This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

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Voltage Stability Of Electric Power Systems

The world population depends highly on fossil fuels, particularly for transportation and power generation. This dependency leads to oil price increases because of the depletion of fossil fuels. Burning of fossil fuels also increases greenhouse gas emissions that are greatly responsible for global warming. Thus, electric vehicles (EVs) are considered one of the premium solutions in the land transportation system because they can significantly reduce the dependency on crude oil and minimize transportation-related carbon dioxide emissions along with other pollutants. This study presents an extensive review of the three key areas of EV research, namely, EV charging technologies, the various impacts of EVs, and optimal EV charging station (CS) placement and sizing. Several technical publications related to EV charging technologies are highlighted, and the performance comparison of different EV technologies is discussed. A review of literature on these key areas reveals an increasing interest in these topics in the last decade, with the impacts of EV on the electric power system and the optimal placement and sizing of CS issues widely investigated. By providing an overview of these areas, this study demonstrates the current issues and challenges of widespread deployment of EVs in the market as well as the future research direction in this field. A total of 185 publications are arranged and appended for quick referencing.

A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles

Traditional power grids are being transformed into smart grids (SGs) to address the issues in the existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability, and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution, and utilization systems. SGs employ various devices for the monitoring, analysis, and control of the grid, deployed at power plants, distribution centers, and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation, and the tracking of such devices. This is achieved with the help of the Internet of Things (IoT). The IoT helps SG systems to support various network functions throughout the generation, transmission, distribution, and consumption of energy by incorporating the IoT devices (such as sensors, actuators, and smart meters), as well as by providing the connectivity, automation, and tracking for such devices. In this paper, we provide a comprehensive survey on the IoT-aided SG systems, which includes the existing architectures, applications, and prototypes of the IoT-aided SG systems. This survey also highlights the open issues, challenges, and future research directions for the IoT-aided SG systems.

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Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

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Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Model-based analysis tools, built on assumptions and simplifications, are difficult to handle smart grids with data characterized by volume, velocity, variety, and veracity (i.e., 4Vs data). This paper, using random matrix theory (RMT), motivates data-driven tools to perceive the complex grids in high-dimension; meanwhile, an architecture with detailed procedures is proposed. In algorithm perspective, the architecture performs a high-dimensional analysis and compares the findings with RMT predictions to conduct anomaly detections. Mean spectral radius (MSR), as a statistical indicator, is defined to reflect the correlations of system data in different dimensions. In management mode perspective, a group-work mode is discussed for smart grids operation. This mode breaks through regional limitations for energy flows and data flows, and makes advanced big data analyses possible. For a specific large-scale zone-dividing system with multiple connected utilities, each site, operating under the group-work mode, is able to work out the regional MSR only with its own measured/simulated data. The large-scale interconnected system, in this way, is naturally decoupled from statistical parameters perspective, rather than from engineering models perspective. Furthermore, a comparative analysis of these distributed MSRs, even with imperceptible different raw data, will produce a contour line to detect the event and locate the source. It demonstrates that the architecture is compatible with the block calculation only using the regional small database; beyond that, this architecture, as a data-driven solution, is sensitive to system situation awareness, and practical for real large-scale interconnected systems. Five case studies and their visualizations validate the designed architecture in various fields of power systems. To our best knowledge, this paper is the first attempt to apply big data technology into smart grids.

A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory

The operating status of power systems is influenced by growing varieties of factors, resulting from the developing sizes and complexity of power systems. In this situation, the model-based methods need to be revisited. A data-driven method, as the novel alternative on the other hand, is proposed in this paper. It reveals the correlations between the factors and the system status through statistical properties of data. An augmented matrix as the data source is the key trick for this method and is formulated by two parts: 1) status data as the basic part; and 2) factor data as the augmented part. The random matrix theory is applied as the mathematical framework. The linear eigenvalue statistics, such as the mean spectral radius, are defined to study data correlations through large random matrices. Compared with model-based methods, the proposed method is inspired by a pure statistical approach without a prior knowledge of operation and interaction mechanism models for power systems and factors. In general, this method is direct in analysis, robust against bad data, universal to various factors, and applicable for real-time analysis. A case study based on the standard IEEE 118-bus system validates the proposed method.

A Correlation Analysis Method for Power Systems Based on Random Matrix Theory

Model-based analysis tools, built on assumptions and simplifications, are difficult to handle smart grids with data characterized by 4Vs data. This paper, using random matrix theory (RMT), motivates data-driven tools to perceive the complex grids in highdimension; meanwhile, an architecture with detailed procedures is proposed. In algorithm perspective, the architecture performs a high-dimensional analysis, and compares the findings with RMT predictions to conduct anomaly detections. Mean Spectral Radius (MSR), as a statistical indicator, is defined to reflect the correlations of system data in different dimensions. In management mode perspective, a group-work mode is discussed for smart grids operation. This mode breaks through regional limitations for energy flows and data flows, and makes advanced big data analyses possible. For a specific large-scale zone-dividing system with multiple connected utilities, each site, operating under the group-work mode, is able to work out the regional MSR only with its own measured/simulated data. The large-scale interconnected system, in this way, is naturally decoupled from statistical parameters perspective, rather than from engineering models perspective. Furthermore, a comparative analysis of these distributed MSRs, even with imperceptible different raw data, will produce a contour line to detect the event and locate the source. It demonstrates that the architecture is compatible with the block calculation only using the regional small database; beyond that, this architecture, as a data-driven solution, is sensitive to system situation awareness, and practical for real large-scale interconnected systems. Five case studies and their visualizations validate the designed architecture in various fields of power systems. To our best knowledge, this study is the first attempt to apply big data technology into smart grids.

Future power grids are fundamentally different from current ones, both in size and in complexity. This trend imposes challenges for situation awareness (SA) based on classical indicators, which are usually model-based and deterministic. As an alternative, this paper proposes a statistical indicator system based on linear eigenvalue statistics (LESs) of large random matrices: 1) from a data modeling viewpoint, we build, starting from power flows equations, the random matrix models (RMMs) only using the real-time data flow in a statistical manner; 2) for a data analysis that is fully driven from RMMs, we put forward the high-dimensional indicators, called LESs that have some unique statistical features such as Gaussian properties; and 3) we develop a 3-D power map to visualize the system, respectively, from a high-dimensional viewpoint and a low-dimensional one. Therefore, a statistical methodology of SA is employed; it conducts SA with a model-free and data-driven procedure, requiring no knowledge of system topologies, units operation/control models, causal relationship, and so on. This methodology has numerous advantages, such as sensitivity, universality, speed, and flexibility. In particular, its robustness against bad data is highlighted, with potential advantages in cyber security. The theory of big data-based stability for online operations may prove feasible along with this line of work, although this critical development will be reported elsewhere.

Xing He

Papers

A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory

A Correlation Analysis Method for Power Systems Based on Random Matrix Theory

A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory

Designing for Situation Awareness of Future Power Grids: An Indicator System Based on Linear Eigenvalue Statistics of Large Random Matrices

The impact of large-scale distributed generation on power grid and microgrids