Optimising 280 GW of North Sea energy against ecology & fisheries

hero image — North Sea scenario map (1600×900)

The Ecology scenario: wind capacity redistributed to protect Natura 2000 habitat, at a modelled cost of +€5.2 B/yr in operating expenditure.

01The problem

The North Sea is one of the most contested seas in the world. There are seven different countries with territory within the sea, each of which has its own spatial plan for their EEZs; on top of that the EU has set various energy and environmental targets.

The question the project set out to answer was simple: can we initiate discussion between all the actors within the North Sea? The solution for us was to provide a transparent model that let policymakers see, in hard numbers, what each priority costs the others.

constraint-layers.png — drop into images/north-sea-msp/

Input constraint layers: protected habitat, shipping lanes, cable corridors and bathymetry, harmonised across seven national datasets.

02The approach

I framed the sea as a linear optimization problem. The study area was divided into candidate zones, each of which could be assigned to wind, fishery, conservation or mixed use. An objective function weighed energy yield, ecological value and food production; a set of hard constraints encoded the things that simply cannot be violated — shipping separation, protected sites, minimum spacing between farms.

The whole thing was built in open-source Python — PuLP to express the model and HiGHS as the solver — sitting on a GeoPandas spatial backbone that harmonised the seven countries' data into one coordinate system and one set of units.

Rather than argue about a single plan, the model lets each stakeholder watch their priority's true cost appear in the numbers.

03What I built

A configurable optimization model expressing energy, ecology and fishery objectives with swappable weightings.
A data pipeline harmonising marine protected areas, EEZ boundaries, cable routes and fishing intensity across seven national sources.
A scenario engine that runs the model under different priority weightings and writes comparable CSV outputs.
A visualization layer producing the scenario maps and trade-off charts used in stakeholder workshops.

scenario-comparison.png — drop into images/north-sea-msp/

Four scenarios compared across levelised cost, emissions and an ecological index — the core decision-support output.

04The outcome

The model produced four contrasting scenarios — business-as-usual, energy-first, fishery-protective and ecology-protective — each with a full set of comparable metrics. For the first time the trade-offs were quantified rather than asserted: an ecology-protective layout, for instance, raised the levelised cost of energy but measurably increased the ecological index and preserved fishing grounds.

The work fed into a public-facing exhibit and a technical decision-support tool, giving non-technical stakeholders a way to explore the consequences of their preferences directly.