Using data to handle intra-zonal constraints in the upcoming balancing market

Together with almost half of the Data science group at Statnett, I spend my time building automatic systems for congestion management. This job is fascinating and challenging at the same time, and I would love to share some of what our cross-functional team has done so far. But before diving in, let me first provide some context.

A good day at Statnett’s control centre. Photo: Trond Isaksen

The balancing act

Like other European transmission system operators (TSOs), Statnett is keeping the frequency stable at 50 Hz by making sure generation always matches consumption. The show is run by the human operators in Statnett’s control centre. They monitor the system continuously and instruct flexible power producers or consumers to increase or decrease their power levels when necessary.

These balancing adjustsments are handled through a balancing energy market. Flexible producers and consumers offer their reserve capacity as balancing energy bids (in price-volume pairs). The operators select and and activate as many of them as needed to balance the system. To minimize balancing costs, they try to follow the price order, utilizing the least expensive resources first.

While busy balancing the system, control centre operators also need to keep an eye on the network flows. If too much power is injected in one location, the network will be congested, meaning there are overloads that could compromise reliable operation of the grid. When an operator realizes a specific bid will cause congestion, she will mark it as unavailable and move on to use more expensive bids to balance the system.

In the Norwegian system, congestion does not only occur in a few well-known locations. Due to highly distributed generation and a relatively weak grid, there are hundreds of network constraints that could cause problems, and the Norwegian operators often need to be both careful and creative when selecting bids for activation.

A filtering problem

The Nordic TSOs are transitioning to a new balancing model in the upcoming year. A massive change is that balancing bids will no longer be selected by humans, but by an auction-like algorithm, just as in many other electricity markets. This algorithm (unfortunately, but understandably) uses a highly aggregated zonal structure, meaning that it will consider capacity restrictions between the 12 Nordic bidding zones, but not within them.

Consequently, the market algorithm will disregard all of the more obscure (but still important) intra-zonal constraints . This will -of course- lead to market results that simply do not fit inside the transmission grid, and there is neither time nor opportunity after the market clearing to modify the outcome in any substantial way.

My colleagues and I took the task of creating a bid filtering system. This means predicting which bids would cause congestion, and mark them as unavailable to prevent them from being selected by the market algorithm.

Filtering the correct bids is challenging. Network congestions depend on the situation in the grid, which is anything but constant due to variations in generation, consumption, grid topology and exchange with other countries. The unavailability decision must be made something like 15-25 minutes before the bid is to be used, and there is plenty of room for surprises during that period.

How it works

Although I enjoy discussing the details, I will give a only a short summary of the system works. To decide the availability of each bid in the balancing market, we have created a Python universe with custom-built libraries and microservices that follows these steps

  1. Assemble a detailed model of the Norwegian power system in its current state. Here, we combine the grid model from Statnett’s equpment database and combine it with fresh data from Statnett’s SCADA system.
  2. Adjust the model to reflect the expected state.
    Since we are looking up to 30 minutes ahead, we offset the effect of current balancing actions, and apply generation schedules and forecasts to update all injections in the model.
  3. Prepare to be surprised.
    To make more robust decisions in the face of high uncertainty in exchange volumes, we even apply a hundred or more scenarios representing different exchange patterns on the border.
  4. Find the best balancing actions for each scenario of the future.
    Interpreting the results of an optimal power flow calculation provides lots of insight into which bids should be activated (and which should not) in each exchange scenario.
  5. Agree on a decision.
    In the final step, the solution from each scenario is used to form a consensus decision on which bids to make unavailable for the balancing market algorithm.

An example result and how to read it

My friend and mentor Gerard Doorman recently submitted a paper for the 2022 CIGRE session in Paris, explaining the bid filtering system in more detail. I will share one important figure here to illustrate the final step of the bid filtering method. The figure shows the simulated result of running the bid filtering system at 8 AM on August 23, 2021.

Simulated bid filtering results from August 23, 2021. Bids are sorted horizontally, according to price, and grouped by their bidding zone (NO1 through 5) and direction (up/down).

Before you cringe from information overload, let me assist you by explaining that the abundance of green cells in the horizontal bar on top shows that the vast majority of balancing bids were decided to be available.

There are also yellow cells, showing bids that likely need to be activated to keep the system operating within its security limits, no matter what happens.

The red cells are bids that have been made unavailable to prevent network congestion. To understand why, we need to look at the underlying results in the lower panel. Here, each row presents the outcome of one scenario, and purple cells show the bids that were rejected, i.e. not activated in the optimal solution, although being less expensive than other ones that were activated for balancing in the same scenario (in pink).

The different scenarios often do not tell the same story, a bid that is rejected in one scenario can be perfectly fine in the next, it all depends on the situation in the grid and which other bids are also activated. Because of this ambiguity, business rules are necessary to create a reasonable aggregate result, and the final outcome will generally be imperfect.

So, does this filtering system have any postive impact on network congestions at Statnett? I will leave the answer for later, but if you’re curious to learn more, don’t hesitate to leave a comment.

Author: Martin Håberg

Data scientist at Statnett. Product owner for Automatic congestion management systems. Loves maps, languages and complex problems.

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