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Modeling and scheduling of residential electric loads for energy self‐sufficiency
Author(s) -
Narayanankutty Ravishankar Achathuparambil,
Sankar Ashok,
Sundaramoorthy Kumaravel
Publication year - 2020
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2782
Subject(s) - renewable energy , demand response , scheduling (production processes) , computer science , peaking power plant , automotive engineering , environmental economics , reliability engineering , simulation , distributed generation , engineering , operations management , electrical engineering , electricity , economics
The utility company enforces a limit on the amount of energy injected into the utility grid from residential renewable plants due to the stability/protection concerns caused by the reverse power flow. Under this scenario, a proper load scheduling at the consumer end is necessary to ensure maximum renewable energy utilization during the surplus generation. This article proposes a novel objective function to impart energy self‐sufficiency for residential consumers who own electric vehicles and renewable energy power plants. The proposed objective function aims to promote the consumers to utilize the local sources such as the battery of an electric vehicle, solar PV, wind, etc., effectively through load scheduling. A dedicated program in MATLAB platform is developed for load scheduling, and an experimental case study is conducted using the forecasted demand and generation profiles collected from 10 typical residential consumers. Three new techno‐economic parameters namely, aggregate daily exchanged energy, daily average discharged energy, and daily average self‐sufficiency hours are proposed to conduct the comparative study. An Average Rank ranking method is introduced to evaluate the effectiveness of the proposed and conventional objective functions statistically. The result analysis proves that load scheduling using the proposed objective function secure the best Average Rank of 2.02 compared to the conventional objective functions.