Capacity Optimization Configuration of Hydrogen Production System for Offshore Surplus Wind Power

To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power.By studying the mathematical model of wind power output and calculating surplus wind power,as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank,an innovative capacity optimization allocation model was established.The objective of the model was to achieve the lowest total net present value over the entire life cycle.The model took into account the cost-benefit breakdown of equipment end-of-life cost,replacement cost,residual value gain,wind abandonment penalty,hydrogen transportation,and environmental value.The MATLAB-based platform invoked the CPLEX commercial solver to solve the model.Combined with the analysis of the annual average wind speed data from an offshore wind farm in Guangdong Province,the optimal capacity configuration results and the actual operation of the hydrogen production system were obtained.Under the calculation scenario,this hydrogen production system could consume 3,800 MWh of residual electricity from offshore wind power each year.It could achieve complete consumption of residual electricity from wind power without incurring the penalty cost of wind power.Additionally,it could produce 66,500 kg of green hydrogen from wind power,resulting in hydrogen sales revenue of 3.63 million RMB.It would also reduce pollutant emissions from coal-based hydrogen production by 1.5 tons and realize an environmental value of 4.83 million RMB.The annual net operating income exceeded 6 million RMB and the whole life cycle NPV income exceeded 50 million RMB.These results verified the feasibility and rationality of the established capacity optimization allocation model.The model could help advance power system planning and operation research and assist offshore wind farm operators in improving economic and environmental benefits.

Fast object detection based on binary deep convolution neural networks

In this study,a fast object detection algorithm based on binary deep convolution neural networks(CNNs)is proposed.Convolution kernels of different sizes are used to predict classes and bounding boxes of multi-scale objects directly in the last feature map of a deep CNN.In this way,rapid object detection with acceptable precision loss is achieved.In addition,binary quantisation for weight values and input data of each layer is used to squeeze the networks for faster object detection.Compared to full-precision convolution,the proposed binary deep CNNs for object detection results in 62 times faster convolutional operations and 32 times memory saving in theory,what’s more,the proposed method is easy to be implemented in embedded computing systems because of the binary operation for convolution and low memory requirement.Experimental results on Pascal VOC2007 validate the effectiveness of the authors’proposed method.

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution network operation.Methods:This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs.The solution is designed to ensure the reliable and secure operation of the active power distribution networks,the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand,as the PEVs can be considered as mobile energy storage units.Results:As a result,the charging demands of PEVs are optimally scheduled temporally and spatially,which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing(RTP).Conclusions:The proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.

Genetic algorithm optimization of broadband microstrip antenna

Genetic algorithm(GA)is utilized to design microstrip patch antenna shapes for broad bandwidth.A new project based on GA and high frequency simulation software(HFSS)is proposed to perform optimization.Reasonable agreement between simulated results and measured results of the GA-optimized design is obtained.The optimized patch design exhibits a three-fold enhancement in bandwidth when contrasted with a standard square microstrip antenna,showing the validity of this project.