TY - JOUR
T1 - Tutorial on Stochastic Optimization in Energy - Part II
T2 - An Energy Storage Illustration
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. Paper no. TPWRS-01472-2014.
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2016/3
Y1 - 2016/3
N2 - In Part I of this tutorial, we provided a canonical modeling framework for sequential, stochastic optimization (control) problems. A major feature of this framework is a clear separation of the process of modeling a problem, versus the design of policies to solve the problem. In Part II, we provide additional discussion behind some of the more subtle concepts such as the construction of a state variable. We illustrate the modeling process using an energy storage problem. We then create five variations of this problem designed to bring out the features of the different policies. The first four of these problems demonstrate that each of the four classes of policies is best for particular problem characteristics. The fifth policy is a hybrid that illustrates the ability to combine the strengths of multiple policy classes.
AB - In Part I of this tutorial, we provided a canonical modeling framework for sequential, stochastic optimization (control) problems. A major feature of this framework is a clear separation of the process of modeling a problem, versus the design of policies to solve the problem. In Part II, we provide additional discussion behind some of the more subtle concepts such as the construction of a state variable. We illustrate the modeling process using an energy storage problem. We then create five variations of this problem designed to bring out the features of the different policies. The first four of these problems demonstrate that each of the four classes of policies is best for particular problem characteristics. The fifth policy is a hybrid that illustrates the ability to combine the strengths of multiple policy classes.
KW - Approximate dynamic programming
KW - dynamic programming
KW - energy storage
KW - energy systems
KW - optimal control
KW - reinforcement learning
KW - robust optimization
KW - stochastic optimization
KW - stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=84928943903&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928943903&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2015.2424980
DO - 10.1109/TPWRS.2015.2424980
M3 - Article
AN - SCOPUS:84928943903
SN - 0885-8950
VL - 31
SP - 1468
EP - 1475
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 2
M1 - 7100937
ER -