Integration of Dynamic Probabilistic Risk Assessment and Decision Making Under Uncertainty Methodology for Hydrogen Fueling Station Deployment

The successful design, deployment, and management of complex engineering systems require an integrative framework that captures evolving risk dynamics, causal interdependencies, and explicit decision-making under uncertainty. This paper presents the first formal integration of Dynamic Probabilistic Risk Assessment (DPRA) with multistage stochastic optimization to support long-horizon planning under deep uncertainty. This methodology enables causal, risk-informed reliability modeling to be combined with decision analysis for modeling hydrogen fueling station deployment with the objectives of minimizing risk and maximizing profit and availability.
Traditional Probabilistic Risk Assessment (PRA) methods quantify what can go wrong, how likely it is, and what the consequences are, but they do not prescribe optimal actions for managing risk over time. Multi-horizon stochastic programming, a mathematical optimization framework for Decision Making under Uncertainty (DMU) models optimal actions when future states of the world are uncertain, but representable through probabilistic scenarios. By embedding a Dynamic Bayesian Network (DBN) within a multi-horizon stochastic programming framework, this work constructs a decision-dependent DPRA architecture in which reliability and risk evolve in response to design and operational decisions. This yields a fully dynamic, causal representation of system risk, as opposed to the static snapshots in more traditional techno-economic analyses and conventional PRA.
To enhance economic realism, the framework incorporates a capacity-constrained Cournot competition layer formulated as a stochastic mathematical program with equilibrium constraints (SMPEC). Rather than assuming exogenous hydrogen prices, the SMPEC computes endogenous market price-quantity outcomes each period by from strategic quantity decisions under a shared inverse demand curve, with the operator's feasible supply constrained by DBN derived availability. This hybrid integration captures how preventive maintenance policies, downtime, and capacity losses propagate into equilibrium revenue and long-horizon profits. The proposed DPRASMPEC hybrid model links engineering reliability, safety risk, operational policy, and economic performance in the market into a unified risk-informed tool for evaluating long-term hydrogen fueling station deployment.

Keywords: Hydrogen, Hydrogen Reliability, Quantitative Risk Assessment, Probabilistic Risk Assessment, Decision Making Under Uncertainty, Game Theory, Techno-Economic Assessment, Equilibrium, Optimization.