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Examining the benefits of load shedding strategies using a rolling-horizon stochastic mixed complementarity equilibrium model
Author(s)
Date Issued
2018-06-01
Date Available
2019-08-12T07:55:46Z
Embargo end date
2019-12-02
Abstract
As a result of government policies increasing the amount of electricity generated from fluctuating renewable sources in many countries, the requirement for flexibility in the corresponding electricity systems increases. On the demand side, load shedding is one demand response mechanism contributing to an increased flexibility. Traditionally, load shedding was based on rather static or rotational strategies, whereby the system operator chooses the consumers for load shedding. However, ongoing technological developments provide the basis for smarter and more efficient load shedding strategies. We therefore examine the costs and strategies associated with such mechanisms by modelling an electricity market with different types of generators and consumers. Some consumers provide flexibility through load shedding only while others additionally have the ability to generate their own electricity. Focussing on the impacts of how and to whom consumers with own generation ability can supply electricity, the presence of market power and generator uncertainty, we propose a rolling horizon stochastic mixed complementarity equilibrium model, where the individual optimisation problems of each player are solved simultaneously and in equilibrium. We find that a non-static strategy reduces consumer costs while allowing consumers to provide own generation to the whole market results in minimal benefits. The presence of market power was found to increase costs to consumers.
Other Sponsorship
ESRI’s Energy Policy Research Center
Type of Material
Journal Article
Publisher
Elsevier BV
Journal
European Journal of Operational Research
Volume
267
Issue
2
Start Page
643
End Page
658
Copyright (Published Version)
2017 Elsevier
Language
English
Status of Item
Peer reviewed
ISSN
0377-2217
This item is made available under a Creative Commons License
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Name
Devine_resubmitted_EJOR-D-16-02503.pdf
Size
831.66 KB
Format
Adobe PDF
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