Deployment of IoT-Based Smart Demand-Side Management System with an Enhanced Degree of User Comfort at an Educational Institution

S. Charles Raja (), A. C. Vishnu Dharssini, J. Jeslin Drusila Nesmalar and T. Karthick
Additional contact information
S. Charles Raja: Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu, India
A. C. Vishnu Dharssini: Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu, India
J. Jeslin Drusila Nesmalar: Department of Electrical and Electronics Engineering, Tamilnadu Government Polytechnic College, Madurai 625011, Tamil Nadu, India
T. Karthick: Quantanics Techserv Pvt. Ltd., Madurai 625016, Tamil Nadu, India

Energies, 2023, vol. 16, issue 3, 1-24

Abstract: Nowadays, the Internet of Things (IoT) has a wide impact on many potential applications. The impact of IoT on performing demand-side management (DSM) in an Indian educational institution has not been researched in depth before. In this research work, an IoT-enabled SDSMS (Smart DSM System) has been deployed with the main objective of minimizing electricity tariff and also to tweak the quality of user comfort. It can be feasible by prioritizing available renewable PV solar energy during peak hours in an Indian educational institution. DSM has been performed using day-ahead load shifting and rescheduling the different classes of institutional loads by applying hybrid BPSOGSA (Binary Particle Swarm Optimization and Gravitational Search Algorithm). The BPSOGSA performance on DSM has been evaluated based on electricity tariff, peak demand range, and PAR and compared with the outcomes of both binary conventional algorithms BPSO and BGSA, respectively. The proposed method enhances the degree of user comfort (DUC) by tripping the operation of non-critical institutional loads. Simulation results obtained using MATLAB corroborate that BPSOGSA outperforms both BPSO and BGSA under both DSM scenarios. Before DSM, Peak demand, PAR, and Electricity tariffs were found to be 1855.47 kW, 4.1286, and $2030.67 while after DSM, they reduced to 1502.24 kW, 3.263, and $1314.40 respectively. This indicates a 35.273% reduction in electricity tariff, a 19.037% scale down in peak demand, and a 20.97% reduction in PAR. Finally, the real-time IoT-based SDSMS hardware is implemented at the Renewable energy laboratory for real monitoring of energy consumption via the Blynk application.

Keywords: DSM; BGSA; BPSO; smart grid; load categorizing; DUC; load shifting; Internet of Things (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1403/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1403/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1403-:d:1052720

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().