Loading...

Table of Content

    25 October 2024, Volume 46 Issue 10
    New Energy System Optimization
    Capacity configuration optimization of wind‒solar hydrogen production based on life cycle assessment
    BAI Zhang, HAO Wenjie, LI Qi, HAO hongliang, WEN Caifeng, GUO Su, HUANG Xiankun
    2024, 46(10):  1-11.  doi:10.3969/j.issn.2097-0706.2024.10.001
    Asbtract ( 205 )   HTML ( 10)   PDF (2861KB) ( 75 )  
    Figures and Tables | References | Related Articles | Metrics

    A wind-solar-hydrogen production complementary system is an important technical method to promote the local renewable energy utilization and reduce wind and solar power curtailment. However, the fluctuation of wind and solar outputs and the variety of system equipment challenge the capacity allocation optimization of wind‒solar‒hydrogen production complementary systems. A life cycle assessment(LCA)method was used to address this problem. Taking the levelized cost of hydrogen(LCOH),carbon emission intensity per unit of hydrogen, and energy loss rate as optimization objectives, an off-grid wind‒solar‒hydrogen production system with an annual production capacity of 20 000 t of hydrogen was constructed based on the improved NSGA-Ⅲ multi-objective optimization algorithm. The carbon emissions and economic benefits of the system were further analyzed. The results showed that after the optimization based on the wind and solar resource characteristics in Inner Mongolia, the LCOH of the system was 25.88 yuan/kg, with a carbon emission intensity of 0.59 kg/kg. The annual renewable energy utilization rate increased to 91.09%, demonstrating efficient resource utilization. The LCA showed that the system's total carbon emissions amounted to 250 300 t, and the carbon emission per unit of hydrogen was reduced by 97.05% compared to that of a typical coal-based hydrogen production system. The research results provide a reasonable reference for the utilization of wind-solar-hydrogen production complementary systems.

    Pricing mechanism and optimal scheduling of virtual power plants containing distributed renewable energy and demand response loads
    LI Mingyang, DONG Zhe
    2024, 46(10):  12-17.  doi:10.3969/j.issn.2097-0706.2024.10.002
    Asbtract ( 172 )   HTML ( 7)   PDF (1775KB) ( 62 )  
    Figures and Tables | References | Related Articles | Metrics

    Faced with the widespread integration of distributed wind power, photovoltaic power generation, and flexible loads into the grid, aggregating these resources through virtual power plants (VPPs) and adopting a reasonable pricing mechanism to guide users to participate in demand response can effectively enhance the absorption capacity of renewable energy and reduce overall operating costs. Traditional time-of-use pricing mechanisms often struggle to achieve a good match between demand response loads and renewable energy output, which may result in irrational or excessive demand response. To address this, a VPP internal pricing mechanism based on renewable energy output was proposed for VPPs containing distributed wind power, distributed photovoltaic power generation, and flexible loads. Power trading priorities were set to guide the optimal operation of internal VPP resources, with the goal of minimizing the overall operating costs of the VPP. A mixed integer linear programming (MILP) model was constructed for optimal VPP scheduling. Simulation results based on real data from a region in Inner Mongolia show that, compared to optimization results based on traditional time-of-use pricing, this method significantly improves renewable energy utilization and reduces VPP operating costs.

    Load frequency control of renewable energy power systems considering demand response of air conditioning clusters based on fractional-order integral sliding mode
    JIANG Jian, XU Fengliang, LI Xiaoming, SUN Jianchao, JIANG Huahua, ZHANG Jiaxin, MA Siyuan, YANG Zishuai
    2024, 46(10):  18-25.  doi:10.3969/j.issn.2097-0706.2024.10.003
    Asbtract ( 91 )   HTML ( 6)   PDF (2287KB) ( 26 )  
    Figures and Tables | References | Related Articles | Metrics

    With the extensive integration of renewable energy sources and the continuous increase in load demand, the pressure on the safe operation of power systems significantly intensifies, making frequency control in renewable energy power systems a prominent research area. This study addressed the issue of substantial frequency deviation caused by random disturbances in source-load interactions within renewable energy power systems by proposing a fractional-order integral sliding mode load frequency control strategy that incorporated air conditioning cluster demand response (DR). First, an interconnected power system model was established, including wind turbines and air conditioning cluster DR. Second, a fractional-order integral sliding mode algorithm was used to control the generator's speed regulator, aiming to minimize frequency deviation, suppress oscillatory behavior, and enhance the robustness of the power system. Furthermore, DR and wind turbines were engaged as auxiliary devices to further mitigate frequency deviations. Finally, three different operational scenarios were designed, and simulations were conducted using Matlab/Simulink. The simulation results showed that the proposed control strategy effectively reduced frequency deviations in the renewable energy power systems, ensuring stable system operation.

    Research on the access location of distributed generations based on distribution network status
    GUAN Xiaohu, WANG Changyun, ZHANG Yan
    2024, 46(10):  26-31.  doi:10.3969/j.issn.2097-0706.2024.10.004
    Asbtract ( 82 )   HTML ( 3)   PDF (1724KB) ( 20 )  
    Figures and Tables | References | Related Articles | Metrics

    The locating of distributed generators' access points will impact the economic and stable operation of a distribution network. To reduce the impact of distributed wind power's grid connection on network voltage and network loss, a study on locating the generators' access points based on the distribution network status is carried out. Under the premise of ensuring the stable operation of the whole system after the grid connection of distributed wind power, relevant constraint conditions are formulated, and objective functions are established based on indicators including network loss, distribution network voltage deviation and distribution network voltage stability. The objective functions are normalized and weighted,determining the evaluation equation for the power grid operation after the access of distributed wind power. Taking an IEEE 33 node system as an example, the optimal access location can be found by the particle swarm optimization algorithm. Connecting a 600 kW wind generation system to the IEEE 33 node system , the optimal access point is the node 17. With the access of wind power at this node, the network loss and the voltage deviation of the distribution network is reduced by 38.31% and 42.05%, respectively,and the voltage stability of the distribution network is improved by 18.59%. The proposes decision-making method for the access location of distributed power suppliers can significantly improve the distribution network voltage and network loss by selecting the optimal access location under the constraint of ensuring stable and reliable operation of the distribution network system, providing a new approach for distributed power supply access point locating.

    Power Grid and AI
    Decentralized voltage control of distribution network based on multi-agent reinforcement learning
    MA Gang, MA Jian, YAN Yunsong, CHEN Yonghua, LAI Yening, LI Zhukun, TANG Jing
    2024, 46(10):  32-39.  doi:10.3969/j.issn.2097-0706.2024.10.005
    Asbtract ( 210 )   HTML ( 2)   PDF (2033KB) ( 63 )  
    Figures and Tables | References | Related Articles | Metrics

    The integration of large-scale decentralized resources into the distribution network has changed the traditional power flow distribution, resulting in frequent voltage violations. Model-based voltage control methods require a detailed knowledge of power system network topology and have long computation time, making them unsuitable for real-time voltage control.To address this, this paper proposes a multi-agent online learning strategy for decentralized voltage control in distribution networks, considering asynchronous training. The method considered each photovoltaic (PV) inverter as an agent. First, the agents were partitioned and adjusted, then the voltage reactive power control problem of distribution network was modelled as a Markov decision process. Based on distributed system constraints, a multi-agent reinforcement learning decentralized control framework was used, and agents were trained with a multi-agent deep deterministic policy gradient(MADDPG) algorithm. Once trained, the agents could make decentralized decisions using local information without real-time communication, enabling real-time voltage control and reducing network losses by determining the output plan for the PV inverters. Finally, the effectiveness and robustness of the method were verified through simulation.

    Reactive power optimal scheduling of distribution network based on improved sine-cosine algorithm
    QIAN Da, CHEN Hao, MA Gang
    2024, 46(10):  40-47.  doi:10.3969/j.issn.2097-0706.2024.10.006
    Asbtract ( 86 )   HTML ( 1)   PDF (2693KB) ( 22 )  
    Figures and Tables | References | Related Articles | Metrics

    The integration of distributed photovoltaic (PV) systems into the grid significantly impacts power flow distribution and node voltage, posing challenges to the safe operation of the distribution network. Therefore, effectively optimized control on distribution networks with integrated distributed PV has become a pressing issue. A reactive power optimization method based on an improved sine-cosine algorithm (ISCA) is proposed, designed to reduce node voltage deviation and active power loss while accounting for the reactive power output of the distributed PV system. A corresponding objective optimization model was constructed. The ISCA improved the traditional sine-cosine algorithm by incorporating Lévy flights, elite selection strategies, and greedy algorithms, thereby enhancing both the global and local search capabilities. Simulation experiments conducted on an IEEE 33 system with multiple distributed PV stations showed that ISCA significantly reduced node voltage deviation and active power loss. This method ensures the safe and economical operation of distribution networks with distributed PV stations,and demonstrates superior optimization performance compared to other algorithms.

    Research on power transformer fault diagnosis algorithm based on fuzzy reinforcement learning
    ZHANG Kao, HE Kailin, YANG Peihao
    2024, 46(10):  48-55.  doi:10.3969/j.issn.2097-0706.2024.10.007
    Asbtract ( 102 )   HTML ( 4)   PDF (1675KB) ( 24 )  
    Figures and Tables | References | Related Articles | Metrics

    Currently, there are common problems of low accuracy and limited recognition of fault types in power transformer fault diagnosis. An adaptive transformer fault diagnosis model based on fuzzy reinforcement learning and decision tree algorithm is proposed. Firstly, by conducting Dissolved Gas Analysis(DGA) on real transformers, a series of variables that can reflect the status of transformers are extracted. Then, the decision tree J48 algorithm is used to screen these variables and select the 8 most representative variables, aiming to achieve high classification accuracy with the least number of input variables. Finally, the selected variables are input into the fuzzy reinforcement learning classifier for fault diagnosis. The experimental results show that the constructed fault diagnosis model is more accurate, with an accuracy of up to 99.7%.Compared to traditional DGA fault recognition algorithms, the diagnostic algorithm based on fuzzy reinforcement learning proposed has a higher accuracy in diagnosing power transformer faults.

    Low-carbon Energy
    Study on low-carbon demand response considering electricity-carbon price coupling
    WU Qi, ZHAO Xuanming, ZHANG Jiacheng, QIU Zhifeng, WANG Yalin
    2024, 46(10):  56-66.  doi:10.3969/j.issn.2097-0706.2024.10.008
    Asbtract ( 128 )   HTML ( 2)   PDF (2660KB) ( 71 )  
    Figures and Tables | References | Related Articles | Metrics

    The emission reduction incentive signal was integrated into the demand response mechanism of the power market to guide users to improve their electricity usage behavior with the goal of emission reduction, contributing to the achievement of the "dual carbon" goals. First, based on the carbon emission flow theory, an interactive mechanism for low-carbon demand response considering electricity-carbon coupled prices with nodal carbon potential was designed, converting nodal carbon potential signals into price signals that could incentivize users to adopt electricity consumption behaviors with low carbon emissions. Second, a two-stage model was designed: the supply side model was constructed at the first stage with the objective of minimizing electricity-carbon costs in power generation, while the demand side model was constructed at the second stage with the objective of minimizing electricity-carbon consumption costs and demand response costs. The upper and lower models interacted through market clearing of generating units and load demand. Simulation results of the modified IEEE 30-bus system show that the low-carbon demand response mechanism based on electricity-carbon price coupling can incentivize the power supply side to absorb more renewable energy, providing environmental benefits for the user side in the electricity-carbon market considering environment, thereby enriching the existing demand response mechanism.

    Analysis and research on carbon emission reduction from co-firing green ammonia in coal-fired power plants
    SHEN Mingzhong, Hu Xiaofu, SHEN Jianyong, HOU Pengfei
    2024, 46(10):  67-72.  doi:10.3969/j.issn.2097-0706.2024.10.009
    Asbtract ( 89 )   HTML ( 4)   PDF (2296KB) ( 36 )  
    Figures and Tables | References | Related Articles | Metrics

    Co-firing green ammonia with coal-based fuels is a feasible carbon reduction pathway for coal-fired power plants.Analyzing carbon reduction across the entire chain not only facilitates the development of the green hydrogen and green ammonia industries and effectively promotes their efficient utilization,but also supports the advancement of ammonia-coal co-firing technology.In a scenario where green ammonia was produced using renewable energy-powered green electricity and co-fired of ammonia with coal in coal-fired power plants, the total CO2 reduction throughout the chain was calculated.The results showed that using 445 GW·h of green electricity to produce green ammonia and co-firing it in coal-fired units achieved a total CO2 reduction of 100.7 thousand tons/year.The CO2 reduction per unit of green electricity was approximately 226.29 g/(kW·h),and annual carbon reduction cost savings were estimated at 9.5 665×106 yuan.

    Simulation research of charging and discharging processes of compressed air storage based on salt caverns
    HAO Ning, LI Zhenya, WANG Yuxuan, BIAN Wenjie
    2024, 46(10):  73-81.  doi:10.3969/j.issn.2097-0706.2024.10.010
    Asbtract ( 133 )   HTML ( 6)   PDF (4568KB) ( 48 )  
    Figures and Tables | References | Related Articles | Metrics

    In order to quantitatively analyse the thermodynamic behaviour of underground salt cavern gas storage during charging and discharging process, a thermodynamic model of salt cavern charging and discharging was constructed and simulation calculations were conducted. A comprehensive analysis was performed on the dynamic changes of salt cavern gas storage system throughout the continuous process of "charging-storage-discharging-storage". The results showed that, the gas pressure and temperature inside the salt cavern exhibited a regular pattern of "rise-fall-fall-rise"over time. In the spatial dimension, the pressure inside the salt cavern gradually increased from top to bottom due to the gravity effect, while the temperature inside the cavern showed a decreases pattern from the top to the bottom due to natural convection. In addition, if the system was not discharged promptly after the initial static gas storage period, the system would continuously lose energy,negatively impacting energy storage. Moreover,as the static gas storage period extended,the temperature of the compressed air inside the salt cavern dropped to the initial temperature before charging.