An optimal bidding and scheduling method for load service entities considering demand response uncertainty

Rushuai Han; Qinran Hu; Hantao Cui; Tao Chen; Xiangjun Quan; Zaijun Wu

doi:10.1016/j.apenergy.2022.120167

An optimal bidding and scheduling method for load service entities considering demand response uncertainty

Rushuai Han, Qinran Hu, Hantao Cui, Tao Chen, Xiangjun Quan, Zaijun Wu

Electrical Engineering

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Abstract

With the rapid development of demand-side management technologies, load serving entities (LSEs) may offer demand response (DR) programs to improve the flexibility of power system operation. Reliable load aggregation is critical for LSEs to improve profits in electricity markets. Due to the uncertainty, the actual aggregated response of loads obtained by conventional aggregation methods can experience significant deviations from the bidding value, making it difficult for LSEs to develop an optimal bidding and scheduling strategy. In this paper, a bi-level scheduling model is proposed to maximize the net revenue of the LSE from optimal DR bidding and energy storage systems ESS scheduling by considering the impacts of the uncertainty of demand response. An online learning method is adopted to improve aggregation reliability. Additionally, the net profit for LSEs can be raised by strategically switching ESS between two modes, namely, energy arbitrage and deviation mitigation. With Karush–Kuhn–Tucker (KKT) optimality condition-based decoupling and piecewise linearization applied, this bi-level optimization model can be reformulated and converted into a mixed-integer linear programming (MILP) problem. The effectiveness and advantages of the proposed method are verified in a modified IEEE RTS-24 bus system.

Original language	English
Article number	120167
Journal	Applied Energy
Volume	328
DOIs	https://doi.org/10.1016/j.apenergy.2022.120167
Publication status	Published - 15 Dec 2022
Publication type	A1 Journal article-refereed

Keywords

Aggregation deviation
Bi-level scheduling model
Energy storage system
Load serving entity
Online learning

Publication forum classification

Publication forum level 3

ASJC Scopus subject areas

Building and Construction
Mechanical Engineering
Energy(all)
Management, Monitoring, Policy and Law

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An optimal bidding and scheduling methodFinal published version, 3.79 MBLicence: CC BY

https://urn.fi/URN:NBN:fi:tuni-202211248604Licence: CC BY

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title = "An optimal bidding and scheduling method for load service entities considering demand response uncertainty",

abstract = "With the rapid development of demand-side management technologies, load serving entities (LSEs) may offer demand response (DR) programs to improve the flexibility of power system operation. Reliable load aggregation is critical for LSEs to improve profits in electricity markets. Due to the uncertainty, the actual aggregated response of loads obtained by conventional aggregation methods can experience significant deviations from the bidding value, making it difficult for LSEs to develop an optimal bidding and scheduling strategy. In this paper, a bi-level scheduling model is proposed to maximize the net revenue of the LSE from optimal DR bidding and energy storage systems ESS scheduling by considering the impacts of the uncertainty of demand response. An online learning method is adopted to improve aggregation reliability. Additionally, the net profit for LSEs can be raised by strategically switching ESS between two modes, namely, energy arbitrage and deviation mitigation. With Karush–Kuhn–Tucker (KKT) optimality condition-based decoupling and piecewise linearization applied, this bi-level optimization model can be reformulated and converted into a mixed-integer linear programming (MILP) problem. The effectiveness and advantages of the proposed method are verified in a modified IEEE RTS-24 bus system.",

keywords = "Aggregation deviation, Bi-level scheduling model, Energy storage system, Load serving entity, Online learning",

author = "Rushuai Han and Qinran Hu and Hantao Cui and Tao Chen and Xiangjun Quan and Zaijun Wu",

note = "Funding Information: This research is supported by the National Natural Science Foundation of China ( 51907026 ), Natural Science Foundation of Jiangsu Province, China ( BK20190361 ), Key Research and Development Program of Jiangsu Province, China ( BE2020081-2 ), and Chinese Society of Electrical Engineering ( JLB-2020-186 ). Publisher Copyright: {\textcopyright} 2022 The Author(s)",

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doi = "10.1016/j.apenergy.2022.120167",

language = "English",

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T1 - An optimal bidding and scheduling method for load service entities considering demand response uncertainty

AU - Han, Rushuai

AU - Hu, Qinran

AU - Cui, Hantao

AU - Chen, Tao

AU - Quan, Xiangjun

AU - Wu, Zaijun

N1 - Funding Information: This research is supported by the National Natural Science Foundation of China ( 51907026 ), Natural Science Foundation of Jiangsu Province, China ( BK20190361 ), Key Research and Development Program of Jiangsu Province, China ( BE2020081-2 ), and Chinese Society of Electrical Engineering ( JLB-2020-186 ). Publisher Copyright: © 2022 The Author(s)

PY - 2022/12/15

Y1 - 2022/12/15

N2 - With the rapid development of demand-side management technologies, load serving entities (LSEs) may offer demand response (DR) programs to improve the flexibility of power system operation. Reliable load aggregation is critical for LSEs to improve profits in electricity markets. Due to the uncertainty, the actual aggregated response of loads obtained by conventional aggregation methods can experience significant deviations from the bidding value, making it difficult for LSEs to develop an optimal bidding and scheduling strategy. In this paper, a bi-level scheduling model is proposed to maximize the net revenue of the LSE from optimal DR bidding and energy storage systems ESS scheduling by considering the impacts of the uncertainty of demand response. An online learning method is adopted to improve aggregation reliability. Additionally, the net profit for LSEs can be raised by strategically switching ESS between two modes, namely, energy arbitrage and deviation mitigation. With Karush–Kuhn–Tucker (KKT) optimality condition-based decoupling and piecewise linearization applied, this bi-level optimization model can be reformulated and converted into a mixed-integer linear programming (MILP) problem. The effectiveness and advantages of the proposed method are verified in a modified IEEE RTS-24 bus system.

AB - With the rapid development of demand-side management technologies, load serving entities (LSEs) may offer demand response (DR) programs to improve the flexibility of power system operation. Reliable load aggregation is critical for LSEs to improve profits in electricity markets. Due to the uncertainty, the actual aggregated response of loads obtained by conventional aggregation methods can experience significant deviations from the bidding value, making it difficult for LSEs to develop an optimal bidding and scheduling strategy. In this paper, a bi-level scheduling model is proposed to maximize the net revenue of the LSE from optimal DR bidding and energy storage systems ESS scheduling by considering the impacts of the uncertainty of demand response. An online learning method is adopted to improve aggregation reliability. Additionally, the net profit for LSEs can be raised by strategically switching ESS between two modes, namely, energy arbitrage and deviation mitigation. With Karush–Kuhn–Tucker (KKT) optimality condition-based decoupling and piecewise linearization applied, this bi-level optimization model can be reformulated and converted into a mixed-integer linear programming (MILP) problem. The effectiveness and advantages of the proposed method are verified in a modified IEEE RTS-24 bus system.

KW - Aggregation deviation

KW - Bi-level scheduling model

KW - Energy storage system

KW - Load serving entity

KW - Online learning

U2 - 10.1016/j.apenergy.2022.120167

DO - 10.1016/j.apenergy.2022.120167

M3 - Article

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SN - 0306-2619

VL - 328

JO - Applied Energy

JF - Applied Energy

M1 - 120167

ER -

An optimal bidding and scheduling method for load service entities considering demand response uncertainty

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Keywords

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