Stochastic-Robust Planning of Networked Hydrogen-Electrical Microgrids: A Study on Induced Refueling Demand

TL;DR

This work addresses planning networked hydrogen-electrical microgrids under uncertainty by incorporating the demand-inducing effect (DIE) of refueling capacity through a decision-dependent uncertainty (DDU) set $\\Xi^s(n_i)$. It formulates a trilevel $\min-\max-\min$ problem where the upper level makes investment decisions, the middle level selects the worst-case refueling demand given that investment, and the lower level optimizes operation across stochastic scenarios, solved by a customized Parametric Column-and-Constraint Generation (PC&CG) algorithm with an equivalent single-level reformulation. The paper demonstrates, via an IEEE-33 bus case study, that including DIE can yield meaningful economic gains and shifts in refueling demand and grid operation, while also showing that the marginal benefits of DIE taper as system bottlenecks emerge. It further shows that PC&CG offers superior computational efficiency relative to BC&CG and scales well with the number of scenarios, enabling practical deployment of DIE-aware planning for decarbonized energy-transportation networks.

Abstract

Hydrogen-electrical microgrids are increasingly assuming an important role on the pathway toward decarbonization of energy and transportation systems. This paper studies networked hydrogen-electrical microgrids planning (NHEMP), considering a critical but often-overlooked issue, i.e., the demand-inducing effect (DIE) associated with infrastructure development decisions. Specifically, higher refueling capacities will attract more refueling demand of hydrogen-powered vehicles (HVs). To capture such interactions between investment decisions and induced refueling demand, we introduce a decision-dependent uncertainty (DDU) set and build a trilevel stochastic-robust formulation. The upper-level determines optimal investment strategies for hydrogen-electrical microgrids, the lower-level optimizes the risk-aware operation schedules across a series of stochastic scenarios, and, for each scenario, the middle-level identifies the "worst" situation of refueling demand within an individual DDU set to ensure economic feasibility. Then, an adaptive and exact decomposition algorithm, based on Parametric Column-and-Constraint Generation (PC&CG), is customized and developed to address the computational challenge and to quantitatively analyze the impact of DIE. Case studies on an IEEE exemplary system validate the effectiveness of the proposed NHEMP model and the PC&CG algorithm. It is worth highlighting that DIE can make an important contribution to the economic benefits of NHEMP, yet its significance will gradually decrease when the main bottleneck transits to other system restrictions.

Figures (7)

• Figure 1: Framework of the trilevel stochastic-robust NHEMP problem.
• Figure 2: Typical structure of carbon-free town with networked hydrogen-electrical microgrids.
• Figure 3: Illustration of the DDU and DIU sets. (Without loss of generality, indices $s$ and $t$ are suppressed, and $\left|\mathcal{Z} \right|=2$. Then, $\underline{\xi}_{1}$, $\overline{\xi}_{1}$, $\underline{\xi}_{2}$, $\overline{\xi}_{2}$ and $\underline{\zeta}$, $\overline{\zeta}$ are set to 10, 40, 10, 30, 35 and 55, respectively. Besides, $\sum_{i\in\Lambda_1}n_i=2$ and $\sum_{i\in\Lambda_2}n_i=1$. For the DDU set, $\underline{\gamma}_{1}$, $\overline{\gamma}_{1}$, $\underline{\gamma}_{2}$, $\overline{\gamma}_{2}$, $\underline{\alpha}$ and $\overline{\alpha}$ are, respectively, chosen as 2, 5, 4, 8, 2 and 8. For the DIU one, $\overline{\gamma}_{z}=\underline{\gamma}_{z}=\overline{\alpha}=\underline{\alpha}=0$.)
• Figure 4: Topology and zone partitioning of 33-bus exemplary network.
• Figure 5: Networked hydrogen-electrical microgrids planning results.

Theorems & Definitions (19)

• Remark 1
• Proposition 1
• Theorem 1
• Remark 2
• proof
• Lemma 1
• Remark 3
• Theorem 2
• proof
• Remark 4