A stochastic dual dynamic programming approach for optimal operation of DER aggregators

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Title: A stochastic dual dynamic programming approach for optimal operation of DER aggregators
Authors: Fatouros, P
Konstantelos, I
Papadaskalopoulos, D
Strbac, G
Item Type: Conference Paper
Abstract: The operation of aggregators of distributed energy resources (DER) is a highly complex task that is affected by numerous factors of uncertainty such as renewables injections, load levels and market conditions. However, traditional stochastic programming approaches neglect information around temporal dependency of the uncertain variables due to computational tractability limitations. This paper proposes a novel stochastic dual dynamic programming (SDDP) approach for the optimal operation of a DER aggregator. The traditional SDDP framework is extended to capture temporal dependency of the uncertain wind power output, through the integration of an n-order autoregressive (AR) model. This method is demonstrated to achieve a better trade-off between solution efficiency and computational time requirements compared to traditional stochastic programming approaches based on the use of scenario trees.
Issue Date: 20-Jul-2017
Date of Acceptance: 1-Jun-2017
URI: http://hdl.handle.net/10044/1/48884
DOI: https://dx.doi.org/10.1109/PTC.2017.7981213
Publisher: IEEE
Journal / Book Title: PowerTech, 2017 IEEE Manchester
Copyright Statement: © 2017 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.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/K002252/1
EP/L024756/1
EP/N005996/1
Conference Name: IEEE PowerTech 2017
Publication Status: Published
Start Date: 2017-06-18
Finish Date: 2017-06-22
Conference Place: Manchester, UK
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Centre for Environmental Policy
Faculty of Natural Sciences



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