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• Reserve capacity activation is explicitly modelled for hydropower plants. • A new hybrid stochastic-robust optimization model is formulated. • The column-and-constraint generation technique is used to create extreme scenarios. • The new hybrid model is compared to the standard stochastic and robust formulations. As the penetration of variable renewable power generation increases in power systems around the world, system security is challenged. It is crucial to coordinate the available flexible generating resources, such as hydropower, to meet the need for system balancing. However, reserved capacity on hydropower plants should only be activated if there is sufficient energy or storage capacity to either increase or decrease production. The potential change in production will also affect all reservoirs and plants connected by the cascaded topology. These issues are largely ignored or simplified in hydropower reserve scheduling models. To properly account for the possible activation of reserved capacity, several two-stage model formulations based on stochastic and robust optimization are presented and compared in this paper. The uncertainty in net load deviations due to forecasting errors in renewable power generation is considered the source of reserve capacity activation. The case study based on a real Norwegian watercourse clearly shows the benefit of using any of the two-stage model solutions over the standard deterministic reserve procurement. A novel hybrid stochastic-robust model formulation is presented and shown to efficiently increase the robustness of the solution without notably increasing the reserve procurement cost compared to the stochastic and robust models. [ABSTRACT FROM AUTHOR]

• A chance-constrained DCOPF with non-parametric correlated probabilistic forecasts of DLR and wind power. • The model co-optimizes energy and reserves with typical risk aversion levels of TSOs. • Assuming a normal distribution overestimates the probabilistic forecasts of DLR. • DLR reduces wind power curtailment and dispatch costs in highly congested networks. Compared to Seasonal Line Rating (SLR), Dynamic Line Rating (DLR) allows for higher power flows on overhead transmission lines, depending on the actual weather conditions. Nevertheless, the potential of DLR has to be traded off against the additional uncertainty associated with varying ratings. This paper proposes a DC-Optimal Power Flow (DCOPF) algorithm that accounts for DLR uncertainty by means of Chance-Constraints (CC). The goal is to determine the optimal day-ahead dispatch taking the cost of reserve procurement into account. The key contribution of this paper consists in considering both non-parametric predictive distributions of DLR and the combined wind power uncertainty in the optimization problem. Our results highlight the benefits of DLR in wind-dominated power systems, assuming typical risk aversion levels in the line rating estimation. [ABSTRACT FROM AUTHOR]