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This paper presents three control strategies for grid-connected photovoltaic (PV) systems – maximum power point tracking, constant power point tracking and limited power point tracking. The first one is widely used while the latter two are proposed by the authors to fulfil system operator re-quirements. In some cases, when there is an excess of gen...
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... P&O and IC are reliable for simpler setups, while ANN offers a cutting-edge solution for systems demanding adaptability and enhanced performance in dynamic environments (Soumana, Saulo, & Muriithi, 2022). Researchers and practitioners should carefully consider these tradeoffs when selecting the most appropriate MPPT technique for their particular use case (Lazarov, Zarkov, Stoyanov, & Kanchev, 2012). ...
This article conducts a thorough comparison of three Maximum Power Point Tracking control techniques for photovoltaic systems: Perturb and Observe, Incremental Conductance, and Artificial Neural Network. The study aims to identify the most effective MPPT method by subjecting each technique to numerical simulations. The article explores the performance, efficiency, and robustness of Perturb and Observe, Incremental Conductance and Artificial Neural Network in capturing the maximum power output from photovoltaic panels under varying environmental conditions. Following rigorous testing through numerical simulations, the superior technique is selected for implementation in a grid-connected photovoltaic power conversion chain. This research contributes valuable insights into the optimization of photovoltaic system performance through advanced MPPT control strategies, facilitating informed decisions for practical applications in renewable energy systems.
The presented research works aim to develop an energy management system for a cluster of distributed micro gas turbines and controllable PV generators called «active generators». The general principles of electricity generation from renewable and non-renewable energy sources are first presented. The operation of actual electric grids is also recalled in order to highlight the challenges and expected innovations in future Smart Grids. Then, the integration of a novel method for maximum and limited power point tracking in a PV-based active generator is presented. The modeling of micro-gas turbines in a microgrid energy management system is also presented. The main contribution of this thesis concerns the design of an operational planning of generators one day ahead by the means of a dynamic programming-based algorithm, taking into account the PV power production and the consumption forecasts. The proposed method calculates the production planning of generators by performing a global optimization of an objective function. An adjustment algorithm is proposed and executed every ½ hours through a communication network in order to take into account the uncertainty in forecasted values. An urban microgrid is used for testing the developed algorithms through Supervisory Control and Data Acquisition (SCADA) with hardware-in-the-loop and real-time simulations. Comparisons of the microgrid operation in identical situations with different objective functions are performed, as well as evaluations of economic and environmental indicators
