Open AccessDescriptive statistical approach for the assessment of the output of a virtual power plant in a secondary distribution network
Author(s) -
Palanichamy Ponraj,
Samuel Arul Daniel,
Murali Venkatakirthiga
Publication year - 2020
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.2019.1658
Subject(s) - computer science , profiling (computer programming) , generator (circuit theory) , operator (biology) , virtual power plant , descriptive statistics , reliability engineering , data mining , power (physics) , simulation , statistics , mathematics , engineering , electrical engineering , renewable energy , distributed generation , operating system , biochemistry , physics , chemistry , repressor , quantum mechanics , transcription factor , gene
Virtual power plants (VPPs) of secondary distribution network have come into existence due to the installation of generators in the premises of domestic consumers and the recent legislative emphasis on net‐zero energy operation. A predetermined operation of VPP as required by a system operator is possible only when its power output is assessed beforehand. A simple approach for assessment based on load profiling using descriptive statistical parameters such as z ‐scores of the load data is proposed in this study. The proposed method collates historical customer data according to seasons, days of the week and specified time‐intervals of the day. Then, z ‐scores are computed for each of the historical load and generator data. The calculated z ‐scores are subsequently grouped into specified clusters. The grouped data in each of the clusters are then employed to predict the VPP output. Demonstration of the assessment of VPP output using available Pecan street data is presented to validate the proposed approach.