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  • 1 Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, Boszorkány út 2, H-7624Pécs, Hungary
  • | 2 Energy Design Building Technology Research Group, Szentágothai Research Centre, University of Pécs, Ifjúság útja 20, H-7624Pécs, Hungary
  • | 3 Renergy Consulting Ltd, Tettye d. 2/1, H-7625Pécs, Hungary
  • | 4 Marcel Breuer Doctoral School, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány út 2, H-7624Pécs, Hungary
  • | 5 Department of Control Systems, Institute of Information Technology, University of Dunaujvaros, Dunaujvaros, H-2401, Hungary
Open access

Abstract

Energy storage on grid level is still a critical issue. Inventions related to development and control of smart buildings, including integrated solar systems can be easily realized by smart control of building management including storage. At buildings, which have high heat capacitance the part of the stored heat can be used for grid stabilization. This means the grid can be balanced with well-set up heating/cooling strategy and well-scheduled timetable via intelligent control of buildings. A possible solution is introduced in this paper, where the surplus production is used for overcooling the building, while the building thermodynamic properties are making it possible to store this amount of energy for days. This paper analyses a cost-effective solution of grid energy storage through a case study.

Abstract

Energy storage on grid level is still a critical issue. Inventions related to development and control of smart buildings, including integrated solar systems can be easily realized by smart control of building management including storage. At buildings, which have high heat capacitance the part of the stored heat can be used for grid stabilization. This means the grid can be balanced with well-set up heating/cooling strategy and well-scheduled timetable via intelligent control of buildings. A possible solution is introduced in this paper, where the surplus production is used for overcooling the building, while the building thermodynamic properties are making it possible to store this amount of energy for days. This paper analyses a cost-effective solution of grid energy storage through a case study.

1 Introduction

Renewable energy production trend runs on uncertain way, which is shown on the German example where in 11th May of 2016 renewable energy production reached the level, when electric energy market could not accommodate the available renewable energy and the market price turned to negative. This will happen more frequently in the future, so time has come to go smarter. Grid operator, Transmission System Operators (TSOs), Distribution System Operators (DSOs), or grid end-users should provide solutions for storage capacity or any alternative technology solution, which is able to manage the excess energy. The system, which is able to handle the issue is called smart grid. This system is the 21st century vision of electrical grid, including a wide variety of technologies: sensors, smart meters, smart appliances, renewable energy resources, energy efficiency resources and storage systems, intelligent overall central control of smart devices.

Continuous control of the production, distribution, load regulation and storage of electricity are important aspects of smart grid. The US Energy Independence and Security Act of 2007 [1] declares, that peak shaving technologies with electricity storage systems (including electric vehicle storage) and thermal air conditioning are mandatory fields of the smart grid discussion.

A base list of technologies of Energy Storage Systems (ESSs): It can be a battery system (electro-chemical storage - also built-in battery packs of electric vehicles), or compressed/liquid air storage, or hydro pump systems as well as hydrogen storage, or electro mechanical (flywheel) storage system and at last but not at least thermal storage systems. The rated power of hydro pumped systems were 185 GW, 4 GW of thermal storage, 4.4 GW battery, 2.6 GW electro-mechanical, and the other solutions, all together the installed storage power worldwide is far less than 1 TW, in 2017, [2]. The cost starts from 350 $/kWh for sodium/sulfur batteries and goes until 1,200 $/kWh at pumped hydro systems as an average, [3].

Pumped hydro systems are the etalon solution of energy storage. Benchmarking the thermal storage (a Seasonal Thermal Heat Storage (STES), the Latent Thermal Heat Storage (LTES), or Chemical Thermal Heat Storage (CTES) [4, 5]) can have higher energy density and the storage costs are similar. As emerging solution some buildings can be used also as heat storage when these have increased thermal capacitance [6].

New solutions (demand side management, net metering as framework of Internet of Things (IoTs)) as parts of the smart grid help to distribute energy and to optimize the use of energy. The smart net metering effects on the consumption when Renewable Energy Credits (RECs) is used.

1.1 Smart micro-grids with energy storage systems

Smart micro-grids have been identified as essential components of future power systems with the potential to assist with energy security, environmental sustainability and energy equity challenges the population is facing globally [7].

Smart grid (Fig. 1) today means the local grid is connected to electrical network, but this can work autonomously as a balanced local grid. From this sight the following systems can name [8]:

  1. Autonomous: Total energy need is provided by built-in capacity of the grid;

  2. Partially autonomous: Taken into consideration the economic aspects the predictable demands will be covered, only. Typically it works with renewable energy production. It does not produce high amount excess energy. It identifies the future load demands and storage capacities according to the real consumer needs;

  3. Grid connected micro-grid, designed for large scale consumption need.

Fig. 1.
Fig. 1.

An example of a smart grid with distributed generation and distributed control (on the basis of [8])

Citation: Pollack Periodica 16, 2; 10.1556/606.2020.00207

Energy storage ensures the smart grid’s autonomy. The key criteria of a smart city are that the produced goods (also the energy) have to be used up locally and following the principle this is how the smallest ecological footprint will be achieved.

Numerous control methods for energy management systems have been developed for smart grids to distribute energy between consumers, ESS and production sites. Newton–Raphson technique-based method exist [9], recursive least square methods [7], space vector pulse-width modulation [10], and also genetic algorithm-based control systems [11], but all agrees, that weather forecast must be applied for lower uncertainty in the system.

The micro-grid can be Alternating Current (AC) or Direct Current (DC) base network, but it becomes even more perspective, as the small size DC micro-grid has lower losses, based on the demonstration experiment at Okinawa Campus project verified the effectiveness of DC based Open Energy System (DCOES) [12].

Let’s take into consideration an ideal model for distributed energy storage system in micro-grid. When electrical storage is full, then peak excess energy generation is fed as heat into buildings. This cannot be regenerated and “repumped” to electrical grid but when the capacity and the heat resistance of the building envelope is high enough the stored energy can be used for long period of time, making heating system more effective and increasing the personal comfort level, in parallel.

The building mass can be used for Demand Side Management (DSM) by its coupling with buildings’ air conditioning system, where it can provide a capacity stress reservoir on the demand side with minimal changes to conventional operation and without disturbing thermal comfort in a sacrificial side [13].

It is also accepted, that a DSM is the most important issue for the smart grid balancing, e.g., to utilize the energy produced at the same place and time, to lower the dependence from the distribution grid control. Office and education buildings are said to be ideal for DSM purposes, because of shorter utilization period. It is an interesting conclusion that thermal energy storage can result higher energy demand, but lower costs by shifting away demand from peak hours to lower prices period [14].

DSM can also be practically combined with passive building ventilation for cooling purposes as it was investigated by the authors [15].

Researchers have compared the dynamic behavior of different heat emission systems and highlighted their large influence on thermal comfort: Not only the availability of the thermal mass, but also the interaction between the heating system and the thermal mass has significant importance [16]. This statement is considerable in cooling interaction, too. Direct cooling of the building structure is more acceptable from one side, but that needs special planning and financial investment.

The level of insulation has an important influence on the heat reservoir, at advanced insulation the time constant can be high enough and the stored energy can last for more than 8 h in a residential building [17] and even more in a building with higher thermal capacitance.

1.2 Thermal storage connected to electrical micro-grid

In spite of low conversion efficiency from thermal energy to electrical grid there are some applications, which can work in building environment. A thermoelectric solution can make it possible to use thermal storage in a smart grid bi-directionally, in this new approach the cooling and the warming sides of thermoelectric module are realized by the two sides of a building wall (inside-outside) [18]. Other method for the conversion can be a Stirling engine operating as a generator or a water pump [19].

The less complicated application is one directional demand control. In a smart grid there are programmable consumers and non- or partially programmable consumers. The most referred programmable DSM load is washing machine since its operation can be delayed or scheduled to a period where renewable energy production in the grid is over demand. A thermal storage also can be a programmable load. The buildings’ thermal storage can be activated by the Heating, Ventilation and Air Conditioning (HVAC) system or heat pump in cooling mode if present. Smart grid control strategies allow reduction of energy procurement costs by up to 15% and the consumer’s cost by 13% [20].

Also thermal energy grid can be used for wide-spreading the cooling energy produced in overproduction period. These thermal energy grids are called 4th generation one since they can be used for cooling in district, [21]. Unlike the first three generations’ solutions, the development of 4th generation district heating system involves the challenge of more energy efficient buildings as well as being part of an integrated operation of smart energy systems, i.e., integrated smart electricity, gas and thermal grids. The economic advantages of a thermal grid can be based on its energy analysis as it is described by Rezaie and Rosen [22].

This all means above that a thermal storage is absolutely suitable for peak shifting, resulting cost reduction and increasing efficiency of Photo Voltaic (PV) systems [23]. In special cases the legal regulations of a given country are inspiring the stakeholders to realize a smart micro-grid even if the electricity grid is not ‘smart’ in that relevant area but the Building Management System (BMS) can partly overtake its role.

In this paper an existing governmental higher-educational building is investigated from DSM purposes. The building itself has been renovated in 2002, insulation applied, windows changed that time. On the top of the building, a 22 kWp PV system is installed and has been producing electricity since 2015.

2 Dynamic control solutions for building management system

Finding solutions for DSM needs a better understanding of the thermodynamic behavior of the building and the existing BMS. The BMS in this case is a simple central HVAC control, which has been modified by a device, which sends fake temperature data from the tested room. The thermodynamic properties and the control method are described in the followings.

2.1 Heat capacity and heat resistance

A building, which has been built by traditional technologies from concrete, should have big heat capacitance. To utilize this well, the structure has to be heat-insulated as much as possible and other energy saving technologies has to be used (at least triple layer glazing, high air tightness, heat recovery ventilating system, etc.). This heat capacitance can be used for store energy inside the structure of the building, with keeping the user habits and comfort in focus. Therefore, it needs a smart or well programmed BMS, which takes the building’s heat capacity and the heat losses into consideration for the control of the temperature.

There are several techniques to determine the total resistance (RT) and capacitance (C) of a building. For new houses it is mandatory in Hungary to issue an Energy Certificate [24], according to decree 7/2006 TNM, which is based on MSZ EN ISO 6946. This certificate contains the conductive loss (or gain) [W/K] for the building, as in line ΣAU + Σ lΨ it is described, where A is the envelope area [m2]; U is the heat transfer coefficient [W m−2 K−1]; the l is conduction length [m] and Ψ is thermal conductivity [W m−1 K−1]. The heat capacity for the structure is not calculated in this section of the simplified method, but the specific heat and density values for all construction materials are given, thus solid volume and mass can be calculated easily. The air volume is also given in the certificate. The method could even contain energy analyses, also [25].

Other simulation technique is to build up a thermal resistance model (Fig. 2), to calculate the total resistance, and finally to summarize it [26].

Fig. 2.
Fig. 2.

An example for thermal resistance model of a building

Citation: Pollack Periodica 16, 2; 10.1556/606.2020.00207

The main building of the Faculty of Engineering and Information Technology, University of Pecs (Fig. 3) was chosen to calculate how much amount of energy can be stored in this building beside good optimization of energy use. A simplified thermal resistance model based in Fig. 2 was drawn and the heat storage capacity of the building was calculated. The total thermal resistance of the building is RT = 0.039 K/W and the capacitance is C = 1,140 kWh/K, was rough-calculated according to Table 1.

Fig. 3.
Fig. 3.

A photo and the simplified model of the building

Citation: Pollack Periodica 16, 2; 10.1556/606.2020.00207

Table 1.

Physical parameters of the building envelope

Density (kg·m−3)Specific heat (J·kg−1·K−1)Thermal conductivity (W·m−1·K−1)Volume in the building (m3)
Concrete2,2008801.22,121
Thermal isolation1001,3000.04540
Air1.121,0200.0227,092

The calculated thermal capacity is the same amount to 1,000 m3 water tank, which could be considered as investment benchmark of building envelope.

Using the building as cooling storage at 2 K range of temperature change in the structure is doubling the capacity, which has low influence in the thermal comfort. For better understanding of the thermal behavior, a LabView simulation has been completed. It shows that the time constant of the building is high enough; even 2 days can be considered (Fig. 4). As boundary condition 22 °C outside temperature was given, with an estimated 2 °C cooling success.

Fig. 4.
Fig. 4.

The simulated temperature change diagram (time in hours, temp. in °C)

Citation: Pollack Periodica 16, 2; 10.1556/606.2020.00207

Presenting heat properties of the building, better models can be applied than this simple RC circuit model, but this can simply be implemented in control systems. In this specific case a legal regulation made it necessary to start investigation if any surplus energy could be produced by the installed PV system.

The Hungarian Law about the electric energy [27] states, if a facility having its own electric transformer system (from middle to low voltage), they have to make the authorization of the planned PV system as it was a MW size power plant, which would practically around 20 times higher planning and licensing costs.

The solution in this case could have been an isolated system installation, which still can feed into the grid, but a smart control solution switches the PV system off the grid when positive balance occurs (at peak production). That would mean significant loss of potential green energy. A HVAC system is applied for better utilization of potential renewable energy.

Positive balance in energy production and demand can occur on weekends, only and especially in summertime when the building is not occupied (Fig. 5).

Fig. 5.
Fig. 5.

Production and consumption of the building on a typical weekend day, in middle of June

Citation: Pollack Periodica 16, 2; 10.1556/606.2020.00207

A photovoltaic systems’ telemetry data monitor connected to the grid-feeding inverters. LabView-based environmental monitoring system is connected by RS485 line to USS protocol. The monitoring system gives the switching sign when grid connected system must be turned into isolated system when grid feeding is higher than a pre-defined threshold value (currently the value is zero).

At this special case, student hostel building is hosting the 0.4 kV transformer, where 3 pcs of current switches were installed. A current direction sensor was placed (PQRM 5100 31 type, manufactured by DATCON) at the transformer which senses the current flow and then it switches the operation mode.

2.2 Building management system

In normal operation when the energy balance turns into grid feeding, the transmitter sends a sign through its High-Voltage (HV) isolated contact. This operates an Internet Protocol (IP) relay, and sends the sign into the structured IP network with high reliability. On the opposite side, the same hardware converts the sign back to a discrete sign and disconnects the AC side of the inverter from the grid, so the PV production stops immediately. When the protection sign is canceled, the inverter follows the normal protocol and after few minutes it turns back the system into production mode.

As it can be seen in Fig. 5, the surplus energy marked with brighter column can be utilized the way consumers generate heat or cooling energy to store in thermal storage of the building for later use.

A scheme of energy consumption timetable would be necessary to set up, which guarantees the PV system’s normal operation. When surplus energy is generated, the BMS involves switchable loads into the system, which are easily controllable (HVAC, heating, domestic hot water production, etc.) and this stored amount of energy types can be used up by residential later.

The solution uses inverters’ open channel USS protocol. When the energy production is approaching the energy use, the controller can decide whether to switch the system off the grid, or to use the surplus energy for cooling.

The aim was to develop a control and monitoring module. With the help of the described hardware the investigation of the consumption habits is feasible; the control of extra loads is manageable. The overall system efficiency can be optimized, connecting the system to BMS system.

3 Conclusion

In the described application success of integrating the PV system in the BMS means that the surplus energy will not be lost. This case the building uses energy for cooling and the amount of cooling will active until it is needed. It is also clarified, that control strategy increases the consumption, but the overall system reaches higher efficiency at the end. Due to the relatively small sized PV system, the positive balance occurs only few days a year and uses 5–15% of the heat capacity of the building in ratio.

Increasing the peak power of the PV production it would be necessary to reach 1–2 °C in cooling range, therefore the comfort values could be analyzed, besides other important parameters and processes. According to the heat resistance model based simulation, the building has a time-constant of 3.5 days by one degrees Kelvin. The useful part of it is 2 days, since the function is exponentially changing.

This kind of thermodynamic control could be a basic feature or the first step for smart buildings. The only thing we need here to integrate the thermodynamic resistance (R) and capacity (C) based function into the control system or the simpler thermostat control. The R and C values can be extracted from the building’s energy certificate, which is required in Europe for new buildings and at change of ownership or occupier. The prediction of demand and production is mandatory to maximize the savings; therefore the Authors’ previous work [28] in analytic prediction can be also connected, as well as methods based on Artificial Intelligence (AI).

Acknowledgements

István Háber has got PhD in renewable energy engineering; he is active in smart city topics, smart building researches. Istvan Szabo has got PhD in renewable energy generation system optimization. Gergely Bencsik was researcher at Energy Consulting Ltd and Basma Naili are PhD students and supported the work with programming the BMS system and helped in construction.

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  • [1]

    Energy independence and security act of 2007 . Public Low 110–140, Dec. 19, 2007 110th US Congress, 2007.

  • [2]

    DOE Global Energy Storage Database .Sandia National Renewable Laboratories, [Online]. Available: www.energystorageexchange.org. Accessed: Oct. 9, 2018.

    • Search Google Scholar
    • Export Citation
  • [3]

    S. Schoenung, “Energy storage system costs update”, Sandia National Energy Laboratories, Report, vol. 2730, pp. 130, 2011.

  • [4]

    J. Thakur and B. Chakraborty, “Smart net metering models for smart grids in India”, in International Conference on Renewable Energy Research and Applications, Palermo, Italy, Nov. 22–25, 2015, 2015, pp. 173177.

    • Search Google Scholar
    • Export Citation
  • [5]

    S. Sabihuddin, A. Kiprakis, and M. Mueller, “A numerical and graphical review of energy storage technologies”, Energies, vol. 8, no. 1, pp. 172216, 2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [6]

    H. Zsiborács, et al., “Electricity market challenges of photovoltaic and energy storage technologies in the European Union: Regulatory challenges and responses”, Appl. Sci., vol. 10, no. 4, pp. 14721498, 2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [7]

    W. Doorsamy and W. A. Cronje, “State estimation on stand-alone DC microgrids through distributed intelligence”, in International Conference on Renewable Energy Research and Applications, Palermo, Italy, Nov. 22–25, 2015, 2015, pp. 227231.

    • Search Google Scholar
    • Export Citation
  • [8]

    J. B. Ekanayake, N. Jenkins, K. Liyanage, J. Wu, and A. Yokoyama, Smart Grid Technology and Applications. Wiley, 2012.

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    R. Atia and M. Yamada, “Distributed renewable generation and storage systems sizing in deregulated energy markets”, in International Conference on Renewable Energy Research and Applications, Palermo, Italy, Nov. 22–25, 2015, 2015, pp. 258262.

    • Search Google Scholar
    • Export Citation
  • [10]

    K. A. Alfaitori, A. Khalil, and A. Asheibi, “Distributed control of photovoltaic-based microgrid”, in 4th International Conference on Renewable Energy Research and Applications, Palermo, Italy, Nov. 22–25, 2015, 2015, pp. 203208.

    • Search Google Scholar
    • Export Citation
  • [11]

    Y. Utsugi, S. Obara, Y. Ito, and M. Okada, “Planning of the optimal distribution of renewable energy in Hokkaido, Japan”, in International Conference on Renewable Energy Research and Applications, Palermo, Italy, Nov. 22–25, 2015, 2015, pp. 254257.

    • Search Google Scholar
    • Export Citation
  • [12]

    T. Sakagami, A. Werth, M. Tokoro, Y. Asai, D. Kawamoto, and H. Kitano, “Performance of a DC-based microgrid system in Okinawa”, in International Conference on Renewable Energy Research and Applications, Palermo, Italy, Nov. 22–25, 2015, 2015, pp. 311316.

    • Search Google Scholar
    • Export Citation
  • [13]

    S. N. Palacio, K. J. Kircher, and K. M. Zhang, “On the feasibility of providing power system spinning reserves from thermal storage”, Energy and Build., vol. 104, pp. 131138, 2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [14]

    A. Arteconi, et al., “Thermal energy storage coupled with PV panels for demand side management of industrial building loads”, Appl. Energ., vol. 185, part 2, pp. 19841993, 2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [15]

    I. Haber, I. Kistelegdi, and I. Farkas, “Investigation of the solar- and wind energy usage of a positive energy factory building”, Tech. Gaz., vol. 21, no. 6, pp. 12431248, 2014.

    • Search Google Scholar
    • Export Citation
  • [16]

    G. Reynders, T. Nuytten, and D. Saelens, “Potential of structural thermal mass for demand side management in dwellings”, Building and Environ., vol. 64, pp. 187199, 2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [17]

    H. Wolisz, A. Constantin, R. Streblow, and D. Müller, “Performance assessment for heat distribution systems for sensible heat storage in building thermal mass”, in Proceedings of Cleantech for Smart Cities & Buildings, Lausanne, Switzerland, Sep. 6, 2013, vol. 2, 2013, pp. 945950.

    • Search Google Scholar
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Senior editors

Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

Scientific Secretary

Miklós M. Iványi

Editorial Board

  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
  • Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
  • Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
  • Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
  • Ján Bujňák (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
  • Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
  • Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
  • Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
  • Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
  • Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
  • Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
  • Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
  • Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Rita Kiss  (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
  • István Kistelegdi  (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
  • László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu 

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

  • SCOPUS

 

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,257
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
340/243=1,4
Scopus
Cite Score Rank
Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP
1,09
Scopus
Cites
321
Scopus
Documents
67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate
48%

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,262
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
269/220=1,2
Scopus
Cite Score Rank
Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP
0,933
Scopus
Cites
290
Scopus
Documents
68
Acceptance
Rate
67%

 

Pollack Periodica
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Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Publication
Programme
2021 Volume 16
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

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