TY - JOUR
T1 - Tutorial on Stochastic Optimization in Energy - Part I
T2 - Modeling and Policies
AU - Powell, Warren Buckler
AU - Meisel, Stephan
N1 - Funding Information:
The work of W. B. Powell was supported by the National Science Foundation under grant ECCS-1127975 and the SAP initiative for energy systems research. The work of S. Meisel was supported by the German Research Foundation.
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2016/3
Y1 - 2016/3
N2 - There is a wide range of problems in energy systems that require making decisions in the presence of different forms of uncertainty. The fields that address sequential, stochastic decision problems lack a standard canonical modeling framework, with fragmented, competing solution strategies. Recognizing that we will never agree on a single notational system, this two-part tutorial proposes a simple, straightforward canonical model (that is most familiar to people with a control theory background), and introduces four fundamental classes of policies which integrate the competing strategies that have been proposed under names such as control theory, dynamic programming, stochastic programming and robust optimization. Part II of the tutorial illustrates the modeling framework using a simple energy storage problem, where we show that, depending on the problem characteristics, each of the four classes of policies may be best.
AB - There is a wide range of problems in energy systems that require making decisions in the presence of different forms of uncertainty. The fields that address sequential, stochastic decision problems lack a standard canonical modeling framework, with fragmented, competing solution strategies. Recognizing that we will never agree on a single notational system, this two-part tutorial proposes a simple, straightforward canonical model (that is most familiar to people with a control theory background), and introduces four fundamental classes of policies which integrate the competing strategies that have been proposed under names such as control theory, dynamic programming, stochastic programming and robust optimization. Part II of the tutorial illustrates the modeling framework using a simple energy storage problem, where we show that, depending on the problem characteristics, each of the four classes of policies may be best.
KW - Approximate dynamic programming
KW - dynamic programming
KW - energy systems
KW - optimal control
KW - reinforcement learning
KW - robust optimization
KW - stochastic optimization
KW - stochastic programming
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U2 - 10.1109/TPWRS.2015.2424974
DO - 10.1109/TPWRS.2015.2424974
M3 - Article
AN - SCOPUS:84928741896
SN - 0885-8950
VL - 31
SP - 1459
EP - 1467
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 2
M1 - 7097741
ER -