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Distributed demand response market model for facilitating wind power integration

Authors: Javad Saebi; Duy Thanh Nguyen;

Distributed demand response market model for facilitating wind power integration

Abstract

To cope with wind power uncertainty, balancing authorities are required to procure adequate ancillary services (ASs) with the aim of maintaining the security of the power system operation. The transmission system operator (TSO) is responsible for maintaining the balance between supply and demand in delivery hours. Besides the generating units, demand response (DR) has the potential capabilities to be considered as a source of AS. The demand‐side AS can be used both locally (by the local entities in distribution networks) and system‐wide (by the TSO). However, the optimal coordination between the local and global beneficiaries is a challenging task. This study proposes a distributed DR market model, in which the DR is traded as a public good among the providers and beneficiaries through the local DR markets. The local DR markets can be run in each load bus to trade the DR provided by retail customers connected to that bus with the buyers. To include the interactions between the energy/reserve market and the local DR markets, a bi‐level programming model is proposed. The bi‐level problem is translated into a single‐level mixed‐integer linear programming problem using the duality theorem. The proposed model is verified by simple and realistic case studies.

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Keywords

distributed demand response market model, tso, transmission system operator, local beneficiaries, wind power plants, local dr markets, wind power uncertainty, local entities, integer programming, power generation economics, global beneficiaries, demand side management, energy-reserve market, linear programming, ancillary services, TK1-9971, wind power integration, power system operation, distribution networks, demand response, power markets, bilevel programming model, Electrical engineering. Electronics. Nuclear engineering, distributed dr market model

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    11
    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
gold