
demonstrations, etc.) are designed to reduce the gap between
perceived costs and engineering cost estimates, in order to promote
diffusion of energy efficient technologies and savings
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Final energy demand and representation of
behaviour of agents
 The demand model solves in time forward with anticipations over a 10
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years horizon; technology dynamics follow a rotating vintage approach
(ordinary, intermediate, advanced and future technology generations)
accounting for equipment stock, investment, decommissioning and
premature scrapping
Technology learning effects are represented as improvement of unit costs
and performance over time, which may be updated from past technology
diffusion
Decisions at each nesting level are influenced by equivalent perceived cost
for candidate technologies and energy savings investment
Capital decisions use weighted average cost of capital (WACC) and
subjective discount rates defined by sector
The decisions can be influenced by policies, such as
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Taxes and subsidies
Promotion of new technologies (reduce perceived costs)
Promotion of energy efficiency, including standards
Energy service companies, etc.
Features of industrial energy demand modelling
 Energy demand is part of macroeconomic production functions by sector
 Sectoral value added derived using GEM-E3, transformed in physical output
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indicators for certain heavy industries
Substitution between industrial processes allows handling of different energy
intensity for scrap or recycling processes and for basic processing of
materials
mix of technologies and fuels, including the use of self-produced by-products
(e.g. black liquor, blast furnace gas)
engineering-oriented representation of energy saving possibilities (e.g. shift
to more efficient process technologies, heat recovery, use of control systems,
etc.)
Technology vintages and dynamics
Interaction with Power and Steam sub-model for industrial CHP and boilers
Substitutions are possible between processes, energy forms, technologies
and energy savings
Influence from standards, emission constraints, pollution permits etc.
Features of energy demand modelling for
buildings
 Useful energy demand, final energy demand, equipment
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choice, energy efficiency investment and fuel mix derived
from utility maximization under budget constraint
Useful energy demand depends on behavioural
characteristics partly influenced by costs and prices
Distinction of households types according to energy
consumption patterns (5 types with endogenous shifts
between types) and for agriculture and services breakdown
by sub-sector (e.g. market services, trade)
Separate treatment of electric appliances
Final energy demand linked with thermal integrity of building,
with consideration of renovation investment
Heat pumps and direct use of Renewables included
Influence from standards, emission constraints, pollution
permits and energy efficiency policies
Features of energy demand modelling for
transport sector
 Transport activity for passengers and freight is linked to macroeconomic
drivers and the generalised price of transportation determined by transport
mode; modal shifts are possible; possibility to calibrate to projections of
specialised transport activity models (e.g. TRANSTOOLS)
 A complex nested tree decomposes trips by area, sector and purpose, as a
result of utility maximisation under budget constraint influenced by
generalised cost of transportation (incl. congestion costs)
 Choice of fleet technology/fuels derives from cost minimisation aiming at
meeting demand for transportation services; fleet vintages are dynamic with
possibility of premature scrapping; choices are influenced by standards,
regulations and technology progress; possible mismatching between vehicle
ranges and trip distances as well as the density of refuelling/recharging
infrastructure increase cost of technology/fuel types
 Balancing between demand for transportation services and fleet is obtained
by solving concatenated first order conditions; the ticket price of public
transportation services is computed so as to recover fixed costs.
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How policy targets can influence energy demand
 Targets for CO2 emissions, energy efficiency, renewables and others are handled
through their dual (shadow) variable
 Carbon value (€/tCO2) is perceived as a cost influencing the mix and energy
demand without entailing carbon payments, unless it is a tax or a price of
permits (emission allowances)
 Renewable value (€/toe from RES) is perceived as a benefit influencing the mix
without entailing monetary revenues, unless it is a subsidy or a price of permits
(green certificates)
 Efficiency value (€/toe) is perceived as a cost influencing the mix and energy
demand without entailing direct payment, unless it is a tax or a price of permits
(white certificates)
 Other policies represented by dual cost variables: congestion value in
transport, overall standards/regulations as penalties, energy saving obligations
represented by dual cost variables (penalty), etc.
 Other policies for energy efficiency are represented in a more straightforward way:
 Standards influence technology availability and choice
 Campaigns and promotion of new technologies represented by lowering
perceived costs
 Energy service companies imply a reduction of subjective discount rates
 Building renovation policies and building codes represented as penalties
influencing choices over the thermal integrity cost-potential functions
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Formation of final energy demand
 Total final demand for energy fuels formed by adding
demand projections by sector
 Using load profile patterns by type of energy use, load
profiles are projected for electricity (high, medium, low
voltage), industrial steam, distributed heat and gas;
the load profiles are represented as a typical day for
summer and winter (11 segments)
 Final demand for fuels is an input to
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Power and steam supply sub-model
Oil supply sub-model
Gas supply sub-model
Biomass supply sub-model
Hydrogen supply sub-model
Power and Steam Supply
 Commodities: electricity (high, medium, low voltage and self-supply), industrial
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steam and distributed heat from boilers, district heating and CHP
Plant types: a large number different types with distinct technologies,
distinguishing between industrial, utilities and self supply plant types
Possible auxiliary equipment: CHP (several types), DESOX, DENOX, ESP,
CCS retrofitting
Data for existing plants: a large inventory of existing power plants
Inputs to plants: all types of fuels and energy forms
Model Outputs:
 Production by plant type
 Investment in new plants (on new or existing site, extension of lifetime,
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replacement, retrofitting or adding auxiliary equipment, premature
decommissioning)
Flows over a stylized grid of electricity, steam, heat
Power flows over interconnectors
Emissions and allowances
Costs of supply, grid costs and other monopolistic charges and Electricity Prices
by customer type (sectors)
Input fuels – link to fuel supply non linear curves
IND Auto
Power
plants
Utility
Power
plants
Electricity
auto
District
Heating
Plants
Heat or
Steam
Self supplying
Power
plants
Industrial
Boilers
IND Auto Heat
or
Steam
Electricity
Transmission
HV
Heat
Distribution
Electricity
Distribution
MV
Steam
Distribution
Electricity
auto
HV Grid
MV
Grid
Electricity
Demand HV
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Electricity
Demand MV
Electricity
Distribution
LV
Electricity
Demand LV
Steam
Demand
Link to pricing sub-model and the Demand Sub-models
Heat
Demand
Power and Steam Supply
 Solves a least cost non-linear optimization problem under constraints (demand,
operation and grid, reliability and reserve, fuel availability and their cost/supply
curves, policy restrictions)
 Two options for foresight: perfect (inter-temporal), perfect for shorter time horizons
 The model solves simultaneously
 a unit commitment-dispatching problem
 a capacity expansion problem
 a DC-linearized optimum power flow problem (over interconnectors)
 The model solves simultaneously for all commodities (electricity, steam, heat)
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connected over a stylized grid, and with CHP electricity-steam possibility frontiers
Intermittent RES are handled in a deterministic way with pre-determined time
profiles of operation (over the typical load segments)
Power reserve constraints take into account capacity credits for RES, which
depend on total RES deployment and trade flows
Detailed operation constraints are handled in one model version (which requires
more computer time) which includes ramping constraints and technical minimum of
thermal plants
Storage possibilities are modeled endogenously (pumped storage and hydrogen),
involving investment in storage, balancing of storage within a year and conversion
losses
Power and Steam Supply
 Cost of capital of plants represented through annuity payments for
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capital, using a WACC and a risk premium specific to each plant type
Technology progress is exogenous and feedback from learning
handled exogenously by scenario
Cost of fuels through cost-supply curves, varying over time
Cost of CCS storage seen in the model as cost/supply curves
For RES and nuclear the model includes site development costs,
formulated as non linear functions relating unit costs with potential;
cost of investment on an existing site is lower
Different costs for extension of lifetime, retrofitting, and for adding
auxiliary equipment
Cost of transmission/distribution grids formulated through reduced
cost functions depending on the deployment of variable RES,
decentralized generation, efficiency and assumptions about smart
metering (e.g. electrification of transport), DC lines, etc.
Cost of CHP and heat distribution formulated as non linear cost
curves related to potential (no spatial modeling)
Interconnections and electricity trade
 Electricity trade modeled for the typical load profile (11 segments)
 The network of current and future interconnectors is mapped with
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all details provided by ENTSOe. Capacities, reactance and
resistance parameters are exogenous. Possibility to handle new
DC lines for RES (offshore wind, Desertec) endogenously.
A single node by country (35 countries)
Power flows determined over a DC linearized network (PTDF)
included as a constraint in the least cost optimization of power
supply. Possibility to run the whole Europe, or by region
Constraints in the form of NTCs included
Because of computer capacity limitations the model is solved in
two steps: the full Europe power model runs separately; the
resulting trade flows are introduced as constraints in the PRIMES
models per country
Electricity Prices
 A finance and pricing sub-model runs after the power/steam
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optimization model
Firstly the model simulates wholesale power prices and links marginal
costs with demand sectors depending on their load profiles
Secondly the model applies a Ramsey-Boiteux algorithm to determine
electricity tariffs by sector, based on marginal costs, recovery of fixed
generation costs (incl. stranded investment costs) and priceelasticities by sector; exogenous cost mark-ups are applied in the
short-term to mimic current market distortions; mark-ups tend to
vanish in the long term assuming a well functioning market.
Thirdly, the model determines prices for transmission and distribution
(as regulated monopolies with regulated WACC), recovery of RES and
other subsidies so as to recover costs and allocates costs to
consumer types according to pre-determined rules
Steam or distributed heat prices reflect costs, taking account of CHP
Mathematical and computer structure
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PRIMES is a modular system, each sub-model having its own mathematical structure;
modeling 35 countries one by one
The energy demand sub-models are solved as mixed complementarity problems
(concatenation of demand MCP with a technology/fuel “supply” MCP and equilibrium
conditions within the sector); they depend on prices received from the rest of the model
and address demand to the rest of the model; for households the objective function is to
maximise utility, for business it is to minimise cost
The power/steam sub-model is solved as a least-cost optimisation model; the results
are used by the finance/pricing model (MCP) to determine tariffs
The other energy supply models are least cost optimisation models and are also
followed by a pricing MCP sub-model; the gas supply model is solved as an MCP
formulating Cournot oligopoly
The overall equilibrium is obtained for each time period following a Gauss-Seidel
iteration between the modules
The whole model runs with GAMS on a set of 48 parallel processors; input and outputs
are organised and stored in Excel files; the full trade electricity model takes 7-8 hours
for a run, a single country model takes 2 hours (countries run in parallel) and the gas
supply Eurasian model takes 5-6 hours on state-of-the-art Intel processors
Each sub-model runs over the entire time horizon; the balancing iterations are also
carried out for the entire horizon; so the entire model is not doing typical recursive time
forward, but can handle perfect foresight
clearing
GDP, Economic Activity by sector, Households’ Income, Demographics
(exogenous)
CORE MODEL
Demand for
Electricity and
Distributed
Steam/Heat
CO2 Prices
and Costs
Prices of Hydrogen
Prices of Nat. Gas
Prices of Oil
Products
Prices of Solids
CO2 Emissions
Hydrogen
Supply Model
Demand for RES
Biomass
Supply Model
Demand for
Hydrogen
Gas Supply
Model
Demand for Bioenergy
Solids Supply
Model
Demand for Nat.
Gas
Refineries
Imports/Exports
Extraction
Prices of Bioenergy
Prices of Electricity
and Distributed
Steam/Heat
Industrial Energy Demand
excl. Fuels for steam
production from CHP and
boilers
Demand for Solids
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Electricity
, District
Heating,
CHP and
industrial
boilers
Transport Energy Demand
Demand for Oil
Products
World Energy Prices (exogenous)
Tertiary and Agriculture
Energy Demand
RES Cost
Supply
Curves
Demand, Supply and Prices of energy commodities exchanged within the Energy branch
Emission Trading scheme, Carbon transport and storage, Carbon prices, etc.
Cost of RES
Residential Energy Demand
Foresight, expectations, risk
PRIMES is designed for long term planning and scenarios, not for short term forecasting or for
simulating shock impacts
 PRIMES generally applies perfect foresight over a limited time horizon, e.g. 10 years in
demand, 20 or 30 or more years in power generation
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 The demand sub-models run over the entire time horizon taking into account all future
technology costs and fuel prices with anticipation horizons over 10 years (adjustment of prices in
the context of iteration).
 The resulting projection of demand goes to the supply models which run with perfect foresight (or
under other options) over the entire horizon, taking into account all future costs, prices and
demand
 For market clearing, the model running process is repeated with each sub-model running for the
entire time horizon (with iterations)
Adaptive or rational expectations are not modeled but could be modeled in the demand side
as the basic architecture is time forward with 10 years anticipation
 Risk factors are generally introduced in the perception of costs of technologies and the
WACCs both in demand and supply sectors
 Uncertainty is not modeled; modeling uncertainty would imply probabilistic approaches which
would render the already complex structure difficult to manage in a reasonable computer time;
experimental work on power plant modeling that instead of least cost maximizes the
probability of avoiding financial failure under probabilistic constraints is underway, but
integration into PRIMES seems technically very difficult
 Cases of unforeseen failures (e.g. CCS delay, delay in transport electrification, etc.) are
simulated by assuming different risk premium factors or technology costs over time
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ETS modelling
 Volume of ETS allowances are set exogenously
 ETS carbon prices are determined endogenously so as emissions
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comply with allowances (with banking but not borrowing from the
future, taking into account permissible use of CDM)
Depending on ETS legal provisions, the model can handle
grandfathering or auctioning or mixed
In case of grandfathering in power sector, model options handle the
degree of passing through opportunity costs to consumer prices;
industrial costs take into account free allocation of allowances; in
case of auctioning, the model simulates impacts on wholesale power
markets and determines electricity tariffs so as to recover auction
payments
In the reporting on costs all the EU provisions related to redistribution
of the auction revenues are also accounted for
Note: The ETS carbon price can also be applied to the non-ETS
sectors as a carbon value, which would represent the marginal value
of the abatement costs in non-ETS sectors; this approach was
followed to model scenarios seeking cost-effective sharing of
emission cut effort by sector
Renewable scenarios
The model accounts in detail the RES % indicators of Eurostat and can handle targets for
these indicators, both overall and by sector (heating/cooling, transport, electricity)
 Penetration of RES in heating and cooling derive from relative costs and is influenced by RES
promoting policies reflected in the model through the “RES-value”, which is perceived by
consumers as a benefit
 Penetration of RES in transport takes place through higher use of biofuels: a) blended in oilbased fuels influenced by regulations and relative costs, b) diffusion of vehicles using pure
biofuels supported by development of refuelling infrastructure. Biomass supply simulation
determines prices of biofuels
 Penetration of RES in power sector derives from RES promotion (direct subsidies and/or
facilitation reflected by RES-value) and by carbon prices. Key points of simulation of high or
very high RES in power generation:
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 Variable RES have fixed generation possibilities by load segment
 Potential is limited and cost increase as approaching potential
 Balancing requirements determined considering ramping and reserve requirements constraints
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(with RES capacity credits diminishing with penetration)
Storage possibilities endogenous: hydro-pumping, hydrogen (produced by excess RES or the grid
mix, stored and then used in other load segments wither mixed with gas or directly in gas
turbines). Storage development increase costs.
Exploitation of remote RES potential (North Sea, North Africa) with development of super-grid DC
network over EU countries (endogenous capacities)
Very decentralised RES development is endogenous, taking into account reduction of grid costs
because of self-supply, but also increase in grid costs because of smart networking and metering
High development of indigenous RES generally implies higher grid costs
RES subsidies are recovered through adjusting consumer prices (RES levy)
PRIMES Biomass Supply
 Projects optimal use of biomass/waste resources and investment in secondary
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and final transformation, so as to meet a given demand of final biomass/waste
energy products, projected to the future by the rest of the PRIMES model
Projects land, agricultural, forest and waste resources used in production of
bio-energy products, as well as investment in biomass processing technologies
Determines endogenously imports-exports of bio-energy products and
feedstock
Evaluates energy demand and emissions in bio-energy production
Determines the consumer prices of the final bio-energy products
FEEDSTOCK
Energy crops: starch, sugar, oil
Woody crops: herbaceous ligno-cellulosic,
short rotation wood
Forestry: wood platform, forestry residues,
wood waste
Waste: agricultural residues, industrial solid,
black liquor, industrial pulp, used fats and
oil, municipal solid, sewage sludge landfill
gas, manure, etc.
Aquatic biomass
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conversion
processes
FINAL COMMODITIES
Solid: solid biomass for direct
combustion, pellets, charcoal,
mass burn wastes, refuse
derived fuel
Liquid: bio-ethanol, biodiesel,
heavy bio-oil, bio-kerosene
Gaseous: biogas, bio methane,
bio-hydrogen
CO2 Capture
 CCS develops at different speeds depending on the scenario under consideration; it
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competes with other means, such as carbon free power generation (renewable energies,
nuclear), fuel switching and the reduction of energy consumption
Power plants with carbon capture are more expensive in terms of capital investment and
operation costs than similar plants without carbon capture; net thermal efficiency is also
lower, since carbon capture needs energy to operate.
As CCS technology is assumed to evolve over time (as technology becomes commercially
mature), as carbon prices also change over time and as storage costs depend on
cumulative quantities stored, investment in CCS involves arbitraging over time: perfect
foresight in the model allows for simulating such decisions.
The CCS investment decisions are integrated within the PRIMES sub-model on power and
steam generation. The CCS technology for power plants are represented in two ways:
 as typical new power plants enabled with CCS considered as candidate for investment
 as auxiliary technologies candidate for retrofitting existing power plants or plants built
(endogenously by the model) without initially having CCS.
This flexible representation allows assessment of various policy options, as for example
the “capture-ready” options or optionally mandatory CCS measures.
Capture technologies in PRIMES: post-combustion, pre-combustion and oxyfuel applied
on different technology/fuel combinations (biomass CCS is not yet included); CCS for
emissions from industrial processes is also included.
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CO2 transport and storage
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The costs of transporting and storing CO2 are modelled through non linear cost curves
by country, bounded by storage potential (set exogenously for each scenario per time
period). Costs increases with quantity stored. The costs and potentials are derived
from publications from TNO and JRC.
It is assumed that CO2 transportation and storage are offered by regulated monopolies
operating by country (CO2 exchanges between countries are not modelled in the
current model version)
Both activities operate under strong economies of scale, bear very high fixed costs
(and small variable costs) and face high uncertainty about future use of infrastructure
Prices for transportation and storage services are determined on the basis of
levelizing total development costs and investments over time on an anticipated
cumulative demand for the service
Public acceptance issues and other uncertainties are expressed through parameters
shifting the cost-supply curve to the left and up (making more expensive the service
and lowering potential)
Scenarios involving delays in CCS development may be simulated by introducing
particularly high storage and transport costs for a limited period of time
The pilot CCS plants envisaged for 2020 are assumed to have reserved specific sites
for CO2 storage at rather short distances with small marginal costs for storage
PRIMES Model Presentation for
Peer Review – Part 2
E3MLab
Brussels 26 September 2011
World modeling for international fossil fuel
prices
 PRIMES takes fossil fuel prices from world energy
modeling handled either by POLES model or the
Prometheus model
 Both global models represent energy demand and
production for several world regions and simulate
market clearing for fossil fuels at a global level
 The models project production of fossil fuels, taking
into account conventional and unconventional
resources; fossil fuel prices depend on equilibrium
between global demand and supply, with supply costs
depending on exploration and production costs as
well as on rents
 They also include power generation sector,
renewables, emission constraints and policies, etc.
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Gas prices for Europe
 In the core PRIMES model gas prices are projected as functions of gas
import prices (determined by the global models) which include gas
transportation and LNG costs
• A detailed gas supply model is available (as a satellite model to PRIMES),
which simulates an oligopoly market over multiple countries (Eurasian area),
involving many actors (consumers, TSOs, traders and upstream producers)
 Consumers are price takers with demand being elastic with prices
 TSOs manage gas hubs and minimize cost of gas supply
 Traders maximise profits, perform arbitraging operations and are price takers
from upstream producers
 Upstream producers compete along a Nash-Cournot game (with conjectural
variations)
 The number of competitors acting on each node change over time to reflect
growing competition (long term trend towards a well functioning market)
 Operations and flows are constrained by a physical system involving
pipelines, LNG terminals, gas storage facilities, liquefaction plants and gas
producing wells
 The gas model projects physical and commercial gas flows and determines
gas prices by country and by consumer type
30
Macroeconomic and sectoral projections
 GDP and sectoral activity projections are exogenous
inputs to the PRIMES model
 The projections are derived as follows:
 Short term GDP trends are taken from published forecasts
(e.g. DG ECFIN)
 Long term demographic and growth trends are taken from
published studies, as for example the DG ECFIN 2009
Ageing Report
 The GEM-E3 model is used to build a detailed sectoral
projection which matches with short and long term GDP
trends; the model-based scenario makes assumptions about
productivity trends by production factor and sector, public
consumption and investment, foreign trade barriers,
consumer habits etc. The model produces a European and a
global projection.
31
Feedbacks on GDP
 PRIMES can be linked with GEM-E3 to perform closed-loop analysis. The
linkage is programmed as follows:
 PRIMES takes projection of economic activities from GEM-E3 and produces




energy system projections; the results are used to calibrate parameters of
the GEM-E3 model:
The electricity production function of GEM-E3 (it follows a bottom-up MCP
formulation) is calibrated to reproduce the technology/fuel mix projected by
PRIMES
Energy saving parameters of GEM-E3 (entering production and
consumption functions) are calibrated to reproduce the energy efficiency
trends projected by PRIMES
Biomass production and the structure of the transport sector as projected by
PRIMES are reproduced by GEM-E3 after calibrating parameters of the
production functions
Environmental constraints (e.g. ETS allowances or targets) are introduced in
GEM-E3 as also in PRIMES
 So GEM-E3 model runs and produces a new projection of economic
activity and also impact information (on investment, growth, employment,
prices, terms of trade, etc.)
 PRIMES may run again with the adjusted economic activity projections
 The linked model system has been used to assess the impacts of RES and
energy efficiency policies
32
Modeling of transport activity and modal
shifts
 PRIMES has no spatial information and is not designed to
model transport flows
 However, the transport sector model is designed to take
as inputs results from transport flow models, such as
SCENES and TRANSTOOLS
 These models simulate modal shifts as a result of
transport policies and changes in infrastructure
 The PRIMES transport model simulates further modal
shifts and changes in transportation activity driven by
relative and overall transportation costs, which are
influenced by policies, fuel prices and technology costs
33
Links to modeling GHG other than energyrelated CO2
 PRIMES is formally linked with the IIASA GAINS model:
 GAINS uses energy demand and supply projections from PRIMES and
calculates emissions (air quality, non CO2 GHGs, acid rain pollutants)
 GAINS constructs marginal abatement cost curves for each non CO2





34
GHG and by country
PRIMES uses these marginal abatement cost curves to determine costeffective emission cut sharing between CO2 and non CO2 GHGs;
PRIMES handles energy related and process related CO2 emission cuts;
the marginal costs of cutting energy related emissions is a result of the
model simulating energy system restructuring
PRIMES includes a module for process-related CO2 emissions. The
marginal costs of abatement is modeled using cost curves for measures
and represent possibility of CCS in industry
The optimal sharing is obtained by applying the same level of carbon
prices and by iterating over the carbon prices until the desired overall
GHG emission level is met.
Abatement costs for non CO2 GHGs are calculated as the area below the
marginal abatement cost curves of GAINS
PRIMES Model Presentation for
Peer Review – Part 3
E3MLab
Brussels 26 September 2011
Comparison with US DOE estimates for 2010
Data for year 2010
Steam Turbine Coal Conventional
Steam Turbine Coal Supercritical
Integrated Gasification Combined Cycle Coal
Pulverised Coal Suprcritical CCS post combustion
Gas Turbine Combined Cycle Gas Conventional
Gas Turbine Combined Cycle Gas Advanced
Gas combined cycle CCS post combustion
Nuclear second generation
Nuclear third generation
IG Biomass CC
Biogas Advanced CHP
Wind onshore (average)
Wind offshore (near coast)
Wind offshore (remote)
Solar thermal
Photovoltaic small scale
Photovoltaic large scale
36
Overnight capital costs
PRIMES
NEMS
€'2010/kW $'2010/kW
$'2010/kW
Min
Max
1448
2042
2844
3167
2338
3296
2304
3248
3221
3565
3408
4805
4579
5099
724
1021
978
897
1265
1003
1546
2180
2060
3056
4309
5335
4057
5721
2449
3452
7894
1536
2165
3860
1272
1793
2438
2032
2866
5975
2788
3932
4661
6573
4692
4972
7010
6050
4091
5769
4755
Development of capital costs over time (non-RES)
Development of capital costs over time (non-RES)
6000
5000
4382
3985
EUR'2010/kW
4000
3481
2232
1000
2315
2050
2035
1450
2199
1637
1899
1741
1724
762
822
37
1542
1577
929
1115
856
0
2010
3618
3064
3000
2000
3859
2015
2020
2025
2030
713
2035
2040
2045
Pulverised Coal Suprcritical CCS oxyfuel
Steam Turbine Coal Supercritical
Integrated Gasification Combined Cycle Coal
Gas Turbine Combined Cycle Gas Advanced
Gas combined cycle CCS pre combustion
Nuclear third
2050
Development of capital costs over time (RES)
Development of capital costs over time (RES)
6000
5562
5000
4450
4203
EUR'2010/kW
4000
4169
4171
3839
3000
3805
2959
2678
2000
1940
1932
1739
1847
1663
1000
0
2010
2015
Wind Power
38
2020
1366
1085
1104
1106
2025
Wind Power Offshore
2030
Solar PV
1750
1074
2035
2040
Solar Thermal
2045
Geothermal
2050
Decommissioning and lifetime extension costs
 For existing plants decommissioning schedules
are based on information in the power plant
inventory; however extension of lifetime and
retrofitting are handled endogenously if
permissible
 For new plants, decommissioning is scheduled at
the end of pre-determined lifetime; extension of
lifetime is handled endogenously
 Extension of lifetime entails investment costs
(much lower than overnight capital costs);
retrofitting costs are higher than extension of
lifetime costs
39
Sustainable bio-energy potential
 Biomass supply is handled by the PRIMES biomass model,
which includes sustainability constraints, as follows:
 Land availability for energy crops is limited so as not to develop to
the detriment of other land uses (food) and not to change land use
(e.g. permanent grass land is largely excluded)
 Availability of forestry resources for energy purposes is limited
maintaining current forestry uses and areas
 Lifecycle CO2 emissions in the production of bio-energy
commodities (e.g. biofuels) are constrained to stay below norms
(Fuel quality Directive and Renewables Directive)
 Sustainability issues for bio-energy imports to the EU are checked
exogenously (import volumes or types may be restricted)
 The demand of bio-energy commodities by sector is handled by
the core PRIMES model as a function of relative prices and
other costs; the demand vectors are inputs to the PRIMES
biomass model which further determines the supply
technologies, land uses, imports and thus costs and prices.
40
Demand side appliances (1)
Appliance
Source
Washing machine EuP and IEA
PRIMES
Lighting
Base Case
0.998kWh/cycle
443EUR
Improved
BAT
-10%
(+25% cost)
BNAT
Technical performance
limit might soon be
reached
1.57kWh/cycle
582EUR
40%
improvement,
0.95kWh/cycle
-50%
(+32% cost)
further -5%, at 25%
cost increase
Residential: -70%
LEDs and OLEDs
EuP
Services: -70%
Street: -30%
-26% at 30% cost -80%
(+250% cost)
TVs: -20%
PRIMES
Entertainment/offi EuP
ce equipment
PRIMES
41
815EUR
further -2% at 35%
cost
TVs:-30 to -50%
compared to current
Computers: -65 to - Computers: software
75%
and consumer
behaviour
-10% at 32% cost further -10% (+32% further -5%, at 25%
cost )
cost increase
Demand side appliances (2)
Appliance
Source
Base Case
Boilers (Water EuP (Gas?)
heating)
Primes (Gas) 500-1500EUR
Boilers
(Central
heating)
Primes (Gas) 1000-3000EUR
BAT
BNAT
30-40%
60%
21%
42% (add. Inv.
Cost 100%)
EuP (Gas?)
Air
EuP
conditionining Primes (Elec) 500-1500EUR
42
Improved
47%
30%-40%
9%
23% (add. Inv.
Cost. 49%)
-57%
-47% (add. Inv.
Cost 61%)
30%
Electric vehicle assumptions
Reference scenario
Updated Reference
Decarbonisation
scenario
Reference scenario
Updated Reference
Decarbonisation
scenario
Reference scenario
Updated Reference
Decarbonisation
scenario
43
Battery cost (USD/kWh) of a medium sized vehicle
2015 2020 2025 2030 2035 2040 2045
700
680
670
660
660
660
660
700
626
570
539
509
479
449
2050
660
419
p.a. %
-0.2%
-1.5%
700
578
332
271
258
246
-2.9%
2015
0.15
0.15
Efficiency (kWh/km)
2020 2025 2030 2035
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.14
2040
0.15
0.14
2045
0.15
0.13
2050
0.15
0.13
p.a. %
0.0%
-0.5%
0.15
0.15
0.13
0.12
0.12
-0.7%
456
0.14
394
0.14
0.13
2015
140
140
Range (km)
2020 2025 2030
140
140
140
140
160
164
2035
140
168
2040
140
172
2045
140
176
2050
140
180
140
140
320
380
440
500
200
260
Transport assumptions
 The transport sector was calibrated to the detailed
results of the PRIMES-TREMOVE transport model
 Base year vehicle prices are taken from the DG
COMPETITION “Car prices within the European
Union” of 1st January 2010 for each Member State
 Battery costs used in the scenarios are within the
ranges of costs found in literature including IEA, US
DOE, McKinsey and industry sources
 Infrastructure development is taken into account in
the PRIMES-TREMOVE model
44
Correspondence of transport modes, vehicle Technologies and fuels in PRIMESTREMOVE
Buses
Liquid
Fuels
Gaseous
Fuels
Hydrogen
Electricity
45
Gasoline
blend
Ethanol
Diesel
Blend
DME
B100
Fuel oil
blend
Jet fuel
ICE
Natural
gas
/hydroge
n blend
Natural
gas/biog
as blend
Biogas
LPG
ICE
ICE
ICE
ICE
ICE
ICE
Two
wheelers
Passenger
cars
X
X
Light
duty
vehicles
Heavy
duty
vehicles
Rail
X
Navigation
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Turbines
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
ICE
ICE
ICE
FCEV
BEV
PHEV
On-grid
Aviation
X
X
Discount rates
Base
Case
Industry
Private
individuals
(houses and
cars)


17.5%
Tertiary
12%
Public transport
8%
Power
generation
investment
Power sector
grids
46
12%
 To simulate capital budgeting decisions of agents
9%
7%


(consumers, producers) in a realistic way, the
model uses sector specific discount rates
Subjective discount rates are used for decisions by
households and for private cars, whereas WACC
are used for business decisions
A vast literature has provided statistical evidence
about subjective discount rates, which can be
substantially high for low income classes; these
rates reflect risk aversion, cash flow shortages,
access to bank lending, etc.
If the model used social discount rates to simulate
private capital budgeting decisions, the results
would be unrealistic (historical developments could
not be explained) and the energy saving or
abatement possibilities would be overestimated
Social discount rates are used to calculate
cumulative energy system overall costs by scenario
and serves scenario comparison purposes
Translation of policy into modelling
parameters
 Information campaigns and product labelling: increased market





47
acceptance parameter for the specific technologies
Energy service companies (ESCOs): changes in perceived
discount rates for private households
Energy saving obligatio: through efficiency values representing
the marginal cost of the energy saving obligation
Eco-design standards: availability of only certain more advanced
technology options from certain time periods onwards
Promotion of CHP and micro-generation: priority grid access for
CHP, CHP values representing marginal benefits for CHP can
be introduced. Micro-generation is included only in the low
voltage grid, reducing the transmission costs.
smart meters: implicitly considered in cost of grid when share of
decentralised generation increases or when electrification of
road transport develops