A smart grid is a modernized electric power grid that uses information and communication technologies (ICT) to improve the reliability, security, sustainability, and efficiency of an electric power system. One key characteristic of a smart grid is to provide two-way information exchange via communication technology. This allows building applications that link producers and consumers for a better planning of resources, new comfort features, and for integrating renewable energy sources into the grid.
To realize power grids with a high number of renewable energy resources, a number of research problems need to be solved. Out of all, balancing production and consumption in such a power grid is a major issue, as renewable energy resources are intermittent in nature- their output heavily depends on weather conditions.With increased focus on this integration of renewable energy resources, a framework of smart microgrids is a viable approach to grid modernization. A smart microgrid can be described as a decentralized grouping of electricity generation, energy storage, and loads. Usually, the generation and loads in a microgrid are connected at low-voltage (LV) level and medium-voltage (MV) level.
Professor Elmenreich and his team work on different areas of Smart Microgrids which include:
- Simulation of smart microgrids
- Prediction models for photovoltaic systems
Due to the unpredictable nature of photovoltaics and wind power and their dependence on meteorological conditions, it is important to model and simulate these sources in power system to study their impact on power flow and the quality of the power system.
Renewable Alternative Power System Simulator- RAPSim
Professor Elmenreich and his team developed a Renewable Alternative Power System Simulator- RAPSim. "RAPSim is a free and open-source software, microgrid simulation framework to better understand the power flowing behavior in smart microgrids with renewable sources and load demands. It is able to simulate grid-connected or standalone microgrids with solar, wind or other renewable energy sources," explains researcher Manfred Rabl-Pöchacker.
In order to analyze the impact of added renewable energy sources on the microgrid variables, power flow analysis is important. The proposed software allows to define the power generated or consumed by each source in the microgrid and then provides a power flow analysis. RAPSim is designed for application in science and classroom with a simple to use graphical interface. It is an easily extendable framework that supports users in implementing their own models, in particular, grid objects such as new types of producers or consumers and algorithms for grid control.
RAPSim Video
Prediction Models for Photovoltaics
Photovoltaic (PV) systems have received a lot of attention due to their ecological property of efficiently converting the usable solar power into electricity. “Forecasting the PV output power of smart microgrid is essential for an efficient use of an electricity grid.,“ states Ekanki Sharma. She is currently working on different aspects of solar power forecasting techniques.
The team lead by Elmenreich has investigated how computational methods and principles can assist in planning smart microgrids. “In a recent case study, we trained a neural network with sensor data as well as with energy production data of renewable energy plants. The results indicate that neural networks are able to forecast the production of photovoltaic and wind power plants,“ reports Professor Elmenreich.
The group also contributed towards machine learning applications, in particular for modeling solar radiation. They presented the possibility of using an Artificial Neural Network (ANN) to grasp the correlation of input parameters and predict global and diffuse solar radiation. Experiments revealed that applying ANN can achieve higher accuracy than existing models.
Selected Publications
M. Pöchacker, T. Khatib, and W. Elmenreich. The microgrid simulation tool RAPSim: Description and case study. In Proc. IEEE Innovative Smart Grid Technologies Asia, 2014.
T. Khatib, and W. Elmenreich. A model for hourly solar radiation data generation from daily solar radiation data using a generalized regression artificial neural network. International Journal of Photoenergy, 2015.
T. Khatib, and W. Elmenreich. Novel simplified hourly energy flow models for photovoltaic power systems. Energy Conversion and Management, 2014.
Modeling of Photovoltaic Systems Using MATLAB: Simplified Green Codes
When investigating the field of photovoltaic systems as a student, one faces the situation that, despite a high number of publications on the topic, it is hard to find out how to start with your own experiments and simulations. To address this issue, the book Modeling of Photovoltaic Systems Using MATLAB: Simplified Green Codes (Wiley, 2016) describes models of photovoltaic systems interleaved with short MATLAB programs showing how to set up a simulation based on the content of the current chapter. The chapters of the book cover different systems including photovoltaic energy sources, storage, and power electronic devices.
The book resulted from the collaboration of Tamer Khatib and Wilfried Elmenreich at the NES Institute in the years 2013 to 2015.
Book Preview
When investigating the field of photovoltaic systems as a student, one faces the situation that, despite a high number of publications on the topic, it is hard to find out how to start with your own experiments and simulations. To address this issue, the book Modeling of Photovoltaic Systems Using MATLAB: Simplified Green Codes (Wiley, 2016) describes models of photovoltaic systems interleaved with short MATLAB programs showing how to set up a simulation based on the content of the current chapter. The chapters of the book cover different systems including photovoltaic energy sources, storage, and power electronic devices.
The book resulted from the collaboration of Tamer Khatib and Wilfried Elmenreich at the NES Institute in the years 2013 to 2015.
Smart Microgrid Lab
With the roll-out of smart grid technology, the importance of metering, communication, and distribution has risen rapidly. To give students a hands-on experience on research activities going on in the domain of smart grids, the Smart Microgrid Lab was established. The lab is part of the Lakeside Labs GmbH and is operated in cooperation with the Smart Grids Group at Alpen-Adria-Universität Klagenfurt.
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| Figure: Lucas-Nülle Advanced Photovoltaics Trainer |
Experimental setups include the combination of simulation scenarios with actual hardware in the loop. This way, students, and researchers can perform experiments and test modeling an entire power grid from power generation to power consumption. A special focus is given to the integration of photovoltaics so that students can design their own microgrid.
The lab is connected to a Photovoltaic (PV) plant installed on the rooftop of the building with a capacity of 4.8kWp, which compares to a PV system for typical single-family houses. Several test loads with common household appliances are available in the lab to perform experiments in this context.
Usually, this system is connected to the grid, but in the case of a power outage, the lab can sustain itself in an off-grid (island) mode. “If everything goes dark, students can still continue learning in our lab,” Elmenreich adds with a wink.
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