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.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

Review on Capacity Optimization of Traction Transformer for High-Speed Railway

In electrified railways,traction load not only fluctuates between peaks and valleys,but also has a situation of low utilization rate of average load throughout the day and short overload.The traction transformer selects the capacity with the peak load as the demand boundary,which will cause the capacity utilization rate to be low and even lead to the economic decline of the traction power supply system.This article summarizes the existing configuration methods for capacity optimization of traction transformer.Then under the conditions of energy storage and new energy access to traction power supply system,the three aspects are described as follows.Firstly,the energy storage device is connected to the system,which can pull the capacity of traction transformer to achieve peak shifting and valley filling.Then,the possibility of integrated configuration of new energy and traction power supply system to optimize the capacity of traction transformer and the methods of optimal configuration of traction transformer capacity by using new energy such as wind and light are summarized.Finally,this paper discusses the current structure of new energy access to traction power supply system,and it looks forward to the feasibility of new energy access to traction power supply system cooperating with energy storage devices to optimize the capacity of traction transformer.

Analysis on the Development Trend of Contemporary Local Governance Mode from the perspective of Economic Policy and Environmental Capacity Optimization

As the basic level of national governance, local governance is deeply influenced by administrative tradition, political system and economic development level of a country. With more than thirty years of reform and opening up, the pattern of local governance has become diversified. The diversity of governance model cannot be managed in the same way, however, it cannot affect the government’s ability to play the initiative role of governance. We need to innovate and improve the existing local governance models.

An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect.The contemporary methods of steganography are at best a compromise between these two.In this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic embedding.The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret data.This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images.The ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual quality.Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement.The levels of pheromone are modified to reinforce successful pixel choices.Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of 0.98.The approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.

Use the Power of a Genetic Algorithm to Maximize and Minimize Cases to Solve Capacity Supplying Optimization and Travelling Salesman in Nested Problems

Using Genetic Algorithms (GAs) is a powerful tool to get solution to large scale design optimization problems. This paper used GA to solve complicated design optimization problems in two different applications. The aims are to implement the genetic algorithm to solve these two different (nested) problems, and to get the best or optimization solutions.

MOF-derived porous graphitic carbon with optimized plateau capacity and rate capability for high performance lithium-ion capacitors

The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived from a new triply interpenetrated co-balt metal-organic framework(Co-MOF)was prepared through the facile and robust carbonization at 1300°C and washing by HCl solu-tion.The as-prepared PGC-1300 featured an optimized graphitization degree and porous framework,which not only contributes to high plateau capacity(105.0 mAh·g^(−1)below 0.2 V at 0.05 A·g^(−1)),but also supplies more convenient pathways for ions and increases the rate capability(128.5 mAh·g^(−1)at 3.2 A·g^(−1)).According to the kinetics analyses,it can be found that diffusion regulated surface induced capa-citive process and Li-ions intercalation process are coexisted for lithium-ion storage.Additionally,LIC PGC-1300//AC constructed with pre-lithiated PGC-1300 anode and activated carbon(AC)cathode exhibited an increased energy density of 102.8 Wh·kg^(−1),a power dens-ity of 6017.1 W·kg^(−1),together with the excellent cyclic stability(91.6%retention after 10000 cycles at 1.0 A·g^(−1)).

PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.

Multi-objective capacity allocation optimization method of photovoltaic EV charging station considering V2G

Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.

Capacity Analysis and Information Optimization of WSDM

Wireless statistic division multiplexing (WSDM) is a multiplexing scheme that transmits multiple signals simultaneously in the same frequency band over wireless channels. Based on the Shannon capacity of band-limited waveform AWGN channel with input power constraint, we obtain channel capacity of WSDM. Compared to time division multiplexing (TDM), frequency division multiplexing (FDM), and code division multiplexing (CDM), WSDM is more effective in raising spectrum efficiency. What’s more, we propose information optimization method to separate time-frequency mixed signals. Computer simulations also verify that the proposed method is feasible and performs better than traditional algorithms.

Optimal Configuration Method for the Installed Capacity of the Solar-Thermal Power Stations

Because of the randomness of wind power and photovoltaic(PV)output of new energy bases,the problem of peak regulation capability and voltage stability of ultra-high voltage direct current(UHVDC)transmission lines,we proposed an optimum allocation method of installed capacity of the solar-thermal power station based on chance constrained programming in this work.Firstly,we established the uncertainty model of wind power and PV based on the chance constrained planning theory.Then we used the K-medoids clusteringmethod to cluster the scenarios considering the actual operation scenarios throughout the year.Secondly,we established the optimal configuration model based on the objective function of the strongest transient voltage stability and the lowest overall cost of operation.Finally,by quantitative analysis of actual wind power and photovoltaic new energy base,this work verified the feasibility of the proposed method.As a result of the simulations,we found that using the optimal configuration method of solar-thermal power stations could ensure an accurate allocation of installed capacity.When the installed capacity of the solar-thermal power station is 1×106 kW,the transient voltage recovery index(TVRI)is 0.359,which has a strong voltage support capacity for the system.Based on the results of this work,the optimal configuration of the installed capacity of the solar-thermal power plant can improve peak shaving performance,transient voltage support capability,and new energy consumption while satisfying the Direct Current(DC)outgoing transmission premise.

Modeling and optimization of a multi-carrier renewable energy system for zero-energy consumption buildings

For the carbon-neutral,a multi-carrier renewable energy system(MRES),driven by the wind,solar and geothermal,was considered as an effective solution to mitigate CO2emissions and reduce energy usage in the building sector.A proper sizing method was essential for achieving the desired 100%renewable energy system of resources.This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm(GA)coupled with the loss of power supply probability(LPSP)method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement.An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand.A case study of a swimming pool building was used to demonstrate the process of the proposed design method.Compared to the conventional distributed energy system,the MRES is feasible with a lower annual total cost(ATC).Additionally,the ATC decreases as the power supply reliability of the renewable system decreases.There is a decrease of 24%of the annual total cost when the power supply probability is equal to 8%compared to the baseline case with 0%power supply probability.

Evaluation of optimal UPFC allocation for improving transmission capacity

A unified power flow controller(UPFC)combines the advantages of various flexible alternating current transmission system(FACTS)devices into a powerful format.Using a 500 kV power grid,this study evaluates the selection and use of a UPFC to improve transmission capacity.The"UPFC unit capacity control proportionality coefficient"is introduced to quantify the control effect of the UPFC,and an optimal calculation method for the UPFC capacity is presented.Following the proposal of a UPFC site selection process,the data of an existing power grid is used to conduct simulations.The simulation results show that the UPFC has a strong ability to improve transmission capacity,and its use is greatly advantageous.Additionally,by applying the proposed selection method,the control effect and economic benefits of the UPFC can be comprehensively considered during project site selection.These findings have a guiding significance for UPFC site selection in ultra-high voltage power grids.

Station-and-network–coordinated planning of integrated energy system considering integrated demand response

The integrated energy system(IES)is an important energy supply method for mitigating the energy crisis.A station-and-network–coordinated planning method for the IES,which considers the integrated demand responses(IDRs)of flexible loads,electric vehicles,and energy storage is proposed in this work.First,based on load substitution at the user side,an energy-station model considering the IDR is established.Then,based on the characteristics of the energy network,a collaborative planning model is established for the energy station and energy network of the IES,considering the comprehensive system investment,operation and maintenance,and clean energy shortage penalty costs,to minimize the total cost.This can help optimize the locations of the power lines and natural gas pipelines and the capacities of the equipment in an energy station.Finally,simulations are performed to demonstrate that the proposed planning method can help delay or reduce the construction of new lines and energy-station equipment,thereby reducing the investment required and improving the planning economics of the IES.

Air route network optimization in fragmented airspace based on cellular automata

Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas（PRDs）.In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors（NSLF） are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in China's Mainland is collected as the origin-destination（OD） airport pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67%,while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively.The NSLF decreases by 5.82% with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22%.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.

Generic security analysis framework for quantum secure direct communication

Quantum secure direct communication provides a direct means of conveying secret information via quantum states among legitimate users.The past two decades have witnessed its great strides both theoretically and experimentally.However,the security analysis of it still stays in its infant.Some practical problems in this field to be solved urgently,such as detector efficiency mismatch,side-channel effect and source imperfection,are propelling the birth of a more impeccable solution.In this paper,we establish a new framework of the security analysis driven by numerics where all the practical problems may be taken into account naturally.We apply this framework to several variations of the DL04 protocol considering real-world experimental conditions.Also,we propose two optimizing methods to process the numerical part of the framework so as to meet different requirements in practice.With these properties considered,we predict the robust framework would open up a broad avenue of the development in the field.

Cost reduction of a hybrid energy storage system considering correlation between wind and PV power

A hybrid energy storage system(HESS)plays an important role in balancing the cost with the performance in terms of stabilizing the fluctuant power of wind farms and photovoltaic(PV)stations.To further bring down the cost and actually implement the dispatchability of wind/PV plants,there is a need to penetrate into the major factors that contribute to the cost of the any HESS.This paper first discusses hybrid energy storage systems,as well as chemical properties in different medium,deeming the ramp rate as one of the determinants that must be observed in the cost calculation.Then,a mathematical tool,Copula,is explained in details for the purpose of unscrambling the dependences between the power of wind and PV plants.To lower the cost,the basic rule for allocation of buffered power is also put forward,with the minimum energy capacities of the battery ESS(BESS)and the supercapacitor ESS(SC-ESS)simultaneously determined by integration.And the paper introduces the probability method to analyze how power and energy is compensated in certain confidence level.After that,two definitions of coefficients are set up,separately describing energy storage status and wind curtailment level.Finally,the paper gives a numerical example stemmed from real data acquired in wind farms and PV stations in Belgium.The conclusion presents that the cost of a hybrid energy storage system is greatly affected by ramp-rate and dependence between the power of wind farms and photovoltaic stations,in which dependence can easily be determined by Copulas.

Optimal electromagnetic hybrid negative current compensation method for high-speed railway power supply system

To achieve economical compensation for the huge-capacity negative sequence currents generated by high-speed railway load, an electromagnetic hybrid compensation system(EHCS) and control strategy is proposed.The EHCS is made up of a small-capacity railway static power conditioner(RPC) and a large-capacity magnetic static var compensator(MSVC). Compared with traditional compensation methods, the EHCS makes full use of the SVC’s advantages of economy and reliability and of RPC’s advantages of technical capability and flexibility. Based on the idea of injecting a negative sequence, the compensation principle of the EHCS is analyzed in detail. Then the minimum installation capacity of an EHCS is theoretically deduced. Furthermore, a constraint optimization compensation strategy that meets national standards, which reduces compensation capacity further, is proposed. An experimental platform based on a digital signal processor(DSP) and a programmable logic controller(PLC) is built to verify the analysis. Simulated and experimental results are given to demonstrate the effectiveness and feasibility of the proposed method.