Decarbonisation modelling in the electricity sectorsled.rec.org/documents/SLED_Albania_ELEC_ENG.pdf · 2016-03-09 · Decarbonisation modelling in the electricity sector Albania Authors

Decarbonisation modelling in the electricity sector

Albania

Support for Low-Emission Development in South Eastern Europe (SLED)

ALB

AN

IAEN

G


Albania

Authors András Mezősi, PhD

László szabó, PhD

(Regional Centre for Energy Policy Research)

support for Low-Emission Development in south Eastern Europe (sLED)

December 2015

AcknowLEDgEMEntsREC project management: József Feiler, Vaiva Indilaite, Ágnes kelemen, gordana kozhuharovaDesign and layout: tricia Barna, Juan tornerosCopyediting and proofreading: rachel hidegPublisher: the regional Environmental center for central and Eastern Europe (rEc)Photo credits: istockThe REC is implementing the project “Support for Low-Emission Development in South Eastern Europe”(SLED) to help policy makers in the project countries (Albania, the former Yugoslav Republic of Macedonia,Montenegro and Serbia) to establish realistic but ambitious decarbonisation pathways for their electricityand building sectors by 2030.The SLED project is funded by the Austrian Development Cooperation through the Austrian DevelopmentAgency (ADA). Special thanks are due to Hubert Neuwirth and Monika Tortschanoff of ADA.

Table of contents

I. ExEcutIVE suMMAry 6

II. IntroDuctIon 10

III. MEthoDoLogy 12

Scenario development framework 13

Models 14 The European Electricity Market Model 14

The EKC network model 15

Model assumptions 16 Cross-border network capacities 16

Current generation capacities 17

Fossil fuel prices 17

European Union Emissions Trading System price 17

European Union minimum tax levels for energy products 17

IV. scEnArIo AssuMPtIons 19

Introduction of the EU Emissions Trading System 21

Introduction of minimum excise duty on energy products 21

Environmental standards enforcement 21

Deployment of renewable energy sources for electricity 21

Conventional power plants 21

Electricity demand 23

V. MoDELLIng rEsuLts 24

Price development 25

Regional outlook 26

Generation mix 28

CO₂ impacts 29

Net import position 30

Investment costs 30

Support budget for renewable energy sources for electricity 31

Albania DEcArBonIsAtIon MoDELLIng In thE ELEctrIcIty sEctor 3

DEcArBonIsAtIon MoDELLIng In thE ELEctrIcIty sEctor Albania4

VI. sEnsItIVIty AssEssMEnt 35

VII. nEtwork IMPActs 39

Planned new network elements 41

Results of network modelling 42 Steady-state and contingency analyses 42

System balances 42

(N-1) Security criteria 42

Net transfer capacity 45

Transmission grid losses 45

VIII. AnnEx 49

The European Electricity Market Model 50

Geographical scope 50

Market participants 50

Equilibrium 51

Network representation 52

The EKC network model 52 Load-flow data collection 53

Demand 53

Network modelling methodology 53 Steady-state and contingency analyses 53

Evaluation of net transfer capacity 53

Transmission grid losses 54


Tables and figuresTable 1 Present net transfer capacity values in the region (MW) 17

Table 2 Planned interconnectors and their investment status (MW) 18

Table 3 Electricity generation capacities, 2014 18

Table 4 Fuel prices, 2015–2030 18

Table 5 Main scenario assumptions for Albania 20

Table 6 Capacity deployment of renewable energy sources for electricity in the scenarios (MW) 22

Table 7 Gross electricity consumption in Albania (GWh) 23

Table 8 Cumulated investment costs in 2015–2030 in the three scenarios 32

Table 9 Contingencies in 2020 43

Table 10 Contingencies in 2025 44

Table 11 Transmission losses in 2015, 2020 and 2025 in Albania, for all scenarios and regimes 48

Executive summary Figure 1 Generation mix, net imports and CO₂ emissions in the three scenarios 8

Executive summary Figure 2 Tax-based revenues and expenditure on support for renewable energy sources for electricity 9

Figure 1 Modelled countries 15

Figure 2 Base-load price evolution in the various scenarios (EUR/MWh) 25

Figure 3 Peak-load price evolution in the various scenarios (EUR/MWh) 26

Figure 4 Planned new fossil fuel–based capacities in South Eastern Europe, 2015–2030 27

Figure 5 Planned new renewable-based capacities in South Eastern Europe, 2015–2030 27

Figure 6 Generation mix, net imports and CO₂ emissions in the three scenarios 28

Figure 7 CO₂ emission levels per capita 29

Figure 8 Revenues from the Emissions Trading System and excise tax 30

Figure 9 Net import position changes in Albania in the three scenarios 31

Figure 10 Annual support budget for renewable energy sources for electricity (EUR million) 33

Figure 11 Average support for renewable energy sources for electricity from end consumers (EUR/MWh) 34

Figure 12 Change in the electricity mix in the case of low hydro availability 36

Figure 13 Changes in the net import position of Albania 37

Figure 14 Base-load price changes in Albania (EUR/MWh) 38

Figure 15 Geographical coverage of the network analysis 40

Figure 16 Planned interconnection lines in South Eastern Europe 41

Figure 17 Net transfer capacity values for 2020 (winter regime) 46

Figure 18 Net transfer capacity values for 2020 (summer regime) 46

Figure 19 Net transfer capacity values for 2025 (winter regime) 47

Figure 20 Net transfer capacity values for 2025 (summer regime) 47

Figure 21 Annual transmission losses in Albania for all scenarios 48

Figure 22 Analysed countries 50

Figure 23 Operation of the model 51

I. Executive summary

The objective of the project “Support for Low-Emission Development in South Eastern Europe(SLED)” is to help policy makers in Albania, the formerYugoslav Republic of Macedonia, Montenegro andSerbia) to set realistic but ambitious decarbonisationpathways for their electricity sectors up to 2030. Inthe case of Albania, the project results were also usedin the assessment process for the country’s intendednationally determined contributions (INDC).

This study assesses the effect of decarbonisation scenarios on the Albanian electricity system.

The scenarios (Reference – REF; Currently PlannedPolicies – CPP; and Ambitious – AMB) use different as-sumptions related to electricity demand and supply.Supply-side factors include the deployment levels ofrenewable energy sources for electricity (RES-E), thefirst year of operation of the planned Vlora I and IIcombined-cycle gas turbine (CCGT) power plant, anddifferences in energy efficiency measures.

The scenarios and assumptions were agreed with themain stakeholders in Albania (relevant ministries andelectricity experts).

The assessment was carried out using the EuropeanElectricity Market Model (EEMM) developed by the Regional Centre for Energy Policy Research (REKK);and the network model of the Electricity CoordinatingCenter (EKC). The EEMM is a detailed, bottom-up eco-nomic simulation model covering the whole Euro-pean Network of Transmission System Operators forElectricity (ENTSO-E) region, while the EKC networkmodel covers the medium- and high-voltage networkof the South East European (SEE) region.

The following main conclusions could be drawn fromthe scenario modelling:

The stringency of climate policy commitments has

limited impact on wholesale price development.The wholesale price is dependent on regional gen-erational capacity expansion rather than on theambition level of climate policy. Since, at regionallevel, significant capacity expansion is foreseen inthe coming five years in both fossil- and renewable-based generation, it will drive wholesale electricityprices down in the whole SEE region until 2025.

Future generation mix and production levels are

more sensitive to demand assumptions. Albaniais currently a net importer of electricity and con-tinues to be an importing country in the REF andCPP scenarios. However, in the AMB scenario thecountry could become an exporter of electricity by

2030. This is due to the lower demand and the sig-nificant increase in hydro-based generation, whichis assumed to be doubled by 2030 in the AMB sce-nario. As a result of higher hydro deployment andthe intensive growth in demand there is a signifi-cant impact on the trade position of the countryand, consequently, on the security of the supplysituation in the country.

CO₂ emissions remain minimal in the modelled

period even with the construction of the two CCGTplants. Albania has the least carbon intensive elec-tricity system in Europe, due to its hydro-basedgeneration, and continues to hold this position. Albania could still develop significant capacities inhydro generation, which could be a very valuableasset for the future operation of the electricity sys-tem. At present, further deployment is con-strained by security of supply considerations: thecountry would like to reduce its dependence onhydro, which is very sensitive to meteorologicalconditions (precipitation levels and patterns). Thisis the main reason for planning two gas-basedCCGT plants in the country. However, our resultsshow that their utilisation would be very low,which would not allow for the economical opera-tion of the two plants due to the relatively highprice of natural gas in the region. In addition, thenecessary infrastructure to supply gas in the coun-try is still lacking. The Trans-Adriatic Pipeline (TAP)for natural gas and its connections to the countryare still in the planning phase.

The level of support needed for the assumed

RES-E deployment shows significant expansionbut would not reach the 2012 levels of the CzechRepublic, Greece or Portugal by 2020 (EUR 12/MWh compared to EUR 13.4/MWh). In addition, the 2020 value would be the peak, asthe required support level subsequently de-creases. The modelling results also show that Albania would not be able to generate significantcarbon tax–based revenues (either by a nationaltaxation scheme or the EU Emissions Trading System [ETS]) to finance part of the required RES-E support budget after 2020.

Generation capacity investments in Albania are

concentrated in hydro technologies. Althoughhydro technologies make up the bulk of the in-vestment costs due to their relatively expensiveconstruction costs, from a generation unit costperspective (based on levelised cost of electricity)hydro is the cheapest renewable technology.



The security of supply concern was further

analysed in order to check the impact of a dryyear on the Albanian electricity system. This sen-sitivity assessment confirms that Albania is sen-sitive to meteorological conditions: in the shortterm, severe droughts — modelled as the driestof the past eight years in the region — coulddrive up prices by EUR 10/MWh, and in the longterm by EUR 4/MWh. In such a year the countrywould still rely heavily on imports, and only in theAMB scenario would its imports be reduced to 18 percent of consumption. In other scenarios,imports could peak up to close to 60 percent indry years. However, the results also show thatgas-fired CCGT plants would not help much interms of the country’s security of supply (SoS), asimports would be more competitively priced.

The hydro sensitivity assessment also points to an

important future policy direction for the country.

If further cooperation is enhanced within the region and with EU member states, the countrycould further utilise its hydro potential. In this casethe country should be very supportive towards astricter EU renewable policy, as it would createmore demand for its hydro-based generation.

The assessment of network impacts shows that

the Albanian electricity system would requiresome network reinforcements in the future tocope with the planned RES capacity increase in thescenarios. If the planned network additions arenot built, contingencies would appear in the sys-tem. The net transfer capacities would also in-crease toward Serbia, but with a more mixedeffect in the direction of other neighbouring coun-tries. Network losses would increase in the scenar-ios with higher consumption levels by 2030, butdistributed RES-E generation would slightly con-strain the increase in network losses.

Executive summary Figure 1 Generation mix, net imports and CO₂ emissions in the three scenarios


Executive summary Figure 2 Tax-based revenues and expenditure on support for renewable energy sources for electricity

II. Introduction

The main objective of the SLED project is to help policy makers in Albania, the former Yugoslav Republic of Macedonia, Montenegro and Serbia insetting up realistic but ambitious decarbonisationpathways for their electricity sectors up to 2030. Policy developments should be evidence based, as faras possible building on quantified modelling results obtained from the possible set of future decarboni-sation scenarios. The SLED project assisted the coun-tries with modelling, accompanied by a continuousconsultation process to enable national policy makers

to influence the scenario development process according to their needs for their future energy sectorand climate strategy developments. During the mod-elling exercise, policy options related to productionlevels and/or the fuel mix for electricity generation —such as supply-side energy efficiency improvements,the accelerated retirement of old power plants, increasing shares of renewable energy sources (RES),and electricity demand — were assessed from theperspective of CO₂ emissions, generation capacityinvestment costs and renewable support needs.

Albania DECARBONISATION MODELLING IN THE ELECTRICITY SECTOR 11

III. Methodology

In this section we introduce the framework for the sce-narios, including the differentiation dimension, and thetwo models used in the assessment of the scenarios.

Scenario development frameworkIn order to assess the full range of the decarbonisa-tion potential in the assessed countries, three scenar-ios were constructed for each country in the SLEDproject: Reference (REF); Currently Planned Policies(CPP); and Ambitious (AMB). Scenario assumptionswere related to six dimensions:

carbon value;

energy/excise tax;

environmental standards;

deployment of renewable energy technologies;

deployment of conventional generation technolo-

gies; and

electricity demand (integrating assumptions on

end-use energy efficiency improvement).

The above factors all affect national CO² emissions either via the level of electricity production or by theirimpact on the fuel mix for electricity generation. As faras taxation is concerned, two factors were identified.First, the introduction of the EU ETS either as a conse-quence of EU membership or the transposition of EUlaw required for members of the Energy Community;and second, simply the introduction of a national pol-icy instrument placing value on carbon emissions,which alters the cost of the respective generation tech-nologies and hence the production possibilities. Thesame logic applies to the introduction of the minimumtax level on energy products required by EU legislation.The electricity supply mix is affected by the introduc-tion of European air pollution regulations: the LargeCombustion Plants (LCP) Directive, for example, mayforce the most polluting coal plants out of operationor limit their operating hours. The development of re-newables and conventional (fossil) generation capaci-ties is the outcome of national policy decisions and —in the case of renewables — support levels. Electricitydemand growth triggers higher production from theavailable power plant portfolio or imports.

The REF scenario reflects the business-as-usual devel-opments in the country, meaning that the official en-ergy policy and legislative instruments that were inplace by the closing date of the scenario definition(July 2015) are included. The CPP scenario reflects

those policies that are under consideration and thatcould have an impact on GHG emissions. The thirdscenario, AMB, represents the most advanced climatepolicy stand.

The options included in the scenarios are assessednot only for the individual countries but also in termsof possible synergies from more collaborative actionsamong SEE countries.1 Modelling the various optionslisted above can help to identify the most effective op-tions to reduce CO₂ emissions in the assessed coun-tries. Other impacts, such as those relating to securityof supply and network reliability, are also included inthe analysis.

conDItIons sPEcIFIc to ALBAnIA

The main data and policy inputs for the scenarioswere agreed with relevant stakeholders (the Ministryof Energy, the Ministry of Environment, the Faculty ofEngineering and the National Agency for Natural Resources) at meetings organised in two rounds. In December 2014, we agreed with stakeholders onthe main assumptions of the scenarios (demandgrowth rates, RES and conventional capacity expan-sion, cross-border capacities, energy efficiency meas-ures). We agreed at this first meeting to use thescenarios in the USAID report (Reference, Energy Efficiency–Natural Gas [EE-NG] and Energy Efficiency,Renewables and Natural Gas [EE-RES-NG]).2 Based onthese assumptions, the European Electricity MarkedModel (EEMM) was run and preliminary results weredelivered. These results were sent to the stakehold-ers and were also presented during a second stake-holder meeting in July 2015. Based on the feedback,the scenarios were redesigned for the following reasons. First, between the two meetings energy policy was undergoing considerable development inAlbania. A draft national renewable energy actionplan (NREAP) ( July 2015 version) was already underrevision by the Ministry of Energy, including impor-tant assumptions on future renewable deployment.3

This document also includes a more up to date pro-jection of electricity demand, with which we replacedthe USAID reference projections assuming a verysteep increase in electricity consumption. The newprojection is more in line with the expected develop-ment. Second, the Ministry of Energy expressed itsinterest in using the modelling results in Albania’sINDC commitment, thus the scenarios were re-designed with these new elements in order to reflectmore closely the country’s latest energy policy devel-opments. This means that we built a more up to date



and more realistic scenario that can give input to theINDC planning process. (For the final assumptions ofthe three scenarios, see Table 5 on page 20.)

ModelsDecarbonisation scenarios for the four assessedcountries and the region as a whole were developedusing the state-of-the-art EEMM in tandem with thedetailed technical network model of the Electricity Coordinating Center (EKC). The EEMM has been fre-quently applied in the region in the past in relation toProjects of Energy Community Interest (PECI) assess-ment, while the network model has been used inmany network expansion and upgrade projects in theregion.4 The EEMM is a partial equilibrium model fo-cused on generation capacities, while the EKC net-work model focuses on the transmission system, inparticular on the development of cross-border capac-ities. The two models are introduced briefly in thissection: more detailed model descriptions can befound in the Annex.

The reliability of model results was ensured by work-ing closely together with stakeholders in the region.The EKC network modelling team is from Serbia,which means that the modelling experts have in-depth regional knowledge. In addition to this insiderinvolvement, three project factors gave unique addedvalue to the assessment:

The models were updated with the most recent

data from the beneficiary countries, with the helpof local experts.

Throughout the project, the involvement of stake-

holders — including representatives of relevantministries dealing with climate- and energy-related issues and representatives of the trans-mission system operators (TSOs) — was ensuredby setting up a project “task force”. These expertsand policy makers were involved in defining policy-relevant scenarios and in the assessment ofmodel results already at an early stage of the proj-ect. They also provided up-to-date information onnational energy policies and checked the validityof information and data during two stakeholdermeetings in November 2014 and July 2015.

The dissemination of project results will be en-

sured by means of workshops in all participatingcountries.

The relevant experts and stakeholders in the projectcountries were reached with the help of a local expertconsultancy (EKC from Serbia), as well as the local of-fices of the Regional Environmental Center (REC),which has long-term expertise and a solid network inthe region.

The European Electricity Market Model

The EEMM is a simulation model of the Europeanelectricity wholesale market that works in a stylisedmanner with perfect competition assumptions.

The EEMM covers 36 countries with rich bottom-uprepresentation. In Figure 1, in the countries colouredorange, electricity prices are derived from the demand-supply balance, and in the blue countriesprices are exogenous. The ENTSO-E countries of theEU (Malta and Cyprus are not included in the model)and Balkan countries are modelled in full detail. In theelectricity production sector we differentiated 12 technologies. We assume one interconnector perpair of countries, which means modelling 85 trans-mission lines. The EEMM models the production sideat unit level, which means that at the greater Euro-pean level almost 5,000 units are included in themodel runs. Equilibrium (in prices and quantities) isreached simultaneously in the producer and thetransmission segments. These units are characterisedby various technological factors, allowing the con-struction of the merit order for the particular time period. In each year we have 90 reference hours torepresent the load curve with sufficient detail for eachEuropean country.

There are three types of market participants in themodel: producers, consumers and traders. All ofthem behave in a price-taking manner: they take theprevailing market price as given, and assume thatwhatever action they decide upon has a negligible effect on this price.

Producers are the owners and operators of powerplants. Each plant has a specific marginal cost of pro-duction, which is constant at the unit level. In addi-tion, generation is capacity constrained at the level ofavailable capacity.

The model only takes into account short-term variablecosts with the following three main components: fuelcosts, variable operational expenditure (OPEX), andCO₂ costs (where applicable). As a result, the approachis best viewed as a simulation of short-term (e.g. day-ahead) market competition.


Price-taking producer behaviour implies that when-ever the market price is above the marginal genera-tion cost of a unit, the unit is operated at full availablecapacity. If the price is below the marginal cost thereis no production at all; and if the marginal cost andthe market price coincide, the level of production isdetermined by the market clearing condition (supplymust equal demand).

Consumers are represented in the model in an aggre-gated way by price-sensitive demand curves. In eachdemand period there is an inverse relationship be-tween the market price and the quantity consumed:the higher the price, the lower the consumption. Thisrelationship is approximated by a downward slopinglinear function.

Finally, traders connect the production and consump-tion sides of a market, export electricity to more expen-sive countries and import it from cheaper ones.Cross-border trade takes place on capacity-constrainedinterconnectors between neighbouring countries. Elec-tricity exchanges always occur from a less expensivecountry to a more expensive one, until one of twothings happens: either prices, net of direct transmis-sion costs or export tariffs, equalise across the twomarkets; or the transmission capacity of the intercon-

nector is reached. In the second case, a considerableprice difference may remain between the two markets.

The model calculates the simultaneous equilibriumallocation in all markets with the following properties:

Producers maximise their short-term profits given

the prevailing market prices.

Total domestic consumption is given by the aggre-

gate electricity demand function in each country.

Electricity transactions (exports and imports)

occur between neighbouring countries until mar-ket prices are equalised or transmission capacityis exhausted.

Energy produced and imported is in balance with

energy consumed and exported.

Given our assumptions about demand and supply,market equilibrium always exists and is unique inthe model.

The EKC network model

Electric power systems in SEE are modelled with theircomplete transmission networks at 400 kV, 220 kVand 150 kV. The power systems of Albania, the formerYugoslav Republic of Macedonia, Montenegro and

Figure 1 Modelled countries


Serbia are also modelled at the 110 kV voltage level.The network equivalent of Turkey (i.e. European part)and the rest of ENTSO-E Continental Europe (modelledover the X-node injections) are used in the model.

The network model in this assessment provides thefollowing results:

Contingency analyses, which include:

• an assessment of the existing electricity networksituation within Albania, the former Yugoslav Republic of Macedonia, Montenegro and Serbia,together with the regional context; and

• a definition of the network topologies andregimes for 2015, 2020 and 2025, using realisticscenarios of demand growth, generation expan-sion, transit flows, RES integration and high-voltage direct current (HVDC) links.

Total and net transfer capacity (TTC/NTC) evalua-

tion between Albania, the former Yugoslav Repub-lic of Macedonia, Montenegro and Serbia in alldirections, for all topology scenarios.

An assessment of transmission grid losses with

and without a level of energy production from RES.

stEADy-stAtE AnD contIngEncy AnALysEs

For the defined scenarios, steady-state load flowswere calculated and contingency (n-1) analyses per-formed. Security criteria were based on the loadingsof lines and voltage profile and were checked foreach scenario analysed.

Load-flow assessment is a basic step for NTC evalua-tion, and it comprises the following analyses:

steady-state AC load-flow analysis;

a security (n-1) assessment where the tripping of

lines is simulated. This means that one line is con-sidered out of service while load flow is calculatedand the security of the system assessed (circuitoverloads and voltage violations); and

a voltage profile analysis.

In the analysis of voltage profiles, voltage limits areaccording to the respective national grid codes.

EVALuAtIon oF nEt trAnsFEr cAPAcIty

Total and net transfer capacity were evaluated between Albania, the former Yugoslav Republic ofMacedonia, Montenegro and Serbia, as well as be-tween these countries and their neighbours, in all

directions and for all topology scenarios, with refer-ence to each target year and regime, and a final as-sessment was made of the TTC/NTC additional valuesas a result of the new interconnections and thestrengthening of major internal energy transit routes.

General definitions of transfer capacities (TTC andNTC) and the procedures for their assessment weregiven by ENTSO-E, as well as by the practice and ex-perience of regional SEE TSO working groups.

The methodology used in performing this study wasbased on the prerequisites outlined below.

trAnsMIssIon grID LossEs

The assessment of electricity losses is based on lossesover the equivalent time duration in the winter peakand summer peak periods. This approach takes intoaccount that the effect on losses may be different inthese two regimes, as a result of which losses on ayearly level can be determined more accurately.

Model assumptionsIn this section we introduce those assumptions thatremain constant across the various scenarios for allthe assessed countries and the regional assessment.

Cross-border network capacities

Even though countries in the SEE region are well con-nected with their neighbours, further capacity exten-sions are envisaged in the future. The model uses theNTC values of ENTSO-E to reflect the trading possibil-ities between countries. Tables 1 and 2 show the pres-ent NTC values in the region, including neighbouringcountries, and the planned new connections in themodelling timeframe.

The Montenegro–Italy 1,000 MW submarine cable isplanned to start operation in 2018. Construction hasalready started and is proceeding according to the in-vestment plan. The other submarine cable connectingItaly with Albania is very uncertain and might not berealised as planned, or might even be cancelled. Themodelling considers the “approved” and “under con-struction” categories of ENTSO-E in all three scenarios.The Albania–Serbia line in fact connects Albania withKosovo*. As there is no constraint between the elec-tricity systems in Kosovo* and Serbia, the new line in-creases the interconnectedness of the three countries.


Table 1 Present net transfer capacity values in the region (MW)

Origin and destination country NTC value

From To O➞D D➞O

AL MK 0 0

BA RS 488 403

BA ME 483 440

GR MK 329 151

GR AL 250 250

HR RS 507 429

HU RS 689 758

ME AL 400 400

MK BG 96 215

RO RS 570 347

RS ME 540 583

RS MK 491 253

RS AL 223 223

RS BG 162 250

Source: ENTSO-E

Current generation capacities

Table 3 provides information on electricity generationcapacities for the base year, 2014.

Fossil fuel prices

Table 4 shows the fossil fuel prices applied in themodelling for the period 2015–2030.

European Union Emissions Trading System price

Concerning the carbon price assumptions, we fol-lowed the carbon value path of the latest impact as-sessment of the EU (GHG40EE scenario5) andassumed an ETS carbon price of EUR 22/tCO₂ for Europe by 2030. The ETS price goes linearly from its2014 value of EUR 6/t to EUR 22/t by 2030 in all

scenarios. The ETS price applied in Albania differs intiming in the various scenarios. The exact year of in-troduction and level are shown in Table 5 (page 20).

European Union minimum tax levels for energy products

Excise duty is differentiated according to the fuel used(coal, natural gas and heavy fuel oil [HFO]). The mini-mum excise duty level applied is equal to the 2014level applicable by EU law:

EUR 0.3/GJ for natural gas;

EUR 0.15/GJ for coal; and

EUR 0.38/GJ for HFO

The tax applied in Albania differs in timing in the various scenarios. The relevant year of introductionis shown in Table 5.


notEs

See the regional assessment.1

Assessment of Energy Developments in Albania for the period 2012–2030. United States Agency for International Development (USAID), January 20152

National Renewable Energy Action Plan, June 15, 2015. Received from the Ministry of Energy in July 2015 and under revision in July.3

In the network assessment of the 400 kV line between Montenegro, Serbia and Bosnia and Herzegovina, for example.4

Commission Staff Working Document, “Impact Assessment: A policy framework for climate and energy in the period from 2020 up to 2030”.5

SWD(2014) 15 final.

* This designation is without prejudice to positions on status, and is in line with UNSCR 1244 and the ICJ Opinion on the Kosovo declarationof independence.

Table 2 Planned interconnectors and their investment status (MW)

Country 1 Country 2 Year of commissioning Investment status O➞D D➞O

RS RO 2017 Approved 800 800

BA ME 2023 Planned 600 600

IT AL 2020 Planned 500 500

RS MK 2015 Under construction 400 1,000

MK AL 2019 Approved 600 600

AL RS 2016 Under construction 500 500

IT ME 2018 Under construction 1,000 1,000

RS BA 2022 Planned 600 600

RS ME 2022 Planned 600 600

Source: ENTSO-E

Table 3 Electricity generation capacities, 2014

Coal andlignite Natural gas HFO/LFO Hydro Wind Biomass PV Total

AL 0 0 0 1,801 0 5 2 1,807

ME 210 0 0 661 0 7 3 881

MK 824 290 210 644 37 0 15 2,020

RS 4,672 0 0 2,357 331 1 7 7,368

Source: REKK and PLATTS database

Table 4 Fuel prices, 2015–2030

Coal price (EUR/GJ) Lignite price (EUR/GJ) West European naturalgas price (EUR/GJ)

East European natural gasprice (EUR/GJ)

2015 2.0 1.2 5.5 8.3

2020 2.2 1.3 5.9 8.0

2025 2.2 1.3 6.0 8.1

2030 2.2 1.3 6.2 8.3

Source: IEA and EIA projections

IV. Scenario assumptions


Table 5 summarises the scenario assumptionsgrouped under taxation, supply-side measures anddemand-side measures.

These assumptions are based on Albania’s existingenergy strategy documents and the outcomes of thetwo stakeholder meetings held in Tirana in December2014 and July 2015. The energy policy documents thatwere used are:

Assessment of Energy Developments in Albania

for the period 2012–2030 (USAID, January 2015)

Draft National Renewable Energy Action Plan, June

15, 2015 (received from the Ministry of Energy inJuly 2015 as a document under revision)

National Renewable Energy Action Plan of Albania

(Ministry of Energy, 2014)

Table 5 Main scenario assumptions for Albania

Scenario assumptions

Reference GHG scenario(REF)

Currently Planned PoliciesGHG scenario (CPP)

Ambitious GHG policyscenario (AMB)

Taxation

Introduction of EU ETS ETS to be introduced in 2025 CO₂ cost in 2020 is 40% of

the ETS price; from 2025 ETSis introduced

ETS to be introduced in 2020

Introduction of minimumexcise duty Year of introduction: 2020 Year of introduction: 2020 Year of introduction: 2018

Electricity supply

Enforcement ofenvironmental standards

(LCP Directive)

Due to the requirements ofthe LCP Directive, Fier TTP is

not in operation in themodelled period.

Due to the requirements ofthe LCP Directive, Fier TTP is


Due to the requirements ofthe LCP Directive, Fier TPP is


RES-E deployment

NREAPs: 2,324 MW hydro;30 MW wind; 32 MW PV;

and 5 MW biomass by 2020.By 2030: 2,710 MW hydro;100 MW wind; 79 MW PV;

and 24 MW biomass.


and 5 MW biomass by 2020.By 2030: 3,097 MW hydro;170 MW wind; 126 MW PV;

and 42 MW biomass.


and 5 MW biomass by 2020.By 2030: 3,869 MW hydro;310 MW wind; 220 PV; and

80 MW biomass.

Conventional capacitydevelopments

CCGT Vlora I ( 200 MW)comes online in 2020 andCCGT Vlora II (160 MW)comes online in 2025.

CCGT Vlora I. ( 200 MW)comes online in 2020 andCCGT Vlora II (160 MW)comes online in 2025.

CCGT Vlora I (200 MW)comes online in 2020 andCCGT Vlora II (160 MW)comes online in 2025.

Electricity demand Electricity demand

According to the June 2015draft NREAP projections up

to 2020. In the next period, a3.1% growth rate is applied,

which is a continuation ofthe trend between 2010 and

2020. 2030: 12,990 GWh.

According to the USAIDEnergy Efficiency–Natural

Gas scenario. 2030: 10,918 GWh

According to the USAIDEnergy Efficiency–

Renewable–Natural Gasscenario.

2030: 10,857 GWh


National Energy Efficiency Action Plan for the Re-

public of Albania 2010

National Background Report on Energy for Alba-

nia, 2012 (WBC-Inco)

Introduction of the European UnionEmissions Trading SystemWe used different assumptions with respect to Alba-nia joining the EU ETS. In the REF scenario, Albaniajoins the ETS in 2025, while in the CPP scenario thepower sector already faces a carbon value equal to 40 percent of the EU ETS price in 2020. In the AMBscenario, the Albanian power sector joins the ETS already in 2020. “Joining the ETS” does not necessarilyimply EU membership: we only assume that nationalpolicy makers will apply some instruments with simi-lar effects on the electricity sector as the EU ETS (e.g.by a voluntary or legal obligation, through a nationalcommitment or Energy Community commitments).

Introduction of minimum excise duty on energy productsConcerning other taxes in the energy sector, we usedthe assumption that the country introduces the min-imum level of excise duties in 2020 in the REF and CPPscenarios, while in the AMB scenario it is introducedin 2018.

Environmental standards enforcementWe assume that the country fulfils the require-ments of the EU LCP Directive, which means thatthe Fier oil-fired TPP will not be in operation in themodelled period.

Deployment of renewable energy sourcesfor electricityAlbania finalised its NREAP for the Energy CommunitySecretariat in December 2014. The document isunder revision, and the revised version is the basis forour modelling, providing planned capacity values upto 2020. Figures beyond 2020 are based on the USAID

document “Assessment of Energy Developments inAlbania for the Period 2012–2030”, which containsforecasts up to 2030.

Up until 2020, all scenarios use the NREAP numbers.Hydro generation is assumed to grow quite signifi-cantly: by 2030 capacities are more than double the2015 levels in the AMB scenario. This is quite a stronggrowth but still well within the available potential ofthe country. The main factor that gives rise to uncer-tainty about such development is the environmentalconcerns related to such massive hydro capacity ex-pansion. The other RES technologies also assume sig-nificant growth rates in the modelled period, but theirlevel remains moderate, staying below 20 percent ofthe total capacities of the country in the AMB sce-nario. In the CPP scenario, the growth rate of capaci-ties is assumed to be half of the AMB scenario, whilein the REF scenario the growth rate is only 25 percentof the growth rate in the AMB scenario.

The scenarios assume normal utilisation conditionsfor weather-dependent technologies (solar and wind),meaning average working hours and efficiency. Con-cerning hydro generation in these scenarios, averagehydrological conditions are assumed. This assump-tion will be relaxed in the sensitivity assessment,where a low precipitation pattern is also assessed.The EEMM treats RES-E capacities in a “must run” op-eration mode to reflect the priority dispatch of renew-able technologies.

Conventional power plantsAlbania currently has only one fossil fuel plant, Fier TPP,which operates on fuel oil. The power plant has notbeen in operation since 2009 but acts as a standing re-serve. The plant uses outdated technology and is notexpected to be put into operation in the future. Albaniais planning to build two gas-fired CCGT plants in the fu-ture. Vlora I is planned to come online by 2020 with acapacity of 200 MW, and Vlora II is due to come onlinein 2025 with a capacity of 160 MW. The construction ofthese two units is foreseen in all scenarios. However,it should be emphasised that these plants are condi-tional on the construction of a gas pipeline (TAP orother source) that will provide natural gas to the SEEregion. Albania currently has no access to a natural gassource: the TAP is still at the negotiation phase andconstruction has not yet started. This poses seriousrisks to the realisation of the country’s CCGT plans.


Table 6 Capacity deployment of renewable energy sources for electricity in the scenarios (MW)

REF scenario 2015 2016 2017 2018 2019 2020 2025 2030

Hydro* 1,801 1,893.5 1,941 1,991 2,264 2,324 2,538 2,710

Pumpedstorage 0 0 0 0 0 0 0 0

Geothermal 0 0 0 0 0 0 0 0

Solar 2 5 10 16 24 32 54 79

Wind 0 0 4 10 20 30 63 100

Biomass 5 5 5 5 5 5 24 24

CPP scenario 2015 2016 2017 2018 2019 2020 2025 2030

Hydro* 1,801 1,894 1,941 1,991 2,264 2,324 2,752 3,097


Geothermal 0 0 0 0 0 0 0 0

Solar 1.6 4.8 9.6 16 24 32 76 126

Wind 0 0 4 10 20 30 95 170

Biomass 5 5 5 5 5 5 42 42

AMBscenario 2015 2016 2017 2018 2019 2020 2025 2030

Hydro* 1,801 1,894 1,941 1,991 2,264 2,324 3,179 3,869


Geothermal 0 0 0 0 0 0 0 0

Solar 2 5 10 16 24 32 120 220

Wind 0 0 4 10 20 30 160 310

Biomass 5 5 5 5 5 5 80 80

*Excluding pumped storage


Electricity demandElectricity consumption usually closely follows a coun-try’s GDP development. We did not prepare our ownforecast based on GDP assumptions, but used the lat-est official energy forecasts (Draft NREAP of 2015 andthe USAID forecast) for the Albanian power system(Table 7).

In the REF projection of electricity demand, the latestdraft NREAP forecast is used up to 2020, and thegrowth rate is then kept at the average rate for theperiod 2010–2020 (3.1percent) up to 2030. For theCPP and AMB scenarios, the USAID forecasts wereused, which assume different levels of energy efficiency improvements, resulting in different levelsof electricity consumption. Table 7 shows that the scenarios are coherent: the main differences appearafter 2020 and there is a reasonable gap between theelectricity consumption values in 2030. This reflectsthe general characteristics of energy efficiency meas-ures: significant time is needed to bring about resultsin terms of reducing electricity consumption, but onceenergy efficiency programmes are in place, their impacts are cumulative, which increases the gap between the reference and mitigation scenarios.

Table 7 Gross electricity consumption in Albania (GWh)

GWh 2015 2016 2017 2018 2019 2020 2025 2030

REF 8,229 8,493 8,757 9,021 9,286 9,550 11,138 12,990

CPP 8,153 8,145 8,363 8,616 8,895 9,165 9,165 10,918

AMB 7,909 7,857 8,084 8,319 8,703 9,121 9,095 10,857

V. Modelling results

In this section we discuss the results of the modellingrelated to wholesale price development, the electric-ity generation mix, CO₂ emissions, renewable sup-port and investment needs for the generationcapacities included in the respective scenarios.

Price developmentOne of the most important indicators of the func-tioning of an electricity market is the developmentof the wholesale price. Sudden and significant pricechanges are a sign of the malfunctioning of the elec-tricity market, as they generally indicate shortagesof certain generation capacities or problems withcross-border trade. As hydro capacities are almostthe exclusive source of electricity in Albania (supple-mented only by imports), changes in precipitationpatterns can also change the price pattern in theelectricity sector. This issue is addressed in SectionVI (Sensitivity assessment).

Figure 2 shows the base-load price development in Albania in the various scenarios. The figure shows theyearly base-load price development as the calculatedaverage of the modelled base-load hours of the year.In this way it is possible to smooth out the cyclical be-haviour of electricity prices over a year in order to illustrate the main trends.

One of the most important things shown by the figureis the minor differences between the scenarios. Whilethere is a big fluctuation in terms of price betweenthe years, there is no significant variation between thescenarios. This shows that, from the point of view ofwholesale price, more ambitious GHG policies arefeasible in the Albanian electricity system, thus thepower system (together with import possibilities)would be able to cope with the assumed GHG policyinstruments without any significant price increase.The impacts on electricity imports and the RES sup-port budget will be analysed later in the presentstudy. What can be observed as a general trend in theprojection is a falling base-load price level in the com-

Figure 2 Base-load price evolution in the various scenarios (EUR/MWh)



ing five years, followed by a slightly increasing trendup to 2030. The reason for the significant price dropin the near future is the dynamically growing capacitypool in the region, which will be analysed below under“Regional outlook”.

Another interesting trend is the reduction in peak-load prices, which are getting closer to the base-loadprice by 2030. Albania’s increasing connectivity andmore available capacities make shortages on the sup-ply side less frequent, which — in turn — results inthe convergence of base and peak prices, as illus-trated in Figure 3. The same conclusion holds forpeak-load prices as for base-load prices: the scenar-ios do not show any significant differences in thewholesale market prices.

Regional outlookThe plummeting price trend in the first five years re-quires a more detailed explanation. The main drivingforce behind this development is the dynamic capac-ity expansion in the region.6 As the country is wellconnected with its neighbours, any increase in gener-

ation capacities in neighbouring countries will also in-crease supply in Albania, thus reducing prices in thewhole region. As illustrated by Figures 4 and 5, thereis a significant peak in the construction of new powerplants in the region in the period 2015–2020.

Figures 4 and 5 show that most of the new fossil-based generation is due to be built in the coming fiveyears, meaning a significant and quite rapid increaseon the generation side. This increase in generation iscomplemented by an even more sizeable increase inrenewable capacities, mainly wind and hydro. Allthese new plants represent a significant pressure onthe supply side, and, if realised, could lead to signifi-cant price reductions in the coming years. As many ofthese plants are under construction or have a final in-vestment decision (FID), they will probably be built,even in an environment of falling electricity prices.Under these investment conditions, project develop-ers are trying to finalise their projects as soon as pos-sible, as new entrants will deter other investors fromentering the market. However, this might lead to a sit-uation in which all new entrants lose money, as thefalling price trend would undermine the long-termprofitability of the fossil fuel–based plants (mainly

Figure 3 Peak-load price evolution in the various scenarios (EUR/MWh)


Figure 4 Planned new fossil fuel–based capacities in South Eastern Europe, 2015–2030

Figure 5 Planned new renewable-based capacities in South Eastern Europe, 2015–2030

Source: EEMM database, Platts

Source: EEMM database, Platts


coal-based generation), especially if accompanied byan increasing carbon price trend.

Generation mixWhile we can observe less-pronounced changes in thewholesale electricity prices, the various policies haveprofound impacts on the electricity generation mix, onthe export-import position of the country, and thus onCO₂ emissions. Figure 6 summarises the above-mentioned impacts of the three assessed scenarios.

In terms of generation mix, the most importantchanges can be observed in hydro-based generationand in the import share needed for the country to bal-ance its electricity supply and demand. Hydro genera-tion increases throughout the modelled period,reflecting the increasing generation capacities in thescenarios. This increase is more intensive in the caseof the CPP and AMB scenarios, which has a very posi-tive impact on Albania’s electricity balance. In the AMB

scenario, the country could eliminate net import de-pendency and could become self-sufficient. The twofactors that contribute to this positive trend are the in-creasing hydro generation and the falling electricity de-mand assumed in the scenario. Another importantelement shown in the figure is the low utilisation rateof the planned CCGT capacities in the electricity mix.Even if the two CCGT units are built, their utilisation islimited to a few hours a year, showing their very lowcompetitiveness. They cannot compete with the lower-priced coal generation in the region, even in the AMBscenario with carbon taxation. The EUR 22/tCO₂ car-bon tax is not able to stimulate a swap between coal-and gas-fired generation. This brings into question theconstruction of the second CCGT plant, as even the firstplant has a very low contribution to the system load,which will not make its operation economical in the as-sumed environment characterised by a low carbon tax-ation level and relatively high natural gas prices in theregion. Apart from hydro, other RES generation ap-pears mainly after 2025, and by 2030 will make a size-able contribution to Albanian electricity generation.

Figure 6 Generation mix, net imports and CO₂ emissions in the three scenarios


CO₂ impactsIn this sub-section we assess the impacts of the vari-ous scenarios on CO₂ emission using four indicators:

CO₂ per capita;

CO₂ per GWh production;

CO₂ per GWh consumption; and

the fiscal impact of the introduced taxes.

We will look at the first three indicators in relation tothe ENTSO-E average in order to measure the coun-try’s relative performance.

With its exclusive dependence on hydro, Albaniahas barely any CO₂ emissions in its electricity sec-tor. What is more surprising is that the constructionof the two CCGT plants does not change the picture,as they are hardly utilised — just for some hours inthe REF scenario in 2025. The country’s carbon im-pact is via its imports, but those emissions are ac-counted in the source countries. As the previousresults indicate, these imports are eliminated in the

AMB scenario, thus in this case electricity consump-tion in Albania becomes fully carbon neutral. Theother two indicators (CO₂ per GWh of productionand CO₂ per GWh of consumption) are not pre-sented in this assessment, as they show the samepicture as in Figure 7.

We have calculated the revenues from a possible car-bon taxation and excise duty scheme in the country,and the revenues from these sources are shown inFigure 8.

Excise and carbon taxes mean government revenuesfrom the electricity sector. As Figure 8 shows, carbonrevenues would be significantly more importantsources than excise taxes on energy products, butdue to the fossil-fuel-free (and thus carbon-free) na-ture of the Albanian electricity sector, these revenuesare insignificant. Projected revenues would reach onlyEUR 0.6 million in the REF scenario, and would evenfall subsequently.

Figure 7 CO₂ emission levels per capita


Net import positionThe most significant variation between the scenar-ios appears in relation to import levels. The countryis currently a heavy importer of electricity: around40 percent of domestic demand is covered by im-ports. In this context the future scenarios aremarkedly different. While the REF scenario shows asimilar level of import balance in the modelled period, in the CPP and AMB scenarios we can observe a steeply decreasing import balance. In theAMB scenario, the country even becomes an exporter of electricity (Figure 9).

Figure 9 illustrates an important co-benefit of higherRES deployment in Albania: import dependency willbe significantly reduced. There are two main driversbehind this development. The first is lower demanddue to energy efficiency measures. The second is thatgreater RES-E capacity, mainly hydro, will enablehigher production rates and greater exports to neigh-bouring countries, facilitated by the increasing NTCassumed in the scenarios.

These results will be further analysed in the sensitivityassessment (Section VI), where the assumption regarding the average hydro utilisation rate will be relaxed and the production pattern checked with newassumptions on the availability of hydro generation.

Investment costsThe foregoing sections present the multiple benefitsthat appear in the AMB scenario characterised bystronger GHG reduction policy instruments. However,it is important to assess the financial consequencesof more stringent climate policies targeting the elec-tricity sector. This will be done by considering twotypes of costs: the investment costs for generation capacities; and the RES-E support budget.

The investment costs required to build up the new ca-pacities in the electricity sector are shown in Table 8.

The sources of information concerning unit investmentcosts (shown in the second column, EUR/kW) are a2013 publication by the Fraunhofer research organisa-

Figure 8 Revenues from the Emissions Trading System and excise tax


tion7; and the Serbian Energy Strategy, which gives region-adjusted values for the investment costs. Whilemost of the renewable and natural gas–based esti-mates are in a similar range, estimates in the case ofhydro and coal generation investment costs deviatesignificantly. We use benchmark investment cost val-ues, as national quotes generally underestimate costs.8

As Table 8 shows, the AMB scenario doubles the cu-mulated investment cost compared to the REF sce-nario, due to the higher RES capacity deployment(mainly hydro generation). While the unit investmentcosts are higher for hydro than for solar or windtechnologies, the levelised cost of electricity (LCOE)in the case of hydro technology is lower than forwind or solar (see below). In addition, its higher util-isation rates and regulatability makes hydro more at-tractive in the region. On the other hand, hydrogeneration raises greater environmental concerns,as the construction of new dams and reservoirs isgenerally more difficult now, with many new environ-mental regulations in place, and many of the newlyplanned hydro plants are located in environmentally

sensitive areas. It is important to emphasise that al-though gas plants contribute less to the overall in-vestment cost level, their very low utilisation ratesmake the investments very ambiguous. In normalyears (with average precipitation patterns assumed)their contribution would be minimal, thus their rolewould be merely to serve as a reserve for dryweather conditions. This assumption will be furtherchecked in the sensitivity assessment (Section VI).

Support budget for renewable energysources for electricityThe next assessed cost element is the RES supportbudget, which indicates the overall financial burdenof RES-E deployment. We calculate with a benchmarksupport need, as present national support levelsmight not reflect the LCOE of the technologies.

The support need for 1 MWh of RES-E is calculatedby taking the LCOE of the various generation tech-

Figure 9 Net import position changes in Albania in the three scenarios


nologies, which reflects the full average cost of re-newable generation, including not only the marginaloperating costs but also the financial returns neededto cover the investments.9 To make RES productionbreak even, the difference between the LCOE valueand the market price (P) must be given to producersfor every produced MWh of renewable electricity,which is the support need for RES-E production.Base-load and peak-load prices are used from EEMMruns, making it possible to calculate the supportneed for each MWh of RES electricity produced. Weassume that this support need is independent of thesupport type applied (feed-in tariff [FIT] or feed-inpremium). If this support need is multiplied by theprojected quantity of generated RES-E, we arrive atthe support budget. The calculation is shown in thefollowing equation:

Support budget = (LCOEt-P)*generated electricity

• LCOEt: levelised cost of electricity generationof technology t ~ average cost of electricityproduction

• P: modelled base-load electricity price (exceptPV, where peak-load electricity prices aretaken into account)

We use a differentiated LCOE for all RES-E technolo-

gies, based on data from the literature. One of themost recent reliable calculations (Ecofys, 201410) givesthe following benchmark LCOE data, which were usedin this study:

EUR 55/MWh for hydro;

EUR 90/MWh for wind;

EUR 110/MWh for biomass;

EUR 105/MWh for PV; and

EUR 80/MWh for geothermal.

Present FIT support levels in the country for newhydro capacities are set at around EUR 60/MWh forcapacities up to 10 GW for existing plants, and EUR 70/MWh for new plants up to 15 MW. For otherRES technologies no FIT exists. The benchmark LCOEvalues show that the present level of support in Albania will be sufficient to cover hydro technology.

If the RES-E support budget is divided by the totalelectricity consumption — assuming that all electricityconsumers have to pay for RES-E support — we canalso calculate the average RES-E support fee that eachend user has to pay according to their consumption.These values — the total annual RES-E supportbudget and the average RES support fee — are shownin Figures 10 and 11.

Table 8 Cumulated investment costs in 2015–2030 in the three scenarios

Investmentcost (EUR/kW)

New capacity (MW) Investment cost (EUR million)

REF CPP AMB REF CPP AMB

Natural gas 1,000 360 360 360 360 360 360

Coal 2,000 0 0 0 0 0 0

Hydro 2,500 909 1,296 2,068 2,273 3,239 5,170

Geothermal 4,000 0 0 0 0 0 0

Solar 1,100 77 124 218 85 137 240

Wind 1,000 100 170 310 100 170 310

Biomass 3,000 19 38 75 56 113 226

Total – 1,465 1,988 3,032 2,875 4,018 6,306


Figure 10 shows a steeply increasing support budgetbetween 2015 and 2020 (mainly due to wholesaleprice fluctuations), but also a decreasing budget after2020 despite continuously growing RES-E capacitiesin the CPP and REF scenarios. There is a peak in thesupport budget in around 2020 in the REF and CPPscenarios, after which financing becomes less costlyin overall terms. In the REF and CPP scenarios, thedrop in budget is significant, while in the AMB sce-nario RES financing will slightly increase further, evenup to 2025, reaching over EUR 110 million per year.In 2015 the support budget is close to zero (or slightlynegative), as the country’s hydro generation has alower cost than the calculated benchmark LCOE.

Figure 11 illustrates what this support means for endconsumers in terms of the price increase they willface due to the higher deployment of RES-E.

The average RES-E charge on consumed electricity fol-lows a similar trend as in the case of the overall RES-E budget. The extra charge is the highest in theAMB scenario, reaching a peak of EUR 12/MWh (orEUR 0.12/kWh) by 2020. By 2020, the support levelsin the scenarios comes close to the levels in many European member states in 2012. According to a2015 report by the Council of European Energy Regu-lators (CEER) on EU renewable support schemes, EUmember states supported RES-E with an average ofEUR 13.68/MWh in 2012.11 Thus, in spite of the signif-icant increase, the level of the financial burden of theprojected support in 2020 is comparable to the levelof support in the Czech Republic, Greece or Portugalin 2012. The projected average charge also peaks in2020 (and in 2025 for the AMB scenario) and subse-quently follows a declining trend.

Figure 10 Annual support budget for renewable energy sources for electricity (EUR million)


Figure 11 Average support for renewable energy sources for electricity from end consumers (EUR/MWh)

notEs

1 “The region” includes Albania (AL), Bosnia and Herzegovina (BA), Croatia (HR), Bulgaria (BG), Greece (GR), Hungary (HU), the former YugoslavRepublic of Macedonia (MK), Montenegro (ME), Romania (RO) and Serbia (RS).

2 Levelized Cost of Electricity Renewable Energy Technologies, Fraunhofer Institute for Solar Energy Systems ISE, November 2013

3 A recent example from the region is the Sostanj coal-fired TPP in Slovenia. The initial investment cost estimate was EUR 700 million for agross 600 MW coal plant (net output 545 MW), and the final investment cost was EUR 1,400 million, equal to EUR 2,500/kW. Source: BalkanEnergy News, June 2015.

4 The most common way to calculate LCOE is:

where I = investment costs; M = maintenance costs; F = fuel costs; E = electricity generated in time t; r = discount rate; and t = time pe-riod.

5 Subsidies and costs of EU energy, Ecofys, November 2014, Final report DESNL14583

6 Status Review of Renewable and Energy Efficiency Support Schemes in Europe in 2012 and 2013, Council of European Energy Regulators,2015. Ref: C14-SDE-44-03.

VI. Sensitivity assessment


Hydropower plays an important role in the region, ashigh levels of hydro capacity exist in almost all the as-sessed countries. Albania leads these countries, rely-ing almost exclusively on hydropower (and imports).This poses a challenge, as in dry years imports of elec-tricity can rise rapidly. As it also raises security of sup-ply concerns for the country, this aspect needs to beassessed in greater detail. It should be emphasisedthat this is another reason why most SEE countriesare cautious about further increasing the share ofhydro capacities, as to do so would be to furtherdeepen their exposure to meteorological conditions(i.e. to the quantity and seasonality of precipitation).

In order to investigate this issue, a sensitivity assess-ment was carried out that assumed lower precipita-tion levels than in the previous three scenarios. In theREF, CPP and AMB scenarios, hydro utilisation ratesare modelled on the average level over the past eightyears, while in the sensitivity runs we checked thesescenarios using the lowest utilisation rate during thepast eight years, mimicking the situation in a dry year.As droughts usually occur in the same years through-out the region, we modelled the sensitivity runs ac-

cordingly: all neighbouring countries experience alower level of precipitation. This is an important as-sumption, since droughts affect these countries in asimilar way and drive import and export prices up-wards in a similar pattern.

We focus on two aspects in this sensitivity assess-ment: the substitution possibilities within the countryto compensate for the loss in hydro generation; andthe impacts on export-import positions. Figure 12 il-lustrates the substitution effects in the case of lower-than-usual hydro generation.

As shown in Figure 12, the decrease in hydro genera-tion is mainly substituted by imports. This indicatesthat the construction of the second CCGT unit is notjustifiable, even if assuming limited hydro generationdue to dry weather. Security of supply concerns aresupported by this result, as imports do indeed growsignificantly. On the other hand, cross-border capac-ities are sufficient to provide the required power inthe long term.

Figure 12 also shows that when hydro generation issubstituted by gas, there is an increase in CO₂ emis-

Figure 12 Change in the electricity mix in the case of low hydro availability


Figure 13 Changes in the net import position of Albania

sions as well. However, imports are not accounted inthe national GHG emissions of Albania, while CO₂emissions would definitely grow in the neighbouringcountries (carbon leakage).

Figure 13 shows the impact on the export/import position of Albania in greater detail.

Lower than usual levels of rainfall have a significantimpact on the country’s import position. The net im-port position decreases by 1,500–2,200 GWh, oraround 15 percent of Albania’s gross consumption,in 2030. While in the AMB scenario the countrywould become a significant exporter in a “normal”year, these exports would be cancelled out bydrought, and the country would remain an importerin such years.

The overall conclusion to be drawn from the sensitiv-ity assessment is that security of supply concerns dueto exposure to higher levels of hydro generation arewell founded. However, this statement only holds iftaken purely from a national point of view. If a moreregional view is taken (see the regional assessment

for more detail), we can say that, due to the high levelof interconnectedness between SEE countries, thereis no shortage of capacity to satisfy overall regionaldemand even in a dry year if the planned capacitiesincluded in the scenarios are built. The market oper-ating model of the Nordic countries could also servefor this region. Under favourable hydrological condi-tions the region could sell hydro-based electricity toits neighbours, while in dry years imports would in-crease. This market model would enable the buildingof more hydro capacities in the region without impos-ing extra costs on consumers, and would also reducesecurity of supply concerns. The high (and increasing)interconnection rates in the region would allow forsuch cooperation, and countries would be in a win-win situation. In this case, the region would not be dis-advantaged by a more stringent European climateand renewables policy, as it would create greater de-mand for their expanding hydro capacities.

Figure 14 illustrates the wholesale price changes inthe scenario runs.


Although in 2015 the price impact is significant(above EUR 10/MWh), mainly due to a constrainedsupply side, after 2020 the impact is limited to around EUR 4/MWh in all scenarios. This latter development is probably the result of the capacitydevelopments in the region, which are explored inSection V under “Regional outlook”.

Figure 14 Base-load price changes in Albania (EUR/MWh)

VII. Network impacts


The electricity transmission system in SEE is relativelywell developed for the current level of power exchangesin the region. However, the exchange possibilities in theregion are limited by bottlenecks in both internal net-works and interconnections. Improving the balance between energy supply and demand is crucial in orderto boost and sustain economic development in SEE.This also means that TSOs should be prepared to sup-port energy trading between their control areas andwith their neighbours through the appropriate devel-opment of their transmission networks.

The network analysis below focuses on Albania, theformer Yugoslav Republic of Macedonia, Montenegroand Serbia. However, representative trade flows withneighbouring countries (e.g. Romania and Bulgaria)are also included in the assessment. The main networkelements in the region are presented in Figure 15.

Commercial congestion is permanently present in flowdirections from Romania to Serbia and from Bulgariato Serbia, due to the fact that Romania and Bulgariahave a surplus of electrical energy, and that Serbia isused as a transit area towards Montenegro, the formerYugoslav Republic of Macedonia and Greece (countrieswith an electrical energy deficit).

Prior to October 2004, the SEE power system was notconnected for unified parallel operation. Following re-connection with the first synchronous zone of theUnion for the Coordination of Transmission of Elec-tricity (UCTE) in October 2004, power system condi-tions in SEE changed dramatically. The region’s powerutilities began a process of deregulation and privati-sation. Due to the post-socialist collapse in industrialconsumption, the region was initially characterised bya surplus of installed generation capacity. Relatively

Figure 15 Geographical coverage of the network analysis


cheap electricity from SEE became a great market op-portunity. Countries in the region agreed to create astable common regulatory and market framework ca-pable of attracting investment in power generationand transmission networks.

All these factors have a substantial impact on the op-eration and development of the regional transmissionnetwork. Compared to other European regions, SEEis characterised by large interconnection capacities ata 220 kV voltage level and above.

Planned new network elementsThere are comprehensive, realistic plans for the de-velopment of the transmission network in SEE, andcurrent practice suggests that these plans are moreor less being implemented. Aside from the fact thatSEE countries can be regarded as well connected, newinvestments are expected, especially for cross-borderelements or internal connections that will have a sig-nificant impact on cross-border capacities.

The planned new transmission lines, listed in accor-dance with the ENTSO-E Ten-Year Network Develop-ment Plan (TYNDP), as well as strategic developmentand investment projects in each country, are shownin Figure 16.

In the three SLED scenarios (REF, CPP and AMB), gen-eration capacities — both distributed RES generationand conventional generation — and the assumedtotal electricity consumption change according to theagreed scenario definitions. The introduction of thesechanges in the network model has many impacts,which are assessed under the following categories:

steady-state and contingency analyses;

the evaluation of NTC; and

the calculation of transmission grid losses.

The detailed network modelling methodology forthese three areas is presented in the Annex followingthe network modelling description.

Figure 16 Planned interconnection lines in South Eastern Europe


Results of network modellingThe winter and summer operating regimes for the2015, 2020 and 2025 development stage in all sce-narios were assessed in the network modellingstage. The year 2015 was considered as the refer-ence year in the assessment, reflecting the presentnetwork topology and the currently available gener-ation capacities.

Steady-state and contingency analyses

Calculations within system studies were conductedon regional network models for SEE prepared for2020 and 2025. The power systems of the four as-sessed countries were modelled according to the datacollected and the Southeast Europe Cooperation Initiative (SECI) regional model for 2020/2025, theavailable respective national development plans, andthe transmission system development set out inENTSO-E’s TYNDP 2012–2022.

System balances

Power system balances (in MW) in the assessed coun-tries, analysed for all regimes (winter and summer)and scenarios, are presented below.

In 2020:

Montenegro is an importing country in the sum-

mer regime, while in the winter regime it is an ex-porting country due to the significant number ofRES.

Serbia is an exporter of 1,000 MW in both regimes.

Albania is an exporting country.

Due to new capacities (conventional, especially TPPs),in 2025:

Serbia becomes a large exporter.

Montenegro is an exporter in the winter peak

regime (due to a certain number of RES), but in thesummer peak regime it still imports a smallamount of power.

Albania only exports in the winter peak regime

(AMB and REF) at 150 MW, and in other regimes itis balanced.

The former Yugoslav Republic of Macedonia is an im-porter in all regimes and scenarios.

(N-1) Security criteria11

In 2015, there are no high-loaded elements at 220 kVand 400 kV voltage levels in the assessed countries.

The results of the security assessments for 2020 and2025 are shown in Tables 9 and 10 for the whole ofthe assessed region, as contingencies appear at regional but not at country level.

In 2020, the following strengthening is necessary:

In all scenarios, the tripping of the OHL 220 kV

Fierza (AL)–Titan (AL) line leads to the overloadingof the OHL 220 kV VauDejes (AL)–Komani (AL) line.The new OHL 220 kV Komani (AL)–Titan (AL) linesolves the problem (70 km). Some windfarms that are to be constructed within

the Serbian power utility EPS will be connected tothe OHL 220 kV Zrenjanin (RS)–Pancevo (RS) line.As a consequence of overloading in that area, inthe CPP scenario the conductor on the OHL 220kV Pancevo (RS)–Zrenjanin (RS) line should be replaced with a higher capacity one (length of approximately 22+44 km).In the AMB scenario, only the replacement of a

1 km length of conductor on the OHL 220 kVHIP (RS)–Pancevo (RS) line is required (in addi-tion to the OHL 220 kV VauDejes [AL]–Komani[AL] line).Generally speaking, the CPP scenario requires

greater additional investment than the other two(as a consequence of more new elements). TheREF and AMB scenarios require the same level ofinvestment, but less than the CPP scenario.

In 2025, the following strengthening is necessary:

In all scenarios, the tripping of the line OHL 220 kV

Fierza (AL)–Titan (AL) leads to the overloading ofthe OHL 220 kV VauDejes (AL)–Komani (AL) line.The new OHL 220 kV Komani (AL)–Titan (AL) linesolves that problem (70 km). Some windfarms to be constructed within the Ser-

bian EPS will be connected to the OHL 220 kV Zren-janin (RS)–Pancevo (RS) line. As a consequence ofoverloading in that area, in the CPP and AMB scenar-ios the conductor on the OHL 220 HIP (RS)–Beograd8 (RS) line should be replaced with a higher-capacityone (length of approximately 14.5 km).

The REF scenario requires less additional investmentthan the other two (as a consequence of fewer newelements). The CPP and AMB scenarios require thesame level of investment.


Table 9 Contingencies in 2020

Scenario2020 Tripping Overloading Solution

REF

Winter max. OHL 220 kV Fierza (AL)–Titan (AL) OHL 220 kV VauDejes (AL)–Komani (AL) New OHL 220 kV Komani (AL)–Titan (AL)

Summer max.

OHL 400 kV Podgorica (ME)–Tirana (AL) OHL 220 kV Podgorica (ME)–Koplic (AL) Remedial action; Disconnection of theOHL 220 kV Podgorica (ME)–Koplic (AL)

OHL 220 kV Zrenjanin (RS)–WPP (RS) OHL 220 kV HIP (RS)–Beograd8 (RS) Changing of the conductors and earth

wires and OPGW across the Danube Riverwith higher capacity (1 km)

CPP

Winter max. OHL 220 kV Komani (AL)–Kolace (AL) OHL 220 kV VauDejes (AL)–Komani (AL) New OHL 220 kV Komani (AL)–Titan (AL)

Summer max.

OHL 220 kV Komani (AL)–Kolace (AL) OHL 220 kV VauDejes (AL)–Komani (AL) New OHL 220 kV Komani (AL)–Titan (AL)

OHL 400 kV Pancevo (RS)–Beograd20 (RS) OHL 220 kV HIP (RS)–Beograd8 (RS)Changing of the conductors and earthwires and OPGW with higher capacity

(14.5 km)

OHL 400 kV Drmno (RS)–Smederevo (RS)

OHL 220 kV WPPs (RS)–Zrenjanin (RS)Changing of the conductors (on

WPPs–Zrenjanin) and earth wires andOPGW with higher capacity (44 km)

OHL 400 kV Mladost (RS)–TENT B (RS)

TR 400/220 SS Pancevo (RS)

OHL 220 kV WPPs (RS)–Pancevo (RS)

OHL 220 kV WPPs (RS)–Zrenjanin (RS) OHL 220 kV WPPs (RS)–Pancevo (RS)Changing of the conductors (on

WPPs–Pancevo) and earth wires andOPGW with higher capacity (22 km)

AMB

Winter max. OHL 220 kV Komani (AL)–Kolace (AL) OHL 220 kV VauDejes (AL)–Komani (AL) New OHL 220 kV Komani (AL)–Titan (AL)

Summer max. OHL 220 kV HIP (RS)–Beograd8 (RS)Changing of the conductors and earth

wires and OPGW across the Danube Riverwith higher capacity (1 km)


Table 10 Contingencies in 2025

Scenario2025 Tripping Overloading Solution

REF

Winter max. OHL 220 kV Fierza (AL)–Titan (AL) OHL 220 kV VauDejes (AL)–Komani (AL) New OHL 220 kV Komani (AL)–Titan (AL)

Summer max.

OHL 220 kV WPPs (RS)–Zrenjanin (RS) OHL 220 kV HIP (RS)–Beograd8 (RS)

Changing of the conductors (on OHL 220kV HIP–BG8) and earth wires and OPGW

across the Danube River with highercapacity (1 km)

OHL 400 kV RP Drmno (RS)–Smederevo (RS) OHL 400 kV Pancevo (RS)–Beograd (RS)

Changing of the conductors and earthwires and OPGW across the Danube River

with higher capacity (1 km)

CPP

Winter max.


Several contingencies OHL 220 kV HIP (RS)–Beograd8 (RS)Changing of the conductors (on OHL 220kV HIP–BG8) and earth wires and OPGWwith higher capacity (whole line 14.5 km)

Summer max.

OHL 400 kV Pancevo (RS)–Beograd20 (RS)

OHL 220 kV HIP (RS)–Beograd8 (RS)Changing of the conductors (on OHL 220kV HIP–BG8) and earth wires and OPGWwith higher capacity (whole line 14.5 km)OHL 220 kV WPPs (RS)–Zrenjanin (RS)

AMB

Winter max.


Several contingencies OHL 220 kV HIP (RS)–Beograd8 (RS)Changing of the conductors (on OHL 220kV HIP–BG8) and earth wires and OPGWwith higher capacity (whole line 14.5 km)

Summer max.

OHL 400 kV Pancevo (RS)–Beograd20 (RS) OHL 220 kV HIP (RS)–Beograd8 (RS)Changing of the conductors (on OHL 220kV HIP–BG8) and earth wires and OPGWwith higher capacity (whole line 14.5 km)

OHL 220 kV WPPs (RS)–Zrenjanin (RS) OHL 220 kV HIP (RS)–Beograd8 (RS)Changing of the conductors (on OHL 220kV HIP–BG8) and earth wires and OPGWwith higher capacity (whole line 14.5 km)


We can conclude from the results that the connectionof new RES causes some new overloading in Serbia andAlbania, while in Montenegro and the former YugoslavRepublic of Macedonia RES capacity development hasno impact on the 400 kV and 220 kV transmission net-work. Thus, for Albania, the newly installed RES gener-ation capacities assumed in the scenarios pose someproblems to the electricity network. The present sys-tem, with the planned line extensions, is unable tocope with the modelled electricity flows without somereinforcements of the above-mentioned lines.

Net transfer capacity

According to the ENTSO-E methodology, the resultsof the gross transfer capacity (GTC) calculation shouldbe used for market analysis. However, since in thecurrent operation of the power systems NTC valuesare used to describe limitations in transfer capacitiesbetween countries, NTCs were calculated on the bor-ders of the analysed countries. These capacities wereused as inputs in the market analysis.

Capacity calculation is always related to a given powersystem scenario — that is, generation schedule andpattern, consumption pattern and available networkstate. These constitute the data that make it possibleto build up a mathematical model of the power sys-tem (load flow equations). The solution of this modelprovides knowledge of the voltages in the networknodes and the power flows in the network elements,which are the parameters monitored by a TSO inorder to assess system security.

Before the results are presented, it is important to un-derline that NTC values, beside network topologies,depend on the generation pattern of the region aswell as the engagement of the generation units in oneparticular system.

The NTC values for the three assessed scenarios for2020 and 2025 are presented in Figures 17 to 20.

The NTC values for the AMB and CPP scenarios arehigher than for the REF scenario on most of the bor-ders for both the winter and summer regimes. Theoverall conclusion for 2020 is that the replacement ofconventional sources with RES increases NTC valueson most of the borders, except in the direction Albania–Montenegro where there is a slight decrease.

Similarly to 2020, in 2025 the deployment of renew-ables increases NTC values on most of the borders in

both the winter and summer regimes. In the case ofthe Montenegro–Albania and Albania–Former Yu-goslav Republic of Macedonia directions, there is aslight decrease in the NTC values for the AMB sce-nario compared to the REF scenario for both the win-ter and summer regimes. In both regimes the highestincrease is in the Serbia–Albania direction.

By modelling the scenarios, we can observe that inthe Albanian electricity system a bi-directionalchange is taking place. In the direction to Serbia, theCPP and AMB scenarios would increase the NTC val-ues, while in the other directions there are mixed effects for Albania by 2025. The CPP and AMB scenarios generally reduce the NTC compared to theREF scenario, although the relation between the CPPand AMB scenarios is mixed. This is probably due tothe changing directions of the base export-importflows in the scenarios.

Transmission grid losses

Transmission losses were calculated for all fouranalysed countries. The analyses were carried out forthree scenarios with different levels of RES, tworegimes (winter maximum and summer maximum)and three target years (2015, 2020, 2025).

The total losses in Albania’s power systems are shownin Table 11 (page 48).

The losses are highly dependent on electricity ex-changes, transmission reinforcements, levels of pro-duction and consumption, as well as the connectionpoints of power plants and consumers. Power lossesare higher in the winter regime and lower in the sum-mer regime due to the large exchanges between thecountries during the winter regime in the period2015–2030.

An increase in capacities and consumption levels gen-erally increases losses over the modelled period, al-though the results also show that the AMB scenario,with an increased level of distributed generation, willreduce the overall loss level compared to the CPP sce-nario. A regional comparison of losses can be foundin the regional study.

Total annual losses in Albanian power systems areshown in Figure 21 (page 48).

Figure 18 Net transfer capacity values for 2020 (summer regime)


Figure 17 Net transfer capacity values for 2020 (winter regime)

Figure 20 Net transfer capacity values for 2025 (summer regime)


Figure 19 Net transfer capacity values for 2025 (winter regime)


notEs

1 (N-1) Security criteria refer to the assessment of the electricity system when the largest capacity (either network or gen-eration) is removed from the system to simulate the state of the system with the outage of the largest capacity element.In this case, an outage of a network element is modelled.

Figure 21 Annual transmission losses in Albania for all scenarios

Table 11 Transmission losses in 2015, 2020 and 2025 in Albania, for all scenarios and regimes

2015 2020 2025

Winter Summer Winter Summer Winter Summer

Equivalent duration time of maximum losses (h) 2,961 2,375 2,961 2,375 2,961 2,375

Transmission losses

(MW)

REF 33.4 27.7 51.6 41.9 60.5 36.1

CPP - - 54.3 35.9 72.2 49.9

AMB - - 52.4 36.2 62.0 37.8

Yearly transmissionlosses (GWh)

REF 164.7 252.3 264.9

CPP - - 246.0 332.3

AMB - - 241.1 273.4

VIII. Annex


The two applied models — the EEMM and the EKCnetwork model — are described in greater detail inthis annex.

The European Electricity Market Model The EEMM simulates the operation of a Europeanelectricity wholesale market. It is a partial equilibriummodel.

Geographical scope

Figure 22 shows the geographical coverage of themodel. In the countries coloured orange, electricityprices are derived from the demand–supply balance.In the countries shown in blue, prices are exogenous.

Market participants

There are three types of market participant in themodel: producers, consumers and traders. Marketsare assumed to be perfectly competitive — that is, actors are price takers.

Producers are the owners and operators of powerplants. Each plant has a specific marginal cost of pro-duction, which is constant at the unit level, and gen-eration is capacity constrained at the level ofinstalled capacity.

The EEMM works with power plants at the unit level,and there are close to 5,000 power plant units in themodel. For individual power plants, the following es-sential information is contained by the model: in-stalled capacity, year of construction, technology andmain fuel type.

Within the electricity sector we can distinguish 12 dif-ferent technologies: biomass-fired power plants; coal-fired plants; lignite-fired plants; geothermal plants;heavy fuel oil–fired plants; light fuel oil–fired plants;hydropower plants; wind power plants; solar powerplants; nuclear plants; natural gas–fired plants; andtidal power plants.

The model takes into account short-term variablecosts with the following four main components: fuelcosts; variable OPEX; excise tax; and CO₂ costs (whereapplicable). The fuel cost in each generation unit de-pends on the type and price of the fuel and the overall

Figure 22 Analysed countries


efficiency of electricity generation. The latter is takenfrom the literature and empirical observation for var-ious power plant types and commissioning dates.When the market price is above the marginal genera-tion cost of a unit, the unit is operated at full availablecapacity, and if the price is below the marginal costthere is no production.

Consumers are represented in the model in an aggre-gated way by price-sensitive demand curves. The slopeof the demand curve is the same for all countries.When determining future consumption we considerthe relationship between past GDP and electricity con-sumption figures separately for each country. Basedon this relation and the GDP forecast we establish theexpected annual electricity consumption.

Finally, traders connect the production and con-sumption sides of a market, export electricity tomore expensive countries and import it fromcheaper ones. Within the model, a country appearsas a node — that is, there are no network constraintswithin the country, only between countries. Cross-border trade takes place on capacity-constrained in-terconnectors between neighbouring countries.Electricity exchanges occur until either prices, net ofdirect transmission costs or export tariffs, equaliseacross the markets; or the transmission capacity ofthe interconnector is reached.

Equilibrium

The model is a partial equilibrium model and calcu-lates the equilibrium allocation in all domestic elec-tricity markets under the following constraints:

Producers maximise their short-term profits,

given market prices.

Total domestic consumption is given by the aggre-

gate electricity demand function in each country.

Electricity transactions (export and import) occur

between neighbouring countries until marketprices are equalised or transmission capacity is ex-hausted.

Energy produced and imported is in balance with

energy consumed and exported.

Market equilibrium always exists and is unique inthe model.

The calculated market equilibrium is static: it onlydescribes situations with the same demand, supplyand transmission characteristics. To simulate theprice development of more complex electricity prod-ucts, such as those for base-load or peak-load deliv-ery, we perform several model runs with typicalmarket parameters and take the weighted averageof the resulting prices.

When modelling, hourly markets are simulated, andthese simulations are independent from one another

Figure 23 Operation of the model

Output

Input

Marginal generation cost Available generation capacity

Supply curves by countryDemand curves by countryCross-border transmission capacity

Model

Equilibriun prices by country Electricity trade between countries Production by plant


— that is, ramp-up costs are excluded. Within themodel, the equilibrium for a given hour (with respectto quantities and prices) is reached simultaneously bythe producer and transmission segments. Figure 23describes the operation of the model.

By determining the short-run marginal cost and avail-able capacity for each power plant we can constructthe supply curve for each country — in other words,the merit order curve. Taking into consideration theconstraints of cross-border capacities and the de-mand curves characterising each country, we arriveat the input parameters of the model. The model ap-plies these data to maximise European welfare, whichis the sum of producer and consumer surpluses. As aresult of model computations we get the hourly equi-librium price for each country, the hourly commercialtransfers between the countries, and the productionof each power plant unit.

We simulate the short-term market represented by aselected hour. We typically aim to model an annualperiod, rather than a single hour, therefore on the de-mand side it is necessary to settle on a given numberof reference hours through which annual averageprices are approximated. In the model, 90 referencehours are established.

Network representation

The EEMM assumes that each country is a node —that is, network constraints do not exist within any ofthe countries. Cross-border capacities, on the otherhand, may impose a serious limitation on the tradingof electricity. Scarcity is expressed through the NTC.

The EKC model provided modelled NTC values for thefour target countries of the SLED project and theneighbouring regions (directly connected to the targetcountries), while for the rest of the countries mod-elled by the EEMM, data from ENTSO-E were used.

The EKC network model A regional load-flow model on which analyses are per-formed was developed based on SECI regional trans-mission models for 2015 (also used as the currentmodel), 2020 and 2025, updated according to the as-sumptions of the ToR.

All analyses were performed for the years 2015, 2020and 2025 with two typical regimes: winter peak (third

Wednesday in January at 19:30), and summer peak(third Wednesday in July at 10:30)

The topology of transmission networks in SEE coun-tries (Albania, Bosnia and Herzegovina, Bulgaria, Croa-tia, Greece, the former Yugoslav Republic ofMacedonia, Romania and Serbia) is taken according tothe SECI regional model, updated according to gener-ation surplus projections in the SEE region. Otherneighbouring countries (France, Switzerland, Germany,Ukraine and Slovakia) are modelled as injections (overinterconnection lines); while Austria and Hungary arepresented with the full model adopted according to theUCTE system adequacy forecast 2014–2024.

The system study was performed on already existingstudies using the current regional system models de-veloped under SECI, checked and updated by theTSOs. System studies and planning were done on aregional basis, as was the definition of the bordercrossing points.

Electric power systems in SEE were modelled with theircomplete transmission networks (at 400 kV, 220 kV and150 kV). The power systems in the four assessed coun-tries were modelled in addition at the 110 kV voltagelevel. The network equivalent of Turkey (i.e. Europeanpart) and the rest of ENTSO-E Continental Europe(modelled over the X-node injections) were also usedin the model.

The following assessment was carried out in the net-work study:

Load-flow data collection, which includes:

• an assessment of the existing electricity networksituation within Albania, the former Yugoslav Republic of Macedonia, Montenegro and Serbia,together with the regional context; and

• a definition of the network topologies andregimes for 2015, 2020 and 2025, using realisticscenarios for demand growth, generation expansion, transit flows, RES integration andHVDC links.

TTC/NTC evaluation among Albania, the former

Yugoslav Republic of Macedonia, Montenegro andSerbia in all directions, for all topology scenarios,with reference to each target year and regime.

An assessment of transmission grid losses with

and without a level of energy production from RES.


Load-flow data collection

In the process of collecting the data needed for load-flow studies, representatives of the four countries re-viewed and updated the proposed datasets, whichinclude:

Demand level in the agreed regimes and power

exchanges in 2015, 2020 and 2025 for two charac-teristic regimes:

• third Wednesday in January at 19:30 (winterpeak); and

• third Wednesday in July at 10:30 (summer peak).

A list of new generation facilities and generation

units to be decommissioned up to 2015, 2020 and2025.

A list of new elements in the transmission network.

The level of transmission reliability margin used in

the evaluation of the NTC.

These data were used for the preparation of the net-work models for 2015, 2025 and 2025, which wereused for the load-flow analyses.

Demand

The development of demand in two characteristicregimes (third Wednesday in January at 19:30 [winterpeak] and third Wednesday in July at 10:30 [summerpeak]) in 2015, 2020 and 2025 were analysed on thebasis of:

ENTSO-E online datasets;

ENTSO-E scenario outlook and adequacy forecasts;

national demand projections; and

consultants’ datasets from relevant projects in the

SEE region.

Since network forecast models are used in our analy-sis, demand excludes transmission and distributionlosses, as well as the power plants’ own consumptionand pumping.

Network modelling methodologyThe network modelling methodology comprises:

steady-state and contingency analyses;

an evaluation of NTC; and

The calculation of transmission grid losses.

These three parts are introduced in detail below.

Steady-state and contingency analyses

For the defined scenarios, steady-state load flowswere calculated and contingency (n-1) analyses per-formed. Security criteria were based on the loadingsof lines and voltage profile, and were checked foreach scenario analysed.

Load-flow analyses provide an insight into transmissionnetwork adequacy for the observed scenarios of ex-changes and a comparison of observed configurations,under steady-state and (n-1) operating conditions.

Load-flow assessment is a basic step in NTC evalua-tion and comprises the following analyses:

steady-state AC load flow;

security (n-1) assessment; and

voltage profile analysis.

In the analysis of voltage profiles, voltage limits aretaken according to the respective national grid codes.

It should be stressed that only the 400 kV and 220 kVnetworks were assessed from a security point of view.There are many new RES to be connected to lower-voltage networks, which might cause problems in net-works of 110 kV or lower, although this should besolved via the national transmission network plans.

Evaluation of net transfer capacity

The TTC and NTC were evaluated between Albania,the former Yugoslav Republic of Macedonia, Mon-tenegro and Serbia, as well as between these coun-tries and their neighbours, in all directions, for alltopology scenarios, and with reference to each targetyear and regime. A final assessment was also madeof the TTC/NTC additional values as a result of new in-terconnections and the strengthening of the majorenergy transit routes.

It is also important to note that TTC, NTC, base caseexchange (BCE), already allocated capacity (AAC) andavailable transfer capacity (ATC) are the exchange pro-gramme values; these are not the physical flows andgenerally differ from the physical flows at the inter-connection lines (except in particular cases of radialoperation).

The solution of this model is the so-called base caseand is the starting point for the computation. This basecase can already contain exchange programmes be-


tween TSOs and control areas. These are the varioustransactions likely to exist in the forecasted situationaccording to what has been observed in the past.

The TTC value may therefore vary (increase or de-crease) when approaching the time of programme ex-ecution as a result of a more accurate knowledge ofgenerating unit schedules, load pattern, networktopology and tie line availability.

The general definitions of transfer capacities (TTC,NTC) and the procedures for their assessment are ac-cording to ENTSO-E and the practice and experienceof regional SEE TSO working groups.

BAsE cAsE ExchAngE

In the base case, for a given pair of neighbouring con-trol areas A and B, for which capacities are to be com-puted, the global exchange programme known asbase case exchange (BCE) exists. The BCEs are theprogramme (contractual) values related to the basecase model.

MAxIMuM ADDItIonAL ExchAngE (ΔEMAx)

The maximum additional programme exchange (overthe BCE) that meets the security standards is indi-cated by ΔEmax. An additional programme exchangeis performed by decreasing generation in area A, andsimultaneously increasing generation in area B.

totAL trAnsFEr cAPAcIty

The TTC is the maximum exchange programme be-tween two areas, compatible with the operational se-curity standards applicable to each system, if futurenetwork conditions, generation and load patterns areperfectly known in advance.

TTC = BCE + ΔEmax

trAnsMIssIon rELIABILIty MArgIn (trM)

The TRM is a security margin that deals with uncer-tainties in the computed TTC values arising from:

unintended deviations in physical flows during

operation due to the physical functioning of loadfrequency control (LFC);

emergency exchanges between TSOs to deal with

unexpected unbalanced situations in real time; and

inaccuracies, for example in data collection and

measurements.

In the present study, the TRM value used was accordingto the collected load-flow data sent by the TSOs.

nEt trAnsFEr cAPAcIty

The NTC is the maximum exchange programme be-tween two areas compatible with the security stan-dards applicable in both areas, taking into account thetechnical uncertainties in future network conditions.

NTC = TTC-TRM

Transmission grid losses

The assessment of electricity losses is based on theequivalent duration time of losses in winter peak andsummer peak periods. This approach takes into ac-count that the impact on losses can be different inthese two regimes, meaning that annual losses canbe determined more accurately.

The assessment of electricity losses is based on theequivalent duration time of maximum losses. Themethod used to determine this equivalent durationtime requires two parameters as input: maximum de-mand and load factor. These two parameters are ob-tained from an analysis of the load duration diagramof the analysed year for the respective power system.

Yearly losses are calculated based on grid losses inMW calculated for the two analysed regimes — winterpeak and summer peak — and the equivalent loadduration time of the respective loads in theseregimes. With the calculated equivalent duration timeof maximum losses for the respective period, yearlytransmission grid losses (GWh) are calculated by mul-tiplying this value by power losses (MW):

- Active power losses in MW in specific regime i

- Equivalent duration time in hours for the respec-

tive load in regime i


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