Optimeering to support Nordic TSOs in joint reserve market project

Optimeering was recently awarded a major project on behalf of the four Nordic TSOs, Statnett, Svenska kraftnät, Fingrid and Energinet.dk, due to its core competance in numerical optimisation.

The four Nordic TSO are planning to implement a common Nordic capacity market for FRR-A (automatic frequency restoration reserves), and as a part of this work an optimisation tool for bid selection for a common Nordic market needs to be developed. 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 TSOs have asked Optimeering to provide external expertise to help develop the FRR-A clearing algorithm.

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 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 FRR-A capacity market that accounts for the complex bid structures via the use of advanced mathematical optimization techniques.

It is very encouraging to see that our core competence in modelling and algorithm development is well understood and highly valued by our clients, says Optimeering CEO Gavin Bell. It is critical for this market (as with many others) that bids are selected in an efficient, timely and transparent manner, and we are well-placed to deliver on this goal.

Optimeering welcomes Giulio Gola

GiulioGolaWe are pleased announce that Giulio Gola will be joining our team shortly. Giulio holds a PhD in Nuclear Engineering from Polytechnic of Milan/OECD Halden Reactor Project, Norway. His previous work experience includes General Electric, Institute for Energy Technology (IFE) and FirstSensing AS.

He has a spent a major part of his career developing complex computer systems for condition monitoring, system diagnostics and maintenance optimization within the energy sector. These systems consist of using (among others) ensembles of data-driven models (auto-associative neural networks, PCA, clustering) for fault detection and both empirical and stochastic methods for forecasting remaining lifetime for use in maintenance optimisation.

Giulio will undoubtedly strengthen Optimeerings competence in terms of analytics, data-driven prediction and optimisation.