TY - GEN
T1 - Optimal planning of distribution network based on k-means clustering
AU - Gao, Hongjun
AU - Liu, Youbo
AU - Liu, Zhenyu
AU - Xu, Song
AU - Wang, Renjun
AU - Xiang, Enmin
AU - Yang, Jie
AU - Qi, Mohan
AU - Zhao, Yinbo
AU - Pan, Hongjin
AU - Ma, Wang
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by Science and Technology Project of State Grid Corporation of China (Research and Application on Power Grid Planning Data Automatic Collection and Intelligent Execution Management Technology Based on Novel Information and Communication Technology under Grant SGIT0000YXJS1900324.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/30
Y1 - 2020/10/30
N2 - The reform of electricity marketization has bred multiple market agents. In order to maximize the total social benefits on the premise of ensuring the security of the system and taking into account the interests of multiple market agents, a bi-level optimal allocation model of distribution network with multiple agents participating is proposed. The upper level model considers the economic benefits of energy and service providers, which are mainly distributed power investors, energy storage operators and distribution companies. The lower level model considers end-user side economy and actively responds to demand management to ensure the highest user satisfaction. The K-means multi scenario analysis method is used to describe the time series characteristics of wind power, photovoltaic power and load. The particle swarm optimization (PSO) algorithm is used to solve the bi-level model, and IEEE33 node system is used to verify that the model can effectively consider the interests of multiple agents while ensuring the security of the system.
AB - The reform of electricity marketization has bred multiple market agents. In order to maximize the total social benefits on the premise of ensuring the security of the system and taking into account the interests of multiple market agents, a bi-level optimal allocation model of distribution network with multiple agents participating is proposed. The upper level model considers the economic benefits of energy and service providers, which are mainly distributed power investors, energy storage operators and distribution companies. The lower level model considers end-user side economy and actively responds to demand management to ensure the highest user satisfaction. The K-means multi scenario analysis method is used to describe the time series characteristics of wind power, photovoltaic power and load. The particle swarm optimization (PSO) algorithm is used to solve the bi-level model, and IEEE33 node system is used to verify that the model can effectively consider the interests of multiple agents while ensuring the security of the system.
KW - Electricity marketization
KW - K-means
KW - Market agents
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=85101598673&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101598673&partnerID=8YFLogxK
U2 - 10.1109/EI250167.2020.9347346
DO - 10.1109/EI250167.2020.9347346
M3 - Conference contribution
AN - SCOPUS:85101598673
T3 - 2020 IEEE 4th Conference on Energy Internet and Energy System Integration: Connecting the Grids Towards a Low-Carbon High-Efficiency Energy System, EI2 2020
SP - 2135
EP - 2139
BT - 2020 IEEE 4th Conference on Energy Internet and Energy System Integration
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE Conference on Energy Internet and Energy System Integration, EI2 2020
Y2 - 30 October 2020 through 1 November 2020
ER -
