EFEU / FLEXe Krooks Jan optimization of gas distribution logistics_optimization of gas distribution logistics

Optimization of gas distribution logistics

31.10.2016

Jan Krooks Senior Development Manager Wärtsilä Energy Solutions

Gas distribution enabling renewable energy

•  Increases in use of renewable energy (solar and wind) requires dynamic power producers.

•  Gas fired solutions, especially gas engines, gives a high efficient and rapid support for the electricity grids.

•  Today natural gas is mainly available in gas grids and as liquified natural gas (LNG) in large scale terminals. •  Large scale LNG shipping

>120 000 m3

•  Large scale LNG storage >160 000 m3

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Loadcurve,future- highwindSolar Wind Windcurtailment Flexiblecapacity Low-carbonbaseload

What will small scale LNG bring?

•  Small scale LNG enables: •  Gas fired power plants in areas with no

gas grid and in islands •  Gas to industial consumers •  Gas bunkering to ferrys, supply vessels

and smaller transport vessels •  Gas for road transportation •  Utilisation of smaller stranded gas

sources •  Back-up for liquid biogas

•  An affordable small-scale LNG infrastructure brings a more environmentally friendly fuel to completely new parts of the world.

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Optimization of LNG supply – Small scale LNG

Objective function Min (Costs)

Costs = Shipping cost (renting cost, propulsion cost, port fee) + Investment cos (terminal and storage)

+ LNG purchased at the supply terminals

The optimization model can give answers to: •  How many vessels are needed? Which sizes? •  Time the vessels are in use? •  Vessel routes (from-to)? •  Storage size and “inventory”

The optimization model (mixed-integer linear programming (MILP)) helps setting up LNG supply chains and determining how the inputs affect the feasibility of the supply chains

Optimization cases – Small scale LNG

Optimization of CNG container distribution chain

Problem formulation Determine the best supply chain and the number of containers to be transported to the operating tanking stations. Initial information •  Potential container filling stations (•) •  Potential vehicle tanking stations (n) •  Maximum tanking station demand •  Container capacity and costs

(investment, transport, maintenance) •  Transportation times and constraints

Problem solution The problem was tackled by mixed integer linear programming (MILP). Results for example case: transportation routes (lines) with number of containers (numbers) transported per time period.