Distributed Virtual Power Plant Optimization via Scenario Approach

The shift towards decentralised energy systems has given rise to Virtual Power Plants (VPPs), essential for managing Distributed Energy Resources. This study proposed a distributed management architecture for VPPs that resiliently provides aggregated power production to the main grid, irrespective of uncertainties in non-dispatchable resources. Exploiting a scenario optimisation formulation and a Lagrangian decomposition of the problem, we develop a distributed stochastic optimisation framework for VPP management that scales with the number of agents. The method’s performance is tested across various network sizes and real generation and consumption data, showing that the constraint violation probability on the minimum service level remains constant as the number of agents increases, while maintaining a fixed amount of considered scenarios.