As computer technology continues to advance, more and more people are using databases, leading to variations in data backup and transfer between databases. This paper proposes a small sample inter-database discrepancy data elimination method based on cloud computing architecture as a way to solve the problem of data synchronization discrepancy between different databases. The semantic features of the data in the database are represented in the form of a directed graph, and the semantic Gaussian marginalized data fusion system is constructed by combining the rectangular window function of Gaussian marginalization to realize the fusion filtering processing of discrepant data. Then, the particle swarm discriminant tree algorithm is used to extract the features of the difference data between the small sample databases, and the KL transform is used to compress the difference data to improve its confidence level. The rough weighted average single dependency method is introduced to detect and identify the difference data between small sample databases and combined with the artificial intelligence algorithm to construct the principal component feature set of the difference data in the small sample databases, thus realizing the elimination of the difference data between small sample databases. When the proportion of difference data is increased from 0.05% to 1.00%, the leakage alarm rate and false alarm rate of this paper’s method for the difference data between small sample databases are 0.113% and 0.099%, respectively. When eliminating the inter-database discrepancy data, its time consumption is between 0.06μs and 0.3μs, and the average value of the removal rate of discrepancy data can reach 95.54%. Small sample databases that utilize cloud computing technology can utilize a variety of differential data elimination algorithms to ensure high-quality migration and synchronized backup of inter-database data.

Currently, the provincial and local power grids are facing increasing pressure of frequency regulation and peak load management. This paper analyzes the coordinated optimal control strategy of provincial and local power grids with the participation of multiple regulating resources. An optimization model for the provincial and local two-level grids is constructed considering the characteristics of thermal power units, energy storage systems, wind power, photovoltaics, hydropower and other new energy sources. Then the coordinated automatic generation control scheme based on opportunity constraints is designed for the provincial and local grids. Finally, the proposed strategy is tested on the provincial and local grids. Simulation results demonstrate that the proposed strategy effectively regulates the frequency fluctuation at the provincial and local levels under the uncertainty of wind power.

Access control is a security technique that can restrict access to protected resources, and data to only authorized users. In this paper, we design a blockchain-based access control scheme for cloud storage that is enabled by revocation. First, initialize blockchain to generate global parameters, generate complete user encryption keys and decryption keys, and perform data encryption. When the blockchain receives a user’s access request, the authorization contract determines if it is on the revocation list. If not, the key is checked. Then, it determines abnormal access and adds its identity to the revocation list, preventing further access to the database. The access control model is created by combining attribute-based encryption. After the security analysis and operational efficiency test, it can be considered that the model meets the security features, such as IND-CPA security. Regarding the time overhead of generating encryption keys, the computational overhead of this paper is the lowest, and the time required to generate encryption keys for 10 attributes is only 0.09 seconds, and for 100 attributes is only 1.62 seconds, which is better than the performance of the two attribute-based access control schemes, FIFC and AACE. The user access time overhead for 10 to 100 attributes at user encryption time is 1.38, 1.56, 1.98, 2.1, 2.53, 2.76, 3.03, 3.27, 3.66, and 3.94 seconds, respectively. The lowest decryption time consumed ensures data security and a good access experience. This study achieves fine-grained access control while protecting data privacy.

Reliability evaluation is a fundamental problem to be solved in power system planning and operation, which is of great significance to ensure power supply and provide support for security warning of the power grid. A reliability evaluation method for transmission grid based on risk theory is proposed in this paper, which effectively combines the fault occurrence probability and the result of load loss due to the contingencies, quantitatively describes the reliability evaluation index under different contingencies. This method can provide a comprehensive total reliability evaluation index of the system under different line fault probabilities, which cam reflect the relative reliability of the transmission grid network. Finally, simulation tests are conducted in the IEEE-24 bus system and the effectiveness of the proposed method is verified.

Reconfiguring the distribution network is a key problem that needs to be addressed during power system restoration, especially in the case of widespread outages. After a power outage occurs, the power system should restore power usage as quickly as possible and as much as possible. Thus, a network reconfiguration method that considers load restoration after power outages is proposed. This method takes the minimum repair time of the line after outages and the maximum amount of restored load supply as the objective function. Besides, the level of loads is considered here and the “virtual flow” method is utilized to guarantee the network structure’s connectivity. Finally, some simulation tests are performed on the IEEE33 bus system to validate the effectiveness of the proposed reconfiguration method.

针对现有大多数方法难以兼顾系统经济性与新能源消纳的问题,提出一种基于改进遗传算法的源网荷储协同控制方法.该方法综合考虑源网荷储系统的特性,构建了以运行成本和弃风弃光量最小化为目标的模型.通过采用混沌优化算法来对遗传算法进行改进,进而设计一种基于双层嵌套结构及改进遗传算法相结合的高效方法用于目标函数的求解,以得到最佳的系统控制模式.基于IEEE33 节点系统对所提方法进行实验论证的结果表明,优化后系统的弃风弃光量和运行成本仅为0.872 MW及2.330 万元,说明了该方法可有效减少系统的运行成本,并提高新能源的消纳.

As a clean energy source, wind power has incomparable advantages in terms of low carbon energy. The inherent randomness and intermittentness of wind power have brought difficulties to the economic dispatch of the power system, and the consumption of a higher proportion of wind power is an urgent problem to be solved. Motivated by this, we propose a robust multi-period economic dispatch framework for system under high penetration wind power over rolling horizon. Firstly, we model the procedure of economic dispatch as a Markovian decision process, and the overall multi-period dispatch is divided into a two-stage optimization. Secondly, the objective of economic dispatch is of a min-max-min form, which is solved by bisection method. The inner max-min problem is calculated by the dual form. Finally, the simulation implemented on a system under significant wind demonstrates the effectiveness of the proposed method and shows the benefits in contrast to the traditional deterministic dispatch models.

To help to bring about “carbon peaking and carbon neutrality”， the traditional power system with fossil energy as its mainstay will be transformed into a new power system dominated by new energy. Zhejiang province， against such a background， has seen rapid growth in the installed capacity of photovoltaic power generation. Due to the randomness and fluctuation of photovoltaic power generation， high-proportion photovoltaic power has imposed new impact and challenges on the dispatch and operation of power system. Therefore， based on the existing research on the impact of photovoltaic power generation integration on power system， this paper summarizes and analyzes the impact of photovoltaic power generation on the balance of power generation and consumption in Zhejiang power grid， and the countermeasures as well. First， based on the characteristics of Zhejiang power grid， this paper analyzes the impact of high-proportion photovoltaic power integration on Zhejiang power grid from three aspects： typical load curve， power supply， and photovoltaic power generation support. Then， on the basis of the known impact， the relevant challenges are summarized on the short-term and medium and long-term time scales. Finally， a solution that can adapt to the effective consumption of photovoltaics and ensure the balance of the power system with the integration of high-proportion photovoltaic power in the future is proposed.

Virtual power plant （VPP） technology can realize the effective aggregation and coordinated control of distributed resources to reduce the carbon emissions of power system operation and improve renewable energy consumption. The technology has become an effective way to promote the realization of “dual-carbon” goals. Firstly， the general situation and research status of VPP technology are introduced in terms of intelligent metering， information communication， and coordinated control. Next， given the promotion effect of VPP on the “dual-carbon” goals， the VPP dispatch modes for carbon emission reduction and renewable energy consumption are introduced respectively. Then， the construction status of three different types of VPPs in Shanghai， Jiangsu and a city in Zhejiang are introduced. And the impact of various factors on the key technologies and low-carbon dispatch mode selection of VPPs is analyzed. The results show that VPPs should be further developed in terms of intelligent metering， information communication and coordinated control； key technologies for VPPs and low-carbon dispatch mode should be selected rationally in accordance with load characteristics， resource distribution， and other factors.

To promote inter-regional resource complementarity and improve the level of new energy consumption， a collaborative optimal scheduling model for coupled transmission and distribution systems considering DC tie-line power adjustment and electricity-gas-heat coupling is proposed. Firstly， a power adjustment model of DC tie-line is established to make full use of the adjustment capacity of the tie-line to improve the operation flexibility of the system. Secondly， an active distribution network model considering the electricity-gas-heat coupling is established for the receiving-end grid. Moreover， the coordinated optimization of the HVDC tie-line and the multi-energy complementary system of receiving-end is carried out. The linearized AC power flow is used for approximate calculation of the distribution network loss， and nonconvexity the Weymouth equation of natural gas pipeline flow is incrementally linearized. In so doing， the model in this paper is transformed into a mixed-integer linear programming （MIP） problem. Finally， an example is given to verify the effectiveness of the proposed model and method.

Power grid diagram is an important tool for power grid dispatching operation. The automatic generation of power grid diagram is a complex multi-objective optimization problem, in which the difficulty of automatic station layout is related to the number of stations and lines. A mathematical model of station grid uniform diagram layout is built, and an improved force-directed grid layout algorithm is proposed for the supply area. For the division of supply area, a method is designed for supply area layout division and station global optimization. For the station uniform diagram layout under any canvas, the station weight coefficients are introduced, and the repulsive force and attractive force calculation formula of the force-directed algorithm are redefined. The two-way simulated annealing algorithm is used to control the layout uniformity. The experimental results show that the grid layout obtained by the algorithm has the advantages of reasonable layout area division, uniform station layout, no overlapping of station coordinates, and fewer line intersections.

Abstract Accurate parameters of transmission line (ParTL) are the basis of power system analysis. For high‐voltage transmission line (TL), remote terminal units (RTUs) are widely installed to provide supervisory control and data acquisition (SCADA) measurements. Based on multiple snapshots of SCADA data at both ends of the TL, a four‐step method to identify ParTL is proposed in this paper. In detail, based on the π‐model of TL, the relationship between voltage phase angle difference (VPADs) and parameter identification is presented. And then, the general estimation formulation for VPADs is established. Furthermore, four steps to simplify and solve the formulation are detailed, that is, firstly after the approximated relationship between VPADs and reactance are derived, the estimation of VPADs at multiple snapshots is converted into the optimal problem with reactance of TL, just one variable, and the VPADs are estimated roughly; secondly based on the median estimation, the ParTL are identified roughly; thirdly, combined with Taylor series expansions and the alternating current power flow model, the VPADs are estimated accurately; fourthly, the ParTL are identified accurately. Finally, the results with simulated and field data verify the effectiveness and practicality of the proposed method.

With the rapid development of UHV AC/DC hybrid power grid, the safe and stable operation of interconnected large power grid is facing great pressure, which puts forward higher requirements for real-time analysis and dynamic early warning application of power grid control system. At present, the branch parameter identification of power grid regulation and control system is mainly carried out by the least square method, but this method has some shortcomings, such as easy over-fitting. One cross neural network model (CRSNet) is proposed in this paper, which adjusts the branch parameters to different scales in the calculation process and then updates the parameters through cross perception, so that the neural network can consider the characteristic information of other nodes in the power grid and it can fit accurately. Compared with the least square method commonly used in practice and other machine learning algorithms, the experiment shows that the proposed model has greatly improved the fitting accuracy and enhanced robustness, which has a broader application prospect.

Large-scale distributed generation grid-connection brings huge economic and environmental benefits, but also threatens the stability of the grid. To make the grid consume a higher proportion of distributed generation, it is necessary to optimize the location and capacity of the distributed generation connected to the grid. Firstly, the uncertainty analysis model of wind speed, illumination intensity, and load of grid is established. Secondly, a distributed generation location and capacity planning model with the lowest annual comprehensive cost as the objective function is constructed. Then, a novel fractional particle swarm optimization algorithm is proposed, and the performance of the algorithm on complex optimization problems is tested. Finally, the simulation results of the IEEE 33-bus system example verify the rationality of the established model and the effectiveness of the proposed algorithm.

Based on the virtual power plant platform, this paper studies the aggregation adjustment and optimization strategy of multiple agents, and uses the multi-agent reinforcement learning strategy to realize the game behavior among multiple power generation companies, energy storage companies and load users, which meets the adjustment in the virtual platform. Nash equilibrium of the overall income of resource participants. Based on the modeling of the adjustable resource aggregation of the virtual power plant, the game strategy is divided into the overall cooperative game and the partial non-cooperative game according to the game characteristics, and different game strategies are adopted respectively. The comparison results of field operation examples and methods prove that the strategy has advantages in terms of model training time-consuming, execution time-consuming, convergence, etc., and it has theoretical guidance and field promotion value.

针对传统依靠人工编制事故预案无法覆盖全部事故情况,且编制过程智能化和自动化不足等问题,设计和开发了大电网事故预案自动生成推演系统.依据大电网事故处置预案的特点,采用电网运行方式分析、灵敏度分析、拓扑分析和电网风险分析等方法,结合DTS的仿真模拟功能,自动生成涵盖事故前运行方式、事故情况、事故后运行方式、紧急控制方案、风险预控方案、设备恢复方案的事故预案.应用表明,该系统可以针对灵活设定的运行方式和假想故障,生成包含具体控制措施的事故预案,具有较好的实用性.

Time series anomaly data detection has always been a research hotspot in different fields and is also one of the prerequisite tasks for effective data analysis. At present, a large number of research models are based on machine learning methods, which fail to well extract the correlation between data, fail to obtain the development trend of data, and often lead to wrong abnormal judgment. However, due to the large number of parameters, the new deep learning method takes too long to compute and is difficult to deploy. Aiming at the above problems, Light Gradient Boosting Machine (LightGBM) is used in this paper to significantly improve the detection accuracy of the model. Through experimental comparison, this method can obtain high accuracy in a very short time.

文章提出求解电网最优电压调节问题的两阶段方法.利用无功功率隔离开关调节电压,同时最小化电网损耗.首先将电网线性化,并求解最优电压调节问题,以确定隔离开关运行计划.采用无功功率分配法,结合树路径最优化法对隔离开关进行无功功率分配,得到帕累托前沿解以降低电压损耗,选择电网电压运行的最优方案.使用Matlab对电网电压性能进行评估,结果表明:与其它非线性优化算法相比,所提算法具有更优的精度与效率.

Transmission line parameters are the basis of power system calculation, and their accuracy directly affects the safe and stable operation of the system. At the same time, with the improvement of computing power and the rapid growth of the amount of power grid operation data, deep learning has developed rapidly and applied in power systems. However, there is little research on line parameter identification combined with deep learning. Therefore, from the perspective of line model and deep learning, combined with median estimation and modified Supervisory Control And Data Acquisition (SCADA) data based on line model, this paper proposes a robust method for parameter identification of transmission line based on Long Short-Term Memory (LSTM) and modified SCADA data. Speciffically, the overall process of the line parameter identification method based on LSTM is given first. Then the settings of the proposed method are introduced in detail. Combined with the line model, a modified input data based on the SCADA data is constructed, and a multi-case training set considering different operating conditions and different line parameters is established. According to a large number of tests, the optimized parameter configuration of the LSTM is given. Finally, the median estimation is used to give the parameter identification results. Case studies in simulated and measured data verify the effectiveness and robustness of the proposed method.

According to the characteristics of the “physical distribution and logical integration” of the new generation dispatching automation system, this paper designs a lightweight human-machine terminal display architecture. It also proposes the componentization of the screen and its rendering technology to realize the display of electrical primitives, chart primitives, and non-viewable primitives on lightweight man-machine terminals. Besides, this paper proposes the GIS visual display technology to meet the needs of geographic information display in the dispatch automation system and put forward the interaction technology of picture linkage to improve the interaction flexibility of picture components. Relevant research results have developed prototype system and applied in the field pilot.