Towards fully Decentralized Multi-Objective Energy Scheduling

Future demand for managing a huge number of individually operating small and often volatile energy resources within the smart grid is preponderantly answered by involving decentralized orchestration methods for planning and scheduling. Many plaiming and scheduling problems are of a multi-objective nature. For the single-objective case - e.g. predictive scheduling with the goal of jointly resembling a wanted target schedule -fully decentralized algorithms with self-organizing agents exist. We extend this paradigm towards fully decentralized agent-based multi-objective scheduling for energy resources e.g. in virtual power plants for which special local constraint-handling techniques are needed. We integrate algorithmic elements from the well-known S-metric selection evolutionary multi-objective algorithm into a gossiping-based combinatorial optimization heuristic that works with agents for the single-objective case and derive a number of challenges that have to be solved for fully decentralized multi-objective optimization. We present a first solution approach based on the combinatorial optimization heuristics for agents and demonstrate viability and applicability in several simulation scenarios.