Forecast and Energy Management of a Microgrid with Renewable Energy Sources Using Artificial Intelligence

• Published in: Intelligent Computing Systems Vol. 820; pp. 81 - 96
• Main Authors: Cruz May, E., Ricalde, L. J., Atoche, E. J. R., Bassam, A., Sanchez, E. N.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Artificial neural networks; Microgrid; Power measurement; Prediction; Renewable energy
• ISBN: 9783319762609; 3319762605
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-319-76261-6_7

This paper presents a design of an artificial neural network algorithm for prediction and management of electric loads for the optimal operation of a microgrid with sources of renewable energy. The hybrid power generation system is composed of a photovoltaic array, wind turbines, public power grid, electric loads and battery bank as a storage system. A dynamic neural network is implemented to determine the optimal amounts of energy that must be obtained from the sources, to reduce costs and improve efficiency. Simulation results demonstrate that generation of each energy source can be reached in an optimal form using the proposed design.