A probabilistic chance-constrained day-ahead scheduling model for grid-connected microgrid

Chunyang Liu; Xiuli Wang; Yuntao Zou; Haitao Zhang; Wei Zhang

doi:10.1109/NAPS.2017.8107180

A probabilistic chance-constrained day-ahead scheduling model for grid-connected microgrid

Chunyang Liu
, Xiuli Wang
, Yuntao Zou
, Haitao Zhang
, Wei Zhang

School of Electrical Engineering

Xi'an Jiaotong University

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

6 Scopus citations

Abstract

The forecast data of the renewable energy generation and loads cannot be exactly accurate because of their intermittence and fluctuation characteristics. To handle this problem, a probabilistic chance-constrained model for day-ahead scheduling is proposed in this paper. The proposed model is established not only by the aggregated scenarios but also by the eliminated ones which are used in chance constraints. The mixed integer linear programming algorithm is applied to solve the schedule problem efficiently. Finally, a grid-connected microgrid consisting of a photovoltaic system (PV), a wind turbine (WT), a micro turbine (MT), a diesel engine (DE), a fuel cell (FC), and a battery energy storage system (BESS) is studied, and the simulation results show the effectiveness of the probabilistic chance-constrained model.

Original language	English
Title of host publication	2017 North American Power Symposium, NAPS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538626993
DOIs	https://doi.org/10.1109/NAPS.2017.8107180
State	Published - 13 Nov 2017
Event	2017 North American Power Symposium, NAPS 2017 - Morgantown, United States Duration: 17 Sep 2017 → 19 Sep 2017

Publication series

Name	2017 North American Power Symposium, NAPS 2017

Conference

Conference	2017 North American Power Symposium, NAPS 2017
Country/Territory	United States
City	Morgantown
Period	17/09/17 → 19/09/17

Keywords

chance constraints
day-ahead scheduling
grid-connected microgrid
mixed integer linear programming
probabilistic model

UN SDGs

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

Access to Document

10.1109/NAPS.2017.8107180

Cite this

@inproceedings{8f0457e7ab5d4924b39f08fc8dc528df,

title = "A probabilistic chance-constrained day-ahead scheduling model for grid-connected microgrid",

abstract = "The forecast data of the renewable energy generation and loads cannot be exactly accurate because of their intermittence and fluctuation characteristics. To handle this problem, a probabilistic chance-constrained model for day-ahead scheduling is proposed in this paper. The proposed model is established not only by the aggregated scenarios but also by the eliminated ones which are used in chance constraints. The mixed integer linear programming algorithm is applied to solve the schedule problem efficiently. Finally, a grid-connected microgrid consisting of a photovoltaic system (PV), a wind turbine (WT), a micro turbine (MT), a diesel engine (DE), a fuel cell (FC), and a battery energy storage system (BESS) is studied, and the simulation results show the effectiveness of the probabilistic chance-constrained model.",

keywords = "chance constraints, day-ahead scheduling, grid-connected microgrid, mixed integer linear programming, probabilistic model",

author = "Chunyang Liu and Xiuli Wang and Yuntao Zou and Haitao Zhang and Wei Zhang",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 North American Power Symposium, NAPS 2017 ; Conference date: 17-09-2017 Through 19-09-2017",

year = "2017",

month = nov,

day = "13",

doi = "10.1109/NAPS.2017.8107180",

language = "英语",

series = "2017 North American Power Symposium, NAPS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 North American Power Symposium, NAPS 2017",

}

Liu, C, Wang, X, Zou, Y, Zhang, H & Zhang, W 2017, A probabilistic chance-constrained day-ahead scheduling model for grid-connected microgrid. in 2017 North American Power Symposium, NAPS 2017., 8107180, 2017 North American Power Symposium, NAPS 2017, Institute of Electrical and Electronics Engineers Inc., 2017 North American Power Symposium, NAPS 2017, Morgantown, United States, 17/09/17. https://doi.org/10.1109/NAPS.2017.8107180

A probabilistic chance-constrained day-ahead scheduling model for grid-connected microgrid. / Liu, Chunyang; Wang, Xiuli; Zou, Yuntao et al.
2017 North American Power Symposium, NAPS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8107180 (2017 North American Power Symposium, NAPS 2017).

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

TY - GEN

T1 - A probabilistic chance-constrained day-ahead scheduling model for grid-connected microgrid

AU - Liu, Chunyang

AU - Wang, Xiuli

AU - Zou, Yuntao

AU - Zhang, Haitao

AU - Zhang, Wei

PY - 2017/11/13

Y1 - 2017/11/13

N2 - The forecast data of the renewable energy generation and loads cannot be exactly accurate because of their intermittence and fluctuation characteristics. To handle this problem, a probabilistic chance-constrained model for day-ahead scheduling is proposed in this paper. The proposed model is established not only by the aggregated scenarios but also by the eliminated ones which are used in chance constraints. The mixed integer linear programming algorithm is applied to solve the schedule problem efficiently. Finally, a grid-connected microgrid consisting of a photovoltaic system (PV), a wind turbine (WT), a micro turbine (MT), a diesel engine (DE), a fuel cell (FC), and a battery energy storage system (BESS) is studied, and the simulation results show the effectiveness of the probabilistic chance-constrained model.

AB - The forecast data of the renewable energy generation and loads cannot be exactly accurate because of their intermittence and fluctuation characteristics. To handle this problem, a probabilistic chance-constrained model for day-ahead scheduling is proposed in this paper. The proposed model is established not only by the aggregated scenarios but also by the eliminated ones which are used in chance constraints. The mixed integer linear programming algorithm is applied to solve the schedule problem efficiently. Finally, a grid-connected microgrid consisting of a photovoltaic system (PV), a wind turbine (WT), a micro turbine (MT), a diesel engine (DE), a fuel cell (FC), and a battery energy storage system (BESS) is studied, and the simulation results show the effectiveness of the probabilistic chance-constrained model.

KW - chance constraints

KW - day-ahead scheduling

KW - grid-connected microgrid

KW - mixed integer linear programming

KW - probabilistic model

UR - https://www.scopus.com/pages/publications/85040615766

U2 - 10.1109/NAPS.2017.8107180

DO - 10.1109/NAPS.2017.8107180

M3 - 会议稿件

AN - SCOPUS:85040615766

T3 - 2017 North American Power Symposium, NAPS 2017

BT - 2017 North American Power Symposium, NAPS 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 North American Power Symposium, NAPS 2017

Y2 - 17 September 2017 through 19 September 2017

ER -

A probabilistic chance-constrained day-ahead scheduling model for grid-connected microgrid

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this