期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 31, 期 2, 页码 1468-1475出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2015.2424980
关键词
Approximate dynamic programming; dynamic programming; energy storage; energy systems; optimal control; reinforcement learning; robust optimization; stochastic optimization; stochastic programming
资金
- National Science Foundation [ECCS-1127975]
- SAP initiative for energy systems research
- German Research Foundation
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.
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