(467h) Moving Horizon Dynamic Optimization of a Renewable-Driven Chemical-Energy Community Under Varying Disruptions

In response to the urgent need for climate action and the transition toward sustainable industrial practices, green hydrogen and ammonia are emerging as key energy carriers in future multifunctional networks. Their roles are critical in sectors susceptible to variability and disruptions, such as agriculture and electricity generation [1-5]. Recent studies emphasize the value of integrated chemical-energy systems, enhanced producer-consumer interconnectivity, and the decentralization, parallelization, and automation of manufacturing processes as resilient strategies for improving adaptability to disturbances [6,7]. Adaptive scheduling frameworks—incorporating real-time monitoring, predictive analytics, and advanced optimization algorithms—have also been proposed to mitigate uncertainty and enhance operational efficiency [8-10]. Furthermore, the integration of process design, control, and scheduling has been extensively explored, with moving horizon methods (akin to model predictive control, MPC) gaining traction for closed-loop scheduling and integrated planning in chemical supply chains [10-12].

A major challenge in large-scale decision-making studies is the limited incorporation of detailed dynamic or phenomenological process models. The simultaneous solution of complex process dynamics and network-level optimization problems often becomes computationally prohibitive [7,12]. In addition, idealized production conditions are frequently assumed, neglecting real-world disruptions such as equipment failures or fluctuations in process parameters. However, such disturbances—ranging from shifts in temperature, pressure, and reaction rates to unplanned maintenance and equipment downtimes—can significantly disrupt coordinated operations, degrade product quality, and reduce system reliability.

This study investigates the monthly dynamic scheduling of a renewable-powered chemical-energy community using a moving horizon optimization approach. The community comprises hydrogen and ammonia production units driven by renewable energy sources and is designed to continuously meet consumer ammonia demand, even during disruptions. These disruptions are modeled as either complete outages or overproduction periods in renewable energy supply, each with defined durations. Two key performance metrics are used to assess community operations: (i) the supply-to-demand ratio and (ii) a Gaussian-based penalty function that quantitatively penalizes both overproduction and undersupply, weighted by consumer-specific demand profiles. Since all renewable energy is dedicated to ammonia production, buffer storage units are introduced to decouple production from demand, mitigate economic inefficiencies, and store excess ammonia. Results indicate that the system’s decision variables are particularly sensitive to the behavior of the renewable energy modules, more so than other process components. This underscores the importance of siting and operating energy modules based on a thorough understanding of consumer demand profiles to avoid economic inefficiencies. Additionally, integrating grid-connected energy export options further enhances system performance when multiple energy generation units are included for resilience.

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