Modelling Distributed Generation and Variable Loads from RESec.europa.eu/energy/intelligent/projects/sites/iee-projects/files/... · RES2020 – Modelling Distributed Generation and

Project no: EIE/06/170/SI2.442662

RES2020 Monitoring and Evaluation of the RES directives

implementation in EU27 and policy recommendations for 2020

Modelling Distributed Generation and Variable

Loads from RES

Deliverable D.3.1

RES2020 – Modelling Distributed Generation and Variable Loads from RES

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The RES2020 project

RES2020 aims at analysing the present situation in the RES implementation, defining future

options for policies and measures, calculating concrete targets for the RES contribution that

can be achieved by the implementation of these options and finally examining the

implications of the achievement of these targets to the European Economy.. A number of

future options for policies and measures will be defined and they will be studied with the use

of the TIMES energy systems analysis model, in order to analyze the quantitative effects on

the RES development. TIMES offers the possibility of developing an aggregate parameter in

order to quantify the impact of a wide range of support schemes. The results will be combined

to provide recommendations of optimal mix scenarios for policy measures, in order to ensure

the achievement of the targets.

The countries modelled in the RES2020 project the codes used are:

AT Austria FR France NL Netherlands

BE Belgium GR Greece NO Norway

BG Bulgaria HU Hungary PL Poland

CY Cyprus IE Ireland PT Portugal

CZ Czech Republic IS Iceland RO Romania

DE Germany IT Italy SE Sweden

DK Denmark LT Lithuania SI Slovenia

EE Estonia LU Luxembourg SK Slovakia

ES Spain LV Latvia UK United Kingdom

FI Finland MT Malta

The project aim at delivering results for the year 2020. However in order to avoid “end

effects”, the run of the model will be done until the year 2030. Therefore the base year of the

model will be 2000, as in the case of NEEDS and the “milestone years” will be 2001, 2005,

2010, 2015, 2020, 2025 and 2030.


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Contents

1 INTRODUCTION -------------------------------------------------------------------------------- 4

2 CURRENTLY AVAILABLE DATA ON WIND, DISTRIBUTED ELECTRICITY 6 2.1 NEEDS-VEDA BASE-YEAR TEMPLATES-------------------------------------------------- 6 2.2 NEEDS-VEDA SUBRES TECHNOLOGY REPOSITORY------------------------------------- 6 2.3 NEEDS-VEDA SUBRES TRANSFORMATION FILE----------------------------------------- 6

3 WIND ENERGY, VARIABLE LOAD MODELLING------------------------------------ 7

4 DISTRIBUTED ELECTRICITY-------------------------------------------------------------- 9

4.1 STRUCTURE OF THE COMMON RES2020 REFERENCE ENERGY SYSTEM FOR ELECTRICITY DECENTRALISED GENERATION------------------------------------------------------- 9 4.2 MODIFICATION OF THE NEEDS-TIMES MODEL -----------------------------------------12 4.3 REQUESTED DATA FOR ELECTRICITY GENERATION AND ELECTRICAL NETWORK ---13

5 CONCLUSIONS AND RECOMMENDATIONS -----------------------------------------17

6 REFERENCES-----------------------------------------------------------------------------------18

APPENDIX I - WIND ENERGY, VARIABLE LOAD MODELLING---------------------19

APPENDIX II – PROPOSAL FOR THE STRUCTURE OF THE COMMON RES 2020 REFERENCE ENERGY SYSTEM FOR ELECTRICITY DECENTRALISED GENERATION – VERSION 1----------------------------------------------------------------------26


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1 INTRODUCTION

The current document describes the data required for the RES 2020 project concerning wind

power (and other intermittent renewables), and distributed electricity. These activities are

described in the project proposal WP2, Task 1 and WP3, Task 1 and 3.

Work Package 2: Detailed description of RES framework,

Task 1. Development of a template for the data collection for:

Installed capacities

Financial & political framework

Technological potential

Issues regarding market penetration of RES in the EU-25 (and EU27).

Work Package 3: Expansion of existing tools

Task 1. RES-E issues (coordination POLITO).

This task aims at the analysis and inclusion in the model of grid issues in order to account for

distributed generation (coordinated by POLITO), and issues of modelling variable loads (wind farms)

(coordinated by RISOE).

Task 2. RES-Heat and Biofuels Issues (coordination ECN).

This task focuses on the technology characterisation, estimation of potentials for biofuels on the level

of individual technologies, and renewable heating/cooling. Drawing on the BRED study (Biomass

strategies for greenhouse gas emission reduction) and ECN’s BIOTRANS model, the following

information will be used as basis for detailed country representation:

• Domestic bio-energy crop growing - in competition with other land uses such as agriculture and

forestry. The estimation of biomass potentials will only include crops that can be grown in an

environmentally sustainable way.

• Supply of waste wood and of by- product waste from agricultural activities

• Import of a (limited) number of bio-energy forms (e.g. HTU oil from South America)

• Harvesting and transport of the primary bio-energy

• Processing and conversion of primary bio-energy into derived products

• Direct use or further processing of derived products

• Bio-energy consuming technologies in power/heat sector, industry, residential and tertiary and

transport sectors. These technologies will cover both dedicated bio-energy based applications as

well as co-firing in fossil fuelled installations.


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• Use biomass as feedstock for petrochemical industry or as primary material for manufacturing

industries.

Data to be collected:

1. Country specific

• Availability of different types of biomass and waste for energy use. For modelling trade of

biomass among different countries, restrictions on internal trade in biomass or derivatives,

if existent, will have to be listed.

• Land use data for the other (competing) activities, a likely source here being FAO

statistics, and future projections of these activities or other known land use change. This

should be connected to the possible impact of abandoning or transforming EU’s

subsidising politics on agriculture. The project will build on the data collected in the EU-

funded REFUEL project, which ECN coordinates.

2. Technology specific parameters such as investment and O&M costs, efficiencies, annual

availability, including outlook on possible improvements, for biomass technologies for electricity,

conversion technologies for biofuel production, renewable heat technologies.

Task 3. Development of the new templates for the Reference Energy System.

The inclusion of all the modelling issues of the previous tasks will require the development of

extensions to the existing templates used in defining the country models. The task includes testing of

the internal consistency as well as functional testing of the templates.

In this report the enhancements of the representation of the electricity system and the

modelling of wind generation are presented.

Chapter 2 describes the data that are currently available within the NEEDS project. These data

are organised in “Base-Tear templates” for each country and a “SubRes technology

repository”, which consists of two Excel workbooks, containing technology data that are

common for all countries and a “transformation file” with national specific data.


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2 CURRENTLY AVAILABLE DATA ON WIND, DISTRIBUTED ELECTRICITY

Most of the data for the enhanced NEEDS-TIMES models are already in the VEDA templates

for the NEEDS model. These templates have been used during 2006 to build up national

models on the basis of Eurostat data for 2000 for 29 countries and techno-economic data

developed within the NEEDS project. The templates for the national models have been

reviewed and developed by the national modelling teams within the NEEDS project Research

Stream 2a, WP 2.

2.1 NEEDS-VEDA Base-Year templates

The NEEDS-VEDA templates are divided into five sectors

• SUP – Energy supply sector (mining and refineries)

• ELC – Electricity with CHP

• IND – Industry, including autoproducers of electricity and heat

• RCA – Residential, Commercial, Agriculture

• TRA - Transport.

In RES2020 only parts of SUP, ELC and RCA will be considered. Generic autoproducers will

be included into the ELC sector for the development of the enhanced model on distributed

electricity in RES 2020.

2.2 NEEDS-VEDA SubRes technology repository

The file consists of sheets similar to the base-year sectors containing techno-economic data

for a large number of new technologies.

2.3 NEEDS-VEDA SubRes transformation file

The file has been updated by the county modellers in order to be used in the calibration

process of the model.


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3 WIND ENERGY, VARIABLE LOAD MODELLING

Large hydro should not be included in RES2020 distributed generation. However, it may be

consider together with wind.

Wave and Tidal power are future technologies, for which only limited capacities may be

deployed by the end of the period

Wind energy is divided into two technologies, on-shore and off-shore. In the base year, 2000,

off-shore wind was negligible, so only existing on-shore wind turbines will be considered.

The key parameters are installed capacities and an average availability factor in each time

slice.

In future years average availabilities shall be calculated for each country and milestone year

on the basis of disaggregated data and assumptions. This simplified structure is compatible

with the detailed assumptions on availability in time slices. Introducing different classes of

wind power may be considered after test runs of the model. However, it is currently preferred

to model availability factors depending on calculation based on disaggregated technologies.

For each country the maximum potential for on-shore and off-shore wind will be requested.

Data for planned installed capacities are requested as minimum capacities for the optimisation

model. Further installed capacities will be considered as scenario assumptions.

For each country the maximum potential for on-shore and off-shore wind will be requested. In

the NEEDS-TIMES Pan-European model the electricity generation from wind power are give

upper and lower limits on the scenario file Scen_UC_ELC.xls.

Data for planned installed capacities are requested as minimum capacities for the optimisation

model. Further installed capacities will be considered as scenario assumptions.

Considering the variation in wind resources from year to year an even more simplified

structure is recommended. Monthly data for wind power are available from UCTE and Nordel

for most countries from 2005. These data are easily converted to seasonal data following the

TIMES time slices. For all countries with a significant capacity of wind power there is a

common pattern of seasonal variation. Winter: 20-30 %, Fall: 20-25 % , Spring 15-25 %, and

Summer: 10-20 %. Statistics for diurnal variations are available from few countries only

(Denmark and Greece). Although the average daytime availability tends to be slightly higher

than nighttime availability, it is not recommended to consider this variation in the model.

Data for installed capacities by the end of the year are available from EWEA since 2004. At

the end of 2006, offshore wind farm installations represented 1.8% of total installed wind


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power capacity, generating 3.3% of Europe's wind power (press release from EWEA 9 Oct.

2007). The largest share of offshore wind capacity is 13% in Denmark. For countries with

coasts to the Atlantic Ocean, the North Sea the annual availability factor is set as 40 % with

seasonal variations similar to onshore wind power. For countries in the Baltic Sea the Finnish

assumption at 34 % is used.

Average seasonal availabilities calculated from the monthly statistics for 2005 and 2006 are

used for BE, DE, DK, ES, FR, IT, NL, NO, PL, PT. National studies are used for FI and GR,

because the generation of the currently small capacity will not represent future investments.

For UK monthly data on ‘Other electricity’ from BERR are used for 2006 only. For the rest of

the countries the national data from countries with similar climate and large wind capacity are

used.

The availability factors calculated following the above approach are presented in Appendix I,

together with the data used.

The NEEDS-TIMES model also considers the availability for each technology in peak load

hours. In the Pan-European model an availability for wind power at 6 % had been assumed

for most countries. However, before making any assumption of an arbitrary number, the

impact of such assumption should be tested by running the model. Many methods for demand

peak shaving are currently being tested by European system operators. These include – among

other measures - international trade and flexible demand, using far more detailed models than

TIMES focusing on the short-term operation of the electricity system. In contrast, TIMES is

designed to give the right answers for long-term technology choices.


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4 DISTRIBUTED ELECTRICITY

The initial consideration about the various possibilities of expanding the Reference Energy

system in order to account for the decentralised generation was included in the document

“Proposal for the structure of the common RES2020 Reference Energy System for Electricity

Decentralised Generation, Version 1”, which was distributed by POLITO 7 November 2006.

The document is included in Appendix II, in order to show the different possible approaches

compared to the one that was finally implemented.

The following Section 4.2 is based on the document “NEEDS_new_ELC_Structure.doc”,

received from Markus Blesl, IER on 15-02-07. And Section 4.3 is based on the file

“Requested Data for Electricity generation and electricity network”.

4.1 Structure of the common RES2020 Reference Energy System for Electricity Decentralised Generation

Distributed Generation (DG) is the implementation of various power generating resources,

near the site of demand, either for reducing reliance on, or for feeding power directly into the

grid. DG may also be used to increase the transmission and distribution system reliability.

Figure 4.1 shows the final proposal for the new electricity structure in the reference energy

system. The structure of electricity transportation and distribution systems includes processes

that describe the grids.

The idea is to add in the initial electricity templates, the transmission and distribution grids (as

processes), and to differentiate between the commodities produced from the centralised power

plants from those consumed and produced form decentralised technologies.

In this way the new scheme will represent the difference between centralised production of

electricity, and decentralised production of electricity.

The structure of the electricity transportation and distribution systems is shown in Figure 4.1.

Some hypotheses are necessary to simplify the network issues.

1) The distribution networks should be operating in a radial or meshed configuration.

Modelling meshed networks would however require the implementation of advanced

load flow calculation, which is not the purpose of this model. It is therefore assumed

here a simplified approach.


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2) Each voltage level of the network is modelled by an equivalent simplified system

(EVTRANS_X-X) composed of lines, transformers, infrastructure for electricity

transport and distribution.

The requested minimum data for these technologies are efficiency (losses for each

voltage level, already put in the NEEDS project) and variable cost to simulate the grid

tariff on each voltage level.

The technology EVTRANS_H-H change input commodity of process from ELCHIG to

ELCHIGG and describe the high voltage transport before the use and the transformation in

medium voltage. The efficiency values of EVTRANS_H-H reflect grid losses of high voltage

level and set variable cost for the transmission tariffs (see ETSO paper on transmission tariffs

in Europe). For the efficiency of this process, the commodity efficiency of ELCHIG (used in

NEEDS project) can be used (see TE(ENT) in the NEEDS project “constant sheet of ELC

template).

The technology EVTRANS_H-M is a transformation / grid process reflecting high – medium

voltage transformation and the medium voltage grid (from ELCHIGG to ELCMED) and add

transmission losses as process efficiency according to medium voltage grid loss values. The

commodity efficiency of ELCMED (TE(ENT) of ELCMED) can NOT be used directly, since

former reference energy system did not reflect electricity flows (ELC quantities) of medium

voltage grid correctly. Moreover, add variable cost as grid tariffs for medium voltage grid

(usually grid utilization fees of medium voltage level include the fee of high voltage level,

thus deduct high voltage level fees to gain single medium fees).

The technology EVTRANS_M-L is a transformation / grid process reflecting medium – low

voltage transformation and the low voltage grid. The new process EVTRANS_M-L has

ELCMED as input and ELCLOW as output. Efficiency is adopted from commodity efficiency

of ELCLOW (check, if efficiency corresponds to real low voltage grid losses) and variable

cost are represented by grid utilization cost of low voltage consumers minus costs of medium

and high voltage level.


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Figure 4.3. Final NEEDS and RES2020, Electricity Structure


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4.2 Modification of the NEEDS-TIMES model

This section is based on the document produced by IER, which describes the steps for the

enhancement of the NEEDS templates, performed by IER in order to model decentralized

electricity. The basic steps to change the existing electricity structure of the NEEDS TIMES

model to the new proposed structure that are adopted for introducing decentralized generation

are:

A. Adding commodity ELCHIGG

1. Add the commodity in ELC template in sheet ELC_comm

2. Change input commodity of process INDELC00 and if available of INDELC01 from

ELCHIG to ELCHIGG

3. Change input commodity of SUPELC00 from ELCHIG to ELCHIGG

4. Change input commodity of TRAELC00 from ELCHIG to ELCHIGG

5. Change output commodity of EXPELCHIGA from ELCHIG to ELCHIGG in SUP

template, TRADE sheet

B. Add / modify transformers

Since there is no single process for reflecting the electricity grid of a specific voltage level,

the grid costs are added to the transformer processes, thus the EVTRANS processes

contribute to all grid costs (incl. transformation costs)

1. Add new transformer process EVTRANS_H-H in ELC template, sheet “ELC_fuel” to

reflect high voltage grid; Input commodity = ELCHIG, Output = ELCHIGG;

efficiency values reflect grid losses of high voltage level and set variable cost for the

transmission tariffs (see ETSO paper on transmission tariffs in Europe) For the

efficiency of the process, the commodity efficiency of ELCHIG can be used (see

TE(ENT) in “constant sheet of ELC template)

2. Change input commodity of EVTRANS_H-M process from ELCHIG to ELCHIGG

and add transmission losses as process efficiency according to medium voltage grid

loss values. The commodity efficiency of ELCMED (TE(ENT) of ELCMED) can

NOT be used directly, since former reference energy system did not reflect electricity

flows (ELC quantities) of medium voltage grid correctly. Moreover, add variable cost

as grid tariffs for medium voltage grid (usually grid utilization fees of medium voltage

level include the fee of high voltage level, thus deduct high voltage level fees to gain

single medium fees)


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3. The transformer EVTRANS_H-L is replaced by a transformation / grid process

reflecting medium – low voltage transformation and the low voltage grid. The new

process EVTRANS_M-L has ELCMED as input and ELCLOW as output. Efficiency

is adopted from commodity efficiency of ELCLOW (check, if efficiency corresponds

to real low voltage grid losses) and variable cost are represented by grid utilization

cost of low voltage consumers minus costs of medium and high voltage level.

C. Removing commodity efficiency

et \I: to ignore attribute TE(ENT) in “constant”-sheet ELC template for ELCHIG,

ELCMED, ELCLOW or use the scenario file “NEEDS_BAU_General” in which

commodity efficiencies of all electricity commodities of all countries is set to “1”

4.3 Requested Data for Electricity Generation and Electrical Network

This section is based on the document “Requested Data for Electricity generation and

electricity network” and describes the data required for the RES2020 project concerning

renewable electricity generation and network issues.

This is a brief description of the data in the files “RES2020_Electricity Generation.xls”.

4.3.1 RES2020_Electricity Generation

This file presents the data for the electricity generation from renewable energy sources and

data for distributed generation.

The file is composed from five sheets:

• Comments

• TS

• AF

• NEEDS_Tech

• New_Tech

• Technological_Potentials

4.3.2 Comments sheet

In this first sheet there are some definitions related to the parameters requested in the other

sheets and the country codes.


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4.3.3 TS sheet

The TS sheet contains the time slice definition of the model, i.e. the fraction of the year into

seasons and periods of the day. In this model, the final agreement was a number of 12 time

slices, defined with four seasons in a year (spRing, Summer, Fall, Winter) and three periods

in a day (Day, Night and a Peak, Figure 4.1). See Figure 4.2 for the detailed definition of the

time slices: number of days considered for each season and hours considered for each period

of the day.

Figure 4.1. Time slices of the model.

Figure 4.2. Definition of the time slices

4.3.4 AF sheet

Processes and commodities can be modelled in VEDA-TIMES on different timeslice levels.

Some of the input parameters, which describe a process or a commodity, are timeslice

specific, i.e. they have to be provided by the user for specific timeslices, e.g. the availability

factor AF of a power plant operating on a ‘DAYNITE’ timeslice level.

For the renewables technologies, the AF does not represent only the technical process

availability but also the resources availability. For example the AF of wind power plant

should be 0.3; this means that the power plant on the year (8760 hours) should not be working

more than about 2600 hours (=0.3*8760). The value 0.3 is related to the availability of wind

during the period of one year.


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In the model of RES2020, it is important for the renewable technologies to give a country

specific AF on a season and day-night level, as requested in the AF sheet of the excel file.

The data should be collected with the same definition of the time slices shown in Figure 4.2.

4.3.5 NEEDS_Tech sheet

This sheet presents the renewable electricity production technologies described in the NEEDS

project. The values are not country specific.

4.3.6 New_Tech

In this sheet there are some new technologies (missing in the NEEDS project). The values are

not country specific.

For the first two tables in this sheet, presenting the wave, tidal and micro CHP technologies it

is important to verify the column in light blue which is related to the output from the process

(i.e. it must be verified that these technologies can produce on a medium or high or low

voltage grid). This parameter is very important for the decentralized generation. Generally the

processes that produce on the low voltage grid, are small technologies near the site of

electricity consumption.

4.3.7 Technological_Potentials

There are four tables in this sheet. The first and the second table are request to the maximum

and minimum technological potentials (GW) for the relative technologies. The third and

fourth table are related to the annual electricity generation (PJ) from the related technologies.

The bound presented in the third and fourth table must be consistent with the capacity data

presented in the first and second table.

4.3.8 Countryxx Worksheet

This worksheet presents some extracts from the various templates of the NEEDS model that

refer to renewable electricity and will be enhanced in RES2020.

From the ELC Template, the PV electricity generation in the base year (2000) will be

included and the PV installed capacity should appear in the base-year technologies (although

this will be very small).

In the SUP template the “Renewable Potentials” table will be filled in with the data from the

‘Technological Potentials” worksheet tables 3 and 4.


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Finally the capacity constrain in the ELC template will be filled in for the lower installed

capacity from data from the ‘Technological Potentials” worksheet tables 1 and 2. An upper

limit constrain might be considered in the case of some scenarios.

Regarding the electricity network, the requested country specific data are:

- efficiency (from the losses) for each voltage level;

- tariff for each voltage level

as shown in the excel file, Countryxx worksheet, table RES2020_Network.


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5 CONCLUSIONS AND RECOMMENDATIONS

Regarding the representation of the decentralised electricity generation, the enhancements that

have been introduced in the NEEDS templates, offer the possibility of modelling RES

generation more realistically.


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6 REFERENCES

Loulou, Richard; Remme, Uwe; Kanudia, Amit; Lehtila, Antti; Goldstein, Gary (2005),

Documentation for the TIMES Model: Part III: GAMS Implementation. Energy Technology

Systems Analysis Programme.

RES2020, Annex I. Description of the Action. EIE-060170 RES2020 AnnexI_27_06_cleaned

version.doc


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APPENDIX I - WIND ENERGY, VARIABLE LOAD MODELLING


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Table I.1. Electricity production from Wind (Data provided by UCTE, Nordel statistics 2006, Table S7, UK: DTI/BERR electricity statistics)

Country (GWh)

Year Month BE DE DK ES FI FR GR IT NL PL PT NO SE UK 2005 1 24 5907 1076 1873 19 101 74 188 338 15 98 46 143 264 2005 2 16 3328 639 1700 16 82 88 197 186 10 113 35 93 266 2005 3 18 2758 586 1677 11 67 68 161 209 17 140 24 65 271 2005 4 15 1732 502 1783 11 69 77 215 123 11 127 29 68 207 2005 5 19 1615 438 1388 12 72 59 197 158 9 115 31 53 179 2005 6 13 1398 462 1140 9 53 59 109 102 8 84 39 57 157 2005 7 14 1525 305 1167 5 74 70 203 109 13 131 23 42 156 2005 8 13 1519 433 1210 12 77 66 198 123 6 118 42 59 148 2005 9 14 1601 384 925 20 57 56 130 105 6 129 66 72 165 2005 10 24 2337 450 1346 21 103 108 101 151 10 183 50 83 189 2005 11 27 2302 602 1401 20 107 99 227 190 14 208 59 103 274 2005 12 28 2859 738 1733 11 130 122 404 215 13 280 63 93 314 2006 1 26 3677 512 1518 18 138 103 219 190 12 186 77 90 257 2006 2 28 2203 385 1911 7 149 86 301 222 15 175 48 66 420 2006 3 35 2701 517 2772 9 203 98 357 257 18 274 40 70 442 2006 4 25 2152 499 1954 10 156 85 272 211 18 189 43 72 167 2006 5 30 3008 602 1664 8 176 88 256 254 23 176 41 79 174 2006 6 13 1130 324 1366 12 105 73 201 111 9 164 44 54 147 2006 7 14 929 193 1166 11 98 168 183 82 9 176 38 40 158 2006 8 21 1943 258 2157 5 197 74 360 160 15 260 24 41 128 2006 9 20 2173 505 1444 13 155 101 270 136 23 204 47 82 137 2006 10 35 3172 501 2283 12 235 122 259 288 11 391 64 91 153 2006 11 44 4477 868 2175 18 295 100 220 382 38 341 89 129 231 2006 12 54 4730 943 2066 24 315 101 255 404 43 356 118 173 232 2007 1 61 7512 2134 427 130 334 550 65 249 284


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Table I.2. Installed capacity at the end of each month and each year (Data from EWEA, Nordel statistics 2004-2006, Table S1a)

Country (MW) Year Month BE DE DK ES FI FR GR IT NL NO PL PT SE UK 2004 12 95 16629 3122 8263 79 386 465 1125 1078 158 63 522 442 888 2005 1 101 16779 3123 8410 79 417 474 1174 1090 168 64 564 449 927 2005 2 107 16929 3125 8557 80 448 483 1224 1102 179 65 605 456 966 2005 3 113 17079 3126 8704 80 479 492 1273 1113 189 66 647 463 10042005 4 119 17229 3127 8851 80 510 501 1322 1125 199 66 689 470 10432005 5 125 17379 3129 8998 80 541 510 1372 1137 209 67 730 477 10822005 6 131 17529 3130 9145 81 572 519 1421 1149 220 68 772 484 11212005 7 137 17678 3131 9292 81 602 528 1470 1160 230 69 814 490 11592005 8 143 17828 3133 9439 81 633 537 1520 1172 240 70 855 497 11982005 9 149 17978 3134 9586 81 664 546 1569 1184 250 71 897 504 12372005 10 155 18128 3135 9733 82 695 555 1618 1196 261 71 939 511 12762005 11 161 18278 3137 9880 82 726 564 1668 1207 271 72 980 518 13142005 12 167 18428 3138 10027 82 757 573 1717 1219 281 73 1022 525 13532006 1 169 18611 3138 10159 82 825 587 1751 1247 285 80 1080 530 14042006 2 171 18794 3138 10292 83 892 602 1785 1276 290 86 1138 534 14552006 3 174 18977 3137 10424 83 960 616 1819 1304 294 93 1196 539 15062006 4 176 19159 3137 10556 83 1027 631 1852 1333 298 100 1253 543 15562006 5 178 19342 3137 10689 84 1095 645 1886 1361 303 106 1311 548 16072006 6 180 19525 3137 10821 84 1162 660 1920 1390 307 113 1369 553 16582006 7 182 19708 3136 10953 84 1230 674 1954 1418 311 119 1427 557 17092006 8 184 19891 3136 11086 85 1297 688 1988 1446 316 126 1485 562 17602006 9 187 20074 3136 11218 85 1365 703 2022 1475 320 133 1543 566 18112006 10 189 20256 3136 11350 85 1432 717 2055 1503 324 139 1600 571 18612006 11 191 20439 3135 11483 86 1500 732 2089 1532 329 146 1658 575 19122006 12 193 20622 3135 11615 86 1567 746 2123 1560 333 153 1716 580 1963


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Table I.3. Calculated Monthly Availability Factors Year Month BE DE DK ES FI FR GR IT NL NO PL PT SE UK 2005 1 0.319 0.473 0.463 0.299 0.322 0.326 0.210 0.215 0.417 0.367 0.316 0.234 0.428 0.3842005 2 0.223 0.293 0.304 0.296 0.299 0.272 0.271 0.240 0.251 0.292 0.230 0.278 0.304 0.4112005 3 0.214 0.217 0.252 0.259 0.185 0.188 0.186 0.170 0.252 0.171 0.349 0.291 0.189 0.3622005 4 0.175 0.140 0.223 0.280 0.191 0.188 0.213 0.226 0.152 0.202 0.230 0.256 0.201 0.2762005 5 0.204 0.125 0.188 0.207 0.201 0.179 0.155 0.193 0.187 0.199 0.180 0.212 0.149 0.2232005 6 0.138 0.111 0.205 0.173 0.155 0.129 0.158 0.107 0.123 0.247 0.163 0.151 0.164 0.1952005 7 0.137 0.116 0.131 0.169 0.083 0.165 0.178 0.186 0.126 0.135 0.254 0.216 0.115 0.1802005 8 0.122 0.115 0.186 0.172 0.199 0.163 0.165 0.175 0.141 0.235 0.116 0.185 0.159 0.1662005 9 0.130 0.124 0.170 0.134 0.342 0.119 0.142 0.115 0.123 0.366 0.118 0.200 0.198 0.1862005 10 0.208 0.173 0.193 0.186 0.346 0.199 0.262 0.084 0.170 0.258 0.188 0.262 0.218 0.1992005 11 0.233 0.175 0.267 0.197 0.340 0.205 0.244 0.189 0.219 0.303 0.269 0.295 0.276 0.2892005 12 0.225 0.209 0.316 0.232 0.180 0.231 0.286 0.316 0.237 0.301 0.239 0.368 0.238 0.3122006 1 0.207 0.266 0.219 0.201 0.294 0.225 0.236 0.168 0.205 0.363 0.203 0.232 0.228 0.2462006 2 0.243 0.174 0.183 0.276 0.126 0.249 0.213 0.251 0.259 0.247 0.259 0.229 0.184 0.4302006 3 0.271 0.191 0.221 0.357 0.146 0.284 0.214 0.264 0.265 0.183 0.260 0.308 0.175 0.3952006 4 0.198 0.156 0.221 0.257 0.167 0.211 0.187 0.204 0.220 0.200 0.251 0.209 0.184 0.1492006 5 0.227 0.209 0.258 0.209 0.129 0.216 0.183 0.182 0.251 0.182 0.291 0.180 0.194 0.1462006 6 0.100 0.080 0.143 0.175 0.198 0.126 0.154 0.145 0.111 0.199 0.111 0.166 0.136 0.1232006 7 0.103 0.063 0.083 0.143 0.175 0.107 0.335 0.126 0.078 0.164 0.101 0.166 0.097 0.1242006 8 0.153 0.131 0.111 0.262 0.079 0.204 0.144 0.243 0.149 0.102 0.160 0.235 0.098 0.0982006 9 0.149 0.150 0.224 0.179 0.212 0.158 0.200 0.186 0.128 0.204 0.241 0.184 0.201 0.1052006 10 0.249 0.210 0.215 0.270 0.189 0.221 0.229 0.169 0.258 0.265 0.106 0.328 0.214 0.1112006 11 0.320 0.304 0.385 0.263 0.292 0.273 0.190 0.146 0.346 0.376 0.362 0.286 0.311 0.1682006 12 0.376 0.308 0.404 0.239 0.375 0.270 0.182 0.161 0.348 0.476 0.379 0.279 0.401 0.159


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Table I.4. Calculated Seasonal Availability Factors

BE DE DK ES FI FR GR IT NL NO PL PT SE UK Spring 0.19 0.15 0.21 0.25 0.19 0.18 0.18 0.20 0.19 0.19 0.23 0.25 0.18 0.27

Summer 0.13 0.11 0.17 0.17 0.15 0.15 0.17 0.16 0.13 0.20 0.18 0.18 0.15 0.18Fall 0.18 0.15 0.20 0.17 0.34 0.17 0.21 0.12 0.16 0.31 0.18 0.24 0.22 0.21

Winter 0.25 0.29 0.34 0.26 0.27 0.26 0.25 0.24 0.29 0.30 0.27 0.29 0.30 0.36

2005 average 0.19 0.19 0.24 0.22 0.24 0.20 0.21 0.18 0.20 0.26 0.22 0.25 0.22 0.26Spring 0.22 0.18 0.24 0.26 0.15 0.23 0.19 0.21 0.24 0.19 0.27 0.22 0.19 0.20

Summer 0.12 0.09 0.11 0.19 0.15 0.15 0.21 0.17 0.11 0.16 0.12 0.19 0.11 0.11Fall 0.22 0.20 0.25 0.23 0.22 0.20 0.21 0.17 0.22 0.26 0.21 0.26 0.23 0.12

Winter 0.28 0.25 0.28 0.26 0.26 0.26 0.21 0.19 0.28 0.34 0.29 0.26 0.27 0.28

2006 average 0.22 0.19 0.22 0.24 0.20 0.21 0.21 0.19 0.22 0.25 0.23 0.23 0.20 0.19Averages 2005/2006 BE DE DK ES FI FR GR IT NL NO PL PT SE UK

Spring 0.21 0.17 0.23 0.25 0.17 0.21 0.19 0.20 0.21 0.19 0.25 0.23 0.18 0.24Summer 0.13 0.10 0.14 0.18 0.15 0.15 0.19 0.16 0.12 0.18 0.15 0.19 0.13 0.15

Fall 0.20 0.18 0.22 0.20 0.28 0.19 0.21 0.14 0.19 0.28 0.19 0.25 0.22 0.17Winter 0.26 0.27 0.31 0.26 0.26 0.26 0.23 0.22 0.28 0.32 0.28 0.28 0.28 0.32

Year 0.21 0.19 0.23 0.23 0.22 0.20 0.21 0.19 0.21 0.25 0.22 0.24 0.21 0.23 .


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Wind 2005

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

BE DE DK ES FI FR GR IT NL NO PL PT SE UK

Spring Summer Fall Winter

Wind 2006

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

BE DE DK ES FI FR GR IT NL NO PL PT SE UK

Spring Summer Fall Winter

Figure I.1. Calculated Seasonal Availability Factors


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AT BG BE CH CY CZ DE DK EE ES FI FR GR HU IE IS IT LT Onshore

Spring 0.167 0.257 0.210 0.167 0.257 0.167 0.167 0.202 0.21 0.252 0.21 0.206 0.257 0.167 0.099 0.192 0.204 0.21 Summer 0.103 0.333 0.126 0.103 0.333 0.103 0.103 0.107 0.16 0.182 0.16 0.149 0.333 0.103 0.057 0.180 0.164 0.16

Fall 0.178 0.238 0.202 0.178 0.238 0.178 0.178 0.199 0.27 0.200 0.27 0.187 0.238 0.178 0.060 0.284 0.144 0.27 Winter 0.272 0.341 0.265 0.272 0.341 0.272 0.272 0.296 0.30 0.259 0.30 0.256 0.341 0.272 0.138 0.322 0.217 0.30

Offshore Spring 0.343 0.41 0.343 0.356 0.36 0.39 0.31 0.45 0.31 0.40 0.34 0.427 0.306 0.44 0.31

Summer 0.445 0.24 0.445 0.218 0.22 0.25 0.23 0.32 0.23 0.29 0.44 0.247 0.286 0.35 0.23 Fall 0.318 0.39 0.318 0.380 0.38 0.39 0.39 0.35 0.39 0.37 0.32 0.257 0.453 0.31 0.39

Winter 0.455 0.52 0.455 0.579 0.58 0.53 0.42 0.46 0.42 0.50 0.46 0.591 0.513 0.47 0.42

LU LV MT NL NO PL PT RO SE SI SK UK Onshore

Spring 0.167 0.21 0.257 0.214 0.192 0.252 0.232 0.167 0.182 0.204 0.167 0.099Summer 0.103 0.16 0.333 0.121 0.180 0.151 0.187 0.103 0.128 0.164 0.103 0.057

Fall 0.178 0.27 0.238 0.191 0.284 0.193 0.253 0.178 0.223 0.144 0.178 0.060Winter 0.272 0.30 0.341 0.283 0.322 0.281 0.276 0.272 0.284 0.217 0.272 0.138

Offshore Spring 0.31 0.343 0.41 0.31 0.39 0.39 0.356 0.35 0.441 0.43

Summer 0.23 0.445 0.23 0.29 0.23 0.31 0.218 0.24 0.353 0.25 Fall 0.39 0.318 0.37 0.45 0.30 0.42 0.380 0.42 0.311 0.26

Winter 0.42 0.455 0.54 0.51 0.43 0.46 0.579 0.54 0.467 0.59

Table I.5. Seasonal Availability Factors to be used in the model


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APPENDIX II – PROPOSAL FOR THE STRUCTURE OF THE COMMON RES 2020 REFERENCE ENERGY SYSTEM FOR

ELECTRICITY DECENTRALISED GENERATION – VERSION 1


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POLITECNICO DI TORINO

Dipartimento di Energetica

Laboratorio di Analisi e Modelli Energetici

Proposal for the structure of the common

RES2020 Reference Energy System for Electricity

Decentralised Generation Version 1


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1 INTRODUCTION

The construction of a common RES2020 to be used by all Country Modellers, is the first task that must be completed as part of Work Packages 3, task 1. The construction of the common RES-ELC (for the electricity sector) will be accomplished in two phases. In the first phase, we need to decide the structure of the ELC sector in the RES, the list and names of commodities and the common methodology to evaluate some data. In the second phase, we need to arrive eventually a revision of the common list and descriptions of technologies. The choice of a common RES-ELC is influenced by data availability.

2 ELECTRIC POWER TRANSMISSION & DISTRIBUTION

2.1 Electricity transmission

Electric power transmission is the second process in the delivery of electricity to consumers. Electricity is generated by power plants and is then sold as a commodity to end consumers by retailers. The electric power transmission and electricity distribution networks allow the delivery of the generated electricity to consumers. The grid allows large generation facilities such as hydroelectric dams, fossil fuel plants, nuclear power plants, etc. run by large public and private utility organizations to produce large quantities of energy and then deliver it to distribution networks for delivery to retail customers for consumption. Electricity is usually sent over long distance through a combination of overhead power transmission lines or buried cables. A transmission grid, is made up of power stations, substations and transmission circuits. Power is usually transmitted as a 3-phase alternating current. At the generating plants the power is produced at a relatively low voltage of 10-15 kV, then stepped up by the power station transformer to a high voltage (220 - 400 kV) alternating current (AC) for transmission over longer distances to a grid exit point substation. It is necessary to transmit the electricity at high voltage to reduce the percentage of power lost. The higher the voltage the lower the current that flows, which reduces the size of cable needed and the amount of energy wasted. Substations are also used to step the voltage down and supply electricity to low voltage local power lines for distribution to commercial and residential users. Typically, the electricity is transformed to a sub-transmission voltage (66 - 132 kV) using interconnecting transformers and then transformed to a medium voltage (10 - 50 kV). Finally, in the distribution substation, the power is transformed to low voltage (220 - 330 V). The universal method of supply from distribution lines to small end users is via single phase or three phase connections.

http://www.wordiq.com/definition/Electricity

http://www.wordiq.com/definition/Electricity

http://www.wordiq.com/definition/Power_plant

http://www.wordiq.com/definition/End_consumer

http://www.wordiq.com/definition/Electricity_retailing

http://www.wordiq.com/definition/Electricity_distribution

http://www.wordiq.com/definition/Hydroelectricity

http://www.wordiq.com/definition/Fossil_fuel

http://www.wordiq.com/definition/Nuclear_reactor



http://www.wordiq.com/definition/Grid

http://www.wordiq.com/definition/Substation

http://www.wordiq.com/definition/Alternating_current

http://www.wordiq.com/definition/Transformer

http://www.wordiq.com/definition/Voltage

http://www.wordiq.com/definition/Alternating_current

http://www.wordiq.com/definition/Substation


http://www.wordiq.com/definition/Single_phase_electric_power

http://www.wordiq.com/definition/Three_phase


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2.2 Electricity distribution

Electricity distribution is the penultimate process in the delivery of electric power, i.e. the part between transmission and user purchase from an electricity retailer. It is generally considered to include medium-voltage (less than 50kV) power lines, low-voltage electrical substations and pole-mounted transformers, low-voltage (less than 1000V) distribution wiring and sometimes electricity meters. Other processes in power delivery are generation, transmission and retailing. Increasing the voltage reduced the current in the transmission and distribution lines and hence the size of conductors required and distribution losses incurred. This made it more economic to distribute power over long distances. The ability to transform to extra-high voltages enabled generators to be located far from loads with transmission systems to interconnect generating stations and distribution networks. European systems used higher voltages, generally 3300 volts to ground, in support of the 220Y/380 volt power systems used in those countries. In the UK, urban systems progressed to 6.6 kV and then 11 kV (phase to phase), the most common distribution voltage. In Europe a typical urban or suburban low-voltage substation might be rated at 2 MW and supply a whole neighbourhood. This is because the higher voltage used in Europe (230 V vs 120 V) may be carried over a greater distance without unacceptable power loss. In this way is possible to use large and efficient transformers.

2.3 Distributed generation

Distributed generation is a new trend in electric power generation. The concept permits the electricity "consumer", who is generating electricity for their own needs, to send their surplus electrical power back into the power grid. Distributed generation is not confined to fossil fuel. Some countries already have a significant renewable power source in power grid-tied wind turbines and biomass combustion. Increasing amounts of distributed generation will require changes in the technology required to manage transmission and distribution of electricity. There will be an increasing need for network operators to manage networks 'actively' rather than 'passively' as is currently the case. Increased active management will bring additional benefits for consumers in terms of the introduction of greater choice with regard to energy supply services and greater competition. However, the switch to more active management may be a difficult one. Until recently, regulatory and technology issues meant that domestic consumer-generated electricity could not be easily or safely coupled with the incoming electric power supply. Electric companies need to have the ability to isolate parts of the power grid; when a line goes down workmen have to be sure the power is off before they work on it. They also spend much effort maintaining the quality of power in their grid. Distributed power installations can make control of these issues more difficult. With the advent of extremely reliable power electronics it is becoming economic and safe to install even domestic scale co-generation equipment. These installations can produce domestic hot water, home heating and electricity, with surplus energy being sold back to the

http://www.wordiq.com/definition/Power_delivery

http://www.wordiq.com/definition/Electric_power

http://www.wordiq.com/definition/Electric_power_transmission

http://www.wordiq.com/definition/Electricity_retailer

http://www.wordiq.com/definition/Electrical_substation

http://www.wordiq.com/definition/Transformers

http://www.wordiq.com/definition/Electricity_meter

http://www.wordiq.com/definition/Electricity_generation

http://www.wordiq.com/definition/Electricity_transmission


http://www.wordiq.com/definition/MW

http://www.wordiq.com/definition/Electric_power

http://www.wordiq.com/definition/Electricity_generation

http://www.wordiq.com/definition/Power_grid

http://www.wordiq.com/definition/Wind_turbine


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power company. Advances in electronics have eased the electric companies safety and quality concerns. Regulators can act to remove barriers to the uptake of increased levels of distributed generation by ensuring centralized and distributed generation are operating on a 'level playing field. As there is the potential for major portion of the electricity power supply to come from decentralized power sources, billing and energy credits, generation control and system stability remain significant issues limiting the widespread use of this technology. To maintain control and stability of the power system in some networks, the neighbouring consumers need to consume all the electric power that a producing consumer may produce. This ensures there is a net flow of electric power from generators to consumers in the distribution network, even though there may be a local outflow within the local distribution. With the growth of electricity markets and the requirement for open access to networks, the distributed generator may have more options for selling the excess production, either through physical or financial contracts (Hedges). Some countries have significantly better resources than others in particular for the renewable resource sectors. Some nations have significant resources at distance from the major population centers where electricity demand exists. Exploiting such resources on a large scale is likely to require considerable investment in transmission and distribution networks as well as in the technology itself. If renewable and distributed generation were to become widespread, electric power transmission and electricity distribution systems would no longer be the main distributors of electrical energy but would operate to balance the electricity needs of local communities. Those with surplus energy would sell to areas needing "top ups". That is, network operation would require a shift from 'passive management' - where generators are hooked up and the system is operated to get electricity 'downstream' to the consumer - to 'active management', wherein generators are spread across a network and inputs and outputs need to be constantly monitored to ensure proper balancing occurs within the system.

http://www.wordiq.com/definition/Hedge

http://www.wordiq.com/definition/Distributed_generation





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3 RES-ELC

3.1 NEEDS RES-ELC

The figure 1 shows the existing NEEDS RES-ELC.

Figure 1. NEEDS RES-ELC In the existing NEEDS structure, the decentralised power plants produce ELCMED and ELCLOW. In figure 2 are explained the centralised and decentralised technologies used in the NEEDS project for ELC production. The figure shows four groups of technologies for decentralised production; in particular the cogenerations power plants for commercial (COM) and refineries (REF) should be produce at medium level, instead the electricity power plants public (PV technologies and micro-cogenerations???) and cogenerations power plants for agricultural sector should be produce at low level. In the NEEDS model the losses are evaluated with the parameter TE(ENT) applied to the commodities ELCHIG, ELCMED and ELCLOW, therefore also the decentralised technologies, that produce ELCMED and ELCLOW, they should be loaded of losses. But in reality these losses for the decentralised technologies are missing because the electricity produced is directly consumed from the end use technologies and only the exceeding electricity is sold at the transmission grid.


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Figure 2. NEEDS RES-ELC with Decentralised technologies In the existing scheme, the commodities ELCHIG, ELCMED and ELCLOW, can be add with the transmission and distribution cost using the technology “electricity voltage transformers” and “local infrstructures”. The problem is that generally the decentralised electricity is not distribute, so there aren’t these cost. For example, in the existing NEEDS technology we have a production of ELCLOW from the PV roof technologies, that is “distributed” and so loaded of loss, but PV is a very specific process used in an house to produce directly the electricity and not distributed with the grid.

3.2 RES2020 project, RES-ELC first proposal

The figure 3 shows the first proposal for the new RES-ELC, for the RES2020 project in which the structure of electricity transport and distribution it’s similar to the NEEDS project. The difference are on electricity voltage transformers (High to Low replaced with Medium to Low and the possibility to transform from LOW to MED and from MED to HIGH; these technologies are in light blue), on the name of decentralized technologies electricity output (ELCLOW replaced to sectorial-ELC for example RSDELC; technologies in light yellow) and on infrastructure to describe the possibility to use electricity produced in a specific sector also in other sectors (“grid-connected” RSDELC to ELCLOW). Figure shows this process named Dummy Tech RSD for instance (technologies in light green). In order to limit the electricity losses, only if (or when) there’s a real distribution by grid, it’s necessary to modify the SubRes adding sectorial decentralised technologies, as reported in the figure 3 (light yellow technologies). Losses are not computed on processes (grids) but on commodities, so grids are described in terms of capacity, costs and input-output flows.

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Figure 3. RES2020 project, RES-ELC first proposal

3.3 RES2020 project, RES-ELC second proposal

The figure 4 shows the second proposal for the new RES-ELC, for the RES2020 project. The structure of electricity transport and distribution it’s not the same of NEEDS Project, because includes processes that describe the grids and involves to double the number of electricity commodities to represent the commodities before and after transport and distribution. The idea is to add in the existing NEEDS RES-ELC the transmission and distribution grids and to differentiate produced commodities from centralised power plants (ELCHIGP, ELCMEDP and ELCLOWP) from those consumed and produced form decentralised technologies (ELCHIG, ELCMED and ELCLOW). In this way the new scheme will present a difference between centralised (see in the figure 4 red, violet and blue vertical lines) and decentralised produced electricity (see in the figure 4 the black vertical lines), in terms of cost and losses.


- 34 -

Figure 4. RES2020 project, RES-ELC


- 35 -

The losses in the NEEDS scheme are automatically loaded on the commodities (ELCHIG, ELCMED and ELCLOW), independently on the “distance” between production and consumption; this approach is not convenient to describe decentralised power generation. In the figure 4 grids are modelled as process with costs, efficiency, capacity, input and output flows; losses are attributes of grids and not of commodities. The structure proposed is more realistic and allows to represent a better competition among technologies because the commodity output from power plant has to pass by the grids to be used. For example, if the production is on hig-level, the commodity produced in NEEDS is ELCHIG, that is consumed from industry and transport without additional transport cost; instead in the figure 4 the produce commodity is ELCHIGP that has to pass by the transport grid-hig to produce ELCHIG; this commodity can be used from industry sector and for export. The grid-hig loading the losses and the additional transport cost on the commodity ELCHIGP to produce ELCHIG, while the decentralised technologies are able to produce electricity (on the ELCMED and ELCLOW level, see the light green technologies) without losses and additional cost due to transport and distribution grids.

Documents

Modelling Distributed Generation and Variable Loads from RESec.europa.eu/energy/intelligent/projects/sites/iee-projects/files/... · RES2020 – Modelling Distributed Generation and