Open AccessFlexible‐regulation resources planning for distribution networks with a high penetration of renewable energy
Author(s) -
Huaniu,
Lu Yang,
Jingxiang Zhao,
Yuzhu Wang,
Weizhou Wang,
Fuchao Liu
Publication year - 2018
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.2018.5531
Subject(s) - renewable energy , flexibility (engineering) , environmental economics , variable renewable energy , computer science , energy supply , investment (military) , reliability engineering , electric power system , operations research , engineering , power (physics) , economics , energy (signal processing) , electrical engineering , statistics , physics , management , mathematics , quantum mechanics , politics , political science , law
Aimed at the status of distribution networks with a high penetration of renewable energy starved of flexible‐regulation resources for improving their ability to absorb renewable‐energy sources, this study puts forward flexibility‐supply and flexibility‐demand indexes for distribution networks and establishes a bi‐level programming model for the distribution of flexible‐regulation resources within the system‐flexibility‐balance constraints. The lower‐level planning model seeks the lowest annual operating cost, lowest annual network loss, and lowest annual abandonment of wind and solar sources; in addition to the traditional power balances and constraints of various control variables and state variables, the requirement to balance constraints involving the flexibility of the system's supply and demand is even more crucial. The upper‐level model aims to minimise the annual investment and operational costs. Combining the models, a double‐layer iterative optimisation algorithm for flexible‐regulation‐resource allocation is proposed. Simulation results for an actual distribution network show that the proposed bi‐level programming model can increase the capacity to absorb renewable energy and reduce system losses with the lowest investment and operational cost.