TY - GEN
T1 - Learning Solutions for Intertemporal Power Systems Optimization with Recurrent Neural Networks
AU - Mohammadian, Mostafa
AU - Baker, Kyri
AU - Dinh, My H.
AU - Fioretto, Ferdinando
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Learning mappings between system loading and optimal dispatch solutions has been a recent topic of interest in the power systems and machine learning communities. However, previous works have ignored practical power system constraints such as generator ramp limits and other intertemporal requirements. Additionally, optimal power flow runs are not performed independently of previous timesteps-in most cases, an OPF solution representing the current state of the system is heavily related to the OPF solution from previous timesteps. In this paper, we train a recurrent neural network, which embeds natural relationships between timesteps, to predict the optimal solution of convex power systems optimization problems with intertemporal constraints. In contrast to traditional forecasting methods, the computational benefits from this technique can allow operators to rapidly simulate forecasts of system operation and corresponding optimal solutions to provide a more comprehensive view of future system states.
AB - Learning mappings between system loading and optimal dispatch solutions has been a recent topic of interest in the power systems and machine learning communities. However, previous works have ignored practical power system constraints such as generator ramp limits and other intertemporal requirements. Additionally, optimal power flow runs are not performed independently of previous timesteps-in most cases, an OPF solution representing the current state of the system is heavily related to the OPF solution from previous timesteps. In this paper, we train a recurrent neural network, which embeds natural relationships between timesteps, to predict the optimal solution of convex power systems optimization problems with intertemporal constraints. In contrast to traditional forecasting methods, the computational benefits from this technique can allow operators to rapidly simulate forecasts of system operation and corresponding optimal solutions to provide a more comprehensive view of future system states.
KW - Recurrent neural networks
KW - learning optimal solutions
KW - power systems forecasting
UR - http://www.scopus.com/inward/record.url?scp=85135068131&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135068131&partnerID=8YFLogxK
U2 - 10.1109/PMAPS53380.2022.9810638
DO - 10.1109/PMAPS53380.2022.9810638
M3 - Conference contribution
AN - SCOPUS:85135068131
T3 - 2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022
BT - 2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022
Y2 - 12 June 2022 through 15 June 2022
ER -