The Smart Grid Need

Touching video about the transformation of our energy system into a smart grid:

 "All over the world countries are pursuing grid modernization for the benefits it provides to their environment, their economy and their energy security."

"By modernizing the grid we can squeeze much more power out of the system than we already have and that means saving tens of billions of dollars in power plants and power lines that we do not have to build."

Simulating the Smart Grid

In our PowerTech2013 paper Simulating the Smart Grid we propose a meta-model for a complete view onto the Smart Grid.

Power systems face increased complexitiy because of developments leading to more interdependencies between the power system components. For example:

• The progress of ICT in the last decades allows new applications in the field of energy.
• With increasing energy production in small distributed and private plants, the power flow direction is no longer unidirectional, from power plants to endusers. The former consumers becomes a prosumers.
• Increased use of renewable energies makes the energy production less schedulable. Loads need to be stored or shifted in time. A high need for efficient storage possibilites is generated.
• The energy market has been liberalized what gives rise to new energy related products and services.

The Smart Grid as Agents operating on several layers of complex flow networks.

 Power systems, as we knew them, are geting smarter by use of ICT. Liberalization of power market is expected to increase efficiency and energy product variety. New developments enable new perspectives which further drive new developments - it is hard, sometimes impossible, to distinguish between the drivers and the outcomes of this process.

The make this highly complex system more comprehensive, we propose to view it as agents operating on different flow networks. The agents optimize their flow according their individual utility function. The rules for the different types of flow are resulting from the subsystem design.

The model is generic but as flows are a measurable quantities it is suitable for quantitative extensions.

M. Pöchacker, A. Sobe, W. Elmenreich: Simulating the Smart Grid, IEEE PowerTech2013, Grenoble, June, 2013. 

EvoENERGY - Evolutionary Algorithms in Energy Applications @EvoStar 2013, Vienna

EvoStar comprises several co-located conferences on the topic of evolutionary computing. The track of EvoENERGY contained five paper presentations of interesting ideas for the Smart Grid.

Ana Soares from the University of Coimbra presented her work on "Domestic Load Scheduling Using Genetic Algorithms" where a Genetic Algorithm is used to optimize for an objective function considering energy consumption, end user preferences, peak power, and presently available energy. Encoding of solutions was done as string of integers where the recombination was done by a bit mask over the integer string (so no typical crossover). The evolved results define scheduling of loads from household appliances in order to fulfill the above defined objectives.

Stephan Hutterer from FH Hagenberg approached the optimal power flow problem with an evolutionary algorithm. Optimal control policies are learned offline for a given power grid resulting in general abstract rules for optimal power flow.

"Prediction is difficult, especially of the future" (Nils Bohr) - the prediction of power load profiles can be improved with the approach presented by Frédéric Krüger from the Université de Strasbourg. They show how a genetic algorithm generated with the EAsy Specification of Evolutionary Algorithms (EASEA) language can be applied to solve a noisy blind source separation problem and create accurate power load profiles using real world data.

Another approach for forecasting electrical consumption was presented by Martina Friese and Oliver Flasch from FH Köln in his talk on "Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data". They apply state-of-the-art time-series forecasting methods to electrical energy consumption data recorded by smart meters and show that genetic programming is an attractive alternative to custom-built approaches for electrical energy consumption forecasting.

Dominik Egarter from Alpen-Adria-Universität Klagenfurt presented the paper "Evolving Non-Intrusive Load Monitoring" [PDF]. Here, an evolutionary algorithm is used to determine a set of devices for a given load curve - in other words, your smart meter knows what devices you have on even if they are not smart. The work on evolving non-intrusive load monitoring shows the capabilities of the approach but also its limits. The latter basically tell you how much you have to masquerade your power profile so that it does not give away information about the devices that constituted it. See also this blog article on Dominik's work.

Open-Source Energy Monitoring Hardware

OpenEnergyMonitor hardware: raspberry pi, emonTX
 and emonGLCD

In our smart microgrid laboratory at the Alpen-Adria-Universität Klagenfurt we need to be able to measure energy flows at different places in the network. Our criteria had been to be able to measure power, current, and voltage with adjustable measurement time intervals. The meters should be networked wirelessly with a visualisation possibility via an embedded device or a web page. In order to implement appropriate measurement strategies (for example measuring with a time-triggered architecture) the system should be fully programmable, in other words open-source. 

emonCMS web-app visualization tool

To meet this requirements we decided to use the OpenEnergyMonitor. It provides a metering board emonTx, which is based on Arduino and communicates to some base station. This can be either a own-built base station emonBase from OpenEnergyMonitor or the nowadays trendy Raspberry Pi. All necessary software is provided and easy to use. The OpenEnergyMonitor also provides an energy visualisation tool called emonCMS, which can be installed on the Raspberry Pi. It can be used for processing, logging and visualizing energy. Like the other software also the emonCMS is open source.

Links:

• Raspberry Pi, a credit-card-sized embedded computer running Linux
• OpenEnergyMonitor, open-source energy monitoring tools
• A. Monacchi, D. Egarter, W. Elmenreich. Integrating Households into the Smart Grid. Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, Berkeley, CA, May 2013.
• W. Elmenreich, G. Bauer, and H. Kopetz. The time-triggered paradigm. In Proceedings of the Workshop on Time-Triggered and Real-Time Communication Systems, 2003.

How do you use your electrictiy?

MONERGY

The MONERGY project aims to develop innovations that contribute to a more efficient energy consumption in households. We look specifically at the situation in the regions of Carinthia and Friuli-Venezia Giulia.

To guide our research efforts, we need your help in the form of a short survey (approximately 10 minutes) on the use of electrical appliances at home:

• Survey: Energieverbrauch (in German language)
• Survey: Consumo di energia (in Italian language)

Since we especially aim at the situation in Carinthia and Friuli-Venezia Giulia, the survey is only available in Italian and German. If you don't live in that region, your contribution is still helpful and appreciated, but there is no English version, sorry.

The results and conclusions will be published later on via the MONERGY website (in German/Italian) and in this blog (in English language).

Looking forward to your participation in the survey!

Energy Aware Software-Engineering and Development (EASED@BUIS)

2. Workshop on Energy Aware Software-Engineering and Development (EASED@BUIS)

25.04.2013

Oldenburg (Oldb.)
Kulturzentrum PFL
Peterstraße 3

The EASED workshop will be held in conjunction with the 5th BUIS-Days: IT-based resource and energy management.
Utilization of mobile and embedded devices, and thus their induced energy consumption, is constantly increasing. Reducing the energy consumption of such devices will not only improve the carbon footprint of contemporary mobile IT usage, but will also extend the device lifetime, improve user acceptance and reduce operational costs.
Next to serious and ongoing efforts in hardware design and on operating system level, software engineering techniques will also contribute to optimize energy consumption by improving software design and software quality. The EASED@BUIS workshop, which follows up the Workshop on Developing Energy Aware Software Systems (EEbS 2012), held at the annual GI Conference in September 2012, provides a broad forum for researchers and practitioners to discuss ongoing works, latest results, and common topics of interest regarding the improvement of software induced energy consumption.
Intensive discussions at the first workshop identified a major challenge in optimizing energy efficiency, which is to precisely measure energy consumption of software regarding user behavior.
Thus, the follow workshop EASED@BUIS will focus on the following topics:

• approaches and techniques to estimate or measure the energy consumption of software components,
• approaches to define standardized usage scenarios of applications on mobile devices to provide repeatable measurement of energy consumption in concrete application settings,
• approaches to model the energy consumption of software components, and
• experiences on measuring and improving the energy consumption of software components. 

Well elaborated and standardized measurement means will provide an important foundation to detecting sources of wasting energy caused by software systems and will enable validation means to verify energy savings by software improvements.
EASED@BUIS will be organized as a one day discussion-intensive workshop to provoke intensive collaborations among the participants. It is intended to initiate collaborative works on standardizing (static and dynamic) measuring techniques for energy consumption.
To further stimulate these discussions, authors are invited to submit position papers one on the        workshop´s topics. Accepted papers will be presented at the workshop and will be published in Softwaretechnik-Trends.
EASED@BUIS is supported by the GI special Interest groups Software Technology and Environmental Informatics.

Submissions and important Dates
Authors are encouraged to submit their position papers (2 pages in two column form) not later than March 15, 2013 through easychair.
paper submission deadline: March 15, 2013
author notification: March 25, 2013
camera-ready deadline: April 1, 2013
Organizing Committee
Christian Bunse (University of the Applied Sciences Stralsund)

Stefan Naumann (University of the Applied Sciences Trier, Environmental Campus Birkenfeld)

Andreas Winter (Carl von Ossietzky University, Oldenburg)

Program Committee
Colin Atkinson (University Mannheim)
Paris Avgeriou (University of Groningen)
Holger Eichelberger (University Hildesheim)
Sebastian Götz (TU Dresden)
Theo Härder (TU Kaiserslautern)
Mirco Josefiok (OFFIS, Oldenburg)
Ákos Kiss (University of Szeged)
Sonja Klingert (University Mannheim)
Patricia Lago (VU University Amsterdam)
Thierry Leboucq (KaliTerre, Nantes)
Birgit Penzenstadler (TU München)
Giuseppe Scanniello (University of Basilicata)
Maximilian Schirmer (Bauhaus-University Weimar)
Gunnar Schomaker (OFFIS Oldenburg)
Joost Visser (Software Improvement Group, Amsterdam)
Claas Wilke (TU Dresden)
Alexandru Telea (University of Groningen)
Local Organization
Marion Gottschalk (Carl von Ossietzky University, Oldenburg)
Andreas Winter (Carl von Ossietzky University, Oldenburg)

Evolving Non-Intrusive Load Monitoring

Our paper Evolving Non-Intrusive Load Monitoring by Dominik Egarter, Anita Sobe and Wilfried Elmenreich has been accepted for the conference track EvoEnergy (Evolutionary Algorithms in Energy Applications) of the EvoApplication (16th European Conference on the Applications of Evolutionary Computation) 2013, taking place in Vienna form 3rd to 5th of April.

Basic principle of the ON/OFF time genome appliance
detection. Given is the total power consumption over
time. The goal is to deduce the on/off times of devices
(colored blocks) that add up to the measured power profile.

Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. In this paper we propose an evolutionary optimization algorithm to identify appliances, which are modeled as on/off appliances. We evaluate our proposed evolutionary optimization by simulation with Matlab, where we use a random total load and randomly generated power profiles to make a statement of the applicability of the evolutionary algorithm as optimization technique for NILM. Our results shows that the evolutionary approach is feasible to be used in NILM systems and can reach satisfying detection probabilities.



Dominik Egarter, Anita Sobe, Wilfried Elmenreich,   Evolving Non-Intrusive Load Monitoring,   EvoApplication 2013,   16th European Conference on the Applications of Evolutionary Computation, April, 2013