Research Article
Hopfield Neural Network-based Security Constrained Economic Dispatch of Renewable Energy Systems
@ARTICLE{10.4108/eai.25-1-2021.168224, author={Shewit Tsegaye and Fekadu Shewarega and Getachew Bekele}, title={Hopfield Neural Network-based Security Constrained Economic Dispatch of Renewable Energy Systems}, journal={EAI Endorsed Transactions on Energy Web}, volume={8}, number={35}, publisher={EAI}, journal_a={EW}, year={2021}, month={1}, keywords={Hopfield neural networks, Security constraints, economic dispatch, renewable energy systems, and optimization}, doi={10.4108/eai.25-1-2021.168224} }- Shewit Tsegaye
Fekadu Shewarega
Getachew Bekele
Year: 2021
Hopfield Neural Network-based Security Constrained Economic Dispatch of Renewable Energy Systems
EW
EAI
DOI: 10.4108/eai.25-1-2021.168224
Abstract
This paper presents Security Constrained Economic Dispatch (SCED) of Renewable Energy Systems (RES) using Hopfield Neural Networks (HNN) to address power mismatch problems of the Ethiopian power grid. The mathematical formulations of SCED for RES comprising biomass, hydro, solar PV, waste to energy plant, wind, and geothermal are presented. Each of these sources requires problem formulation and constraint handling mechanisms considering security limits and credible contingencies. This enables renewable energy systems to provide secure and reliable electric service. Modified IEEE 118 bus system and Ethiopian renewable energy systems were used as case studies. Modelling and simulation were conducted on MATLAB. According to the results obtained, it can be deduced that employing HNN based SCED is a promising step in connection to developments needed in the adoption and realization of smarter grids as it reduces execution time, production cost and the number of blackouts while increasing the security level of a power system of RES.
Copyright © 2021 Shewit Tsegaye et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
