Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price

Yanhong Hu; Xiong Wu; Jingjing Cao; Penglei Wang; Yunyi Wu; Xuyan Zhou

doi:10.1109/EI256261.2022.10116635

Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price

Yanhong Hu
, Xiong Wu
, Jingjing Cao
, Penglei Wang
, Yunyi Wu
, Xuyan Zhou

School of Electrical Engineering

Xi'an Jiaotong University
China Three Gorges Corporation

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

4 Scopus citations

Abstract

With a large number of photovoltaics (PVs) and electric vehicles (EVs) connected to the distribution network, the distribution system will face the risk of bidirectional congestion. However, the output of PV and the flexibility of EVs are uncertain, which will bring challenges to congestion management. This paper proposes a distributed optimal scheduling strategy for aggregated EVs and PV considering dynamic distribution locational marginal price (DLMP). Firstly, the static robust optimization method is used to deal with the uncertainty of EVs and PVs. Then, the dual decomposition theory is used to formulate the interactive rules between the distribution system operator (DSO) and aggregators. Finally, the distribution network congestion is alleviated according to the DLMP. The case study based on the improved ieee-33 bus system verifies the rationality and effectiveness of the strategy. The results show that the proposed strategy can effectively solve the congestion of distribution network considering the willingness of the aggregators to purchase and sell electricity.

Original language	English
Title of host publication	EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2100-2105
Number of pages	6
ISBN (Electronic)	9798350347159
DOIs	https://doi.org/10.1109/EI256261.2022.10116635
State	Published - 2022
Event	6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022 - Chengdu, China Duration: 11 Nov 2022 → 13 Nov 2022

Publication series

Name	EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

Conference

Conference	6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022
Country/Territory	China
City	Chengdu
Period	11/11/22 → 13/11/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

aggregated electric vehicles
distribution locational marginal price
distribution network congestion
photovoltaic
static robust optimization

Access to Document

10.1109/EI256261.2022.10116635

Cite this

Hu, Y., Wu, X., Cao, J., Wang, P., Wu, Y., & Zhou, X. (2022). Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price. In EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration (pp. 2100-2105). (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EI256261.2022.10116635

Hu, Yanhong ; Wu, Xiong ; Cao, Jingjing et al. / Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price. EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 2100-2105 (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration).

@inproceedings{58e98db1244549a2b40f1f3692d093da,

title = "Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price",

abstract = "With a large number of photovoltaics (PVs) and electric vehicles (EVs) connected to the distribution network, the distribution system will face the risk of bidirectional congestion. However, the output of PV and the flexibility of EVs are uncertain, which will bring challenges to congestion management. This paper proposes a distributed optimal scheduling strategy for aggregated EVs and PV considering dynamic distribution locational marginal price (DLMP). Firstly, the static robust optimization method is used to deal with the uncertainty of EVs and PVs. Then, the dual decomposition theory is used to formulate the interactive rules between the distribution system operator (DSO) and aggregators. Finally, the distribution network congestion is alleviated according to the DLMP. The case study based on the improved ieee-33 bus system verifies the rationality and effectiveness of the strategy. The results show that the proposed strategy can effectively solve the congestion of distribution network considering the willingness of the aggregators to purchase and sell electricity.",

keywords = "aggregated electric vehicles, distribution locational marginal price, distribution network congestion, photovoltaic, static robust optimization",

author = "Yanhong Hu and Xiong Wu and Jingjing Cao and Penglei Wang and Yunyi Wu and Xuyan Zhou",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022 ; Conference date: 11-11-2022 Through 13-11-2022",

year = "2022",

doi = "10.1109/EI256261.2022.10116635",

language = "英语",

series = "EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration",

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

pages = "2100--2105",

booktitle = "EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration",

}

Hu, Y, Wu, X, Cao, J, Wang, P, Wu, Y & Zhou, X 2022, Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price. in EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration, Institute of Electrical and Electronics Engineers Inc., pp. 2100-2105, 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022, Chengdu, China, 11/11/22. https://doi.org/10.1109/EI256261.2022.10116635

Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price. / Hu, Yanhong; Wu, Xiong; Cao, Jingjing et al.
EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. Institute of Electrical and Electronics Engineers Inc., 2022. p. 2100-2105 (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration).

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

TY - GEN

T1 - Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price

AU - Hu, Yanhong

AU - Wu, Xiong

AU - Cao, Jingjing

AU - Wang, Penglei

AU - Wu, Yunyi

AU - Zhou, Xuyan

PY - 2022

Y1 - 2022

N2 - With a large number of photovoltaics (PVs) and electric vehicles (EVs) connected to the distribution network, the distribution system will face the risk of bidirectional congestion. However, the output of PV and the flexibility of EVs are uncertain, which will bring challenges to congestion management. This paper proposes a distributed optimal scheduling strategy for aggregated EVs and PV considering dynamic distribution locational marginal price (DLMP). Firstly, the static robust optimization method is used to deal with the uncertainty of EVs and PVs. Then, the dual decomposition theory is used to formulate the interactive rules between the distribution system operator (DSO) and aggregators. Finally, the distribution network congestion is alleviated according to the DLMP. The case study based on the improved ieee-33 bus system verifies the rationality and effectiveness of the strategy. The results show that the proposed strategy can effectively solve the congestion of distribution network considering the willingness of the aggregators to purchase and sell electricity.

AB - With a large number of photovoltaics (PVs) and electric vehicles (EVs) connected to the distribution network, the distribution system will face the risk of bidirectional congestion. However, the output of PV and the flexibility of EVs are uncertain, which will bring challenges to congestion management. This paper proposes a distributed optimal scheduling strategy for aggregated EVs and PV considering dynamic distribution locational marginal price (DLMP). Firstly, the static robust optimization method is used to deal with the uncertainty of EVs and PVs. Then, the dual decomposition theory is used to formulate the interactive rules between the distribution system operator (DSO) and aggregators. Finally, the distribution network congestion is alleviated according to the DLMP. The case study based on the improved ieee-33 bus system verifies the rationality and effectiveness of the strategy. The results show that the proposed strategy can effectively solve the congestion of distribution network considering the willingness of the aggregators to purchase and sell electricity.

KW - aggregated electric vehicles

KW - distribution locational marginal price

KW - distribution network congestion

KW - photovoltaic

KW - static robust optimization

UR - https://www.scopus.com/pages/publications/85160205264

U2 - 10.1109/EI256261.2022.10116635

DO - 10.1109/EI256261.2022.10116635

M3 - 会议稿件

AN - SCOPUS:85160205264

T3 - EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

SP - 2100

EP - 2105

BT - EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

Y2 - 11 November 2022 through 13 November 2022

ER -

Hu Y, Wu X, Cao J, Wang P, Wu Y, Zhou X. Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price. In EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. Institute of Electrical and Electronics Engineers Inc. 2022. p. 2100-2105. (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration). doi: 10.1109/EI256261.2022.10116635

Distributed Optimal Scheduling for Aggregated Electric Vehicles and Photovoltaic Considering Dynamic Distribution Locational Marginal Price

Abstract

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UN SDGs

Keywords

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