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• 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]