Bi-level stochastic planning and energy management of a multi-carrier energy system with electrical, cooling, heating, and hydrogen hubs

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Smart multi-carrier energy systems (MCESs) have emerged as a promising solution for the coordinated operation of multiple energy carriers, enabling significant reductions in operating costs and emissions. However, their optimal planning and operation under uncertainty, particularly with the integration of hydrogen-based technologies, remain a major challenge. This paper proposes a bi-level stochastic planning and energy management framework for a smart MCES integrating electrical, heating, cooling, and hydrogen hubs. The system supplies conventional energy demands as well as fuel cell electric vehicle stations and an ammonia production plant while accounting for uncertainties in renewable energy generation, load demands, and transportation behavior. In the proposed framework, the upper level optimally designs and dispatches generation and storage units, whereas the lower level applies a demand-side response strategy based on time-of-use tariffs for electricity, heating, cooling, and ammonia production. To efficiently solve the resulting large-scale and non-linear optimization problem, an enhanced Artificial Hummingbird Algorithm (EAHA) is developed by incorporating chaotic mutation, guided evolution mutation, and quasi-oppositional-based learning (QOBL) strategies to avoid premature stagnation and enhance global search capability. Simulation results show that integrating renewable energy resources with hydrogen production using steam methane reforming reduces total cost and emissions by 35.86% and 23.39%, respectively, compared to the base case without renewable energy integration, while corresponding reductions of 22.47% and 21.91% are achieved when hydrogen is produced via water electrolysis. Furthermore, applying demand-side response reduces total operating costs by 12.15% and 10.23% for the two hydrogen production pathways, respectively. The results confirm the effectiveness of the proposed framework for achieving economically efficient and low-carbon operation of hydrogen-integrated MCESs under uncertainty.