TY - JOUR
T1 - Risk-based multi-objective optimization of distributed generation based on GPSO-BFA algorithm
AU - Xiong, Xiaoping
AU - Wu, Wenliang
AU - Li, Ning
AU - Yang, Lu
AU - Zhang, Jie
AU - Wei, Zhi
N1 - Funding Information:
This work was supported in part by the National Natural Science Fund under Grant 51867004, and in part by the Guangxi University Youth Basic Quality Promotion Project under Grant T3020094004.
Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - With the rapid evolution of technology, growing accessibility, and environmental appeal of wind and solar electric systems, distributed generation (DG) has been pushed from the fringe to a mainstream factor in the grid. However, due to the randomness and uncertainty of environmental and operational conditions, DG also brings many risks and may adversely affect the reliability and safety of the power grid when connected to the distribution network. Therefore, it is necessary to introduce the risk theory into the allocation and placement of DGs. This paper establishes a comprehensive set of risk and economic indexes by modeling the randomness and uncertainty of DG outputs. In addition, islanded operation, which is a promising development direction of microgrids, is explicitly studied and the related indexes are modeled. Putting them together, we propose a risk-based multi-objective optimal allocation model to optimize the placement and configuration of DGs and provide a reliable and cost-effective system. We solve the formulated multi-objective optimization problem by combining the gradient particle swarm optimization algorithm and the bacterial foraging algorithm. We demonstrate the validity and rationality of the proposed method through the analyses of the American PGE 69-node system.
AB - With the rapid evolution of technology, growing accessibility, and environmental appeal of wind and solar electric systems, distributed generation (DG) has been pushed from the fringe to a mainstream factor in the grid. However, due to the randomness and uncertainty of environmental and operational conditions, DG also brings many risks and may adversely affect the reliability and safety of the power grid when connected to the distribution network. Therefore, it is necessary to introduce the risk theory into the allocation and placement of DGs. This paper establishes a comprehensive set of risk and economic indexes by modeling the randomness and uncertainty of DG outputs. In addition, islanded operation, which is a promising development direction of microgrids, is explicitly studied and the related indexes are modeled. Putting them together, we propose a risk-based multi-objective optimal allocation model to optimize the placement and configuration of DGs and provide a reliable and cost-effective system. We solve the formulated multi-objective optimization problem by combining the gradient particle swarm optimization algorithm and the bacterial foraging algorithm. We demonstrate the validity and rationality of the proposed method through the analyses of the American PGE 69-node system.
KW - Distribution generation
KW - GPSO-BFA
KW - distributed network
KW - multi-objective optimization
KW - risk theory
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U2 - 10.1109/ACCESS.2019.2902886
DO - 10.1109/ACCESS.2019.2902886
M3 - Article
AN - SCOPUS:85064718191
SN - 2169-3536
VL - 7
SP - 30563
EP - 30572
JO - IEEE Access
JF - IEEE Access
M1 - 8672434
ER -