Resilience Hubs: Power & Logistics

• Resilience hubs are multipurpose facilities integrating hardened infrastructure with advanced power, communication, and logistics systems to maintain critical services during disruptions.
• They employ techniques like stochastic optimization, AI-driven forecasting, and composite resilience indices to manage uncertainties and optimize performance.
• Implementation relies on regulatory frameworks and socio-economic targeting to ensure equitable service delivery and robust recovery in compound hazard scenarios.

Resilience hubs are multipurpose facilities or network nodes designed to absorb, recover from, and adapt to disruptive events while maintaining continuity of critical services for surrounding communities or logistics systems. In power systems, they serve as physical anchor points with enhanced electrical, thermal, and communications infrastructure, supporting populations during high-impact, low-probability (HILP) events. In hyperconnected logistics, resilience hubs enable persistent and efficient freight movement amid supply, demand, and network instabilities. This article surveys the operational models, resilience quantification, optimization methodologies, enhancement strategies, regulatory frameworks, and identified research gaps in resilience hub implementation, focusing on evidence from recent technical literature.

1. Definitions and Functional Objectives

Community-centric power systems:

A resilience hub is a public facility (e.g., school, community center) with hardened infrastructure, distributed energy resources (DERs), and emergency services, intended to maintain shelter, power, communications, and coordination during and after disruptions. Key objectives are shock absorption, rapid restoration of services, upstream infrastructure support (water, transport, telecom), and equitable access for vulnerable populations (Nazaria et al., 29 Dec 2025).

Logistics networks:

Resilience hubs are open-access nodes within hyperconnected transportation systems, tasked with maintaining on-time delivery, high consolidation rates, and manageable operational costs under stochastic demand and hub-level disruptions. Formally, resilience is the ability to serve all demands within deadlines, consolidate shipments efficiently, and minimize budget increases even as uncertainties escalate (Liu et al., 2024).

2. Mathematical Modeling and Resilience Metrics

Power systems

Fragility-curve modeling:

Each hub’s component (e.g., batteries, building envelope) is assigned a log-normal fragility curve, $$P\{D \ge d\} = \Phi\left(\frac{\ln d - \mu}{\sigma}\right),$$ where $\Phi(\cdot)$ is the standard normal CDF, $\mu$ and $\sigma$ fit to stress/failure data. In seismic risk, $$P_f(\mathrm{PGA}) = \Phi\left[\frac{\ln(\mathrm{PGA}/\mathrm{PGA}_{mi})}{\beta_{\text{tot}}}\right],$$ with $\mathrm{PGA}_{mi}$ as the median threshold and $\beta_{\text{tot}}$ the log-dispersion.

Fuzzy set representations:

Triangular/trapezoidal membership functions model epistemic uncertainty in hub performance, e.g., power quality as a fuzzy set over state-of-charge (SOC) intervals.

Composite resilience indices:

The weighted sum $R_{\text{hub}} = w_1\,A + w_2\,C + w_3\,D$ captures absorptive, restorative, and adaptive capacities. Area-under-curve metrics $$RI_1 = \frac{\int_{t_1}^{t_4} P_{\text{hub}}(t)\,dt}{\int_{t_1}^{t_4} P_T\,dt}$$ normalize hub performance over the disruption and recovery window.

Logistics networks

Two-stage stochastic optimization model:

Let $O$, $D$, $H$, $A$, $V$, $W$ denote sets of origins, destinations, hubs, arcs, OD pairs, scenarios, respectively. Decision variables include hub openings $X_h$, capacities $C_h$, shipments flows $F_{od}(a,\omega)$, and fleet allocations $\mathrm{Tr}(a,\omega)$. The objective is: $$\min \left[\sum_{h\in H}(f_n X_h + S_h C_h) + \frac{1}{|W|}\sum_{\omega\in W} \sum_{a \in A} V_a t(a,\omega) \mathrm{Tr}(a,\omega)\right]$$ subject to constraints on flow conservation, truck loading, hub capacity, and activation.

Metrics:

• On-time delivery rate:

$$R_{\text{on-time}} = \frac{\sum_{w\in W}\sum_{(o,d)\in V} 1\{\text{shipment}_{o,d,w} \text{ delivered within deadline}\}}{\sum_{w\in W}\sum_{(o,d)\in V} 1}$$

• Consolidation rate:

$$\rho_{\rm cons} = \frac{\sum_{w\in W}\sum_{a\in A}\sum_{(o,d)\in V} F_{od}(a,w)}{\sum_{w\in W}\sum_{a\in A} m\,\mathrm{Tr}(a,w)}$$

• Daily cost resilience:

$$C_{\rm daily}(N;level) = \sum_{h\in N} (f_n X_h + S_h C_h) + \frac{1}{|W|}\sum_{\omega\in W}\sum_{a\in A} V_a t(a,\omega)\mathrm{Tr}(a,\omega)$$

3. Enhancement Strategies and Optimization

Power system resilience hubs

Network hardening:

Binary variables $x_i$ for line reinforcement and $y_j$ for structural retrofit, with objectives to minimize capital plus expected outage costs: $$\min_{x,y} \sum_i C^{\text{hard}}_i x_i + \sum_j C^{\text{build}}_j y_j + \mathbb{E}_\xi[\text{Cost}_\text{outage}(x,y;\xi)]$$ Budget and minimum resilience constraints apply.

Resource allocation (DER sizing/siting):

Nominal PV/battery capacities $(g^{\text{pv}}_k,g^{\text{bat}}_k)$ are chosen by two-stage stochastic programming, balancing investment and expected load-shed costs.

Optimal scheduling of repairs:

Crew assignments $z_{l,t}$ maximize time-discounted critical load at hubs, under travel-time and flow conservation constraints.

Network reconfiguration:

Smart switching variables $s_{ij}$ enable topology adjustments to shape load flows and maintain radiality, minimizing weighted load-shedding across hubs.

Logistics system resilience hubs

Stress scenario generation:

Demand scenarios via kernel density estimation; disruption scenarios via probabilistic hub outages. Optimization is conducted under four scenario regimes:

• BDN: deterministic, no disruption
• SDN: stochastic demand
• SDiN: stochastic disruption
• ISN: combined uncertainty

Each yields a different optimal network in terms of number of active hubs, average capacity, and connectivity degree.

4. Interdependency and Socioeconomic Integration

Resilience hubs are embedded in multiple critical infrastructure networks:

• Water systems: Hubs with backup power maintain local pump operations; simulations show increases in water-system resilience from co-location.
• Telecommunications: Hubs provide emergency comms and support cell-site recovery.
• Transportation: Siting hubs at transit nodes supports traffic management and post-seismic recovery.
• Socioeconomic targeting: Deployment guided by Social Vulnerability Index (SoVI) metrics maximizes benefits for medically and economically vulnerable populations, aligning technical resilience with equity goals.

5. AI Integration in Planning and Operations

AI methods are deployed for both planning and dynamic operation of resilience hubs:

• Load and outage forecasting: Adaptive Random Forests and Bayesian Additive Regression Trees achieve hub-level demand prediction accuracy of 85–95 %. CNN–LSTM networks enable fine-grained solar output forecasts for DER scheduling.
• Reconfiguration and restoration: Markov Decision Processes with DQN and DDPG algorithms control switches, DER setpoints, and crew routing, improving post-disaster performance.
• Generative AI: Transformer models (e.g., eGridGPT) synthesize scenario ensembles for planning under uncertainty, and LLMs assist regulatory compliance.

A plausible implication is that integration of AI augments decision quality and resilience index outcomes under both normal and extreme operational contexts.

6. Regulatory and Techno-Legal Frameworks

European Union

• GDPR enforces strict personal and socioeconomic data protections in hub siting and management.
• NIS2 mandates risk management and incident reporting for energy-sector hubs.
• EU AI Act and CER Directive classify hub-control AI as high-risk, demanding transparency, oversight, and audits.

United States

• NERC CIP applies binding cybersecurity standards for microgrid controllers and hub communications.
• CISA/NIST CSF supplies voluntary resilience guidelines.
• State privacy laws (CCPA, HIPAA) control socio-medical data disclosure associated with operations.
• NIST AI RMF guides AI risk management but lacks binding force.

Key contrast: EU adopts a compliance-driven, top-down regime, while the US regulatory environment is sectoral and largely voluntary.

7. Current Research Gaps and Prospects

Identified research gaps for resilience hubs include:

• Spatiotemporal failure correlations: Current fragility models lack cross-component and time-cascade effects; remote-sensing and simulation are needed for refinement.
• Data scarcity and confidentiality: Proprietary network topologies hinder open research; synthetic model generation and privacy-preserving techniques (federated learning, differential privacy) are advocated.
• Extreme and compound hazard scenarios: Generative models offer potential for multi-hazard stress-testing but require substantial development.
• Stochastic–AI hybridization: Few works integrate stochastic optimization directly with AI-driven scenario generation or dispatch optimization.
• Techno-legal metrics: Harmonization between resilience indices and regulatory KPIs (e.g., incident reporting time) remains an open challenge.
• Human-centered adaptation: More sophisticated agent-based and ML-guided behavioral models are needed to dynamically allocate services and measure efficacy among critical subpopulations.

This suggests future scholarship will benefit from interdisciplinary approaches, integrating engineering, AI, regulatory analysis, and socio-behavioral modeling to maximize both technical and social resilience at hub sites (Nazaria et al., 29 Dec 2025, Liu et al., 2024).