Open AccessTwo‐stage restoration strategies for power systems considering coordinated dispatch between plug‐in electric vehicles and wind power units
Author(s) -
Zhang Can,
Zhang Huayi,
Liu Shengyuan,
Lin Zhenzhi,
Wen Fushuan
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0167
Subject(s) - wind power , computer science , electric power system , flexibility (engineering) , integer programming , plug in , linear programming , mathematical optimization , electric power , power (physics) , reliability engineering , automotive engineering , engineering , algorithm , electrical engineering , mathematics , statistics , physics , quantum mechanics , programming language
With increased penetration of wind power units and plug‐in electric vehicles (PEVs), their control flexibility and quick response potentially provide an alternative way to fulfil the need for rapid restoration. A two‐stage restoration strategy optimisation approach is presented with the coordination of PEVs and wind power units considered. The optimisation model is to maximise the restored generation capability and minimise the fluctuation of cranking power. In the first stage, the aim is to provide reliable cranking power remotely for black start generators through coordinated dispatch of PEVs and wind power units and quadratic programming (QP) models for dispatching electric vehicle aggregators (EVAs) subject to wind power fluctuations, and for dispatching numerous PEVs within each EVA are developed. To ensure close coordination between these two dispatching procedures, bi‐level programming‐based hierarchical decomposition approach is used to solve the QP models in an iterative way. In the second stage, an integer linear programming model is proposed to optimise the restoration schedules through an effective transformation of the original non‐linear formulation, so as to reduce the computing time and effort significantly. Finally, a case study is presented to demonstrate the effectiveness and essential features of the developed models and methods.