Against the backdrop of complex control and management requirements, the development of islanded microgrids (MGs) faces severe challenges. To effectively address frequency restoration, voltage regulation, and economic operation in MGs under dynamic environments, this paper proposes an economic distributed secondary control (DSC) scheme based on the consensus protocol of multi-agent systems. This method decentralizes the control logic to each distributed generator (DG) and achieves global coordination through sparse communication among adjacent nodes, significantly enhancing the flexibility and economy of secondary control. Unlike traditional distributed strategies that focus solely on power sharing, the proposed approach integrates economic dispatch into the secondary control layer, enabling real-time incremental cost equalization. The stability of the closed-loop system is rigorously proven using Lyapunov theory. Finally, simulations verify the effectiveness and robustness of the proposed DSC in terms of frequency/voltage restoration and economic optimization, demonstrating its performance under load variations and plug-and-play operation. Notably, the proposed method achieves approximately 5% reduction in total generation cost during load switching events, highlighting its significant economic benefit.

In the first stage, the multi-objective optimal scheduling model is established with the objectives of microgrid operation cost and environmental control cost and power supply reliability, and the solution with the smallest expected value of the comprehensive affiliation function is selected as the scheduling strategy of microgrid in the first stage, so as to obtain the microgrid scheduling plan and the power shortage that make the first-stage objective as optimal as possible in a comprehensive way. The results show that the effectiveness of the proposed two-stage optimal scheduling strategy is verified by comparing the expectation and variance values of the total cost function values of each sample in the context of a large number of sampling samples.

To address the challenge of clean heating for rural households in extremely cold regions such as Northeast China, this study investigates a photovoltaic (PV)-direct-driven ground source heat pump (GSHP) system assisted by solar greenhouse waste heat, using a case study of a rural household near a solar greenhouse in the suburbs of Shenyang. The system's heating performance during the heating season was analyzed using TRNSYS. Simulation results demonstrate that the solar greenhouse can provide 4,203.92 kWh of heat per heating cycle, reducing the GSHP's heating output by 20%. Over the system's lifecycle, heat extraction from the ground decreased by 5,501.01 kWh, stabilizing ground temperature and extending the GSHP's operational life. Additionally, the GSHP's cumulative heating output decreased by 25,953.89 kWh, reducing carbon emissions by 287.61 kg. Although the total electricity consumption of the solar greenhouse-assisted PV-GSHP system increased by 5% compared to a standalone GSHP system, the GSHP's energy consumption decreased by 11.75%. The annual operating cost of the hybrid system was approximately 1,814.6 RMB, representing an 11.75% reduction compared to systems without auxiliary heating and a 5.37% reduction compared to standalone GSHP systems. This study provides theoretical and technical support for applying PV-GSHP systems in agricultural settings.

With the continuous development of economic strength in our country, the pattern of electric power system is also developing and changing. The access capacity of distributed photovoltaic is also increasing year by year, and its impact on the distribution network is also increasing. How much installation capacity should be set for distributed photovoltaics and where it should be installed is an urgent problem to be solved. Aiming at voltage, network loss and access cost, this paper established an objective function and improved Flower Pollination Algorithm (FPA). Finally, the improved flower pollination algorithm was verified through IEEE33-node system. The verification results show the effectiveness and feasibility of the improved algorithm, and provide some references for the location and capacity determination of distributed PV grid connection.

The scientific and reasonable operation of power station reservoir can optimize the utilization efficiency of water resources, improve the comprehensive benefit of power station and promote the development of social economy. In this work, the optimization model of power station reservoir operation was established based on five constraint conditions, including water level constraint, output constraint etc. The model was solved based on genetic algorithm (GA), ant colony algorithm (ACO) and simulated annealing (SA) algorithm, and the parallel joint framework of these optimization algorithms was proposed to give full play to the advantages of different algorithms and improve the robustness of the result. A case study was conducted on a typical reservoir as the case study. The results showed that GA and ACO could optimize the model effectively, and the results were better than that of SA algorithm. The average output of power station reservoir solved by ACO was 2392 MW, with the annual average electric power production of 20.95 billion kW • h. Moreover, the results obtained based on the parallel joint framework were better than 93.33% of the results obtained based on a single algorithm run in the case, which further proved the effectiveness of the parallel joint framework of optimization algorithms. This work gives insights on Optimal operation of power station reservoir.

With the gradual advancement towards the goal of carbon neutrality, photovoltaic power generation, as a relatively mature zero-carbon power technology, will be connected to the grid in an increasing proportion. A voltage control strategy, involving distributed energy storage, is proposed in order to solve the voltage deviation problem caused by the high proportion of PV connected to the low voltage distribution network (LVDN). A voltage calculation method of the LVDN node with a high proportion of PV is proposed. According to the voltage distribution of the LVDN nodes running throughout the day, fuzzy cluster analysis was used to partition the nodes in the LVDN. A two-objective mathematical model for optimizing distributed energy storage in a partition was constructed, and a particle swarm optimization algorithm was used to solve the model. A voltage control strategy based on sensitivity analysis is proposed, and a mathematical model is established to analyze the relationship between the node current increment and the node off-limit voltage. The mathematical relationship among the node off-limit voltage, node off-limit power and energy storage exchange power is derived, and the voltage deviation of the distribution network can be suppressed by adjusting the injected power of the distributed energy storage. Through case analysis, it is verified that the proposed voltage control strategy can reduce the voltage deviation of the distribution network nodes, effectively solve the problem of the distribution network voltage deviation, and reduce the active power loss.

Rural power grids are essential for rural development, impacting the lives of farmers, the agricultural economy, and the overall efficiency of agricultural production. To ensure the reliable operation of these grids, finding ways to provide high-quality power is imperative. In recent years, the penetration rate of distributed photovoltaic (PV) in the distribution network has been increasing. When the output of PV and load are not matched, the voltage fluctuation of the network affects the safe and stable operation of the distribution network. In this study, we propose that the stable operation of rural power grids can be achieved by employing a photovoltaic-electric spring (PV-ES) device. A state space model of PV-ES is established and a single PV-ES voltage control method, based on a PI controller, is proposed, taking a rural user household with a monthly power consumption of about 120 access to distributed power supply as an example. We analyzed the device’s effectiveness in addressing voltage fluctuation issues as well as how light intensity impacts its effectiveness. The implementation of the PV-ES device solves the most significant problem faced by rural power grids, namely, the unstable power supply that occurs during peak electricity consumption periods. In addition, the PV-ES device ensures a high-quality electricity consumption experience for consumers.

在积极推进新工科建设的背景下,在几十年教学实践的基础上,立足农业院校电气类专业实际,开展沈阳农业大学电气类专业课程建设研究.经过教学团队多年的积淀和传承,沈阳农业大学电气类专业课程建设目标明确,课程建设成果显著,已形成了集立体化的课程育人体系、多元化的课程教学体系和多样化的课程评价体系于一体的较为健全的课程建设体系.秉承立德树人理念,依托新工科建设,沈阳农业大学电气类专业课程建设将面向未来形成特色,并持续改进不断提升.

In recent years, rapid industrialization has driven higher energy demand, depleting fossil-fuel reserves and causing excessive emissions. China’s “dual carbon” strategy aims to balance development and sustainability. This study optimizes microgrid efficiency with a tiered carbon-priced economy. A Stackelberg game establishes microgrid-user equilibrium, solved iteratively with a multi-population algorithm (MPGA). Comparative analysis can be obtained without considering demand response scenarios, and the optimization cost of microgrid operation considering price-based demand response scenarios was reduced by 5%; that is 668.95 yuan. In addition, the cost of electricity purchase was decreased by 23.8%, or 778.6 yuan. The model promotes user-driven energy use, elevating economic and system benefits, and therefore, the scheduling expectation of “peak shaving and valley filling” is effectively realized.

植物柔性传感器具有轻量化、透气、植物表面共形性高等特点,可以实现无损、实时、原位监测,提供精准的植物生理与环境信息,实现植物语言识别,开创"植物半导体新赛道".介绍了柔性电子技术在植物健康监测方面的最新进展,概述了植物柔性传感器用于植物激素、信号分子、植物电信号、挥发性有机物等的监测,可有效反映出植物健康状态;通过监测地表和空气中植物代谢产物反映环境胁迫条件,分析了新型植物可穿戴设备的传感技术,制备材料、结构以及监测方法.最后,对植物柔性传感器在现代农业中的应用进行总结,并讨论了未来植物可穿戴设备面临的机遇和挑战.

The problem of distribution network operation optimization is diversified and uncertain. In order to solve this problem, this paper proposes a method of distribution network operation optimization considering wind-solar clustering, which includes source load and storage. Taking the total operating cost as the objective function, it includes network loss cost, unit operating cost, and considers a variety of constraints such as energy storage device constraints and demand response constraints. This paper aims to optimize the operation according to different wind-solar clustering scenes to improve the economy of distribution network. Taking the 365-day wind-solar output curves as the research object, K-means clustering is carried out, and the best k value is obtained by elbow rule. The second-order cone programming method and solver are used to solve the optimization model of each typical scenario, and the operation optimization analysis of each typical scenario obtained by clustering is carried out. Taking IEEE33 system and local 365-day wind-solar units output scenes as examples, the period is 24 h, which verifies the effectiveness of the proposed method. The proposed method has guiding significance for the operation optimization of distribution network.

The large-scale grid connection of new energy will affect the optimization of power flow. In order to solve this problem, this paper proposes a power flow optimization strategy model of a distribution network with non-fixed weighting factors of source, load and storage. The objective function is the lowest cost, the smallest voltage deviation and the smallest power loss, and many constraints, such as power flow constraint, climbing constraint and energy storage operation constraint, are also considered. Firstly, the equivalent load curve is obtained by superimposing the output of wind and solar turbines with the initial load, and the best k value is obtained by the elbow rule. The k-means algorithm is used to cluster the equivalent load curve in different periods, and then the fuzzy comprehensive evaluation method is used to determine the weighting factor of the optimization model in each period. Then, the particle swarm optimization algorithm is used to solve the multi-objective power flow optimization model, and the optimal strategy and objective function values of each unit output in the operation period are obtained. Finally, IEEE33 is used as an example to verify the effectiveness of the proposed model through two cases: a fixed proportion method to determine the weighting factor, and this method to determine the weighting factor. The proposed method can improve the economy and reliability of distribution networks.

In the daily maintenance, inspection and operation monitoring of transformers, inspection and monitoring personnel need to detect a large number of data, it is undoubtedly very difficult to accurately locate fault information from these data, and the combination of transformer digital twin system and deep learning algorithm provides a new idea to solve the problem of transformer running state detection and early fault diagnosis. This paper designs an intelligent system based on digital twin system, which is highly visualized and deeply combined with the characteristics of reality, real-time detection data, online rapid diagnosis, accurate alarm to assist transformer detection. Through the combination of digital twinning system and actual monitoring hardware, the data can be displayed directly, which is conducive to the quick inspection of transformer running state and the improvement of working efficiency.

"双碳"目标以来,我国农村地区大力发展清洁能源,积极构建农村清洁能源体系,各个地区因地制宜推动农村电气化发展和乡村清洁能源的改造.但随着屋顶分布式光伏的大量接入,农村光伏并网消纳问题依旧存在,还须通过加快分布式光伏发电就近消纳,以提高农村电网整体承载能力.对于以粮食作物为主的农村地区,可以结合农业区位因素、地区地域特色,建设"光伏+现代农业"的绿色可持续发展模式.土壤关系到农业生产的可持续性,土壤肥力的形成变化对土壤发育、生物地质循环具有主导作用.针对我国部分农村地区土壤肥力不足而引起的氮素流失问题,设计了一种基于低温等离子体技术的大气土壤固氮装置,配合农村清洁能源,提出了光伏固氮技术方案,促进农村地区光伏消纳的同时,还可以用于补足农田庄稼中土壤肥力.

In recent years, a large number of countries have connected and distributed photovoltaics in remote rural areas, aiming to promote the use of clean energy in rural areas. The solar energy that is not used in time needs to be discarded, resulting in a large amount of wasted energy. Rural areas are closely related to agricultural production, and solar energy can be used for agricultural nitrogen fixation to supplement the nitrogen needed by crops and effectively use the upcoming waste of solar energy. A photovoltaic-driven plasma reactor for nitrogen fixation in agriculture was designed in this study. The air inlet and outlet holes are arranged above and below the reactor to facilitate air entry and directly interact with the gliding arc generated at the bottom of the electrode to achieve atmospheric nitrogen fixation in agriculture. The characteristics of gliding arc development in the process of nitrogen fixation in agriculture were studied experimentally. There are two discharge modes of the gliding arc discharge: one is steady arc gliding mode (A-G Mode), and the other is breakdown gliding mode (B-G Mode). By collecting discharge signals, different discharge modes of gliding arc discharge were analyzed, and the effect of the air flow rate on the discharge period and discharge mode ratio distribution is discussed. The effects of the air flow rate on the yield, specific energy input, and energy consumption in plasma agriculture were studied. The experimental results show that with an increase in the air flow rate, the B-G mode takes up a larger proportion and the gliding arc discharge period is shortened. However, the higher the proportion of the B-G mode, the more unfavorable the production of nitrogen oxides. Although the nitrogen oxides generated by the system are not particularly excellent compared with the Haber-Bosch ammonia process (H-B process), the access to distributed photovoltaic roofs in rural and remote areas can effectively use available resources like water, air, and solar, and avoid energy waste in areas where wind and solar are abandoned.

In order to improve the quality of undergraduate graduation design, this paper studies and improves the guidance method of electrical engineering graduation design based on the OBE concept. The graduation design teaching process was discussed around the topic selection stage, the topic opening stage, and the design stage. The main goal is to expand students' knowledge, enhance their engineering knowledge and improve their ability to solve practical engineering problems. The process inspection and guidance methods were optimized from the three levels of instructors, majors and colleges. The process inspection and guidance of colleges and specialties were online or offline. After the student selected the topic, the graduation design guidance and inspection of the instructor was conducted face to face or online. Through the questionnaire survey, this paper completed the achievement degree analysis of graduation design goals. The method proposed in this paper is beneficial to improving the practical ability and innovation ability of electrical engineering undergraduates.

In order to effectively improve the online teaching effect of graduate courses, it is very important to plan the online teaching operation mode with the full help of information technology. This paper analyzes the characteristics and existing problems of postgraduate online teaching. In view of the characteristics and existing problems of online teaching, according to the teaching experience and the characteristics of postgraduate courses, this paper designs the teaching operation mode from the three aspects of teaching, auxiliary teaching and interactive platform, and puts forward the methods of developing online teaching from the three aspects of classroom teaching, course management and course assessment. This paper discusses the process of online teaching in detail, which provides a reference for the efficient online teaching of graduate courses.

This paper proposes a spectral clustering method in consideration of the operational issues of distribution networks like load fluctuation, intermittent power output, reactive power flow, and daily switching frequency of reactive power compensation. We divide the daily load curve of the distribution network with distributed generation units (DG) into time periods, and set the minimum network loss and voltage offset of each time period as the objective function. Then we use this method to establish a time-divided dynamic reactive power optimization (RPO) mathematical model of DG. Since the traditional random lion swarm optimization (LSO) can hardly escape a local optimum, a random black hole mechanism is introduced to improve the LSO algorithm, and to formulate a random black hole based lion swarm optimization (RBH-LSO) algorithm. This paper takes the improved IEEE 33-node system as the sample object. The RBH-LSO algorithm, the LSO algorithm and the particle swarm optimization (PSO) algorithm are mutually used to realize the optimization of this system. After the simulation results of the optimization are analyzed, this paper demonstrates, as a summary, that the RBH-LSO algorithm has exceeding excellence in performance and proves to be an effective mechanism for dynamic RPO of distribution networks with DG.

Nowadays, countries and regions around the world are doing a solid and effective job of carbon peaking and carbon neutralization, and environment friendly artificial nitrogen fixation pathways have become a major development trend. Based on the nitrogen fixation by sliding arc plasma, the power supply side is directly driven by photovoltaic panels and the design of a photovoltaic-driven nitrogen fixation plasma reactor is improved. The PV-driven plasma nitrogen fixation process is described and a PV-driven plasma nitrogen fixation testbed is built. The scope of application in some rural areas and agricultural production processes is discussed. The paper is a significant theoretical basis for the construction of a test platform for a new nitrogen fixation plant combining clean energy and plasma nitrogen fixation. This is of guiding significance for future data analysis of the electrical parameters of photovoltaic driven nitrogen fixation.

课程思政与思政课程协同育人是涉农高校思想政治教育改革创新的重要举措,但当前涉农高校在理念、教学、队伍等方面尚未形成协同育人格局.面对上述问题,涉农高校要以理念协同为先决条件,以教学协同为核心要素,以队伍协同为关键环节,从而确保思想政治教育质量得到稳步提升.