Demand side management of a university load in smart grid environment

Basumatary, Jwngsar; Singh, Brijendra Pratap; Gore, Manoj Madhava

doi:10.1145/3170521.3170524

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Mirjalili et al [25] created a new evolutionary algorithm known as gray wolf optimization (GWO) based on wolves' hunting behavior. In this algorithm, alpha (α) is considered as the fittest solution, beta (β) and delta (δ) are the second and third fittest solutions correspondingly.…”

Section: Binary Gray Wolf Optimizationmentioning

confidence: 99%

“…The optimal design and economical aspects of grid-interactive PV system configuration for an educational campus have been carried out using hybrid optimization of multiple electric renewable (HOMER) software [24]. The load pattern of energy consumption has been studied for Motilal Nehru national instituteof technology (MNNIT), Allahabad, India, using a bottom-up load model [25]. Artificial intelligence techniques are also proposed to solve the energy demand planning in smart homes [26].…”

Section: Introductionmentioning

confidence: 99%

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

Tamilarasu¹,

Sathiasamuel²,

Joseph

et al. 2021

Energies

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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.

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Section: Binary Gray Wolf Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%