Risk constrained battery energy storage planning in active distribution networks

Yongxi Zhang; Fengji Luo; Ke Meng; Zhao Yang Dong; Hongming Yang; K. P. Wong

doi:10.1109/POWERCON.2016.7754057

Risk constrained battery energy storage planning in active distribution networks

Yongxi Zhang, Fengji Luo, Ke Meng, Zhao Yang Dong, Hongming Yang, K. P. Wong

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

4 Citations (Scopus)

Abstract

This paper proposes a stochastic planning framework for the battery energy storage system (BESS) with abundant distributed renewable energy sources. The uncertainties of wind power, solar power, and load demand variations are modeled through the scenario analysis. The risks of the planner in the uncertain environment is also considered. The conditional value at risk (CVaR) theory is introduced into the objective model to model the risk. A series of case studies are conducted to validate the efficiency of the proposed method.

Original language	English
Title of host publication	2016 IEEE International Conference on Power System Technology, POWERCON 2016
Publisher	IEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781467388481
DOIs	https://doi.org/10.1109/POWERCON.2016.7754057
Publication status	Published - 22 Nov 2016
Event	2016 IEEE International Conference on Power System Technology, POWERCON 2016 - Wollongong, Australia Duration: 28 Sept 2016 → 1 Oct 2016

Conference

Conference	2016 IEEE International Conference on Power System Technology, POWERCON 2016
Country/Territory	Australia
City	Wollongong
Period	28/09/16 → 1/10/16

UN SDGs

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

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10.1109/POWERCON.2016.7754057

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@inproceedings{1715e8c3e7e340a7b341644fd2343533,

title = "Risk constrained battery energy storage planning in active distribution networks",

abstract = "This paper proposes a stochastic planning framework for the battery energy storage system (BESS) with abundant distributed renewable energy sources. The uncertainties of wind power, solar power, and load demand variations are modeled through the scenario analysis. The risks of the planner in the uncertain environment is also considered. The conditional value at risk (CVaR) theory is introduced into the objective model to model the risk. A series of case studies are conducted to validate the efficiency of the proposed method.",

keywords = "Active distribution network, battery energy storage system, conditional-value-At-risk, renewable energy, smart grid",

author = "Yongxi Zhang and Fengji Luo and Ke Meng and Dong, {Zhao Yang} and Hongming Yang and Wong, {K. P.}",

year = "2016",

month = nov,

day = "22",

doi = "10.1109/POWERCON.2016.7754057",

language = "English",

booktitle = "2016 IEEE International Conference on Power System Technology, POWERCON 2016",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2016 IEEE International Conference on Power System Technology, POWERCON 2016 ; Conference date: 28-09-2016 Through 01-10-2016",

}

Zhang, Y, Luo, F, Meng, K, Dong, ZY, Yang, H & Wong, KP 2016, Risk constrained battery energy storage planning in active distribution networks. in 2016 IEEE International Conference on Power System Technology, POWERCON 2016., 7754057, IEEE, Institute of Electrical and Electronics Engineers, 2016 IEEE International Conference on Power System Technology, POWERCON 2016, Wollongong, Australia, 28/09/16. https://doi.org/10.1109/POWERCON.2016.7754057

TY - GEN

T1 - Risk constrained battery energy storage planning in active distribution networks

AU - Zhang, Yongxi

AU - Luo, Fengji

AU - Meng, Ke

AU - Dong, Zhao Yang

AU - Yang, Hongming

AU - Wong, K. P.

PY - 2016/11/22

Y1 - 2016/11/22

N2 - This paper proposes a stochastic planning framework for the battery energy storage system (BESS) with abundant distributed renewable energy sources. The uncertainties of wind power, solar power, and load demand variations are modeled through the scenario analysis. The risks of the planner in the uncertain environment is also considered. The conditional value at risk (CVaR) theory is introduced into the objective model to model the risk. A series of case studies are conducted to validate the efficiency of the proposed method.

AB - This paper proposes a stochastic planning framework for the battery energy storage system (BESS) with abundant distributed renewable energy sources. The uncertainties of wind power, solar power, and load demand variations are modeled through the scenario analysis. The risks of the planner in the uncertain environment is also considered. The conditional value at risk (CVaR) theory is introduced into the objective model to model the risk. A series of case studies are conducted to validate the efficiency of the proposed method.

KW - Active distribution network

KW - battery energy storage system

KW - conditional-value-At-risk

KW - renewable energy

KW - smart grid

UR - http://www.scopus.com/inward/record.url?scp=85006789466&partnerID=8YFLogxK

U2 - 10.1109/POWERCON.2016.7754057

DO - 10.1109/POWERCON.2016.7754057

M3 - Conference paper

AN - SCOPUS:85006789466

BT - 2016 IEEE International Conference on Power System Technology, POWERCON 2016

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 2016 IEEE International Conference on Power System Technology, POWERCON 2016

Y2 - 28 September 2016 through 1 October 2016

ER -

Risk constrained battery energy storage planning in active distribution networks

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