Scenario‐Based Distributed Robust Optimization for Optimal Virtual Power Plant Scheduling Under Uncertainty

ABSTRACT Distributed robust optimization algorithms focus on developing decision‐making strategies that can operate effectively under uncertain conditions. This paper examines a scenario‐based distributed robust optimization algorithm for optimal scheduling of virtual power plants (VPPs). The proposed algorithm follows three key steps: scenario sampling, scenario reduction, and distributed optimization using the Alternating Direction Multiplier Method (ADMM). This approach balances robustness with computational complexity and ensures convergence, offering a practical solution for multi‐agent optimization. By employing an uncertainty set to represent the variabilities of wind and photovoltaic power generation, which leads to the establishment of a distributed robust optimization model for optimal virtual power plant scheduling. Experimental simulations validate the algorithm's feasibility and efficacy in economically optimal scheduling, offering methodological support for enhancing both robustness and economic efficiency in VPPs' operations.