Europe’s power grid is rapidly becoming a crowded airspace. We’ve spent billions on controllable storage, virtual power plants, and responsible industrial demand, yet the cost of managing the traffic is skyrocketing.

The math simply doesn’t add up.

One major reason for this quite expensive paradox is that while we have successfully built the clean resources, we are still managing traffic using a 'voice radio' infrastructure.

To thoroughly analyze the digital rewiring of European grid operations and the tools required, we dug in deep and created a five-part series on grid congestion management. In today’s first installment, we examine the mechanics behind this multi-billion-euro bottleneck - and the first steps on how we can solve it.

To understand the issues that arise with grid congestion, first let’s look at Germany. In 2023, the country spent a staggering €3.13 billion on grid congestion management, effectively paying wind and solar farms to shut down while ramping up fossil-fuel plants elsewhere. Even  slight structural relief and favorable weather in 2024 could only bring that number down to a still-eye-watering €2.7 billion in 2024. These grids are clearly choking. Under a business-as-usual pathway, uncoordinated renewable growth could force operators to throw away up to 310 TWh of clean power by 2040.

We have been told for years that grid congestion is a physical hardware problem. The conventional wisdom says we just need to lay more high-voltage copper cables, build more substations, and wait for the grid to absorb more solar and wind.

But that diagnosis is not complete. We don’t have a shortage of physical flexibility, nor do we have a shortage of trading platforms. What we have is a crisis in orchestration. The exploding cost of redispatch was long perceived to be an infrastructure deficit - when it is increasingly becoming a software and integration failure.

Grid mechanics

This software failure stems from a fundamental mismatch: how we trade electricity versus how we physically move it.

Our wholesale power markets are built on a beautiful fiction: the so-called "copper plate" model. This design assumes that the grid is a frictionless, infinite plane. The market matches the cheapest generation with the highest demand across massive national bidding zones, completely ignoring whether the physical wires have the capacity to carry that power.

But the physical grid doesn't care about market clearing prices; it cares about thermodynamics. When a sunny, windy day clears a massive volume of cheap power in the north, and the market schedules it to flow to industrial centers in the south, physical reality intervenes. The lines reach their thermal limits.

Congestion is the literal gap between what the market wants to do and what the physical wires can actually handle.

And how are we handling it?

Think of it like a busy airport. If a runway suddenly closes due to icy conditions right as a dozen planes are on final approach, what you might expect is an automated airspace vectoring software instantly recalculating trajectories. But what we have instead is a controller in the tower picking up a radio and manually calling each pilot one by one to tell them to circle, while contacting other airports to find an open slot. 

Pretty inefficient, right?

Control tower toolkit

When physical reality clashes with commercial market schedules, grid operators must step in to keep the lights on. They have a standard, four-step toolkit to resolve these bottlenecks:

Topology Reconfiguration (Zero-Cost): System operators first utilize discrete, no-cost remedial actions to reroute power flows. These include line switching, node splitting, and adjusting Phase-Shifting Transformer (PST) tap setpoints. Specialized Remedial Action Optimization (RAO) software (such as N-SIDE's platform) can evaluate hundreds of topological combinations in less than one second, ensuring native N-1 security and reducing necessary redispatch volumes before any commercial generators are touched.

Countertrading (Costly): If topological measures cannot resolve a constraint, particularly at cross-zonal boundaries, TSOs pay to execute offsetting commercial trades in adjacent bidding zones to reroute flows across borders.

Redispatch (Last Resort): The system operator directly instructs - and pays a premium for - specific generators or flexible loads on either side of the bottleneck to adjust their active power schedules (downregulating generation on the congested side and upregulating on the downstream side).

Decentralized Flexibility Marketplaces: In advanced regimes, TSOs and DSOs co-procure this physical adjustment from aggregated distributed energy resources (DERs) through structured, localized marketplaces.

In theory, this toolkit should protect the system elegantly. 

In practice, the underlying grid structure makes it incredibly difficult to execute.

Redispatch was designed to be the ultimate emergency override - a costly, manual last resort when physics disagreed with the market. But because operators lack the high-speed digital coordination required to deploy dynamic local flexibility, that override has become their everyday habit.

Instead of dynamically tapping into local batteries or demand response, busy control room operators default to what is familiar, safe, and manual: shutting down clean wind farms and paying expensive, centralized fossil-fuel plants to fire up. The last resort has become the default setting, simply because the software layer isn’t fast enough to make the alternatives viable.

Balancing vs. congestion

These two concepts are similar, yet they solve entirely different problems across different scales:

The structural mess occurs because the exact same physical assets - like utility-scale batteries or industrial loads - are expected to do both jobs.

When you use a single asset base to solve two distinct grid problems, the activation paths cross. A battery activated to stabilize national frequency might suddenly dump power onto a local distribution grid that is already operating at its thermal capacity. This operational divide forces operators to pull conflicting levers, muddying localized price signals and cannibalizing their own system flexibility.

If you've been following our work on aFRR and mFRR markets, think of congestion management as the geographic boundary that balancing cannot cross. You cannot efficiently stabilize system-wide frequency when the assets you need are being throttled by a highly localized network bottleneck.

Costs keep soaring anyway

If we have the assets and we have the marketplaces, why are congestion costs still exploding? The answer lies in three structural software hurdles:

1. Single-zone wholesale markets (like Germany) ignore internal physical bottlenecks and treat the network like a frictionless copper plate. This suppresses localized price signals, forcing administrative software and manual operators to correct what the market design itself fails to resolve.
    
2. The vast majority of new flexible assets - like residential batteries, heat pumps, and EV fleets - sit at the distribution level managed by DSOs. Yet, TSOs and DSOs operate separate, isolated software stacks with no live, automated data interface between them. Activating a DSO-connected asset for a TSO-level constraint remains slow and heavily manual.
    
3. Even when flexibility bids exist in a marketplace, the physical chain of command takes too long. The journey from "congestion detected" in a SCADA system to "asset setpoint changed" still involves human checkpoints and legacy data exchange protocols built for day-ahead planning. This adds minutes of delay and megawatts of operational imprecision.

Breaking the flex barrier

The frontier of automated, software-driven congestion management is already active, proving what is possible when data flows smoothly:

🇫🇮FinFlex (Finland): Fingrid and Helen Electricity Network have partnered to co-procure local flexibility through the NODES platform. Operating as a live TSO-DSO co-procurement market, it allows both operators to access the same pool of localized resources without tripping over each other's networks.

🇳🇱GOPACS (Netherlands): This platform acts as an automated coordinating layer between TSOs and DSOs. When an operator flags a local bottleneck, GOPACS automatically searches and matches order books on EPEX SPOT or Nord Pool to pair a buy order inside the congested zone with a counter-bid outside it, resolving the constraint without unbalancing the grid.

🤖Algorithmic engines, such as N-SIDE’s Remedial Action Optimiser, are demonstrating that topology changes alone can reduce redispatch costs from €3.33/MWh to €0.49/MWh. By evaluating hundreds of network configurations in seconds, it allows operators to maximize step one of the cascade before a single commercial asset is even touched.

While these pioneering platforms represent the leading edge of grid optimization, none of them solve the entire problem out of the box. Instead, they point to a massive operational vacuum that the market has to fill: total automation of the decision cascade, backed by a flawless, live data interface running all the way from the TSO's grid model down to the asset layer.

The current reality on the ground remains deeply fragmented. When you peel back the software layers of these implementations, the underlying execution gaps quickly become visible.

The missing hand-off

All of these projects share one critical architectural requirement: the flexibility signal must reach asset-level dispatch automatically, in near real-time, without a human in the loop.

Today’s commercial flexibility marketplaces are excellent at procuring capacity. They are far less equipped to ensure that the asset on the receiving end actually delivers at the exact millisecond of activation. Because the aggregator or Virtual Power Plant (VPP) platform managing the capacity is rarely integrated into the same real-time data flow, a severe execution gap remains.

This is the exact operational bottleneck that automated VPP orchestration and modern trading suites are built to eliminate. When a DSO issues an automated activation instruction via an API, the physical asset - whether an industrial battery or a chilled warehouse - needs to execute its ramp-rate response within a tight operational window.

Fixing this does not require rewriting market rules from scratch. What it requires is a direct, low-latency data bridge from the marketplace platform straight to the edge of the physical asset.

The question every operator should be asking

Rising redispatch costs are a symptom of an IT architecture built for a grid that no longer exists - a world where most flexibility lived at the transmission level and could be managed with a simple phone call.

Grid congestion is both a financial threat and a massive chance for integration. The energy transition does not need more copper cables nearly as fast as it needs smarter flight management.

The friction of manual interfaces must be replaced with modular, API-driven software architectures that act as a high-speed link between physical grid operations and the assets that keep power flowing smoothly. This automated integration layer allows us to finally stop pulling the dispatch lever as a default fix, and start managing grid congestion at the top flight speed of the energy market.

If you're still debating whether to automate your orchestration layer, perhaps you need to ask yourself: How much more money can we afford to burn?

Coming up next is Part 2 of the Grid Congestion Management Series, where we will talk about software orchestration. Grid bottlenecks are not confined within national borders: a loop flow triggered in northern Germany can instantly choke a transformer in Poland or Austria. If TSOs try to solve these bottlenecks in isolation, they end up passing the problem (and the cost) along to their neighbors. We need to zoom out to the macro-regional level.