Design of a Rule-based Decisive Model for Optimizing the Load Balancing in a Smart Grid Environment

Design of a Rule-based Decisive Model for Optimizing the Load Balancing in a Smart Grid Environment
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Nishant Jakhar, Rainu Nandal, Kamaldeep
DOI : 10.14445/22315381/IJETT-V70I8P209

How to Cite?

Nishant Jakhar, Rainu Nandal, Kamaldeep, "Design of a Rule-based Decisive Model for Optimizing the Load Balancing in a Smart Grid Environment," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 97-103, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P209

Abstract
The electricity demand of users is increasing day by day. This increasing demand also increases the load on power stations, resulting in frequent power drops or failures. This unequal and increasing energy demand is a growing concern of the power sector. The smart grid keeps track of the user demands over the traditional distribution system and ensures the balanced distribution of electricity. Even though the uninterrupted power supply and heavy load situations are key challenges in the smart grid environment, this paper proposes a demand and load-driven rule-based model to achieve effective resource allocation and usage. The proposed intelligent system will predict the expected overload situation at the time allocation and perform load-balanced usage of available resources. The proposed model is simulated with different load situations and analyzed regarding the number of power failures. The analysis results are obtained in average power delay, power switches and power failure measures. The analysis results identified that the proposed rule-based decisive model optimized the performance of the smart grid in extreme load situations and achieved effective results with minimum delay and lesser power failures. The proposed system achieved the reliable and effective distribution of power.

Keywords
Power Distribution, Optimization, Resource Allocation, Rule-based, Smart Grid.

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