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Title: Optimal distributed generation planning in active distribution networks considering integration of energy storage

Abstract

A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the 'best' compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG & E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60%.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability
OSTI Identifier:
1466386
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 210; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
active distribution network; chance-constrained programming; distributed generation planning; energy storage; multi-objective ant lion optimizer; two-stage optimization

Citation Formats

Li, Yang, Feng, Bo, Li, Guoqing, Qi, Junjian, Zhao, Dongbo, and Mu, Yunfei. Optimal distributed generation planning in active distribution networks considering integration of energy storage. United States: N. p., 2018. Web. doi:10.1016/j.apenergy.2017.08.008.
Li, Yang, Feng, Bo, Li, Guoqing, Qi, Junjian, Zhao, Dongbo, & Mu, Yunfei. Optimal distributed generation planning in active distribution networks considering integration of energy storage. United States. doi:10.1016/j.apenergy.2017.08.008.
Li, Yang, Feng, Bo, Li, Guoqing, Qi, Junjian, Zhao, Dongbo, and Mu, Yunfei. Mon . "Optimal distributed generation planning in active distribution networks considering integration of energy storage". United States. doi:10.1016/j.apenergy.2017.08.008.
@article{osti_1466386,
title = {Optimal distributed generation planning in active distribution networks considering integration of energy storage},
author = {Li, Yang and Feng, Bo and Li, Guoqing and Qi, Junjian and Zhao, Dongbo and Mu, Yunfei},
abstractNote = {A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the 'best' compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG & E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60%.},
doi = {10.1016/j.apenergy.2017.08.008},
journal = {Applied Energy},
issn = {0306-2619},
number = C,
volume = 210,
place = {United States},
year = {2018},
month = {1}
}