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DOI:10.35833/MPCE.2022.000193
A Multi-objective Chance-constrained Information-gap Decision Model for Active Management to Accommodate Multiple Uncertainties in Distribution Networks
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Author: Shida Zhang1, Shaoyun Ge1, Hong Liu1, Junkai Li1, Chenghong Gu2, Chengshan Wang1

Author Affiliation: 1.School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2.Department of Electrical and Electronic Engineering, University of Bath, Bath BA2 7AY, U.K.

Foundation:
This work was supported by the National Natural Science Foundation of China (No. U1866207).
Abstract: The load demand and distributed generation (DG) integration capacity in distribution networks (DNs) increase constantly, and it means that the violation of security constraints may occur in the future. This can be further worsened by short-term power fluctuations. In this paper, a scheduling method based on a multi-objective chance-constrained information-gap decision (IGD) model is proposed to obtain the active management schemes for distribution system operators (DSOs) to address these problems. The maximum robust adaptability of multiple uncertainties, including the deviations of growth prediction and their relevant power fluctuations, can be obtained based on the limited budget of active management. The systematic solution of the proposed model is developed. The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term. Considering the stochastic characteristics and correlations of power fluctuations, the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution. The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network. The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties, which corresponds to an optional active management strategy set for future selection.

Keywords:
Active management ; distribution network ; multiple uncertainties ; information gap decision theory ; chance constraint
Received:April 02, 2022               Online Time:2023/01/28
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