article · eTransportation
Managing hydrogen refuelling stations powered by renewable energy sources requires addressing uncertainties in fuel demand, generation forecasts, and market prices. A control strategy uses a model predictive control framework with Boolean relaxations and stochastic scenario-based modelling to oversee stations servicing multiple vehicles simultaneously, such as buses and light vehicles. The system functions both off-connected and on-connected to hydrogen markets, permitting fuel purchases during supply deficits and sales during surpluses. A buffer tank stores excess hydrogen to convert back into electricity when market prices are favourable. Numerical simulations demonstrate that the strategy successfully coordinates parallel refuelling across multiple storage tanks while meeting demand and operational limits. Accounting for equipment degradation reduces unnecessary electrolyser switching by more than 2,000 events annually, cutting operating expenses by 30 percent, while the mathematical relaxation halves computation time across open-source and commercial solvers.
Hydrogen fuel cell vehicles offer clean transport, but refuelling stations struggle with fluctuating renewable energy supplies and volatile energy prices. Optimising station operations protects expensive equipment like electrolysers from premature wear while lowering operational costs. More efficient, faster computational control allows station operators to respond reliably to changing market prices and vehicle refuelling demands, supporting the broader adoption of hydrogen-powered transport.
This simulation-tested strategy is aimed at operators of hydrogen refuelling stations, fleet logistics providers, and renewable energy managers seeking to lower running costs and participate in electricity markets. While validated through numerical simulations and tested with commercial solvers such as GUROBI, the method remains at an applied computational stage. Real-world deployment will require field validation on physical refuelling infrastructure to verify operational stability under live grid and fuelling conditions.
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The growing demand for hydrogen-based mobility highlights the importance of management strategies for hydrogen refueling stations (HRSs), particularly in handling uncertainties related to hydrogen demand, energy forecasts, and market prices. This paper presents a sophisticated approach for managing an HRS powered by renewable energy sources (RESs) that addresses these uncertainties. The HRS is designed to support the simultaneous refueling of multiple hydrogen electric vehicles, including light vehicles and buses, and operates in both off-connected without access to the hydrogen market and on-connected with access to the hydrogen market. The connection to the hydrogen market allows for the purchase of hydrogen when RESs are insufficient and the sale of excess hydrogen. Additionally, a buffer-tank is integrated into the system to store surplus hydrogen, which can be converted to energy and sold to the electrical market when prices are favorable. The proposed strategy incorporates Boolean relaxations and a stochastic scenario-based approach within a model predictive control framework to enhance robustness against uncertainties and reduce computational complexity. Numerical simulations show that the strategy optimizes the use of multiple tanks for parallel refueling and ensures effective HRS operation by meeting hydrogen demands, satisfying operational constraints, minimizing costs, and maximizing profits. Furthermore, when compared to other strategies in the literature with a modeling and control perspective, incorporating degradation factors into control settings significantly reduces unnecessary electrolyzer switching, leading to a 30% decrease in operating expenses and over 2,000 fewer switching events annually, while the relaxed framework achieves nearly a 50% reduction in computation time with both open-source and commercial solvers (e.g., GUROBI). • Advanced hydrogen refueling station design with RES-powered parallel refueling. • Buffer-tank for electricity sales to maximize revenue from the energy market. • Integrated control for operation in both on- and off-hydrogen market modes. • Scenario-based MPC strategy for robustness against demand and RES generation uncertainties. • Simplified control strategy with Boolean relaxations to reduce computational complexity.
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DOI: 10.1016/j.etran.2024.100393
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