Open AccessSelection of appropriate load compositions for predicting the dynamic performance of distribution grids
Author(s) -
Roy Naruttam Kumar,
Pota Hemanshu Roy
Publication year - 2019
Publication title -
iet energy systems integration
Language(s) - English
Resource type - Journals
ISSN - 2516-8401
DOI - 10.1049/iet-esi.2019.0020
Subject(s) - network topology , electric power system , voltage , grid , computer science , control theory (sociology) , topology (electrical circuits) , stability (learning theory) , process (computing) , power (physics) , engineering , mathematics , machine learning , physics , geometry , control (management) , quantum mechanics , artificial intelligence , electrical engineering , operating system
In this study, the significant effects of the load models used to analyse low‐voltage power systems are presented and a systematic process for selecting appropriate load compositions to evaluate the performance of distribution systems containing distributed generation is proposed. The driving force behind, and key reasons for, voltage instability are analysed by evaluating different load models subjected to grid disturbances. The stability conditions of distribution systems are also checked through time‐domain simulations performed on various network configurations, such as radial, loop, and mesh topologies. The system's responses to changes in the substation's voltage and contingency events are also demonstrated. It is found that the types and order of load models used greatly affect a system's voltage stability and therefore, an accurate load modelling is necessary to support the renewables integration in distribution systems. As the system's response is found to be sensitive to the network's topology and actual load composition, an approach that selects appropriate load compositions which, in turn, reduces the huge investment costs of improper planning, is proposed. The effectiveness of the proposed approach is verified by connecting static synchronous compensators to the network under varying load compositions.