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

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

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 c...

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Bibliographic Details
Published in:Energies (Basel) Vol. 16; no. 18; p. 6681
Main Authors: Parangusam, Kanakaraj, Lekshmana, Ramesh, Gono, Tomas, Gono, Radomir
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-09-2023
Subjects:
Algorithms
Alternative energy sources
Artificial intelligence
Automation
Building management systems
Case studies
Consumer behavior
Cost control
Demand side management
demand side management (DSM)
Economic incentives
Electric vehicles
Electricity
Energy consumption
Energy efficiency
Energy management
Energy prices
Energy resources
Force and energy
Home appliances
Marketing research
multi-home energy management system (MHEMS)
Nuclear energy
Optimization
Python
Renewable resources
residential distribution transformer (RDT)
Scheduling
Simulation
Smart houses
summer peak intelligent controller (SPIC)
Tamil Nadu Electricity Board (TNEB)
Tariffs
India
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16186681