Open AccessUncertainty level of voltage in distribution network: an interval model and application in centralised storage location
Author(s) -
Chen Pengwei,
Tao Shun,
Xiao Xiangning
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0445
Subject(s) - interval (graph theory) , mathematical optimization , voltage , computer science , node (physics) , quadratic programming , quadratic equation , stochastic programming , control theory (sociology) , linear programming , mathematics , engineering , geometry , electrical engineering , structural engineering , control (management) , combinatorics , artificial intelligence
Intermittent distributed generations, stochastic loads and other uncertainties have uncertain impacts on the node voltages in distribution network. To quantitatively assess the impact boundaries of uncertainties on node voltages, an interval model is proposed in this study from the perspective of linear description. The model parameters defined as the voltage influence intervals are solved for as follows: first, conduct the analysis sample collection through the affine power flow and the expected operating point estimated by point‐estimate method; then, calculate the voltage influence intervals via interval regression for the above sample. To meet the different demand of solving efficiency and conservative level for parameter identification, two solution methods of interval regression based on quadratic programming and stochastic programming are proposed, respectively. To further illustrate the practical application of the interval model, an optimal location method for centralized energy storage is presented. The verification results based on the modified IEEE 33‐bus system and practical 113‐bus system demonstrate the interval model and its solution methods have an excellent performance in assessing the impact boundaries of uncertainties. The application case also shows that the proposed decision method can produce a global and robust location scheme with considering the bound uncertainty of distributed generation output fluctuations.