Earlier this year, I wrote an introduction to the bid filtering problem, and explained how my team at Statnett are trying to solve it. The system we’ve built at Statnett combines data from various sources in its attempt to make the right call. But how well is it doing its job? Or, more precisely, what is the effect on network congestion of applying our bid filtering system in its current form?
Without calling it a definitive answer, a paper I wrote for the CIGRE Symposium contains research results that provide new insight. The symposium was in Kyoto, but a diverse list of reasons (including a strict midwife) forced me to leave the cherry blossom to my imagination and test my charming Japanese phrases from a meeting room in Trondheim.
A quick recap
European countries are moving toward a new, more integrated way of balancing their power systems. In a country with highly distributed electricity generation, we want to automatically identify power reserves that should not be used in a given situation due to their location in the grid. If you would like to learn the details about the approach, you are likely to enjoy reading the paper. Here is the micro-version:
To identify bids in problematic locations, we need a detailed network model, we try to predict the future situation in the power grid, and then we apply a nodal market model which gives us the optimal plan for balancing activations for a specific situation. But since we don’t really know how much is going to flow into or out of the country, we optimize many times with different assumptions on cross-border flows. Each of the exchange scenarios tells its own story about which bids should -and shouldn’t- be activated. The scenarios don’t always agree, but in the aggregate they let us form a consensus result, determining which bids will be made unavailable for selection in the balancing market.
An unfair competition
Today, human operators at Statnett select power reserves for activation when necessary to balance the system, always mindful of their locations in the grid and potential bottlenecks. Their decisions on which balancing bids to activate – and not activate – often build years of operational experience and an abundance of real-time data.
Before discussing whether our machine can beat the human operators, it’s important to keep in mind that the bid filtering system will take part in a different context: the new balancing market, where everyday balancing will take place without the involvement of human operators. This will change the rules of the balancing game completely. While human operators constantly make a flow of integrated last-minute decisions, the new automatic processes are distinct in their separation of concerns and must often act much earlier to respect strict timelines.
Setting up simulations
The quantitative results in our paper come from simulating one day in the Norwegian power grid, using our detailed, custom-built Python model together with recorded data. The balancing actions -and the way they are selected- are different between the simulations.
The first simulation is Historical operation. Here, we simply replay the historical balancing decisions of the human operators.
The second simulation is Bid filtering. Here, we replace the historical human decisions with balancing actions selected by a zonal market mechanism that doesn’t see the internal network constraints or respect the laws of physics. The balancing decisions will often be different from the human ones in order to save some money. But before the market selects any bids, some of them are removed from the list by our bid filtering machine in order to prevent network congestion. We try not to cheat, the bid filtering takes place using data and forecasts available 30 minutes before the balancing actions take effect.
The third simulation is No filtering. Here we try to establish the impact on congestion of moving from today’s manual, but flexible operation to zonal, market-based balancing. This simulation is a parallel run of the market-based selection, but without pre-filtering any bids, and it provides a second, possibly more relevant benchmark.
Power flow analyses
The interesting part of the simulation is when we inject the balancing decisions into the historical system state and calculate all power flows in the network. Comparing these flows to the operational limits reveals which balancing approaches are doing a better job at avoiding overloads in the network.
The overloads are similar between the simulation, but they are not the same. To better understand the big picture, we created a congestion index that summarizes the resulting overload situation in a single value. The number doesn’t have any physical interpretation, but gives a relative indication of how severe the overload situation is.
When we run the simulation for 24 historical hours, we see that with market-based balancing, there would be overloads throughout the day. When we apply bid filtering and remove the bids expected to be problematic, overloads are reduced in 9 of the 24 hours, and we’re able to avoid the most serious problems in the afternoon.
No matter the balancing mechanism, the congestion index virtually never touches zero. Even the human operators with all their extra information and experience run into many of the same congestion problems. This shows that balancing activations play a role in the amount of congestion, but they are just one part of the story, along with several other factors.
With that in mind, if you’re going to let a zonal market mechanism decide your balancing decisions, it seems that bid filtering can have a clear, positive effect in reducing network overloads.
What do you think? Do you read the results differently? Don’t be afraid to get in touch, my team and I are always happy to discuss.