concept

IT sector

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Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 12 facts

measurementThe BAT Algorithm reduces the peak-to-low energy demand difference to 2.44 MW in the residential sector and 1.64 MW in the IT sector.

claimThe GreenTech Nexus (GN) system provides interactive dashboards to users in residential and IT sectors, allowing them to actively manage their energy consumption.

claimIn an IT sector company, critical infrastructure such as servers and networking equipment are classified as non-flexible loads due to their 24/7 operational requirements, with power consumption (Pc) multiplied by consistent operational hours (Ht.c).

measurementIn the IT sector, using general demand-side management (DSM) formulas, the peak demand is 4.23 MW and the low demand is 0.99 MW, resulting in a 3.24 MW difference.

measurementThe Chaotic Harris Hawk Optimization algorithm for demand-side management achieves a peak load of 3.4 MW in the residential sector and 3.1 MW in the IT sector, compared to a normal demand-side management strategy with electric vehicle constraints which operates at 4.3 MW in the residential sector and 3.5 MW in the IT sector.

claimDemand Side Management (DSM) provides a framework for intelligently adjusting energy demand patterns in residential and IT sectors to align consumption with sustainable practices.

claimGeneric demand-side management (DSM) models reduce the variation between residential and IT sector energy loads, reducing the peak load to 4.52 MW for residential and 3.61 MW for IT, and decreasing the peak-to-low difference to 3.39 MW for residential and 2.44 MW for IT.

claimThe GreenTech Nexus system connects with an aggregator to transmit power demands, including energy consumption by residential and IT sectors, to the grid for the following day.

measurementThe Slime Mould Algorithm (SMA) for Demand Side Management achieves a peak load of 3.4 MW in the residential sector and 3.1 MW in the IT sector.

measurementThe CS algorithm achieves a peak-to-low energy demand difference of 3.6 MW in the residential sector and 2.56 MW in the IT sector, which is less effective than the CHHO algorithm but better than the baseline DSM.

claimThe GreenTech Nexus input model is designed to shift energy consumption to off-peak hours in residential and IT sectors.

claimThe CHHO algorithm is the most effective optimization strategy for demand-side management, achieving peak-to-low energy demand differences of 2.36 MW for the residential sector and 1.62 MW for the IT sector.