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

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  • 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)

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.

Suggested Citation

  • S. Charles Raja & A. C. Vishnu Dharssini & J. Jeslin Drusila Nesmalar & T. Karthick, 2023. "Deployment of IoT-Based Smart Demand-Side Management System with an Enhanced Degree of User Comfort at an Educational Institution," Energies, MDPI, vol. 16(3), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1403-:d:1052720
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    References listed on IDEAS

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    1. Karthick Tamilarasu & Charles Raja Sathiasamuel & Jeslin Drusila Nesamalar Joseph & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2021. "Reinforced Demand Side Management for Educational Institution with Incorporation of User’s Comfort," Energies, MDPI, vol. 14(10), pages 1-22, May.
    2. Nadeem Javaid & Sakeena Javaid & Wadood Abdul & Imran Ahmed & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid," Energies, MDPI, vol. 10(3), pages 1-27, March.
    3. Huang, Shan-Huen & Lin, Pei-Chun, 2010. "A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(5), pages 598-611, September.
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    1. Maria G. Ioannides & Anastasios P. Stamelos & Stylianos A. Papazis & Erofili E. Stamataki & Michael E. Stamatakis, 2024. "Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems," Energies, MDPI, vol. 17(3), pages 1-28, January.

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