Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104690
Title: A risk-averse stochastic dynamic programming approach to energy hub optimal dispatch
Authors: Moazeni, Somayeh
Miragha, Amir H.
Defourny, Boris
Keywords: Engineering::Electrical and electronic engineering
Energy Hub
Stochastic Optimization
Issue Date: 2019
Source: Moazeni, S., Miragha, A. H., & Defourny, B. (2019). A risk-averse stochastic dynamic programming approach to energy hub optimal dispatch. IEEE Transactions on Power Systems, 34(3), 2169-2178. doi:10.1109/TPWRS.2018.2882549
Series/Report no.: IEEE Transactions on Power Systems
Abstract: This paper studies the optimal operation problem of an energy hub with multiple energy sources to serve stochastic electricity and heat loads in the presence of uncertain prices as well as operational constraints, such as minimum uptime and downtime requirements. Price and demand uncertainties are modeled by stochastic processes. The goal is to minimize some risk functional of the energy hub operational cost. A stochastic dynamic optimization formulation is introduced for the problem. An approximate dynamic programming framework, based on cost function approximation, is proposed to obtain dynamic dispatch policies. The approach enables a risk-sensitive energy hub operator to consider a non-differentiable risk measure and various constraints. The performance of the approach for the energy hub dispatch problem and characteristics of the storage levels are numerically investigated.
URI: https://hdl.handle.net/10356/104690
http://hdl.handle.net/10220/50052
ISSN: 0885-8950
DOI: http://dx.doi.org/10.1109/TPWRS.2018.2882549
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TPWRS.2018.2882549
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Journal Articles

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