Enhancing the Efficiency of Multi-Electrolyzer Clusters with Lye Mixer:Topology Design and Control Strategy

The rise in hydrogen production powered by renewable energy is driving the field toward the adoption of systems comprising multiple alkaline water electrolyzers.These setups present various operational modes:independent operation and multi-electrolyzer parallelization,each with distinct advantages and challenges.This study introduces an innovative configuration that incorporates a mutual lye mixer among electrolyzers,establishing a weakly coupled system that combines the advantages of two modes.This approach enables efficient heat utilization for faster hot-startup and maintains heat conservation post-lye interconnection,while preserving the option for independent operation after decoupling.A specialized thermal exchange model is developed for this topology,according to the dynamics of the lye mixer.The study further details startup procedures and proposes optimized control strategies tailored to this structural design.Waste heat from the caustic fully heats up the multiple electrolyzers connected to the lye mixing system,enabling a rapid hot start to enhance the system’s ability to track renewable energy.A control strategy is established to reduce heat loss and increase startup speed,and the optimal valve openings of the diverter valve and the manifold valve are determined.Simulation results indicate a considerable enhancement in operational efficiency,marked by an 18.28%improvement in startup speed and a 6.11%reduction in startup energy consumption inmulti-electrolyzer cluster systems,particularlywhen the systems are synchronized with photovoltaic energy sources.The findings represent a significant stride toward efficient and sustainable hydrogen production,offering a promising path for large-scale integration of renewable energy.

Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources.However,there are challenges in building models through centralized shared data due to data privacy concerns and industry competition.Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally.In this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model.We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach.Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

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.

Low-Carbon Dispatching for Virtual Power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value

The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness.To address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness.Based on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator.Then,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both considered.To achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear relationships.Subsequently,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and feasibility.Finally,the simulation verification based on the IEEE 33-bus DN is conducted.The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond.Meanwhile,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

Research of simulation and experiment on arc characteristics of gas mechanical switch

An arc is the high-temperature discharge plasma produced in the opening process of mechanical switches,which directly affects the breaking capability of a hybrid DC circuit breaker.According to the physical mechanism of an electric arc,the construction of an arc model for simulation analysis is an important technical means in the electrical field.In this study,based on the theory of magneto hydrodynamics(MHD),a gas mechanical switch model of a natural commutation DC circuit breaker with a compound gap is established.The arc motion process under different conditions is simulated and calcu-lated.The influence of different initial pressures,different opening speeds,and different striking currents on the arc voltage characteristics is analysed.The results show that the larger the gas pressure,the smaller the arc volume and the higher the arc voltage.The faster the opening speed,the longer the arc and the higher the arc voltage;with the increase of the current,the arc voltage increases rapidly at a low current,while the arc voltage increases slowly at a high current.

Optimal Virtual Battery Model for Aggregating Storage-Like Resources with Network Constraints

A virtual battery(VB)provides a succinct interface for aggregating distributed storage-like resources(SLR)to interact with a utility-level system.To overcome the drawbacks of existing VB models,including conservatism and neglecting network constraints,this paper optimizes the power and energy parameters of VB to enlarge its flexibility region.An optimal VB is identified by a robust optimization problem with decision-dependent uncertainty.An algorithm based on the Benders decomposition is developed to solve this problem.The proposed method yields the largest VB satisfying constraints of both network and SLRs.Case studies verify the superiority of the optimal VB in terms of security guarantee and less conservatism.

Parallel Computing Based Solution for Reliability-constrained Distribution Network Planning

The main goal of distribution network(DN)expansion planning is essentially to achieve minimal investment con-strained by specified reliability requirements.The reliability-constrained distribution network planning(RcDNP)problem can be cast as an instance of mixed-integer linear programming(MILP)which involves ultra-heavy computation burden especially for large-scale DNs.In this paper,we propose a parallel computing based solution method for the RcDNP problem.The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination.Then,a parallelizable augmented Lagrangian algorithm with acceleration method is developed to solve the coordination planning problems.The lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem.Gauss-Seidel iteration is adopted on the subset of the convex hull of the feasible region constructed by decomposition.Under mild conditions,the optimality and convergence of the proposed method are verified.Numerical tests show that the proposed method can significantly reduce the solution time and make the RcDNP applicable for real-worldproblems.

Comparison of DES and URANS:Estimation of fluctuating pressure from URANS simulations in stilling basins

This study conducts a comparative analysis between detached eddy simulation(DES)and Unsteady Reynolds-averaged Navier-Stokes(URANS)models for simulating pressure fluctuations in a stilling basin,aiming to assess the URANS mode’s performance in modeling pressure fluctuation.The URANS model predicts accurately a smoother flow field and its time-average pressure,yet it underestimates the root mean square of pressure(RMSP)fluctuation,achieving approximately 70%of the results predicted by DES model on the bottom floor of the stilling basin.Compared with DES model’s results,which are in alignment with the Kolmogorov−5/3 law,the URANS model significantly overestimates low-frequency pulsations,particularly those below 0.1 Hz.We further propose a novel method for estimating the RMSP in the stilling basin using URANS model results,based on the establishment of a quantitative relationship between the RMSP,time-averaged pressure,and turbulent kinetic energy in the boundary layer.The proposed method closely aligns with DES results,showing a mere 15%error level.These findings offer vital insights for selecting appropriate turbulence models in hydraulic engineering and provide a valuable tool for engineers to estimate pressure fluctuation in stilling basins.

A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.

An ANN-Based Short-Term Temperature Forecast Model for Mass Concrete Cooling Control

Concrete temperature control during dam construction(e.g.,concrete placement and curing)is important for cracking prevention.In this study,a short-term temperature forecast model for mass concrete cooling control is developed using artificial neural networks(ANN).The development workflow for the forecast model consists of data integration,data preprocessing,model construction,and model application.More than 80000 monitoring samples are collected by the developed intelligent cooling control system in the Baihetan Arch Dam,which is the largest hydropower project in the world under construction.Machine learning algorithms,including ANN,support vector machines,long short-term memory networks,and decision tree structures,are compared in temperature prediction,and the ANN is determined to be the best for the forecast model.Furthermore,an ANN framework with two hidden layers is determined to forecast concrete temperature at intervals of one day.The root mean square error of the forecast precision is 0.15∘C on average.The application on concrete blocks verifies that the developed ANN-based forecast model can be used for intelligent cooling control during mass concrete construction.

Adaptive Reference Power Based Voltage Droop Control for VSC-MTDC Systems

Featuring low communication requirements and high reliability,the voltage droop control method is widely adopted in the voltage source converter based multi-terminal direct current(VSC-MTDC)system for autonomous DC voltage regulation and power-sharing.However,the traditional voltage droop control method with fixed droop gain is criticized for over-limit DC voltage deviation in case of large power disturbances,which can threaten stable operation of the entire VSCMTDC system.To tackle this problem,this paper proposes an adaptive reference power based voltage droop control method,which changes the reference power to compensate the power deviation for droop-controlled voltage source converters(VSCs).Besides retaining the merits of the traditional voltage droop control method,both DC voltage deviation reduction and power distribution improvement can be achieved by utilizing local information and a specific control factor in the proposed method.Basic principles and key features of the proposed method are described.Detailed analyses on the effects of the control factor on DC voltage deviation and imbalanced power-sharing are discussed,and the selection principle of the control factor is proposed.Finally,the effectiveness of the proposed method is validated by the simulations on a five-terminal VSC based high-voltage direct current(VSC-HVDC)system.

Value Evaluation Method for Pumped Storage in the New Power System

When integrating the generation of large-scale renewable energy,such as wind and solar energy,the supply and demand sides of the new power system will exhibit high uncertainty.Pumped storage power stations can improve flexible resource supply regulation in the power system,which is the key support and important guarantee for building low-carbon,safe,and efficient new power systems.Limited by the current operation mode and electricity price mechanism,the pumped storage power station cost cannot be effectively recovered,and the value cannot be reasonably compensated,resulting in difficult return on investment,single investment subjects,and notable industry development difficulties.According to the operational requirements of the new power system,combined with the various functions of pumped storage power stations,the value of pumped storage power stations in the new power system was analyzed.Based on the equivalent value substitution principle and system operation simulation,a pumped storage value evaluation method for the new power system was proposed.The new power system operation was simulated considering the dispatching model of wind and photovoltaic power abandonment penalties.Under the same dispatching objectives,the output of various power sources and power generation operating costs with and without pumped storage power stations in the system were compared.From economic,safety,social,and environmental benefit perspectives,a quantitative model of the pumped storage power station value was established,covering seven dimensions:asset investment savings,power generation operating cost reduction,flexible adjustment capability improvement,system resilience enhancement,power outage loss reduction,renewable energy consumption,and emission reduction promotion.Based on the new power system operation and planning data for southern China,the value of typical pumped storage power stations was analyzed,and the results showed that with new power system’s construction and development,the value of pumped storage power stations is increasing,and the value structure is closely related to power grid characteristics.This value evaluation method could provide references for pumped storage investment decisions,subsidy policies,and price mechanisms to fully utilize the role of pumped storage power stations and promote high-quality development of new power systems.

A modular parallelization framework for power flow transfer analysis of large-scale power systems

Power flow transfer(PFT) analysis under various anticipated faults in advance is important for securing power system operations. In China, PSD-BPA software is the most widely used tool for power system analysis, but its input/output interface is easily adapted for PFT analysis,which is also difficult due to its computationally intensity.To solve this issue, and achieve a fast and accurate PFT analysis, a modular parallelization framework is developed in this paper. Two major contributions are included. One is several integrated PFT analysis modules, including parameter initialization, fault setting, network integrity detection, reasonableness identification and result analysis.The other is a parallelization technique for enhancing computation efficiency using a Fork/Join framework. The proposed framework has been tested and validated by the IEEE 39 bus reference power system. Furthermore, it has been applied to a practical power network with 11052 buses and 12487 branches in the Yunnan Power Grid ofChina, providing decision support for large-scale power system analysis.

Investigation of sensitivity of the ultra-high frequency partial-discharge detection technology for micro-crack in epoxy insulator in GIS

The ultra-high frequency(UHF)method has been widely used in a gas-insulated system(GIS)for partial-discharge detection,and many achievements have been realised.In addition,many studies based on artificial defects have been made to confirm its validity.Therefore,the UHF method is generally believed to be sufficiently effective for GIS monitoring.However,in practical application,the authors find that for some micro-crack discharge in GIS insulator,the UHF method has low sensitivity.To fully study the characteristics of the micro-crack discharge in the GIS insulator,an experiment is conducted in this study using an actual post insulator with a micro-crack defect.The current signal based on IEC 60270 standard and the radio-frequency electromagnetic signal is simultaneously measured for thorough analysis.The results show that some submillimetre crack defects may occur in the GIS insulator.Its discharge is mainly presented as glow discharge,and the discharge signal frequency usually cannot reach the UHF band;thus,it cannot be effectively detected by the UHF method.This study provides complementary information to the applicability of the UHF method and inspires further study of the GIS insulator and its monitoring technology.

Operation of Off-grid Power Supply System Using IoT Monitoring Platform for Oil and Gas Pipeline Based on RESOC

An oil and gas pipeline monitoring platform uses internet of things(IoT)to ensure safe operation in remote and unattended areas,through automatic monitoring and systematic control on equipment such as the cut-off valves and cathodic protection systems.The continuity and stability of power supplies for various equipment of an oil and gas pipeline IoT monitoring platform is crucial.There is no single universal off-grid power supply method that is optimal for an oil and gas pipeline IoT monitoring platform in all different contexts.Therefore,it is necessary to select a suitable one according to the specific geographical location and meteorological conditions.This paper proposes an off-grid power supply system comprised of a reversible solid oxide fuel cell(RESOC),photovoltaic(PV)and battery.Minimum operating costs and the reliability of system operations under constraint conditions are the key determining objectives.A“PV+battery+RESOC”system operational optimization model is established.Based on the model,three types of off-grid power supply schemes are proposed,and three geographical locations with different meteorological conditions are selected as practical application scenarios.The Matlab Cplex solver is used to solve the different power supply modes of the three regions.And finally,the power supply scheme with the best reliability and economy under different geographical environments and meteorological conditions is obtained.

Superconducting fault current limiter(SFCL)for fail-safe DC-DC conversion:From power electronic device to micro grid protection

In DC micro grids and networks,DC-DC power converters having a large number of semiconductor-based power electronic devices are usually adopted to interconnect the renewable sources and flexible loads.Most of the semiconductor-based devices suffer from poor fault withstanding abilities,but conventional power electronic protection schemes have the bottlenecks of the time-delay,self-malfunction and mis-judgement.This paper presents a novel solution using the superconducting fault current limiter(SFCL)to protect a power electronic device and extend the usage to a micro grid.This SFCL is actually a self-triggering,recoverable,and passive current limiter,which does not involve any additional circuit hardware and software.Experimental investigations and simulation analyses clarify the feasibility of using this superconductor-based protection scheme to implement the self-acting fail-safe protection of DC-DC converters.Further system-level simulations explore the SFCL to suppress the over-current and stabilize the bus voltage of a photovoltaic based DC micro grid,particularly facing millisecond-level transients and faults.Our experimental and theoretical investigations lay some technical bases to establish a superconductor-semiconductor-coupled interdisciplinary application from the view from the applied superconductivity,to power electronics,and to micro grids.

Study of degradation of fuel cell stack based on the collected high-dimensional data and clustering algorithms calculations

Accurate perception of the performance degradation of fuel cell is very important to detect its health state.However,inconsistent operating conditions of fuel cell vehicles in the test result in errors in the data.In order to obtain a more credible degradation rate,this study proposes a novel method to classify the experimental data collected under different working conditions into similar operating conditions by using dimensionality reduction and clustering algorithms.Firstly,the experimental data collected from fuel cell vehicles belong to high-dimensional data.Then projecting high-dimensional data into three-dimensional feature vector space via principal component analysis(PCA).The dimension-reduced three-dimensional feature vectors are input into the clustering algorithm,such as K-means and density-based noise application spatial clustering(DBSCAN).According to the clustering results,the fuel cell voltage data with similar operating conditions can be classified.Finally,the selected voltage data can be used to precisely represent the true performance degradation of an on-board fuel cell stack.The results show that the voltage using the K-means algorithm declines the fastest,followed by the DBSCAN algorithm, finally the original data, which indicates that the performance of the fuel cell actually declines faste. Early intervention can prolong its life to the greatest extent.