Saturday, April 22, 2017

Master Thesis topics

Maximum power point tracking (MPPT) method for efficient Photovoltaic systems at high-altitudes

Maximum power point tracking, photovoltaic, high-altitudes

At high-altitudes, the availability of full solar radiation allows to form an efficienct PV system as compared to ground mounted PV systems. The output power of PV module highly depends on three important parameters, i.e. temperature, irradiation and latitude [1]. However, these parameters impede the diffusion of solar radiation in countries with cold climatic conditions. So an optimum solution to this problem is to employ photovoltaic system at high altitudes.
For building an efficient Photovoltaic system in cold climatic conditions, the maximum power point tracking is an important aspect to be exploited [2].  The aim of this thesis is to develop power point tracking algorithm that can ensure high efficiency of the Photovoltaic system at high altitudes. 
Master thesis objectives:
  • to investigate state-of-the-art methods of tracking algorithms that has been employed to Photovoltaic systems
  • to analyse the parameters affecting maximum power point tracking in Photovoltaic systems
  • to develop a modified tracking algorithm for achieving high efficiency from Photovoltaic systems
  • to test and validate the modified MPPT algorithm on the PV system

Students should have a bachelor degree in Information and Communication Technology or a similar degree with an academic level equivalent to the bachelor degree in Engineering.


  1. G.S. Aglietti, S. Redi, A.R. Tatnall, T. Markvart (2009) Harnessing high altitude solar power, IEEE Transactions on Energy Conversion, vol.24, no.2, pp. 442-451.
  2. L.G. Antonio,M.B. Saldivar Marquez, O.P. Rodriguez (2016) Maximum power point tracking techniques in photovoltaic systems: A brief review, 13th International Conference on Power Electronics (CEIP), pp. 317-322.

Contact Details:  Ekanki SHARMA : 

To build an algorithm for measuring and storing the data in LEGO robot

At high altitudes the Photovoltaic system has the ability to work efficiently as compared to the ground-mounted Photovoltaic system. To test the implemented PV system, it is necessary to measure and store the data for further analysis. However it is a tedious job due to the unsuitable weather conditions.
The goal of this thesis is to build an algorithm to measure and store data in LEGO robot.

Master thesis objectives:
  • to investigate the requirements of LEGO robot for efficient measurement and storage of data
  • to model or program the robot to implement the functions mentioned above
  • to test and validate the performance of robot on site

Students should have a bachelor degree in Information and Communication Technology or a similar degree with an academic level equivalent to the bachelor degree in Engineering.

Contact Details:  Ekanki SHARMA : 

Friday, January 6, 2017

Comparison of the Open Source Smart Grid Simulation Tools RAPSim and GridLAB-D

Renewable Alternative Powersystems Simulation
When you work on smart grid simulations with renewable energy, you most likely have heard about GridLAB-D which is an open source simulator allowing you to combine renewable energy sources with power flow problems in smart grid networks.
The other tool, RAPSim, comes with a similar goal, however was built based on a different design philosophie. While GridLAB-D comes with a textfile-based configuration of your simulation scenario and is run from command line, RAPSim comes with a graphical user interface allowing to generate your scenarios in a Sim City-like interface.
Midhat Jdeed has compared the two tools in his thesis by defining a number of very simple case studies which have been modeled in both tools.
Running GridLAB-D
In the end, he came to the following findings and conclusions: As expected, GridLAB-D comes with a large user base and a a comprehensive library of models. It was more surprising to find out that making a simple model were sometimes more difficult in GridLAB-D. For example, a household with constant load and a PV system could not be well described with the standard elements, because there is no household with a constant load. This makes sense from a realism point of view, but made it difficult to reproduce the same results in different simulators.
As a conclusion, both tools have their merits, be it for the researcher who wants to run simulations online on a simulation server or a teacher that wants to give some students a quick hands on experience with modeling a microgrid with a renewable energy source.

M. Jdeed. Comparison of the Smart Grid Simulation Tools RAPSim and GridLAB-D. Master Thesis, Alpen-Adria-Universität Klagenfurt, November 2016.

D. P. Chassin, J. C. Fuller, and N. Djilali. GridLAB-D: An Agent-Based Simulation Framework for Smart Grids. Journal of Applied Mathematics, vol. 2014, Article ID 492320, 12 pages, 2014. doi:10.1155/2014/492320

M. Pöchacker and W. Elmenreich. Model implementation for the extendable open source power system simulator RAPSim. In Proceedings of the 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES'15), pages 103–108, Ancona, Italy, October 2015.

Friday, July 22, 2016

PhD Position (Research and Teaching Assistant) in Smart Grids

The Smart Grids Group at the Institute of Networked and Embedded Systems of Alpen-Adria-Universität Klagenfurt has an opening for a research and teaching staff member position (“Universitätsassistent/in”). The start of employment is soonest with a contract duration of four years.

Your work will comprise:
  • Research in the field of energy informatics with emphasis on state-of-the-art methods for smart microgrids
  • Independent research with the aim to submit a dissertation
  • Teaching in the field of electrical and computer engineering (e.g. “Smart Grid Lab” and “Circuit Systems”)
  • Participation in administrative and organizational tasks of the Institute
  • Student mentoring
  • Assistance in public relations activities of institute and faculty
The Smart Grids Group led by Professor Wilfried Elmenreich works on the design, modeling, and analysis of solutions for future energy applications. Big challenges like the transition of our energy system to a smart, low-carbon emitting approach require an interdisciplinary research approach. The group, composed of engineers, computer scientists, and physicists is engaged in research on self-organizing systems, complex systems, communication networks and computational intelligence for this purpose.

Our team is very international and dedicated to quality research and teaching. The offices and laboratories are located in the well-equipped Lakeside Science & Technology Park. Working language is English. The Institute cooperates with national and international partners in research and industry. It is part of the research cluster Lakeside Labs (self-organizing networked systems) and the European Erasmus-Mundus Doctoral College (interactive and cognitive systems).

Required Qualifications:
  • A university degree (Master or Diplom-Ingenieur) in the field of electrical engineering, computer engineering, communications engineering, power engineering, or physics graded with “good” or better
  • Fluent in written and spoken English
  • Experience in two or more of the following fields: energy informatics, power engineering, communication protocols, artificial intelligence, renewable energy generation, complex systems
  • Good programing skills in C, Java or Python
Additional Qualifications
  • Good social and communicative competences
  • Basic knowledge of German
  • First relevant scientific publications
  • Relevant international experience
This position serves to enhance the expertise and scientific education of graduates of a Master or Diploma study program and aims at completing a PhD in Technical Sciences. Applications of people who already have a respective PhD cannot be considered.
The university strives at raising the number of female scientific staff members and therefore specifically invites women to apply. In case of equal qualifications, women will receive priority consideration.

Handicapped persons or persons with chronic illnesses who comply with the qualification criteria mentioned are specifically invited to apply.

The annual gross salary is € 2.022,40 (30 hours weekly according to Uni-KV: B1). It is intended to complement this with a 10 hours project contract depending on availability of funds.

Your application has to be uploaded before August 10 with the Human Resource Department at the University of Klagenfurt, reference 448/16. Please take the time to complete the online application form carefully, providing as much detail as possible. Once you have completed the form, please upload your application documents (cover letter, curriculum vitae, written documents, certificates, official records).

Further information can be obtained from Professor Wilfried Elmenreich (Tel.: +43-463-2700-3649).

Travel expenses in connection with the application procedure cannot be reimbursed.
The call for applicants is conformable to § 107 Abs. 1 Universitätsgesetz 2002. The official announcement in German language can be found in the Mitteilungsblatt 23_2015_2016.

Saturday, November 21, 2015

New Version of Renewables Alternativ Powersystems Simulation (RAPSim) Supports Your Own Model Implementations

We are proud to annouce a new release of our Renewables Alternativ Powersystems Simulation (RAPSim) software. The current version 0.92 is now available on the sourceforge page of RAPsim.

It has been some time since the RAPSim software had been presented at  the IEEE Innovative Smart Grid Technologies Asia and also announced in this blog.

Renewable Alternative Powersystems Simulation
Since then we improved the graphical user interface to allow a smooth interactoin between user and simulation system and improved the software structure to allow for an easy extension of the simulator with your own models. At the 12th Workshop on Intelligent Solutions in Embedded Systems we presented this feature in detail. The main steps are:
  1. Select the correct abstract class and define general attributes. A structure of abstract models is provided to handle all the interaction with the objects and the other simulation parts. The user has to name, describe the model and add an appropriate icon for the model. 
  2. Model parameters must be defined for being available in the GUI. The provided data type deals also with complex numbers. Dependent on the type of implemented model the user can define which variables are editable and visiable. This is also the place to set initial values. 
  3. Define the update procedure. This is the main part of the model implementation, meaning the mathematical part is done here. This includes also the possible allignment of data from an external source, like a file, with the simulation time. 
For details about model implementation please see the paper on this topic which was presented at the WISES 2015:

M. Pöchacker and W. Elmenreich. Model implementation for the extendable open source power system simulator RAPSim. In Proceedings of the 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES'15), pages 103–108, Ancona, Italy, October 2015.

and the RAPSim introduction paper:

M. Pöchacker, T. Khatib, and W. Elmenreich. The microgrid simulation tool RAPSim: Description and case study. In Proceedings of the IEEE Innovative Smart Grid Technologies Asia (ISGT-ASIA'14), Kuala Lumpur, Malaysia, 2014. IEEE.

Sunday, November 15, 2015

Photovoltaics Energy Payback Time

Photovoltaic systems are great in producing clean energy without CO2 emissions. A question that remains however, is the amount of energy invested into production and transport of the materials, cells and panels. To answer this question, we had a look into the annual Photovoltaics Report of the Fraunhofer Society (German: Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V.).

To give a short summary: The energy payback time for current modules is around one to two years. Considering a lifetime of 20 years, this means that photovoltaic systems are quite effective in providing clean energy. The energy payback time depends mainly on three parameters: (i) the  material usage for the system, (ii) the effiency of the cells and panel, and (iii) the irradiation striking onto the panel. Regarding the first aspect, the report shows that material usage for silicon cells went down by a factor of 2.5 over the last ten years due to increased
efficiencies and thinner wafers. Efficiency is improving slower, currently the best multicrystalline modules provide an efficiency of 18.5%, panels with monocrystalline cells a 22.9% and upcoming thin film technologies have a range between 10.9% and 17.5%.

The largest influence for the energy payback time currently is the place where you put your module: In regions with an annual irradiation of around 1000 kWh/m2 - this is basically the value for PV panels installed in Germany, they energy payback time is 2 years, while in sunny areas, the annual irradiation can be double or more, leading to an energy payback time of around 1 year.

Energy payback time for typical PV systems in different regions of Europe

Sunday, October 4, 2015

Call for Papers on Optimization of Photovoltaic Power Systems

The Hindawi Jounral of Engineering is publishing a special issue on optimization of photovoltaic power systems. Journal of Engineering is a peer-reviewed, open access journal that publishes original research articles as well as review articles in several areas of engineering.

PV generation system is one of the most popular uses of direct solar energy and its installation is rapidly growing because it is considered as a clean and environmentally friendly source of energy. The primary obstacle to increased use of PV systems is their high initial cost. Currently, many research works are carried out focusing on optimization of PV systems in order to reduce the capital cost of the PV system without affecting its reliability. The optimization of a PV system means that the system parameters such as number of PV modules, capacity of storage battery, capacity of inverter, and PV array tilt angle must be selected optimally. In addition, diesel generator and wind turbine capacities must be optimized in case of hybrid PV systems. Moreover, the optimization term includes the electronic features that maximize the yield of these systems such as sun trackers, MPPT, and smart inverters.

This special issue aims to discuss the recent developed and contribution of photovoltaic system optimization science.

Potential topics include, but are not limited to:
  • Modeling and characterization of photovoltaic systems
  • Optimal sizing and installation of standalone photovoltaic system
  • Optimal sizing of hybrid photovoltaic systems
  • Optimal sizing of grid connected photovoltaic systems
  • Optimal placement and management of photovoltaic systems in power system
  • Power electronics for photovoltaic system
  • Sun trackers
  • MPPTs
  • Solar inverters
  • Solar chargers
  • Photovoltaic field performance assessment
  • Modeling of solar radiation

Authors can submit their manuscripts via the Manuscript Tracking System
Manuscript Due:    Friday, 29 January 2016
First Round of Reviews:    Friday, 22 April 2016
Publication Date:    Friday, 17 June 2016

Lead Guest Editor

Tamer Khatib, An-Najah National University, Nablus, State of Palestine

Guest Editors

Wilfried Elmenreich, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
Azah Mohamed, National University of Malaysia, Bangi, Malaysia
Suvash Saha, Queensland University of Technology, Brisbane, Australia
Hussein Kazem, Sohar University, Sohar, Oman

Monday, September 28, 2015

Interoperability Between Smart and Legacy Devices in Energy Management Systems

Energy management systems can help to decrease energy consumption by giving user feedback or by directly controlling devices. Smart appliances create a network of devices that can be addressed and controlled via a defined network interface. However, legacy devices will establish a significant portion of a system’s power consumption and, therefore, need to be included into the management system. We propose an open architecture to integrate smart and non-smart devices by using smart plugs and non-intrusive load monitoring methods. Devices are connected either as (i) smart appliances via a fieldbus or wireless network, (ii) legacy devices connected to a smart plug, or (iii) other legacy devices being detected from a time sequence of power consumption values, which are disaggregated into the power draws of different devices. At a service layer, device properties are presented in a unified way including a machine-readable description of their features and properties. The data layer provides an abstract representation of data and functionalities. It connects to the application layer where different applications can access the data. The system supports mechanism for service discovery, service coordination, and service and resource description.

D. Egarter, A. Monacchi, T. Khatib, and W. Elmenreich. Integration of legacy appliances into home energy management systems. Journal of Ambient Intelligence and Humanized Computing, 2015.

My talk at Workshop Energieinformatik 45. GI-Jahrestagung "Informatik, Energie und Umwelt":