Summary In this paper, a fuzzy energy management algorithm for a hybrid renewable power system based on lifetime extending is presented. When the system contains two storage elements or more, the selection of the suitable element to be charged or discharged becomes of paramount importance. When the storage elements are of different types, the decision will be difficult. Conventional algorithms that make series of tests to select the storage element choose always the first available element. This way of testing affects badly the most used element and may affect the other storage elements too as they rarely operate under hard load scenarios. In this study, and in order to solve this problem, two fuzzy controllers have been used to manage the energy flow for a hybrid renewable power system. It is composed of: a photovoltaic generator as a main source, a fuel cell and batteries as a storage elements. The controllers operate as master and slave. The master controller gives orders to all the system power converters and to the slave controller as well. The latter is activated only when the storage elements are at the same state of charge. It is charged, instead of the master's, to select the suitable element to be charged or discharged. Its orders are given based on lifetime functions for each element. To examine the proposed algorithm, simulations have been performed under Matlab/Simulink (The MathWorks, Inc., Massachusetts, USA). Comparison and statistics have been carried out to give the percentage of the worked hours for each element in each operating mode. The obtained results show the high performance of the proposed algorithm. Copyright © 2017 John Wiley & Sons, Ltd.

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Objectives: To improve the reduction of photovoltaic system's power output under various resistance load. Additionally, partial shaded conditions (PSCs) lead to several peaks on photovoltaic (PV) curves, which decrease conventional techniques' efficiency and in these (PSCs), standard equations might not be implemented entirely, therefore, the mathematical model of PV array is compulsory to modify and re-establish as well as it is compulsory to apply some methods based on artificial intelligence to develop the performance of traditional techniques. Methods: This work has modified and re-established the mathematical model of PV array under (PSCs) which are recognized and verified using MATLAB/Simulink environment. Also, heuristic algorithms (Cuckoo Search Algorithm (CSA) and Modified Particle Swarm Optimization (MPSO)) have been suggested and applied with PV system to promote output power under various resistance load, varying weather conditions and (PSCs). Moreover, these suggested algorithms can improve the dynamic response and steady-state PV systems' performance simultaneously and effectively comparing to the Modified Perturb and Observe (MP&O) and Artificial Neural Network (ANN) methods. Findings: The proposed methods are examined under various resistance load, several scenarios for (PSCs) and non-uniform irradiation levels to investigate its effectiveness. The results ensure that proposed tracker based on Cuckoo Search Algorithm (CSA) can distinguish between the global and local maximum peaks of PV system effectively with efficiency of 99% comparing to other MPPT approaches. So, all approaches mentioned above are implemented to improve the output power of PV system in Yemen. Novelty: Modified and re-established the mathematical model of PV array under (PSCs) and also, proposed a heuristic algorithms (CSA) and (MPSO)