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Optimal Energy Storage Strategies in Microgrids

Bhattacharya, Arnab (2018) Optimal Energy Storage Strategies in Microgrids. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Microgrids are small-scale distribution networks that provide a template for large-scale deployment of renewable energy sources, such as wind and solar power, in close proximity to demand. However, the inherent variability and intermittency of these sources can have a significant impact on power generation and scheduling decisions. Distributed energy resources, such as energy storage systems, can be used to decouple the times of energy consumption and generation, thereby enabling microgrid operators to improve scheduling decisions and exploit arbitrage opportunities in energy markets. The integration of renewable energy sources into the nation's power grid, by way of microgrids, holds great promise for sustainable energy production and delivery; however, operators and consumers both lack effective strategies for optimally using stored energy that is generated by renewable energy sources.

This dissertation presents a comprehensive stochastic optimization framework to prescribe optimal strategies for effectively managing stored energy in microgrids, subject to the inherent uncertainty of renewable resources, local demand and electricity prices. First, a Markov decision process model is created to characterize and illustrate structural properties of an optimal storage strategy and to assess the economic value of sharing stored energy between heterogeneous, demand-side entities. Second, a multistage stochastic programming (MSP) model is formulated and solved to determine the optimal storage, procurement, selling and energy flow decisions in a microgrid, subject to storage inefficiencies, distribution line losses and line capacity constraints. Additionally, the well-known stochastic dual dynamic programming (SDDP) algorithm is customized and improved to drastically reduce the computation time and significantly improve solution quality when approximately solving this MSP model. Finally, and more generally, a novel nonconvex regularization scheme is developed to improve the computational performance of the SDDP algorithm for solving high-dimensional MSP models. Specifically, it is shown that these nonconvex regularization problems can be reformulated as mixed-integer programming problems with provable convergence guarantees. The benefits of this regularization scheme are illustrated by way of a computational study that reveals significant improvements in the convergence rate and solution quality over the standard SDDP algorithm and other regularization schemes.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Bhattacharya, Arnabarb141@pitt.eduarb1410000-0002-3727-350X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKharoufeh, Jeffreyjkharouf@pitt.edujkharouf
Committee MemberZeng, Bobzeng@pitt.edubzeng
Committee MemberJiang, Danieldrjiang@pitt.edudrjiang
Committee MemberMao, Zhi-Hongzhm4@pitt.eduzhm4
Date: 25 January 2018
Date Type: Publication
Defense Date: 9 October 2017
Approval Date: 25 January 2018
Submission Date: 16 October 2017
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 155
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Energy, Microgrids, Stochastic Programming, Optimization, Decomposition Procedures, Smart Grid
Date Deposited: 25 Jan 2018 21:49
Last Modified: 25 Jan 2018 21:49


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