Open AccessTowards robust OPF solution strategy for the future AC/DC grids: case of VSC‐HVDC‐connected offshore wind farms
Author(s) -
Nikoobakht Ahmad,
Aghaei Jamshid,
Niknam Taher,
Vahidinasab Vahid,
Farahmand Hossein,
Korpås Magnus
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2017.0575
Subject(s) - offshore wind power , grid , mathematical optimization , high voltage direct current , computer science , electric power system , wind power , voltage source , linear programming , power flow , scheduling (production processes) , integer programming , voltage , control theory (sociology) , power (physics) , engineering , mathematics , electrical engineering , direct current , control (management) , artificial intelligence , physics , geometry , quantum mechanics
This study jointly addresses two major challenges in power system operations: (i) sustained growth of intermittent offshore wind farms (OWFs) connected to AC grid via multi‐terminal voltage source converter (VSC)‐based high‐voltage DC (HVDC) grid that brings new challenges to the power system operation, and (ii) dealing with non‐linearity of the AC power flow equations with the multi‐terminal VSC‐based HVDC grid model. To overcome these challenges, firstly, to deal with the uncertainties caused by the high penetration of the intermittent OWFs, this study introduces a robust optimisation approach. The proposed framework is computationally efficient and does not require the probability density function of the wind speed. The proposed decision‐making framework finds the optimal decision variables in a way that they remain robust against the set of uncertainties. Secondly, the mathematical representation of the full AC optimal power flow (OPF) problem, with the added modelling of multi‐terminal VSC‐based HVDC grid in a day‐ahead scheduling problem, is a mixed‐integer non‐linear programming (MINLP) optimisation problem, which is computationally burdensome for large‐scale systems. Accordingly, this paper proposes a computationally efficient method for adjustment of solutions set points, which is also compatible with existing customary solvers with minimal modification efforts.