Evolving a Self-organizing Energy Market for Microgrids

Graphical user interface of the FREVO plugin

Andrea Monacchi and Wilfried Elmenreich went to Zurich to present their paper about a home energy market simulator at Energieinformatik '2014.
In the presented work, self-organizing behavior between prosumers is implemented at device level via market-based energy allocation solutions. The approach is implemented in a simulator extension to the open framework FREVO, which is released for open use. With the help of FREVO, artificial neural network controllers for energy prosumers are designed using an evolutionary algorithm. Minimizing individual and overall running costs enables a better use of local energy production from renewable sources, while considering residents' necessities to minimize discomfort.

• Andrea Monacchi, Sergii Zhevzhyk, Wilfried Elmenreich. HEMS: A Home Energy Market Simulator. Energieinformatik. Zurich, November 2014.

HEMS: A Home Energy Market Simulator from Andrea Monacchi

Final two Interns@SmartGrid_Group

For the last two months we had further two interns working at our institute. We report a short interview about their work and experiences at our research group.

What's your name?

Julia

What are you studying?

I am studying electrical engineering and industrial electronics at the HTL Mössingerstraße in Klagenfurt and this year I've finished the 4th class.

What are your expectations for the future?

I want to conclude the HTL with good marks, then I would like to study something that combines electrical engineering and economics.

What are you working on?

LabView GUI

I have worked on a LabVIEW measurement program for a photovoltaic system and have also created a consumer box for the lab, where we want to simulate a household. I have installed input modules, which are used for measuring voltage and current, and an output module, which is used for turning the devices on and off. These modules are implemented into my LabView program and further I have created an interface, where the user is able to input data to the program and view power and current consumption. There is also the possibility to read the measured data and control the devices through a web service in the network. I have also installed the hardware elements in the cabinet, created the wiring plan and ordered appliances. The aim of the program is to be used as a control of consumer devices like a refrigerator, water heater and radiator, which are turned on at the time, when the current price is on the lowest peak. Thus this could be an innovative application for saving costs and has to be researched.

Laboratory Installations

What did you like of the project

I liked about my work that I had the chance to contribute my own ideas and expand my programming skills in LabVIEW. It was a very varied project and I liked the mix of manual work and logical thinking.

What's your name?

Johannes

What are you studying?

I'm studying electronics and technical informatics at the HTL Mössingerstraße in Klagenfurt. I have finished my second year.

What are you expectations for the future?

After finishing the HTL, I want to study computer science.

What are you working on?

I was working on Java classes for a Smart Grid Simulator which make it possible to use algorithms from the GridLAB-D simulator. To submit this, I converted all data stored by objects from the Smart Grid Simulator into a file which is then used by GridLAB-D. The output files made by GridLAB-D are read and the values are assigned to the proper object.

OpenEnergyMonitor

GridLAB-D to Smart Grid Simulator 

I was also working on an open source energy monitor made by Openenerymonitor. My task was to enable it to change the refresh rate on the transmitter (emonTX) by using buttons on the receiver (emonGLCD). The difficult thing was to send and receive data at a time using the same transceiver.

Power Profile Generation in Java

My final project was the implementation of a Java program, which generates random power profiles of different appliances out of a given database, place this random profiles randomly in time and finally, tries to detect which appliance was used or not. The technique of appliance detection is called Non-Intrusive Load monitoring and enjoys currently an great interest in research.
What did you like of the project?

I like programming a lot. So I had the opportunity to improve my skills.

Smart Grid @ GECCO 2013

On July 6th - 10th we attended the Genetic and Evolutionary Conference GECCO 2013 in Amsterdam. GECCO is the largest conference in field of genetic and evolutionary computation. The conference was organized by 18 different tracks like Genetic Programming, Genetic Algorithms or Real World Applications, had 35 different tutorials and hosted 13 workshops.

From the Smart Grid group, Dominik Egarter took part at the Green and Efficient Energy Applications of Genetic and Evolutionary Computation Workshop 2013, where he presented his paper EvoNILM - Evolutionary Appliance Detection for Miscellaneous Household Appliances.
In this workshop 5 different papers were presented, which topics were widely spread from power flow optimization, wind power forecasting to wind power siting.

• D. Egarter, W. Elmenreich. EvoNILM - Evolutionary Appliance Detection for Miscellaneous Household Appliances Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation, Amsterdam, July 2013.

Evolutionary Appliance Detection for Miscellaneous Household Appliances

The paper "EvoNILM - Evolutionary Appliance Detection for Miscellaneous Household Appliances" was accepted to the Workshop "Green and Efficient Energy Applications of Genetic and Evolutionary Computation" at Gecco 2013.

To improve the energy awareness of consumers, it is necessary to provide them with information about their energy demand, not just on the household level. Non-intrusive load monitoring (NILM) gives the consumer the opportunity to disaggregate their consumed power on the appliance level. The consumer is provided with information about the energy demand of each individual appliances. In this paper we present an evolutionary optimization algorithm, applicable to NILM purposes. It can be used to detect appliances with a probabilistic power demand model. We show that the detection performance of the evolutionary algorithm can be improved if the single population approach of the evolutionary algorithm is replaced by a parallel population approach with individual exchange and by the introduction of application-oriented pre-processing and mutation methods. The proposed algorithm is tested with Matlab simulations and is evaluated according to the fitness reached and detection probability of the algorithm.

This paper is an improvement and follow up paper of the previous work "Evolving Non-Intrusive Load Monitoring".

• D. Egarter, W. Elmenreich. EvoNILM - Evolutionary Appliance Detection for Miscellaneous Household Appliances Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation, Amsterdam, July 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.

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