Smart Grid Data Research Articles

Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation

The edge of the smart grid has a massive number of power and resource-constrained interconnected devices. Mainly, smart meters report power consumption data from consumer homes, industrial buildings, and other connected infrastructures. Multiple approaches were proposed in the literature to preserve the privacy of consumers by altering the data via additive noise, masking, or other data obfuscation techniques. A significant body of work in the literature employs differential privacy methods with constraining predefined parameters to achieve the optimal trade-off between privacy and utility of the data. However, billing accuracy can be degraded by using such additive noise techniques. We propose a differentially-private model that perturbs data by adding noise obtained from a virtual chargeable battery, while maintaining billing accuracy. Our model utilizes fog-computing data aggregation with lightweight cryptographic primitives to ensure the authenticity and confidentiality of data generated by low-end devices. We describe our differentially-private model with flexible constraints and a dynamic window algorithm to maintain the privacy-budget loss in infinitely generated time-series data. Our experimental results show a possible decrease in data perturbation error by 51.7% and 61.2% for smart meters and fog-computing data aggregators perturbed data, respectively, compared to the commonly used Gaussian mechanism.

Supervised learning‐based demand response simulator with incorporation of real time pricing and peak time rebate

Demand response (DR) program empowers the dynamic prices to actively optimize the consumption. This optimized consumption plays a vital role in resolving the complex operation and reliability issues in the electricity market. The human behavior aspect of consumers explained by several models that have been reported in the literature. These models depend on the classical utility factor. The effect of price on the consumer's decision in the field of energy efficiency and reduction of consumption based on behavioral characteristics are two important aspects of DR programs. In absence of such characteristics, results become non-viable. In this paper, the footprint of two time-based DR programs is explored on the peak reduction namely; Real-Time Pricing (RTP) and Peak Time Rebate (PTR). Artificial Neural Network (ANN) based topologies for two DR programs are proposed. The proposed topologies employ variation in demand and price, subsequently for simulating an online DR simulator. Demand before and after the RTP and PTR was calculated and compared with four ANN-based DR topologies namely; Radial Basis Function Neural Network-Demand Response (RBFN-DR), Feedforward Backprop-Demand Response (FFBP-DR), Layer Recurrent-Demand Response (LR-DR), and Generalized Regression-Demand Response (GR-DR). The proposed models are tested on three test cases. The first case tested on hourly data of New England ISO of Connecticut on August 18, 2014, the second case tested on hourly data of the same system on August 18, 2020, and the third case tested on hourly residential data of test smart grid. By assessing the results from all three test cases, depicted that RBFN-DR proved its efficacy by giving better results for both price-based programs namely; RTP and PTR.

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

Cost-Effective Data Aggregation Method for Smart Grid

Finding a more efficient use of energy is an important problem that needs attention. Compared with the traditional power grid, a smart grid can monitor users’ electricity situation and electricity consumption instantly. However, it involves many problems of deploying network equipment. Consequently, it is vital to promote smart grids by collecting data from smart meters efficiently and keeping costs low. In this article, we propose a two-stage method of data collection for smart grids. The main contribution of this paper is to lower the number of data aggregation points (DAPs) so that the cost can be reduced. By using the K-means method, an entire smart grid can be divided into many smaller parts. In addition, the needs of transmitting and receiving data in the entire smart grid can be met by installing the least number of DAPs. Finally, the simulations show that the proposed two-stage method of data collection can use fewer DAPs to collect data than other methods which use one-stage methods, so the proposed scheme is more cost-effective.

Enhanced RSA key encryption application for metering data in smart grid

Purpose Digitizing of the electrical power grid promotes the advantages of efficient energy management alongside the possibilities of major vulnerabilities. A typical inadequacy that needs critical attention to ensure the seamless operation of the smart grid system remains in the data transmission between consumer premises smart devices and the utility centres. Many researches aim at establishing security protocols to ensure secure and efficient energy management resulting in perfect demand–supply balance. Design/methodology/approach In this paper, the authentication of the smart meter data has been proposed with enhanced Rivest–Shamir–Adleman (RSA) key encryption using an efficient way of generating large prime numbers. The trapdoor one-way function applied in the RSA algorithm makes it almost impossible for the reverse engineering attempts of cracking the key pair. Findings The algorithm for generating prime numbers has been tested both with the convention method and with the enhanced method of including a low-level primality test with a first few hundred primes. The combination of low-level and high-level primality tests shows an improvement in execution time of the algorithm. Originality/value There is a considerable improvement in the time complexities when using the combination method. This efficient generation of prime numbers can be successfully applied to the smart meter systems, thereby increasing the strength and speed of the key encryption.

Research on the Structure of Distribution Network Voltage Smart Grid Information System Based On Flexible DC

Since the concept of smart grid was defined, it has aroused the interest of many researchers, and many countries have also regarded smart grid as a key research project. Because it is the field of power grid research in the future, the structure of the smart grid becomes more complex with the deepening of research, the scale becomes larger, and the data contained in the system is bound to increase rapidly. Flexible DC grid technology has the characteristics of high reliability, good fault tolerance and good scalability, and can be used to solve some problems encountered in the application of smart grids. Based on the structure of the smart grid information system, this paper studies the characteristics of the smart grid data, as well as some safety issues encountered in the data, applies flexible DC power distribution technology, designs a new smart grid equipment management architecture, and System implementation was carried out. Through experimental verification, the new architecture proposed in this paper has high feasibility and safety. Finally, the realization of the management system function based on the new equipment management architecture and the analysis of the system function effect are given.

Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model

The power consumption model can be represented in multiple dimensions, and it is proliferating to include structured and unstructured data. Dealing with such heterogeneous data and analyzing it in real-time is an ongoing challenge in the energy sector. Moreover, converting these data into useful information remains an open research area. This study focuses on modeling realistic and efficient power consumption data management in the heterogeneous environment for the Iraq energy sector and suggested a novel hybrid load forecasting model. The proposed system is named the Power Consumption Information and Analytics System (PIAS), which can perform various roles such as data acquisition from mechanical and smart meters, data federation, data management, data visualization, data analysis, and load forecasting. The proposed system has a four-tier framework (Data, Analytics, Application, and Presentation). Each layer is discussed in detail in this study to overcome the anticipated challenges. Furthermore, this study discusses the proposed system by applying two case studies. The first case study discusses power consumption data management, while the second introduces a novel hybrid load forecasting model using Fuzzy C-Means clustering, Auto Regressive Integrated Moving Average (ARIMA), and Gradient Boosted Tree Learner. The dataset used in this forecasting is based on a 1-year duration dated 1 January 2019 to 31 December 2019, on an hourly basis (365 * 24) for the Baghdad governorate. The results showed high accuracy in load forecasting with improved error rates (MAPE, MAE, and RMSE) achievements in comparison with other evaluated models such as standalone ARIMA and Gradient Boosted Trees methods.

Demand response program for smart grid through real time pricing and home energy management system

Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer.

Discussion on the Problems in the Application of Smart Grid and Big Data Technology

Discussion on the Problems in the Application of Smart Grid and Big Data Technology

Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

Weighted entropy and modified MDL for compression and denoising data in smart grid

Denoising and compression of power system data from the measurement and monitoring instruments in smart grid is an important topic. Compression is essential for the transmission and storage of a big amount of smart grid data through the communication channels and denoising is essential as the noise produces erroneous results in further analysis of the power system data. This paper presents a technique for denoising and lossy compression of data in smart grid communication using wavelet packet transform (WPT). The paper proposes weighted entropy to calculate the best basis of a signal from the complete WPT. Then the paper presents the application of the proposed best basis algorithm for the denoising and compression of the signal. A modified minimum description length (MDL) algorithm is proposed that allows to adjust the threshold for the denoising that does not require the use of a noise estimation formula. A set of real power system data recorded by various measurement and monitoring instruments in smart grid is utilized to assess the effectiveness of the proposed technique. Both the denoising and compression performance on the simulation show promising results such that the proposed algorithm could be considered as a potential technique for the real time noise removal and compression application. Results from the comparison are presented with Shannon entropy - MDL based WPT, wavelet transform (WT) based method, fuzzy transform and Matlab’s state of art technique.

Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning

Smart Grid uses electricity and information flows to set up a highly developed, fully automated, and distributed electricity grid system. To identify the reliability of work and availability, cyber attacks detection in the smart grids play a significant role. This paper highlights the integrity of false data cyber-attacks in the physical layers of smart grids. As the first contribution, the Proposed True Data Integrity provides an attack exposure metric through an Agent-Based Model. Next, the research focuses on the decentralization of Data Integrity Security in the system with an Agent-based approach. Finally, the productivity and efficiency of the developed modeling techniques are experimentally evaluated and compared with the existing state-of-the-art supervised deep-learning models. The obtained results of the studies have shown the improved false data detection accuracy of 98.19% through replay cyber-attacks using the Artificial Feed-forward Network. Based on the research findings, deep neural network can be used to assess cyber data in smart grids to detect malware incidents and attacks.

A cybersecurity user authentication approach for securing smart grid communications

The smart grid provides technology that offers power solutions by integrating wireless access, renewable energy resources, smart meters, and smart appliances. Energy harvesting and distribution is automated through remotely operated commands using information and communication technology. Common issues in integrating power grid with technology include security threat that reduces the expected assimilation performance. In the present work, a cybersecurity-assisted authentication method for smart grids is introduced to overcome false data flow. This method pre-estimates the energy requirement of the meters, depending on previously acquired information. Authentication-dependent security is provided according to the pre-estimated energy requirement and distribution manner. Variations in smart grid data for energy allocation are monitored based on network and end-user consumption until the users' current connection time. This method provides individual authentication for power-sharing and user verification, thereby improving false data's detection rate. Results show that the proposed method required less detection time (4.67 s) without increasing the overload for end-users.

Energy Monitoring Platform: AHigh-Performance Smart Meter Data Analytics Engine

4th Industrial revolution or Industry 4.0 is a concept of factories in which machines are augmented with wireless connectivity and sensors, connected to a system that can visualize the entire production line and make decisions on its own. To remain competitive, Industries or factories are one of the major factors leading to the consumption of energy. However, these industries are facing two obstacles. The first one is the low availability of energy and the other is increased cost of the current available energy. Different alternatives like Generators and Inverters have their limitations when it comes to cost and supply. The industries need to optimize the usage to stay price in check. It has become a necessity for the industries reduce energy consumption for a comparable level of activity to observe it. The platform is the answer to these problems. It is a solution that puts the client/user in control of their energy consumption by enabling live monitoring of the energy smart grid data via a dedicated online platform.

Sliding window-based LightGBM model for electric load forecasting using anomaly repair

Smart grids have attracted much attention recently for their potential to reduce power system operating and management costs. Smart grid core components include energy storage, renewable energy source(s), and smart meters. Smart meters collect diverse data regarding smart grid operation, which can lead to inefficient operation if the meter data are damaged or tampered with during collection or transmission. Therefore, it is important to identify abnormalities in smart grid data and process them accordingly. Various anomaly detection models have been proposed using statistical methods, but they cannot detect some anomaly patterns accurately, and the models generally did not consider repair strategies for the detected anomalies. Anomaly repair should be included with model training to improve forecasting performance. This paper proposes a robust sliding window-based LightGBM model for short-term load forecasting using anomaly detection and repair. We first show how to detect anomalies using a variational autoencoder and then how they can be repaired using a random forest method. Finally, we verify that the proposed sliding window-based LightGBM achieves superior forecasting performance in combination with anomaly detection and repair.

Overall Design of Artificial Intelligence Applications Based on the Electric Power Big Data Platform

Electric power big data is a new cross-domain data that appears with the development of smart grid and big data technology, and it is a specific application of big data technology in modern smart grid. This article mainly introduces the overall design of artificial intelligence applications based on the electric power big data platform. This article will explain in detail the construction process of the electric power big data platform and the overall design of artificial intelligence applications. This article introduces the specific significance of power big data as the main technical department of artificial intelligence, from the main links of demand analysis, architecture design, detailed design, and system implementation to the overall process of artificial intelligence application design from demand to realization. First, the overall requirements of the system are analyzed in detail from two aspects: functional requirements and non-functional requirements, and then the business architecture, application architecture, data architecture and technical architecture of the system are designed. Taking the functional modules of the electric power big data platform as an example, the detailed design of the functions is explained from the point of view of the class diagram, sequence diagram, and data model. This paper uses a self-organizing patch antenna calculated based on the power big data platform to search for the lowest voltage standing wave ratio of 1.0068 and the corresponding return loss of 45dB. However, regarding the antenna bandwidth, the self-organizing patch antenna still has the characteristics of a traditional patch antenna with a relatively narrow working bandwidth, and the working bandwidth of 13dB is less than 12%.

Application Research of Big Data Real-time Processing Technology in Smart Grid

Big data is valuable, and big data processing technology can efficiently process massive data and get the value in big data, so the research and application of big data technology become popular nowadays. Smart grid is one of the most potential field for application of big data technology, applying big data technology to smart grid can improve the reliability and economy of smart grid. The operating conditions of power system changes rapidly, therefore, real-time processing technology became the focus of application field. This paper reviewed the data sources, data characteristics and data application scenarios of big data in smart grid, analyzed data processing modes of big data processing technology: batch processing and stream processing, and compared the realization methods of real-time big data processing, summarized the application of big data real-time processing technology in smart grid, and the challenges in the application, and finally, prospected the application of real-time big data processing technology in smart grid.

Smart Grid Security Applied to the Brazilian Scenario: A Visual Approach

Many countries are experiencing a transformation from the traditional generation power system into a smart grid which incorporates communication and data processing technologies. New services become possible and are important to improve the system operation, but there is a large increase in data gathering. This new paradigm increases the complexity of the network and brings new security issues. This article simplifies the understanding of security in power networks by surveying one hundred and eight recent studies in smart grid security, mapping graphically the type of risk they represent to their location in the smart grid, organizing them by the lifecycle of smart grid data as proposed by 1, and comparing the ideal case to the current Brazilian scenario.

Fault recognition method of smart grid data acquisition system based on FNN and sequential DS fusion

It is of significant practical importance to ensure the operational safety of the smart grid, which requires real-time fault diagnosis and identifying what causes it based on an enormous amount of data. This article further studies the intelligent fault-identification method based on the combination of multi-machine learning methods on the bases of researching on Fault Diagnosis of Smart Grid Data Acquisition System. Firstly, we should apply statistical analysis and feature extraction for fault data. Then, we can use fuzzy neural network (FNN) to calculate the probability of fault prediction of power distribution stations, manufacturers and operation businesses, and use the membership function to calculate the corresponding fault membership and uncertainty. Secondly, it makes use of Dempster/Shafer (DS) evidence sequential fusion method to realize fault membership fusion, and gives the corresponding decision criteria for failure causes. Thirdly, a fault-identification method of smart grid data-acquisition system is established based on FNN and DS Evidence Fusion. Finally, the experimental results based on the actual operation data of smart grid show that the new method has a very good application effect at fault cause identification.

A Data-sharing Model Based on Blockchain for Power Grid Big Data

In the existing smart grid, wireless sensor networks are widely deployed to monitor the entire grid’s status to ensure the safe, reliable, and efficient operation of the grid. However, due to the storage mode of the infinite sensor network perception data based on the trusted central node in the smart grid, the smart grid exists information security issues such as the central node’s vulnerability to malicious attacks and deliberate data tampering. In response to these information security issues, this paper proposes a data-sharing model based on blockchain technology and digital digest matching algorithm, which has the characteristics of decentralization, security and credibility, non-tampering, and protection of private data. The model uses the consortium blockchain technology to build a smart grid Data Storage Consortium Blockchain (DSCB). The data capability is taken as a unique token through the method of data capability to control the data access to achieve fine-grained access control with a resource as the unit. The capability update is convenient, and the capability withdrawal is supported to ensure the data security sharing between sensor nodes. Security analysis shows that the model can realize safe, effective, and privacy-protected data storage and sharing.