Open AccessStochastic assessment of distributed generation hosting capacity and energy efficiency in active distribution networks
Author(s) -
Quijano Darwin Alexis,
Wang Jianhui,
Sarker Mushfiqur R.,
PadilhaFeltrin Antonio
Publication year - 2017
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.2017.0557
Subject(s) - distributed generation , mathematical optimization , computer science , ac power , compensation (psychology) , voltage , stochastic programming , power (physics) , reliability engineering , control theory (sociology) , engineering , mathematics , electrical engineering , renewable energy , control (management) , psychology , physics , quantum mechanics , artificial intelligence , psychoanalysis
Active network management (ANM) aims to increase the capacity of variable distributed generation (DG), which can be connected to existing distribution networks. In this study, it is proposed to simultaneously consider the efficient use of energy resources when high shares of DG are procured through the ANM approach. To that end, a multi‐period and multiobjective optimisation algorithm, based on the linearised optimal power flow, is formulated. The algorithm seeks to maximise the installed capacity of DG while minimising the energy losses and consumption of voltage‐dependent loads. The objectives are optimised considering the coordinated operation of voltage regulators and on‐load tap changers, and the management of DG generation curtailment and reactive power compensation from DG. Additionally, the effects of load and generation uncertainties are addressed through a two‐stage stochastic programming formulation of the multiobjective problem. The result is a set of non‐inferior solutions, which allows exploring the degree of conflict among the objectives. The proposed approach was tested on two IEEE test feeders and the solutions show a significant improvement in the system's energy efficiency with a low impact on the amount of connected DG.