1 / 19

Where Green Really Matters

Where Green Really Matters. Diego Klabjan & Yue Geng Industrial Engineering and Management Sciences Northwestern University. Agenda. Project overview Model Energy and emission aspects Current r esults Future actions. Project Overview. Scope Features Objectives. Scope.

iago
Download Presentation

Where Green Really Matters

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Where Green Really Matters Diego Klabjan & Yue Geng Industrial Engineering and Management Sciences Northwestern University

  2. Agenda • Project overview • Model • Energy and emission aspects • Current results • Future actions

  3. Project Overview Scope Features Objectives

  4. Scope • Assess long term economical viability of Summit • Supporting transportation logistics needs • Operational considerations at Summit • Energy requirements • Environmental impact • Emissions from transportation • Renewable on-site generation

  5. The Greenland Physics Problem • THE KEY RESOURCE QUESTIONS • How much does it cost? • How much will the cost escalate? • How can we use less of it? • PRIMARY NODES • Kangerlussuaq • Summit Station • Thule Air Base • Remote Sites… Field • KEY METRICS • Pounds of cargo moved (O-D) • Number of Pax moved (O-D) • Hourly airlift costs • Ship costs per pound • Volume of fuel consumed for flights and infrastructure • Annual researcher days supported • Energy consumption at Summit • ON-ISLAND MODES • Military airlift (LC-130) • Commercial air (Twin Otter, Helos, ?) • Oversnow Traverse Field

  6. Features • Highly seasonal demand • Long planning period – over 5 years or more • Different modes of transportation are available • Emission should be controlled • Different emission factors for different modes of transportation • Transportation of fuel • Renewable on-site generation

  7. Travel on Greenland

  8. Objectives • A tool to analyze scenarios • Cost • Emissions • Other key performance indicators • Given a possible scenario • Determine by means of optimization logistics requirements and expenses • Trade-off between cost and emissions

  9. Model Network flow model Calibration

  10. Network flow model • Standard time based model • Keeping track of fuel consumption at Summit • Base fuel consumption plus fuel consumed by people

  11. Time (Days/Weeks) LC-130 from Scotia on day 8 arrive Kanger on day 9 Inventory left from previous day Vessel from VA Beach on day 2 arriving to Kanger on day 32 Time window for shipment A at origin Shipment A Location in the US 1 2 3 4 5 6 7 8 31 32 … … 1 2 3 4 5 6 7 8 31 32 … … Kanger 1 2 3 4 5 6 7 8 31 32 … … Summit … Time window for shipment A at destination Locations Aggregate by week Shipment A

  12. Constraints • Flow balance for each commodity at each node • Consumable commodity • Non-consumable commodity • Capacity is not exceeded for each arc and each transportation mode • Inventory capacity is not exceeded at specific nodes • Certain matchup of commodity and transportation mode is forbidden

  13. Energy and Emission Aspect Background on transportation emission Features Methods

  14. Methods – Bi-level Goal Programming • Phase 1 • Minimize cost • Constraints are imposed on the constructed network • Phase 2 • Minimize emissions • Constraints are imposed on the constructed network • Cost ≤ (1+f)∙ optimal cost from Phase 1 • Control tractability by aggregation • During season aggregate by week • Off-season by month

  15. Size of the Model • Before aggregation of demand, thousands of commodities • After aggregation of demand • Around 150 commodities • Over 3, 000 arcs • Over 30, 000 variables • Over 10, 000 constraints

  16. Preliminary Results – Install renewables (f = 0.05)

  17. Current Results – Install renewables tons

  18. Future Actions • Calibrate the model based on data from more years • Develop decomposition algorithms to handle the long planning horizon • Handling possible future scenarios • Develop the decision support tool with the above ingredients

More Related