Intelligent approach for optimal sizing in photovoltaic panel-battery system and optimizing smart grid energy-Reference-Cited by-同舟云学术

Intelligent approach for optimal sizing in photovoltaic panel-battery system and optimizing smart grid energy

Published:2021-07-19 Issue: Volume: Page:014233122110270
ISSN:0142-3312
Container-title:Transactions of the Institute of Measurement and Control
language:en
Short-container-title:Transactions of the Institute of Measurement and Control

Author:

Naceur Ferdaws Ben¹²,Salah Chokri Ben³^ORCID,Telmoudi Achraf Jabeur⁴,Mahjoub Mohamed Ali²

Affiliation:

1. University of Sousse, ISITCom, Tunisia

2. University of Sousse, National Engineering School of Sousse, LATIS – Laboratory of Advanced Technology and Intelligent Systems, Tunisia

3. LASEE Laboratory, Department of Electrical Engineering, University of Sousse, ISSAT of Sousse, Tunisia

4. University of Tunis, LISIER Laboratory, Higher National Engineering School of Tunis, Tunisia

Abstract

Renewable energy plays a very important role in solving energy problems, and solar energy is one of the most important renewable sources, especially in sunny countries. This paper deals with two problems: The first one is about optimal sizing in a photovoltaic panel (PVP)-battery system, and the second consists in energy management in smart grids. To achieve the first objective, an adaptive neuro-fuzzy inference system (ANFIS) estimation algorithm is developed in order to estimate a database of instantaneous photovoltaic power. The estimated instantaneous photovoltaic power is used in an optimal algorithm to size a PVP-battery power station to supply a 1.5 kW AC load. For the second objective, a deep learning forecasting algorithm is realized to estimate the smart grid parameters so as to optimize the consumption energy. All results are checked carrying out Matlab simulation using real weather data. The simulation results give a good performance of our proposed sizing and management systems.

Publisher

SAGE Publications

Subject

Instrumentation

Link

http://journals.sagepub.com/doi/pdf/10.1177/01423312211027027

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