DECEMBER 2022
www.fe.um.si/jet.html
2 JET JET 3
VOLUME 15 / Issue 4
Revija Journal of Energy Technology (JET) je indeksirana v bazah INSPEC© in Proquest’s Technology Research Database.
The Journal of Energy Technology (JET) is indexed and abstracted in database INSPEC© and Proquest’s Technology Research Database.
4 JET
JOURNAL OF ENERGY TECHNOLOGY
Ustanovitelj / FOUNDER
Fakulteta za energetiko, UNIVERZA V MARIBORU / FACULTY OF ENERGY TECHNOLOGY, UNIVERSITY OF MARIBOR
Izdajatelj / PUBLISHER
Fakulteta za energetiko, UNIVERZA V MARIBORU / FACULTY OF ENERGY TECHNOLOGY, UNIVERSITY OF MARIBOR
Glavni in odgovorni urednik / EDITOR-IN-CHIEF
Jurij AVSEC
Souredniki / CO-EDITORS
Bruno CVIKL Miralem HADŽISELIMOVIC Gorazd HREN Zdravko PRAUNSEIS Sebastijan SEME Bojan ŠTUMBERGER Janez USENIK Peter VIRTIC Ivan ŽAGAR
Uredniško izdajateljski svet / PUBLISHING & EDITORIAL COUNCIL Dr. Anton BERGANT,
Litostroj Power d.d., Slovenia
Prof. dr. Marinko BARUKCIC,
Josip Juraj Strossmayer University of Osijek, Croatia
Prof. dr. Goga CVETKOVSKI,
Ss. Cyril and Methodius University in Skopje, Macedonia
Prof. dr. Nenad CVETKOVIC,
University of Nis, Serbia
Prof. ddr. Denis ĐONLAGIC,
University of Maribor, Slovenia
Doc. dr. Brigita FERCEC,
University of Maribor, Slovenia
JET 5
Prof. dr. Željko HEDERIC,
Josip Juraj Strossmayer University of Osijek, Croatia
Prof. dr. Marko JESENIK,
University of Maribor, Slovenia
Prof. dr. Ivan Aleksander KODELI,
Jožef Stefan Institute, Slovenia
Prof. dr. Rebeka KOVACIC LUKMAN,
University of Maribor, Slovenia
Prof. dr. Milan MARCIC,
University of Maribor, Slovenia
Prof. dr. Igor MEDVED,
Slovak University of Technology in Bratislava, Slovakia
Prof. dr. Matej MENCINGER,
University of Maribor, Slovenia
Prof. dr. Greg NATERER,
Memorial University of Newfoundland, Canada
Prof. dr. Enrico NOBILE,
University of Trieste, Italia
Prof. dr. Urška LAVRENCIC ŠTANGAR,
University of Ljubljana, Slovenia
Izr. prof. dr. Luka SNOJ,
Jožef Stefan Institute, Slovenia
Prof. Simon ŠPACAPAN,
University of Maribor, Slovenia
Prof. dr. Gorazd ŠTUMBERGER,
University of Maribor, Slovenia
Prof. dr. Anton TRNIK,
Constantine the Philosopher University in Nitra, Slovakia
Prof. dr. Zdravko VIRAG,
University of Zagreb, Croatia
Prof. dr. Mykhailo ZAGIRNYAK,
Kremenchuk Mykhailo Ostrohradskyi National University, Ukraine
Prof. dr. Marija ŽIVIC,
Josip Juraj Strossmayer University of Osijek, Croatia
6 JET
Tehnicni urednik / TECHNICAL EDITOR
Sonja Novak
Tehnicna podpora / TECHNICAL SUPPORT
Tamara BRECKO BOGOVCIC
Izhajanje revije / PUBLISHING
Revija izhaja štirikrat letno v nakladi 100 izvodov. Clanki so dostopni na spletni strani revije – https://www.fe.um.si/jet.html / The journal is published four times a year. Articles are available at the journal’s home page – www.fe.um.si/en/jet.html. Cena posameznega izvoda revije(brez DDV) / Price per issue (VAT not included in price): 50,00 EUR Informacije o narocninah: https://www.fe.um.si/naro%C4%8Dnine.html / Subscription information: https://www.fe.um.si/en/subscriptions.html
Lektoriranje / LANGUAGE EDITING
TAIA INT d.o.o.
Oblikovanje in tisk / DESIGN AND PRINT
Tiskarna Koštomaj d.o.o.
Naslovna fotografija / COVER PHOTOGRAPH
Jurij AVSEC
Oblikovanje znaka revije / JOURNAL AND LOGO DESIGN
Andrej PREDIN
Ustanovni urednik / FOUNDING EDITOR
Andrej PREDIN
Izdajanje revije JET financno podpira Javna agencija za raziskovalno dejavnost Republike Slovenije iz sredstev državnega proracuna iz naslova razpisa za sofinanciranje domacih znanstvenih periodicnih publikacij / The Journal of Energy Technology is co-financed by the Slovenian Research Agency.
JET 7
Spoštovani bralci revije Journal of energy technology (JET)
Revija Journal of Energy Technology (JET) redno izhaja že petnajsto leto zapored. V tem casu so bili v reviji objavljeni številni zelo zanimivi clanki priznanih strokovnjakov. Marsikateremu mlademu raziskovalcu je prav revija JET predstavljala odskocno desko za njegov nadaljnji razvoj, številni so pozneje objavili svoje prispevke celo v najbolj priznanih znanstvenih publikacijah na svojem raziskovalnem podrocju. Revija je namenjena objavi znanstveno-raziskovalnih in strokovnih dosežkov na podrocju energetike ter zajema širok spekter tehnicnih, ekonomskih in pravnih dosežkov s podrocja proizvodnje, transporta in pretvorbe energije. Velik poudarek je namenjen tudi izrabi obnovljivih in alternativnih virov energije, kakor tudi varovanju okolja. Revija je ves cas izhajala pod okriljem Fakultete za energetiko, dokler Senat Fakultete za energetiko Univerze v Mariboru ni sklenil, da bo z letom 2023 znanstveno revijo JET zacela izdajati Univerzitetna založba Univerze v Mariboru. To avtorjem prispevkov in bralcem ne prinaša bistvenih sprememb. Kot odgovorni urednik revije se bom skupaj s svojo ekipo še naprej trudil za objavljanje zanimivih clankov. Rad bi se zahvalil vsem soustvarjalcem revije, avtorjem ter seveda tudi recenzentom za njihov trud in neprecenljiv prispevek, še posebej pa mag. Sonji Novak za zelo dobro sodelovanje pri ustvarjanju revije JET, ter upam, da bo naše sodelovanje še naprej potekalo v tem duhu.
Vsem bralcem želim obilo užitkov pri branju nove številke revije.
Jurij AVSEC
odgovorni urednik revije JET
8 JET
Dear Readers of the Journal of Energy Technology (JET)
The Journal of Energy Technology (JET) is entering its 15th consecutive year of regular publication. During this time, the Journal has published a number of very interesting articles by renowned experts. For many young researchers, JET has been the catalyst for their further development, and many have gone on to publish their papers in some of the most acclaimed journals in their field of research. The Journal is dedicated to the publication of scientific research and professional developments in the field of energy and covers a wide range of technical, economic and legal breakthroughs in the field of energy production, transport and conversion. A strong emphasis is also placed on the use of renewable and alternative energy sources, as well as on environmental protection. The Journal used to be published by the Faculty of Energy Technology until the Senate of the Faculty of Energy Technology of the University of Maribor decided that the scientific journal JET will be published by the University Publishing House of the University of Maribor as of 2023. I am confident that the authors of the articles and our readers will not in any way be affected by this change. As Editor-in-Chief, I will continue to work hard with my team to publish interesting articles. I would like to thank all the contributors to the Journal, the authors and, of course, the reviewers for their efforts and invaluable contributions, and especially mag. Sonja Novak for her priceless input in the creation of JET, and I hope that our cooperation will continue in this spirit.
I wish all readers a lot of pleasure in reading the new issue of the magazine.
Jurij AVSEC
Editor-in-chief of JET
JET 9
Table of Contents / Kazalo
Energo-economics payback investment calculation modelling of recently built gas-steam combined
cycle power plant
Energetsko-ekonomsko model vracilne dobe plinsko-parne elektrarne s kombiniranim procesom
Dušan Strušnik, Jurij Avsec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Monitoring and assessment mechanism of just energy transition trajectories. Do Just Transition Observatories matter?
Mehanizem spremljanja in ocenjevanja trajektorij pravicnega prehoda na podrocju energije. Ali so opazovalnice pravicnega prehoda pomembne?
Dionysios Giannakopoulos, Ioannis Karagiannis and Lefteris Topaloglou . . . . . . . . . . . . . . 29
Energy source replacement in a thermal power plant
Zamenjava energenta v termoelektrarni
Matej Fike, Andrej Predin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Energy analysis of hydrogen use in road transport of the Republic of Croatia
Energetska analiza uporabe vodika v cestnem prometu Republike Hrvaške
Franco Krog, Jurij Avsec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Instructions for authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
10 JET
JET Volume 15 (2022) p.p. 11-28 Issue 4, 2022 Type of article: 1.01 www.fe.um.si/si/jet.html
ENERGO-ECONOMICS PAYBACK INVESTMENT CALCULATION MODELLING OF GAS-STEAM COMBINED CYCLE POWER PLANT
ENERGETSKO-EKONOMSKO MODEL
VRACILNE DOBE PLINSKO-PARNE
ELEKTRARNE S KOMBINIRANIM PROCESOM
Dušan Strušnik1R, Jurij Avsec2
Keywords: calculation, efficiency, investment, market, modelling, payback
Abstract
The paper deals with energo-economic payback calculation modelling of the combined gas-steam cycle operation, demonstrating the basic characteristic properties of cycle behaviour in different operating regimes and calculating the payback period of the investment. The calculation of the payback period of the investment is based on the calculation of the net present value and with actual obtaining data sets from a recently built gas-steam combined cycle power plant. The results of the calculation modelling show that the gas-steam combined cycle power plant can achieve a useful efficiency of up to 88% in the back-pressure operation of the steam turbine. The useful efficiency of the gas turbine is up to 40%. The payback period of the investment depends on the investment costs, the quantity and market price of the consumed fuel, the quantity
R Corresponding author: Doc. Dr. Dušan Strušnik, Energetika Ljubljana d.o.o., TE-TOL Unit, Toplarniška 19, 1000 Ljubljana, E-mail: dusan.strusnik@gmail.com
1 Energetika Ljubljana d.o.o., TE-TOL Unit, Toplarniška 19, SI-1000 Ljubljana, Slovenija
2 University of Maribor, Faculty of Energy Technology, Hocevarjev trg 1, SI-8270 Krško, Slovenia
and market price of the generated electricity and thermal energy. The results show that with a price ratio fuel/electricity of 0.36, the payback period of the investment is 4 years, with a price ratio fuel/electricity of 0.54, the payback period of the investment is as much as 17 years.
Povzetek
Prispevek obravnava energetsko-ekonomski model izracuna vracila delovanja kombiniranega plinsko-parnega cikla s prikazom osnovnih znacilnih lastnosti obnašanja cikla v razlicnih režimih obratovanja in izracunom vracilne dobe investicije. Izracun vracilne dobe investicije temelji na izracunu neto sedanje vrednosti in ob dejanskem pridobivanju nizov podatkov iz nedavno zgrajene plinsko-parne kombinirane elektrarne. Rezultati racunskega modeliranja kažejo, da lahko plinsko-parna elektrarna pri protitlacnem obratovanju parne turbine doseže koristni izkoristek do 88 %. Koristni izkoristek plinske turbine je do 40 %. Vracilna doba investicije je odvisna od stroškov investicije, kolicine in tržne cene porabljenega goriva ter kolicine in tržne cene proizvedene elektricne in toplotne energije. Rezultati kažejo, da je pri cenovnem razmerju gorivo/elektrika 0,36 vracilna doba investicije 4 leta, pri cenovnem razmerju gorivo/elektrika 0,54 pa kar 17 let.
INTRODUCTION
The construction of new thermal energy systems enables the conversion of internal fuel energy into electricity and thermal energy in a more environmentally friendly way. In energy conversion, ecological awareness in thermal power plants is mainly reflected in the appropriate choice of fuel. To this end, more environmentally friendly processes are increasingly being used in practical applications, which enables the combined conversion of electricity and thermal energy using natural gas. [1] Such a cycle is called a gas-steam combined cycle power plant (GSCCP). GSCCP consists of a gas turbine (GT), a heat recovery steam generator (HRSG), a steam turbine (ST) and a thermal station for district heating (DH). [2] Some other authors also researched the environmental and ecological influences of using of different types of fuels for heat and power generated by GSCCP. Luis et al. presented the energy-ecologic efficiency of waste-to-energy plants and carried out the influence of emission abatement and biogenic carbon offset due to biomass regrowth regarding waste-fired plants. [3] Skorek-Osikowska et al. analysed thermodynamic and ecological assessment of selected coal-fired power plants integrated with carbon dioxide capture where they discovered that the post-combustion system allowed for a reduction of the value of the average annual carbon dioxide (CO2) emission rate aggravating the unit of net electricity produced for 735 kg CO2/MWh [4]. Silveira et al. studied the ecological efficiency and thermoeconomic analysis of a cogeneration system at a hospital. [5] In reviewing the scientific literature, we have not yet found a paper analysing the energo-economics payback investment model of
GSCCP.
The GT consists of a compressor part, combustion chambers, a turbine part and a generator of GT. The compressor part of the GT is used to compress the air, which then enters the combustion chambers. Combustion chambers are used for the combustion of natural gas or for the chemical process of converting the internal energy of natural gas into thermal energy. [6] Thermal energy is used to increase the enthalpy value of compressed gas. After the combustion process, compressed gases with increased enthalpy value or flue gases enter the turbine part of GT. In the turbine part of the GT, the thermal energy of the flue gases is converted into mechanical energy, which is converted into electrical energy by means of the GT generator. The flue gases are discharged from the turbine part of the GT to HRSG GT at a temperature of approx. 560 °C. [7] The main purpose of HRSG is to use the residual heat energy of flue gases for the production of high pressure (HP) steam, the production of low pressure (LP) steam, and for the production of heat for district heating (DH). The remaining unused flue gas heat is discharged from the HRSG to the surroundings via a chimney at a temperature of approx. 75 °C. [8]
At a pressure of approx. 95 bar and a temperature of approx. 520 °C, HP steam is discharged from HRSG to a steam turbine (ST), where the thermal energy of HP steam is converted into mechanical energy, which is converted by means of generator ST into electricity. LP steam is discharged from HRSG to industrial consumers at a pressure of approx. 9 bar and a temperature of approx. 260 °C. The amount of DH thermal energy from the HRSG depends on the flue gas temperature in the chimney, as the HRSG DH system maintains the flue gas temperature above the condensing flue gas temperature which is approx. 75 °C.
ST plant consists of an expansion cylinder, a generator part and a DH system. A special feature of the ST plant is the backpressure mode of operation, as the ST plant does not have a condenser. This means that all the outlet steam from the expansion cylinder is used to generate DH heat. A schematic representation of the operation of the GSCCP is shown in Fig. 1.
Figure 1: A schematic representation of the operation of the GSCCP.
Other authors have also researched the GSCCP thermodynamics operation concepts. Maheshwari et al. studied thermodynamic different configurations of gas-steam combined cycles employing
intercooling and different means of cooling in the topping cycle. [9] With vapour absorption inlet air-cooling, Shukla et al. researched thermodynamic investigation of parameters affecting the execution of steam injected cooled gas turbine-based combined cycle power plant. [10] Kafaei et al. researched the best angle of hot steam injection holes in the steam turbine blade cascade.
[11] Srinivas et al. carried out sensitivity analysis of steam-injected gas turbine-based combined cycle with dual pressure HRSG [12]. When reviewing the literature, we found no paper describing the payback investment models of GSCCP with actually obtaining data sets from the recently
built plant.
The data sets are obtained from the supervisory control and data acquisition (SCADA). [13] SCADA continuously, 24 hours a day and 365 days a year, records the most important data sets of the GSCCP operation. [14]
The innovation, originality, and contribution to the new knowledge; however, are expressed in the validated energo-economics payback investment calculation modelling of GSCCP with actually obtained data from SCADA by the recently-built plant. The recently-built plant is located in the middle of Slovenia, which lies in southern central Europe, Fig. 2.
Figure 2: The recently-built GSCCP in the middle of Slovenia.
This paper first presents the operation of the system, before turning its attention to the presentation of the energo-economics payback investment calculation model, actual data sets filtration and model validation. Following this, the results are presented. Finally, the concluding part presents the most important findings and discussion.
ENERGO-ECONOMICS PAYBACK INVESTMENT CALCULATION MODEL
The energo-economics payback investment calculation model consists of auxiliary units and calculation units. The auxiliary unit of input data contains the database of electric power of the GT generator (PGTe), which represents a set of input data to the energo-economic calculation model.
Using a set of input data, the GT calculation unit calculates the characteristic properties of GT operation, such as consumption and power of natural gas GT useful efficiency, etc. The HRSG calculation unit calculates the amount of generated HP steam, the amount of generated LP steam and the amount of generated heat for DH using the data obtained from the input unit and the data obtained from the GT calculation unit. The results of the GT calculation unit and the results of the HRSG calculation unit enter the ST calculation unit and the calculation unit of the payback period of the investment. All the results of all calculation units are finally combined in a results report monitoring unit. A schematic representation of the operation of the energo-economics payback investment calculation model is shown in Fig. 3.
Figure 3: Schematic representation of the energo-economics payback investment calculation model.
The energo-economic calculation model is made using actual data sets obtained from SCADA. In addition to the consumption of natural gas for GSCCP operation, it also calculates the total amount of generated electricity and thermal energy of GSCCP, GT useful efficiency, ST useful efficiency, total GSCCP useful efficiency, etc. Beyond the stated values, the mathematical model also calculates the energy flows generated in 5400 hours of GSCCP operation. The auxiliary ST calculation unit contains an artificial neural network (ANN), that calculates the ST exhaust steam thermodynamic properties in dependence on the ST entering steam quantity and quality. The ANN, feed-forward type, is aimed at identifying and modelling the complex nonlinear relationships between the input and the output target of a system. [15] The ANN approach is an evolutionary and fast calculation methodology that does not require complex mathematical equations to explain a non-linear and multi-dimension system. [16] The ANN that was used in the auxiliary ST calculation unit was selected using a validation process. However, the ANN architecture that gave the best results in the validation procedure was used in the auxiliary ST calculation unit.
The calculation of the payback period of the investment is based on the calculation of the net present value of cash flows and depends on the net cash flow, something which in turn varies according to investment costs, maintenance costs, tax rate, quantity and market price of fuel consumed, quantity and market price of generated electricity and thermal energy, etc.
The GT calculation model calculates natural gas consumption for GSCCP operation using an equation generated based on GT manufacturer data GSCCP operation using an equation generated based on GT manufacturer data [17]:
...,.....·............·............·............
.....=./0.752.·3600 (1)
......·..........
where m.NG is natural gas flow and PGTe is the power of GT generator. Now that the natural gas flow for GSCCP operation is known, the GT calculation unit calculates the power of the natural gas consumed in two different ways. The power of natural gas, taking into account higher calorific value (HHV), is calculated by the GT calculation unit using the equation: [18]
.......=.......·............ =.......·0.011348 (2)
where PHHV is the power of the natural gas taking into account HHV. PHHV is used in the economic calculation of natural gas consumption. The GT calculation unit calculates the power of the natural gas taking into account lower calorific value (LHV) using the equation: [18]
....... =.......·............ =.......·0.01028 (3)
where PLHV is the power of the natural gas taking into account LHV. PLHV is used in all other process calculations, for example to calculate process useful efficiency, etc. The amount of generated HP steam, LP steam and generated heat for DH is calculated by the HRSG calculation unit using the generated equations below. calculations, for example to calculate process useful efficiency, etc. The amount of generated HP steam, LP steam and generated heat for DH is calculated by the HRSG calculation unit using the generated equations below. The equations are generated based on the data of the HRSG manufacturer. [19]
.......=0.00007994·........-0.01236·........+0.7599·.......+0.003202 (4)
.......=0.00002231·........-0.002278)·........+0.1225·.......+0.001664 (5)
........... =0.00006536·........-0.00707·.......+0.3642·.......+0.003177(6)
where m.HP is HP steam mass flow from HRSG, m.LP is LP steam mass flow from HRSG and PDH-HRSG is generated DH heat from HRSG. Now that the amount of generated HP steam and LP steam is known, the ST calculation unit can also calculate the power generated by ST generator: [20]
....... =.......·(h..-h...)·0.9 (7)
where PSTe is the power of the ST generator, hHP is specific enthalpy of HP steam and hOUT is specific enthalpy of steam from ST expansion cylinder. The thermal power of LP steam, which is used for industrial purposes and the heat generated from ST for DH is calculated by the mathematical model using the equations: [21]
......=.......·(h..-0.126) (8)
(9)
where PLP is power of LP steam, hLP is specific enthalpy of LP steam, 0.126 is specific enthalpy of water at 1 bar and 30 °C, PDH-ST is the heat generated from ST for DH system and 0.251 is specific enthalpy of water at 1 bar and 60 °C. The GT useful efficiency and the total GSCCP useful efficiency is calculated by the mathematical model using the equations: [22]
......= .... ·100% (10)
................. ...........
......... =.... .·100% (11)
where .GT is GT useful efficiency and .GSCCP is GSCCG useful efficiency.
In the calculation of the payback period of the investment, the payback calculation unit takes into account the remaining costs and carries out the calculation in several steps. The payback calculation unit calculates the economic eligibility of the investment, assesses the profit that the investment will yield and, based on the duration of the investment and the discount rate, determines whether the investment will be repaid or not. The payback calculation unit calculates profit by first estimating annual income and deducting energy costs, maintenance costs, operating costs, and depreciation: [23]
................ =............-............-..................... (12) where Prof is annual profit, Inc is annual income, Dep is the annual depreciation, and costs are annual costs. In the case of electricity and heat production, the annual income is the product of the annual production of energy products and the price of energy products: [23]
............ =(.............. ·......)+(................ ·........) (13)
where Prel-ann is the annual production of electricity, Cel is the price of electricity, Prther-ann is the annual production of heat and Cther is the price of heat. The payback calculation unit adds to operating costs, financing costs, maintenance costs, etc. Depreciation costs are calculated by the payback calculation unit as the ratio of the value of the investment and the duration of the investment, linear depreciation: [1]
............ = (14)
where Inv is investment value, Dur and is investment duration. Profit is subject to state tax determined by the effective tax rate. After paying the tax, the net profit remains: [1]
................... =(1-............)·................ (15)
where Profnet is net profit, and tax is the effective tax rate. The money coming from the investment is called the net cash flow and consists of the net profit that the investor can freely dispose of and the depreciation that they must allocate for new investments: [1]
............ =................... +............(1 (16) where NCF is net cash flow. The sum of all discounted values of net cash flow over the life of the investment gives the present value of revenue and, if the value of the investment is deducted from it, the payback calculation unit can calculate the net present value: [1]
............ ...... (.........).-............ (17)
where NPV is the net present value, which is a basic indicator of the cost-effectiveness of the investment. Only when NPV is positive is the investment economical. When we compare two or more investments, the most economical is the one that reaches the highest NPV. [1]
ACTUAL DATA SET FILTRATION AND MODEL VALIDATION
Actual data set filtration and model validation is the process of determining whether the model accurately represents the behaviour of the actual system. However, it is important to consider the quality of the data, whether it truly represents the system, and if it is the best test of the model. [24] Before the validation procedure, all actual data set should be properly prepared. All data that do not belong to the actual data group, error data, are removed in the filtration process. Error data in an individual data actual group are caused by measurement errors, recording errors, or turbine trip and other measurement failures. An example of an unfiltered and filtered actual data set from the SCADA is shown in Fig. 4.
Figure 4: (a) Unfiltered data from the SCADA system with error data and (b) filtered data used for training and validation of architectures applied in the ST calculation unit.
Filtered data from the SCADA has been used in the ANN training and validation process to choose the ANN architecture that gave the best results which were applied in the auxiliary ST calculation unit. Training an ANN is an iterative process in which training data examples are presented to the network one by one, and the values of the weights are adjusted each time. [25] In the ST calculation unit development process, an input data set was used during the learning phase, expressed in the [3x5400] matrix form and an output data set equally expressed in the [2x5400] matrix form. For each input data set, which in our case are steam mass flow, steam temperature and steam pressure into the ST, there is a specific output data, which are exhaust steam temperature and exhaust steam pressure from the ST.
The validation of ANN algorithm structures is carried out by means of the calculations of the error between the results provided by the ANN algorithm structure and the actual process data. The errors can be computed in several ways. The most useful way of error computation is called the mean square error (MSE) and is defined as: [26]
............ =..... ......-....... (18)
whereas the root mean square (RMS) is defined as follows:
. ../. ............=.(1/....). ......-....... (19)
The correlation coefficient (R2) and mean absolute error (MAE) are respectively defined as: [27], [28]
........
.....=1-..... (20)
. .....
............ =..... ......-...... (21)
where tj is the target value, oj is the output value, and p is the pattern. The R2 are normalised ranges between 0 and 1. A very good fit yields an R2 value of 1, whereas a poor fit result in a value near 0. [27], [28]
Using Eq. 18-21, the ANN structures of different architectures have been validated, where the number of hidden layers and the number of neurons in each hidden layer has been changed. The results of the validations of ANN structures of various architectures for the selection of the winning structure used in the auxiliary ST calculation unit are shown in Table 1.
Table 1: Results of validations of ANN structures of various architectures for the selection of the winning ANN algorithm structure used in the auxiliary ST calculation unit.
Algorithm Layers At Epochs Data Set MSE RMSE R2 MAE
Architecture Size
ANN 5 120 5400 13.5643 3.6830 0.9994 1.5018
20-18-25
ANN 5 112 5400 10.7807 3.2834 0.9996 1.3753
12-10-11
ANN 5 164 5400 12.4752 3.5320 0.9995 1.5122
9-7-6
ANN 4 112 5400 12.6885 3.5621 0.9995 1.5140
45-37
ANN 4 149 5400 12.5996 3.5496 0.9995 1.5002
22-21
ANN 4 167 5400 11.5722 3.4018 0.995 1.4968
12-9
ANN 4 187 5400 13.2776 3.6438 0.9995 1.5565
7-9
ANN 3 276 5400 11.7001 3.4205 0.9995 1.4307
60
ANN 3 108 5400 14.7699 3.8432 0.9994 1.6032
40
ANN 3 317 5400 12.6844 3.5615 0.9995 1.5384
20
ANN 3 258 5400 13.1586 3.6275 0.9995 1.5341
12
ANN 3 187 5400 15.6925 3.9661 0.9994 1.6541
5
Table 1 shows that the winning ANN structure used in the auxiliary GT calculation unit is the structure with 12-10-11 architecture (written in bold), as it has the lowest error rate. The process of creation and the regression of the ANN structure, used in the auxiliary GT calculation unit, are shown in Fig. 5.
Figure 5: Process of creation and regression of the winning ANN non-linear structure used in the simulation model of the non-linear ANN unit.
The creation of the ANN structure, used in the auxiliary ST calculation unit, was performed with 212 epochs. The best validation agreement of the MSE is 10.7807 and it was reached at the 112th epoch, whereby regression is R2 0.9996.
THE RESULTS OF THE ENERGO-ECONOMIC PAYBACK INVESTMENT CALCULATION MODEL
The results of the energo-economic payback investment calculation model are designed so that the amount and power of natural gas consumed and required for the operation of the GSCCP is presented first. Then, the quantities of HP steam, LP steam and thermal power generated by GSCCP are presented. Following this is a presentation of the useful efficiency of GT and GSCCP operation, energy flows and the amount of greenhouse CO2 gas released into the atmosphere after 5400 hours of GSCCP operation. At the end of the chapter, the results of the calculations of the payback period of the investment depending on the price ratio of the fuel required for the operation of the GSCCP and the total generated electricity are presented.
Fig. 6 shows the natural gas consumption for GSCCP operation as a function of generator power GT. At the power of the GT generator of 5 MW, the natural gas consumption amounts to 4341.1 Nm3/h, a standard cubic metre per hour defined at a natural gas reference temperature of 0 °C and a natural gas reference pressure of 1.013 bar. At the power of the GT generator of 30 MW, the consumption of natural gas amounts to 8859.8 Nm3/h, and at a maximum load of 55 MW of the GT generator, the consumption of natural gas for GSCCP operation is as much as 13465 Nm3/h.
Figure 6: The results of GT calculation unit; (a) natural gas consumption depending on the load
of the GT generator; and (b) power of consumed natural gas taking into account HHV and LHV.
Fig. 6(b) shows the power consumption of natural gas needed for the GSCCP operation. The power of natural gas considering HHV is taken into account in the analysis of the payback period of the investment, as the economic cost calculations of gas consumption take into account the HHV. The power of natural gas considering the LHV is used in all other technological calculations, such as the useful efficiency calculations, etc. It is evident from Fig. 6(b) that at the power of the GT generator of 5 MW, the power of the consumed fuel when considering HHV is 49.2 MW and when considering LHV the power of the consumed fuel is 44.6 MW. At a maximum load of the GT generator of 55 MW, the power of consumed fuel amounts to 152.8 MW, when taking into account HHV, and 138.4 MW when taking into account LHV. However, if the process useful efficiency calculations were based on the HHV power of the fuel consumed, they would be significantly lower.
Fig. 7 shows the results of the HRSG and ST calculation unit. It is evident from Fig. 7(a) that at the power of the GT generator of 5 MW, the amount of generated HP steam is 3.5 kg/s, the amount of generated LP steam is 0.5 kg/s and the generated DH heat from HRSG is 1.6 MW. At the power of the GT generator of 30 MW, the amount of generated HP steam is 13.8 kg/s, the amount of generated LP steam is 2.2 kg/s and the generated DH heat from HRSG is 6.3 MW. At the maximum power of the GT generator of 55 MW, the amount of generated HP steam is 17.7 kg/s, the amount of generated LP steam is 3.5 kg/s and the generated DH heat from HRSG is 9.5 MW.
Figure 7: The results of HRSG and ST calculation unit; (a) flows of generated HP steam, flows of generated LP steam, generated DH heat from HRSG and (b) generated DH heat from ST, electrical power of ST generator and power of LP steam for industrial use.
It is evident from Fig. 7(b) that at the power of the GT generator of 5 MW, the generated DH heat from ST is 7.7 MW, the power of the ST generator is 1.8 MW and the power of LP steam is 1.4 MW. At the power of the GT generator of 30 MW, the generated DH heat from ST is 30.5 MW, the power of the ST generator is 7.4 MW and the power of LP steam is 5.8 MW. At the maximum power of the GT generator of 55 MW, the generated DH heat from ST is 39.1 MW, the power of the ST generator is 9.5 MW and the power of LP steam for industrial purposes is 9.3 MW.
Figure 8: GT useful efficiencies and GSCCP useful efficiencies as a function of the power of the GT generator.
Fig. 8 shows GT useful efficiencies and GSCCP useful efficiencies as a function of the power of the GT generator. At the power of the GT generator of 5 MW, the GT useful efficiency amounts to 10% and the GSCCP useful efficiency to 39%. At the power of the GT generator of 30 MW, the GT useful efficiency amounts to 34% and the GSCCP useful efficiency amounts to 87%. At the maximum power of the GT generator of 55 MW, the GT useful efficiency amounts to 40% and the GSCCP useful efficiency amounts to 88%.
Fig. 9 shows the energy flows and the amount of greenhouse CO2 gas released into the atmosphere after 5400 hours of GSCCP operation, as the constant 50 MW power of the GT generator is taken into account. As much as 768,624 MWh of natural gas are required for 5400 hours of uninterrupted operation, taking into account HHV, while 696,475 MWh of natural gas are required when taking into account LHV. The GSCCP generates 319,793 MWh of electricity, 250,729 MWh thermal energy for DH and 45,549 MWh thermal energy of LP steam used for industrial purposes. At the same time, the GSCCP emits 140,660 tons of greenhouse CO2 gas into the atmosphere.
Figure 9: Energy flows and the amount of greenhouse CO2 gas released into the atmosphere after 5400 hours of GSCCP operation.
The results of the payback calculation unit depending on the ratio of the price of natural gas, taking into account HHV, and the price of electricity excluding and taking into account the cost of purchasing CO2 carbon offsets is shown in Fig. 10. The calculations take into account that GSCCP operates 5400 hours per year, the investment costs amount to 75,000,000.00 monetary units, the discount rate is 7%, tax rate is 22%, maintenance costs are 2% of investment costs per year, the price of district heating heat is fixed and amounts to 70.00 monetary units per MWh, the purchase price of carbon offset is fixed and amounts to 70.00 monetary units per tonne of CO2 emitted, and the power of the GT generator is fixed and amounts to 50 MW. In addition to this, the calculation does not take into account a possible subsidy for the production of high-efficiency electricity. The said subsidy can in fact be offset by the cost of purchasing CO2 carbon offsets. The grey areas in Figure 10 represent the zero balance or payback period of the investment. This subsidy can in fact be offset by the cost of purchasing CO2 carbon offsets. The grey area in Fig. 10 represents the zero balance or payback period of the investment.
It is evident from Fig. 10(a) that the payback period of the investment, excluding the cost of purchasing CO2 carbon offsets, ranges from 4 years for the 0.35 fuel/electricity price ratio and up to 20 years for the 0.49 fuel/electricity price ratio. Without taking into account the cost of purchasing CO2 carbon offsets at a fuel/electricity price ratio of 0.46, the payback period is 10 years. However, with fuel/electricity price ratios higher than 0.49, the investment does not pay off even across 20 years.
It is evident from Fig. 10(b) that the payback period of the investment, taking into account the cost of purchasing CO2 carbon offsets, ranges from 7 years for the 0.35 fuel/electricity price ratio and up to 20 years for the 0.42 fuel/electricity price ratio. Taking into account the costs of purchasing CO2 carbon offsets, the payback period of the investment is 10 years at 0.39 fuel/ electricity price ratio. However, with fuel/electricity price ratios higher than 0.42, the investment does not pay off even across 20 years. With fuel/electricity price ratios higher than 0.49, taking into account the cost of buying CO2 carbon offsets, the cash flow of net present value becomes negative, which means that we start to generate a negative return or loss. The essential importance of the cost-effective operation of the GSCCP is therefore dictated by the market dynamics of fuel, electricity and thermal energy prices.
CONCLUSION
This paper has presented both the energo-economic calculation modelling of GSCCP operation with the basic characteristic properties of the system behaviour in different operating regimes, and the energy flows generated by the uninterrupted 5400-hour operation of the GSCCP. The results of the calculation modelling show that the GSCCP can achieve a useful efficiency of up to 88% in the backpressure operation of a steam turbine. The useful efficiency of the gas turbine is up to 40.5%. In 5400 hours of continuous operation and 50 MW of uninterrupted constant power, the GT generator requires as much as 768,628 MWh of natural gas at HHV value and 696,475 MWh of natural gas at LHV value. The GSCCP generates 319,793 MW of electricity, 250,729 MWh of thermal energy for DH, and 45,549 MWh of thermal energy for industrial purposes, while 140,660 tons of CO2 greenhouse gas are emitted into the environment. The calculation of the payback period of the investment is based on the calculation of the net present value taking into the account the HHV value of natural gas. The results of the calculation of the payback period of the investment show that the payback period depends mainly on the market conditions of energy products. At a price ratio of fuel/electricity of 0.35, without factoring in the costs of purchasing CO2 carbon offsets, the payback period of the investment is 4 years, while at a price ratio of fuel/electricity of 0.49, the payback period is greater than 20 years. The costs of purchasing CO2 carbon offsets; however, can be offset by revenues from subsidies for high-efficiency electricity generation.
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Nomenclature
Abbreviations
ANN artificial neural network DH district heating GSCCP gas-steam combined cycle power plant GT gas turbine HHV higher heating calorific value HRSG heat recovery steam generator HP high pressure LHV lower heating calorific value LP low pressure
MAE mean absolute error
MSE mean square error
RMS root mean square
R2 correlation coefficient
SCADA supervisory control and data acquisition
ST steam turbine
CO2 carbon dioxide
Parameters
Cel electricity price, monetary unit
Cther thermal price, monetary unit
costs annual coast, monetary unit
Dep annual depreciation, monetary unit
Dur investment duration, years
hHP specific enthalpy of HP steam, MJ/kg
hLP specific enthalpy of LP steam, MJ/kg
hOUT specific enthalpy of steam from expansion cylinder of ST, MJ/kg
Inc annual income, monetary unit
Inv investment value, monetary unit
NCF net cash flow, monetary unit
NPV net present value, monetary unit
PDH-HRSG DH generated heat from HRSG, MW
PDH-ST DH generated heat from ST, MW
PGTe GT generator power, MW
PHHV power of natural gas taking into account HHV, MW
PLHV power of natural gas taking into account LHV, MW
Prel-ann annual production of electricity, MWh
Prther-ann annual production of heat, MWh
JET Volume 15 (2022) p.p. 29-50 Issue 4, 2022 Type of article: 1.01 www.fe.um.si/si/jet.html
MONITORING AND ASSESSMENT MECHANISM OF JUST ENERGY TRANSITION TRAJECTORIES. DO JUST TRANSITION OBSERVATORIES MATTER?
MEHANIZEM SPREMLJANJA IN OCENJEVANJA TRAJEKTORIJ PRAVICNEGA PREHODA NA PODROCJU
ENERGIJE. ALI SO OPAZOVALNICE
PRAVICNEGA PREHODA POMEMBNE?
Dionysios Giannakopoulos1, Ioannis Karagiannis2 and Lefteris Topaloglou3
Keywords: just transition, monitoring mechanisms, observatory, governance
Abstract
The primary aim of the European Green Deal is to move towards climate neutrality in a socially just and inclusive way. To this end, the EU has set up the so-called ‘Just Transition Mechanism’ (JTM), which will provide funding and technical assistance to the regions of the EU most affected
1R Corresponding author: Dr Dionysios Giannakopoulos, Senior Researcher, CERTH Hellas, Address: Mesogeion 357-359, 152 31, Halandri, Athens, HELLAS, Tel.: +306932264181, E-mail address:d.giannakopoulos@certh.gr
2 Ioannis Karagiannis, Researcher, ANKO S.A., Address: Fon Karagianni 1-3, 501 31, Kozani, HELLAS, Tel.: +306973990561, E-mail address: ikaragiannis@anko.gr
3 Dr Lefteris Topaloglou, Associate Professor, University of Western Macedonia, Address: Kila 501 00, Kozani, HELLAS, Tel.: +306944291835, E-mail address: etopaloglou@uowm.gr
by the transition to a green economy. However, in addition to securing sufficient resources and providing technical assistance for their utilisation, a critical factor for the successful outcome of the transition in coal-dependent regions is the establishment of effective monitoring/assessment mechanisms and place-based governance models. This reflects the departure of European politics from horizontal and one-size-fits-all policies. This is done for two reasons: (a) the utilisation of local knowledge and local territorial capital deals better with problems and (b) the transfer of the level of decision-making and implementation of policies as close as possible to the citizens to whom they concern. An effective governance model; however, should be accompanied by an effective mechanism for scientifically monitoring, analysing, evaluating, and formulating substantiated policy proposals. Setting up and operating a Just Transition Observatory could be a valuable support mechanism, provided it has the presumption of multi-level expertise and objectivity, deep knowledge of local specificities and legitimacy to institutionally represent the public interest at the local level. Such an Observatory could function as an independent evaluation body (with periodic reports or focused studies) in the course of the Energy Transition Pro-grammes, identifying potential difficulties, recording impacts, results and outcomes, as well as providing policy recommendations. Given that Just Transition Plans include a number of important transformational policies and transformative plans, the crucial question that arises is whether regional and central policy makers have the tools to evaluate these transformational policies and transformative projects. In this regard, it is necessary to design evaluation models based on predefined indicators and criteria that will be jointly defined in the context of public consultation. This will ensure objectivity on the basis of commonly agreed indicators and objectives, while also guaranteeing the legitimacy and acceptance of both strategic policies and specific investment plans. This paper attempts to examine to what extent there is room for the establishment of ‘Just Transition Observatories’ Platforms along the coal-dependent areas. More specifically, the potential of such mechanism to monitor, analyse and evaluate clean energy transition, and provide robust policy recommendations, will be explored. To this end, Just Transition Observatories will operate as an independent evaluation mechanism producing evidence-based reports and studies on clean energy transition. Thus, the Observatories will be able to deliver periodic reports based on indicators, comparative analysis and policy recommendations. In addition to this, such mechanisms could enhance networking at the EU, national and local level, mobilising scientific dialogue and debate on clean energy transition.
Povzetek
Primarni cilj evropskega zelenega dogovora je premik k podnebni nevtralnosti na socialno pravicen in vkljucujoc nacin. V ta namen je EU vzpostavila t. i. Mehanizem za pravicni prehod (JTM), ki bo zagotovil financno in tehnicno pomoc regijam EU, ki jih je prehod v zeleno gospodarstvo najbolj prizadel. Poleg zagotavljanja zadostnih virov in tehnicne pomoci za njihovo up-orabo pa je kljucni dejavnik za uspešen prehod v regijah, odvisnih od premoga, vzpostavitev ucinkovitih mehanizmov spremljanja/ocenjevanja in modelov upravljanja na podlagi krajev, kar izraža odmik evropske politike od horizontalnih politik in politike ene velikosti za vse. To se izvaja iz dveh razlogov: (a) uporaba lokalnega znanja in lokalnega teritorialnega kapitala ucinkovitejše rešuje probleme in (b) prenos ravni odlocanja in izvajanja politik cim bližje državljanom, ki se jih neposredno ticejo. Ucinkovit model upravljanja pa mora spremljati ucinkovit mehanizem znanstvenega opazovanja, analiziranja, ocenjevanja in oblikovanja utemeljenih politicnih predlogov. Vzpostavitev in delovanje Observatorija za pravicni prehod bi lahko bil dragocen podporni mehanizem, ce bi imel predpostavko o vecnivojskem strokovnem znanju in objektivnosti, globokem poznavanju lokalnih posebnosti in legitimnosti za institucionalno zastopanje javnega interesa na lokalni ravni. Takšen observatorij bi lahko deloval kot neodvisno ocenjevalno telo (z obcasnimi porocili ali osredotocenimi študijami) poteka programov energetskega prehoda, pri cemer bi prepoznaval morebitne težave, beležil vplive, rezultate in izide ter tudi predlagal politicna priporoci-la. Glede na to, da nacrti pravicnega prehoda vkljucujejo številne pomembne transformacijske politike in transformativne nacrte, se postavlja kljucno vprašanje, ali imajo regionalni in centralni oblikovalci politik orodja za ovrednotenje teh transformacijskih politik in transformativnih projektov. Pri tem je potrebno oblikovati modele vrednotenja na podlagi vnaprej dolocenih indikatorjev in meril, ki jih bomo opredelili v okviru javne razprave. To bo na eni strani zagotovilo objektivnost na podlagi skupno dogovorjenih kazalnikov in ciljev ter na drugi strani legitimnost in sprejemljivost tako strateških politik kot posebnih naložbenih nacrtov. Ta dokument skuša preuciti, v kolikšni meri obstaja prostor za vzpostavitev platform observatorijev za pravicni prehod vzdolž obmocij, ki so odvisna od premoga. Natancneje bo preucen potencial mehanizma za opazovanje, analizo in vrednotenje prehoda na cisto energijo ter za zagotavljanje trdnih politicnih priporocil. V ta namen bodo observatoriji pravicnega prehoda delovali kot neodvisen ocenjevalni mehanizem, ki bo pripravljal porocila in študije o prehodu na cisto energijo, ki temeljijo na dokazih. Na podlagi tega bodo observatoriji lahko predložili redna porocila, ki bodo temeljila na kazalnikih, primer-jalnih analizah in priporocilih politike. Poleg tega bi lahko takšni mehanizmi okrepili povezovanje tako na ravni EU kot na nacionalni in lokalni ravni ter spodbudili znanstveni dialog in razpravo o prehodu na cisto energijo.
INTRODUCTION
Clean energy transition represents a topic that has received increased attention from policy makers across Europe, shedding light on the multifaceted interplay of socio-economic, technological and environmental aspects of the transition. To this end, just transition means that society shares both tangible and intangible costs and benefits of transitioning to a low-carbon economy in a socially-just trajectory (Petrakos et al, 2021, Topaloglou 2020, 2021). In this sense, transition can be seen as a means to tackle persistent problems related to transformative and cross-cutting changes, that calls for multi-level governance in light of climate challenges and all-embracing major shifts in governance approaches (Topaloglou and Ioannidis, 2022; Van Engelenburg and Maas, 2018; Jordan et al., 2015). However, in addition to securing sufficient resources and providing technical assistance for their utilisation, a critical factor for the successful outcome of the transition in coal-dependent regions is the establishment of effective monitoring and assessment mechanisms and place-based governance models.
The added value of efficiency measurement for policy formation and evaluation is plainly cited by Lord Kelvin 1824-1907, ‘To measure is to know. If you cannot measure it, you cannot improve it. When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind’ (Thomshon, 1916). Given that Just Transition Plans include a number of important transformational policies and transformative plans, the critical question that arises is whether Just Transition Observatories in coal-dependent regions matter, and whether they aim to provide to policy makers the tools to monitor and evaluate transformational policies and transformative projects. In this regard, it is necessary to design evaluation models based on predefined indicators and criteria that will be jointly defined in the context of public consultation.
This paper’s scope is, first, to address the main challenges of green strategy and climate neutrality in Europe in terms of policymaking and the need for monitoring mechanisms. Secondly, it seeks to critically review the relative scientific discussion. Thirdly, this paper aims to introduce a conceptualisation of the Just Transition Observatory by testifying its need against various critical socio-economic factors.
The paper is organised as follows. The subsequent section provides a critical theoretical review on the nature of energy transition and the need for monitoring mechanisms. Then, the following section outlines just energy transition policymaking. Section 3 attempts to provide a conceptualisation of a Just Energy Observatory, while Section 4 focuses on the analysis of two selected indicators that could be embedded in the Just Energy Observatory. The final section provides various conclusions and policy recommendations.
LITERATURE REVIEW
Over the last decade, innumerable studies have examined the complex interplay of social, economic, environmental, and technological aspects of energy transitions. Although the evidence is mixed, it should be a relatively safe prediction to argue that energy transition can be delivered only if there is a co-evolution of technology, policy, infrastructure, scientific knowledge, social, and governance practices in order to foster regime destabilisation (Geels, 2011). Given the mul-ti-level and multi-faceted character of climate change, the issue of monitoring energy transition has received surprisingly little attention, not just by scholars but also by practitioners and policymakers. While a plethora of scholarly publications are available addressing governance of energy transition at the local and national level (Betsil and Bulkelev, 2006; Kemp et al, 2007; Loorbach et al, 2007), there have been remarkably few that have focused on the governing of energy transition based on monitoring and assessing mechanisms. The need for tailored approaches to energy transition is very apparent in regions that currently have or have an inheritance of carbon-intensive productive bases (Topaloglou, 2021). For these regions, due to the dominant presence of carbon intensive industry in local economies, the move to a low-carbon economy will deeply influence local jobs, industry infrastructure, and the entirety of the regional economy.
From a policy-making point of view, transition management takes place in a multi-actor environment, placing particular emphasis on the role of central government as an agent of change. At the same time, the role of local and regional authorities, energy business actors, key stakeholders and civil society organisations appears to be of great importance. OECD (2013), in a working paper entitled ‘monitoring the transition to a low-carbon economy’, argues that local governments, together with other local institutions, will be central agents in the success of the transition of regional areas to low-carbon economies (OECD 2013). Local governments typically hold multiple roles as decision-makers, planning authorities, managers of municipal assets, operators of local energy providers and role models for the public (ICLEI 2012). Local government also has an important amount of land property and a large degree of impact over land-use policies, which means they can influence, to some extent, the regulatory limits for energy activities (Miranda and Larcombe, 2012). Seen in this respect, Petrakos et al, (2021), argue that the endeavour of transition requires a multi-level governance environment that influences the decision-making process, the financial means, and interventions.
As a result, transition arenas usually unfold in a multi-actor development of visions, deliberative decision, and policymaking, as well as coordination of actions to set up, manage and evaluate transition experiments that contribute to niche development (Loorbach, 2008). To this end, a major limitation of an effective clean energy transition process is the lack of a comprehensive and defensible monitoring mechanism for collecting, collating, analysing, assessing, and providing policy recommendations at multiple scales. Evidence shows that so far, the existing data are very incomplete, fragmented, heterogeneous and not easily available to end users (Hoppe and Miedema, 2020).
This observational framework seems to be lacking at present, yet it would significantly help in identifying, monitoring, and evaluating the just energy transition. The outcomes of such an Observatory, could be used to develop and evaluate a targeted and effective just energy transition. In this aftermath, the multidimensional nature of energy transition calls for a multi-indicator framework for effective measuring and monitoring, gaining greater recognition in literature (Kagimu & Ustun, 2016) and deserves to be replicated. Such a framework would offer the possibility of establishing specific monitoring mechanisms to observe just energy transition in each area, strongly affected by climate change. Through the deliberative process of deciding ‘what’ to measure, monitor and use as indicators for performance, stakeholders can develop a shared understanding and responsibility for developing and populating indicators (OECD, 2015). Therefore, this monitoring mechanism should reflect the views of different actors and emphasise the engagement of all key-stakeholders (Topaloglou and Ioannidis, 2022), taking into account the various institutionalised modes of social coordination to provide collective goods (Börzel and Risse, (2010).
A crucial factor in the transition monitoring mechanism seems to be the prioritisation of indicators to track progress and effectiveness of interventions, given the amount of public and private investment and projects being made in each of these areas to facilitate the transition. Evidence shows that agreement on a set of common indicators for measuring and populating these indicators requires stakeholder engagement to inform and validate the indicator selection process. It is noteworthy that while many initiatives at the national and local level can certainly be found, there is also a significant number of inconsistencies and a lack of agreement concerning the indicators needed to be analysed and assessed. It is widely recognised that energy transition indicators must begin from a baseline. If we are to accurately assess energy transition, then this baseline will need to incorporate the critical local assets. Each area has an array of existing production base, including the labour force and capital stock (OECD, 2015). These initial conditions and assets shape the trajectory of the local area’s pathway to a climate neutral paradigm. To this end, local trajectories will be unique for each area because of the different compositions of stocks and activities (Topaloglou, 2021).
JUST ENERGY TRANSITION POLICYMAKING
The adoption of the 17 Sustainable Development Goals (SDGs) by the UN in the 2030 Agenda reflects the just transition policymaking at a global level. The SDGs aim to ensure sustainable global, social and economic development as well as universal peace. At European level, seen from a just transition perspective within the context of the European Green Deal, the European Commission has set out a roadmap for a new growth policy for the EU towards climate neutrality by 2050, aiming to leave no person and no region behind. The European Green Deal for the European Union (EU) and its citizens is a new growth strategy that aims to transform the EU into a fair and prosperous society, with a modern, resource-efficient and competitive economy where there are no net emissions of greenhouse gases in 2050 and where economic growth is decoupled from resource use. The main goals that need to be monitored and assessed deal with climate ambition targets, supplying clean, affordable and secure energy, accelerating sustainable mobility, designing the ‘Farm to Fork Strategy’, preserving and restoring biodiversity and supporting a zero-pollution policy. From the above, an effective mechanism able to monitor and assess the fulfilment of these ambitious goal is vital.
In this setting, the Just Transition Mechanism focuses on the regions and sectors that are most affected by the transition due to their dependence on fossil fuels. The mechanism consists of three pillars. Firstly, the Just Transition Fund, secondly, a dedicated scheme under the InvestEU programme, and thirdly, a public sector loan facility provided by the European Investment Bank to mobilise additional investments in the regions concerned. The Just Transition Fund receives the lion’s share aiming to support the territories most affected by the transition and for preventing an increase in regional disparities, as well as mitigating any negative repercussions on employment.
To accomplish the above objectives, the Just Transition Fund supports transformative investments in the involved areas, the reduction of GHG emissions, the regeneration of local economies, the reskilling of workers and technical assistance. Support will also be available for all Member States, focused on regions that are the most carbon-intensive or with the most people working in fossil fuels. Member States can gain access by preparing Territorial Just Transition Plans that cover the period up to 2030, identifying the territories that should receive the most support. These Plans should also set out ways to best address social, economic, and environmental challenges.
Given that many people and citizens are vulnerable to the transition, the Just Transition Mechanism aims to protect them by facilitating employment opportunities in new sectors, offering both reskilling and upskilling support, improving energy-efficient housing, fighting energy poverty, and facilitating access to clean, affordable and secure energy. On the other hand, companies and sectors active in carbon-intensive industries can be supported by the Just Transition Mechanism through creating attractive conditions for public and private investment, providing easier access to loans and financial support, investing in the creation of new firms, SMEs and start-ups, and devoting themselves to research and innovation activities.
In parallel, all Member States have embedded in their National Energy and Climate Plans (NECPs) targets, policies, and measures to reach the ambitious goals towards EU climate neutrality by 2050. It is worth noting that according to EC working Documents (EC 275, 2021), proposals to extend the geographical scope to territories where there is no clear transition process with an associated negative socio-economic impact by 2030 or before, should not be accepted. In this context, the TJTPs should describe the transition process at national and local levels, including a timeline for key transition steps towards 2030 and 2050 climate targets, in compliance with the respective NECP. The TJTP will also need to demonstrate clear evidence of a transition process and its impact at the level of the concerned territory in the near future. Based on the requirements of the TJTPs, several social, economic and environmental indicators are mandatory for monitoring and reporting (EC, 2020).
Having touched upon just transition policymaking, it is also necessary to address an appropriate framework of a Just Transition Observatory in the coal-dependent regions aiming to create a replicable model focusing on the governance of the energy transition applicable in each Euro
Monitoring and assessment mechanism of just energy transition trajectories. Do Just Transition Observatories matter?
pean region. These regions must integrate just energy transition policies into social, energy and environment policies from a multi-level governance perspective.
CONCEPTUALISATION OF A JUST TRANSITION OBSERVATORY
In order to conceptualise the proposed monitoring mechanism, it is useful to start from the assumption that the Just Transition Process deals with a drastic restructuring of the regional economic model and environmental and societal status, something which is well-established and has been operative for many decades. Thus, this transition process requires fast and robust actions in order to shift the local economy towards a sustainable, resilient and just development model by introducing coal-dependent areas to a carbon-free regime (Giannakopoulos et al, 2022).
Furthermore, we consider that the transition process goes beyond the boundaries of the phasing out of the carbon/lignite industry, tackled by Just Transition Mechanism, and instead involves shaping a new holistic regional development paradigm, which requires new infrastructure, new skills, and new business activities under a new regional branding. Within this context, a significant amount of information should be collected, assessed and discussed in order to support actions and measures, as well as to evaluate their progress and success. Due to the multidisciplinary character of the transition process and the involvement of EU, national, regional and local authorities/stakeholders, centralised and specialised data handling monitoring is considered a crucial parameter of success.
Figure 1: Conceptualization of a Just Transition Observatory.
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Dionysios Giannakopoulos, Ioannis Karagiannis and Lefteris Topaloglou JET Vol. 15 (2022)
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Additionally, we suggest that the Just Transition Observatories act as an independent mechanism that combine the Just Transition needs with the broader climate policies, measures and initiatives as formed in different levels (national, regional, municipal), in order to facilitate the transformation of local entities to a zero-carbon era. To this end, Just Transition Observatories’ Platform along coal-dependent areas could incorporate data and information indicatively regarding NCEPs, TJTPs, ROPs, OECD SDGs reports, Covenant of Mayors reports, EUROSTAT, National Statistics etc., producing evidence-based reports and studies on clean energy transition, as well as comparative studies on transition processes on a regional and EU basis.
Taking into consideration the above analysis, Figure 1 illustrates the conceptual approach of the Just Transition Observatory articulated on an input-output logic. The input segment involves measurements based on predefined indicators and implementation analysis. The output part consists of periodic reports which assess the implementation and consistency with the predefined goals and policy recommendations aiming to improve transition policy efficiency.
Figure 2 demonstrates the critical stakeholders who should be actively involved in a Just Transition Observatory, such as central and local governments, academic and research institutions, the energy companies, the main societal actors and the European Commission.
Figure 2: Stakeholders of a Just Transition Observatory.
Attempting to ensure the viability of the proposed monitoring mechanism, Figure 3 identifies the potential funding resources of the Just Transition Observatory, consisting of EU, national and
private funds.
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Monitoring and assessment mechanism of just energy transition trajectories. Do Just Transition Observatories matter?
Figure 3: Funding of a Just Transition Observatory.
ANALYSIS
In order to ascertain the foreseen outcome of the discussed structure of the Just Transition Observatory, a preliminary analysis of selected parameters was carried out. Taking into consideration that employment, Gross Value Added and business environment growth are critical issues for just regional transition, an analysis of their characteristics at the regional level was carried
out.
This work focused on the Just Transition Mechanism (JTM) territories, initiating their radical change from a heavily-dependent on CO2 emissions technological and economical model to a climate-neutral economy. It must be mentioned that during the period 2021-2027, a goal of around €55 billion will be mobilised for the most-affected regions under the target, in order to ensure that no one will be left behind.
An analysis of the position of sectors’ shares in the TJTP areas with respect to the national share, as well as the maximum and minimum share in all NUTS3 countries’ areas, was carried out. In the following Map 1, we can see the dominant economic sector regarding employment in TJTP areas. For reasons of discussion, only four selected cases are presented, dealing with Germany, Greece, Bulgaria, and the Czech Republic, illustrated in Figure 4. Based on a level of further analysis, conclusions can be drawn for each individual area, with respect to their national position and national/local policies, as well as for groups of areas. For example, for many sectoral cases, a wide spread is observed, while for other cases, all national TJTP areas are either below the average national share (e.g. [K-N] Greece, [G-J] Bulgaria, [K-N] the Czech Republic) or above it ([B-E] & [O-U] the Czech Republic).
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The selection of the countries examined, namely Germany, Greece, Bulgaria and the Czech Republic, was made with respect to the representation of significant dependency on the coal/lignite industry, phase out discussion readiness, and countries’ profiles.
Map 1: Employment: Dominant sector per TJTP area (2019). Data source: Eurostat
Amongst TJTP areas, [O-U] sector is dominant in 55 cases, representing 16.1% of the total number of employees, which deals with 19.9 million people for total areas (28.9% sectors total share in all TJTP areas). In addition to this, significant impact presents [G-J] dealing with 33 areas of 8.6% employees (27.0% share in total TJTP areas) and [B-E] in 26 areas with 7.5% (18.5% share in total TJTP areas). Sector [A] is dominant in only four areas related to 0.9% employees (5.3% share in total TJTP areas) and [K-N] deals with 1 area and 0.7% (13.4% share in total TJTP areas). Sector [F] has a total share of 6.8% in all TJTP areas in not governing any examined area. It must be noticed that [O-U] presents a key position as a local and total areas sector, while [G-J] and [B
E] are crucial in long scale with less significance in local scale.
Figure 4: Percentage of employment per TJTP areas in Germany, Greece, Bulgaria, Germany and the Czech Republic compared to national characteristics (2019).
Taking into consideration Gross Value Added (GVA), as we can see in Map 2, amongst TJTP areas, [B-E] sector is dominant in 50 cases representing 10.1% of the total examined areas GVA, which deals with 1,019.486 €, (21.6% sectors total share in all TJTP areas), followed by sector [O-U] dealing with 42 areas of 11.5% total GVA (24.0% share in total TJTP areas). Sector [G-J] is dominant in 21 areas with 5.0% (22.2% share in total TJTP areas) and [K-N] in only 6 areas related to 5.5% (23.5% share in total TJTP areas). Sectors [A] with a total share of 2.4% and [F] with 6.2% share in all TJTP areas are not governing any examined area. Moreover, in this case, we can see that [O-U] presents a key position as a local and total areas sector, followed by [B-E], while [G-J] and [K-N] are significant in long-scale analysis.
In Figure 5, the analysis of the position of the sectors’ shares in the TJTP areas GVA, with respect to the national share, as well as the maximum and minimum share in all NUTS3 countries’ areas, is illustrated for the same selected countries as above. In addition to this, in this case, a wider spread is observed for numerous sectoral contributions in individual areas amongst countries, resulting in required specific local policies, while in other cases sectoral measures may arise. In the countries examined, all national TJTP areas are either below the average national share (e.g. [G-J] Bulgaria, [G-J] & [K-N] the Czech Republic) or above it ([A] Bulgaria).
Map 2: Gross Value Added: Dominant sector per TJTP area (2019). Data source: Eurostat.
Figure 5: Share of Gross Value Added per TJTP areas in Germany, Greece, Bulgaria and Czech Republic (2019) compared to national characteristics.
The examination of the ratio of employment per GVA could provide a valuable indicator for further analysis of growth potential at the national and regional level. Figure 6 illustrates the respective indicator for the four examined countries.
Figure 6: Ratio of employment / GVA per TJTP areas in Germany, Greece, Bulgaria and the Czech Republic (2019) compared to national characteristics.
Map 3: Business demography: Dominant sector per TJTP area (2019). Data source: Eurostat.
The examination of Business demography amongst TJTP areas, Map 3, results in the requirement for further data availability, since published data are only available for 73 of the 119 areas examined. From this arises a further work and usefulness of proposed Observatory to act as an intermediate stakeholder amongst regional, national and EU authorities to identify information needed, data gaps and contribute to further actions for common EU indicators development.
In terms of the Business demography analysis, [G-J] sector is dominant in 48 cases, followed by 15 of [P-S], while [K-N-X-K642] is related to only 9 areas and [F] to one.
The analysis of the newly-born businesses in the last 3 years achieving the continuation of their activities as a share of total business activities in the examined area is proposed as an economic growth indicator to be used in the Observatory, as illustrated in the Figure 7. It must be noted that TJTF areas achieved a higher score than 90% of the examined cases areas in Austria, Belgium, Latvia, Lithuania, Malta and Romania. Specific analysis of these may provide useful results for replication, as well as Best Practices that could then be developed and disseminated.
Figure 7: Percentage of newly-born active enterprises in the last 3 years per TJTP area (2019, Eurostat).
CONCLUSIONS
Given the longitudinal nature of green transition and climate change, the theme of establishing an objective monitoring mechanism of just transition policies per each affected region across the EU is challenging and necessary, particularly at this time, in view of the Just Transition Fund’s unfolding. Considering that climate neutral goals will remain in place over the next few decades, comprehensive and inclusive policies, as well as efficient means, instruments and valid indicators, are indispensable.
Hence, a consensus on both mechanisms and a set of common indicators for measuring these indicators requires stakeholder engagement and agreement to inform and validate the indicator selection process. By selecting, through consultation processes, ‘what’ exactly to measure, monitor and use as indicators for performance, stakeholders can develop a shared understanding and responsibility for developing and updating indicators. In turn, this has the potential to create a local platform for co-operation amongst institutions based on different and multi-layered responsibilities and expertise.
This paper addresses the need of establishing an Observatory, aiming to monitor and assess the just transition endeavour in each coal-dependent area in Europe, based on co-created and pre-defined indicators and criteria. For the sake of the discussion, three pilot indicators have been selected to be analysed in a comparative perspective at a European, national and regional level. In particular, the critical parameters of employment, gross value added, and business environment growth have been explored and evaluated per economic sector in Germany, Greece, Bulgaria and the Czech Republic.
The preceding analysis has shown that establishing a comprehensive local transition monitoring mechanism and developing collectively-agreed indicators in a participatory way, could essentially assist the Just Transition Goals of local policies. Multi-level governance practices and co-ordination between multi-layered stakeholders in these planning, implementation, and monitoring processes within and amongst regions could prove crucial. The truth is that institutional and regional boundaries do not often match up in real life. As a result, a tailor-made approach that ensures a process of stakeholder engagement must be developed per each involved area.
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ANNEX
Table 1: NUTS3 territories proposed or included in TJTPs
NUTS3 Territory Status Area (%)
AT121–Mostviertel-Eisenwurzen Included in approved TJTP 100%
AT122–Niederösterreich-Süd Included in approved TJTP 100%
AT211–Klagenfurt-Villach Included in approved TJTP 50%
AT212–Oberkärnten Included in approved TJTP 13%
AT213–Unterkärnten Included in approved TJTP 100%
AT221–Graz Included in approved TJTP 89%
AT223–Östliche Obersteiermark Proposed by EC and included in approved TJTP 100%
AT225–West- und Südsteiermark Included in approved TJTP 38%
AT226–Westliche Obersteiermark Included in approved TJTP 100%
AT312–Linz-Wels Included in approved TJTP 29%
AT314–Steyr-Kirchdorf Included in approved TJTP 55%
AT315–Traunviertel Proposed by EC and included in approved TJTP 100%
BE323–Arr. Mons Proposed by EC 100%
BE328–Arr. Tournai-Mouscron Proposed by EC 86%
BE32B–Arr. Charleroi Proposed by EC 100%
BE32C–Arr. Soignies Proposed by EC 23%
BG344–Stara Zagora Proposed by EC 100%
BG415–Kyustendil Proposed by EC 100%
CY000–Kýpros Proposed by EC and included in approved TJTP 100%
CZ041–Karlovarský kraj Proposed by EC 100%
CZ042–Ústecký kraj Proposed by EC 100%
NUTS3 Territory Status Area (%)
CZ080–Moravskoslezský kraj Proposed by EC 100%
DE402–Cottbus, Kreisfreie Stadt Proposed by EC 100%
DE406–Dahme-Spreewald Proposed by EC 100%
DE407–Elbe-Elster Proposed by EC 100%
DE40B–Oberspreewald-Lausitz Proposed by EC 100%
DE40G–Spree-Neiße Proposed by EC 100%
DEA15–Mönchengladbach, Kreisfreie Stadt Included in approved TJTP 100%
DEA1D–Rhein-Kreis Neuss Proposed by EC and included in approved TJTP 100%
DEA26–Düren Proposed by EC and included in approved TJTP 100%
DEA27–Rhein-Erft-Kreis Proposed by EC and included in approved TJTP 100%
DEA29–Heinsberg Included in approved TJTP 100%
DEA2D–Städteregion Aachen Included in approved TJTP 100%
DEA31–Bottrop, Kreisfreie Stadt Included in approved TJTP 100%
DEA36–Recklinghausen Included in approved TJTP 39%
DED2C–Bautzen Proposed by EC 100%
DED2D–Görlitz Proposed by EC 100%
DED51–Leipzig, Kreisfreie Stadt Proposed by EC 100%
DED52–Leipzig Proposed by EC 100%
DEE02–Halle (Saale), Kreisfreie Stadt Proposed by EC 100%
DEE05–Anhalt-Bitterfeld Proposed by EC 100%
DEE08–Burgenlandkreis Proposed by EC 100%
DEE0A–Mansfeld-Südharz Proposed by EC 100%
DEE0B–Saalekreis Proposed by EC 100%
DK050–Nordjylland Proposed by EC 100%
EE00A–Kirde-Eesti Proposed by EC 100%
EL411–Lesvos, Limnos Proposed by EC and included in approved TJTP 100%
EL412–Ikaria, Samos Proposed by EC and included in approved TJTP 100%
EL413–Chios Proposed by EC and included in approved TJTP 100%
EL421–Kalymnos, Karpathos, Kasos, Kos, Rodos Proposed by EC and included in approved TJTP 100%
EL422–Andros, Thira, Kea, Milos, Mykonos, Naxos, Paros, Syros, Tinos Proposed by EC and included in approved TJTP 100%
EL431–Irakleio Proposed by EC and included in approved TJTP 100%
EL432–Lasithi Proposed by EC and included in approved TJTP 100%
EL433–Rethymni Proposed by EC and included in approved TJTP 100%
EL434–Chania Proposed by EC and included in approved TJTP 100%
EL531–Grevena, Kozani Proposed by EC and included in approved TJTP 100%
EL532–Kastoria Proposed by EC and included in approved TJTP 100%
EL533–Florina Proposed by EC and included in approved TJTP 100%
EL651–Argolida, Arkadia Proposed by EC and included in approved TJTP 49%
EL653–Lakonia, Messinia Included in approved TJTP 6%
ES111–A Coruńa Proposed by EC 100%
ES120–Asturias Proposed by EC 100%
ES242–Teruel Proposed by EC 100%
ES413–León Proposed by EC 100%
ES414–Palencia Proposed by EC 100%
NUTS3 Territory Status Area (%)
ES611–Almería Proposed by EC 100%
ES612–Cádiz Proposed by EC 100%
ES613–Córdoba Proposed by EC 100%
FI1D1–Etelä-Savo Proposed by EC 100%
FI1D2–Pohjois-Savo Proposed by EC 100%
FI1D3–Pohjois-Karjala Proposed by EC 100%
FI1D5–Keski-Pohjanmaa Proposed by EC 100%
FI1D7–Lappi Proposed by EC 100%
FI1D8–Kainuu Proposed by EC 100%
FI1D9–Pohjois-Pohjanmaa Proposed by EC 100%
FRE11–Nord Proposed by EC 100%
FRL04–Bouches-du-Rhône Proposed by EC 100%
HR028–Sisacko-moslavacka županija Proposed by EC 100%
HR036–Istarska županija Proposed by EC 100%
HU231–Baranya Proposed by EC 100%
HU312–Heves Proposed by EC 100%
IE063–Midland Proposed by EC 100%
ITF43–Taranto Proposed by EC 100%
ITG2H–Sud Sardegna Proposed by EC 23%
LT022–Kauno apskritis Proposed by EC 100%
LT026–Šiauliu apskritis Proposed by EC 100%
LT028–Telšiu apskritis Proposed by EC 100%
LU000–Luxembourg Proposed by EC 1%
LV005–Latgale Proposed by EC 100%
LV008–Vidzeme Proposed by EC 100%
MT001–Malta Proposed by EC 11%
NL111–Oost-Groningen Proposed by EC 100%
NL112–Delfzijl en omgeving Proposed by EC 100%
NL113–Overig Groningen Proposed by EC 100%
PL225–Bielski Proposed by EC 100%
PL227–Rybnicki Proposed by EC 100%
PL228–Bytomski Proposed by EC 100%
PL229–Gliwicki Proposed by EC 100%
PL22A–Katowicki Proposed by EC 100%
PL22B–Sosnowiecki Proposed by EC 100%
PL22C–Tyski Proposed by EC 100%
PL414–Koninski Proposed by EC 100%
PL517–Walbrzyski Proposed by EC 100%
PT11A–Área Metropolitana do Porto Proposed by EC 3%
PT16I–Médio Tejo Proposed by EC 100%
PT181–Alentejo Litoral Proposed by EC 100%
RO125–Mures Proposed by EC 100%
RO224–Galati Proposed by EC 100%
RO316–Prahova Proposed by EC 100%
RO411–Dolj Proposed by EC 100%
NUTS3 Territory Status Area (%)
RO412–Gorj Proposed by EC 100%
RO423–Hunedoara Proposed by EC 100%
SE214–Gotlands län Included in approved TJTP 100%
SE331–Västerbottens län Proposed by EC and included in approved TJTP 100%
SE332–Norrbottens län Proposed by EC and included in approved TJTP 100%
SI034–Savinjska Proposed by EC 100%
SI035–Zasavska Proposed by EC 100%
SK022–Trenciansky kraj Proposed by EC 100%
SK042–Košický kraj Proposed by EC 100%
50 JET
JET Volume 15 (2022) p.p. 51-58 Issue 4, 2022 Type of article: 1.01 www.fe.um.si/si/jet.html
ENERGY SOURCE REPLACEMENT IN A THERMAL POWER PLANT
ZAMENJAVA ENERGENTA V TERMOELEKTRARNI
Matej Fike1R, Andrej Predin1
Keywords: wood biomass, thermal power plant, electricity production
Abstract
In Slovenia, approximately one third of electricity is produced in thermal power plants. Electricity prices have risen sharply recently. There are several reasons for this. One of them is the large increase in the price of CO2 coupons that thermal power plants have to buy, since they emit large amounts of greenhouse gas into the environment during the production of electricity. Emissions of the greenhouse gas CO2 are the result of burning fossil fuels, in this case coal. We want to use renewable energy sources to replace fossil fuels and reduce greenhouse gas emissions. More than half of Slovenia is covered with forest, and as a result, wood biomass energy represents a great energy potential. One possibility is the use of wood biomass in a classic thermal power plant for the production of electricity. This contribution presents the energy potential of Slovenian forests, before evaluating the replacement of the energy source in the existing coal-based thermal power plant with wood biomass. It has been discovered that there is enough wood biomass in Slovenia, and that all the electricity that is currently produced from coal can be produced from wood biomass. The cost of wood biomass is higher than the cost of coal. By changing the energy source, we save on the purchase of CO2 coupons. Therefore, the use of wood biomass represents an economically justified energy source for the production of electricity.
R Corresponding author: Doc. Dr. Matej Fike, Faculty of Energy Technology, Hocevarjev trg 1, SI-8270 Krško, Tel.: +386 7 6202 228, E-mail: matej.fike@um.si,
1 Faculty of Energy Technology, Hocevarjev trg 1, 8270 Krško
Povzetek
V Sloveniji približno eno tretjino elektricne energije proizvedemo v termoelektrarnah. Elektricna energije se je v zadnjem casu mocno podražila – razlogov za to je vec. Eden izmed njih je veliko povišanje cene emisijskih kuponov, ki jih morajo kupovati termoelektrarne, saj pri proizvodnji elektricne energije v okolje emitirajo izdatne kolicine toplogrednega plina. Emisije toplogrednega plina CO2 so posledica zgorevanja fosilnih goriv, v tem primeru premoga. Z obnovljivimi viri en-ergije želimo nadomestiti fosilna goriva in zmanjšati emisije toplogrednih plinov. Vec kot polovica Slovenije je pokrita z gozdom, zato energija lesne biomase predstavlja velik energetski potencial. Ena od možnosti je uporaba lesne biomase v klasicni termoelektrarni za proizvodnjo elektricne energije. V tem prispevku predstavimo energetski potencial slovenskih gozdov in ovrednotimo zamenjavo energenta v obstojeci termoelektrarni na premog z lesno biomaso. Ugotovitve kažejo, da je v Sloveniji dovolj lesne biomase, da lahko iz nje proizvedemo vso elektricno energijo, ki jo sedaj pridobivamo iz premoga. Strošek lesne biomase je višji od stroška premoga, vendar z zamenjavo energenta prihranimo pri nakupu emisijskih kuponov. Tako je uporaba lesne biomase ekonomsko upravicen energent za proizvodnjo elektricne energije.
INTRODUCTION
Renewable energy sources include all energy sources that are captured from constantly repeating natural processes. Renewable energy sources (RES) include solar radiation, wind, water flow in rivers, photosynthesis, ground heat flows and sea currents. Renewable energy sources locally reduce import dependence and increase energy security. The industry related to RES promotes greater employment and rural development. The most important renewable energy source in Slovenia is wood biomass, followed by water energy, while the use of solar energy has also been increasing recently. Slovenia had set a national goal of achieving at least a 25% share of RES in the final gross energy consumption by 2020. However, Slovenia itself did not reach this target. The achieved share of energy use from renewable sources was 24.1%. In order to fulfil the set goal, Slovenia concluded an agreement with the Czech Republic and, with the help of the mechanism of statistical transfer of renewable energy, transferred 465 GWh of energy from another EU member state and thus avoided sanctions. By 2030, Slovenia has set an overarching national goal of achieving at least a 27% share of RES in the final gross energy consumption. In accordance with the National Energy and Climate Plan (NECN), the sectoral target shares of RES in the gross final use of energy are also set for 2030. In the heating and cooling sector, the share of RES is 41.4%, in the electricity sector the share is 43.3% and in the transport sector 20.8%, with a projected share of biofuels of at least 11%. In 2020, the share of RES in the heating and cooling sector was 32.14%, in the electricity sector 35.09% and in the transport sector 10.91%. [1] It follows that we will have to significantly increase the share of RES in all sectors.
The aim of this research is to analyse the replacement of coal as an energy source with wood biomass in a thermal power plant.
Energy source replacement in a thermal power plant
DATA AND METHODS
2.1 Thermal power plant
For the analysis, we will use data from 2019. During this year, the Šoštanj thermal power plant (TEŠ) operated two blocks, block 5 and block 6. Block 5 produced 775,028 MWh and block 6 produced 3,397,672 MWh of electricity. [2] A total of 4,195,305 MWh and 3,720,821 MWh of electricity were produced on both generators. They used 3,040,612 tons of lignite coal from Velenje. The calorific value of coal was 11,874 GJ/t. Depending on the technology used in the individual blocks, the specific CO2 emissions in block 5 are 1.090 kg/MWh and 0.869 kg/MWh in block 6. In total, CO2 emissions amounted to 3,800 tons. Companies need emission coupons for greenhouse gas emissions. For each emission coupon, the company can release 1 ton of CO2 into the atmosphere or an equivalent amount of another greenhouse gas that is equally or similarly harmful to the environment. Companies that need emission coupons buy them on the emissions market. The basic idea behind emission coupons is to encourage companies to focus on operations that are kinder to nature and the planet. Emission coupons are issued by the European Union, and the total number of issued coupons decreases every year. In doing this, the European Union seeks to reduce the amount of greenhouse gases in the atmosphere over time. In the period between 2021 and 2030, 2.2% fewer coupons are issued each year. Figure 1 shows the evolution of emission coupon prices over time. The diagram shows that the price of coupons was low until 2018 and was around €10 per ton of CO2. This price then rose above €20 per ton, with a subsequent sharp jump in 2021. In August 2022, the maximum price was recorded at almost €100 per coupon. The current price is around €80.
Figure 1: Emission coupons prices on the stock exchange
The price of coal and the cost of emission coupons have the greatest influence on the own price of the electricity produced in the thermal power plant. In 2019, the Šoštanj thermal power plant
JET 53
produced 3,721 GWh of electricity and recorded €217.8 million in revenues from the sale of electricity, or a total of €225.9 million in revenues from the sale of electricity and heat. 3,040,612 tons of coal were used for the production of electricity and heat. The price of coal is €2.75/GJ. The cost of coal amounted to €99.3 million. In Slovenia, we have 45 devices that require emission coupons for their operation. In the year under review, 6,253,595 emission coupons were handed over, of which 3,817,347 went to the Šoštanj thermal power plant, which represents 61% of all the coupons handed over. The average price of emission coupons in the year in question was €24.60 per coupon. [3] Based on this data, we can calculate that the cost of coal was €26.7/MWh and the cost of emission coupons, if we consider the average price of all submitted coupons, was €25.1/MWh. On average, electricity was sold at €58.5/MWh. Mainly due to the high cost of emission coupons, the company’s profit was negative. As the prices of emission coupons increase, business is getting worse. One of the possibilities for improving operations is the transition from fossil fuels to renewable energy sources, or the replacement of the energy source coal with wood biomass, thereby avoiding the cost of emission coupons.
2.2 Wood biomass
Slovenia’s natural wealth is the forest, which covers 58% of Slovenia’s surface. The area of commercial forests measures 1,068,484 ha, protected forests 98,762 ha and forest reserves 9,508 ha. The total area of forests is 1,176,754 ha. Taking into account the forest cultivation plans (GGN) made in 2019, the wood stock is estimated at 356,756,000 mł or 303 mł/ha. The annual increase is estimated at 8,827,600 mł or 7.5 mł/ha. In 2019, 5,287,863 mł of woody biomass were cut, of which 3,326,578 mł were conifers and 1,961,285 mł hardwoods (deciduous trees). The recorded logging in 2019 amounted to 87% of the possible logging according to GGN.
One of the main tasks of the Forestry Institute in the field of wood for energy is to provide data on the potential of wood suitable for energy. They spatially show the potential of wood suitable for energy use (supply), its use (demand) and the balance sheet for an arbitrarily selected area. When calculating supply, in addition to other parameters, data on forests and the potential of non-forest land, industrial resources and collected wood waste in collection centres are taken into account. When determining demand, they take into account the estimated use of wood for energy by households. Table 1 shows the energy potential of wood. [4]
Table 1: Potential of wood biomass for energy
Source Parameter Net quantity in tons of dry matter
forest available logging quantity of lower quality assortments 1,603,000
actual logging quantity of lower quality assortments 799,000
logging residue in the forest upon the undertaken of a possible felling 455,000
logging residue in the forest upon the actual realisation of the felling 294,000
the amount of log bark when a possible felling is realised 59,000
the amount of log bark when an actual felling is undertaken 59,000
agricultural areas estimated possible permanent felling of trees on non-forested areas 151,000
wood and non-wood residues from agricultural land usable for energy 197,000
industry the amount of sawmill residues during the processing of logs upon realisation of a possible felling 530,000
the amount of sawmill residues during the processing of logs during the current realisation of the felling 500,000
collection centres collected wood residues 350,000
consumption consumption of lower-quality assortments in households for heating and cooking 776,000
consumption of lower quality assortments in district heating and cogeneration systems 94,000
wood residues used in industry 32,000
total consumption of wood for energy in all sectors 902,000
the amount of competitive use of lower quality wood in the industry 255,000
RESULTS
One of the options for the extended operation of thermal power plants is the replacement of the energy source coal with wood biomass, which is carbon neutral. In 2019, TEŠ consumed 3,040,612 tons of coal with a calorific value of 11,874 GJ/t. If the consumed amount of coal is converted into energy, we get 36 PJ of energy. Taking into account the annual increase in Slovenia and the assumed average wood density of 550 kg/m3, as well as the average calorific value of
16.5 MJ/kg, we find that coal energy can be replaced with 45% of the annual increase. This value is also very close to the percentage of waste when processing logs into quality products with high added value. If we allocated the same amount of money for wood biomass as was spent in 2019 (before the drastic increase in the price of emission coupons) for the payment of coal and emission coupons, then the price of wood biomass delivered to the thermal power plant could be €48/m3.
Table 1 shows a more detailed analysis of the potential of wood biomass for energy production. The possible harvest of lower-quality assortments amounts to 1,603,000 tons. If we add to this the amount of log bark at the realisation of a possible felling (59,000 tons), the amount of sawmill residues from the processing of logs at the realisation of a possible felling (530,000 tons), and the collected wood residues in collection centres (350,000 tons), we get the sum of wood biomass resources at the realisation of a possible felling in the amount of 2,542,000 tons. From this sum, we can subtract the consumption of lower-quality assortments in households for heating and cooking (776,000 tons) and the consumption of lower-quality assortments in district heating and cogeneration systems (94,000 tons), as well as wood residues used in industry. In doing this, we obtain a quantity of lower-quality wood when the possible harvest is realised in the amount of 1,640,000 tons. This is the amount of wood biomass that could be usefully used in a thermal power plant. Taking into account the calorific value of 16.5 MJ/kg, 75% of coal consumption and consequently 75% of CO2 emissions could be replaced with the available amount of lower-quality wood, which corresponds to 2.8 million tons of CO2.
CONCLUSION
The article analyses the replacement of coal as an energy source with wood biomass in a thermal power plant. The analysis was made on the basis of data for the year 2019. This year was chosen because the Covid situation broke out a year later, and it thus represents a normal, average year. The analysis of the data showed a sharp increase in the prices of emission coupons, which have a negative impact on the operation of the thermal power plant. It was found that the entire amount of coal used can be replaced by 45% of the annual increase in wood biomass. Furthermore, it was found that if the coal was replaced only by the consumption of lower quality wood when the possible felling was realised, then 75% of the coal consumption could be replaced.
References
[1] Republika Slovenija Statisticni urad. Available online: https://pxweb.stat.si/SiStatData/pxweb/sl/Data/-/1818002S.px/table/tableViewLayout2/ (accessed on 27 September 2022).
[2] Termoelektrarna Šoštanj. Letno porocilo TEŠ 2019. Available online: https://www.te-sostanj.si/wp-content/uploads/2020/09/Letno-poro%C4%8Dilo_ TE%C5%A0_-2019_slo_web-oblikovano.pdf (accessed on 27 September 2022).
[3] Ministrstvo za okolje in prostor. Sklep o povprecni ceni emisijskih kuponov za leto 2019. Available online: https://www.gov.si/assets/ministrstva/MOP/Dokumenti/Podnebnespremembe/sklep_o_povprecni_ceni_emisijskih_kuponov_2019.pdf (accessed on 27 September 2022).
[4] Zavod za gozdove Slovenije. Porocilo zavoda za gozdove Slovenije o gozdovih za leto 2019. Available online: http://www.zgs.si/fileadmin/zgs/main/img/PDF/LETNA_POROCILA/2019_Porocilo_o_gozdovih.pdf (accessed on 27 September 2022).
[5] Termoelektrarna Šoštanj. Letno porocilo TEŠ 2021. Available online: https://www.te-sostanj.si/wp-content/uploads/2022/09/03_TES_letno_porocilo_2021_slo_-SPLET.pdf (accessed on 27 September 2022).
[6] Termoelektrarna Šoštanj. Okolje TEŠ. Available online: https://www.te-sostanj.si/okolje/zakaj-blok-6/ (accessed on 27 September 2022).
[7] Elektroinštitut Milan Vidmar. Vplivi bloka 6 TE Šoštanj na okolje. Available online: http://www.umanotera.org/upload/files/EIMV.pdf (accessed on 27 September 2022).
Nomenclature
(Symbols) (Symbol meaning) GGN forestry cultivation plan TEŠ Thermal power plant Šoštanj RES Renewable energy sources
58 JET
JET Volume 15 (2022) p.p. 59-68 Issue 4, 2022 Type of article: 1.04 www.fe.um.si/si/jet.html
ENERGY ANALYSIS OF HYDROGEN USE IN ROAD TRANSPORT OF THE REPUBLIC OF CROATIA
ENERGETSKA ANALIZA UPORABE VODIKA V CESTNEM PROMETU REPUBLIKE HRVAŠKE
Franco Krog1R, Jurij Avsec1
Key words: hydrogen, hydrogen technologies, hydrogen acquisition, transport
Abstract
In this paper, we will calculate the needs for hydrogen if all traffic in Croatia was driven by hydrogen. In the article, we will determine the required amount in several ways. First, we will briefly describe fuel cell cars and the extent to which the amount of exhaust gases would be reduced. We will then explain the ways of obtaining hydrogen, its transport and what the purchase costs would be. Finally, we will compare the results and draw some conclusions.
Povzetek
V clanku bomo izracunali, kakšne bi bile potrebe po vodiku, ce bi ves promet na Hrvaškem potekal na vodik. V nalogi bomo na razlicne nacine dolocili potrebno kolicino vodika, potrebnega za transport na Hrvaškem. Opredili bomo avtomobile na gorivne celice ter kolicino izpušnih plinov, ki bi se zmanjšala z uporabo vodikovih tehnologij. Pojasnili bomo tudi nacine pridobivanja vodika, njegovega transporta in kolikšni bi bili stroški nakupa vozila. Na koncu bomo primerjali rezultate
in zapisali ugotovitve.
R Corresponding author: Franco Krog, University of Maribor, Faculty of Energy Technology, Hocevarjev trg 1,8270 Krško, Slovenia, tel.: +386 41 203 208, E-mail: franco.krog1@um.si
1 University of Maribor, Faculty of Energy Technology, Hocevarjev trg 1,8270 Krško, Slovenia
1 INTRODUCTION
Road traffic is an indispensable part of everyday life. About 30% of the total energy used and 25% of the total exhaust gas emissions emitted within the EU come from road transport. The European Commission has proposed several different directions for sustainable development, one of which involves the application of hydrogen technology. Regardless of the environmental protection aspect, the need for a new fuel has arisen due to the high prices of currently-available fuels and the dependence on fuel imports. Fuel cells were found as an alternative to internal combustion engines. Since Europe is at a low level of self-sufficiency, its own hydrogen production would help solve an additional problem. Fuel cells are already available on the market and are still under development. Production of fuel cell cars and this technology are also available for purchase, something which represent a very successful development.
2 HYDROGEN CAR
More and more companies are deciding to develop hydrogen cars. The most successful hydrogen car, developed in serial production, is the Toyota Mirai hydrogen car. In Table 1, we present the technical characteristics of the Toyota Mirai car, developed in December 2021. 17,940 units have already been sold, [1] a small number compared to sales of battery electric vehicles, but intense growth in hydrogen vehicle sales has nevertheless been predicted. In Table 1, you can see the technical data relating to the Toyota Mirai hydrogen car.
Table 1: Technical data of the car Toyota Mirai [2]
Motor power 113 kW / 335 Nm
Number of reservoirs 3
Maximum speed 178 km/h
Nominal working pressure 700 bar
Range 550 km (NEDC)
Tank volume 122,4 l
Maximum mass of stored hydrogen 5 kg
Refill time 3 min
Combined consumption 0,76 kg/100 km
Starting price in Germany 60 000 EUR
A fuel cell car has similar design as a battery electric car except instead of a battery it has fuel
cells and a hydrogen tank.
The main components of a fuel cell car are:
1.
The electric motor;
2.
The high pressure hydrogen tank;
3.
The battery;
4.
The fuel cells;
5.
The boost converter;
6.
The control unit.
In these cars, hydrogen is stored at 700 bar in three polymer high-pressure tanks. They are made of three layers: an inner plastic layer, a middle carbon fibre plastic layer and an outer plastic layer with glass fibre to protect it against damage. The Toyota Mirai car uses PEMFC or polymer fuel cells where the electrodes are separated by a solid polymer electrolyte. The 650 V electrical installation llows for a smaller number of fuel cells. The battery in fuel cell cars is primarily intended to store excess energy generated during regenerative braking. This stored energy is then used by the car during re-acceleration, thus increasing the car’s range. The battery is much smaller than that found in electric vehicles. The size or energy capacity of the Toyota Mirai lithium-ion battery is 1,2 kWh. [1]
Figure 1: Design of the Toyota Mirai II (https://www.toyota-europe.com/news/2020/new-mirai-concept).
THE NEED FOR HYDROGEN
In this article, we have calculated the energy needs for hydrogen in road traffic of the Republic of Croatia. To determine the required amount of hydrogen for road traffic needs, we calculated the required amount of hydrogen with the help of the data presented in Table 2.
Table 2: Data required for calculation in Croatia
Number of registered vehicles 1 666 413 [3]
Energy consumed in traffic 5,34 * 1010 MJ [4]
Average mileage 15 000 km/year [5]
We calculated the need for hydrogen from the car’s declared hydrogen consumption, which was 0.76 kg/100 km: [2]
(..............................................)....... =......*....=0,76 .. *15000........ = 114 kg (3.1)
.....
Where:
–
(Consumption H2)15000km – hydrogen consumption (EU average)) – 15 000 km (kg);
–
PGTM – Toyota Mirai combined consumption (kg/100 km);
–
d – distance (km).
......=....*....=1666413*115........ = 1,92*10. kg (3.2)
The electricity required to produce one kilogram of hydrogen is 55 kWh/kg, which means that 6,270 kWh will need to be supplied for one car. It will also be necessary to obtain at least 10,448,409,510 kWh of electricity, which means that a 1,200 MW power plant would be required.
HYDROGEN PRODUCTION
As shown in the previous sections, hydrogen is required for a fuel cell car. We already solved the first problem, environmental pollution, by introducing green hydrogen, as opposed to grey and blue hydrogen, both of which are also available. Grey hydrogen, which is currently the most common form of hydrogen, is produced from natural gas or methane in the steam reforming process. Blue hydrogen, on the other hand, is the same as grey hydrogen, except that it captures greenhouse gases in the process. The gas capture process is cost prohibitive. Green hydrogen is created with the help of electricity from renewable sources.
Another problem with conventional fuels is their price. Since we want the lowest possible price and zero pollution, our own local production of hydrogen with the help of renewable sources would represent the most economical solution. Hydrogen can be obtained from fossil fuels and from water. The direction of obtaining hydrogen from fossil fuels is not sustainable from an ecological point of view. Extraction from water can take place via electrolysis, thermochemical and photochemical processes. Currently, only electrolysis devices can be purchased from the above on the market.
Obtaining hydrogen by means of electrolysis can be represented by formula 4.1
4.......... 4.....+2..... (4.1)
As seen in the previous section, a 1200 MW plant is not the best idea. Another option for green electricity is to use photovoltaic systems. The advantage of using photovoltaic panels is the production of DC electricity, which is necessary for electrolysis. In Figure 2, you can see the annual solar radiation.
Figure 2: Annual solar radiation. [6]
On average, solar radiation in Croatia amounts to 1250 to 1500 kWh per square metre and 2000 hours of sunshine, [7] which means that our own production of hydrogen could well be something to consider. [6] Currently, the preferred way of producing hydrogen is self-production. If we needed 114 kilograms of hydrogen per car per year, then we would require 6270 kWh of electricity, as well as another 1.35 kWh [8] to compress a kilogram of hydrogen, giving us a total of 6425 kWh. If we, for example, installed 25 photovoltaic modules, each with a power of 300 W, on a roof of 50 m˛, we would theoretically obtain up to 12000 kWh of electricity per year, which means we would be able to meet these requirements.
......=......... *........ *......... *....=300....*2000........*25*0,8=
(4.2)
12000........h
Where:
–
WPV – production of photovoltaic modules [W];
–
Wmodul – power of each module [W];
–
tyear – number of sunny hours per year;
–
Nmodul – number of modules;
–
k– correction factor.
As we can see from this calculation, with 25 power modules of 300 W, we can produce enough energy for hydrogen production and compression, allowing us to install fewer modules.
REDUCTION OF EMISSIONS IN THE CASE OF SWITCHING TO HYDROGEN
We know from experience that an average car emits between 110 and 150 gCO2 . Every machine
km
in which a certain fuel is burned produces emissions of certain gases. Car engines have different emissions, which depend mainly on which fuel is used and the engine’s fuel consumption. To simplify the calculation, we will use the average consumption. Currently, the most prevalent exhaust gas is carbon dioxide. Data on the amount of CO2 are as follows. [9]
–
1 litre of gasoline contains 652 g CO2;
–
1 litre of diesel contains 720 g CO2.
When we calculate emissions for an average consumption of 5.2 l/100 km for diesel and 7 l/100 km for gasoline, we get:
.........,.=(.............................................*..........,.)/100........ =(5,2*2640)/100
(5.1)
=137,3..../100........
.........,.=(.............................................*..........,.)/100........ =(7*2392)/100
(5.2)
=167,44..../100........ 137,3 ....
.........,.,..... =.........,.*....=........ *15000........=2059,5........ (5.3)
167,44 ....
.........,.,..... =.........,.*....=........ *15000........ (5.4)
=2511,6........
If we add the average mileage and the average fuel consumption to the above data, with the help of a calculator to calculate the carbon footprint, we obtain the results shown in Table 3: [10]
Table 3: Total CO2 emissions
Fuel Annual consumption Annual emissions of CO2 Total annual emission
in litres in kg per 15,000 km in kg
Diesel 780 2060 1,75 * 109 kg
Gasoline 1005 2512 2,05 * 109 kg
2
If we calculate the average value of 130 gCOto overall average 15000 kilometres per year, we
km
get the following value:
130 ....
.........,..... =.........*....=........ *15000........=1950........ (5.5)
We can also calculate this for all cars:
.........=.........,..... *....=1950........*1666413=3249505350........ (5.6)
PURCHASE COSTS OF HYDROGEN TECHNOLOGIES
In Table 4, we present the price of the Toyota Mirai and its running costs:
Table 4: Data for Toyota Mirai. [2]
Car Toyota Mirai
Propulsion type Electric motor 113 kW
Fuel consumption 0.76 kg /100 km
CO2 emissions 0 g/100 km
New vehicle price 60 000 EUR
Annual fuel cost and CO2 emissions 1083 EUR and 0 kg CO2
In the Figure 3, we present the costs of several types of vehicles per 15,000 km and the dependence of annual costs on the distance travelled from 5,000 to 100,000 km depending on the type of fuel in Figure 3. [11, 12, 13, 14]
Figure 3: Annual cost per fuel type per 15,000 km.
Franco Krog, Jurij Avsec JET Vol. 15 (2022)
Issue 4
Figure 4: Annual cost per distance from 5,000 to 100,000 km.
Figure 4 shows the annual cost per distance from 5,000 to 100,000 km. In a comparison of hydrogen with today’s most expensive energy source, gasoline, it can be seen that hydrogen is about 200 euros more expensive per 15,000 kilometres travelled. For every kilometre more, the difference is even greater.
In Figure 5, we can see the price difference depending on the fuel used in the car.
Figure 5: The price of buying a new vehicle.
The hydrogen car itself is expensive. The best option at the moment is a hybrid. Therefore, when introducing hydrogen technologies, it will first be necessary to reduce the purchase price of the
66
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car. The competitiveness of the hydrogen car could be achieved through subsidies and reductions
in car taxes
References
[1] Web page Wikipedia: https://en.wikipedia.org/wiki/Toyota_Mirai
[2] Pricelist Toyota.de
[3] Bureau of Statistics, https://podaci.dzs.hr/2022/hr/29136 [19.10.2022]
[4] Environment and Nature Agency: Liquid Petroleum Fuels Report 2015
[5] https://www.odyssee-mure.eu/publications/efficiency-by-sector/transport/distancetravelled-by-car.html [21.10.2022]
[6] M. Percic, B. Frankovic. Solar energy in the coastal area of the Republic of Croatia–today and tomorrow. River; Faculty of Engineering, University of Rijeka, 2016.
[7] H. Lulic, The natural and technical potential of solar energy
[8] M. Gardiner, DOE Hydrogen and Fuel Cells Programe Record, Department of energy, USA
[9] N. Andersen, The fate of fossil fuel CO2 in the oceans, Springer New York, NY, 1977
[10] Web page MyClimate: https://co2.myclimate.org/en/car_calculators [29.11.2022]
[11] Web page Toyota.si, https://www.toyotaadria.com/si/pdf/cenik_vozil/cenik_ novi_chr. pdf [2.11.2022]
[12] Web page Toyota.si: https://www.toyotaadria.com/si/pdf/cenik_vozil/cenik _corolla_ hb_ts.pdf [2.11.2022]
[13] Web page Toyota.si: https://www.toyotaadria.com/si/pdf/cenik_vozil/cenik_prius_ plug_in.pdf [2.11.2022]
[14] Web page Nissan: https://www.nissan.si/vozila/nova-vozila/leaf/cene-specifikacije. html#grade-ZE1-2|overview
Nomenclature (Symbol)
CO2 PTM (Consumption H2)15000km
CO2,D
CO2,B
CO2,D,15000
(the meaning of the symbol)
carbon dioxide
combined consumption of Toyote Mirai consumption (EU average) – 15 000 km CO2 emissions of diesel fuel CO2 emissions of petrol fuel CO2 emissions for diesel fuel every 15000 kilometers
CO2,B,15000
k
d
....,
CO2,D
....,
CO2,D
H2 tleto Wmodul Nmodul O2 WPV
CO2 emissions for gasoline fuel at 15000 kilometers corection factor
distance
emissions after burning a litre of diesel fuel emissions after burning a litre of gasoline
hydrogen
insolation power of each module number of modules
oxygen
energy production of PV modules
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Submission of Manuscripts: All manuscripts must be submitted in English by e-mail to the editorial office at jet@um.si to ensure fast processing. Instructions for authors are also available online at http://www.fe.um.si/en/jet/author-instructions.html.
Preparation of manuscripts: Manuscripts must be typed in English in prescribed journal form (MS Word editor). A MS Word template is available at the Journal Home page.
A title page consists of the main title in the English and Slovenian language; the author(s) name(s) as well as the address, affiliation, E-mail address, telephone and fax numbers of author(s). Corresponding author must be indicated.
Main title: should be centred and written with capital letters (ARIAL bold 18 pt), in first paragraph in English language, in second paragraph in Slovenian language.
Key words: A list of 3 up to 6 key words is essential for indexing purposes. (CALIBRI 10pt)
Abstract: Abstract should be up to 500 words long, with no pictures, photos, equations, tables,
text only.
Povzetek: - Abstract in Slovenian language.
Main text should be structured logically in chapters, sections and sub-sections. Type of letters is Calibri, 10pt, full justified.
R Corresponding author: Title, Name and Surname, Organisation, Department, Address, Tel.: +XXX x xxx xxx, E-mail address: x.x@xxx.xx 1 Organisation, Department, Address 2 Organisation, Department, Address
Authors names and surnames
Units and abbreviations: Required are SI units. Abbreviations must be given in text when first mentioned.
Proofreading: The proof will be send by e-mail to the corresponding author in MS Word’s Track changes function. Corresponding author is required to make their proof corrections with accepting or rejecting the tracked changes in document and answer all open comments of proof reader. The corresponding author is responsible to introduce corrections of data in the paper. The Editors are not responsible for damage or loss of submitted text. Contributors are advised to keep copies of their texts, illustrations and all other materials.
The statements, opinions and data contained in this publication are solely those of the individual
authors and not of the publisher and the Editors. Neither the publisher nor the Editors can accept any legal responsibility for errors that could appear during the process.
Copyright: Submissions of a publication article implies transfer of the copyright from the author(s) to the publisher upon acceptance of the paper. Accepted papers become the permanent property of “Journal of Energy Technology”. All articles published in this journal are protected by copyright, which covers the exclusive rights to reproduce and distribute the article as well as all translation rights. No material can be published without written permission of the publisher.
Chapter examples:
MAIN CHAPTER
(Arial bold, 12pt, after paragraph 6pt space)
1.1 Section (Arial bold, 11pt, after paragraph 6pt space)
1.1.1 Sub-section (Arial bold, 10pt, after paragraph 6pt space)
Example of Equation (lined 2 cm from left margin, equation number in normal brackets (section. equation number), lined right margin, paragraph space 6pt before in after line):
Equation (1.1)
Tables should have a legend that includes the title of the table at the top of the table. Each table
should be cited in the text.
Table legend example:
Table 1: Name of the table (centred, on top of the table)
Paper title
Figures and images should be labelled sequentially numbered (Arabic numbers) and cited in the text – Fig.1 or Figure 1. The legend should be below the image, picture, photo or drawing.
Figure legend example:
Figure 1: Name of the figure (centred, on bottom of figure, photo, or drawing)
References
[1] N. Surname: Title, Journal Title, Vol., Iss., p.p., Year of Publication
[2] N. Surname: Title, Publisher, Year of Publication
[3] N. Surname: Title [online], Publisher or Journal Title, Vol., Iss., p.p., Year of Publication. Available: website (date accessed)
Examples:
[1] J. Usenik: Mathematical model of the power supply system control, Journal of Energy
Technology, Vol. 2, Iss. 3, p.p. 29 – 46, 2009
[2] J. J. DiStefano, A.R. Stubberud, I. J. Williams: Theory and Problems of Feedback and Control Systems, McGraw-Hill Book Company, 1987
[3] T. Žagar, L. Kegel: Preparation of National programme for SF and RW management taking into account the possible future evolution of ERDO [online], Journal of Energy Technology, Vol. 9, Iss. 1, p.p. 39 – 50, 2016. Available: http://www.fe.um.si/images/jet /Volume 9_Issue1/03-JET_marec_2016-PREPARATION_OF_NATIONAL.pdf (7. 10. 2016)
Example of reference-1 citation: In text [1], text continue.
Nomenclature (Symbols) (Symbol meaning) t time
JET I Journal of Energy Technology I Vol. 15, Issue 4, December 2022 I University of Maribor, Faculty of Energy Technology
ISSN 1855-5748
72 JET
9
771855
574008