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

    Authors

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

    Comments

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    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

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