Fractal IoT

Anjana, M.S.; Ramesh, Maneesha Vinodini; Devidas, Aryadevi Remanidevi; Athira, K.

doi:10.1145/3364544.3364826

Cited by 10 publications

(1 citation statement)

References 23 publications

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“…Additionally, strategies for utilizing extra solar energy and interoperability with appliances and electric vehicles must be investigated. Anjana M S et al developed a fractal IoT architecture for smart communities to effectively control energy constraints and generate self-sustaining neighborhoods [25]. The system handled energy shortages and was tested in smart building, smart grid, and microgrid scenarios while combining numerous features.…”

Section: Literature Review On Peak Load Management In Indiamentioning

confidence: 99%

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

Parangusam,

Lekshmana,

Gono

et al. 2023

Energies

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Electricity demand has increased tremendously in recent years, due to the fact that all sectors require energy for their operation. Due to the increased amount of modern home appliances on the market, residential areas consume a significant amount of energy. This article focuses on the residential community to reduce peak load on residential distribution networks. Mostly, the residential consumer’s power demand increases more during the summer season due to many air conditioners (AC) operating in residential homes. This paper proposes a novel summer peak intelligent controller (SPIC) algorithm to reduce summer peak load in residential distribution transformers (RDT). This proposed SPIC algorithm is implemented in a multi-home energy management system (MHEMS) with a four-home hardware prototype and a real-time TNEB system. This hardware prototype is divided into two different cases, one with and one without taking user comfort into account. When considering consumer comfort, all residential homes reduce their peak load almost equally. The maximum and minimum contribution percentages in Case 2 are 29.82% and 19.30%, respectively. Additionally, the real-time TNEB system is addressed in two different cases: with and without incentive-based programs. In the real-time TNEB system during peak hours, the novel SPIC algorithm reduces peak demand in Case 1 by 113.70 kW, and Case 2 further reduces it to 118.80 kW. The peak load decrease in Case 2 during peak hours is 4.5% greater than in Case 1. In addition, we conducted a residential consumer opinion survey to validate the acceptance rate of the proposed design and algorithm.

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Section: Literature Review On Peak Load Management In Indiamentioning

confidence: 99%