Open AccessResilience-oriented Comprehensive Planning Strategy of Distributed Generator in Power Distribution System
Author(s) -
Z.P. Li,
Kecan Huang,
Tong Qian,
Wujing Huang,
Wenyuan Tang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/838/1/012008
Subject(s) - resilience (materials science) , electric power system , computer science , reliability engineering , mathematical optimization , cluster analysis , monte carlo method , integer programming , generator (circuit theory) , power (physics) , stochastic programming , operational planning , event (particle physics) , operations research , engineering , mathematics , algorithm , economics , statistics , physics , management , quantum mechanics , machine learning , thermodynamics
Power distribution system resilience is not considered in traditional planning of distributed generator (DG), which leads to severe power outages and economic losses during extreme weather event. To address this issue, this paper proposes a resilience-oriented comprehensive planning strategy of DG in power distribution system. The problem is formulated as a two-stage stochastic mixed-integer second-order cone programming (SMISOCP). The objective of the first stage is to determine the number, location and capacity of DG and obtain the economic cost of power distribution system. The second stage minimizes the resilience cost under uncertain failure scenarios. First, sufficient failure scenarios are generated by the Monte Carlo method. Then the failure scenarios are reduced to the most representative scenarios by using the K-means clustering algorithm to reduce computational burden. Finally, the two-stage SMISOCP is solved based on the reduced failure scenarios. The simulation results of the IEEE 33-bus test systems illustrate the effectiveness of the proposed two-stage strategy.