Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System

Han Zhang; Xiuli Wang; Yongxi Huang; Xiaokan Gou

doi:10.1109/APPEEC48164.2020.9220412

Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System

Han Zhang
, Xiuli Wang
, Yongxi Huang
, Xiaokan Gou

School of Electrical Engineering

Xi'an Jiaotong University
Qinghai Electric Power Company

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The large-scale renewable energy(RE) integration into power systems in China poses an additional challenge to the day-ahead stochastic scheduling due to the uncertainty. Moreover, when power markets are considered, trading energy in day-ahead scheduling cannot be disregarded. In this paper, a two-stage stochastic day-ahead scheduling model considering market transactions is established, with the goal to use the most economical way to ensure the safe and reliable operation of the power grid and maximize the consumption of new energy. Mid- and long-term electricity contract decomposition, ancillary service transactions, and inter-provincial transactions are considered in the proposed model. Finally, a numerical example based on a real system of a province of China verifies the reasonableness of the proposed model. Four cases are analyzed to understand the effect of market transactions on the consumption of new energy. Furthermore, the results show the superiority of the stochastic method over an equivalent deterministic model.

Original language	English
Title of host publication	12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728157481
DOIs	https://doi.org/10.1109/APPEEC48164.2020.9220412
State	Published - Sep 2020
Event	12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020 - Nanjing, China Duration: 20 Sep 2020 → 23 Sep 2020

Publication series

Name	Asia-Pacific Power and Energy Engineering Conference, APPEEC
Volume	2020-September
ISSN (Print)	2157-4839
ISSN (Electronic)	2157-4847

Conference

Conference	12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020
Country/Territory	China
City	Nanjing
Period	20/09/20 → 23/09/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

day-ahead scheduling
multi-energy system
power market
two-stage stochastic optimization

Access to Document

10.1109/APPEEC48164.2020.9220412

Cite this

Zhang, H., Wang, X., Huang, Y., & Gou, X. (2020). Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System. In 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020 Article 9220412 (Asia-Pacific Power and Energy Engineering Conference, APPEEC; Vol. 2020-September). IEEE Computer Society. https://doi.org/10.1109/APPEEC48164.2020.9220412

@inproceedings{5d5dc17eb7924235ad3b3f5fb3a8f14e,

title = "Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System",

abstract = "The large-scale renewable energy(RE) integration into power systems in China poses an additional challenge to the day-ahead stochastic scheduling due to the uncertainty. Moreover, when power markets are considered, trading energy in day-ahead scheduling cannot be disregarded. In this paper, a two-stage stochastic day-ahead scheduling model considering market transactions is established, with the goal to use the most economical way to ensure the safe and reliable operation of the power grid and maximize the consumption of new energy. Mid- and long-term electricity contract decomposition, ancillary service transactions, and inter-provincial transactions are considered in the proposed model. Finally, a numerical example based on a real system of a province of China verifies the reasonableness of the proposed model. Four cases are analyzed to understand the effect of market transactions on the consumption of new energy. Furthermore, the results show the superiority of the stochastic method over an equivalent deterministic model.",

keywords = "day-ahead scheduling, multi-energy system, power market, two-stage stochastic optimization",

author = "Han Zhang and Xiuli Wang and Yongxi Huang and Xiaokan Gou",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020 ; Conference date: 20-09-2020 Through 23-09-2020",

year = "2020",

month = sep,

doi = "10.1109/APPEEC48164.2020.9220412",

language = "英语",

series = "Asia-Pacific Power and Energy Engineering Conference, APPEEC",

publisher = "IEEE Computer Society",

booktitle = "12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020",

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Zhang, H, Wang, X, Huang, Y & Gou, X 2020, Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System. in 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020., 9220412, Asia-Pacific Power and Energy Engineering Conference, APPEEC, vol. 2020-September, IEEE Computer Society, 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020, Nanjing, China, 20/09/20. https://doi.org/10.1109/APPEEC48164.2020.9220412

Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System. / Zhang, Han; Wang, Xiuli; Huang, Yongxi et al.
12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020. IEEE Computer Society, 2020. 9220412 (Asia-Pacific Power and Energy Engineering Conference, APPEEC; Vol. 2020-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System

AU - Zhang, Han

AU - Wang, Xiuli

AU - Huang, Yongxi

AU - Gou, Xiaokan

PY - 2020/9

Y1 - 2020/9

N2 - The large-scale renewable energy(RE) integration into power systems in China poses an additional challenge to the day-ahead stochastic scheduling due to the uncertainty. Moreover, when power markets are considered, trading energy in day-ahead scheduling cannot be disregarded. In this paper, a two-stage stochastic day-ahead scheduling model considering market transactions is established, with the goal to use the most economical way to ensure the safe and reliable operation of the power grid and maximize the consumption of new energy. Mid- and long-term electricity contract decomposition, ancillary service transactions, and inter-provincial transactions are considered in the proposed model. Finally, a numerical example based on a real system of a province of China verifies the reasonableness of the proposed model. Four cases are analyzed to understand the effect of market transactions on the consumption of new energy. Furthermore, the results show the superiority of the stochastic method over an equivalent deterministic model.

AB - The large-scale renewable energy(RE) integration into power systems in China poses an additional challenge to the day-ahead stochastic scheduling due to the uncertainty. Moreover, when power markets are considered, trading energy in day-ahead scheduling cannot be disregarded. In this paper, a two-stage stochastic day-ahead scheduling model considering market transactions is established, with the goal to use the most economical way to ensure the safe and reliable operation of the power grid and maximize the consumption of new energy. Mid- and long-term electricity contract decomposition, ancillary service transactions, and inter-provincial transactions are considered in the proposed model. Finally, a numerical example based on a real system of a province of China verifies the reasonableness of the proposed model. Four cases are analyzed to understand the effect of market transactions on the consumption of new energy. Furthermore, the results show the superiority of the stochastic method over an equivalent deterministic model.

KW - day-ahead scheduling

KW - multi-energy system

KW - power market

KW - two-stage stochastic optimization

UR - https://www.scopus.com/pages/publications/85094327100

U2 - 10.1109/APPEEC48164.2020.9220412

DO - 10.1109/APPEEC48164.2020.9220412

M3 - 会议稿件

AN - SCOPUS:85094327100

T3 - Asia-Pacific Power and Energy Engineering Conference, APPEEC

BT - 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020

PB - IEEE Computer Society

T2 - 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020

Y2 - 20 September 2020 through 23 September 2020

ER -

Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Multi-Energy System

Abstract

Publication series

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UN SDGs

Keywords

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