Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique

Priyadharshini Ramu (), Sivasankar Gangatharan, Sankar Rangasamy and Lucian Mihet-Popa ()
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Priyadharshini Ramu: Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, India
Sivasankar Gangatharan: Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, India
Sankar Rangasamy: Department of Electrical and Electronics Engineering, Chennai Institute of Technology, Chennai 600069, India
Lucian Mihet-Popa: Electrical Engineering Department, Ostfold University College, 1757 Halden, Norway

Sustainability, 2023, vol. 15, issue 16, 1-28

Abstract: The inclusion of photovoltaics (PV) in electric power supply systems continues to be a significant factor in global interest. However, solar power exhibits intermittent uncertainty and is further unpredictable. Accurate solar generation prediction and efficient utilization are mandatory for power distribution management and demand-side management. Peak demand management and reducing energy costs can be effectively tackled through the implementation of a reliable solar power forecasting system and its efficient utilization. In this regard, the proposed work is related to efficiently managing solar PV power and optimizing power distribution using an enhanced reinforced binary particle swarm optimization (RBPSO) technique. This DSM (demand-side management) strategy involves utilizing a forecast of solar PV generation for the upcoming day and adjusting the consumption schedule of the load to decrease the highest energy demand. The proposed approach improves user comfort by adjusting the non-interruptible and flexible institutional load through clipping and shifting techniques. To evaluate the effectiveness of this approach, its performance is assessed by analyzing the peak demand range and PAR (peak-to-average ratio). It is then compared to the conventional genetic algorithm to determine its effectiveness. Simulation results obtained using MATLAB show that the PAR peak demand before DSM was found to be 1.8602 kW and 378.06 kW, and after DSM, it was reduced to 0.7211 kW and 266.54 kW. This indicates a 29% reduction in Peak demand and performance compared to the conventional genetic algorithm (GA).

Keywords: smart grid; demand response; prediction; educational load; peak shaving; load scheduling; demand side management; BPSO; GA (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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