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Optimal control, investment and utilization schemes for energy storage under uncertainty
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Mirhosseini, Niloufar Sadat. Optimal control, investment and utilization schemes for energy storage under uncertainty. Retrieved from https://doi.org/doi:10.7282/T36971M6

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TitleOptimal control, investment and utilization schemes for energy storage under uncertainty
NameMirhosseini, Niloufar Sadat (author); Jafari, Mohsen A (chair); Boucher, Thomas O (internal member); Wang, Honggang (internal member); Masiello, Ralph D (outside member); Ott, Andrew (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2013
Other Date2013-10 (degree)
SubjectIndustrial and Systems Engineering, Energy storage, Energy security, Electric utilities, Monte Carlo method
Extentxi, 113 p. : ill.
DescriptionEnergy storage has the potential to offer new means for added flexibility on the electricity systems. This flexibility can be used in a number of ways, including adding value towards asset management, power quality and reliability, integration of renewable resources and energy bill savings for the end users. However, uncertainty about system states and volatility in system dynamics can complicate the question of when to invest in energy storage and how best to manage and utilize it. This work proposes models to address different problems associated with energy storage within a microgrid, including optimal control, investment, and utilization. Electric load, renewable resources output, storage technology cost and electricity day-ahead and spot prices are the factors that bring uncertainty to the problem. A number of analytical methodologies have been adopted to develop the aforementioned models. Model Predictive Control and discretized dynamic programming, along with a new decomposition algorithm are used to develop optimal control schemes for energy storage for two different levels of renewable penetration. Real option theory and Monte Carlo simulation, coupled with an optimal control approach, are used to obtain optimal incremental investment decisions, considering multiple sources of uncertainty. Two stage stochastic programming is used to develop a novel and holistic methodology, including utilization of energy storage within a microgrid, in order to optimally interact with energy market. Energy storage can contribute in terms of value generation and risk reduction for the microgrid. The integration of the models developed here are the basis for a framework which extends from long term investments in storage capacity to short term operational control (charge/discharge) of storage within a microgrid. In particular, the following practical goals are achieved: (i) optimal investment on storage capacity over time to maximize savings during normal and emergency operations; (ii) optimal market strategy of buy and sell over 24-hour periods; (iii) optimal storage charge and discharge in much shorter time intervals.
NotePh.D.
NoteIncludes bibliographical references
Noteby Niloufar Sadat Mirhosseini
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T36971M6
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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