Parallel tempering for constrained many criteria optimization in dynamic virtual power plants

Following the long-term goal of substituting conventional power generation, market oriented approaches will lead to interaction, competition but also collaboration between different units. Together with the expected huge number of actors, this in turn will lead to a need for self-organized and distributed control structures. Virtual power plants are an established idea for organizing distributed generation. A frequently arising task is solving the scheduling problem that assigns an operation schedule to each energy resource taking into account a bunch of objectives like accurate resemblance of the desired load profile, robustness of the schedule, costs, maximizing remaining flexibility for subsequent planning periods, and more. Nevertheless, also such dynamic approaches exhibit sub-problems demanding for centralized solutions for ahead of time scheduling of active power. In this paper we develop a hybrid approach combining the advantages of parallel tempering with a constraint handling technique based on a support vector decoder for systematically generating solutions; thus ensuring feasible overall solutions. We demonstrate the applicability with a set of simulation results comprising many objective scheduling for different groups of energy resources.