System design, operational optimization and model predictive control of power-to-gas systems based on modelica/simulink co-simulation

Power-to-Gas (PtG), which integrates water electrolysis and CO2 methanation, is a promising technology for storing renewable energy resources (RES) in the form of chemical energy. However, due to the intermittent and dispatchable nature of RES, the optimization and control of PtG systems are essential to ensure high hydrogen production efficiency. To address this challenge, dynamic mechanistic models of the PtG system were developed, incorporating the solid oxide electrolysis cell (SOEC), methanation reactor (MR), and the balance-of-plant components. A pinch point analysis was employed to design the heat exchanger network for effective waste heat recovery. As a result, the proposed optimization framework enhances the overall system efficiency by about 9 %, while an additional 1.5 % improvement is achieved through the introduction of a heat recovery pathway from methanation reactor, representing a marginal yet meaningful advance at the efficiency frontier. To further enhance hydrogen production efficiency, a two-layer optimization and control framework is proposed. In the upper layer, an optimization module determines the optimal operating conditions—including temperature settings, air ratio, and water utilization—while satisfying system constraints. These optimal setpoints, obtained using a pattern search algorithm, are then transmitted to the lower control layer. In this layer, a model predictive control (MPC) strategy is implemented to regulate the SOEC temperature in response to fluctuations in input power. The MPC ensures that temperature gradients remain within a safe limit of 18 K/cm, thereby protecting system integrity and improving dynamic performance.