Modelling reserve - with some introverted algorithms (?)
New markets for reserve services and near-real-time power delivery require new, sophisticated tools for calculating optimal market clearing. In theory clearing a market is simple – you just need to match supply and demand. In practice though, complex bid and offer structures as well as integration between reserve services often mean that things are not quite so straight forward. Getting it right though is very important - clearing and pricing new markets reliably, transparently and quickly is essential for efficient market operation.
Optimeering has a unique combination of modelling expertise and market know-how that positions us to deliver robust, customised market clearing tools for any type of power market. Recently, we have developed the clearing algorithm for the upcoming pan-Nordic aFRR market for the SVK, Statnett, Energinet.dk and Fingrid TSO consortia.
The four Nordic TSOs are planning to implement a common Nordic capacity market for aFRR (automatic frequency restoration reserves) in 2018, and our part of this work is developing and implementing the market clearing engine used to select that combination of bids that is most efficient ("maximises social welfare surplus"). Given that the bidding rules are quite complex (bids can be linked upwards, downwards, and in time, marked as indivisible, and be asymmetrical, to mention some) and the requirement to ensure that the market operates in a socio-economically efficient manner, the problem is not easy.
The problem itself is what we call a combinatorial optimization problem or (mixed) integer program. The linking and non-divisibility of bids is a critical and fundamental characteristic of the bid selection problem that means traditional clearing methods based on hourly bid price alone are insufficient. Given the size of the problem (hourly bids in multiple bidding zones), checking every single possible combination of bids is also not a feasible approach. Instead, a solution algorithm is needed to select bids for the aFRR market that accounts for the complex bid structures via the use of advanced mathematical optimization techniques. We have come up with some clever formulation approaches (if we do say so ourselves ...) that take advantage of structure in the problem to clear the market optimally and very quickly (here we mean seconds, not minutes or hours). Our approach also works for several alternative pricing mechanisms, including pay-as-bid and marginal-cost pricing.
If you are interested in learning more about the aFRR market, or how we can help in other markets, please contact us.