Saturday, July 29, 2017

Smart Microgrids and Renewable Energies in Smart Grids group AAU

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. 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

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.

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. 

Friday, July 21, 2017

Energy Informatics in Klagenfurt at a Glance

In the present day, the electric power grid faces an evolutionary step towards the smart grid. The smart grid is defined as the enhancement of the electric power grid with information and communication technology. This sort of digitisation will enable a bidirectional flow of energy and information within the power grid and provide several novel applications and allow to unlock the full potential of renewable energy technologies. To cope with the challenge of digitisation in power grids, key elements of future energy systems have to be explored and furthermore, computational methods have to be developed and refined.

The Smart Grids group, located at the University of Klagenfurt, contributes to this challenge by investigating how power meter readings can be analysed to discover solutions to sustainably increase the energy efficiency of energy systems. 

"We carried out a measurement campaign in eight selected households to track power consumption of individual electrical appliances for over one year. The main outcome was the GREEND dataset, which was analysed to gain further insights into energy consumption behaviour", says Andrea Monacchi, the coordinator of the campaign. 

Figure: Overview of the households and appliances

On basis of such data, policies can be formulated to improve energy efficiency by shedding of standby losses, postponement to off-peak periods, replacement of inefficient appliances, and operation curtailment. The networked power meter (smart meter) represents the key element in the transition towards the smart grid since such measurement equipment provides feedback to the users, other appliances, and the electric utility. 

"The smart meter is a vital tool for researchers to record the energy consumption of households and industrial buildings. On basis of the collected data, computational methods and effectivity of efficiency measures are evaluated", states Christoph Klemenjak. "We developed an open-hardware smart metering board called YoMo, which is an extension unit for the Arduino platform. The YoMo is designed to monitor current flow and voltage level, as well as active, reactive, and apparent power at the feed point of households."

Figure: The YoMo smart metering board

In general, smart meters serve several purposes such as billing of consumed energy, providing immediate feedback to the users, or switching loads. Smart metering and fine-grained energy data are one of the major enablers for the future smart grid and an improved energy efficiency in smart homes. On the one hand these fine-grained measurements will lead to improved energy consumption habits, on the other hand the fine-grained data produces many questions with respect to privacy issues. To ensure both, household privacy and smart meter information, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. 

"The load hiding technique we developed works with devices that are already in the system. By controlling these household appliances in a certain way, your power profile becomes scrambled", Professor Elmenreich explains. 

Selected Publications 

C. Klemenjak, D. Egarter, and W. Elmenreich. YOMO-The Arduino based smart metering board. Computer Science - Research and Development (Springer), 2016. 

M. Pöchacker, D. Egarter, and W. Elmenreich. Proficiency of power values for load disaggregation. IEEE Transactions on Instrumentation and Measurement, 2016. 

D. Egarter, V. P. Bhuvana, and W. Elmenreich. PALDi: Online Load Disaggregation based on Particle Filtering. IEEE Transactions on Instrumentations and Measurement, 2015. 

D. Egarter, C. Prokop, and W. Elmenreich. Load Hiding of Household's Power Demand. IEEE International Conference on Smart Grid Communications, 2014. 

A. Monacchi, D. Egarter, W. Elmenreich, S. D'Alessandro, and A. M. Tonello. GREEND: An energy consumption dataset of households in Italy and Austria. In Proc. IEEE International Conference on Smart Grid Communications, 2014. 

The Energy Informatics Lab

Data will be one of the most important resources in the future. Among other timely research questions, activities in energy informatics explore how data provided by advanced metering infrastructure (AMI) can be utilised in the most adequate and efficient way. In order to record energy data, the Energy Informatics Lab provides a wide range of measurement instruments such as smart meters, oscilloscopes, and self-designed power meters. Furthermore, modern computer infrastructure allows to evaluate novel computational methods for prediction and user feedback for future energy systems.

Figure: A small selection of the present measurement equipment

The Energy Informatics lab also integrates a well-equipped soldering work station and a 3D printer, which allows the researchers to craft prototypes and custom enclosures for all kinds of measurement equipment. “Previously our students often had problems apply the theoretic concepts they learned. The Energy Informatics Lab offers the possibility to try out things in practice, which gives them a different perspective,” states Professor Wilfried Elmenreich.

Figure: The present 3D printer allows rapid prototyping

More detailed information can be found in our research blogs Energy InformaticsThe Smart Grid, and on the institute's web page Networked and Embedded Systems.

Tuesday, July 18, 2017

Update on Thesis Topics

Deep Neural Networks for Appliance Detection

The objective of this thesis will be to explore the applicability of deep neural networks as appliance detectors. The student will be provided with training data, which includes the energy consumption of typical household appliances over the time span of one year. By using the data, the student will first label the data, train the DNNs and evaluate their performance. Depending on the thesis type, the student will have to compare the performance to another appliance detector. The selected approach will be implemented in Python or C++.

Type: Scalable to all thesis types

Supervised learning techniques for Energy Advisors

Energy Advisors such as Mjölnir provide valuable feedback to the user. The feedback builds on gathered knowledge and observations of the energy consumption in households. The objective of this thesis will be:
  • Review applicable supervised machine learning techniques and discuss their application in Energy Advisor tools
  • Implement the most preferable technique and embed it into the Mjölnir framework
  • Evaluate the performance by means of a case study

Type: Scalable to all thesis types

Forecast of energy consumption in the residential sector

The energy consumption of users can be seen as an aspect of human behaviour. Without a doubt, this behaviour is influenced by weather conditions. When trained with smart meter readings, neural networks can be applied to predict the energy consumption of households. The question is, if weather forecast can serve as adequate training data to successfully predict the energy consumption of households. The scope of this thesis will be to explore this question.

Type: Scalable to all thesis types

Energy advisor platform

Mjölnir is an open-source energy advisor platform, where different energy consumption feedback mechanisms can be implemented and assessed. The advisor platform analyses and processes energy consumption data, which was collected by measurement hardware such as smart meters or power meters.
The objective of this thesis will be to enhance the advisor platform (adding new widgets) and to explore new feedback mechanisms. The student(s) should be familiar with HTML, PHP, JavaScript or Python.

For more information about the advisor see: and

Type: Bachelor/RP/Master

Load Disaggregation: WebApplication 

Nonintrusive Load Monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. The aim of this thesis is to build up a website which can import the measured power consumption of users, disaggregate the used power profiles of appliances, share power profiles and calculate the power costs for each appliance. We are looking for a student which is fit with HTML5 and object-oriented programming to implement on the one hand the homepage and on the other hand the load disaggregation algorithm.

Further Literature

Type: Bachelor/RP/Master

YaY - An Open-Hardware Energy Measurement System for Feedback and Appliance Detection based on the Arduino Platform

"To analyse user behaviour and energy consumption data in contemporary and future households, we need to monitor electrical appliance features as well as ambient appliance features. For this purpose, a distributed measurement system is required, which measures the entire power consumption of the household, the power consumption of selected household appliances, and the effect of these appliances on their environment.
In this paper we present a distributed measurement system that records and monitors electrical household appliances. Our low-cost measurement system integrates the YaY smart meter, a set of smart plugs, and several networked ambient sensors.
In conjunction with energy advisor tools the presented measurement system provides an efficient low-cost alternative to commercial energy monitoring systems by surpassing them with machine learning techniques, appliance identification methods, and applications based on load disaggregation."

Christoph Klemenjak presenting @ HSU

Christoph Klemenjak and Wilfried Elmenreich. YaY - An Open-Hardware Energy Measurement System for Feedback and Appliance Detection based on the Arduino PlatformIn Proceedings of the 13th International Workshop on Intelligent Solutions in Embedded Systems, Hamburg, Germany, June 2017.

The workshop was held at the Helmut Schmidt University of the Federal Armed Forces in Hamburg. Amongst other interesting talks our paper was presented in the first session of the workshop. The subsequent Q&A session provided helpful insights from fellow researchers.