Getting Started TIMES-VEDA V2p7

8/10/2019 Getting Started TIMES-VEDA V2p7

1/145


2/145


3/145


4/145


5/145


6/145


7/145


8/145


9/145


10/145


11/145


12/145


13/145


14/145


15/145


16/145


17/145


18/145


19/145


20/145


21/145


22/145


23/145


24/145


25/145


26/145


27/145


28/145


29/145


30/145


31/145


32/145


33/145


34/145


35/145


36/145


37/145


38/145


39/145


40/145


41/145


42/145


43/145


44/145


45/145


46/145


47/145


48/145


49/145


50/145


51/145


52/145


53/145


54/145


55/145


56/145


57/145


58/145


59/145


60/145


61/145


62/145


63/145


64/145


65/145


66/145


67/145


68/145


69/145


70/145


71/145


72/145


73/145


74/145


75/145


76/145


77/145


78/145


79/145


80/145


81/145

Version 2.7 May 200981

A user may also need to define the PCG if not all of the commodities in the group define the process activity (e.g., natural gas consumed as part of refining), these are considered associatedflows.

The topology associated with a process defined according to the commodities input and output. The

activity of a standard process is equal to the sum of one or several commodity flows on either theinput or the output side of a process as defined by the PCG. The activity of a process is limited bythe available capacity, so that the activity variable establishes a link between the installed capacityof a process and the maximum possible commodity flows entering or leaving the process during ayear or a subdivision of a year.

3.2.3.2 Use of capacity

In each time period the model may use some or all of the installed capacity according to theAvailability Factor of that technology. For each technology, period, region and time-slice, theactivity of the technology may not exceed its available capacity, as specified by a user definedavailability factor. Thus the model may decide to use less than the available capacity during certaintime-slices, or even throughout one or more whole periods, if such a decision contributes tominimizing the overall cost. Optionally, there is a provision for the modeller to force specifictechnologies to use their capacity to their full potential or with a given number of full load hours (byindicating that the AF is fixed (an exogenous utilization factor) rather than a maximum potential.

3.2.3.3 Defining flow relationships in a process

A process with one or more (perhaps heterogeneous) commodity flows is defined by one or moreindependent input and output flow variables. In the absence of relationships between these flows,the process would be completely undetermined, i.e. its outputs would be independent from itsinputs. We therefore need one or more relationships stating that the ratio of the sum of some of itsoutput flows to the sum of some of its input flows is equal to a constant (which is akin to anefficiency). In the case of a single commodity in, and a single commodity out of a process, thisequation defines the traditional efficiency of the process (see the Electricity Power Plants, section

3.2.4.3) . With several commodities, this constraint may leave some freedom for individual output(or input) flows, as long as their sum is in fixed proportion to the sum of input (or output) flows (seethe Flexible Refinery, section 3.2.4.2) . An important rule for this constraint is that each sum must

be taken over commodities of the same type (i.e. in the same group, say: energy carriers, oremissions, etc.). In TIMES-VEDA, for each process the relationship between the input commoditygroup and the output commodity group must be established to properly define the efficiency ratio.As noted earlier, VEDA- FE assists the user by means of macro parameters that are translated intothe actual TIMES parameters needed to properly represent this key relationship. The variousVEDA-FE efficiency related parameters are briefly described here:


82/145


- EFF - can be used for defining process efficiencies of most processes, the parameter valuedefines the amount of activity that can be produced by one unit of flow of a commodity orcommodities on the shadow side of the process;

- CEFF (commodity specific efficiency) - if the efficiency of the power plant is differentdepending upon the fuel consumed, and

- INPUT (or Consumption ) indicating the amount of a commodity needed for a unit ofoutput. Input and output attributes can be conveniently used for specifying fixed relations

between the process activity and individual input or output flows that are not part of the primary group (PG), or even between two flows (e.g., emissions that are commoditydependent).

As the efficiency of the power plant is different depending upon the fuel consumed it is necessary touse the CEFF (commodity specific efficiency).

3.2.3.4 Limiting flow shares in flexible processes

When either of the commodity groups, input or output, contains more than one element, the previous relationship allows a lot of freedom on the values of flows. The process is therefore quiteflexible. To limit this flexibility within acceptable performance ranges, the share of each flowwithin its own group may be controlled within ranges 48. For instance, a refinery output mightconsist of four refined products with 5% losses. The user may then want to limit the flexibility ofthe slate of outputs with four flow shares, as shown in the section 3.2.4.2, by specifying themaximum shares for the various outputs. The commodity group being subjected to shares may beon the input or output side of the process.

3.2.3.5 Peaking Reserve Requirements (time-sliced commodities only)

In TIMES it is required that the total capacity of all processes producing a commodity at each time period and in each region must exceed the average demand in the time-slice when the highestdemand occurs by a certain percentage. This percentage is the Peak Reserve Factor,COM_PKRSV , and is chosen to insure against several contingencies, such as possible commodity

shortfall due to uncertainty regarding its supply (e.g. water availability in a reservoir), unplannedequipment down time, and random peak demand that exceeds the average demand during the time-slice when the peak occurs. This constraint is therefore akin to a safety margin to protect againstrandom events not explicitly represented in the model. In a typical cold country the peaking time-slice for electricity (and natural gas) will be in the Winter (Winter-Peak if a peak timeslice isdefined), and the total electric plant generating capacity (or gas supply) must exceed the Winter-Peak demand load by a certain percentage. In a warm country the peaking time-slice may be in the

48 It is always advisable to leave at least one commodity in a group unconstrained to avoid over specify the process.


83/145


Summer for electricity (due to heavy air conditioning demand). The peaking constraint is createdfor each time-slice of a time-slice commodity 49.

3.2.4 How to declare specific processes

3.2.4.1 Mining process and import/export processes

The mining and import/export processes are described in the TIMES-TUTORIAL paragraph 2.2.5.1and paragraph 2.2.5.2. The user can see how these processes are described in the TIMES-DEMO inthe workbook VT_ DEMOT_SUP_V5, sheets MIN and IMP-EXP. As can be seen there are two oilcrude and two hard coal domestic production options and one natural gas import option.

3.2.4.2 Flexible refinery

A simplified flexible refinery is an energy process (PRE) that consumes one commodity (Comm-IN) and produces four (or more) commodities ( Comm-OUT ) in a flexible manner, using the

parameter Share~UP (in principle is also possible to use FX or LO) to control the individual flowsas related to the total output. As reflected in Figure 3-9 over the period each output commodity can

be at most equal the fifty percent of the total production from the refinery.

The flexible refinery must be described using a Primary Commodity Group (PCG = OILCRD, forexample) in order to define the activity of the process (SSCDRFLX00) as the sum of the four outputflows (see Figure 3-9) . In this way is possible to normalize the output, thus the efficiency (1.05) is

being defined from output flows group OILCRD (sum of all output flows) to crude oil input, andrepresents a 5% loss across the process.

In addition to the activity of a process, one has to define the relationship of the activity to thecapacity unit of the process. This is done using the capacity-to-activity parameter ( Cap2Act ),applied to the primary commodity group. In the example in Figure 3-9 the capacity of the refinery

process is defined in PJ/a, while activity is measured in PJ. Thus, the conversion factor is 1 (acapacity of 1 PJ/a produces in a year an activity of 1PJ) and can be omitted. By default the Cap2Act

is equal to one.

The last parameter, LIFE , is used to describe the operational lifetime of the refinery in terms of thenumber of years. By default the economic life (payback period) is assumed to equal the operationallife, though the user could distinguish them (using ELIFE ), if desired.

In Figure 3-9 an example of a flexible refinery take from the TIMES-DEMO (workbookVT_DEMOT_SUP_V5 sheet Refinery) is shown. Besides those parameters already discussed a tax

49 The user may control for which time-slices the peaking constraints are generated, by explicitly identifying said time-slices, but as default a peaking constraint is created for all time-slices at the commodity time-slice level.


84/145


is applied to some of the output commodities (via FLO_TAX ) and sector CO2 are tracked basedupon the consumption of the crude oil.

Figure 3-9 Flexible Refinery

3.2.4.3 Electric power plants

The electricity power plants are special process that generate electricity (ELE), consuming one ormore commodities and producing electricity by time-slice.In Figure 3-10 three examples are shown:

- a fossil fuel plant that consumes coal (COAHAR);- a fossil fuel plant that has the flexibility to consume oil and/or natural gas (OILDST and

GASNAT), and

- a seasonal reservoir hydro-electric power plant that consumes a renewable commodity(HYDRO) to produce electricity.

The parameters used to describe the electricity power plants in the Figure 3-10 define the:- topology ( Comm-IN/OUT );- efficiency ( EFF or CEFF );- all the availability factor of a technology are 50:

- NCAP_AF - availability factor relating a unit of production (process activity) intimeslice s to the current installed capacity.

- NCAP_AFA - annual availability factor relating the annual activity of a process to theinstalled capacity. P rovided when ANNUAL level process operation is to becontrolled.

- NCAP_AFS - availability factor relating the activity of a process in a time slice(SEASON/WEEKLY/DAYNITE) being at or above the process time slice level to theinstalled capacity. If for e xample the process time slice level is DAYNITE andNCAP_AFS is specified for time slices on the SEASONAL level, the sum of theDAYNITE activities within a season are restricted, but not the DAYNITE activitiesdirectly.

50 For more details see TIMES documentation Part II, table 12.


85/145


- NCAP_AFC - can be used to describe commodity specific availability factors. It is thusmeaningful for only processes with several commodities in the primary group.NCAP_AFC is automatically combined with any normal annual availability factorsdefined for the process. If no normal availability factors have been defined on thetimeslice level tslvl, an upper limit of 1 is always used as the default. If NCAP_AFC is

specified for only some but not all commodities in the primary group, a default value of1 will be used for any missing commodities. NCAP_AFC can be used for any

processes, including storage and trade processes. In the case of storage processes, onlythe output flow of commodity COM is considered in the availability constraint.

- NCAP_AFAC , is just a shorthand alias for NCAP_AFC (,ANNUAL).- the contribution of the power plant towards meeting the peak requirement ( PEAK ).- Investment along with fixed and variable operating and maintenance costs ( NCAP_COST ,

FIXOM and VAROM )51;- capacity to activity conversion factor ( Cap2Act ,by default equal to 1);

- level of the existing installed capacity in the base year ( STOCK );- the flow share ( SHARE ~) establishing the relationship for the commodities available

to the dual-fuelled power plant, and- emission coefficients ( ENVACT ~ for emissions other than CO 2.

The first power plant (ECOASTM000) is described using the commodity input, the commodityoutput, the overall process efficiency ( EFF ), the annual availability factor ( AF ), thecapacity/activity transformation ( CAP2ACT ), the existing stock by region(STOCK~ ), a lower production bound ( BNDACT~LO ), an emission coefficient on

the output ( ENVACT~ ) and the peak parameter ( PEAK ).

The peak parameter (never larger than 1) specifies the fraction of a technology capacity that isconsidered to be secure and thus will most likely be available to contribute to the peak load in thehighest time-slice of a year for a commodity (electricity or heat only); many types of supply

processes can be regarded as predictably available with their entire capacity contributing during the peak and thus have a peak coefficient equal to 1, whereas others (such as wind turbines or solar plants in the case of electricity) are attributed a peak coefficient less than 1, since they are onaverage only fractionally available at peak (e.g., a wind turbine typically has a peak coefficient of

0.25 or 0.3).

In addition to the activity of the power plant, one has to define the relationship to the capacity unitof the process. This is done using the parameter Cap2Act , applied to the primary commodity group.In this example the capacity of the power plants is defined in GW. Since the capacity and activityunits are different (GW for the capacity and PJ for the activity), the user has to supply theconversion factor from the energy unit embedded in the capacity unit to the activity unit. Thisconversion factor is 31.536 PJ/GW.

51 Although for simplicity FIXOM and VAROM are omitted for these plants.


86/145


Figure 3-10 Electricity power plants

The second power plant (EGASOIL000) needs to relate the two input commodities to the process invarious ways. As the efficiency of the power plant is different depending upon the fuel consumed itis necessary to use the CEFF (commodity specific efficiency, see section 3.2.3.3) rather than theoverall process efficiency parameter EFF , and provided a flow share to reduce the freedom of themodel (as explained in the section 3.2.3.4) . For EGASOIL000 a lower flow share on input ( Share-

I~LO ) is assigned for the commodity OILDST, requiring that at least the 20% of the fuel consumedis distillate.

With regard to the hydro-electric power plant An efficiency of 0.33 is a conventional value used,representing the estimated fossil equivalent that would be necessary if this plant was not used), andis also valid for the wind technologies, other renewable power plants, and for nuclear power plants.

This is the minimum sets of parameters to describe an electricity power plant, some other efficiencyand advanced parameters are described in VEDA-FE interface under Tools, Supported Attributes.

The emissions in the TIMES-DEMO are related to the commodity consumption, as described in theTIMES-TUTORIAL, but it is also possible to assign the emissions directly to the technology (seesection 2.2.5.4) . This is especially important for pollutants for which the emission coefficientsdepend on the individual technologies. Some other examples are shown in the TIMES-DEMO(workbook VT_DEMOT_ELC_V5 sheet ELC).For other power plant examples see the workbook VT_DEMO_ELC_V5 sheet ELC.

3.2.4.4 Cogeneration power plant

Cogeneration power plants or combined heat and power plants (CHP) are plants that consume oneor more commodities and produce two commodities, electricity and heat. One can distinguish twodifferent types of cogeneration power plants according to the flexibility of the outputs, a back

pressure process and a condensing process, as shown in Figure 3-14.


87/145


A typical cogeneration power plant includes many of the same parameters mentioned in section3.2.4.3 for electric generation plants 52 as well as:

- PEAK - the cogeneration power plants are technologies that work on the DAYNITEtimeslice level, thus the peak parameter must be related to this timeslice level. For exampleis possible to relate the peak to the winter-peak timeslice (WP, as in the Figure 3-14) or the

summer-peak (SP);- CHPR (FX, LO or UP) is defined to be the heat-to-power ratio, and- the ratio of electricity lost to heat gained ( CEH ).

Back pressure turbineBack pressure turbines are system in which the ratio of the production of electricity and heat isfixed, the electricity generation is directly proportional to the steam.

Figure 3-11 Back pressure turbine characteristic curve

In a real system a back pressure turbine is defined using the electrical efficiency (ETA elBP), thethermal efficiency (ETA thBP), and the load utilization. Thus in TIMES-VEDA, a back pressuresystem is characterised as follows.

ETAel = 45%, ETAth = 30%, Load utilization period = 3500 h/a in TIMES-VEDA become

EFF = ETA elBP + ETA thBP = 75%NCAP_AFA = load utilization period/duration of a year =3500/8760 = 0.40CHPR~FX = ETA thBP /ETA elBP = 0.67CEH = 1

If the CHPR parameter is fixed (FX) the production of electricity and heat is in a fixed proportion, but one could also use a (LO) CHPR for defining the back-pressure point, if so desired (to allow by- passing the turbine to produce more heat). CEH can be either 0 (or missing) or 1 for fixed back pressure mode processes. If it is zero, the activity represents the electricity generation and the

52 Many other parameters can be given.


88/145


capacity represents the electrical capacity; if it is 1, the activity represents the total energy outputand the capacity represents the total capacity (power+heat). The comment about the fixed CHPR is

basically correct, but one could also use a 'LO' CHPR for defining the BP point, if so desired (toallow by-passing the turbine to produce more heat).

It is also possible to describe a back pressure CHP process by means of the CEFF parameter. In thiscase the equations shown are still valid. The first technology shown in Figure 3-14 is an example of

back pressure cogeneration power plant. The others parameters are the same as those described forthe electricity power plant.

Condensing combined heat and power plantThe condensing pass-out or extraction turbine version of a CHP process does not have to produceheat, permitting only electricity to be generated, and permitting the amount of heat generated to bedirectly adjusted to the heat demand, where the electricity generation is reciprocally proportional to

heat generation (electricity losses because of heat extraction).

Figure 3-12 Condensing combined heat and power characteristic curve

Pass-out or extraction turbines are thus described slightly differently.1. Electricity to heat ratio, via parameter CEH such that:

a) = 1: heat loss per unit of electricity gained (moving from backpressure to condensingmode), indicating that activity is measured in terms of total output (electricity plus heat).

2. Efficiencies, according to 1:a) are specified for the condensing point, or

b) are specified for backpressure point.3. Costs, according to 1:

a) are specified based according to condensing mode, or

b) are specified based on total electricity and heat output at backpressure point.


89/145


4. Electric loss per unit of heat gained ( CHPR ): Ratio of heat to power at backpressure point; atleast a maximum value is required, but in addition also a minimum value may be specified

Figure 3-13 Condensing combined heat and line fuel

Thus in TIMES-VEDA, a condensing system is characterised as follows.ETA el = 54% at the condensing point; ETA elBP = 45%; ETA thBP = 44%Load utilization period heat = 3500 h/aLoad utilization period electricity = 7500 h/a in TIMES-VEDA become :

EFF = ETA el = 0.54CHPR~UP = ETA thBP / ETA elBP = 0.98VDA_CEH = (ETA el ETA elBP)/ETA thBP = 0.2045AFAC~ELC = 7500/8760 = 0.856AFAC~HEAT = 3500/8760 = 0.40

Also for the condensing CHP processes it is possible to use the CEFF parameter. In this case theequation shown remains valid.

The first technology shown in Figure 3-14 is an example of a back pressure cogeneration power plant and its parameters and the second one is an example of a condensing cogeneration power plant. Besides the CHP specific parameters other parameters related to power plants may be used.This is the minimum sets of parameters need to describe a CHP, other relevant parameters aredescribed in VEDA-FE interface under Tools, Supported Attributes.

Figure 3-14 Cogeneration power plants


90/145


3.2.4.5 District heating plants

The district heating plants are heat generation technologies (HPL) that consume one or morecommodities and produce one time-sliced commodity (heat).

In Figure 3-15 is shown an example of district heating plants, the parameters used to describe this process are basically the same parameters mentioned in section 3.2.4.3 for electric generation plants, except that the output commodity is heat and the capacity unit PJ/a, and thus the Cap2Act is1.

Figure 3-15 District heating plants

3.2.4.6 Cars, trains, bus converting activity, capacity, demand units; and dual-purpose devices

In this section various transport technologies are described (cars, train, buses, etc.). Generally thesetechnologies are defined as demand technologies (DMD). This means that they consume an energycommodity to produce directly the energy services demanded.

The parameters to describe a demand technology are:- topology Comm-IN/OUT ;- efficiency ( EFF or CEFF );- availability factor ( AF );- commodity dependent availability factor (upper limit) ( AFAC );

- conversion factor Cap2Act ;- cost ( VAROM );- installed capacity in the base year ( STOCK ), and- process activity to commodity flow ( ACTFLO ).

It might be the case that the unit in which the commodity(ies) of the primary commodity group aremeasured is different from the activity unit. An example is shown in Figure 3-16. The activity of thetransport technology passenger car (CAR) is defined by the two demands they service (cars shortdistance CAR_SD and cars long distance - CAR_LD), which are measured in million of

passenger kilometres (MPKms). The activity of the process is, however, defined in million vehiclekilometres (MVKms), while the capacity of the process CAR is defined as thousand units of cars


91/145


(000 units). Th us the conversion factor from capacity to activity ( Cap2Act ) needs to describe theaverage mileage of a car per year. The process parameter ( ACTFLO ) contains the conversionfactor from the activity unit to the commodity unit of the primary commodity group PCG). In thisexample the factor corresponds to the average number of persons per car (1.5). The availabilityfactor ( AF ) describes the maximum kilometers available for a car in a year. In addition a

commodity dependent availability factor (upper limit, AFAC ) corresponding to the maximum kmsrelated to short/long distance relationship relative to the overall AF parameter. In this example theAF is 20000 and the AFAC~ CAR_SD is 0.8, this means that the maximum availability of the carfor short distance travel (CAR_SD) is 80% of the overall usage and is thus equal to 20000*0.8 =16000.

The same parameters used for the cars can also characterize trains, buses, and so on for other multi-use demand technologies. Other examples of transportation processes are available in theVT_DEMOT_TRA_V5 workbook.

Figure 3-16 Cars

3.2.4.7 Car process- defining demand and the load shape

The demand devices are used to satisfy an energy service demand. For example cars are used tosatisfy private transport demand on short and long distance. In VEDA is necessary to define a tablewith base year energy service demand (see Figure 3-17) . Then at the column level is defined thedemand by region. The load curve describes seasonal, weekly and diurnal variations in demands(externally given as inputs). In this example is shows the transport cars demand, with seasons anddiurnal variations, described in the TIMES_DEMO with a variable load curve, as shows in Figure3-17. In figure Figure 3-17 a "Table level declarations" is used. The Table level declaration todefine load curve is ~FI_T: COM_FR. This table level declaration use COM_FR as the attribute forall values in the table that don't have an attribute specification at the column or row level.

Figure 3-17 Cars demand and load shape


92/145


3.2.4.8 Industrial process

In this an example of the industry chain for the Iron and Steel sector is described. In this case onlythe last technology (e.g. finishing process) is described like a demand technology, where the othertechnologies of the chain are described as (upstream) processes in the chain. This means that they

consume energy commodities and/or materials to produce new materials useful for the Iron andSteel chain production. The last process, that is a demand technology, finally consumes energycommodities and materials produced in the chain to satisfy the iron and steel demand.

The parameters used to describe the industrial processes in the Figure 3-10 are similar to those withsome variations being the way the alternative commodities are handled:

- consumption of fuels/materials (INPUT), and- production of materials (OUTPUT).

The first step of the iron and steel production chain is the iron and steel pellet and sinter production,as shows in Figure 3-18. The materials produced in the first step are then used to produce raw iron,as shows in Figure 3-19. The third step is related to the crude steel production and characterizedfrom the consumption of fuels and materials produced in the second step, as shows in Figure 3-20.

Figure 3-18 Industrial processes Iron and steel pellet and sinter production

Figure 3-19 Industrial processes Raw iron production

Figure 3-20 Industrial processes Crude steel production

The last step is the iron and steel production, through a finishing process described as demandtechnology, as shows in Figure 3-21.


93/145


Figure 3-21 Industrial processes Iron and Steel production

3.2.5 How to construct a scenario file

The Scen_.xls files contain additional information and parameters for the entire RES,commodities and technologies (rule-based). The important thing to understand about scenario files

is that they can only manipulate information associated with previously declared RES components,and that new commodities and technologies may not be added via scenario files, though parametersmay be. For this example there are two files:

To construct a scenario file, from VEDA- FE Navigator click NEW in the Sc enario Files windowan put name (e.g. test) in the pop-up window. An excel file with the scenario file structure will beopen, as shows in Figure 3-22.

Figure 3-22 New Scenario file from VEDA-FE Navigator

To define subsets of technologies/commodities a set of headers (see Table 3.1) are used underwhich the user specifies masks using text/wildcards (? for a single character, * for any number) toidentify the qualifying technologies. Technology qualifiers identify candidates based upon setmembers ( Pset_Set ), and/or masks for topology (commodity in/out, Cset_CI/O ), and/or

name/description masks ( Pset_PN/D ). In general if nothing appears below a specification columnthe values provided for the parameter apply to all entries. Exclude is done by -. Multiplemasks may be specified separated by ,.


94/145


Table 3.1: Headers in the scenario files to define subsets of technologies/commodities

Header DescriptionRegion Region namePSet_Set Process set. Comma separated list of process names allowed (wild cards allowed)PSet_PN Process name set. Comma separated list of process names allowed (wild cards allowed)

PSet_PD Process description set. Comma separated list of process description allowed (wildcards allowed)

Pset_CI Process commodity input set. Comma separated list of input commodities to define a setof processes allowed (wild cards allowed)

Pset_CO Process commodity output set. Comma separated list of output commodities to define aset of processes allowed (wild cards allowed)

CSet_Set Commodity set. Comma separated list of process names allowed (wild cards allowed)CSet_CN Commodity name set. Comma separated list of commodity names allowed (wild cards

allowed)CSet_CD Commodity description set. Comma separated list of commodity descriptions allowed

(wild cards allowed)

To filling- in this sheet with the parameter, its enough a to check from VEDA-FE interface, Toolsand Supported Attribute.

Figure 3-23 shows an example of scenario file for the Demo. This is a scenario file to impose a CO2taxation on the Green House Gas (GHG). In the column attribute there is the parameterCOM_TAXNET, below the column year there is the year in which to apply the taxation, the valueis in the column WEU and the commodity name to which apply the taxation is below Cset_CN

(Commodity name of the commodity set).

Figure 3-23 Scenario file from the Demo

3.2.6 How to construct User Constraints (UC)

While TIMES provides most all the core equations that are needed to properly represent theindividual components of the energy system (e.g., commodity balance, process operation, capitalstock turnover), the user will almost always needs to introduce additional constraints to adequatelyrepresent aspects of their particular energy system. Such constraints often involve clusters oftechnologies (e.g., solar, or wind, or hydro each may be subject to a total potential installed

capacity, renewable portfolio (RPS) or CAFE standards may require that a group of technologiesmeet a certain percentage of overall electricity generation or average vehicle fleet efficiency byvehicle class).


95/145


To enable this necessary, and powerful, flexibility User Constraints (UC) may be built. TIMESUser Constraints allow relationships to be established between most any of the TIMES modelvariables, (summed over region, period, time-slice as desired) as well as input parameters. Here acouple of practical examples are described as implemented for TIMES_DEMO.

The majority of User Constraints fall into two general categories, absolute and share. The role ofabsolute user constraints is to control the investment, capacity or operation of a set of processes inabsolute terms. Examples include:

- Electricity generation should consume at least 400 PJ of gas in each period;- Maximum hydro potential is 50MW in 2005 and remains constant from 2010 on at 2010,

see Figure 3-21;- Geothermal should produce at least 3 TWh per year by 2020, and 10 by 2050, and- The total nuclear capacity should be at most 4 GW by year 2020, and at most 10 by year

2050.

The role of share User Constraints are to control the investment, capacity or operation of a set of processes (subset) as the share of a larger set (BigSet). Examples include:

- A maximum of .5PJ and a minimum of .05PJ of coal may be consumed for the generation ofelectricity, while the maximum share from gas is 90%, see Figure 3-21;

- At least 5% of electricity generation should be wind based by 2020;- At least 10% of the residential space heating should be based on natural gas;- Small cars may take at most 30% of the automobile travel demand, and- At least 60% of residential lighting will use the conventional incandescent bulbs.

3.2.6.1 User Constraints (UC) in Scenario Files

In the TIMES_DEMO the user constraints are in the Scen_UCTest workbook. How to construct theUC is defined in User Constraints at www.kanors.com/vedasupport . Declaring UC Sets:

3.2.6.2 How to construct special flow share scenarios

As discussed above, the key to defining a share constraint is to identify two sets of technologies: aBigSet (BS), and a SubSet (SS). For example, in the first case, BS would be all technologies that

produce residential heat for existing rural houses, and the SS would comprise those among the BSthat consume wood. Another case could be the use of renewable hydro power plant in the electricity

production, in which the BS would be all technologies that produce electricity, and SS wouldcomprise those among the BS that consume hydro fuel.

In principle, the BS and SS can be defined in many ways, but a vast majority of cases can behandled by defining the BS based on commodity produced (CP), and the SS based on commodity

consumed (CC). For TIMES VEDA makes it possible to construct such relationships by means of aspecial flo-share scenario (AFS scenario).
http://www.kanors.com/vedasupporthttp://www.kanors.com/vedasupporthttp://www.kanors.com/vedasupporthttp://www.kanors.com/vedasupport


96/145


The Figure 3-24 shows the UConstraints section in the VEDA-FE Navigator. It lists all theUConstraints scenarios stored in the UConstraints folder in SuppXLS folder of the Template pathand allows to create new UC Scenarios and import the existing ones. Use it to create a new UserConstraint share scenario by:

1. Clicking on New button in the UConstraints section of VEDA-Naviator to create a new UCScenario, Figure 3-24;

2. Entering the scenario name in the prompt box (should be unique across all scenarios and thename given can be of max 20 chars long; the Scenario is saved with the name given by theuser preceded by ScenUC_ in UConstraints folder in SuppXLS folder of the template

path);3. Selecting the GDX file associated with a previously run Base scenario from which the base

year fuel shares are to be extracted;

4. After the GDX file is read, a confirmation box appears that asks if an existing UC filestransformation information needs to be inherited: if yes clicked, another open file boxappears indicating the UC file to be inherited here (in this case the years for UCspecification are taken from the source file), while if no clicked, another prompt box appearsasking the years for which UCs are to be specified , as a comma separated string whichgreater than the base-year.- TheUC scenario file is then created, with or without the inherited transformation, where

if inherited the complex definitions are also written in the new UC file.5. Then double click on the created file (in the Figure 3-24 Test_AFS_UC) to open the UC File

for edit (see below), and6. Click SYNC to import the UC File to associated Database (Adratio).

3.2.6.3 The UC scenario file contains the following sheets:1. BaseYrDataValues - that shows the data values of each CP, CC combination in all

regions in the base year (filled by VEDA);2. BaseYrShares - that shows the data shares of each CP, CC combination in all

regions in the base year (filled by VEDA);

3. A transformation sheet Trans (where the user provides the operations to be applied tothe base year shares over time, and4. A Final Data Shares Sheet Final for each year chosen for UC specification (created by

VEDA).

When a file is edited via VEDA-Navigator mode:- The user cannot modify the BaseYrDataValues and the Final Sheets at all, and- New CP, CC combinations can be added to the BaseYrShares sheet. [As a new CP, CC

combination is added to the sheet the corresponding shares for all the regions are

evaluated immediately. The CP and CC can have comma separated values, can includewildcards viz. * %? Underscore is not recognized as a wildcard but a literal.]


97/145


98/145


Figure 3-26 Example of new industry technologies in the SubRes

For other examples is possible to see the SubRES_B-NewTechs template in the SubRES_TMPLfolder.

3.2.7.2 How to construct a SubRes transformation file

See Templates Basic - files n SubRes section at www.kanors.com/vedasupport .

3.2.8 How to construct a demand file

The folder SuppXLS contains the sub-folder Demand where the demand driver (DEMO_Base)and sensitivity series and region-segment driver allocation table (Dem_Alloc+Series), applied overtime to produce the base year projection. Figure 3-27 shows a driver table (~DRVR_Table) with thedrivers (GDP, Population, etc.) for the two regions (ROW and WEU), with their initial allocationfor each region.

Figure 3-27 Demand driver

The next two figures show the sensitivity series (~Series, Figure 3-28) and the drivers allocation(DRVR_Allocation), Figure 3-29) table. A new series may be added by means of a new row withthe name of the series and the new values for each year.
http://www.kanors.com/vedasupporthttp://www.kanors.com/vedasupporthttp://www.kanors.com/vedasupporthttp://www.kanors.com/vedasupport


99/145


Figure 3-28 Demand sensitiviy series

Figure 3-29 shows the driver allocations for each of the regions demands . Each demand isassociated with a driver, along with a calibration and sensitivity series. The calibration andsensitivity column are the series described in the ~Series table, Figure 3-29.

Figure 3-29 Demand drivers allocation table

3.2.9 How to construct a trade scenario

3.2.9.1 How to declare a trade matrix

To exchange commodities between the regions 53 it is necessary to declare a trade matrix and someattributes for the associated inter-region exchange process (IRE). This is accomplished by means ofthe Trade scenario file (ScenTrade_TRADEAttribs) with parameters for the inter-regional tradetechnologies.

The TIMES_DEMO model exchanges electricity (ELC) and oil crude (OILCRD) between the tworegions (ROW and WEU). The associated trade matrix is declared in an Excel file, as shows in theFigure 3-30.

53 To know more about the TIMES trade structures see the Documentation for TIMES model PART II from page215 available at http://www.etsap.org/Docs/TIMESDoc-Details.pdf
http://www.etsap.org/Docs/TIMESDoc-Details.pdfhttp://www.etsap.org/Docs/TIMESDoc-Details.pdfhttp://www.etsap.org/Docs/TIMESDoc-Details.pdfhttp://www.etsap.org/Docs/TIMESDoc-Details.pdf


100/145


Figure 3-30 Trade matrix declaration

3.2.9.2 How to declare a trade parameters

To declare the trade parameters, is possible to construct a scenario file, as shown in Figure 3-31(from the Demo).

Figure 3-31 Trade parameters

3.3 Interpolation and extrapolation

Time-dependent user input parameters are specified for specific years, the so called data years.These data years do not have to coincide with the model years needed for the current run. Reasonsfor differences between these two sets are for example that the period definition for the model has

been altered after having provided the initial set of input data leading to different milestone years or

that statistical data are only available for certain years that do not match the model years. In order toavoid burdening the user with the cumbersome adjustment of the input data to the model years, aninter/extrapolation routine is embedded in the TIMES model generator and in the VEDA software.The inter/extrapolation routine distinguishes between a default inter/extrapolation that isautomatically applied to the input data and an enhanced user controlled inter/extrapolation thatallows the user to specify inter/extrapolation rules for each time series explicitly. Independent of thedefault or user-controlled inter/extrapolation options, TIMES inter/ extrapolates (using the standardalgorithm) all cost parameters in the objective function to the individual years of the model as partof calculating the annual cost details.


101/145


3.3.1 Defaults inter/extrapolation

The standard default inter/extrapolation method interpolates linearly between data points, while itextrapolates the first/last data point constantly backward/forward. The parameters given in Error!Reference source not found .Table 3.2 are by default NOT inter/extrapolated in this standard

default method. All other parameters are by default both interpolated and extrapolated in the defaultmethod.

In many cases VEDA- FE provides defaults that often meet the users needs and leave the user tofocus of the actual data.

Table 3.2: Parameters not being inter/extrapolated by default

Parameter Justification Alternativedefault method

ACT_BND

Bounds, may be intended at specific periodsonly

Migration

CAP_BND NCAP_BNDFLO_FRFLO_SHARSTGOUT_BNDSTGIN_BNDCOM_BNDNETCOM_BNDPRDCOM_CUMNETCOM_CUMPRDCOM_CHRBNDIRE_BNDIRE_XBNDUC_RHST User constraints may be intended for specific

periods onlyMigrationUC_RHSRT

UC_RHSRTS NCAP_AFM

Interpolation is meaningless for these parameters (parameter value is a discrete

number indicating which MULTI curveshould be used).

None NCAP_EFFM NCAP_FOMM NCAP_FSUBM NCAP_FTAXM NCAP_AFX Interpolation is meaningless for these

parameters (parameter value is a discretenumber indicating which SHAPE curve

should be used).

None NCAP_EFFX NCAP_FOMX NCAP_FSUBX NCAP_FTAXX NCAP_PASTI Parameter describes past investments for

individual vintage years so is notinterpolated.

None

NCAP_PASTY Parameter describes number of years overwhich to distribute past investments. None

COM_BLVAL Blending parameters at the moment are notinterpolated. NonePEAKDA_BL

COM_BPRICE Base prices for elastic demands are obtained

from baseline solution None

CM_MAXCO2C Bound may be intended at specific years only None


102/145


As shown in Table 3.2, for bound and RHS parameters an alternative default method ofinterpolation/extrapolation is applied: migration. Migration means that data points are interpolatedand extrapolated within each period but not across periods. This method thus migrates any data

point specified for other than milestoneyr years to the corresponding milestoneyr within the period,so that it will be effective in that period.

3.3.2 Enhanced user-controlled interpolation / extrapolation

The inter/extrapolation facility provides maximum flexibility by allowing the user to control theinterpolation of each time series separately. Many bounding constraints as well as market and

product allocation constraints might be applicable either to only specific years or to the continuoustimes pan of the full time horizon, or to a subset thereof. The possibility of controlling interpolation

on a time series basis improves the independence between the years found in the primary databaseand the data actually used in the individual runs of a TIMES model. In this way the model is mademore flexible with respect to running scenarios with arbitrary model years and period lengths, whileusing basically the very same input database.

The enhanced interpolation/extrapolation facility provides the user with options to control theinterpolation and extrapolation of each individual time series (Table 2). The option 0 does notchange the default behaviour. The specific options that correspond to the default methods are 3 (thestandard default) and 10 (alternative default method for bounds and RHS parameters). Non default

interpolation/extrapolation can be requested for any parameter by providing an additional instanceof the parameter with an indicator in the YEAR index and a value corresponding to one of theinteger valued Option Codes (see Table 2 and example below).

This control specification activates the interpolation/extrapolation rule for the time series, and isdistinguished from actual time series data by providing a special control label (0) in the YEARindex. The particular interpolation rule to apply is a function of the Option Code assigned to thecontrol record for the parameter. Note that for log linear interpolation the Option Code indicates theyear from which the interpolation is switched from standard to log linear mode. TIMES user shell(s)

will provide mechanisms for imbedding the control label and setting the Option Code througheasily understandable selections from a user friendly dropdown list, making the specification simpleand transparent to the user.

The enhanced interpolation/extrapolation facility provides the user with the following options tocontrol the interpolation and extrapolation of each individual time series.

- Interpolation and extrapolation of data in the default way as predefined in TIMES. Thisoption does not require any explicit action from the user.

- No interpolation or extrapolation of data (only valid for non cost parameters).

- Interpolation between data points but no extrapolation (useful for many bounds). See optioncodes 1 and 11 in Table 3.3 below.


103/145


- Interpolation between data points entered, and filling in all points outside the interpolationwindow with the EPS value. This is useful for e.g. the RHS of equality type user constraints,or limitations on future investment in a particular instance of a technology, which shouldoften have a continuous value of EPS to be effective. See option codes 2 and 12 in Table 2

below.

- Forced interpolation and extrapolation throughout the time horizon. Can be useful for parameters that are by default not interpolated. See option codes 3, 4, and 5 as well as 14and 15 in Table 3.3 below.

- Log linear interpolation beyond a specified data year, and both forward and backwardextrapolation outside the interpolation window. Log linear interpolation is guided by relativecoefficients of annual change instead of absolute data values.

Table 3.3: Option codes for the control of data interpolation

Action Option code Applies toDefault interpolation/extrapolation (see above) 0 (or none) All

No interpolation/extrapolation < 0 All

Interpolation but not extrapolation 1 All

Interpolation, but extrapolation with EPS 2 All

Full interpolation and extrapolation 3 AllInterpolation and backward extrapolation 4 All

Interpolation and forward extrapolation 5 Bounds, RHS

Migrated interpolation/extrapolation within periods 10 Bounds, RHS

Interpolation migrated at end-points, no extrapolation 11 Bounds, RHSInterpolation migrated at ends, extrapolation with EPS 12 Bounds, RHS

Interpolation migrated at end, backward extrapolation 14 Bounds, RHS

Interpolation migrated at start, forward extrapolation 15 Bounds, RHS

Log-linear interpolation beyond YEAR YEAR (1000) All

Apart from the migrating options 10 15, all the other enhanced interpolation options describedabove are available for all TIMES parameters. The migrating options are available for all bound andRHS parameters, which are listed in Table 1 above (excluding CM_MAXCO2C, for which

migration is of no use because the parameter is effective for any given year). Note that becauseoption 10 is the default method for bound and RHS parameters, and it is not available for other

parameters, there is no need to ever use this option explicitly. It is mentioned in Table 2 forcompleteness only.

3.3.3 Interpolation of cost parameters

As a general rule, all cost parameters in TIMES are densely interpolated and extrapolated. Thismeans that the parameters will have a value for every single year within the range of years they

apply, and the changes in costs over years will thus be accurately taken into account in the objectivefunction. The user can use the interpolation options 1 5 for even cost parameters. Whenever an


104/145


option is specified for a cost parameter, it will be first sparsely interpolated/extrapolated accordingto the user option over the union of modelyear and datayear, and any remaining empty data pointsare filled with the EPS value. The EPS values will ensure that despite the subsequent denseinterpolation the effect of user option will be preserved to the extent possible. However, one shouldnote that due to dense interpolation, the effects of the user options will inevitably be smoothed.


105/145


4 Appendix A - Getting Started with Problem Defining andDescribing the Area of Study 54

This appendix is a primer on energy systems analysis. Approaching energy as a system instead of aset of elements gives the advantage of identifying the most important substitution options that arelinked to the system as a whole and cannot be understood looking at a single technology orcommodity or sector.

Systems analysis applies systems principles to aid decision-makers in problems of identifying,quantifying, and controlling a system. While taking into account multiple objectives, constraints,resources, it aims to specify possible course of action, together with their risks, costs and benefits.

After an excursus on the peculiarities of energy as a system, this appendix illustrates how to proceed to the three steps of the analyses: identification, quantification and control. It is intended toillustrate how complex energy related matters are, it hints to the complexity of decision-making inenergy related matters and it shows why it helps using ETSAP Tools to represent energy systemsand compile alternative development scenarios.

4.1 The multiple dimensions of energy systems

Present energy systems are the result of complex country dependent, multi-sector developments.Although each decision in this n-step path may have provided rational answers based upon energy,engineering, economic or environmental reasons (for short: 4E), it is hard to find rationality in theoverall system. Furthermore, decisions take into account several other important dimensions that,

broadly speaking, are part of humanities or social sciences.

The four main dimensions encompassed by energy systems analyses will be shortly illustrated inthe following pages. Although social sciences touch aspects as fundamental as the four mentioned

above, they are not treated here because the ETSAP modelling tools are not yet in the position torepresent them explicitly and quantitatively. At most, ETSAP tools help measuring how large is thegap between actual and theoretical systems.

54 Appendix drafted by G.C. Tosato


106/145


4.1.1 Energy: from primary resource to end-use services 55

An energy system comprises an energy supply sector and energy end-use. The energy supply sectorconsists of a sequence of elaborate and complex processes for extracting energy resources,converting these into more desirable and suitable forms of energy, and delivering energy to places

where the demand exists. The end-use part of the energy system provides energy services such ascooking, illumination, comfortable indoor climate, refrigerated storage, transportation, andconsumer goods. The purpose, therefore, of the energy system is the fulfilment of demand forenergy services.

The architecture of an energy system can be represented by a sequential series of linked stages,alternating commodities and processes, connecting various energy conversion and transformation

processes that ultimately result in the provision of goods and services (see Figure 4-1) . A number ofexamples are given for energy extraction, treatment, conversion, distribution, end-use (finalenergy), and energy services in the energy system. The technical means by which each stage isrealized have evolved over time, providing a mosaic of past evolution and future options.

Primary energy is the energy that is embodied in resources as they exist in nature: the chemicalenergy embodied in fossil fuels (coal, oil, and natural gas) or biomass, the potential energy of awater reservoir, the electromagnetic energy of solar radiation, and the energy released in nuclearreactions. For the most part, primary energy is not used directly but is first converted andtransformed into electricity and fuels such as gasoline, jet fuel, heating oil, or charcoal. Primaryenergy is expressed in common units of PJ (international standard) or Tonnes oil equivalent(frequently used) 56.

Final energy is the energy transported and distributed to the point of final use. Examples includegasoline at the service station, electricity at the socket, or fuel wood in the barn. All final energyvectors are expressed in the common energy unit (GJ), but it is clear to all users that the same GJcontent of wood fuels is different from electricity: the latter can produce work directly, the formerhas to undergo the Carnot cycle and losses before producing work.

The next energy transformation is the conversion of final energy in useful energy, basically heat andwork, by means of energy end- use devices, such as boilers, engines or motor drives. Useful energyis measured at the crankshaft of an automobile engine or an industrial electric motor, by the heat ofa household radiator or an industrial boiler, or by the luminosity of a light bulb. In principle useful

55 This paragraph and the following one are taken from N. Nakichenovich et al., CLIMATE CHANGE 1995, Impacts,Adaptation and Mitigation of Climate Change: Scientific Technical Analyses, Contribution of Working Group II tothe Second Assessment Report of the Intergovernmental Panel for Climate Change, WMO UNEP, CambridgeUniversity Press, 1996, 878 pages, Chapter B., pages 75-7856 In other words, primary energy consumption is an abstract concepts, calculated by converted the different forms ofenergy into a common unit. The conversion can be calculated at the physical content equivalent (adopted by IEA), orat the substitution principle (adopted by EIA). The former method converts electricity from nuclear at 33% efficiency,

geothermal at 10% efficiency and all other non-biomass renewable sources at 100% efficiency. The latter convertsevery non fossil / non biomass electricity / heat at the average efficiency of existing fossil power plants. According tothe editor of this report, this second method is more appropriate for economic evaluation (it uses a concept similar to themarginal value) and gives equal weight to each kWh, produced by wither nuclear or renewables.


107/145


energy may be expressed in common energy units, but in practice it is used in sector or applicationspecific energy related units (thermie, lumen, etc.).

In conjunction with non-energy end-use devices, useful energy provides energy services, such asmoving vehicles, warm rooms, process heat, or light. Energy services are expressed in specific

units, such as passengers or tons-kilometre, square-meters of heated flats, tons of cement, and evenvalue added or labour force in economic producing sectors.

Figure 4-1: The energy system: schematic diagram with some illustrative examples of the energysector and energy end-use and services.


108/145


Energy services are the result of a combination of various technologies, infrastructures (capital),labour (know-how), materials, and energy carriers. Clearly, all these input factors carry a price tagand, within each category, are in part substitutable for one another. From the consumer's

perspective, the important issues are quantity, quality and cost of energy services. It often matterslittle what the energy carrier or the source of that carrier is. It is fair to say that most consumers are

often unaware of the "upstream" activities of the energy system. The energy system is service-driven (i.e., from the bottom up), whereas energy flows are driven by resource availability andconversion processes (from the top down). Energy flows and driving forces interact intimately.Therefore, the energy sector cannot be analysed in isolation: It is not sufficient to consider only howenergy is supplied; the analysis also must include how and for what purposes energy is used.

In 1990, 385 EJ of primary energy produced 279 EJ of final energy delivered to consumers,resulting in an estimated 112 EJ of useful energy after conversion in end-use devices. The deliveryof 112 EJ of useful energy left 273 EJ of rejected energy. Most rejected energy is released into the

environment as low-temperature heat, with the exception of some losses and wastes such as theincomplete combustion of fuels.

4.1.2 Engineering: technology efficiency and system efficiency

Energy is conserved in every conversion process or device. It can neither be created nor destroyed, but it can be converted from one form into another. This is the first law of thermodynamics. Forexample, energy in the form of electricity entering an electric motor results in the desired output-say, kinetic energy of the rotating shaft to do work - and losses in the form of heat as the undesired

by-product caused by electric resistance, magnetic losses, friction, and other imperfections of actualdevices. The energy entering a process equals the energy exiting. Energy efficiency is defined as theratio of the desired (usable) energy output to the energy input. In the electric- motor example, this isthe ratio of the shaft power to the energy input electricity. In the case of natural gas for homeheating, energy efficiency is the ratio of heat energy supplied to the home to the energy of thenatural gas entering the furnace. This definition of energy efficiency is sometimes called first-lawefficiency.

A more efficient provision of satisfying energy services not only reduces the amount of primary

energy required but, in general, also reduces adverse environmental impacts. Although efficiency isan important determinant of the performance of the energy system, it is not the only one. In theexample of a home furnace, other considerations include investment, operating costs, lifetime, peak

power, ease of installation and operation, and other technical and economic factors. For entireenergy systems, other considerations include regional resource endowments, conversiontechnologies, geography, information, time, prices, investment finance, operating costs, age ofinfrastructures, and know-how.

The overall efficiency of an energy system depends on the individual process efficiencies, the

structure of energy supply and conversion sector, and the energy end-use patterns. It is the result ofcompounding the efficiencies of the whole chain of energy supply, conversion, distribution, and


109/145


end-use processes. The weakest link in the analysis of the efficiency of various energy chains is thedetermination of energy services and their quantification, mostly due to the lack of data about end-use services and actual patterns of their use.

In 1990, the global efficiency of converting primary energy sources to final energy forms, including

electricity, was about 72%. The efficiency of converting final energy forms into useful energy islower, with an estimated global average of 40%. The resulting average global efficiency ofconverting primary energy to useful energy, then, is the product of the above two efficiencies, or29%. Because detailed statistics for most energy services do not exist and many rough estimatesenter the efficiency calculations, the overall efficiency of primary energy to services reported in theliterature spans a wide range, from 15 to 30%.

Figure 4-2: Major energy and carbon flows through the global energy systems in 1990

How much energy is needed for a particular energy service? The answer to this question is not sostraightforward. It depends on the type and quality of the desired energy service; the type of


110/145


conversion technology; the fuel, including the way the fuel is supplied; and the surroundings,infrastructures, and organizations that provide the energy service. Initially, energy-efficiencyimprovements can be achieved in many instances without elaborate analysis through commonsense, good housekeeping, and leak-plugging practices. Obviously, energy service efficienciesimprove as a result of sealing leaking window frames or installing a more efficient furnace. If the

service is transportation - getting to and from work, for example - using a transit bus jointly withother commuters is more energy- efficient than taking individual automobiles. After the easiestimprovements have been made, however, the analysis must go far beyond energy accounting.

Here the concept that something may get lost or destroyed in every energy device or transformation process is useful. For instance, in terms of energy, 1kWh of electricity and the heat contained in 43kg of 20C water are equal (i.e., 3.6 MJ). At ambient conditions, it is obvious that 1kWh electricityhas a much larger potential to do work (e.g., to turn a shaft or to provide light) than the 43 kg of20C water. Another, more technical, example should help clarify the difference. Furnaces used to

heat buildings are typically 70 to 80% efficient, with the latest, best-performing condensingfurnaces operating at efficiencies greater than 90%. This may suggest that little energy savingsshould be possible, considering the high first-law efficiencies of furnaces. Such a conclusion isincorrect. The quoted efficiency is based on the specific process being used to operate the furnace -combustion of fossil fuel to produce heat. Because the combustion temperatures in a furnace aresignificantly higher than those desired for the energy service of space heating, the service is notwell matched to the source, and the result is an inefficient application of the device and fuel. Ratherthan focusing on the efficiency of a given technique for the provision of the energy service of spaceheating, one needs to investigate the theoretical limits of the efficiency of supplying heat to a

building based on the actual temperature regime between the desired room temperature and the heatsupplied by a technology.

The ratio of theoretical minimum energy consumption for a particular task to the actual energyconsumption for the same task is called second-law (of thermodynamics) efficiency 57. Consideranother example: Providing a temperature of 27C to a building while the outdoor temperature is2C requires a theoretical minimum of about one unit of energy input for every 12 units of heatenergy delivered to the indoors (according to the second law of thermodynamics). To provide 12units of heat with an 80% efficient furnace, however, requires 12/0.8, or 15, units of heat. The

corresponding second-law efficiency is the ratio of ideal to actual energy use (i.e., 1/15 or 7%). Thefirst-law efficiency of 80% gives the misleading impression that only modest improvements are

possible. The second law efficiency of 7% says that a 15-fold reduction in final heating energy istheoretically possible. For example, instead of combusting a fossil fuel, Goldemberg et at. (1988)give the example of a heat pump, which extracts heat from a local environment (outdoor air, indoorexhaust air, ground-water) and delivers it into the building. A heat pump operating on electricitycan supply 12 units of heat for 3 to 4 units of electrical energy. The second-law efficiency improvesto 25-33% for this particular task- still considerably below the theoretical maximum efficiency. Notaccounted for in this example, however, are energy losses during electricity generation. Assuming a

57 The concept of second-low efficiency opens the door to the use of the concept of exergy.


111/145


modern gas-fired, combined- cycle power plant with 50% efficiency, the overall efficiency gain isstill a factor of two compared with a gas furnace heating system. In practice, theoretical maximacannot be achieved. More realistic improvement potentials might be in the range of half of thetheoretical limit. In addition, further improvements in the efficiency of supplying services are

possible by task changes for instance, reducing the thermal heat losses of the building to be heated

via better insulated walls and windows.

There are many difficulties and definitional ambiguities involved in estimating the efficienciesaccording to the second thermodynamic principle for comprehensive energy source-to-servicechains or entire energy systems. The analysis of individual conversion devices is comparativelysimpler than the analysis of energy systems efficiencies to useful energy or even to energy services.All indicate that primary-to-service (second-law) efficiencies are as low as a few percent. Anoverall primary-to-useful energy second law efficiency of 21% has been estimated for Japan, lessthan 15% for Italy, 32% for Brazil, which reduces to 23% when primary energy to service second

law efficiency are estimated (2.5% for the United States). Other estimates of global and regional primary-to-service energy second law efficiencies vary from ten to as low as a few percent.

The theoretical potential for efficiency improvements is very large; current energy systems arenowhere close to the maximum levels suggested by the second law of thermodynamics. However,the full realization of this potential is impossible. Friction, resistance, and similar losses never can

be totally avoided. In addition, there are numerous barriers and inertias to be overcome, such associal behaviour, vintage structures, financing of capital costs, lack of information and know-how,and insufficient policy incentives.

The principal advantage of second-law efficiency is that it relates actual efficiency to the theoretical(ideal) maximum. Although this theoretical maximum can never be reached, low efficienciesidentify those areas with the largest potentials for efficiency improvement. For fossil fuels, thissuggests the areas that also have the highest emission-mitigation potentials.

4.1.3 Economics: the value of energy systems

Since the industrial revolution took place, the economic development as a whole is powered byenergy and the global 2005 GDP of about 55 TUS$2000 58 would not be possible. However thiseconomic development and the associated welfare for the people come at a cost that is considerable,directly and indirectly. What part of the world economic resources are consumed in order to supply

producers and consumers with the energy services they need?

The direct cost of providing primary fuels has been of the order of 1.5 TUS$ globally in 2000. Inthe year 2000, with an average spot price of crude oil of 26.8$/bbl, this cost represented about 4%of the global GDP at market prices (about 37 TUS$). In 2005, with a yearly average spot price of38.1 $/bbl, this share is much higher. But at current future prices of crude oil approaching 100 $/bbl

58 The figure is taken from the IEA Key Energy Statistics, page 48, at purchase power parity.


112/145


(November 2007) the marginal value of primary energy supply approaches 10 percent over theGDP. The additional annual cost of generating electricity, fuels and heat adds another considerableamount. If annual costs of transmission and distribution are added, the total cost of supplying to theeconomic producers and the families the amount of energy demanded is on the order of 15% of theglobal GDP. Slightly more than half of this cost is borne by families, the rest by industries.

However, the economic weight of energy systems as a whole is much higher. Actually, if the energysystem efficiency concept explained above is taken into account, the economic weight of the systemaffected by energy policies ranges between 35 to 50%. In fact it includes all end use devices thattransform final energy into useful energy and into the energy services demanded by final users i.e.motor and engines, heating systems and thermal insulation, industrial boilers and ovens, etc. Theirenergy efficiency improvement potential is much higher than in the primary supply sectors. In asystem analysis view, this part of the system is even more important than the supply side when itcomes to controlling the future development of the system.

4.1.4 Emissions and the environment

The damage to the environment is the major indirect cost caused by present energy systems 59.Substances emitted into the atmosphere by energy technologies such as:

- power plants;- refineries;- incinerators;- factories;- domestic households;- cars and other vehicles;- animals and humans;- fossil fuel extraction and production sites;- offices and public buildings, and- distribution pipelines

are mainly responsible for:- global warming/climate change;- acidification;- air quality degradation, and- damage and soiling of buildings and other structures.

Carbon dioxide is the most important anthropogenic greenhouse gas 60. The primary source of theincreased atmospheric concentration of carbon dioxide since the pre-industrial period results from

59 The level of this indirect cost is highly debated. Methodologies used so far to assess this value (for instance ExternEand its extension elaborated in NEEDS both are EC projects) have some degree of reliability in the evaluation of the

physical impacts but diverge when it comes to converting the damage into monetary units because most damaged goodsare public goods, whose values are not given by the market but have to be assessed from subjective evaluations.60 Quotes from the Summary for Policymakers of the IPCC Fourth Assessment Report Climate Change 2007: ThePhysical Science Basis (February 2007)


113/145


fossil fuel use, (about 85%). The carbon dioxide radiative forcing increased by 20% from 1995to 2005, the largest change for any decade in at least the last 200 years. The radiative forcingcontribution of CO2 equals now the total net anthropogenic contribution to radiative forcing (1.66W/m2).

The acidification of the soil is due to the air emissions of sulphur oxides, nitrogen oxides and partlyammonia. Over 90% of sulphur and nitrogen oxides are emitted from energy systems 61.

Local pollution, mainly urban, originates from anomalous concentrations of carbon monoxide,volatile organic compounds, particulate matter, sulphur and nitrogen oxides. About 90% of carbonmonoxide emissions originate from energy systems (mainly transport) and about 60% of volatileorganic compounds.

Decoupling the benefits of using energy from its disadvantages so far has been partly successful

only in the case of sulphur oxides, with some improvements in for nitrogen oxides, volatile organiccompounds and particulate matters. Decoupling carbon dioxide emissions from the use of energyremains the major environmental issue of energy systems analyses.

4.2 The systems analysis approach: identification of the areas of study

The identification of the system is the first step towards its formal representation in models andtheir use for carrying out mental experiments aimed at exploring how the system might evolve

under different circumstances and how it is possible to control it. The most important elements to beidentified when an energy system is approached for analysis seem:

- Scope of the analyses;- Boundaries;- Time frames;- Components (Elements, Parts), and- Connections, interdependencies and chains.

A short illustration of each element follows.

4.2.1 Scope of the analysis

Before starting the analysis of an energy system the following general elements have to beidentified:

- Who is the client?- What is the aim, what are the specific objectives?- Who will conduct the analysis?

61 You can access to more information from the EMEP/CORINAIR Emission Inventory Guidebook EuropeanEnvironment agency (http://www.eea.europa.eu/ ).
http://www.eea.europa.eu/http://www.eea.europa.eu/http://www.eea.europa.eu/http://www.eea.europa.eu/


114/145


- Are the necessary skills available?- What budget has been allocated for the study?

Establishing these elements can guide one through conflicting requirements of the analysis, forinstance between strategic and operative planning, among different level of comprehensiveness and

detail.

Operative and strategic planning is distinguished by the time span considered and by other factorsrelated to the energy, technological and socio-economic framework. Operative planning looks atshort-term optimisation from minutes to days, and an otherwise fixed technical energy system andsocio-economic framework. Strategic planning tries to include long-term technological and socio-economic developments.

Operative planning requires a large amount of detail within the sub-systems, because high accuracy

is required. In strategic planning, too much detail in the sub-systems can often obstruct the view ofthe total system behaviour for long-term developments. Instead it is more important to consider theinterdependencies between the large numbers of sub-systems. A planner cannot control too manydetails at the same time, since he has only limited resources to collect the necessary data and to

build and exercise a model, aimed at helping him to understand the complex interdependencies.Additionally there are restrictions to the ability of the tools to handle complexity and to process thedata required for very large models in reasonable time. As a result, operative planning is carried outon a sub-system level with a limited time horizon and little consideration of comprehensive aspects.Strategic planning, on the other hand, is done in a comprehensive analysis with a long time horizon

and less detail on the sub-system's level. Despite the different characteristics and purposes ofoperative and strategic planning, insights obtained from one of the model groups can be used to

better describe the behaviour of the other model, e.g. relationships obtained from the operative planning model can be included in a simplified and aggregated form in the strategic planningmodel.

Sub-system and comprehensive analyses are distinguished by the extent of their system boundaries.Sub-system analysis is restricted to a limited number of sub-systems within the whole technicalenergy system. Concentrating only on a sub-system offers the possibility to study the

interdependencies of the system in much detail, but influences from or to other subs-ystems areconsidered only in a simplified manner. A comprehensive analysis, on the other hand, tries to treatall important sub-systems and their interdependencies within one model. In this framework part ofthe details and comprehensiveness of actual systems can be retained, part lost. The expertise of theanalyst helps reaching a balance between the details of an actual system and the synthesis of anymental representations.


115/145


4.2.2 Boundaries

Geographical boundaries are defined by the nature of the problem: energy R&D problems orclimate change mitigation studies may require a global model, security issues require at least aregional approach, designing taxes and subsidies requires a national system, waste disposal options

or local pollution problems are to be studied at the urban / local level. A general description of thesystem should cover the geographic situation, climate, temperature, the population (historicalseries), and the differing conditions between rural and urban areas, the economy, and the mainfeatures of the energy system.

Sometimes the identification is more complex, particularly at the local level. When there are hugeexchanges between the geographical system and the surrounding areas think to vehicle traffic ofcommuters to a town or the trade of energy intensive materials of a country it is sometimes betterto define the boundaries of the logical system.

It is also to be decided whether a single comprehensive energy system has to be considered or it isto be sub-divided into more than one region. In the first case the representation will be based uponan aggregate energy balance and will describe an average situation. If more sub-regions are to beemployed to describe for instance the European Union, such as the Northern, Central and Southern

parts, or each member state, it will necessary to identify and quantify energy flows, technologies,emissions and economic values separately for each region, as well as the possibilities for trade

between the regions.

4.2.3 The time dimension

Statistics and macro variables refer to annual values. Keeping the year as a base, the time dimensionof the system can be explored in two dimensions 62. One looks at the variations from one year toanother (inter-years), the second at the variations inside the typical year (intra-annual). The systemcontrol looks at long term developments along the years; other important energy systems aspects,such as electricity and heat, or gas supply and traffic, refer to intra-annual dimensions.

4.2.3.1 Time horizon

The main concern of energy systems analyses and the rationale for building energy system modelsis the study of possible future developments control policies. This is reflected in the time horizon ofthe analyses: it spans from years (short term) to decades (medium term) to a century (long term) ormore as it happens now in relation to climate changes mitigation policies impact analyses. But

62

In fact the time development of the system along the years can be viewed from two different perspectives, similarly tofluid dynamics. The point of view adopted here is Eulerian in the sense that the time development of the system as awhole is followed year after year. An alternative approach is adopted by Life Cycle Analyses, where the timedevelopment of each element of the system is followed along the years from cradle to grave.


116/145


understanding possible future developments implies studying past behaviour and reconstructing the present layout in an appropriate (maximum) level of detail.

Year by year data are required by the most detailed analyses, such as for local energy environment planning and analyses often focused more on the operation of than the investments in the system.

However, when the time horizon is far way in the future, it is customary to follow the timedevelopment of the system by time periods of variable lengths, yearly in the short term, every fiveyears in the medium, and every twenty or more years in the long period. More periods offer the

possibility to evaluate the system, and thus adjust decisions and strategies, more frequently, but alsoincrease the size of the problem and the amount of data to be processed.

Analysing the intersection between energy and climate change mitigation issues requires theadoption of a very long-term perspective. Energy infrastructure takes a very long time to build andhas a useful life often measured in decades. New energy technologies take time to develop and even

longer to reach their maximum market share. Similarly, the impact of increasing concentrations ofgreenhouse gases from human activities develops over a very long period (from decades tocenturies), while policy responses to climate change threats may only yield effects afterconsiderable delay. Analysis that seeks to tackle these issues must take a similarly long term view looking ahead at least thirty to fifty years.

4.2.3.2 Time granularity

The problem of intra-annual time analyses is easily understandable by looking at the most importantenergy commodity of any system, electricity. Electricity is accessible everywhere in developedcountries and satisfies the demand for many energy services of consumers. Since this demand isdifferent across countries for instance space heating and cooking is provided by electricity insome countries and not in others the primary energy weight of electricity varies from about 50%of Sweden and France to 27% in China. Also the daily, weekly and yearly profile of electricdemand is different across countries. In fact electricity is not a single commodity as there are asmany electricity markets (and prices) as there are hours in a year.

A load curve shows how the demand for electricity, heat or cooling, etc. varies over time. Anexample for district heating production is shown in Figure 4-3. This load curve is made up of dailyaverages, from Jan. 1 to Dec. 31. The diagram shows the typical pattern of high energy productionduring the winter and the small energy production during the summer for water heati

Documents

Getting Started TIMES-VEDA V2p7