Active management of renewable energy sources for maximizing power production

• Published in: International journal of electrical power & energy systems 2014-05, Vol.57, p.64-72
• Main Authors: Calderaro, V., Conio, G., Galdi, V., Massa, G., Piccolo, A.
• Format: Article
• Language: English
• Subjects: Applied sciences; Distributed generation; Distribution networks; Electric power plants; Electrical engineering. Electrical power engineering; Electrical machines; Electrical power engineering; Exact sciences and technology; Miscellaneous; Neural Network; Power electronics, power supplies; Power networks and lines; Reactive power support; Regulation and control; Renewable energy sources; Voltage control
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2013.11.040

•A decentralized control strategy for DG based on Neural Networks is presented.•The innovative control strategy is capable to maximize active power production by DG.•The proposed strategy presents a unified controller for inverter connected DG plants.•The control actions do not require massive communication infrastructures.•The controller immunity from unforeseen operation variations has been proved. The continuous increasing penetration of Distributed Generation systems (DGs) into Distribution Networks (DNs) puts in evidence the necessity to develop innovative control strategies capable to maximize DGs active power production. This paper focuses the attention upon this problem, developing an innovative decentralized voltage control approach aimed to allow DGs active power production maximization and to avoid DGs disconnection due to voltage limit infringements as much as possible. In particular, the work presents a local reactive/active power management control strategy based on Neural Networks (NNs), able to regulate voltage profiles at buses where DGs are connected, taking into account their capability curve constraints. The Neural Network controller is based on the Levenberg–Marquardt algorithm incorporated in the back-propagation learning algorithm used to train the NN. Simulations run on a real Medium Voltage (MV) Italian radial DN have been carried out to validate the proposed approach. The results prove the advantages that the flexibility of the proposed control strategy can have on voltage control performances, generation hosting capacity of the network and energy losses reduction.