Optimal Dynamic Line Rating Forecasts Selection Based on Ampacity Probabilistic Forecasting and Network Operators' Risk Aversion

Romain Dupin (1) , Andrea Michiorri (1) , Georges Kariniotakis (1)

1 PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques

Real-time current-carrying capacity of overhead conductors is extremely variable due to its dependence on weather conditions, resulting in the use of traditionally conservative static ratings. This paper proposes a methodology for exploiting the latent current-carrying capacity of overhead transmission lines taking into account line ampacity forecasts, power flow simulations and the network operator's risk aversion. The procedure can be described as follows: Firstly, probabilistic forecasts for the current rating of transmission lines are generated, paying particular attention to the reliability of the lower part of the distribution. Secondly, a cost benefit analysis is carried out by solving a bilevel stochastic problem that takes into account the reduction in generation costs resulting from a higher power transfer capacity and the increased use of reserves caused by forecast errors. The risk appetite of the network operator is considered in order to accept or penalize high-risk situations, depending on whether the network operator can be described as risk neutral or risk averse.

Mots clés: Probabilistic Forecasts; Risk Management; Dynamic Line Rating; Numerical Weather Predictions

Domaines: Energie électrique; Machine Learning [stat.ML]; Applications [stat.AP]; Optimisation et contrôle [math.OC]; Probabilités [math.PR]; Statistiques [math.ST]

Romain Dupin, Andrea Michiorri, Georges Kariniotakis. Optimal Dynamic Line Rating Forecasts Selection Based on Ampacity Probabilistic Forecasting and Network Operators' Risk Aversion. IEEE Transactions on Power Systems, inPress, pp.1-1. ⟨10.1109/TPWRS.2018.2889973⟩. ⟨hal-01998856⟩