On the 7th of December, our work performed in collaboration with the Swedish TSO, Svenska kraftnät, was accepted in the journal IEEE Transactions on Power Systems.
In this paper, we present how we precisely cast the problem of dimensioning Frequency Restoration Reserves (FRR) in a multi-area setting. This novel methodology aims at decreasing the total reserve needs in both upward and downward directions for each area while keeping the desired reliability at the system level, and accounting for reserve exchange between the four Load Frequency Control (LFC) areas, which are indicated in the figure below.
The problem is modeled as a two-stage chance-constrained optimization problem. The input to our model includes historical data of imbalances, contingencies, and available transmission capacity. Due to the difficulties associated with the nature of the proposed mixed-integer linear program, we introduce a heuristic in order to obtain a feasible solution that is computationally tractable for both commercial and open-source solvers. Furthermore, we adapt our methodology in order to size automatic (aFRR, secondary) and manual (mFRR, tertiary) reserve needs separately while accounting for their interdependence.
Another interesting aspect emerging from the Proof of Concept performed is the fact that multiple solutions exist for the reserve minimization problem. Thus, we execute a second optimization step where we distribute reserves among areas such that the exchange of reserves is minimized and all the constraints of the first step are satisfied.
The presented work illustrates how N-SIDE helps to build the bridge between academia and industry, bringing innovative solutions to support System Operators in their road towards more efficient and reliable operations.
Do you want to have more details of our proposed methodology in the open-access article available in IEEE Transactions on Power Systems?
You can find more information on this topic in our previous blog post here.
Alberte holds a double master's degree in Energy Engineering from KTH (Sweden) and KU Leuven (Belgium) with a specialization in Smart Grids. He works on applications of mathematical programming and machine learning within electric power system operations, such as grid expansion planning or sizing of balancing capacity.Alberte Bouso