Smart Grid Data Analytics Research Articles

Adaptive Energy Management of Big Data Analytics in Smart Grids

The smart grid (SG) ensures the flow of electricity and data between suppliers and consumers. The reliability and security of data also play an important role in the overall management. This can be achieved with the help of adaptive energy management (AEM). This research aims to highlight the big data issues and challenges faced by AEM employed in SG networks. In this paper, we will discuss the most commonly used data processing methods and will give a detailed comparison between the outputs of some of these methods. We consider a dataset of 50,000 instances from consumer smart meters and 10,000 attributes from previous fault data and 12 attributes. The comparison will tell us about the reliability, stability, and accuracy of the system by comparing the output of the various graphical plots of these methods. The accuracy percentage of the linear regression method is 98%; for the logistic regression method, it is 96%; and for K-Nearest Neighbors, it is 92%. The results show that the linear regression method applied gives the highest accuracy compared to logistic regression and K-Nearest Neighbors methods for prediction analysis of big data in SGs. This will ensure their use in future research in this field.

Incentive Mechanisms for Smart Grid: State of the Art, Challenges, Open Issues, Future Directions

Smart grids (SG) are electricity grids that communicate with each other, provide reliable information, and enable administrators to operate energy supplies across the country, ensuring optimized reliability and efficiency. The smart grid contains sensors that measure and transmit data to adjust the flow of electricity automatically based on supply/demand, and thus, responding to problems becomes quicker and easier. This also plays a crucial role in controlling carbon emissions, by avoiding energy losses during peak load hours and ensuring optimal energy management. The scope of big data analytics in smart grids is huge, as they collect information from raw data and derive intelligent information from the same. However, these benefits of the smart grid are dependent on the active and voluntary participation of the consumers in real-time. Consumers need to be motivated and conscious to avail themselves of the achievable benefits. Incentivizing the appropriate actor is an absolute necessity to encourage prosumers to generate renewable energy sources (RES) and motivate industries to establish plants that support sustainable and green-energy-based processes or products. The current study emphasizes similar aspects and presents a comprehensive survey of the start-of-the-art contributions pertinent to incentive mechanisms in smart grids, which can be used in smart grids to optimize the power distribution during peak times and also reduce carbon emissions. The various technologies, such as game theory, blockchain, and artificial intelligence, used in implementing incentive mechanisms in smart grids are discussed, followed by different incentive projects being implemented across the globe. The lessons learnt, challenges faced in such implementations, and open issues such as data quality, privacy, security, and pricing related to incentive mechanisms in SG are identified to guide the future scope of research in this sector.

Big Data analytics in Smart Grids for renewable energy networks: Systematic review of information and communication technology tools

The industrial and economic development of the industrialized countries, from the nineteenth century, has gone hand in hand with the development of electricity, the internal combustion engine, comp...

Smart grid encounters edge computing: opportunities and applications

• A comprehensive review of interdisciplinary works related to the integration of the edge computing and the smart grid is conducted. • The technology roadmap to implement the edge computing in smart grids from the perspective of the power industry is provided. • A fine-grained taxonomy for application scenarios of the edge computing in smart grids is designed. • The synergistic effect is also assessed: smart grids promotes the sustainable development of the edge computing. Smart grids (SGs) are reforming towards utilizing massive data for operations and services. During this reform, the information and communication technologies (ICTs) play a critical role, especially for the computing model, which determines how data analytics in SG can be executed. Edge computing (EC), a novel computing paradigm innovation, has high potential to help with the digitization of SG. This paper seeks to provide a comprehensive review of interdisciplinary research on EC applications in SG. First, an in-depth analysis of EC, including the definition, architectures, characteristics, and key enabling technologies is conducted from the perspective of SG application requirements. Afterward, this paper systematically explores application scenarios of EC in SG based on its characteristics. Beyond that, the synergistic effect of the integration of EC and SG is also assessed: SG not only benefits from EC but also supports EC to become sustainable. Challenges and open questions are then investigated.

Irregularity Detection in Output Power of Distributed Energy Resources Using PMU Data Analytics in Smart Grids

The output power of distributed energy resources (DERs) may experience irregular fluctuations due to variations of renewable sources, which need to be monitored in order to reliably control the grid. This paper proposes a novel approach for centralized detection of such irregularities based on the time-series analysis of the data reported by phasor measurement units (PMUs). In this approach, a network controller constructs datasets of time-aligned real/reactive powers for different zones. The datasets are transformed into sequences of short-time local outlier probability (ST-LOP) that are analyzed to identify the DER events. The network controller estimates features such as the average duration and the similarity degree that is a measure of spatio-temporal correlation between the DER events. As a use case, event-triggered control of solar photovoltaic (PV) systems with energy storage devices is investigated. The simulation results for the IEEE 123-bus network corroborate the effectiveness of the developed analytics for detection and mitigation of ramp-rate solar power fluctuations. Smart microgrids and active distribution networks can employ the developed analytics to improve a range of diagnostic and control functionalities.

Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. General guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

Big data framework for analytics in smart grids

Smart meters are being deployed replacing conventional meters worldwide and to enable automated collection of energy consumption data. However, the massive amounts of data evolving from smart grid meters used for monitoring and control purposes need to be sufficiently managed to increase the efficiency, reliability and sustainability of the smart grid. Interestingly, the nature of smart grids can be considered as a big data challenge that requires advanced informatics techniques and cyber-infrastructure to deal with huge amounts of data and their analytics. For that, this unprecedented smart grid data require an effective platform that takes the smart grid a step forward in the big data era. This paper presents a framework that can be a start for innovative research and take smart grids a step forward. An implementation of the framework on a secure cloud-based platform is presented. Furthermore, the framework has been applied on two scenarios to visualize the energy, for a single-house and a smart grid that contains over 6000 smart meters. The application of the two scenarios to visualize the grid status and enable dynamic demand response, suggests that the framework is feasible in performing further smart grid data analytics.

Smart grid data analytics framework for increasing energy savings in residential buildings

Abstract Human energy consumption has gradually increased greenhouse gas concentrations and is considered the main cause of global warming. Currently, the building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. This paper presents a framework for smart grid big data analytics and components required for an energy-saving decision-support system. The proposed system has a layered architecture that includes a smart grid, a data collection layer, an analytics bench, and a web-based portal. A smart metering infrastructure was installed in a residential building to conduct an experiment for evaluating the effectiveness of the proposed framework. Furthermore, a novel hybrid nature-inspired metaheuristic forecast system and a dynamic optimization algorithm are designed behind the analytics bench for achieving accurate prediction and optimization of future energy consumption. The main contribution of this study is that an innovative framework for the energy-saving decision process is presented; the framework can serve as a basis for the future development of a full-scale smart decision support system (SDSS). Through the identification of consumer usage patterns, the SDSS is expected to enhance energy use efficiency and improve the accuracy of future energy demand estimates. End users can reduce their electricity costs by implementing the optimal operating schedules for appliances, which are provided by the SDSS.

Stream Computing: Opportunities and Challenges in Smart Grid

Traditional Power Grid is transformed to Smart Grid by utilizing the technological advancements of Information and Communication Technology. In Smart Grid data is flowing between different components of the system. Advanced and online analytic on this massive data is required to trigger instantaneous action for grid operation and management. Traditional Big Data handling techniques store and analyze high volume data with variety, but failed to handle high velocity data. Stream computing can analyze high velocity data with variety which is essentially required in online data analytics. This paper compared different Big Data analysis techniques and reveals the importance of stream computing and its opportunities in Smart Grid data analytics.

Demand Forecasting in Smart Grids

Data analytics in smart grids can be leveraged to channel the data downpour from individual meters into knowledge valuable to electric power utilities and end-consumers. Short-term load forecasting (STLF) can address issues vital to a utility but it has traditionally been done mostly at system (city or country) level. In this case study, we exploit rich, multi-year, and high-frequency annotated data collected via a metering infrastructure to perform STLF on aggregates of power meters in a mid-sized city. For smart meter aggregates complemented with geo-specific weather data, we benchmark several state-of-the-art forecasting algorithms, including kernel methods for nonlinear regression, seasonal and temperature-adjusted auto-regressive models, exponential smoothing and state-space models. We show how STLF accuracy improves at larger meter aggregation (at feeder, substation, and system-wide level). We provide an overview of our algorithms for load prediction and discuss system performance issues that impact real time STLF. ® 2014 Alcatel-Lucent.

Cloud-Based Software Platform for Big Data Analytics in Smart Grids

This article focuses on a scalable software platform for the Smart Grid cyber-physical system using cloud technologies. Dynamic Demand Response (D2R) is a challenge-application to perform intelligent demand-side management and relieve peak load in Smart Power Grids. The platform offers an adaptive information integration pipeline for ingesting dynamic data; a secure repository for researchers to share knowledge; scalable machine-learning models trained over massive datasets for agile demand forecasting; and a portal for visualizing consumption patterns, and validated at the University of Southern California's campus microgrid. The article examines the role of clouds and their tradeoffs for use in the Smart Grid Cyber-Physical Sagileystem.

Smart grid data analytics for digital protective relay event recordings

Information systems and intelligent smart grid data analytics will have a critical role in managing the massive amount of data becoming available in power system substations. Digital protective relays are multi-functional intelligent electronic devices based on microprocessors, which are being installed in substations throughout the power grid. New digital relays are replacing old-fashioned electro-mechanical or solid-state relays, and besides their protective function, they are coming equipped with monitoring capabilities. These monitoring capabilities are creating potential for better observability of power systems, redundancy in measurements, and improved decision-making process when operating the system. This article discusses the implementation requirements for a fully automated data analytics solution that provides data integration, fault analysis, and visualization based on the event data recorded by digital protective relays.