Power System Data Research Articles

An integrated planning model in centralized power systems

In the context of centralized electricity markets, we propose an integrated planning model for power pricing and network expansion, which endogenizes the scaling costs from power losses. While the substitutability pattern between pricing and expansion has been overlooked in the power flow optimization literature, this becomes particularly relevant in centralized electricity markets (where the headquarters are enabled to take decisions over a wide range of operational factors). In this paper, we tailor an optimization model and solution approach, that can be effectively applied to large-scale instances of centralized power systems. Specifically, we develop bounds to the optimal operator profit and use them within a mixed-integer linear programming problem, derived from the linearization of an extended power flow model. On the empirical side, we conduct computational tests on a comprehensive power system data set from the Saudi Electricity Company, uncovering the value of the proposed integrated planning. The results reveal the complex substitutability patterns which appear when deciding about integrated operational factors in centralized power systems and support the correctness and efficiency of the proposed resolution mechanism.

Database dynamic update management system for power system

Through the use of smart terminal equipment such as smart meters, the operating data of the entire power system can be collected, and then the collected power big data can be processed and analyzed systematically, thereby real-time monitoring of the power grid; further, combining big data analysis and power system models, Can diagnose, optimize and predict the operation of the power grid, and provide guarantee for the safe, reliable, economical and efficient operation of the power grid. The data sources of the power industry are mainly derived from the power generation, transmission, transformation, distribution, power consumption and dispatching of power production and use of electricity, which can be roughly divided into three categories: one is grid operation and equipment detection or monitoring data; two It is the marketing data of electric power companies, such as data on transaction electricity prices, electricity sales, and customers. Third is the management data of electric power companies. Through the use of smart terminal equipment such as smart meters, the operating data of the entire power system can be collected, and then the collected power big data can be processed and analyzed systematically to achieve real-time monitoring of the power grid; further combining big data analysis and power system models for the power grid Diagnose, optimize and predict the operation, and provide guarantee for the safe, reliable, economical and efficient operation of the power grid.

Research on Application of Deep Learning Based on Mobile Learning in Smart Grid

The rapid economic and social development cannot be separated from the important contribution and guarantee of the power industry. In the development of power industry, substation technology plays a very important role. The development of in-depth learning based on mobile learning provides an effective means for data value extraction. Therefore, it is of great significance to guide power enterprises to make correct decisions by making full use of these data based on the actual power grid and analyzing them in depth to discover and utilize the hidden information. Firstly, the basic framework of in-depth learning is elaborated, and the theoretical basis and technical system of mobile learning are summarized. According to the actual demand of power system, this paper summarizes the key application fields and values of mobile learning-based deep learning in power system data processing based on image data and spatio-temporal data, and puts forward relevant technical development suggestions.

Phasor measuring unit calibration considering topology expansion of electric power utilities

Phasor Measuring Units (PMU) is considered as a feasible alternative for power system data communication and for the preparation of smart grid. To operate multiple PMUs in stable mode, synchronization is a much essential procedure. After examining the concise literature survey on topical PMU applications, this paper step ahead by considering prevailing objectives such as: extension of network topology and PMU calibration considering bias errors. The appropriate deployment of PMUs and intimidating situation influence the reliance on physical configuration. Hence, it is obligatory to extend the topology of the networks. Also, calibrating the PMUs in a definite trail is crucial as the phasor measurements will be corrupted by bias error. This paper proposes a novel framework for bias error detection and calibration of PMU while expanding the topology network. With a whale optimization algorithm based method to improve the accuracy of PMU measurements for enhancing power system state estimation, monitoring and control operations. Case studies on the standard test systems have been conducted to test the efficacy of the intended tool.

Social sensing: towards social media as a sensor for resilience in power systems and other critical infrastructures

ABSTRACT Improving Critical Infrastructure (CI) resilience is a key challenge facing modern society. Increased integration of sensors into infrastructure systems, combined with modern computational capabilities, provides an opportunity to develop novel data-driven methods for improving resilience. Social media serves as a promising data source for such methods, as it has become widely used for information dissemination. This paper aims to demonstrate the value of social media for CI resilience by using this novel data source to model CI behaviors at higher spatiotemporal resolutions than previously shown. We apply this approach, which focuses on statistical analysis and forecasting methods, to a case study of Hurricane Sandy using publicly available Twitter data and power system data for the New York Independent System Operator (NYISO). We find evidence of statistically significant correlations between Twitter and power system data, and develop models for forecasting future behaviors in the NYISO power system using these data.

Widely linear least mean kurtosis‐based frequency estimation of three‐phase power system

We propose a widely linear (augmented) least mean kurtosis (WL-LMK) algorithm for robust frequency estimation of three-phase power system. The negated kurtosis-based algorithms are most celebrated for their computational efficiency and strong robustness against wide range of noise signals which can overcome the inherent performance degradation faced by the well-known minimum mean square error-based algorithms in noisy environments. The proposed widely linear LMK estimation technique utilises all second-order statistical information in the complex domain C for processing of non-circular complex-valued signals. The three-phase power system signal, modelled through Clarke's αβ transformation, is circular for balanced and non-circular for unbalanced systems, based on which, the proposed WL-LMK algorithm is able to achieve improved frequency estimation under unbalanced and other abnormal system conditions. Its estimation performance is evaluated for several cases that encounter in the day-to-day operation of power system. It is observed from simulation studies of synthetic and real-world power system data that the proposed WL-LMK algorithm exhibits superior estimation performance as compared to the standard linear complex LMK (CLMK) and the widely linear least mean square (WL-LMS) algorithms.

The architecture and key technology of fault inversion systemfor Hybrid UHV AC/DC power system

The architecture and key technology of fault inversion system for Hybrid UHV AC/DC power system are introduced in this paper. The complete and accurate fault inversion model of power grid is formed by integrating the off-line model of Power System Data Base(PSDB) with the real-time online model of Smart Grid Dispatching and Control System(D5000). This model not only solves the problem that the real-time online model lacks transient parameter, but also overcomes the difficulty that the off-line model cannot reflect the real-time operation condition of the power grid, Therefore, this model meets the requirements of timeliness and accuracy of fault inversion calculation. Based on this model, the simulation calculation and analysis of power grid are carried out, and the changes of electricity volume in the process of power grid fault are simulated accurately. The system has been installed successfully and put into use in the simulation centre of the State Grid. The fault inversion analysis is carried out for State Grid Dispatch Centre and East China power grid. Besides, the scientific validity of the proposed scheme is verified.

Enhanced data communication models for real‐time power system monitoring in a distributed platform

ABSTRACTThe novelty behind this research is to develop enhanced models for real‐time power system monitoring in a platform‐independent and distributed platform. Distributed models for power system applications have been implemented using Remote Method Invocation (RMI), Common object Request Broker Architecture and. NET remoting technologies. The interoperability among the heterogeneous power system clients in a distributed platform has been achieved through XMLization of power system data which provides meaningful solution to the legacy issues associated with power systems. A RMI‐based model for real‐time power system monitoring in a distributed platform had been implemented and discussed in detail. A component model for real‐time power system monitoring has also been proposed and implemented to increase the reusability and scalability of the system. A location transparent model has been developed using Common object Request Broker Architecture with RMI‐Internet Inter‐Orb Protocol for load flow monitoring and the idea is extended to solve real‐time economic load dispatch and dynamic security monitoring. The detailed monitoring on performance of the proposed models has been done with load flow monitoring, dynamic security monitoring and economic load dispatch problems. A. NET remoting architectural model has been developed for real‐time economic load dispatch in a distributed platform. This research paper explores various issues in deploying power system applications in a distributed platform. It will serve the purpose of stimulating interest among power engineers and also will be extremely useful for electric power utilities.

On the use of dynamic state estimation for the optimal control of power system

Investigations of the application of classical methods of dynamic state estimation on the data of a real power system for the purposes of optimal control have been carried out. A modern feature of state estimation for a large power system is that data from the SCADA system are fed to the calculation subsystem with a fairly small frequency. It is shown that the use of classical methods of dynamic state estimation for such problems is limited. The study was carried out using the ergodic theory of a dynamical system.

О достоверности измерений в электроэнергетической системе с накопителями электроэнергии

The validity and completeness of data in an electric power system are achieved by state estimation, which is aimed at filtering measurement errors and calculating unmeasured operating parameters. Highquality estimates can be obtained if there are no gross errors in measurements, which are revealedand eliminated at the stage of detecting erroneous data, provided the measurements are redundant. With the advent of renewable energy sources, many new facilities have been integrated into the power system, which are not fully equipped with measuring devices, due to which the parameters of such facilities are measured with insufficient redundancy. Under such conditions, and especially when it is difficult to predict the power output produced by stochastic and intermittent sources (wind and solar energy), the conventional validation methods do not always work in a proper way. A new method for revealing erroneous data is proposed, which is based on analyzing the strategy for controlling a storage battery in a power system containing wind farms. A fivenode test system containing a wind farm and a highcapacity storage battery with a simple control strategy (load switching) is considered. It is shown that by using the proposed method it is possible to detect gross errors in active power measurements under the conditions of low information redundancy.

Visualizing the Impacts of Renewable Energy Growth in the U.S. Midcontinent

This paper presents the visualization approach for enhancing analyses of the ongoing Renewable Integration Impact (RIIA) Assessment by the Midcontinent Independent System Operator (MISO), and brings the following contributions. First, this paper details the customization of state-of-the-art, open-source visualization code to complement existing power system visualization tools and data analytic processes used in MISO. Second, this paper describes how MISO integrates this visualization tool within a novel and holistic process for studying renewable integration issues. This visualization tool provides additional insights for RIIA from multiple aspects with fine spatial-temporal granularity, including the comparison of thermal generation performance at different renewable integration scenarios, improved understanding of complex interactions between regions within MISO, the effects of transmission upgrades on curtailment reduction using optimization techniques, and the improved verification of MISO's simulation results. Lastly, we make the updated visualization code package publicly available.

Deep learning in power systems research: A review

With the rapid growth of power systems measurements in terms of size and complexity, discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction, demand response, energy disaggregation, and state estimation is considered a crucial challenge. In recent years, deep learning has emerged as a novel class of machine learning algorithms that represents power systems data via a large hypothesis space that leads to the state-of-the-art performance compared to most recent data-driven algorithms. This study explores the theoretical advantages of deep representation learning in power systems research. We review deep learning methodologies presented and applied in a wide range of supervised, unsupervised, and semi-supervised applications as well as reinforcement learning tasks. We discuss various settings of problems solved by discriminative deep models including stacked autoencoders and convolutional neural networks as well as generative deep architectures such as deep belief networks and variational autoencoders. The theoretical and experimental analysis of deep neural networks in this study motivates long- term research on optimizing this cutting-edge class of models to achieve significant improvements in the future power systems research.

Building Highly Detailed Synthetic Electric Grid Data Sets for Combined Transmission and Distribution Systems

This paper introduces a methodology for building synthetic electric grid data sets that represent fictitious, yet realistic, combined transmission and distribution (T&D) systems. Such data sets have important applications, such as in the study of the wide-area interactions of distributed energy resources, in the validation of advanced control schemes, and in network resilience to severe events. The data sets created here are geographically located on an actual North American footprint, with the end-user load information estimated from land parcel data. The grid created to serve these fictional but realistic loads is built starting with low-voltage and medium-voltage distribution systems in full detail, connected to distribution and transmission substations. Bulk generation is added, and a high-voltage transmission grid is created. This paper explains the overall process and challenges addressed in making the combined case. An example test case, syn-austin-TDgrid-v03, is shown for a 307236-customer case located in central Texas, with 140 substations, 448 feeders, and electric line data at voltages ranging from 120 V to 230 kV. Such new combined test cases help to promote high quality in the research on large-scale systems, particularly since much actual power system data are subject to data confidentiality. The highly detailed, combined T&D data set can also facilitate the modeling and analysis of coupled infrastructures.

육지계통 HVDC 확대와 DC Tap 설정에 따른 융통조류 여유량 산정에 관한 연구

The load continues to be concentrated in the metropolitan area, and the power generation is reduced in the metropolitan area, and the power generation in the non-metropolitan area is increased, so that power transmission is inevitably made through interface lines. In this environment, it is necessary to secure additional transmission lines because existing interface lines have reached the limit to transmit large-scale power. KEPCO has installed a number of HVDC alternatives, which will affect the determination of the limit of the interface power flow that is the basis for operating the system. In this paper, how to calculate the interface flow limit in this environment is described. According to DC tap setting in HVDC facilities, the interface power flow margin changes are monitored. It also discusses how to do with FV(Flow-Voltage) analysis in the future. The simulations were performed using the 8th planned Korean power systems data.

Open data for electricity modeling: Legal aspects

Power system modeling is data intensive. In Europe, electricity system data is often available from sources such as statistical offices or system operators. However, it is often unclear if these data can be legally used for modeling, and in particular if such use infringes intellectual property rights. This article reviews the legal status of power system data, both as a guide for data users and for data publishers. It is based on interpretation of the law, a review of the secondary literature, an analysis of the licenses used by major data distributors, expert interviews, and a series of workshops. A core finding is that in many cases the legality of current practices is doubtful: in fact, it seems likely that modelers infringe intellectual property rights quite regularly. This is true for industry analysis but also academic researchers. A straightforward solution is open data – the idea that data can be freely used, modified, and shared by anyone for any purpose. To be open, it is not sufficient for data to be accessible free of cost, it must also come with an open data license, the most common types of which are also reviewed in this paper.

Big Data Issues in Smart Grids: A Survey

The smart power systems are based upon information and communication technologies, which lead to a deluge of data originating from various sources. To address these challenges concerning accumulated voluminous data, big data analysis in smart power systems is inevitable. This article comprehensively surveys the literature related to the big data issues in smart power systems. The background and motivation of the big data paradigm in smart power systems are first provided, and then the major issues related to the architectures, the key technologies, and standardizations of big data analytics in smart power systems are analyzed. Also, the potential applications of big data in smart power systems based upon the state-of-the-art research are highlighted. Finally, the future issues and challenges of the big data issues in modern power systems are discussed.

Research and Design of Smart Grid Monitoring System Based on Cloud Computing

Real-time data of power system is an important data for power system equipment measurement and acquisition. These data are important basis for analyzing power system stability, predicting grid load and power equipment failure and aging, and are the data that must be monitored for grid operation. Aiming at the problem of massive monitoring data access and processing in smart grid monitoring system, this paper proposes a smart grid monitoring system based on cloud computing framework by comprehensively utilizing geographic information technology, network communication technology and distributed database technology. The system’s workflow is more efficient and reliable for information processing than traditional database models and existing methods.

Analysis of the Jeju Island Power System with an Offshore Wind Farm Applied to a Diode Rectifier HVDC

The Jeju Island power system consists of two-unidirectional high voltage direct current transmission systems (HVDC), thermal power plants, and renewable energy sources. The local government’s policy states that a 100 MW offshore wind farm should be constructed in the future. Due to the small size and sensitivity of the Jeju Island power system, power system analysis must be carried out before the installation of the new facility. Therefore, the objective of this study was to analyze the Jeju Island power system with a new wind farm applied to uncontrolled diode rectifier HVDC. Although there are many studies about the grid connection method of offshore wind farms, its small grid connection analysis has been rarely investigated, especially in the diode rectifier HVDC method. Diode rectifier HVDC is a new grid connection method for offshore wind farms, which reduces the costs and increases the reliability of the offshore platform. To verify the accuracy and effectiveness of simulation models, steady and transient state scenarios were conducted using the PSCAD/EMTDC program. First, the model of the Jeju Island power system without a new wind farm was compared with measured power system data. Second, its power system connected with a diode rectifier HVDC was simulated in a steady state. Finally, disconnection and single line ground fault occurred at the offshore wind farm, respectively. From the simulation results, the grid stability of the Jeju Island power system was confirmed considering a new facility.

The Substation Internet of Things System based on Edge Computing

In response to the demand of the development of energy Internet for the comprehensive perception and wide interconnection of massive data of power system, the concept of ubiquitous power Internet of things(UPIoT), which comes from the deep integration of Internet of things technology and smart grid, has been put forward, becoming an important content and key link in the transformation and upgrading of power grid enterprises. The centralized computing mode previously adopted by smart grid can no longer meet the needs of large-scale computing and rapid response, so the ubiquitous power Internet of things should adopt the operation mode combining centralized computing and distributed computing. Firstly, this paper discusses the basic concepts and main characteristics of ubiquitous power Internet of things. Based on edge computing, this paper constructs the basic architecture of substation internet of things composed of perception layer, network layer, platform layer and application layer. Secondly, it discusses the key technologies in the construction of substation internet of things, and finally analyzes the specific application of edge computing in the construction of substation internet of things.

Utilization of Energy Storage System for Frequency Regulation in Large-Scale Transmission System

As the penetration rate of renewable enery resources (RES) in the power system increases, uncertainty and variability in system operation increase. The application of energy storage systems (ESS) in the power system has been increased to compensate for the characteristics of renewable energy resources. Since ESS is a controllable and highly responsive power resource, primary frequency response and inertia response are possible in case of system contingency, so it can be utilized for frequency regulation (FR) purposes. In frequency regulation, reduction of the Rate of Change of Frequency (RoCoF) and increase the frequency nadir by improving the response characteristics are important factors to secure frequency stability. Therefore, it is important to control ESS with proper parameters according to changing system situation. In this paper, we propose a method to calculate and apply a frequency droop, which is basically required according to the power system condition based on swing equation and effective inertia assessment. In addition, a method to estimate RoCoF droop according to the correlation with frequency by estimating the systematic inertia in the current situation is proposed. The case study for verification of the proposed method was performed through dynamic simulation using actual Korean power system data. The results show that the proposed method is more effective than the governor-free of the conventional thermal generator and conventional droop control-based FR-ESS.