Reinforced Demand Side Management for Educational Institution with Incorporation of User’s Comfort

Karthick Tamilarasu, Charles Raja Sathiasamuel, Jeslin Drusila Nesamalar Joseph, Rajvikram Madurai Elavarasan and Lucian Mihet-Popa
Additional contact information
Karthick Tamilarasu: Department of EEE, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu, India
Charles Raja Sathiasamuel: Department of EEE, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu, India
Jeslin Drusila Nesamalar Joseph: Department of EEE, Kamaraj College of Engineering and Technology, Madurai 625015, Tamil Nadu, India
Rajvikram Madurai Elavarasan: Clean and Resilient Energy Systems (CARES) Laboratory, Texas A&M University, Galveston, TX 77553, USA
Lucian Mihet-Popa: Faculty of Electrical Engineering, Ostfold University College, 1757 Halden, Norway

Energies, 2021, vol. 14, issue 10, 1-22

Abstract: Soaring energy demand and the establishment of various trends in the energy market have paved the way for developing demand-side management (DSM) from the consumer side. This paper proposes a reinforced DSM (RDSM) approach that uses an enhanced binary gray wolf optimization algorithm (EBGWO) that benefits the consumer premises with load scheduling, and peak demand reduction. To date, DSM research has been carried out for residential, commercial and industrial loads, whereas DSM approaches for educational loads have been less studied. The institution load also consumes much utility energy during peak hours, making institutional consumers pay a high amount of cost for energy consumption during peak hours. The proposed objective is to reduce the total electricity cost and to improve the operating efficiency of the entire load profile at an educational institution. The proposed architecture integrates the solar PV (SPV) generation that supplies the user-comfort loads during peak operating hours. User comfort is determined with a metric termed the user comfort index (UCI). The novelty of the proposed work is highlighted by modeling a separate class of loads for temperature-controlled air conditioners (AC), supplying the user comfort loads from SPV generation and determining user comfort with percentage UCI. The improved transfer function used in the proposed EBGWO algorithm performs faster in optimizing nonlinear objective problems. The electricity price in the peak hours is high compared to the off-peak hours. The proposed EBGWO algorithm shift and schedules the loads from the peak hours to off-peak hours, and incorporating SPV in satisfying the user comfort loads aids in reducing the power consumption from the utility during peak hours. Thus, the proposed EBGWO algorithm greatly helps the consumer side decrease the peak-to-average ratio (PAR), improve user comfort significantly, reduce the peak demand, and save the institution’s electricity cost by USD 653.046.

Keywords: smart grid; institutional loads; reinforced demand-side management; load-shifting; user comfort index; enhanced binary gray wolf optimization (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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