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

• 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
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16186681

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.