Toward mitigating wind-uncertainty costs in power system operation: A demand response exchange market framework

Saebi, Javad; Javidi, Mohammad Hossein; Buygi, Majid Oloomi

doi:10.1016/j.epsr.2014.09.017

Cited by 26 publications

(8 citation statements)

References 24 publications

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“…There is no comprehensive approach in the literature to consider this characteristic of the DR in the operation of the power system. Few studies such as [24][25][26] are carried out to consider the concept of the DR exchange in power system operation. A DR market clearing framework is proposed in [24], in which the DR market is cleared after the clearance of the energy/reserve market.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Few studies such as [24][25][26] are carried out to consider the concept of the DR exchange in power system operation. A DR market clearing framework is proposed in [24], in which the DR market is cleared after the clearance of the energy/reserve market. The objective function and constraints of the DR market clearing model are developed based on the outputs of the energy/reserve market.…”

Section: Literature Reviewmentioning

confidence: 99%

“…dual variable of (11), is the price of the DR deployed at scenario w with the probability of π w . Therefore, the DR deployed cost in (24) is calculated by multiplying the probability-removed price of the DR, i.e. γ jtw d /π w , and the deployed DR r jtw .…”

Section: Local/central Markets Cooperationmentioning

confidence: 99%

“…Finally, the equivalent single-level mixed-integer non-linear programming problem of the proposed bi-level model can be expressed as follows: Because of the products of the upper-level variables (R jt and r jtw ) and the lower-level variables (γ jt c and γ jtw d ) in (23) and (24) that appear in the objective function (1), the bi-level problem is nonlinear. Based on the strong duality condition (34), the sum of the bilinear products γ jt c R jt and γ jtw d r jtw can be replaced by its equivalent linear terms.…”

Section: Appendix 2: Solution Approachmentioning

confidence: 99%

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Distributed demand response market model for facilitating wind power integration

Saebi

Nguyen

2020

IET Smart Grid

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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. Remark 1: Some of the abovementioned constants and variables incorporate superscript U, D, NS when referring to the upward, downward, or non-spinning reserve/DR, respectively. Remark 2: A variable, function, or parameter written in bold without one or more indices is a vector form representing the corresponding quantities. For example, the symbol P t S represents the vector of scheduled power of generating units during period t.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Local/central Markets Cooperationmentioning

confidence: 99%

Section: Appendix 2: Solution Approachmentioning

confidence: 99%

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Distributed demand response market model for facilitating wind power integration

Saebi

Nguyen

2020

IET Smart Grid

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“…Some other works [19] have studied the interaction among customers and upper utilities for the accomplishment of DR. DR can be applied in a pool‐based market in [20]. Moreover, DR can be employed in the form of DR exchange (DRX) in day‐head market [21, 22].…”

Section: Introductionmentioning

confidence: 99%