WijkWise Planner

What it is

WijkWise Planner is a simulation-first concept tool that helps Dutch municipalities, housing associations, and neighborhood (wijk) energy planners answer one hard question: can the local electricity grid handle electrification, and what should the neighborhood do first?

The Netherlands is electrifying homes and transport fast, with more heat pumps, solar panels, EV chargers, and batteries every year. WijkWise lets a non-technical planner pick a neighborhood, adjust a few simple controls, and watch projected outcomes resolve for grid congestion risk, transformer headroom, heat-pump readiness, EV charging pressure, solar potential, and battery opportunity. The goal is to move people from uncertainty to action without needing grid engineering knowledge.

Why I started thinking about this

I started thinking about this in the context of the Netherlands after reading news coverage about how grid congestion is becoming a growing problem, and how planning is underway to mitigate it in the years ahead. Those plans are not only about infrastructure. They also involve people working together at the smallest local scale, in neighborhoods, to conserve energy, coordinate demand, and make electrification possible. WijkWise explores what that kind of shared planning could feel like.

How it works

The experience is built around a "what happens if…" loop. You explore questions like: what if more homes install heat pumps? What if EV adoption climbs? What if smart charging is enabled, solar expands, a shared battery is added, or insulation comes first?

Map · scenario · outcome. A stylised wijk choropleth map sits on the left, a Scenario Builder in the center, and a projected-outcome panel on the right. You build a scenario, hit Run simulation, and the result resolves spatially.

Named AI agents. When a simulation runs, four named agents animate in sequence: Demand Modeler → Congestion Forecaster → Driver Analyst → Action Advisor. The outcome then appears with a model confidence score that drops for far horizons and aggressive extrapolation.

Every result explains itself. The outcome leads with a plain-language verdict ("This scenario overloads Oud-West's grid"), then interprets each of the six metric cards in a sentence, breaks down what changed and why with colour-coded raises / lowers / shifts load drivers, and surfaces the single highest-impact recommended first action.

Reading the risk

The four risk tiers describe how much pressure a scenario puts on the neighbourhood’s electricity grid at its busiest moments. As more homes use heat pumps or charge EVs at the same time, demand can approach the capacity of the local transformer — the equipment that supplies electricity to the neighbourhood. Headroom means the spare capacity left before that limit is reached.

• Low · BlueThere is plenty of spare capacity. The neighbourhood can accommodate more electricity use in this scenario.
• Medium · YellowSpare capacity is shrinking. The grid can still cope, but it is a good time to plan how future demand will be managed.
• High · OrangeThere is little room left at peak times. Shifting EV charging or reducing heating demand could help ease the pressure.
• Critical · RedThe grid is close to or beyond its capacity. The scenario needs changes to reduce peak demand or increase available capacity.

These are simulated planning signals, not predictions of a power cut. Compare scenarios to see which choices move the neighbourhood toward a lower risk tier.

To teach the controls, the Scenario Builder opens with three one-click sample scenarios tuned to land on Low, Medium, and High outcomes. A first-time user immediately sees how the levers translate into grid risk, then tweaks from there.

Who it's for

It's aimed at the people making local electrification decisions: municipal energy teams, housing associations, and neighborhood planners who need to sense where the grid is heading before committing to a plan. The interface is intentionally warm and approachable rather than technical: English with Dutch terms, soft civic palette, friendly type.

This is a demo concept built with realistic sample data. It is not factual grid data and the figures should not be treated as real; everything in the app is flagged "Demo data · not real grid figures."