Hydrogen conversion plants based on reversible solid oxide cells (rSOCs) provide a potential solution for large-scale energy storage to facilitate renewable integration. When operating an rSOC plant, the security and performance should be monitored at all times, including the steady-state periods and the transient processes connecting them. Model predictive control (MPC) is an appropriate method for this problem. This paper presents a comprehensive rSOC plant model to describe the multiscale dynamics (such as temperature and mass flows) under both fuel cell and electrolysis modes. Then, a time-varying MPC strategy based on a linear parameter-varying prediction model is proposed to provide fast control to the nonlinear rSOC plant. A numerical case is simulated to validate the effects of the proposed MPC strategy. The results suggest that the automatic coordination of actuators ensures consistent stack security, improves both short-term tracking and long-term production performance, and ultimately benefits the plant economically in practical operation.

中文翻译：

---

基于线性参数变化法的可逆SOC储能厂时变模型预测控制

基于可逆固体氧化物电池（rSOC）的氢气转化厂为大规模储能提供了潜在的解决方案，以促进可再生能源的整合。在运行rSOC工厂时，应始终监控安全性和性能，包括稳态周期和连接它们的暂态过程。模型预测控制（MPC）是解决此问题的合适方法。本文提出了一个全面的rSOC工厂模型，以描述燃料电池和电解模式下的多尺度动力学（例如温度和质量流量）。然后，提出了一种基于线性参数变化预测模型的时变MPC策略，为非线性rSOC工厂提供快速控制。模拟了一个数值案例，以验证所提出的MPC策略的效果。