Evolving a Self-organizing Energy Market for Microgrids

Graphical user interface of the simulator plugin for FREVO

A smart microgrid is the integration of an energy production unit with energy consumers. Production is often done via photovoltaic or wind power, but these require either expensive energy storage or the coordination of all consumers for adjusting energy consumption based on availability of generated energy.
In a paper presented at Energieinformatik in Zurich, Monacchi, Zhevzhyk, and Elmenreich suggest a self-organizing mechanism to coordinate consumer and producer behavior in such a system. The coordination is implemented at device level via market-based energy allocation solutions, where a device has to bet money based on its need to run. If production is insufficient to run all devices, those having the highest utility (reflected in willingness to pay a high price) are able to run. A device that is more flexible will learn to shift its operation to a time when the price is lower due to a higher production or a lower demand. The device behavior is realized via smart controllers with a neural network evolved with the Framework for Evolutionary Design (FREVO). The approach is implemented in a simulator extension to 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.

Links:

• A. Monacchi, S. Zhevzhyk and W. Elmenreich. HEMS: a home energy market simulator. Computer Science – Research and Development, Springer, November 2014.
• A. Sobe, I. Fehérvári, and W. Elmenreich. FREVO: A tool for evolving and evaluating self-organizing systems. In Proceedings of the 1st International Workshop on Evaluation for Self-Adaptive and Self-Organizing Systems, Lyon, France, September 2012.
• I. Fehervari and W. Elmenreich. Evolution as a tool to design self-organizing systems. In Self-Organizing Systems, volume LNCS 8221, pages 139–144. Springer Verlag, 2014.