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Hydrogen Supply Chain Planning with Flexible Transmission and Storage Scheduling

Guannan He, Dharik S. Mallapragada, Abhishek Bose, Clara F. Heuberger, Emre Gençer

arXiv:2008.07611v1math.OCcs.SYeess.SPeess.SY

TL;DR

Existing hydrogen supply-chain planning models do not fully represent technology choices, electricity-price variation, and the spatiotemporal flexibility of trucks. This paper develops a least-cost planning model with flexible truck and pipeline scheduling. In U.S. Northeast scenarios, truck sharing is critical to exploiting flexibility for cost minimization, and electrolyzers can be cost-effective under moderate carbon policy and lower electrolyzer costs.

  • Problem

    HSC planning literature often omits electricity-price variability, important production and transport options, or realistic truck flexibility, despite the need to assess total supply-chain costs.

    Source: §I · p. 2

  • Method

    The model selects a least-cost hydrogen production, storage, transmission, and compression mix under spatiotemporal electricity inputs and demand, with flexible truck and pipeline scheduling.

    Source: §I · pp. 2–3

  • Results

    In the U.S. Northeast, electrolyzers can be cost-effective at a $50/tonne carbon price and $500/kW unit capital cost, while truck sharing is critical to exploiting flexibility for cost minimization.

    Source: §V · p. 8

  • Takeaways & Limitations

    Sharing trucks across zones as storage and routes as transmission is critical to exploiting their flexibility for VRE integration and HSC cost minimization.

    Source: §V · p. 8

Abstract

from arXiv · show

Hydrogen is becoming an increasingly appealing energy carrier, as the costs of renewable energy generation and water electrolysis continue to decline. Developing modelling and decision tools for the H$_{2}$ supply chain that fully capture the flexibility of various resources is essential to understanding the overall cost-competitiveness of H$_{2}$ use. To address this need, we have developed a H$_{2}$ supply chain planning model that determines the least-cost mix of H$_{2}$ generation, storage, transmission, and compression facilities to meet H$_{2}$ demands and is coupled with power systems through electricity prices. We incorporate flexible scheduling for H$_{2}$ trucks and pipeline, allowing them to serve as both H$_{2}$ transmission and storage resources to shift H$_{2}$ demand/production across space and time. The case study results in the U.S. Northeast indicate that the proposed framework for flexible scheduling of H$_{2}$ transmission and storage resources is critical not only to cost minimization but also to the choice of H$_{2}$ production pathways between electrolyzer and centralized natural-gas-based production facilities. Trucks as mobile storage could make electrolyzer more competitive by providing extra spatiotemporal flexibility to respond to the electricity price variability while meeting H$_{2}$ demands. The proposed model also provides a reasonable trade-off between modeling accuracy and solution times.

I. Introduction

Hydrogen supply-chain costs depend on production, transmission, storage, and distribution together, while existing planning models leave important technology choices and operational flexibility unrepresented. The paper proposes a least-cost planning model with broad technology coverage and flexible truck and pipeline scheduling, then applies it to U.S. Northeast scenarios.

I. Introduction

  • Hydrogen supply-chain planning must account for production, transport, storage, and end-use together because transmission, storage, and distribution can represent substantial costs.The motivation links electrolytic hydrogen’s cost competitiveness to declining renewable and electrolyzer costs, while emphasizing that production is only part of total supply cost.Source: §I · p. 2
  • Prior HSC planning models often omit electricity-price variability, key production or transport options, and realistic truck scheduling, leaving no full-fledged model with all options and spatiotemporal flexibility.Existing truck models often use fixed route flow limits, despite travel delays and trucks serving multiple routes and sites as both transmission and storage.Source: §I · p. 2
  • The proposed model selects a least-cost supply-chain technology mix under spatiotemporal variation in electricity inputs and hydrogen demand.It explicitly represents electrolyzers, SMR with and without CCS, storage, gaseous and liquid trucks, pipelines, and associated compression or liquefaction facilities.Source: §I · p. 2
  • Flexible truck routing and scheduling lets trucks serve as shared transmission and mobile storage, while pipeline scheduling represents storage through line packing.The model uses judicious approximations to make flexible truck scheduling computationally tractable at little modeling-accuracy cost.Source: §I · pp. 2–3
  • The case study applies the model to U.S. Northeast infrastructure needs across carbon-policy and hydrogen-demand scenarios using electricity prices from a power-system capacity-expansion model.It analyzes the value of scheduling models and trade-offs and synergies among technologies with different investment and operational flexibility.Source: §I · p. 3

III. Hydrogen Supply Chain Planning Model

The model selects least-cost hydrogen production, storage, transmission, and compression infrastructure and hourly schedules to meet spatiotemporal demand under electricity and natural-gas prices. It represents production, storage, pipelines, and trucks through capacity and operating constraints, including flexible truck routing and pipeline line packing.

Model framework

  • The model jointly chooses hydrogen infrastructure capacities and hourly schedules to meet spatiotemporal demand under energy prices, treating the hydrogen supply chain as a price-taker of electricity.Decisions are continuous except for truck scheduling variables.Source: §III · p. 3

Objective

  • Infrastructure capital and operating costs cover production, storage, pipelines, trucks, compression, electricity, natural gas, truck operations, emissions, and lost load.The objective includes capital costs for the four main supply-chain elements and compression facilities.Source: A. Objective function · pp. 3–4

Production

  • Generation output is bounded, while unit startup and shutdown decisions are subject to minimum up- and down-time constraints.The model limits online units to the available number and constrains the time between startup and shutdown, and vice versa.Source: C. Hydrogen Production Constraints · p. 4

Storage

  • Storage inventory must remain below maximum capacity and above the cushion-gas requirement, with charging constrained by compression capability.Storage charging and discharging efficiency is typically 100% for hydrogen storage.Source: D. Hydrogen Storage Constraints · p. 4

Transmission

  • Pipeline and truck transport jointly serve interzonal hydrogen exchange; pipelines can store hydrogen through line packing, while truck scheduling captures travel delays and shared routing.The truck model relaxes integer truck counts to continuous variables, which the case studies validate as having little cost to modeling and decision accuracy.Source: E. Hydrogen Transmission Constraints · pp. 4–5

IV. Case Studies in the U.S. Northeast

The U.S. Northeast case study evaluates hydrogen infrastructure under varied electricity prices, demand, carbon prices, and technology costs. Flexible truck scheduling changes both system cost and the selected production and transport mix.

  • The case represents the U.S. Northeast as six zones, excludes central SMR in heavily urbanized Zones 2 and 4, and derives electricity-price inputs from a power-system capacity expansion model.Demand assumes 20% FCEV penetration; the model evaluates annual operations over 20 representative weeks and excludes geological storage because regional availability is uncertain.Source: A. Data Descriptions · pp. 5–6, §IV · p. 5
  • Higher carbon prices shift H2 generation from central SMR to SMR with CCS and then electrolysis; at $100/tonne CO2, installed electrolyzer capacity is 59 tonne/hour at $300/kW versus 44 tonne/hour at $700/kW.Electrolyzers produce when electricity prices are below approximately $30/MWh; the authors identify $50/tonne or greater carbon prices and electrolyzer costs of $500/kW or lower as cost-effective conditions.Source: Figure 4 · p. 6, B. Results · p. 6, Figure 5 · p. 7
  • The existing model overinvests in trucks by 86% and stationary storage by 213%, increasing H2 supply cost by 11%; the integer model is intractable for the 20-week case.In the mini case, continuous scheduling changes truck and generation capacities by less than 0.3% versus integer scheduling.Source: Table I · p. 7, B. Results · p. 7
  • In the base case, gas and liquid trucks provide more than 70% of total H2 storage capacity and throughput; with pipeline costs cut by 50%, pipelines carry over half of transported H2.Gas trucks contribute more than liquid trucks, partly because gas compression costs and energy use are lower than liquefaction; with smaller trucks, mobile storage still supplies approximately 60% of stored H2.Source: Table II · p. 7, B. Results · p. 7
  • Cheaper pipeline retrofits bring more pipelines into the system with less electrolytic generation, while trucks complement distributed electrolysis and pipelines suit steady transmission from centralized SMR.The comparison uses a low-electrolysis case with a $500/kW electrolyzer and $50/tonne CO2 price.Source: Table III · p. 7, B. Results · pp. 7–8
  • During off-peak electricity prices, storage charges at low H2 demand and releases at high demand; trucks discharge during a short peak, then deliver H2 from SMR zones during a longer peak.The representative-week periods are Hours 33–47 and 111–168, respectively.Source: Figure 6 · p. 7, B. Results · p. 7

V. Conclusions

The proposed least-cost hydrogen supply-chain model represents demand and renewable variability, technology options, and flexible truck and pipeline scheduling. The U.S. Northeast application identifies conditions for electrolyzer cost-effectiveness and the importance of truck flexibility, while outlining future modeling needs.

V. Conclusions

  • The model represents spatial and temporal variability, multiple hydrogen technology options, and truck and pipeline scheduling flexibility for transmission and storage.It is proposed as a least-cost infrastructure planning model subject to operational and policy constraints.Source: §V · p. 8
  • In the U.S. Northeast, electrolyzers could be cost-effective under a $50/tonne carbon price and $500/kW unit capital cost; truck sharing across zones and routes is critical for flexibility and cost minimization.Shared trucks serve as storage across zones and transmission across routes.Source: §V · p. 8
  • If hydrogen demand grows in heating and industry, planning will require coupled power-system and hydrogen-supply-chain modeling; more detailed pipeline flow, linepack, and gas-pipeline retrofitting are future directions.Source: §V · p. 8

and Storage Scheduling: Supporting Information

The supplied supporting-information material identifies the paper's arXiv version and lists its authors.

1 Transportation Demand

The supporting information describes average hydrogen refuelling demand for light- and heavy-duty vehicles and an hourly refuelling profile normalized by its mean. It identifies these demand components and the profile used in calculating hydrogen demand by zone and time.

1.1 Average Hydrogen Refuelling Demand

  • Table S1 reports average hourly hydrogen refuelling demand for light-duty and heavy-duty vehicles.Source: Table S1 · p. 9

1.2 Hydrogen Refuelling Profile

  • Figure S1 presents the hourly hydrogen refuelling profile normalized by its mean.Source: Figure S1 · p. 10

1.3 Hydrogen Demand Profile Calculation

  • The demand-profile calculation uses zone-level light- and heavy-duty vehicle demand and the hourly hydrogen refuelling profile.Source: §1.3 · p. 10

2 Inter Zone Distances

The supporting information provides inter-zone distances for the hydrogen supply-chain model.

  • Table S2 reports inter-zone distances for the hydrogen supply-chain model in miles.Source: Table S2 · p. 10

3 Electricity Price Profiles

The section specifies assumptions and inputs for electricity-price profiles, alongside technology-cost details and 2050 power-system planning results for the U.S. Northeast.

3 Electricity Price Profiles

  • The electricity-price profiles use a power-system capacity-expansion model, with 20 representative weeks modeled for annual operations using methods described in.The base case does not allow electricity-transmission capacity expansion.Source: §3 · p. 11
  • Projected 2050 load comes from the 2018 NREL Electrification Futures Study under BAU technology advancement and reference electrification.The setup is greenfield, except existing hydro, pumped-storage, and transmission infrastructure are considered.Source: §3 · p. 11
  • Wind and solar inputs use the NREL Wind Toolkit and National Solar Radiation Database; offshore wind is unlimited in zones 2 and 4, based on sampled lease-area sites.The offshore-wind profile uses sampling sites that overlap areas where leases have been auctioned.Source: §3 · p. 11
  • The power model includes flexible reservoir hydro and Canadian hydro imports with weekly energy limits, plus inflexible run-of-river hydro.Source: §3 · p. 11

3.1 Power System Technology Cost and Performance Assumptions

  • Table S3 presents power-system technology cost details in 2019 US$.Source: Table S3 · p. 11

3.2 Power System Capacity Mix

  • Figure S2 presents optimal power-system planning results for the U.S. Northeast in 2050.Source: Figure S2 · p. 12

4 Hydrogen Generation Cost and Performance Parameters

Table S4 lists parameters for hydrogen-generation technologies in 2019 US$.

  • Table S4 presents parameters for H2 generation technologies in 2019 US$ [4–8].Source: Table S4 · p. 12

5 Hydrogen Transmission and Storage Parameters

Table S5 lists parameters for hydrogen transmission and storage in 2019 US$.

  • Table S5 presents parameters for H2 transmission and storage in 2019 US$ [9–13].Source: Table S5 · p. 13
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