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Renewable and Sustainable Energy Reviews

journal homepage:www.elsevier.com/locate/rser

Multi-criteria revision of the Hungarian Renewable Energy Utilization Action Plan – Review of the aspect of economy

Bálint Hartmann

^a,⁎

, Endre Börcsök

^a

, Veronika Oláhné Groma

^a

, János Osán

^a

, Attila Talamon

^a

, Szabina Török

^a

, Márk Alföldy-Boruss

^b

aHungarian Academy of Sciences, Centre for Energy Research, Environmental Physics Department, KFKI Campus, Konkoly-Thege Miklós út 29-33., 1121 Budapest, Hungary

bMOL Group, Október huszonharmadika utca 18., 1117 Budapest, Hungary

A R T I C L E I N F O

Keywords:

Renewable energy Action plan Multi-criteria revision Potential estimation Specific costs Electricity Heat energy Transportation

A B S T R A C T

In 2012 the Hungarian Government has decided to revise the country's National Renewable Action Plan, mostly due to the experienced changes of the economic environment and the continuously decreasing cost of renewable energy technologies. Present paper introduces the methodology and the most important results of this revision process, carried out by the Centre for Energy Research of the Hungarian Academy of Sciences.

First the results of Hungarian initiatives, aiming the increase of the share of renewable, were examined, then by analysis of technological and economic trends of recent years, new scenarios were set to reach the targets set by adopting the 2009/28/EC directive. [1] A multiple-criteria decision analysis (MCDA) of all potential renewable technologies and their respective fuels served as a basis for the most competitive scenarios. The authors focused onfive aspects: economy, environment, climate, job creation and innovation. Due to coverage constraints, present paper dominantly focuses on the review of the economical aspect, but in addition to those mentioned, further aspects may also be considered in the optimisation process. The results of the revision highlight that by 2020 Hungary will still mostly be using bioenergy in its renewable portfolio, which also allows the country to exploit its significant potential.

1. Introduction

In 2012 the Hungarian Government has decided to revise the country's National Renewable Action Plan; present paper introduces the methodology and the most important results of this revision process. The revision focused onfive aspects (economy, environment, climate, job creation and innovation), of which the economical aspect is put into focus in this paper. First the methodology is presented, including determination of specific costs of energy technologies, then results, including the proposed future energy mix, are shown. Based on these results, policy implications were also proposed by the authors.

As defined in Article 4 of the European Renewable Energy Directive [1]each European Member State has to provide a National Renewable Energy Action Plan to the European Commission, detailing projections for renewable energy development up to the year 2020. By that year, the cumulative consumption of renewable energy in all European Member States should result in an overall share of renewable energy of 20% across the European Union. In line with this package, in 2011 the Hungarian Ministry of National Development has announced the

publication of thefinal version of the Hungarian Renewable Energy Utilization Action Plan (hereafter referred as NREAP) for the period between 2010 and 2020.[2]The goal of the NREAP was to guarantee maximal total social recovery by building on the environmental, economic, social, cultural and geopolitical potential of Hungary.

According to Article 22 of[1]all European Union Member States have the obligation to release,“progress reports”. Thefirst versions of the 27 progress reports (with data for 2009 and 2010) were due on 31 December 2011 and are available at the website of the European Commission. The Commission's report from thefirst period[3]has highlighted that a change of the attitude is needed in many European countries to foster developments to achieve the previously undertaken targets. At the end of 2013 new Progress Reports had to be provided by the Member States, with data for 2011 and 2012. The report of the second period[4]raises questions regarding the achievement of the 2020 renewable energy targets. The publication states that in the case of Hungary and Poland, it is only under optimistic assumptions related to the future development of energy demand and country-specific financing conditions that the 2020 renewable energy targets appear

http://dx.doi.org/10.1016/j.rser.2017.05.287

Received 17 April 2016; Received in revised form 3 March 2017; Accepted 29 May 2017

⁎Corresponding author.

E-mail address:hartmann.balint@energia.mta.hu(B. Hartmann).

Available online 02 August 2017

1364-0321/ © 2017 Elsevier Ltd. All rights reserved.

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achievable.[5]also highlights that the European Union could achieve its targets by 2020, but the progress made by renewable energy would slow down due to the reduced investments and reduction in the supporting schemes in many Member States.

This situation encouraged the joint work of the Hungarian Ministry of National Development and the Hungarian Academy of Sciences which aimed to prepare a revision of the NREAP to determine future scenarios to utilize the country's renewable energy potential as efficiently as possible and to reach the 2020 targets. (It has to be noted that to our knowledge, only the Czech Republic has performed a major revision in relation with its action plan: Act 165/2012 contains measures to slow down the further development of energy from renewable sources while still meeting the EU targets for renewable energy under the burden sharing agreement in compliance with the Renewable Energy Directive.)[6].

During the revision, the following objectives and boundary condi- tions were taken into consideration:

•

Total gross energy consumption of Hungary in 2020 is currently anticipated to be 760 PJ. This value has to be used as target for the energy scenarios, instead of the target of the NREAP, which was 823 PJ. 14.65% of the total energy volume must be provided by renew- able energy sources, while the share of renewables in the transpor- tation sector must reach at least 10%, using a multiplication factor of 2.5 for particular raw materials.

•

To ensure the targeted renewable share, the economically optimal scenario should be determined while the use of local energy sources should be facilitated excluding the opportunity of the import renewable energy sources. (This scenario assumes that no major structural changes are necessary in the power system or the district heating infrastructure, e.g. the system of balancing energy remains unchanged.)

•

The study should include the analysis of combined heat and power (CHP) plants, and should primarily focus on mature technologies, due to the short time horizon.

•

Total cost of the estimated energy mix should be revised using MCDA. Based on the results of the analysis, modification of the 2020 energy mix should be proposed. Besides the aspects of the MCDA, possible future decrease of energy consumption and technological developments should also be taken into consideration, when pre- paring the proposal.

•

The increasing penetration of intermittent renewable must not cause operational difficulties in the power system. The authors assume that current balancing reserves and cross-border exchange are technically capable of handling the expected penetration levels for 2020. Costs of balancing energy are taken into consideration as part of the electricity off-take price, which means that power plants that are able to shift their production to high price (peak) periods, are expected to have a shorter payback time.

A multiple-criteria decision analysis (MCDA) of all potential renew- able technologies and their respective fuels served as a basis for the most competitive scenarios. The authors focused on five aspects:

economy, environment, climate, job creation and innovation.

Properly quantified indicators and expert weighting were assigned to thesefive criteria. Although thefine evaluation of thefive criteria could be done using composite indicators and sub-criteria, these were avoided during the MCDA process to improve transparency. The aspect of economy included investment and operation and maintenance (O &

M) costs. In case of investment costs, Hungarian and international values were compared, and learning curves were created for each technology, based on recently completed leading project data to show their future potential as well. As a point of reference international data is also shown on thefigures. The aspect of environment and climate both included the examination of life-cycle physiological effects and greenhouse-gas emissions. The effect on job creation was considered

with the number of new jobs proportioned to the amount of energy generation during the operation of the power plant. Finally, the number of related patents was chosen as the traditional measure of innovation. When examining the electricity and heating/cooling sec- tors, regional potentials (wind, hydro, solar, forestry and agricultural by-products, municipal waste, geothermal energy) and demands (heat demand of family houses, apartment houses and block houses) were assessed. The ranking of the potentials and demands was dynamic, in order to enforce market saturation and potential depletion during the optimisation.

The remainder of the paper is organised as follows; Section 2.

introduces the methodology of the research, emphasizing specific costs of energy technologies and the optimisation process. Results are presented in detail inSection 3., where the proposed future energy mix is also shown. Finally, conclusions are drawn in Section 4 and policy implications are given inSection 5.

2. Methods

The methodology, applied by the authors, consists of three steps.

The very first step focuses on the available potential of various renewable energy sources, aiming to precisely estimate the magnitude of sustainable potential. The second step is independent from thefirst one; the goal is to collect all technologies that might be suitable for energy generation or transportation, using renewable fuels, considering the demand of end-users. For these technologies, specific investment and O & M costs are calculated and future development and decrease of prices is also assessed. The final step of the examination method includes determining sectorial energy use and performing optimisation to prepare the energy mix that fulfils the previously set targets for renewable-based energy utilization. These steps are detailed in the following.

2.1. Estimation of the potential of various energy sources

In recent years, several studies have been published by research entities and NGOs, estimating the renewable energy potential of Hungary, including works of the Hungarian Energy Office[7–9]and the Hungarian Academy of Sciences[10]. The results of these studies are still considered as cornerstones; however, the concept of sustain- ability has significantly changed, especially in the field of biomass- based energy supply, which is still heavily argued in scientific circles.

For the examinations presented in this paper, the authors have used the potentialsfirst published in 2011 in the NREAP (seeTable 1) as a basis for a multiple step approximation of current potentials. The authors have considered the sustainable part of this potential for the optimisation, aiming effective and local utilization, and describing a realistic scenario of renewable energy use. The technological potentials, Table 1

Utilised renewable energy in 2008, technological potential in 2030 and the sustainable potential in 2020, according to[2].

Energy Energy

used in 2008 [PJ]

Technological potential in 2030 [PJ]

Sustainable potential in 2020 [PJ]

Geothermal 4 85 29.3

Solar thermal 0.159 75 15

Biomass 51.068 165 154.3

Biogas 0.906 50 13.2

Hydro 0.767 20 2.3

Wind 0.738 30 15.5

Solar photovolta- ic

0.00198 50 7

Municipal waste

3.863 15 4.3

also shown in this table, represent a more idealistic vision, where an upper margin of technologically available potentials are defined.

In case of several forms of energy, broad studies were already available, however certain results (especially firewood, agricultural residues and biodiesel potential) show inconsistency. Thus, the authors have decided to revise the potential values as well, for all 19+1 administrative divisions (counties and the capital, Budapest).

Estimated energy potentials were ranked infive categories.

2.2. Specific costs of energy technologies

When collecting the specific costs of energy generation technolo- gies, international trends and estimations were also considered, be- sides the expertise of the authors. Due to the relatively short time horizon of the estimation (2013–2020), only mature technologies were considered. Similar reasoning was applied to the building stock and the vehiclefleet, since no rapid changes are expected in thesefields either.

To estimate the specific investment costs of energy generation technologies, three international studies have been reviewed. The study published by the International Renewable Energy Agency[11]in 2015 is a summary of five previous studies, dealing with the costs of renewable-based energy generation technologies, namely: biomass, wind, hydro, solar photovoltaic and solar thermal energy. 8000 medium and large projects have been compared, detailing the cost structure of single power plants. The authors of the study have also considered regional and technological differences. Total investment costs, operation and maintenance costs and levelized cost of energy are all presented. Another comparative study was published in 2011 by Mott MacDonald [12], focusing on low-emission technologies. The primary goal of the study was to compare the costs related to energy generation technologies that are already available on the market, while also presenting an outlook to the future. It has to be noted though that the broad and detailed comparison is based solely on data from the United Kingdom, which is a rather small and constrained market. The third publication studied by the authors was released in 2010 by the National Renewable Energy Laboratory[13]. The study gives an in- depth look into the investment and levelized energy costs of renewable- based electricity generation and storage technologies. Data for the study have been collected from the United States in 2009 and 2010.

Beside the above-mentioned studies, the authors have also collected and examined available cost data of Hungarian power plant projects from the past 10 years, and the future estimations prepared by the Hungarian Energy Office.

All cost data were adjusted to 2013 Hungarian Forint (HUF) values.

For a better and more realistic comparison of electricity (e) and heat (th) generation technologies, costs were scaled to nominal power of the plants ( /C PandC P_e /_th), while in case of combined heat and power production, capacity factor of the plants have also been taken into consideration, as follows:

T E P T E

P P P P T

= ; = ; = + ∙T

e e e

th th th

sum e th

th

max (1)

whereTmax=max T T( ;e th)andTmax≠0.

When further calculating the total energy production of a power plant, no distinction was made between electricity and heat production:

E_sum=E_e+E_th (2)

Since the capacity factor of heat generation technologies is usually lower than for electricity generation technologies, a compensation coefficient was used to eliminate this difference during the comparison.

This coefficient is based on the theoretical maximal capacity factor of the given technology. For example, in case of a wind generator, the theoretical maximal capacity factor is 8760 h annually, while in case of a biogas heater this value is only around 4400 h. (It has to be noted that in case the technology can produce both heat and hot water, the theoretical maximal capacity factor was chosen as 8760 h).

Following the above described equations, levelized cost of energy (LCOE) of different technologies was calculated as:

LCOE=

∑ + ∑ + ∑

∑

i

n I

r i

n M

r i

n F

r i

n E

r

=1 (1 + ) =1 (1 + ) =1 (1 + )

=1 (1 + ) i

i

i i

i i i

i (3)

whereEiis the energy produced in the i^thyear,Iiis the investment cost in the i^thyear,Miare operation and maintenance costs in the i^thyear,Fi

is the fuel cost in the i^thyear,nis the lifetime of the technology andris the discount rate. To simplify the equation,MiandFiwere considered constant throughout the lifetime of the unit, and the whole investment cost is considered to be spent in year zero:

LCOE I

E

M E

F

= E

∙ ∑ + +

i i

n r

i i

i i 0

=1 1

(1 + )ⁱ (4)

whereI0represents total investment costs. Specific investment costs of the energy generation technologies can be calculated as follows:

I E

I

E CRF ACC

∙ ∑ = ∙ = E

i i

n

r i i

0

=1 1 (1 + )

0

i (5)

where CRF is the capital recovery factor and ACC is the annual capital cost.

Specific investment cost(ACC E/ )i and LCOE values of each tech- nology were calculated based on the extreme values of investment and fuel costs, using 5% discount rate.

The transportation sector was handled in a different way. The increase of the share of renewable energy in this sector can be achieved in three ways: by increasing the share during blending of fuels, by converting used vehicles to run purely on biofuels or by purchasing new hybrid, flex or electric vehicles. Since it is difficult to properly determine the lifetime of converted vehicles, instead of LCOE, specific costs were calculated as the ratio of the conversion or investment costs and the volume of the annual utilised renewable energy.

2.3. Determining sectorial use and share of various sources using the optimisation

When developing the base scenarios for the optimal utilization of the Hungarian renewable energy potential, the authors have aimed effective and local utilization. The method is based on an iterative process, which assigns units of primary renewable energy to each of the selected technologies. In every iteration cycle both energy needs and potentials are evaluated, and technologies are ranked. The given energy unit is assigned to the best ranked technology, to which energy needs and potentials can also be associated. The iteration process stops, if the targeted renewable share is reached. It has to be noted that present renewable-based energy generation units are not considered in this iteration process, since present study was prepared to support the decision-making process of the Ministry for National Development, and the difference between present and targeted capacity can be used to determine the direction of development.

The ranking is prepared by a MCDA model, which allows the users to rank different technologies by prioritizing arbitrarily selected criteria, in line with the goals of the decision problem. MCDA tools have become popular in thefield of (renewable) energy planning due to theirflexibility and the ability to consider all criteria simultaneously [14]. Indicators (i piece) are assigned to the evaluation criteria, which are used to rank the possible alternatives (j piece), resulting in Aij

factors. However, the selected evaluation criteria are usually of different importance, so weighting (wi) is also assigned. The final ranking is therefore prepared by taking into consideration both the numerical indicator values and the weighting factors. For the presented study, the authors have used the MCDA-AHP (Analytic Hierarchy Process) model, which is based on the decomposition of the original

problem[15]. After a hierarchical structure of sub problems is created, comparisons are performed to evaluate the importance of the selected aspects. These evaluations are converted to numerical weights, which can be used to create the ranking.

During the optimisation, comparison of electricity and heat energy generation technologies was performed aiming sustainability, security of supply and competitiveness. Beside the quantified boundary condi- tions,five focus areas were selected for the analysis, namely: econom- ics, job creation, innovation, environmental and physiological effects and climate impacts. These focus areas are all represented with a well measurable indicator, which allows clear comparison of the technolo- gies. Use of composite indicators was avoided to improve transparency.

Specific generation costs were selected as the indicator of the econom- ics aspect, which calculates investment, operation and maintenance and fuel costs in proportion to total energy production during the technology's lifetime (see Eq. (3)). Only such technologies were considered that are available on the market. Consistency of cost values was ensured by a control group of the Ministry of National Development. It has to be noted that due to the rather conservative estimation of renewable energy potentials, it was not necessary to include auxiliary costs (e.g. electricity storage, additional balancing power plants) in the calculations.

The transportation sector again had to be handled independently;

therefore, the selected indicators are different than in case of electricity and head energy generation.Table 2shows the indicators for thesefive aspects.

Evaluation of the economic aspect is relatively simple compared to other aspects, since clear majority of the indicators have monetary value, therefore only the unification of the data has to be performed.

The same cannot be said about environmental physiological effects.

During our examinations, the physiological effects were put into focus, since the two mentioned effects are statistically closely related. The indicator values were calculated using impact pathway analysis, based on the emission values for whole lifecycle of 2020 technologies. Results of analysis of lifecycle emission data of a previous project[16] were used as a basis. In case of combustion technologies, the biggest contribution to damage is connected to the narrowly defined energy production period, but in case of other renewable sources (solar PV, wind, etc.), the situation is different, and contribution of the whole lifecycle has to be assessed; manufacturing of the power plant, transportation, fuel-supply, installation and decommissioning. When comparing climatic effects, specific emission of greenhouse gases was based on 2011 report of the Intergovernmental Panel on Climate Change. In case of the transportation sector, only the period of operation was included in the calculations, and the Specific PM10

emission was selected as the indicator of physiological effects.

It has been shown by recent studies that fast increase in the renewable energy sector will lead to new job opportunities and the establishment of new economy sectors [17]. In Europe's leading benchmark country, Germany, 37,7800 workers were employed in 2012, including the production of facilities for renewable energy and their installation, direct operation and maintenance of these facilities and workers in sector of biofuels. Almost half of these jobs were in the wind and solar sectors. The example of the Czech Republic also shows that however no clear correlation can be found between total installed renewable energy capacities and the number of jobs in the sector, certain technologies are performing better. The largest impact was identified in the biomass and biofuel sectors, where 1 MW power increase has created 15.8 jobs, while for example the number for solar photovoltaics is just 6.1 jobs for each MW. This reflects both biophysical suitability and the nature of these sub-sectors where most jobs are in the development and installation phase and relatively few jobs are required for maintenance. Concerning the aspect of job creation, several methods are used widely to estimate the effects. One of these methods focuses on the number of jobs, created by a new power plant investment, relative to the energy production of the plant;

this indicator was used by the authors during the study [18,19].

Another possibility is to also include the number of operational years in the calculation, but due to the relatively low job creation level of renewable technologies and the short time horizon of the examination, this option was ignored. In the amount of new jobs turning out, beside O & M and fuel processing stages, construction, installation and manufacturing were also taken into consideration. To avoid the extreme rising and occurrent drop of job creation, only linear alteration was permitted, for which the gradient was maximized as the double of gradient presence during recent years. (This limitation is also to be recognized inTable 10). It has to be noted that the model is not capable of handling the regional distribution of employment, but in case of Hungary, regions with high biomass potential usually have high unemployment rates as well [20]. Different renewable technologies and different periods of the lifecycle necessitate the employment of workforce with significantly different qualification as well. Research and development is usually done by highly qualified professionals, but operation and maintenance requires less skills. While the former task is usually taking place outside Hungary, the latter option will have more important influence on our calculations. Another social aspect, that are necessary to handle, are the issues of energy poverty and the illegal alienation of forestry products. Since such problems are dominantly characterized by qualitative data, political interference is needed to improve the current situation.

To estimate the effect of innovation, number of submitted patents were examined. It is a commonly used indicator of innovation, and it can also be sorted by energy sources, contrary to other indicators.

International and Hungarian data were compared in thefield of patents directly connected to power plant technology, between 1998 and 2011.

Main patent groups in thefield of energy were determined using the PatBase patent database, while relating technologies were selected manually. The comparison shows that the renewable energy sector has an almost negligible influence on the number of patents, submitted in Hungary. The solefield that has drawn attention lately is wind energy, but even in this case, activity is stagnating, while an exponential increase is observed on international level.

Although dimension of the indicators is different, the resulting values are distinct enough to apply a linear normalization, by assigning 100 points to the best suiting alternative and 0 points to the less suiting one. Therefore, for eachiindicator andjtechnology:

A A

max( ) = 100 and min( ) = 0

j ij

j ij

(8) The initial weighting factors were selected according to the results of a questionnaire assembled by a social survey experts. The sum of the weighting factors is normalized to 1 after the evaluation of each form:

Table 2

Selected aspects and corresponding indicators of the optimisation.

Aspect Indicator

Electricity and heat energy generation

Economics Specific generation costs [HUF/kWh]

Environmental and physiological effects

Lost years per generated energy [c€/kWh]

Climate impacts Greenhouse gas emission per generated energy [gCO2-eq/kWh]

Job creation Jobs per generated energy [job/kWh]

Innovation Number of submitted patents

Transportation

Economics Specific conversion or investment costs plus the volume of the utilised renewable energy [HUF/

PJ]

Environmental and physiological effects

Specific PM10emission [g/PJ]

Climate impacts Specific greenhouse gas emission [gCO2-eq/PJ]

Job creation Specific job creation [job/PJ]

∑

^{w= 1}

i i

=1 5

(9) As the final step, the ranking of the possible renewable energy technologies is prepared by cumulating the multiplication of the weights (wi) and theAijvalues:

∑

^{w A∙ = 1}

i i ij

=1 5

(10) During the analysis, electricity and heat energy sectors were handled jointly due to claiming the same energy sources and the option of combined heat and power production. Possibilities of the heating sector are much more limited: the energy needs of the existing building stock can only be decreased in the long run. Possibilities cover a broad spectrum from building renewal to direct integration of renewable energy sources. The heat energy needs of the Hungarian building stock have been estimated using the building typology mentioned in[21], as shown inTable 3.

Among the 26 renewable-based technologies, examined in our study, 17 are capable of providing heat or hot water; these technologies were assigned with groups of the building typology. During the analysis, only the best performing technology remained with each group, if local energy needs and local primary energy sources are both available. The potential estimation served as a basis for the latter one.

District heating plants were the only exception, since their fuel supply was not restricted to local sources. Modelling of renewable-based electricity generation proved to be less complex, since the vast majority of Hungarian households have access to the public utility grid, there- fore the national electricity consumption is measured very precisely.

However, the geographical location of producers and consumers may also be different, aggregated modelling can be applied.

The next step is to determine the scenario-based ideal values, based on the amount of available energy sources, demand assessment and the determination of evaluation aspects, using the unlimited and feasible primary energy mix as input data. An iterative algorithm was used to create the primary energy mix of the sector, suiting the mentioned aspects, where the targeted amount of renewable-based electricity and heat was distributed in 10 TJ units in every cycle. The distributed energy unit is always assigned to the bestfitting alternative, taking into consideration the boundary conditions of local potentials and local energy demand. Thus, the ideal energy mix is created by this method in an iterative process. As mentioned before, heat utilization was partly determined by the size of the settlements, while such restrictions did not affect electricity utilization. Since local characteristics highly affect investments costs, cheaper technologies were assignedfirst, and as the built-in capacity increased, costs were increased in every iteration step, therefore more expensive alternatives also had to be selected. The flowchart of the iterative process is shown onFig. 1.

To increase the share of renewables in the transportation sector, the authors have focused on exploiting the possibilities of road traffic, since the volume of river shipping is negligible, and the use of fuels in air traffic is strictly regulated. The need of latter sector (estimated 24.7 PJ in 2020) is expected to be served solely by fossil fuels. The energy need of rail systems will be 11.4 PJ, 86% of which is used by electrified

services. The renewable share of this part could reach 1.71 PJ, even if only 7% of the electricity is provided by renewable sources, since the contribution is multiplied by 2.5 according to Article 3(4)(c) of [1].

Considering that rails fuelled by diesel could potentially be converted by blending biodiesel (4.9 V/V%), another 0.06 PJ gain is available. To reach the minimal 10% share (19 PJ) of renewables in this sector, the remaining 17.23 PJ should be utilised during road traffic. Three different options were selected to reach this share (detailed options are shown inTable 4):

•

Increase the blending rate of biofuels (both ethanol and biodiesel) from current 4.9 E/E% levels (E4, B4) to 6.8 E/E% (E10) or 6.5 E/E

% (B7) and 9.2 E/E% (B10) levels

•

Conversion of 2.7 million vehicles, that are expected to remain in service until 2020, to run solely on bioethanol, biodiesel or compressed natural gas

•

Purchase electric, hybrid electric orflex vehicles.

(However, it is technically possible to convert a diesel engine powered car to bioethanol, it would require the replacement of several parts of the powertrain, making the conversion unfeasible.) It has to be noted that in case of the most obvious and cheapestfirst opportunity, the technical limitation of blending varies for different types, and the corresponding literature also shows rather big variations. During our calculations, the maximum values were set according to the compat- ibility of the national vehicle fleet, published by the Faculty of Transportation and Vehicle Engineering, Budapest University of Technology and Economics[22–24].

Energy needs of the transportation sector were calculated using national statistics from 2010. The size of the vehicle fleets was 2,325,000, 656,494, 17,641 and 427,366 for gasoline powered cars, diesel powered cars, diesel powered buses and diesel powered trucks, respectively. Annual energy demand of thefleets was 56, 18.64, 9.7 and 74.66 PJ, respectively. Using the size of the withdrawn and released usedfleet, and taking into consideration the forecasted growth rate of the market, new vehicle purchases until 2020 are estimated as 1,636,877 cars, 6635 buses and 163,625 trucks.

A similar iterative algorithm was used as for the electricity and heat energy sector: the targeted amount of renewable share in transporta- tion was distributed using the energy needs of a single vehicle as base unit. After a vehicle is assigned to a given technology, a new iteration cycle begins and a new optimisation ranking is prepared, until the targeted 10% share is reached. Again, cheaper technologies are assignedfirst, and investment and conversion costs increase with the number of vehicles involved in the process. The flowchart of the iterative process is shown onFig. 2.

3. Results

In this section, detailed results of the revision are presented. The sections are constructed so that each subsection can be handled as an individual result, however the interdependence of them has the biggest value.Section 3.1. shows the volume of potentials that were estimated for various renewable energy sources. InSection 3.2. an overview is Table 3

Building typology used for the study[21].

Number of buildings

Number offlats Total annual energy need [PJ]

Total annual heat energy need [PJ]

Total annual domestic hot water energy need [PJ]

Family house 2,527,151 2,527,151 307 243 64

Medium-scale multi-flat building

81,680 925,516 53 42 11

Large-scale multi-flat building

31,712 703,014 43 34 9

Total 2,640,543 4155 681 403 319 84

presented on the specific costs of renewable energy technologies, formulating the prices for later calculations. Finally,Section 3.3. details results of the optimisation process, how sectorial energy use and the share of different fuels were determined.

3.1. Estimation of the potential of various energy sources

As it was mentioned inSection 2., the estimation of the potential of various energy alternatives considered several existing studies, while

new datasets were also assembled. In the following, potential of the most important energy sources is introduced.

Regarding the renewable energy potential, unexpected information did not occur during the project. The wind energy potential of Hungary has been assessed several times; the latest extensive study has been prepared in 2008 [25]. According to these results, majority of the Hungarian counties lacks harnessable wind power, or have low potential. The highest geographical potentials are between 1 and 1.2 PJ annually. Solar potential of the country has been well documented Fig. 1.Flowchart of the iterative process used to distribute electricity and heat energy among the available technologies.

thanks to of the Hungarian Meteorological Service. Solar photovoltaic potentials have also been calculated in recent years. There is a relatively small spread among the data, since counties with the highest potential are between 0.36 and 0.37 PJ/year while the other end is smaller with just some percent (0.33–0.34 PJ/year)[26]. Hydro potential can be neglected, since Hungarian rivers dominantly have very small slopes, and the only large-scale power plant investment on river Danube has been cancelled due to the lack of public support. The cumulative capacity of small and micro hydro power plants is below 58 MW.

Compared to the previous alternatives, bioenergy potential presents a much more complex issue. Currently, primary biomass is being produced mostly by forestry. Approximately half of the timber is used to create wood products; the rest is mostly used for combustion. The amount of solid biomass forest growth is 10 million m³, annually, 70%

of which is chopped, and even 14% of this amount is exported. The latest statistics on solid biomass production were made by the Hungarian Central Statistics Office in 2012[27]. According their data, three counties have hardly any potential due to the high proportion of agricultural land use, while the counties with the best characteristics could provide up to 3.5 PJ annually. Forestry would be able to provide approximately 40% of the country's biomass potential. Another possi- ble source of solid biomass could be provided by intensive cultivation of energy plants (willow, poplar, etc.), which are usually planted on land, previously excluded from agricultural cultivation. In Hungary, more than 860,000 ha of land has been excluded from such activity, most of which would perfectly suit the needs of energy plants[28]. Additional potential could be exploited by collecting the residues left after the mowing of meadows, pastures, and non-agricultural areas (e.g. curbs of public roads). Such utilization would provide approximately 3–5000 ha of biomass, only considering the roadside of the 150,000 km long Hungarian road network. According to a study, approximately 11% of the growing area of a country can be utilised this way [29]. Further bioenergy potential is available from secondary agricultural and animal products and by-products [30]. The potential of these latter two categories is estimated to be between 8 and 10 PJ/year for agricultural counties and 2–4 PJ/year for mountainous and forest areas.

Biofuels present another potential use of bioenergy: agricultural crops can be used to produce bioethanol, biodiesel or biogas. From this group, the climate of Hungary is best suiting the growing of plants with high starch, cellulose and sugar content, like corn or potato. Since these raw materials are also used by the food industry, the authors have decided to estimate the potentials of energy generation using the volume of exported products only. The results show that counties with the highest biodiesel potential could produce between 2 and 2.6 PJ annually, and the average potential is also relatively high. These numbers are still rather small compared to the bioethanol potential, which reaches almost 13 PJ/year in four counties[31,32].

The high geothermal potential of Hungary is widely known: it is estimated to 60 PJ by several studies[33]. Considering heat energy generation, Hungary is among the top ten countries, worldwide, mostly due to utilization of thermal waters for balneology and agriculture (greenhouse, polytunnel), but promising steps have also been made towards district heating systems as well. Maximal geothermal potential of the 19 Hungarian counties is estimated to be between 0.72–0.8 and 0.54–0.6 PJ/year for geothermal heating plants and geothermal heat pumps, respectively[34].

In summary, renewable energy potential of the country is not outstanding and is very uneven. Bioenergy and geothermal energy are the only sources with high potential, existing in most regions of Hungary. The total ranking of renewable energy potentials is shown inTable 5, where 1 indicates the best potential and 5 indicates the least potential.

3.2. Specific costs of energy technologies

As it was described in Section 2.2., costs of energy generation technologies were collected from numerous sources. However, as the results have shown, there is a rather wide agreement in the, literature concerning cost elements. In the following, a brief description is given for the costs of selected electricity and heat energy production technologies. Since the horizon of the study was 6 years, projected future costs are also indicated on thefigures.

Fig. 3shows the investment costs of wind power plants. It can be well observed from thefigure that majority of Hungarian wind turbines have been built in a 5-year period. Since the installed capacity of the country is still at a very low level (330 MW), and no national industry is available, costs are in the higher region if compared with international projects, and are not expected to decrease significantly. Further expansion of wind power capacity is also hindered by the lack of tenders: new licences have not been issued for years.

Investment costs of solar photovoltaic panels have shown a significant decrease in recent years[35–37]. While experts may argue how long this progress will continue, a moderate further decrease is still anticipated. In this case, evaluation of existing Hungarian projects does not show a strong trend due to their very limited number, but investments costs are estimated to align to international trends, as shown onFig. 4.

The situation of hydro power plants in Hungary is very similar to solar photovoltaics; due to the small number of projects and the low installed capacity values, therefore the decrease of investment costs does not indicate a strong trend. It can also be noticed that investment costs of this technology show a wide distribution on international level as well. Therefore, the authors expect only a slight decrease of investment costs, as it is shown onFig. 5. as well.

Fig. 6. shows the investment costs of geothermal technologies. Two important factors should be noticed: on one hand, costs of the technology are not likely to significantly decrease in the near future.

On the other hand, geothermal potential of a country highly affects investment costs. The data from Mott MacDonald is based on projects executed in the United Kingdom, which country has a lower geothermal grade than Hungary.

As it was mentioned earlier, bioenergy is one of the most important renewable energy sources in Hungary, thanks to the remarkable amount of raw-materials. This fact also has an impact on the invest- ment costs of bioenergy projects; therefore, the authors expect that the use of bioenergy will be further increasing. Comparison of national and international experiences also highlights that Hungarian investment costs for solid biomass (Fig. 7.) and biogas (Fig. 8.) based energy production are below international level.

Besides investment costs, other economical parameters were also assessed in order to allow the authors to properly calculate LCOE of different electricity and heat energy production technologies. Following the methodology described in Section 2.2., minimal and maximal Table 4

Possible changes in the vehicle fleet from the aspect of fuels.

Default Increased blending

Conversion Purchase of new vehicle

Car E4 E10 E85

CNG

E4 B7 or B10 B100 CNG HEV EV

B4 B7 or B10 B100

CNG

Bus B4 B7 or B10 B100

CNG

B7 or B10 B100 HEV EV

Truck B4 B7 or B10 B100

CNG

B7 or B10 B100 HEV EV

investment costs, O & M costs, minimal and maximal fuel costs were collected. Specific investment costs were determined using the volume of energy production, and O & M and fuel costs were adjusted to the

conditions of Hungary. The last column of the table shows the minimal energy price for which the generation unit can sell in order to reach an NPV of 0 in 10 years, assuming 5% discount rate. Production Fig. 2.Flowchart of the iterative process used to distribute renewable share in the transportation sector among the available technologies.

technologies were also differentiated according to their installed capacity.Table 6shows thefinal results of this process.

As mentioned previously, the increase of the share renewables in the transportation sector can be achieved in three ways: by increasing the share during blending of fuels, by conversion of used vehicles to run purely on biofuels or by purchasing new electric vehicles. In all three cases, specific costs were calculated in proportion to the annual energy use of the vehicle. Data were obtained from Hungarian companies and experience gained from previous projects. Conversion costs for cur- rently available technologies are shown inTable 7, while the costs of purchasing new vehicles are shown inTable 8. It can be noticed that certain conversions do not imply extra costs (like increasing the blending up to the technical limit), while other technological options may not even be available or do not offer realistic opportunity since execution costs are higher than the potential gain. In case of new purchases, current vehicle prices of Hungary were used as a basis for Table 5

Distribution of renewable energy potential among the 20 administrative divisions of Hungary, #1=best, #5=worst.

Energy source Category

#1 #2 #3 #4 #5

Wind 1 3 6 1 9

Solar photovoltaic 1 5 9 4 1

Solid biomass 2 5 3 5 5

Biodiesel 4 6 3 5 2

Bioethanol 4 6 3 6 1

Bioenergy by-products 4 6 3 6 1

Geothermal heating plant 6 10 0 0 4

Geothermal heat pumps 6 10 0 0 4

Fig. 3.Historical investment costs of wind power plants(based on data collected by the authors).

Fig. 4.Historical investment costs of solar photovoltaic plants(based on data collected by the authors).

Fig. 5.Historical investment costs of small-scale hydro power plants(based on data collected by the authors).

Fig. 6.Historical investment costs of geothermal power plants(based on data collected by the authors).

Fig. 7.Historical investment costs of solid biomass power plants (based on data collected by the authors).

Fig. 8.Historical investment costs of biogas power plants(based on data collected by the authors).

comparison.

The often significantly different purchase and conversion costs are due to different warranty requirements and conversion processes. Fuel costs also show a big spread, while also playing a huge role in the comparison. Based on production costs, fuel prices offirst generation ethanol,first generation biodiesel and biomethane were calculated as 81.9–90, 71.7–91.8 and 66–98.7 EUR/MWh, respectively.

If we compare the costs of the most competitive technologies of the three sectors (seeFig. 9.), it is clearly indicated that the lowest marginal cost of increasing the renewable share is in the heat energy sector.

Marginal costs of the electricity sector (wind, hydro and solar) are

significantly higher, and the use of biofuels is also a costly option, due to the high fuel costs. The results show that the assumed six years are, in case of an internal combustion engine, enough for the return of the investment in conversion, and after this period, costs converge to those of biofuels. It is also worth noting that under certain circumstances, purchase of new electric vehicles may provide a feasible option as well, especially due to the 2.5 energy multiplication factor. LCOE costs of electric vehicles are currently relatively high, since the lifetime of Table 6

Specific subcategories of costs, summarised total costs of electricity and heat energy production technologies (E–electricity, H–heat, S–small/household scale, M–medium/utility scale, L–large/power plant scale)(based on data calculated by the authors).

Technological parameters Investment costs [EUR/MWh]

O & M costs [EUR/MWh]

Fuel costs [EUR/MWh]

Total costs [EUR/MWh]

Electricity price for NPV = 0, 5%

discount rate [EUR/MWh]

E Wind, onshore S < 50 kW 59.43–139.10 13.47 0.00 72.90–152.57 99.80

E Wind, onshore M < 2 MW 53.50–86.93 13.00 0.00 66.50–99.93 90.70

E Wind park, onshore L > 2 MW 50.93–69.53 10.40 0.00 61.37–79.97 84.40

E Solar PV, rooftop S < 5 kW 92.63–189.20 31.37 0.00 124.00–220.57 183.53

E Solar PV, rooftop M < 5 0 kW 92.63–189.20 26.77 0.00 119.40–215.97 178.93

E Solar PV, rooftop L > 50 kW 92.63–189.20 18.17 0.00 110.80–207.37 170.33

E Hydro, micro S < 100 kW 51.70–86.17 32.27 0.00 83.97–118.43 148.83

E Hydro, small M < 500 kW 51.70–86.17 32.27 0.00 83.97–118.43 148.83

E Hydro, medium L < 5 MW 51.70–86.17 26.00 0.00 77.70–112.17 142.57

E, H Geothermal plant M < 5 MW 22.03–54.20 13.57 7.07–13.70 42.63–81.47 65.27

E, H Geothermal plant L < 50 MW 22.03–54.20 30.77 7.07–15.77 59.83–100.73 63.77

E, H Biomass, wood chips M < 5 MW 10.03–23.03 15.97 25.17–50.77 51.17–89.77 50.53

E, H Biomass, wood chips L < 25 MW 10.03–23.03 15.97 25.17–50.77 51.17–89.77 47.07 E, H Biogas, wood gasification M < 5 MW 13.00–39.90 17.90 25.17–50.77 56.10–108.57 52.30

E, H Biomass, straw M < 5 MW 12.33–27.20 17.30 19.10–38.47 48.73–82.97 50.03

E, H Biogas, biomass based M < 2 MW 15.57–30.23 17.40 22.97–46.33 55.97–93.97 57.37

E, H Biogas, sewage M < 2 MW 11.70–20.53 14.37 0.00 26.07–34.90 48.13

E, H Biogas, landfill M < 2 MW 8.33–14.67 21.90 0.00 30.23–36.57 53.23

E, H Municipal waste M < 5 MW 33.37–63.07 35.30 16.80–16.80 85.47–115.20 72.60

H Solar collector S < 7.5 kW 25.70–61.07 11.03 0.00 36.73–72.10 62.83

H Solar collector M < 50 kW 24.43–57.67 11.03 0.00 35.47–68.70 60.10

H Geothermal heat pump S < 50 kW 10.07–40.53 11.03 9.57–17.67 30.63–69.23 85.73

H Geothermal plant M < 2.5 MW 10.07–40.53 11.03 9.57–17.67 30.63–69.23 57.43

H Biomass, wood chips S < 50 kW 10.43–31.43 15.97 25.17–48.97 51.57–96.40 60.90

H Small heating plant, forestry residues

M < 2.5 MW 8.97–22.20 15.97 25.17–48.97 50.10–87.17 51.47

H Small heating plant, agricultural residues

M < 2.5 MW 8.97–22.20 15.97 19.10–37.13 44.03–75.30 48.70

Table 7

Conversion cost of vehicles in proportion to annual renewable energy use [EUR/GJ], considering 10 and 85 V/V% blending of ethanol (E10, E85), 7 and 100 V/V% blending of biodiesel (B7 and B100) and biomethane based compressed natural gas(based on data collected by the authors).

Conversion Gasoline powered car

Diesel powered car

Diesel powered bus

Diesel powered truck

E10 0 – – –

B7 – 0 0 0

E85 6.66–14 – – –

B100 – 3.33–9.33 0.48–0.4 1.53–2.89

CNG 20.66–110 17.33–93.33 18–90 96.66–289.97

Table 8

Extra cost of different vehicle types, compared to purchasing conventional vehicles, in proportion to annual renewable energy use [EUR/GJ], considering 85 V/V% blending of ethanol, 100 V/V% blending of biodiesel, biomethane-based compressed natural gas, hybrid electric and electric vehicles.(based on data collected by the authors).

E85 car B100 car B100 bus B100 truck CNG car CNG bus CNG truck

Extra costs 33.33–350 70–703.26 4–42.33 7.66–76.66 53.33–110 24–150 19.33–193.31

HEV (electric+gasoline) car HEV (electric+diesel) car HEV bus HEV truck EV car EV bus EV truck Extra costs 706–2123 576.61–1736.49 146.65–266.64 953.23–1333.2 553.28–3319.66 120–240 693.25–1159.88

Fig. 9.Marginal cost of different renewable technologies(based on data collected by the authors).

batteries is limited (the authors have assumed 6 years). However, if the electricity, used to charge the batteries, is also produced using partly renewable sources, specific cost of the technology may decrease to a level that is comparable to biofuels.

3.3. Determining sectorial use and share of various sources using the optimisation

The study presented in this paper was prepared to support the decision-making process of the Ministry for National Economy, to revise the aims of the NREAP. Due to significant changes of the economy on national and global level, total primary energy needs are estimated to reach only 760 PJ in 2020 in contrast to the previously expected 823 PJ. Approximately half of this amount will be consumed in the heat energy sector (showing a decrease), while electricity generation and the transportation sector will account for 25–25%, each (indicating moderate increase). To reach the targeted 14.65%

share in total energy consumption, 116.5 PJ should be provided from renewable sources. The transportation sector separately accounts for 19 PJ, which was determined as the minimal required share (10%) of total needs. The rest (97.5 PJ) should be used during electricity and heat energy production (Table 9). The MCDA presented by this paper was based on these values, although in case the Ministry decides to modify the targets in the future, a new analysis can easily be performed.

Considering electricity and heat production, original targets of the NREAP have to be reconsidered, since the increase of both installed capacity and production is below expected levels in the past three years (since the publication of the NREAP). It is clear that in several cases, Hungary will not be able to increase installed capacity levels to keep pace with the expectations. Therefore, barriers to growth were also implemented in the model; the maximal annual growth of production for each technology was limited to be the double of the highest growth of previous years.

It has to be emphasized that even considering this limitation, the new target of 97.5 PJ (electricity plus heat production) is within reach.

The very high share of biomass is striking, since all other energy sources combined are only able to provide 41.5 PJ, which would still necessitate the use of at least 56 PJ based on solid biomass. In order to reach this production level, annual growth should be above 4.3%.

Biomass plays an even more important role if there no minimal share is considered for the electricity sector; in this case, the heating sector is almost totally able to cover the combined renewable target of both sectors, with biomass and geothermal playing the biggest role.

Evaluating the results of the optimisation, level of biomass usage is over 80 PJ, equivalent to 15% annual growth. Such goal can only be fulfilled with thoughtful use and well-structured development. (It has to be noted that other EU member countries also rely heavily on the use of bioenergy, as recent overview of NREAPs shows in [3]) More realistic results are given by the optimisation if a 10% minimal renewable share is set for the electricity sector. Heat production is still dominant in this scenario, but wind power (3.54 PJ) and solar thermal (1.26 PJ) also has a small role. Detailed results are shown in Table 10.

Marginal costs of the optimised scenario are approximately 61 EUR/MWh. Since marginal costs of the transportation sector are

higher, to the increase the renewable share of the sector over the minimal 10% requirements is not feasible. National energy policy goals of the transportation sector put the increase of blending of renewable- based fuels in focus. However, opinions vary on the rate of the blending; therefore, three options were examined by the authors during the optimisation. Current fuel standards allow 4.9 E/E% blending (E4, B4) as a technological maximum[22]. Keeping the same energy ratios, diesel cars older than 10 years can be supplied with fuels blending with maximum 6.5 E/E% (B7). The third option is to increase per volume level of blending to 10 V/V%, which would necessitate the use of 9.24 E/E% (B10) biodiesel or 6.8 E/E% (E10) bioethanol[23,24]. In order to reach the targeted 10% share (19 PJ) of renewable use in this sector, additional steps should be taken. According to the results of the optimisation, the most feasible technological options to reach the targeted renewable share are to increase of blending until technological limits and to utilize as much B100 fuel in afleet as possible. Partial conversion of the busfleet of the country could be an obvious solution, since these vehicles have high annual energy needs, and the conversion of thefilling station infrastructure would be less costly, compared to other options. The detailed data for these options is shown inTable 11.

The marginal costs of increasing the share of renewables above 10% in the sector are also indicated.

The results for the economically most competitive scenario for all three sectors (electricity, heat, transportation) are shown onFig. 10.

The results of the revision highlight that by 2020 renewable energy in Hungary should mostly be produced from bioenergy sources. The dominance of biomass in the heating sector is playing a huge role in this, since this share is approximately three times bigger compared to all other renewable sources in the total renewable energy mix. Vast majority of the household heating needs could be supplied by briquettes from agricultural residues, besides the existing use of firewood. In the transportation sector, biofuels also will account for 90% of total renewable use. However, sincefirst generation biofuels are used almost exclusively, crop needs of the sector would compete with needs of the food industry. A possible solution may arise if the price of second generation biofuels decreases significantly (at least 30%). These observations confirm that the most feasible way of increasing the share of renewables in the country's energy mix is to emphasize the development of bioenergy use. The share of intermittent renewable electricity production (dominantly wind and solar) is expected to be in the range of 5–7% of total electricity production. According to[38], experiences in many countries (including Denmark, Ireland, Germany, Portugal, Spain, Sweden and the United Kingdom) suggest that system integration of variable renewable energy sources is not a significant challenge at 5–10% share. Current Hungarian experiences are very similar, it is expected that proper use of balancing power plants and cross-border exchange will allow the integration of such share.

4. Conclusions

Present paper has introduced the revision of the Hungarian Renewable Energy Utilization Action Plan, which was prepared by the Hungarian Academy of Sciences after receiving the request of the Hungarian Ministry of National Development. The motivation behind the work was multiple, technological and economic trends of recent years have significantly decreased costs of various renewable energy technologies, and the report of the European Commission in 2013 has highlighted that a change of the attitude is necessary to achieve the targets, undertaken by the country.

Based on the estimations of the Ministry, new scenarios of sectorial energy use were prepared, and targeted renewable energy consumption of the country in 2020 was determined as 760 PJ. A multiple-criteria decision of all potential renewable technologies (electricity, heat and transportation sector) was performed, using five aspects: economy, environment, climate, job creation and innovation. The aspect of economy included investment and operation and maintenance costs;

Table 9

Sectorial energy use, according to 2010 data of the NREAP and plans for 2020 [PJ][2].

Heat energy

Electricity Transportation Total

2010 398.7 152.9 169.4 721

2020 target 372.4 197.6 190 760

2020 renewable target

97.5 19 116.5