Batch Learning SDDP for Long-Term Hydrothermal Planning
(2024) IEEE Transactions on Power Systems — Vol. 39, n° 1, p. 614-627 (2024)
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- Authors
Avila Girardot, Daniel 
Papavasiliou, Anthony
- Abstract
- We consider the stochastic dual dynamic programming (SDDP) algorithm - a widely employed algorithm applied to multistage stochastic programming - and propose a variant using experience replay - a batch learning technique from reinforcement learning. To connect SDDP with reinforcement learning, we cast SDDP as a Q-learning algorithm and describe its application in both risk-neutral and risk-averse settings. We demonstrate the superiority of the algorithm over conventional SDDP by benchmarking it against PSR's SDDP software using a large-scale instance of the long-term planning problem of inter-connected hydropower plants in Colombia. We find that SDDP with batch learning is able to produce tighter optimality gaps in a shorter amount of time than conventional SDDP. We also find that batch learning improves the parallel efficiency of SDDP backward passes.
- Affiliations
Citations
Avila Girardot, D., Papavasiliou, A., & Nils Lohndorf. (2024). Batch Learning SDDP for Long-Term Hydrothermal Planning. IEEE Transactions on Power Systems, 39(1), 614-627. https://doi.org/10.1109/TPWRS.2023.3246724 (Original work published 2024)