Open AccessPredictive control model to manage power flow on a hybrid wind‐photovoltaic and diesel microgeneration power plant with additional storage capacity
Author(s) -
Santos Costa António José Arsénio,
Valério Duarte,
Costa Branco Paulo José
Publication year - 2018
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/iet-cps.2018.5037
Subject(s) - diesel generator , photovoltaic system , renewable energy , automotive engineering , turbine , model predictive control , state of charge , wind power , stand alone power system , hybrid power , power station , engineering , battery (electricity) , diesel fuel , power (physics) , environmental science , computer science , distributed generation , control (management) , electrical engineering , mechanical engineering , artificial intelligence , physics , quantum mechanics
This study proposes and evaluates a predictive control model for the management of the power flow in a hybrid microgeneration power plant with additional storage capacity. The plant integrates a photovoltaic array, a wind turbine, a diesel generator, and a lithium ion battery bank. One objective of the proposed predictive control model is to maximise the use of power from renewable resources looking for the weather predictions and thus minimise the use of fossil power from the diesel generator and corresponding CO 2 emissions. Another aim is to maximise the duration of lithium ion batteries, since extending their lifetime is crucial for the system's economic viability, and since battery disposal brings environmental concerns as well. A numerical evaluation is performed about the evolution of power dispatch decisions and of the batteries state of charge, depending on the available power storage capacity. Model predictive control proves to be a suitable strategy in this system.