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This paper presents the parameter identification of an equivalent circuit-based proton exchange membrane fuel cell model. The model is represented by two electrical circuits, of which one reproduces the fuel cell's output voltage characteristic and the other one its thermal characteristic. The output voltage model includes activation, concentration, and ohmic losses, which describe the static properties, while the double layer charging effect, delays in fuel and oxygen supply, and other effects provide the model's dynamic properties. In addition, a novel thermal model of the studied Ballard's 1.2 kW Nexa fuel cell is proposed. The latter includes the thermal effects of the stack's fan which significantly improve the model's accuracy. The parameters of both, the electrical and thermal, equivalent circuits were estimated on the basis of experimental data by using an evolution strategy. The resulting parameters were validated by the measurement data obtained from the Nexa module. The comparison indicates a good agreement between the simulation and the experiment. In addition to simulations, the identified model is also suitable for usage in real-time fuel cell emulators. The emulator presented in this paper additionally proves the accuracy of the obtained model and the effectiveness of using an evolution strategy for identification of the fuel cell's parameters.

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Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.

Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This paper proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardwarein-the-loop system to obtain the results. The general system does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared to the conventional perturb and observe method, results show the proposed method archives a much superior performance. INDEX TERMS Maximum power point tracking, Photovoltaic system, Artificial bee colony, Hardware in the loop testing. ABBREVIATION Term Description ABC Artificial bee colony algorithm. ACO Ant colony optimization. ADE Adaptive differential evolution. ANFIS Adaptive neuro-fuzzy inference system. ANNs Artificial neural networks. BA Bat algorithm. BI Bio-inspired methods. COA Coyote optimization algorithm. DSC Digital signal controller. ESC Extremum-seeking control. FL Fuzzy logic algorithm. FPA Flower pollination algorithm. FPGA Field-programmable gate arrays. GA Genetic algorithm. HIL Hardware-in-the-loop. INC Incremental conductance algorithm. MOA Moth-flame optimization algorithm.

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