Multi-temporal Optimal Power Flow for voltage control in MV networks using Distributed Energy Resources

•A multi-temporal OPF for voltage control purposes was developed.•The algorithm takes advantage of the available DER, including storage systems.•It aims at providing an operation plan for the day-ahead.•The approach was validated through simulation using real network data. Large-scale integration of...

Full description

Saved in:
Bibliographic Details
Published in:Electric power systems research 2017-05, Vol.146, p.25-32
Main Authors: Meirinhos, J.L., Rua, D.E., Carvalho, L.M., Madureira, A.G.
Format: Article
Language:English
Subjects:
Alternative energy sources
Batteries
Computer simulation
Distributed generation
Distributed power generation
Electric potential
Electricity distribution
Heuristic methods
Large scale integration
Microgrids
Power flow
Renewable Energy Sources
Smart grid technology
Smart grids
Voltage control
Voltammetry
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A multi-temporal OPF for voltage control purposes was developed.•The algorithm takes advantage of the available DER, including storage systems.•It aims at providing an operation plan for the day-ahead.•The approach was validated through simulation using real network data. Large-scale integration of variable Renewable Energy Sources (RES) brings significant challenges to grid operation that require new approaches and tools for distribution system management, particularly concerning voltage control. Therefore, an innovative approach for voltage control at the MV level is presented. It is based on a preventive day-ahead analysis that uses data from load/RES forecasting tools to establish a plan for operation of the different Distributed Energy Resources (DER) for the next day. The approach is formulated as a multi-temporal Optimal Power Flow (OPF) solved by a meta-heuristic, used to tackle complex multi-dimensional problems. The tuning of the meta-heuristic parameters was performed to ensure the robustness of the proposed approach and enhance the performance of the algorithm. It was tested through simulation in a large scale test network with good results.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2017.01.016