Open AccessFeed- Forward Neural Network based Day Ahead Nodal Pricing
Author(s) -
Kaustubh Rokamwar
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36352
Subject(s) - electricity , computation , computer science , artificial neural network , nodal , power (physics) , ac power , voltage , marginal cost , mathematical optimization , transmission (telecommunications) , real time computing , control theory (sociology) , simulation , economics , electrical engineering , telecommunications , engineering , mathematics , algorithm , microeconomics , artificial intelligence , medicine , physics , quantum mechanics , anatomy , control (management)
An electricity locational marginal pricing prediction normally recognized by 24-hour day-ahead nodal price forecast. In this paper first collected all physical and technical data i.e. availability of generation and their cost characteristics, real and reactive demands at various buses, transmission capacity availability at various conditions like peak and off-peak conditions. All these input data are used as input for computation of optimal power flow. The nodal prices are calculated with AC-DC optimal power flow methodology for IEEE 30 bus system. The resulted optimal real electricity bus voltages, nodal prices, reactive and real demands, angles have been given as inputs to Artificial Neural Network (ANN) for predict day ahead nodal prices.