Operational Planning of Hydrogen Energy Storage Systems Using Risk-Averse Stochastic Programming

This paper presents a scenario-based stochastic optimization framework for mobile hydrogen energy storage systems (HESS) integrated with renewable generation and demand response. The model captures relocation dynamics across multiple buses and incorporates financial risk using conditional value-at-risk (CVaR). Key features include scenario reduction for wind and solar uncertainties, downside risk constraints, and temporal coupling between relocation and energy states. The objective function balances expected operating cost and risk exposure, enabling robust dispatch under uncertainty. Simulations on the IEEE 33-bus system show that mobile HESS units outperform stationary hydrogen configurations, achieving up to 8.4% cost reduction despite relocation penalties. Benchmarking against mobile battery systems reveals that battery-based setups yield lower operating costs—approximately 9.9% less—due to higher round-trip efficiency. However, the spatial and long-duration flexibility of HESS remains a strategic advantage, especially under extended congestion. The proposed framework advances mobile energy storage modelling by integrating mobility, uncertainty management, and risk-aware dispatch. It offers a scalable solution for resilient energy planning and can be extended to include load-dependent efficiency, probabilistic demand response, and multi-carrier energy coordination.