Electric Energy Data Storage and Privacy Protection in Edge Computing Mode

In order to solve the problem of untargeted security classification and grading methods in the process of power grid data governance, this paper proposes a classification and grading method that combines the characteristics of power grid services by deeply analyzing the mainstream data security classification and grading standards at home and abroad. This method starts from combing the power grid business lines, goes through the process of service refinement, data classification, and data grading, and finally gives targeted protective measures. This method is in line with the actual operation of the power grid, and clarifies the responsible subject of data classification and grading, so it is easy to implement and facilitate the update of classification and classification. In the process of data grading, the degree of influence and scope of influence on multiple objects are fully considered, and the power grid data is divided into four levels to facilitate data management, which basically conforms to the status quo of power grid data classification and lays a foundation for power grid data governance.

With the continuous construction of intelligent power grids, a large amount of data on power grid operation, production management and market operation have been accumulated in power systems, which are affected by factors such as collection, communication, and environment. There are quality problems in terms of integrity, consistency, and effectiveness of data. In this paper, by studying the characteristics of massive data and data anomaly identification method, a method of error correction for massive data in power grid based on arbitrary partition area is proposed. Taking abnormal data as the center and dividing the area based on the network topology structure, the abnormal data of the network is corrected layer by layer to achieve the purpose of improving the data quality.

Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule.

Big data in recent years (big data) technology has been highly concerned and valued by academia, industry and governments all over the world. Data has been widely considered to be as important as other resources. This paper first analyzes the application of big data in power grid, and then uses Hadoop technology to design a big data system suitable for power grid enterprise decision-making based on the characteristics of power platform in a province of China Data platform system. This paper constructs the operation environment and uses the big data platform to sample all kinds of power generation and user side data. Based on the platform, the behavior data of power customers are analyzed and processed, which proves that the technology of big data can better serve the company's business innovation and decision support.

In order to meet the needs of data availability and practicality in the power grid system to achieve data security and efficient sharing, this paper proposes a power grid data security sharing model based on federated learning, and through the differential privacy, secure multi-party computing, homomorphic encryption, function encryption Four security sharing methods are implemented to ensure the security and feasibility of grid data sharing.

After introducing the present operation situation of Tianjin power grid, this paper reveals the causes of the limited transmission capacity of total cross-sectional area and the circuitous power flow of the double lines in Tianjin. The development and state-of-art of static synchronous series compensator (SSSC) technology are covered in this paper along with the lines running plan of Tianjin power grid in future. A variety of measures to deal with the limited section of transmission capacity and roundabout power flow are proposed and compared, which draws to a conclusion that SSSC is the optimal solution for improving power grid structure and adjusting regional power flow. Therefore a detailed application scheme of SSSC is proposed subsequently for the 220kV power grid in Tianjin. The electricity compensation amount of SSSC demands in Tianjin area is analyzed theoretically, and three alternative capacity of SSSC under steady state control are proposed. The optimized capacity and location scheme of SSSC are derived with equivalent data of Tianjin power grid, and are presented in a transmission capacity effect table. Considering the N-1 transient stability limit and the line rated current constraints, the compensation capacity and the capacity curve of the cross section are given, which provides the basis for the SSSC scheme of various operation modes in Tianjin power grid. Finally, the validity and rationality of the SSSC application in Tianjin 220kV power grid are verified via the power flow data of BPA.

With the rapid development of ultra-high voltage AC and DC power supply and the increase of large-capacity power bases, China’s power system shows obvious integration characteristics of power balance in the whole power grid, which poses new challenges to the existing model of scheduling plan with provincial balance and weakly coupled dispatching at all levels. For this reason, an integrated optimization model of power generation and transmission planning for the entire power grid is proposed, which is suitable for engineering practice and takes into account the characteristics of multi-energy generation complementation on the generation side, transmission operation characteristics of ultra-high voltage lines and flexible load response characteristics on the demand side. The model of multi-energy complementation of generation side is established, which can reduce the fluctuation of clean energy and increase the accommodation of clean energy. The flexible load response model of the demand side is established, which can coordinate with the power adjustment of the power generation to increase the accommodation space of clean energy. The power model of transmission channel is established to connect the energy base with the load center, which can consider the distribution characteristics of the power grid globally and reduce the overall operation cost of the grid. Based on the actual data of a cross-regional interconnected power grid, the validity of the model is verified.

In the modern society with a high dependence on power systems, the stable operation of the power grid is of utmost significance. Traditional approaches for analyzing power grid stability suffer from several limitations, including high computational complexity, notable sensitivity to data quality, and inadequate representation of dynamic characteristics. To address these issues, this paper proposes a power grid stability analysis method predicated on the Convolutional Neural Network (CNN). In this method, the discrimination of power grid stability is modeled as a binary classification problem. By performing preprocessing operations on the input data, such as outlier cleansing based on the 3 criterion and min-max normalization, the CNN's convolutional layers, pooling layers, and fully connected layers are utilized to extract features and classify the states. The case study is founded on the actual monitoring data of a regional power grid. The results demonstrate that the accuracy of the proposed method on both the training set and the test set is remarkably superior to that of the Fully Connected Neural Network (FNN). This method can offer effective technical support for the safe operation of the power grid.

The role of smart meters in the smart cities is becoming increasingly important. These smart meters are always connected to the network and this connectivity may leads to security risks. And, blockchain (BC) technology has recently received a lot of attention due to its data security stability and a distributed platform. BC has been applied to a plethora of different applications. However, BC requires high performance to be applied directly to a smart meter, which is an Internet of Things (IoT) device with low memory and slow CPU computation power. And, it cannot be scale up easily due to the nature of the distributed system. In order to compensate for this shortcoming, we propose a lightweight smart meter framework using BC technology, which is scalable and requires low computation power. In this proposed framework, data blockchain (dBC) and meta blockchain (mBC) are divided, and smart meters are divided into small groups to support scalability. The two proposed grouping algorithms are a delay-aware grouping algorithm that considers network delay and a random grouping algorithm that can improve security with a randomness. We also propose a new Enhanced Privacy Piggy Bank Protocol (EPPB) using bilinear pairing of cryptographic keys to authenticate the communicating parties for smart meters. We presented simulation results and security analysis to verify our proposed framework. From this simulation results, we can see that even if the number of smart meters increases, the performance is not affected.

This contribution addresses the first assessment of the impact of geomagnetically induced currents (GIC) on the 400‐kV power grid of Mexico. As an initial approach, we modeled GIC using a uniform conductivity for the entire Mexican territory and spatially uniform geomagnetic disturbance. Power grid data were provided by the electric operator of Mexico; the geophysical data were inferred from the main features of Mexican geology. We calculate the power grid response during four geomagnetic storms from Solar Cycles 23 and 24 (i.e., 15 July 2000, 20 October 2003, 17 March 2015, and 7 September 2017), as well as during an extreme scenario (a Carrington‐like event). The results show that the Mexican power grid can be affected by three‐phase GIC ranging from 20 to 75 A during geomagnetic disturbances. According to the model, sites located in coastal areas or close to the edges of the network can experience large GIC during time intervals between 3 and 10 hr, depending on the intensity of the geomagnetic disturbance. It is an interesting result that these sites are of the major economic and strategic significance for the country. In the case of a Carrington‐like event, the power grid could be affected by GIC ranging between 25 and 150 A under a uniform 1 V/km EW geoelectric field. Such an event might produce significant distortions in the grid hardware (i.e., transformers and static VAR compensators), potentially leading to widespread damage.

Over the last decade, the Common Information Model (CIM), as specified by IEC 61970 / 61968, has become an important document format for the storage of power grid data. Its object-oriented design makes it easily maintainable and extensible for many use cases in the energy sector. As a result, more and more power grid analysis and simulation tools allow the import and export of CIM based power grid data. Unfortunately, many of them are proprietary and therefore not convenient in the research area since their component models and numerical back-ends often cannot be modified by the user. Thus, open-source alternatives are in demand, such as simulation environments based on the popular modeling language Modelica. Therefore, this paper presents our approach of a template based CIM to Modelica converter. The usage of templates makes it easily adaptable for the generation of Modelica system models targeting arbitrary Modelica libraries. The presented approach is implemented in an open-source project called CIMverter, evaluated on a real-world case with two Modelica power system libraries, and validated against a proprietary simulation tool.

With the development of smart grid, the load data in power grid gradually presents the characteristics of high-dimensional and multivariable, which is difficult to deal with by traditional clustering methods. This paper adopts the improved deep embedded clustering algorithm (IDEC) to extract and cluster the characteristics of daily load curve. In this method, the dimension reduction feature of the daily load curve is extracted by using the encoder part of the autoencoder, which is sent to K-means layer to obtain the initial clustering center; and then the load features are soft allocated; Finally, in order to prevent distorting the embedding space, the reconstruction loss and clustering loss are jointly optimized to obtain the clustering results. The experimental results show that this method can significantly improve the clustering evaluation index.KeywordsIDECDaily load curveAutoencoderJointly optimize

In response to the risk of side channel attacks and the need for data tamper prevention in distributed data storage systems in power grids, a research on side channel attack oriented distributed data tamper prevention storage in power grids is proposed. In order to defend against side channel attacks, a sliding window segmentation method is used to extract key features of distributed storage data in the power grid, and matching measures are calculated to extract key features of distributed data in the power grid. By using the improved MD5 algorithm to calculate the fine-grained data in the core nodes of distributed data encryption in the power grid, the approved re encryption standards are set by the improved MD5 algorithm, and the encryption distance of distributed data in the power grid is adjusted. After completing the above operations, the deep learning structure can be embedded into the initial encryption program to achieve a bidirectional overlap relaxation encryption process, and ultimately achieve tamper proof storage of distributed data in the power grid. The experimental results show that the method proposed in this paper has a high success rate in anti tampering of distributed data in the power grid, and a shorter time consumption for anti tampering storage.

power quality problems such as high line loss, low voltage exist commonly in rural power grid of China due to few supply points, long power transmission and distribution line. Huge fund is required to increase the quantity of supply points in rural power grid; installation of var compensators on line nodes could not solve power quality problems on the whole line. At the same time, as distributed power supply, wind power is also restricted by peak regulation, insufficient line output capacity and other factors in large scale centralized grid-connected output, wind curtailment often occurs. In order to solve the above-mentioned two problems simultaneously, electric power of wind farm may be output via nearby rural power grid line, thus not only utilization rate of wind power is increased, but also power quality of rural power grid is improved.This paper takes an example of a district in Fuxin City, summarizes power quality change data of relevant rural power grid before and after the distributed grid connection of wind farm is made, the analysis shows that distributed grid connection of wind power could improve load flow distribution of the power system, reduce system transmission loss in this district obviously, increase economical efficiency of the system operation effectively. At the same time, effectiveness of static var generator in substation to overcome the impact of such grid connection mode on power grid is verified.

In the day-ahead generation schedule, the rational allocation of spinning reserve (SR) capacity is a necessary means to ensure the safe and reliable operation of the power system. On the basis of analyzing the characteristics of the SR demand of a power grid, the applicability of the traditional reliability indexes is analyzed, and the indexes of the $Tr$ minute power shortage expectation value $(\pmb{EDNS}_{\mathrm{Tr}})$ , the satisfaction probability non-emergency reserve (SPN) and the satisfaction degree of emergency reserve (SDE) are put forward. Based on the historical data of regional power grid, the data requirements and processing methods for the calculation of evaluating the SR capacity in the day-ahead generation schedule are analyzed. Finally, the limited value of the indexes and the SR capacity of a regional grid are evaluated by examples, and that proves the effectiveness of the proposed method in this paper.