Local soft constraints in distributed energy scheduling

Abstract

In this contribution we present an approach on how to include local soft constraints in the fully distributed algorithm COHDA for the task of energy units scheduling in virtual power plants (VPP). We show how a flexibility representation based on surrogate models is extended and trained using soft constraints like avoiding frequent cold starts of combined heat and power plants. During the task of energy scheduling, the agents representing these machines include indicators in their choice for a new operation schedule. Using an example VPP we show that our approach enables the agents to reflect local soft constraints without sacrificing the global result quality.