Open AccessImproved extreme‐scenario extraction method for the economic dispatch of active distribution networks
Author(s) -
Zhang Yipu,
Ai Xiaomeng,
Fang Jiakun,
Wen Jinyu
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0593
Subject(s) - computer science , mathematical optimization , ellipsoid , extreme learning machine , economic dispatch , robustness (evolution) , robust optimization , electric power system , power (physics) , mathematics , artificial intelligence , biochemistry , physics , chemistry , quantum mechanics , astronomy , artificial neural network , gene
Optimisation techniques with good characterisation of the uncertainties in modern power system enable the system operators well trade‐off between security and sustainability. This study proposes the extreme‐scenario extraction‐based robust optimisation method for the economic dispatch of active distribution network with renewables. The extreme scenarios are selected from the historical data using the improved minimum volume enclosing ellipsoid (MVEE) algorithm to guarantee the security of system operation while avoid frequently switching the transformer tap. It is theoretically proved that if the decision can be adaptive to the selected extreme scenarios, it can be robust to all the possible scenarios. Simulation results demonstrate that the proposed improved MVEE algorithm significantly reduces the number of scenarios, so that the computational burden is dramatically cut down. Additionally, compared to the existing robust optimisation approach, the proposed method is less conservative for the smaller size of the uncertainty set.