Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty

Bai, Linquan; Li, Fangxing; Cui, Hantao; Jiang, Tao; Sun, Hongbin; Zhu, Jinxiang

doi:10.1016/j.apenergy.2015.10.119

Title: Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty

Full Record
Other Related Research

Authors:

Bai, Linquan;

; Cui, Hantao;

; Sun, Hongbin; Zhu, Jinxiang

Publication Date:: Fri Apr 01 00:00:00 EDT 2016

Sponsoring Org.:: USDOE

OSTI Identifier:: 1252023

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Applied Energy

Additional Journal Information:: Journal Name: Applied Energy Journal Volume: 167 Journal Issue: C; Journal ID: ISSN 0306-2619

Publisher:: Elsevier

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Bai, Linquan, Li, Fangxing, Cui, Hantao, Jiang, Tao, Sun, Hongbin, and Zhu, Jinxiang. Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty.  United Kingdom: N. p., 2016. 
Web.  doi:10.1016/j.apenergy.2015.10.119.

Copy to clipboard


                    Bai, Linquan, Li, Fangxing, Cui, Hantao, Jiang, Tao, Sun, Hongbin, & Zhu, Jinxiang. Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty.  United Kingdom.  https://doi.org/10.1016/j.apenergy.2015.10.119

Copy to clipboard


                    Bai, Linquan, Li, Fangxing, Cui, Hantao, Jiang, Tao, Sun, Hongbin, and Zhu, Jinxiang. Fri .  
"Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty".  United Kingdom.  https://doi.org/10.1016/j.apenergy.2015.10.119.

Copy to clipboard


                    
@article{osti_1252023,

  title        = {Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty},

  author       = {Bai, Linquan and Li, Fangxing and Cui, Hantao and Jiang, Tao and Sun, Hongbin and Zhu, Jinxiang},

  abstractNote = {},

  doi          = {10.1016/j.apenergy.2015.10.119},

  journal      = {Applied Energy},

  number       = C,

  volume       = 167,

  place        = {United Kingdom},

  year         = {Fri Apr 01 00:00:00 EDT 2016},

  month        = {Fri Apr 01 00:00:00 EDT 2016}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Publisher's Version of Record
https://doi.org/10.1016/j.apenergy.2015.10.119

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 250 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Distributionally-Robust Chance Constrained and Interval Optimization for Integrated Electricity and Natural Gas Systems Optimal Power Flow with Wind Uncertainties

Journal Article Fang, Xin ; Cui, Hantao ; Yuan, Haoyu ; ... - Applied Energy

With increasing penetrations of gas-fired generation in power systems because of reducing gas prices and emissions regulations, the interdependent and coordinated operation of integrated electricity and natural gas systems (IEGSs) is becoming an urgent research topic. Meanwhile, the significantly increasing deployment of wind power necessitates that IEGSs operation considers wind power output uncertainty. How to model the impact of wind power uncertainty on IEGSs power and gas flows dispatch is challenging. In this paper, a hybrid distributionally-robust chance-constrained and interval optimization (DRCC-IO) based model is proposed to consider the influence of wind power uncertainty and its spatial-temporal correlation on IEGSsmore »« less
https://doi.org/10.1016/j.apenergy.2019.113420
Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertainties

Journal Article Fang, Xin ; Cui, Hantao ; Yuan, Haoyu ; ... - Applied Energy
Cited by 42
https://doi.org/10.1016/j.apenergy.2019.113420
Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response

Journal Article Wang, Yongli ; Ma, Yuze ; Song, Fuhao ; ... - Energy (Oxford)
https://doi.org/10.1016/j.energy.2020.118022
Generic modelling and optimal day-ahead dispatch of micro-energy system considering the price-based integrated demand response

Journal Article Chen, Zexing ; Zhang, Yongjun ; Tang, Wenhu ; ... - Energy (Oxford)

As an extension of micro-grid, micro-energy system (MES) is one of the important carriers for energy utilization in the future, and using energy prices as a controllable resource is conducive to improving the optimization potential of MES. Firstly, based on the energy hub model, a generic method for modelling the steady-state energy balance equation of MES is proposed. Then, considering the multi-energy substitution effect in the background of multi-energy coupling, the concept of price-based integrated demand response (P-IDR) is introduced. Meanwhile, based on the price elasticity theory and the discrete choice theory, the energy timing transfer characteristics and energy substitutionmore »« less
Cited by 24
https://doi.org/10.1016/j.energy.2019.04.004

Full Text Available
A robust offering strategy for wind producers considering uncertainties of demand response and wind power

Journal Article Dai, Xuemei ; Li, Yaping ; Zhang, Kaifeng ; ... - Applied Energy

This paper proposes a risk-constrained decision-making approach for a wind power producer participating in the day-ahead market. In the developed model, a flexible demand response trading scheme between the wind power producer and different customers is employed. Through the proposed demand response mechanism, the wind power producer is able to trade demand response resource internally with different customers, and then trade energy externally with the market to increase the expected profit and the wind energy utilization. The uncertainties in the wind power and demand response are modeled by using the information gap decision theory approach from risk averse (robust) andmore »« less
https://doi.org/10.1016/j.apenergy.2020.115742

Full Text Available

Similar Records