A Hierarchical Nonlinear Predictive Controller for Ramp-rate Mitigation in Hybrid Energy Systems with Solar Photovoltaics

Abstract

Stringent ramp-rate constraints due to high solar photovoltaic (PV) penetration in power systems challenge conventional smoothing techniques, where capacity saturation and device degradation become critical bottlenecks. In this context, PV smoothing is posed as a constrained dispatch objective to shape the grid-facing net power trajectory and reduce ramp-rate deviations. This study proposes a grid-compliant coordination framework for PV-integrated Hybrid Energy Systems (HES) that strategically couples battery storage with a hydrogen subsystem, comprising an electrolyzer and a fuel cell, to maximize system-wide efficiency while mitigating ramp-rate excursions, subject to operational limits of the storage devices. We develop a hierarchical supervisory control architecture consisting of an inner-loop real-time battery control and a high-level nonlinear Model Predictive Control (MPC) dispatcher. The MPC framework optimizes the trade-offs between State of Charge (SOC) regulation, conversion efficiency, device switching frequency, and ramp-rate support. Benchmarked against a logic-based control scheme, sensitivity analyses across prediction horizon, battery capacity, and forecast stochasticity demonstrate that MPC significantly improves dispatch smoothness and manages SOC effectively. The results quantify the operational flexibility contributed by hydrogen assets when battery approaches saturation, providing a robust solution for reliable grid integration of high-variability renewables.