Optimal multi-energy storage capacity allocation for electricity-heat-hydrogen-ammonia integrated energy systems with ammonia cycling

Integrated energy system (IES) dominated by coal-fired power generation can effectively reduce carbon emissions through ammonia co-firing. However, the substantial increase in ammonia demand cannot be fully met by green hydrogen produced solely from renewable energy sources, leading to supply shortages. The ammonia cycle, which encompasses ammonia utilization, production, and storage, enables efficient energy recycling. Incorporating the ammonia cycle process can further enhance system operation coordination. This paper establishes a bi-level collaborative optimization model for energy storage capacity configuration in an electricity-heat-hydrogen-ammonia IES, accounting for the ammonia cycle process. Four energy storage forms (electricity, heat, hydrogen, and ammonia) are integrated into the IES framework. First, an IES architecture incorporating electricity-heat-hydrogen-ammonia storage is proposed. A multi-hydrogen coordinated ammonia production method is developed, synthesizing hydrogen from renewable energy-based water electrolysis, natural gas reforming, and fossil fuel-based processes. A hydrogen-to-ammonia conversion model is constructed, considering the storage of electricity, heat, hydrogen, and ammonia. Second, a bi-level optimization model for multi-energy storage configuration in the IES is formulated. The upper-level capacity configuration optimization model aims to minimize the annual operation and maintenance costs of multi-energy storage devices and the system's annual dispatch cost. The lower-level low-carbon dispatch optimization model minimizes the system's typical daily low-carbon dispatch cost. The optimal capacity configuration and low-carbon dispatch scheme are derived through iterative solving between the two levels. Furthermore, the ammonia co-firing ratio in coal-fired units is flexibly constrained to regulate ammonia consumption. Finally, case studies demonstrate that the proposed model reduces both economic costs and carbon emissions in the system.