Open AccessDevelopment of Optimal Day-Ahead Electricity Pricing Scheme using Real Coded Genetic Algorithm under Demand Response Environment
Author(s) -
M. Krishna Paramathma,
D. Devaraj,
V. Agneshidhayaselvi,
M. Karuppasamypandian
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1055/1/012144
Subject(s) - electricity , demand response , genetic algorithm , computer science , smart grid , metering mode , peak demand , scheme (mathematics) , dynamic pricing , software deployment , grid , electricity market , node (physics) , mathematical optimization , real time computing , consumption (sociology) , power (physics) , economics , microeconomics , engineering , electrical engineering , mathematical analysis , mathematics , machine learning , mechanical engineering , social science , physics , geometry , structural engineering , quantum mechanics , sociology , operating system
Real-time pricing in a Smart Grid scenario allows consumers to move their time-insensitive loads to off-peak hours and maintains the power balance between the demand side and supply side. This scheme has a strong impact on customer behaviour, network operations, and overall control of the power grids. In this proposed work, Real coded Genetic Algorithm (RGA) is used to develop a Day Ahead Real-Time electricity-Pricing (DARTP) model. The established scheme maximizes the benefit of the energy provider without reducing the minimum daily consumption rate, the consumer response to the reported electricity prices, and the constraints of distribution networks. The RGA results demonstrate that the calculated optimal prices bring higher benefits for consumers and energy providers than the posting of market prices directly to consumers on the day ahead. The proposed setup is tested with a 32-node distribution bus system. Simulation results reveal that the deployed methodology will help the participants to shift the peak load time to base load time by receiving optimal DARTP, thereby reducing excessive consumption made during the instance of peak load. The obtained DARTP will be sent to the consumers through the deployment of an Advanced Metering System.