31 December 2024, Volume 44 Issue 6

  

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  Two-Layer Energy Management for Integrated Photovoltaic Power to Hydrogen and Refueling System Considering the Operational Logic of Purification Unit

  ZENG Yangjun, LI Jiatong, XU Liuchao, QIU Yiwei, ZHOU Buxiang, ZHENG Yong, HE Ge, JI Xu

  2024, 44(6): 1-9. https://doi.org/10.19718/j.issn.1005-2992.2024-06-0001-09

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  Integrated Photovoltaic Power to Hydrogen and Refueling （IPp2HR）systems effectively utilize solar energy resources,providing green hydrogen for hydrogen-powered transportation and other industries.They are a promising pathway for green hydrogen demonstration.However,current research on IPp2HR systems either overlooks the operational constraints of purification or focuses solely on day-ahead scheduling.Traditional purification systems use fixed operational sequences to dry crude hydrogen,which conflicts with the flexible,variable-load operation required to accommodate renewable energy fluctuations.To address this,a bi-level energy management method is proposed to improve IPp2HR system efficiency.First,a comprehensive model covering power to hydrogen,purification,storage,and refueling is developed.The purification process is transformed into a Mixed-Integer Linear Programming （MILP）model using the Big-M method and integrated into the scheduling framework.Second,a bi-level energy management framework is designed,combining day-ahead and rolling scheduling with real-time control.The day-ahead and rolling stages determine the on/off of electrolyzers based on PV forecasts and hydrogen demand,while the real-time stage adjusts power deviations to enhance PV utilization and operational benefits.A case study based on a hydrogen refueling station in Northeast China validates the proposed method.Results show that considering the purification heating and cooling logic prevents high-cost hydrogen caused by the inability to shutdown at high temperatures.The bi-level framework effectively coordinates day-ahead,rolling,and real-time stages,improving both PV utilization and operational profitability.
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  Research on Security Constrained Unit Commitment of Wind Generationand Hydrogen Storage System Based on Stochastic P-robust Optimization 

  CAI Tingting, ZHAO Yuzhuo

  2024, 44(6): 10-21. https://doi.org/10.19718/j.issn.1005-2992.2024-06-0010-12

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  The high proportion of renewable energy networking,represented by wind energy,has brought new challenges to the power system.Hydrogen energy storage technology is an effective way to smooth out fluctuations in renewable energy power and improve the economic and low-carbon performance of comprehensive energy systems.On the basis of analyzing the power regulation capability of a high proportion wind power interconnection system,it is pointed out that wind hydrogen coupling can reduce system wind abandonment and power shortage.The worst-case scenario cost is an important indicator for evaluating the operational status of a system under uncertain factors.Based on uncertain scenarios,a stochastic p-robust optimization method combining basic stochastic optimization and robust optimization is proposed to ensure stable operation of the system in the worst-case scenario.Taking into account both economic and environmental benefits,a unit commitment optimization model with dual objectives of expected cost and carbon trading cost was established under p-robust constraints.The results of the example show that the stochastic p-robust optimization method effectively reduces the expected cost of the system.The established unit combination optimization model can flexibly optimize the output of multi energy systems based on different objective weights,reduce abandoned wind power,and improve wind power utilization.
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  Multi-timescale Regulation Strategy for Integrated Energy Systems considering Rotational Start/Stop of Electric Hydrogen Arrays

  WANG Yijun, ZHANG Jinming, LIU Ziheng, HE Yuzhe

  2024, 44(6): 22-34. https://doi.org/10.19718/j.issn.1005-2992.2024-06-0022-13

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  The use of electric hydrogen generation to consume the wind power abandoned in the integrated energy system with high percolation rate is an effective method to save energy and reduce carbon,but there exists the problem of improper power distribution of electric hydrogen generation array operation,which leads to the serious imbalance of the life span of each single tank,and greatly reduces the life span of the whole system of electric hydrogen generation, which needs to be solved urgently.The paper proposes a multi-timescale regulation strategy for the integrated energy system that takes into account the rotational start/stop of the electric hydrogen array.In the day-ahead phase,the rotational start/stop strategy of the electric hydrogen array is designed to equalise the system life depreciation；in the intra-day scheduling phase,the economic and low-carbon objective is to ensure the supply of the load demand；and in the real-time phase,the day-ahead real-time purchased power deviation is offset by the flexible use of the energy storage,so as to minimize the impact of the stochastic volatility of the lower-level integrated energy system on the power grid.The real-time phase,the energy storage is used to flexibly offset the day-ahead-real-time power purchase deviation to minimise the random volatility of the lower-level integrated energy system on the grid.Finally,engineering examples are presented to verify the economic,low-carbon and reliability advantages of the strategy.
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  Capacity Optimization of Optical Hydrogen Storage Coupling Access Distribution Network with Flexibility Constraints

  MAO Xinyu, LI Zhenxin, BIAN Yudong, KONG Lingguo

  2024, 44(6): 35-43. https://doi.org/10.19718/i.issn.1005-2992.2024-06-0035-09

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  This paper proposes a capacity configuration method for a photovoltaic hydrogen storage coupling system that takes into account the flexibility constraints of distribution network operation,in response to the problems of high proportion of photovoltaic access leading to voltage exceeding limits,branch power imbalance,and high curtailment rate in the distribution network.Firstly,based on the improved K-means clustering algorithm,load scenarios are divided, and on the basis of considering voltage and power factors,as well as constraints such as Distflow's flow model and second-order cones of line voltage and current,distribution network flexibility indicators are established from both spatial and temporal perspectives；Secondly,taking into account constraints such as flexibility and power balance,an optimization configuration objective function is constructed with the goal of minimizing the cost of electricity per kilowatt hour；Then,an optimization operation strategy for the photovoltaic hydrogen storage coupling system based on net power conditions is proposed,and an improved particle swarm optimization algorithm is used to solve the optimization configuration model.Finally,the effectiveness of the optimization configuration method proposed in this paper is verified through a case study of the actual grid structure of the glass Kezi substation area in Jiaohe City,Jilin Province.
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  Economic Study of Hydrogen-Enriched Natural Gas Combined Cycle Generator Unit

  DONG Jingnan, QI Lei, WANG Yantao, WANG Yanhong, NIU Xiaojuan

  2024, 44(6): 44-51. https://doi.org/10.19718/j.issn.1005-2992.2024-06-0044-08

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  Application of hydrogen-enriched combustion in natural gas generator unit could reduce CO2 emission and promote transformation development for low-carbon in Chinese electric power industry.In this paper,a simplified model of combined cycle using hydrogen-enriched natural gas was established for a 9FA gas-steam combined cycle generator unit.Variations of performance,carbon emission and their influences on power generation economy were all investigated under different hydrogen-enriched ratio and different ambient temperature.The results showed that：Hydrogen-enriched combustion in combined cycle lead a decrease of cycle efficiency and a significantly reduction of CO2 emission.When hydrogen ratio was increased from 0 to 30%，the revenue of generator unit decreased by 52.98%under computational conditions,which significantly affected the economy of combined cycle unit.Cycle performance was better at a higher ambient temperature,and the changing trend was maintained after the introduction of hydrogen.
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  Topology and Control of Medium-Frequency Isolated Centralized Hydrogen Production Equipment for Offshore Wind Power Based on M3C

  LI Juan, LIU Huaibin, ZHU Di, LIU Chuang, PEI Zhongchen

  2024, 44(6): 52-62. https://doi.org/10.19718/i.issn.1005-2992.2024-06-0052-11

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  The production of "green hydrogen"by electrolyzing water from offshore wind power is an important technological direction for promoting the consumption of new energy and achieving deep decarbonization in the power and chemical industries.With the shift of offshore wind power hydrogen production from nearshore hydrogen transmission to offshore hydrogen transmission,utilizing existing offshore oil and gas platforms and pipelines for centralized hydrogen production from offshore wind power is one of the main directions for obtaining"green hydrogen"in the future.However,the design and application of centralized hydrogen production equipment for offshore wind power are constrained by problems such as small insulation margin and difficult optimization design of medium voltage and high-frequency transformers.The article proposes an intermediate frequency isolated offshore wind power centralized hydrogen production equipment based on Modular Multilevel Matrix Converter （M3C）to address the above issues.The equipment uses an M3C converter in the front stage and a 12 pulse thyristor rectifier in the rear stage to achieve intermediate frequency isolation and avoid insulation design difficulties.The high current stress of the thyristor enables high-power hydrogen production,and its key parameters are optimized.Finally,a simulation platform for the proposed hydrogen production equipment was built using MATLAB/Simulink simulation software to verify its effectiveness.
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  A Review on the Research and application of Machine Learning Assisted Optimal Design for Directional Regulation of Radiation Properties

  DU Jiaxin, WANG Fugiang, ZHANG Xinping, SONG Jintao

  2024, 44(6): 63-73. https://doi.org/10.19718/i.issn.1005-2992.2024-06-0063-11

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  How to achieve efficient and precise control of multi-band radiation properties is a common scientific challenge in military camouflage,aerospace,solar energy and other fields.Conventional radiation property control often uses inefficient trial-and-error optimisation of functional groups or micro-nanostructures,which is time-consuming, laborious and difficult to obtain the best radiation properties.The emergence of machine learning has overturned the traditional optimisation methods and greatly improved the efficiency of radiation property optimisation and design by simulating the brain's learning and thinking.In this paper,machine learning algorithms in radiation property regulation are discussed in detail,and their advantages and challenges in terms of accuracy,scalability and efficiency are evaluated；the advanced results of the fusion of machine learning and radiation property directional regulation are summarised in a systematic way,including forward radiation response prediction and material directional optimal design；and finally,the hot spots of the research on the combination of radiation property regulation and machine learning and the direction of future development are explored.By reviewing the existing literature,this paper provides a reference for the design and application of radiation property directional regulation and machine learning algorithms, and makes suggestions for further optimisation and innovation of radiation property directional regulation.
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  Short-term Power Demand Forecasting Based on PSO-SVM Combined Model

  SHI Xiaoyu, WANG Xin, WANG Gang

  2024, 44(6): 74-81. https://doi.org/10.19718/j.issn.1005-2992.2024-06-0074-08

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  In order to adapt to the "dual carbon"transformation goal of future electric power development in Jilin Province,considering the role of multiple influencing factors under the new situation,it is necessary to integrate social and carbon emission related influencing factors to improve the accuracy of power demand forecasting.In the current context,the existing models are still facing challenges in terms of stability and accuracy of electricity demand forecasting.In order to address these challenges,firstly,multiple factors affecting power demand are analyzed through system dynamics model.Based on rigorous correlation analysis,key indicators that have a significant impact on power demand are further screened.Six strongly related indicators,namely permanent population,industrial added value,total energy consumption,low-carbon index of energy consumption structure,per capita GDP and GDP,were determined, and the introduction of carbon emission indicators was increased,highlighting the innovative attention in the "double carbon"aspect.Then,Particle Swarm Optimization （PSO）was used to optimize the key parameters of the Support Vector Machines （SVM）model,and the PSO-SVM power demand prediction model was constructed.The problem that the existing model is easy to fall into the local optimal solution is overcome.The effectiveness of the PSO-SVM model is verified by comparison with the traditional SVM model,BP model and the optimized PSO-BP model.In power forecasting,the model not only has high accuracy,but also shows a faster training speed.Finally,the forecast model is applied to the power demand forecast of Jilin Province from 2023 to 2028，which provides a strong support and reference for power planning and decision-making.
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  SpaceLoad Forecasting based on Modal Decomposition and Improved Neural Networks

  2024, 44(6): 82-90. https://doi.org/10.19718/j.issn.1005-2992.2024-06-0082-09

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  Power System Transient Stability Generator Tripping Control Method Based on I-w Response Characteristics

  LI Shouchao, LIU Cheng, ZHANG Yuchi, WANG Xiangdong, XU Rui, LI Wenbiao

  2024, 44(6): 91-100. https://doi.org/10.19718/j.issn.1005-2992.2024-06-0091-10

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  In order to meet the real-time requirements of the new power system for transient stability emergency control,a transient stability generator tripping control method based on generator current and angular frequency response characteristics is proposed.Firstly,the relationship curves between current and angular frequency are drawn when the system is stable and unstable,and the relationship between current and angular frequency and transient stability is studied.The key characteristics of power angle stability and power angle instability are extracted,and the transient power angle stability criterion based on I-ωresponse characteristics is constructed.Secondly,the slope characteristics of the I-ω relationship curve are studied,and the relationship between the slope of the relationship curve and the transient stability under different proportion of generator tripping control is explored.Based on the generator rotor motion equation,the relationship between the slope of the relationship curve and the amount of generator tripping control is derived,and a calculation method of emergency generator tripping control based on the slope of the relationship curve is proposed.After that,the power angle instability criterion is used as the starting criterion of emergency control,and the generator tripping index considering the influence of generator power angle and kinetic energy contained in the rotor is defined.The fast selection of the generator tripping control location is realized, and a reasonable allocation method of generator tripping control quantity is given.Finally,the proposed method is simulated in the classical second-order one machine infinite bus system and the New England 10-machine 39-bus system with wind turbines,and the effectiveness of the proposed method is verified.
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  Coordinated Optimal Scheduling of Virtual Power Plants Considering Waste Incineration Participation under Stepped Carbon Trading Mechanisms 

  WANG Xiuyun, CUI Benwang

  2024, 44(6): 101-111. https://doi.org/10.19718/j.issn.1005-2992.2024-06-0101-11

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  Under the goal of "dual carbon"，virtual power plant is an effective vehicle for optimizing multi-regional resource allocation and increasing renewable energy penetration.Against this background,the paper proposes a coordinated and optimal scheduling strategy for virtual power plants that considers the participation of waste incineration under the stepped carbon trading mechanism.First,a new power system structure including multiple power plants and multiple energy storage is constructed from the system structure.In order to fully explore the potential of power generation and gas production in waste incineration power plants,an analytical study is carried out for dry and wet waste electrical cogeneration,and a mathematical model of waste incineration power plants is established.Secondly, the joint operation mode of power-to-gas and carbon capture is adopted,and a carbon capture-power-to-gas-hydrogen fuel cell subsystem model is constructed to formulate the joint operation strategy of multiple power plants and multiple energy storage.Again,the carbon trading mechanism is introduced,and a laddered carbon trading calculation model is constructed and analyzed for the price base price,interval length and price growth rate in the model.Finally, with the objective function of minimizing the sum of thermal power cost,purchased energy cost,carbon emission cost, equipment maintenance cost and scenery cost,the coordinated optimal scheduling model of virtual power plant is established,and the model is optimally solved by using the CPLEX solver of Matlab software in multiple scenarios.
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  Optimalallocation of Two Layers of the Distribution Network Considering Distributed PV Utilizations

  ZHAO Haimeng., WANG Yubo, SUN Liang, ZHANG Rufeng

  2024, 44(6): 112-119. https://doi.org/10.19718/j.ssn.1005-2992.2024-06-0112-08

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  Aiming at the uncertainty of distributed photovoltaic （PV）power generation and the overall low utilization rate of the equipment,which leads to the problem of rising cost faced by distribution network planning,a grid planning method considering the utilization rate of distributed PV equipment is proposed in the paper.By using information entropy to extract scenarios from PV output data,a set of typical scenarios is obtained.Based on these scenarios,a joint optimization model of distributed PV storage operation-planning is developed in the paper:at the upper level,the distributed PV and storage are selected and sited with the objective of minimizing the investment and construction cost and maximizing the utilization rate of distributed PV；at the lower level,the distributed PV power and storage are optimized with the objective of minimizing the cost of discarded light,network loss,operation and maintenance,and purchased power,and the planning model solves the optimization problem.In the lower layer,the distributed PV power and storage charging/discharging power in each time period are optimized with the objective of minimizing the abandoned light cost,network loss cost,operation and maintenance cost,and purchasing power cost,and a modified particle swarm algorithm is used as a method for solving the planning model.Finally,the IEEE 33 node system is used as an example for scenario analysis,and the results show that the proposed method can improve the utilization rate of PV equipment,improve the stability of distribution network operation,and reduce the comprehensive cost.