# Procurement of Spinning Reserve Capacity in aHydropower Dominated System Through MixedStochastic-Robust Optimization

## TL;DR

This paper introduces a mixed stochastic-robust optimization model for procuring spinning reserve capacity in hydropower systems, effectively balancing cost efficiency and system security amid uncertain net load and geographic coordination challenges.

## Contribution

It develops a novel two-stage model that captures the impact of reserve activation on hydropower water flow, improving reserve procurement strategies under uncertainty.

## Key findings

- The proposed model outperforms traditional methods in case studies.
- It effectively balances cost and security without excessive conservatism.
- The model captures geographic coordination effects in reserve procurement.

## Abstract

As the penetration of variable renewable powergeneration increases in power systems around the world, systemsecurity is challenged. Ensuring that enough reserve capacity isavailable to balance the increased forecast errors introduced bywind and solar power are and will be an important challengeto solve for system operators. This is also true in systems thatalready have flexible balancing resources, such as hydropower.The main challenge in this case will be the coordination ofthe reserve procurement between connected hydropower plants,as the water flow is changed when the reserve capacity isactivated. However, the activation step is often ignored orsimplified in hydropower scheduling models that include reservecapacity procurement. In this paper, a two-stage model forscheduling power and procuring symmetric spinning reserves in ahydropower system with uncertain net load is proposed to capturethe effect of geographic reserve capacity coordination. To modelthe uncertainty in the net load, a new mixed stochastic-robustoptimization model is proposed to achieve cost efficiency andsystem security. We show that this proposed model outperformsits natural contenders in the given case study, and so does notsuffer from the typical overly conservative nature of pure robustmodels.