Optimal Dynamic Line Rating Forecasts Selection Based on Ampacity Probabilistic Forecasting and Network Operators’ Risk Aversion
Tóm tắt
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: First, probabilistic forecasts for the current rating of transmission lines are generated, paying particular attention to the reliability of the lower part of the distribution. Second, 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.
Từ khóa
#Dynamic line rating #numerical weather predictions #probabilistic forecasts #risk managementTài liệu tham khảo
10.1049/cp.2016.0722
lincoln, 2017, PyPower
10.1109/59.780914
10.1109/PESGM.2015.7286604
10.1109/TPWRD.2017.2691671
10.1109/ICCEP.2015.7177652
nguyen, 0, Dynamic line rating and ampacity forecasting as the keys to optimise power line assets with the integration of res. The European project Twenties Demonstration inside Central Western Europe, 946
nguyen, 0, Operational experience with dynamic line rating forecast-based solutions to increase usable network transfer capacity, Proc 45th Session Counc Large Electr Syst CIGRE
hoekstra, 0, Weather forecasted thermal line rating model for the Netherlands, Proc CIGRE Session
10.1007/s00202-012-0244-8
10.1109/TPWRD.2016.2543818
2018, DLR Forecasting Presented at FERC—Nexans
10.1109/TPWRS.2015.2449753
chen, 0, Impact of dynamic line rating with forecast error on the scheduling of reserve service, Proc Power Energy Soc General Meet, 1
10.1109/TSG.2016.2542922
ke, 2017, LightGBM: A highly efficient gradient boosting decision tree, Adv Neural Inf Process Syst, 3146
10.1109/T-PAS.1977.32393
10.1145/2939672.2939785
seppa, 2006, Guide for selection of weather parameters for bare overhead conductor ratings, 2
10.1109/EPEC.2010.5697195
snoek, 2012, Practical bayesian optimization of machine learning algorithms, Adv Neural Inf Process Syst, 2951
10.1109/PESMG.2013.6672679
10.1109/TPWRS.2014.2305872
10.1109/TSG.2014.2341353
2013, IEEE standard for calculating the current-temperature relationship of bare overhead conductors, 1
10.1016/j.rser.2015.07.134
2002, Thermal Behavior of Overhead Conductors
10.1109/AUPEC.2014.6966636
10.1109/TPWRS.2017.2786470
10.1109/PESGM.2015.7286387
10.1109/PES.2006.1709107
10.1109/PESGM.2014.6939381
10.1109/PESGM.2015.7285696
meinshausen, 2006, Quantile regression forests, J Mach Learn Res, 7, 983