Title

Distributed Control and Generation Estimation Method for Integrating High-Density Photovoltaic Systems

Authors

Authors

H. H. Xin; Y. Liu; Z. H. Qu;D. Q. Gan

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Energy Convers.

Keywords

Consensus; distributed estimation; doubly stochastic; photovoltaic (PV); power dispatch; VIRTUAL POWER-PLANT; NETWORKS; STRATEGY; ENERGY; CONSENSUS; Energy & Fuels; Engineering, Electrical & Electronic

Abstract

The presence of distributed generators (DGs) such as photovoltaic systems (PVs) is increasing significantly in distribution networks, and in order to accommodate a higher penetration of DGs, technical issues arising from fluctuation and unpredictability of their power output must be addressed. It is beneficial if DGs of high penetration can be dispatched when necessary. To this end, a distributed control and generation estimation approach is developed to dispatch multiple DGs, each of which consists of a PV and a controllable load. A strongly connected digraph with a row stochastic adjacency matrix is a sufficient requirement for the communication topology. A distributed weights adjustment algorithm adaptively makes the adjacency matrix doubly stochastic so that the aggregated power generation capacity can be estimated. Then, the expected consensus operational point of the DGs is calculated by those DGs that can obtain power dispatch command from the supervisory control and data acquisition system and is propagated to the rest of the DGs with a consensus algorithm. With this method, all the DGs operate at the same ratio of available power, while their aggregated power meets the power dispatch command. Simulations in the IEEE standard 34-bus distribution network verify the effectiveness of the proposed approach.

Journal Title

Ieee Transactions on Energy Conversion

Volume

29

Issue/Number

4

Publication Date

1-1-2014

Document Type

Article

Language

English

First Page

988

Last Page

996

WOS Identifier

WOS:000345578600020

ISSN

0885-8969

Share

COinS