Flapping wing devices have attracted considerable attention as a new means of power extraction. The present study applies transient numerical calculations based on the dynamic mesh technique to investigate the influences of leading edge, trailing edge, or overall circular arc airfoil deformations on the power extraction efficiencies of a flapping wing device under the condition of maintaining a constant arc length along the mean camber line of the foil. The results indicate that airfoil deformations can improve the power extraction efficiency of the flapping wing device. The primary reason is that the active synchronised deformation of the foil increases the effective curvature and the velocity circulation around the foil. In addition, the performance curve of the flapping wing device exhibits two frequency ranges with relative high efficiency. In the low frequency region, if the leading edge vortex (LEV) detaches from the trailing edge just as the foil reverses its heaving direction, the foil will gain much more energy due to the positive power extraction of the pitching moment. In the high frequency region, a small steady body-attached vortex (BAV) maintains a high lift, and, thus, the device extracts more power due to the enhanced heaving force.

Gasoline and two types of biomass, i.e. shrub boughs and pine boughs were investigated experimentally. The gasoline was the mixture of 500 mL water and 300 mL 92-octane gasoline. The experimental measurement was conducted on a device that contained a colour camera (SP-20000C) and a fibre optic spectrometer (AvaSpec-2048). The device captured spectral intensities via the spectrometer. Colour images were captured on the colour camera. The radiation characteristics, i.e. the wavelength-dependent emissivity in this study, were determined with a multi-wavelength thermometry in accordance with the spectral intensities. The results of emissivity were used so that the temperature distribution was calculated directly using the colour image, with the consideration of the broad response spectrum of the channels of the camera. Radiation characteristics and temperature distributions for these three types of flame were experimentally determined and presented. Results show that, in the visible light region, excluding the spectral lines of alkali metals, the continuous radiation of these flames should result from the similar substance. What is more, the combustion of gasoline (watered) is the most incomplete, resulting in a mass of product of incomplete reaction, and pine boughs were more flammable than shrub boughs.

Precise price forecasting can lessen the risk of participation in the deregulated electricity market. On account of a large amount of historical data, deep learning based methods can be a promising solution to achieve an accurate forecast. This study presents a deep neural network algorithm to estimate the probability density function of price, incorporating the prediction of wind speed and residential load as two other high volatile parameters. To this end, first, a combination of convolution neural network (CNN) and gated recurrent unit (GRU) is utilised to predict wind speed and residential load. Then, the results are integrated into historical price information to form the input dataset for price forecasting. The proposed price forecast procedure consists of CNN, GRU, and adaptive kernel density estimator (AKDE). AKDE is used as a numerical algorithm to capture probabilistic characterisation of real-time and day-ahead prices. Several deep and shallow networks and the proposed algorithm are implemented, and the results are compared. Furthermore, the effectiveness of AKDE in providing complete statistical information is verified through comparison with conventional and fixed smooth KDEs. In addition, the gradient boosting tree method is incorporated to verify the dependence of the price to the wind and the residential loads.

This study presents the transient stability and low voltage ride through (LVRT) improvement of IEEE 68-bus system connected with wind farms and photovoltaic (PV) plant through coordination of static synchronous series compensator (SSSC) and power system stabilisers (PSSs) controllers. It is generally accepted that there is a threat to the stability of power systems with the penetration of wind farm and PV plant. Therefore, in order to improve transient stability and LVRT in the power system, this study proposes the application of proportional and integral controllers in PV and wind farm controllers and a combination of PSS and SSSC controllers. Also, adaptive velocity update relaxation particle swarm optimisation algorithm and genetic algorithm have been applied and compared for controllers design.

This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30-bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.