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It is shown that due to power take-off losses, optimal control provides maximum energy absorption, but not maximum energy production. A new reactive control criterion in the frequency-domain is deduced assuming constant power take-off efficiency, respectively, in the power feeding and power absorption parts of the wave cycle. If applied in the time-domain, this criterion requires the incident wave to be predicted some time into the future. Whilst the OWC type of Wave Energy Converters (WEC) is presented in the paper, the extension to WECs of the floating body type is also considered. Illustrative numerical results for a two-dimensional OWC of simple geometry are presented, which include the performance of this device in three wave spectra with increasing demands of active control for improved energy production. Linear hydrodynamic theory is considered throughout the paper.

In this paper, we investigate how to apply industrial unmanned aerial vehicles (UAVs) for autonomous power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization and the small-timescale optimization. Then, the NP-hard joint optimization problem is transformed to a two- stage optimization problem based on energy consumption magnitude and optimization timescale differences. Next, the first-stage and second-stage problems are solved by exploring dynamic programming (DP) and auction matching, respectively. Finally, the proposed algorithm is verified based on realistic power grid topology. Simulation results demonstrate that the proposed scheme achieves significant energy consumption reduction.

Abstract Non-fragile load frequency control scheme is provided to solve the problem of multi-area power system with energy storage system and wind power. A dynamical model is established in term of multi-area power system with time-delay and uncertainties, which subjects to circular pole constraints simultaneously. Firstly, it is convenient to be operated by computer, the discrete-time signal is attained by sampling the dynamical model, which results in that long time-delay is equivalently turned into two pairs of successive state time-delay. Secondly, non-fragile control is employed to guarantee the closed-loop poles of the equivalent discrete-time one locate in the specified circular region inside the unit disk. Besides, the specified circular region can be transformed into the unit disk by the appropriate linear transformation, thereby, D-stabilization is converted into asymptotical stabilization. Thirdly, the direct distributed scheme of time-delay is adopted, in comparison with the state augmented scheme, less computational burden is derived. Finally, numerical examples are put forward to verify the effectiveness of the proposed methods.

Abstract This paper addresses the importance of the probabilistic analysis in determining the wave energy potential to support the design of electrical generators for wave energy conversion. The irregular activity of ocean waves induces variable frequencies and amplitudes of operation for point absorbers electrical generators, which may lead to the construction of inefficient electrical machines. Applying probabilistic models to describe the ocean wave's behavior allows the determination of the values most likely to be sampled for ocean-state parameters. Knowing these values, the most expected ocean wave potential can be determined and a proper device design can be performed. This work also refers to the importance of using the joint probability models instead of the marginal ones. Seven locations at the Portuguese coast are evaluated in order to determine their wave energy resource potential.

AbstractThe preparation of meso‐aryl hydroporphyrins, namely chlorins (II) and bacteriochlorins (IV), proceeds via reduction of porphyrins (I) and (III) with diimide in the absence of solvents and bases.

The feasibility and operation performance of the gasification of rice straw in an atmospheric fluidized-bed gasifier was studied. The gasification was carried out between 700 and 850 °C. The stoichiometric air-fuel ratio (A/F) for rice straw was 4.28 and air supplied was 7-25% of that necessary for stoichiometric combustion. Mass and power balances, tar concentration, produced gas composition, gas phase ammonia, chloride and potassium concentrations, agglomeration tendencies and gas efficiencies were assessed. Agglomeration was avoided by replacing the normal alumina-silicate bed by a mixture of alumina-silicate sand and MgO. It was shown that it is possible to produce high quality syngas from the gasification of rice straw. Under the experimental conditions used, the higher heating value (HHV) of the produced gas reached 5.1 MJ Nm(-3), the hot gas efficiency 61% and the cold gas efficiency 52%. The obtained results prove that rice straw may be used as fuel for close-coupled boiler-gasifier systems.

AbstractMachine learning techniques (MLTs) can create accurate predictions of solar outputs that are used in photovoltaic system performance analysis. The issue with MLT application is the requirement for large amounts of historical data for training the prediction models that is not always available. Since the photovoltaic system behaviour is non‐linear due to the unpredictable nature of the weather conditions throughout the year, MLT training requires annual historical data to create the prediction model. The photovoltaic system production meters only store up to 3 months of historical power values. This information served as motivation to research different types of MLTs, in search of one that would accurately predict the daily solar energy values of a photovoltaic system based on the available 3‐month historical data. The aim of this work is to implement a photovoltaic system performance analyser that estimates daily solar alternating current energy outputs for any rooftop photovoltaic system, based on daily solar irradiation values, without being influenced by the seasons of the year nor the photovoltaic system installation location. Therefore, 5 MLTs were studied and compared, which include the regression tree, artificial neural network, multigene genetic programming, Gaussian process, and the support vector machine for regression. Results show that the regression tree MLT provides acceptable results to be used in all locations and all seasons of the year, while the support vector machine for regression is best for spring and summer training dataset months, and the Gaussian process is best for the autumn and winter training dataset months.

Abstract High power pseudocapacitors are extremely relevant to answer specific needs in the current energy transition arena and to implement an efficient renewable energy society. However, literature shows that are still open gaps concerning improvement of their energy density at high power, conversion efficiency, cost and cycle life. Electrodes based on active transition metal compounds, and in particular metal sulphides, evidence high potential to meet these objectives. This work discusses the dependence on the synthesis route of the charge storage mechanism of manganese sulphide-based materials and relates the pseudocapacitive response of these electrodes with their polycrystalline nature. Results reveal that a manganese oxy-sulphide mixture can achieve a high specific capacitance of 231 F.g−1 at 0.5 A/g in a 0.65 V active window. These values represent a 31.5 % increase compared to pure rambergite, γ-MnS, and 436 % compared to pure hausmannite Mn3O4 prepared under the same conditions. Moreover, the results show that manganese oxy-sulphide electrodes are characterized by good charge retention (73%), and superior long term capacity retention (above 86%) after 5000 cycles, evidencing potential for high power energy storage applications.