Enhanced Genetic Algorithm for Optimal Demand-Side Control of Time-of-Use Pricing in the Live Central University Building

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : J. Praveena, I. Jacob Raglend

10.14445/23488379/IJEEE-V11I11P101

How to Cite?

J. Praveena, I. Jacob Raglend, "Enhanced Genetic Algorithm for Optimal Demand-Side Control of Time-of-Use Pricing in the Live Central University Building," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 11, pp. 1-11, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I11P101

Abstract:

The energy auditing process collects data on the type and quantity of connected loads, their ratings, energy consumption, and the amount of money spent on power. Identifying sites with high energy demand and waste in academic buildings, hostels, food courts, and other facilities. Solutions for energy-efficient and cost-effective ways to conserve energy and reduce electricity prices. The demand response based, balancing the energy supply and demand, creates a grid with greater economic and environmental advantages. This was made possible by an academic building that reduced electricity costs and optimized power. An energy audit includes observations, measurements, system surveys, data collection, and analysis. Demand-side management is used to balance supply and demand. We then determine and create a demand response program that emphasizes the potential to lower energy costs and increase efficiency by employing a methodical process for measuring the current energy use. The early convergence problem in the process is resolved by the suggested Enhanced Genetic Algorithm, which employs Fitness Distance Balance selection (FDB). When the weather changes or there is a power outage, demand is met by renewable energy. The best time to use renewable energy is when it is most economical. The three objective functions for optimization are energy efficiency, the overall cost per hour for the electrical power supply (computer, water, lights, fans), and the motors' carbon dioxide emissions. By using an enhanced genetic algorithm to optimize power and lower electricity bills, an academic institution backed this. Reduce costs and peak demand by managing consumer energy usage patterns to preserve customer comfort and maximize the use of renewable energy sources.

Keywords:

Enhanced Genetic Algorithm (EGA), Peak to Average load Ratio (PAR), Demand Side Management (DSM), Renewable energy, Electricity.

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