  Recent years have seen an increasing integration of distributed renewable
energy resources into existing electric power grids. Due to the uncertain
nature of renewable energy resources, network operators are faced with new
challenges in balancing load and generation. In order to meet the new
requirements, intelligent distributed energy resource plants can be used which
provide as virtual power plants e.g. demand side management or flexible
generation. However, the calculation of an adequate schedule for the unit
commitment of such distributed energy resources is a complex optimization
problem which is typically too complex for standard optimization algorithms if
large numbers of distributed energy resources are considered. For solving such
complex optimization tasks, population-based metaheuristics -- as e.g.
evolutionary algorithms -- represent powerful alternatives. Admittedly,
evolutionary algorithms do require lots of computational power for solving such
problems in a timely manner. One promising solution for this performance
problem is the parallelization of the usually time-consuming evaluation of
alternative solutions. In the present paper, a new generic and highly scalable
parallel method for unit commitment of distributed energy resources using
metaheuristic algorithms is presented. It is based on microservices, container
virtualization and the publish/subscribe messaging paradigm for scheduling
distributed energy resources. Scalability and applicability of the proposed
solution are evaluated by performing parallelized optimizations in a big data
environment for three distinct distributed energy resource scheduling
scenarios. The new method provides cluster or cloud parallelizability and is
able to deal with a comparably large number of distributed energy resources.
The application of the new proposed method results in very good performance for
scaling up optimization speed.
