Energy conversion - MESSAGE-GLOBIOM

Model Documentation - MESSAGE-GLOBIOM
Model scope and methods - MESSAGE-GLOBIOM Model concept, solver and details - MESSAGE-GLOBIOM Temporal dimension - MESSAGE-GLOBIOM Spatial dimension - MESSAGE-GLOBIOM Policy - MESSAGE-GLOBIOM Socio-economic drivers - MESSAGE-GLOBIOM Population - MESSAGE-GLOBIOM Economic activity - MESSAGE-GLOBIOM Macro-economy - MESSAGE-GLOBIOM Trade - MESSAGE-GLOBIOM Energy - MESSAGE-GLOBIOM Energy resource endowments - MESSAGE-GLOBIOM Fossil energy resources - MESSAGE-GLOBIOM Uranium and other fissile resources - MESSAGE-GLOBIOM Bioenergy - MESSAGE-GLOBIOM Non-biomass renewables - MESSAGE-GLOBIOM Energy conversion - MESSAGE-GLOBIOM Electricity - MESSAGE-GLOBIOM Heat - MESSAGE-GLOBIOM Gaseous fuels - MESSAGE-GLOBIOM Liquid fuels - MESSAGE-GLOBIOM Grid, pipelines and other infrastructure - MESSAGE-GLOBIOM Energy end-use - MESSAGE-GLOBIOM Transport - MESSAGE-GLOBIOM Residential and commercial sectors - MESSAGE-GLOBIOM Industrial sector - MESSAGE-GLOBIOM Energy demand - MESSAGE-GLOBIOM Technological change in energy - MESSAGE-GLOBIOM Land-use - MESSAGE-GLOBIOM Emissions - MESSAGE-GLOBIOM GHGs - MESSAGE-GLOBIOM Pollutants and non-GHG forcing agents - MESSAGE-GLOBIOM Climate - MESSAGE-GLOBIOM Modelling of climate indicators - MESSAGE-GLOBIOM Non-climate sustainability dimension - MESSAGE-GLOBIOM Air pollution and health - MESSAGE-GLOBIOM Water - MESSAGE-GLOBIOM Other sustainability dimensions - MESSAGE-GLOBIOM Appendices - MESSAGE-GLOBIOM References - MESSAGE-GLOBIOM
Corresponding documentation
AIM-Hub BLUES C3IAM COFFEE-TEA DNE21+ GCAM GEM-E3 GRACE IFs IMACLIM IMAGE MESSAGE-GLOBIOM POLES PROMETHEUS REMIND-MAgPIE TIAM-UCL WITCH
Previous versions
Archive of MESSAGE-GLOBIOM version 1.0
Model information
Model link	https://docs.messageix.org http://data.ene.iiasa.ac.at/message-globiom/ https://github.com/iiasa/message ix https://github.com/iiasa/ixmp
Institution	International Institute for Applied Systems Analysis (IIASA), Austria, http://data.ene.iiasa.ac.at.
Solution concept	General equilibrium (closed economy)
Solution method	Optimization
Anticipation

Energy technologies are characterized by numerical model inputs describing their economic (e.g., investment costs, fixed and variable operation and maintenance costs), technical (e.g., conversion efficiencies), ecological (e.g., GHG and pollutant emissions), and sociopolitical characteristics. An example for the sociopolitical situation in a world region would be the decision by a country or world region to ban certain types of power plants (e.g., nuclear plants). Model input data reflecting this situation would be upper bounds of zero for these technologies or, equivalently, their omission from the data set for this region altogether.

Each energy conversion technology is characterized in MESSAGE by the following data:

• Energy inputs and outputs together with the respective conversion efficiencies. Most energy conversion technologies have one energy input and one output and thereby one associated efficiency. But technologies may also use different fuels (either jointly or alternatively), may have different operation modes and different outputs, which also may have varying shares. An example of different operation modes would be a passout turbine, which can generate electricity and heat at the same time when operated in co-generation mode or which can produce electricity only. For each technology, one output and one input are defined as main output and main input respectively. The activity variables of technologies are given in the units of the main input consumed by the technology or, if there is no explicit input (as for solar-energy conversion technologies), in units of the main output.
• Specific investment costs (e. g., per kilowatt, kW) and time of construction as well as distribution of capital costs over construction time.
• Fixed operating and maintenance costs (per unit of capacity, e.g., per kW).
• Variable operating costs (per unit of output, e.g. per kilowatt-hour, kWh, excluding fuel costs).
• Plant availability or maximum utilization time per year. This parameter also reflects maintenance periods and other technological limitations that prevent the continuous operation of the technology.
• Technical lifetime of the conversion technology in years.
• Year of first commercial availability and last year of commercial availability of the technology.
• Consumption or production of certain materials (e.g. emissions of kg of CO2 or SO2 per produced kWh).
• Limitations on the (annual) activity and on the installed capacity of a technology.
• Constraints on the rate of growth or decrease of the annually new installed capacity and on the growth or decrease of the activity of a technology.
• Technical application constraints, e.g., maximum possible shares of wind or solar power in an electricity network without storage capabilities.
• Inventory upon startup and shutdown, e.g., initial nuclear core needed at the startup of a nuclear power plant.
• Lag time between input and output of the technology.
• Minimum unit size, e.g. for nuclear power plants it does not make sense to build plants with a capacity of a few kilowatt power (optional, not used in current model version).
• Sociopolitical constraints, e.g., ban of nuclear power plants, or inconvenience costs of household cook stoves.
• Inconvenience costs which are specified only for end-use technologies (e.g. cook stoves)

The specific technologies represented in various parts of the energy conversion sector are discussed in the following sections on Electricity, Heat, Other conversion, and Grid, Infrastructure and System Reliability below.

Electricity

MESSAGE covers a large number of electricity generation options utilizing a wide range of primary energy sources. For fossil-based electricity generation technologies, typically a number of different technologies with different efficiencies, environmental characteristics and costs is represented. For example, in the case of coal, MESSAGE distinguishes subcritical and supercritical pulverized coal (PC) power plants where the subcritical variant is available with and without flue gas desulpherization/denox and one internal gasification combined cycle (IGCC) power plant. The superciritical PC and IGCC plants are also available with carbon capture and storage (CCS) which also can be retrofitted to some of the existing PC power plants. <xr id="tab:MESSAGE-GLOBIOM_elec"/> below shows the different power plant types represented in MESSAGE.

Four different nuclear power plant types are represented in MESSAGE-GLOBIOM, i.e. two light water reactor types, a fast breeder reactor and a high temperature reactor, but only the two light water types are included in the majority of scenarios being developed with MESSAGE in the recent past. In addition, MESSAGE includes a representation of the nuclear fuel cycle, including reprocessing and the plutonium fuel cycle, and keeps track of the amounts of nuclear waste being produced.

The conversion of five renewable energy sources to electricity is represented in MESSAGE-GLOBIOM (see <xr id="tab:MESSAGE-GLOBIOM_elec"/>). For wind power, both on- and offshore electricity generation are covered and for solar energy, photovoltaics (PV) and solar thermal (concentrating solar power, CSP) electricity generation are included in MESSAGE (see also sections on Non-biomass renewables of MESSAGE-GLOBIOM and :ref:syst_integration).

Most thermal power plants offer the option of coupled heat production (CHP, see <xr id="tab:MESSAGE-GLOBIOM_elec"/>). This option is modeled as a passout turbine via a penalty on the electricity generation efficiency. In addition to the main electricity generation technologies described in this section, also the co-generation of electricity in conversion technologies primarily devoted to producing non-electric energy carriers (e.g., synthetic liquid fuels) is included in MESSAGE (see section on :ref:other).

<figtable id="tab:MESSAGE-GLOBIOM_elec">

List of electricity generation technologies represented in MESSAGE-GLOBIOM by energy source

Energy source	Technology	CHP option
Coal	subcritical PC power plant without desulphurization/denox	yes
	subcritical PC power plant with desulphurization/denox	yes
	supercritical PC power plant with desulphurization/denox	yes
	supercritical PC power plant with desulphurization/denox and CCS	yes
	IGCC power plant	yes
	IGCC power plant with CCS	yes
Oil	heavy fuel oil steam power plant	yes
	light fuel oil steam power plant	yes
	light fuel oil combined cycle power plant	yes
Gas	gas steam power plant	yes
	gas combustion turbine gas	yes
	combined cycle power plant	yes
Nuclear	nuclear light water reactor (Gen II)	yes
	nuclear light water reactor (Gen III+)	yes
	fast breeder reactor
	high temperature reactor
Biomass	biomass steam power plant	yes
	biomass IGCC power plant	yes
	biomass IGCC power plant with CCS	yes
Hydro	hydro power plant (2 cost categories)	no
Wind	onshore wind turbine	no
	offshore wind turbine	no
Solar	solar photovoltaics (PV)	no
	concentrating solar power (CSP)
Geothermal	geothermal power plant	yes

</figtable>

Technological change in MESSAGE is generally treated exogenously, although pioneering work on the endogenization of technological change in energy-engineering type models has been done with MESSAGE (Messner, 1997 MSG-GLB_messner_endogenized_1997). The current cost and performance parameters, including conversion efficiencies and emission coefficients is generally derived from the relevant engineering literature. For the future alternative cost and performance projections are usually developed to cover a relatively wide range of uncertainties that influences model results to a good extent. As an example, <xr id="fig:MESSAGE-GLOBIOM_ther"/> and <xr id="fig:MESSAGE-GLOBIOM_nonth"/> below provide an overview of costs ranges for a set of key energy conversion technologies (Fricko et al., 2016 MSG-GLB_fricko_marker_2016).

<figure id="fig:MESSAGE-GLOBIOM_ther">

Cost indicators for thermoelectric power-plant investment (Fricko et al., 2016)

MSG-GLB_fricko_marker_2016

</figure>

In <xr id="fig:MESSAGE-GLOBIOM_ther"/>, the black ranges show historical cost ranges for 2005. Green, blue, and red ranges show cost ranges in 2100 for SSP1, SSP2, and SSP3, respectively (see descriptions of the SSP narratives in section :ref:narratives). Global values are represented by solid ranges. Values in the global South are represented by dashed ranges. The diamonds show the costs in the “North America” region. CCS – Carbon Capture and Storage; IGCC – Integrated gasification combined cycles; ST – Steam turbine; CT – Combustion turbine; CCGT – Combined cycle gas turbine (Fricko et al., 2016 MSG-GLB_fricko_marker_2016).

<figure id="fig:MESSAGE-GLOBIOM_nonth">

Cost indicators for non-thermoelectric power-plant investment (Fricko et al., 2016)

MSG-GLB_fricko_marker_2016

</figure>

In <xr id="fig:MESSAGE-GLOBIOM_nonth"/>, the black ranges show historical cost ranges for 2005. Green, blue, and red ranges show cost ranges in 2100 for SSP1, SSP2, and SSP3, respectively. Global values are represented by solid ranges. Values in the global South are represented by dashed ranges. The diamonds show the costs in the “North America” region. PV – Photovoltaic (Fricko et al., 2016 MSG-GLB_fricko_marker_2016).

Heat

A number centralized district heating technologies based on fossil and renewable energy sources are represented in MESSAGE (see <xr id="tab:MESSAGE-GLOBIOM_heat"/>). Similar to coupled heat and power (CHP) technologies that are described in the :ref:electricity sector, these heating plants feed low temperature heat into the district heating system that is then used in the end-use sectors. In addition, there are (decentralized) heat generation options in the :ref:industrial and :ref:resid_commerc.

<figtable id="tab:MESSAGE-GLOBIOM_heat">

List of centralized heat generation technologies represented in MESSAGE by energy source

Energy Source	Technology
coal	coal district heating plant
oil	light fuel oil district heating plant
gas	gas district heating plant
biomass	solid biomass district heating plant
geothermal	geothermal district heating plant

</figtable>

Other conversion

Beyond electricity and heat generation there are three further subsectors of the conversion sector represented in MESSAGE, liquid fuel production, gaseous production and hydrogen production.

Liquid Fuel Production

Apart from oil refining as predominant supply technology for liquid fuels at present a number of alternative liquid fuel production routes from different feedstocks are represented in MESSAGE (see <xr id="tab:MESSAGE-GLOBIOM_liqfuel"/>). Different processes for coal liquefaction, gas-to-liquids technologiesand biomass-to-liquids technologies both with and without CCS are covered. Some of these technologies include co-generation of electricity, for example, by burning unconverted syngas from a Fischer-Tropsch synthesis in a gas turbine (c.f. Larson et al., 2012 MSG-GLB_larson_chapter_2012). Technology costs for the synthetic liquid fuel production options are based on Larson et al. (2012) (MSG-GLB_larson_chapter_2012).

<figtable id="tab:MESSAGE-GLOBIOM_liqfuel">

Liquid fuel production technologies in MESSAGE by energy source

Energy Source	Technology	Electricity cogeneration
Biomass	Fischer-Tropsch biomass-to-liquids	yes
	Fischer-Tropsch biomass-to-liquids with CCS	yes
Coal	Fischer-Tropsch coal-to-liquids	yes
	Fischer-Tropsch coal-to-liquids with CCS	yes
	coal methanol-to-gasoline	yes
	coal methanol-to-gasoline with CCS	yes
Gas	Fischer-Tropsch gas-to-liquids	no
	Fischer-Tropsch gas-to-liquids with CCS	no
Oil	simple refinery	no
	complex refinery	no

</figtable>

Gaseous Fuel Production

See <xr id="tab:MESSAGE-GLOBIOM_gasfuel"/> for a list of gaseous fuel production technologies in MESSAGE.

<figtable id="tab:MESSAGE-GLOBIOM_gasfuel">

Gaseous fuel production technologies in MESSAGE by energy source

Energy Source	Technology
Biomass	biomass gasification
	biomass gasification with CCS
Coal	coal
	coal gasification with CCS

</figtable>

Hydrogen Production

See <xr id="tab:MESSAGE-GLOBIOM_hydtech"/> for a list of gaseous fuel production technologies in MESSAGE.

<figtable id="tab:MESSAGE-GLOBIOM_hydtech">

Hydrogen production technologies in MESSAGE by energy source

Energy source	Technology	Electricity cogeneration
Gas	steam methane reforming	yes
	steam methane reforming with CCS	no
Electricity	electrolysis	no
Coal	coal gasification	yes
	coal gasification with CCS	yes
Biomass	biomass gasification	yes
	biomass gasification with CCS	yes

</figtable>

As already mentioned in the section for :ref:`electricity`, technological change in MESSAGE is generally treated exogenously, although pioneering work on the endogenization of technological change in energy-engineering type models has been done with MESSAGE (Messner, 1997 MSG-GLB_messner_endogenized_1997). The current cost and performance parameters, including conversion efficiencies and emission coefficients is generally derived from the relevant engineering literature. For the future alternative cost and performance projections are usually developed to cover a relatively wide range of uncertainties that influences model results to a good extent. As an example, <xr id="fig:MESSAGE-GLOBIOM_costind"/> below provides an overview of costs ranges for a set of key energy conversion technologies (Fricko et al., 2016 MSG-GLB_fricko_marker_2016).

<figure id="fig:MESSAGE-GLOBIOM_costind">

Cost indicators for other conversion technology investment (Fricko et al., 2016)

MSG-GLB_fricko_marker_2016

</figure>

In <xr id="fig:MESSAGE-GLOBIOM_costind"/>, the black ranges show historical cost ranges for 2005. Green, blue, and red ranges show cost ranges in 2100 for SSP1, SSP2, and SSP3, respectively. Global values are represented by solid ranges. Values in the global South are represented by dashed ranges. The diamonds show the costs in the “North America” region. CCS – Carbon capture and storage; CTL – Coal to liquids; GTL – Gas to liquids; BTL – Biomass to liquids (Fricko et al., 2016 MSG-GLB_fricko_marker_2016).

Grid, Infrastructure and System Reliability

Energy Transmission and Distribution Infrastructure

Energy transport and distribution infrastructure is included in MESSAGE at a level relevant to represent the associated costs. Within regions the capital stock of transmission and distribution infrastructure and its turnover is modeled for the following set of energy carriers:

• electricity
• district heat
• natural gas
• hydrogen

For all solid (coal, biomass) and liquid energy carriers (oil products, biofuels, fossil synfuels) a simpler approach is taken and only transmission and distribution losses and costs are taken into account.

Inter-regional energy transmission infrastructure, such as natural gas pipelines and high voltage electricity grids, are also represented between geographically adjacent regions. Solid and liquid fuel trade is, similar to the transmission and distribution within regions, modeled by taking into account distribution losses and costs. A special case are gases that can be traded in liquified form, i.e. liquified natural gas (LNG) and liquid hydrogen, where liquefaction and re-gasification infrastructure is represented in addition to the actual transport process.

Systems Integration and Reliability

The MESSAGE framework includes a single annual time period characterized by average annual load and 11 geographic regions that span the globe. Seasonal and diurnal load curves and spatial issues such as transmission constraints or renewable resource heterogeneity are treated in a stylized way in the model. The reliability extension described below elevates the stylization of temporal resolution by introducing two concepts, peak reserve capacity and general-timescale flexibility, to the model (Sullivan et al., 2013 MSG-GLB_sullivan_electric_2013). To represent capacity reserves in MESSAGE, a requirement is defined that each region build sufficient firm generating capacity to maintain reliability through reasonable load and contingency events. As a proxy for complex system reliability metrics, a reserve margin-based metric was used, setting the capacity requirement at a multiple of average load, based on electric-system parameters. While many of the same issues apply to both electricity from wind and solar energy, the description below focuses on wind.

Toward meeting the firm capacity requirement, conventional generating technologies contribute their nameplate generation capacity while variable renewables contribute a capacity value that declines as the market share of the technology increases. This reflects the fact that wind and solar generators do not always generate when needed, and that their output is generally self-correlated. In order to adjust wind capacity values for different levels of penetration, it was necessary to introduce a stepwise-linear supply curve for wind power (shown in the <xr id="fig:MESSAGE-GLOBIOM_windcap"/> below). Each bin covers a range of wind penetration levels as fraction of load and has discrete coefficients for the two constraints. The bins are predefined, and therefore are not able to allow, for example, resource diversification to increase capacity value at a given level of wind penetration.

<figure id="fig:MESSAGE-GLOBIOM_windcap">

File:Wind cv.png

Parameterization of Wind Capacity Value

</figure>

The capacity value bins are independent of the wind supply curve bins that already existed in MESSAGE, which are based on quality of the wind resource. That supply curve is defined by absolute wind capacity built, not fraction of load; and the bins differ based on their annual average capacity factor, not capacity value. Solar PV is treated in a similar way as wind with the parameters obviously being different ones. In contrast, concentrating solar power (CSP) is modeled very much like dispatchable power plants in MESSAGE, because it is assumed to come with several hours of thermal storage, making it almost capable of running in baseload mode.

In order to ensure adequate reserve dispatch, dynamic shadow prices are placed on capacity investments of intermittent technologies (e.g., wind and solar). The prices are a function of the cumulative installed capacity of the intermittent technologies, the ability for the convential power supply to act as reserve dispatch, and the demand-side reliability requirements. For instance, a large amount of storage capacity should, all else being equal, lower the shadow price for additional wind. Conversely, an inflexible, coal- or nuclear-heavy generating base should increase the cost of investment in wind by demanding additional expenditures in the form of natural gas or storage or improved demand-side management to maintain system reliability.

Starting from the energy metric used in MESSAGE (electricity is considered as annual average load; there are no time-slices or load-curves), the flexibility requirement uses MWh of generation as its unit of note. The metric is inherently limited because operating reserves are often characterized by energy not-generated: a natural gas combustion turbine (gas-CT) that is standing by, ready to start-up at a moment’s notice; a combined-cycle plant operating below its peak output to enable ramping in the event of a surge in demand. Nevertheless, because there is generally a portion of generation associated with providing operating reserves (e.g. that on-call gas-CT plant will be called some fraction of the time), it is posited that using generated energy to gauge flexibility is a reasonable metric considering the simplifications that need to be made. Furthermore, ancillary services associated with ramping and peaking often do involve real energy generation, and variable renewable technologies generally increase the need for ramping.

Electric-sector flexibility in MESSAGE is represented as follows: each generating technology is assigned a coefficient between -1 and 1 representing (if positive) the fraction of generation from that technology that is considered to be flexible or (if negative) the additional flexible generation required for each unit of generation from that technology. Load also has a parameter (a negative one) representing the amount of flexible energy the system requires solely to meet changes and uncertainty in load. <xr id="tab:MESSAGE-GLOBIOM_flex"/> below displays the parameters that were estimated using a unit-commitment model that commits and dispatches a fixed generation system at hourly resolution to meet load an ancilliary service requirements while hewing to generator and transmission operation limitations (Sullivan et al., 2013 MSG-GLB_sullivan_electric_2013). Technologies that were not included in the unit-commitment model (nuclear, H2 electrolysis, solar PV) have estimated coefficients.

<figtable id="tab:MESSAGE-GLOBIOM_flex">

Flexibility Coefficients by Technology (Sullivan et al., 2013 MSG-GLB_sullivan_electric_2013)

Technology	Flexibility Parameter
Load	-0.1
Wind	-0.08
Solar PV	-0.05
Geothermal	0
Nuclear	0
Coal	0.15
Biopower	0.3
Gas-CC	0.5
Hydropower	0.5
H2 Electrolysis	0.5
Oil/Gas Steam	1
Gas-CT	1
Electricity Storage	1

</figtable>

Thus, a technology like a simple-cycle natural gas plant, used almost exclusively for ancillary services, has a flexibility coefficient of 1, while a coal plant, which provides mostly bulk power but can supply some ancillary services, has a small, positive coefficient. Electric storage systems (e.g. pumped hydropower, compressed air storage, flow batteries) and flexible demand-side technologies like hydrogen-production contribute as well. Meanwhile, wind power and solar PV, which require additional system flexibility to smooth out fluctuations, have negative flexibility coefficients.