Towards coding strategies for forecasting-based scheduling in smart grids and the energy lab 2.0


Development of the power supply system towards a more decen-Tralized system with a growing share of renewable energies con-stitutes an additional complexity for its reliable, secure, and eco-nomic operation. This has a strong impact on a variety of opti-mization tasks, such as power plant resource scheduling, reactive power management, or the expansion of the system by addition-Al transmission lines, power generators or storage systems. In particular, scheduling and expansion planning depend strongly on a reliable forecast of expected demands and electricity pro-duction, the latter being a demanding task for volatile sources, such as wind power plants or photovoltaic power generators (PV). For testing new approaches and strategies, the Karlsruhe Institute of Technology (KIT) develops a test bed comprising dif-ferent energy grids called Energy Lab 2.0. This test bed will al-low studying the effects of new tools, forecasting and scheduling techniques, and other algorithms aimed at managing a smart grid. The lab and applied forecasting techniques will be briefly introduced in the present contribution. First ideas about metaheuristic scheduling of different energy sources based on production and demand forecasts with the aim of ensuring a reliable and economic energy supply are intro-duced. Appropriate representations for Evolutionary Algorithms (EAs) are discussed and some experience from earlier scheduling projects for fast scheduling of many jobs to heterogeneous re-sources are given.

GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion