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.