TY - GEN
T1 - A comparison of approximate dynamic programming techniques on benchmark energy storage problems
T2 - 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2014
AU - Jiang, Daniel R.
AU - Pham, Thuy V.
AU - Powell, Warren Buckler
AU - Salas, Daniel F.
AU - Scott, Warren R.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/14
Y1 - 2014/1/14
N2 - As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These sequential decision problems are often modeled as stochastic dynamic programs, but when the state space becomes large, traditional (exact) techniques such as backward induction, policy iteration, or value iteration quickly become computationally intractable. Approximate dynamic programming (ADP) thus becomes a natural solution technique for solving these problems to near-optimality using significantly fewer computational resources. In this paper, we compare the performance of the following: various approximation architectures with approximate policy iteration (API), approximate value iteration (AVI) with structured lookup table, and direct policy search on a benchmarked energy storage problem (i.e., the optimal solution is computable).
AB - As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These sequential decision problems are often modeled as stochastic dynamic programs, but when the state space becomes large, traditional (exact) techniques such as backward induction, policy iteration, or value iteration quickly become computationally intractable. Approximate dynamic programming (ADP) thus becomes a natural solution technique for solving these problems to near-optimality using significantly fewer computational resources. In this paper, we compare the performance of the following: various approximation architectures with approximate policy iteration (API), approximate value iteration (AVI) with structured lookup table, and direct policy search on a benchmarked energy storage problem (i.e., the optimal solution is computable).
UR - http://www.scopus.com/inward/record.url?scp=84946686297&partnerID=8YFLogxK
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U2 - 10.1109/ADPRL.2014.7010626
DO - 10.1109/ADPRL.2014.7010626
M3 - Conference contribution
AN - SCOPUS:84946686297
T3 - IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - ADPRL 2014: 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, Proceedings
BT - IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - ADPRL 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2014 through 12 December 2014
ER -