Privacy-preserving Self-organization in Algorithmic-level Decomposed Predictive Scheduling

Cooperative algorithmic-level decompositions are a parallel computation method that executes several local algorithms.These local algorithms in turn exchange information to find better or more robust solutions.In energy management, this paradigm is used for multi-agent systems to manage generation and/or consumption of electric power within coalitions of energy resources.The exchanged data contains operational schedules of other group members.Such data can be aggregated and exploited by agents to find better solutions and derive sensitive information.It is thus desirable to keep the data private.We apply the Paillier cryptosystem to the use case of distributed predictive scheduling in virtual power plants or energy districts.We had to extend the interaction protocol as simply securing the communication channel between the agents is not targeted for this type of algorithm.Calculations of the global objective function are now done as a joint distributed task with homomorphic encryption.We show that the privacy version has a competitive performance in terms of solution quality with only a constant increase in message exchange for larger scenarios.