Zonotope Group Search Optimizer for Multi-Objective Stochastic Optimal Power Flow

The paper proposes a Zonotope-based Search Optimizer as a Group (ZonoGSO) method to address the uncertainty in stochastic multi-objective optimization problems (MaOPF) caused by the increased penetration of renewable resources such as wind and solar in the power system. This method uses Zonotope to characterize all possible ranges of renewable energy (REs) output, and captures the uncertain output range of REs output with a linear combination of center point and generation vector, which can better simulate the uncertainty of Res in real situations. The stochastic multi-objective problem is transformed into a deterministic three-objective problem to evaluate the superiority of the Pareto front solution. It is tested on three modified bus systems that integrate renewable energy and is compared with the SetGSO algorithm. The results show that the Zonotope-based ensemble search algorithm outperforms the ensemble-based SetGSO method in various performance indicators, further demonstrating the importance of Zonotope in addressing uncertain outputs in complex power systems.