This article investigates the effects of cyber attacks on the global economical dispatch of vicinity interconnected DC MG clusters. First, it articulates an analytical detection and mitigation strategy integrated with logical operation aided event generators for retaining the feasible operation region of the economic dispatch problem in multiple MG clusters. Second, separate defense actions are designed for leader and follower nodes in a MG, unlike common mitigation action for all nodes in previous works. Third, a variable averaging gain for processing the neighbor’s information at every node is proposed. It aids in distinguishing an actual instance of power generation unit saturation from a cyber attack. The entire system is modelled in a real time digital simulator and the results obtained demonstrate the efficacy of the proposed algorithm for attaining a secure global economic dispatch. It also exhibits an important feature of prioritising resiliency over economics by transitioning from economic dispatch mode to supplying to critical loads under adverse events. Finally, for testing the application of the proposed algorithm to a large-scale system, it is extended to five interconnected MG clusters containing 20 DC-DC power electronic converters.Note to Practitioners—Cybersecurity becomes a critical concern for vicinity connected microgrids as an increasing amount of IoT devices are adopted for communication, monitoring, and operation support purposes. This cyber-physical structure provides an attack surface because adversaries can compromise the physical structure by attacking its dependent cyberspace. For mitigation of the attacks, twining the efforts of securing from both the cyber and the physical testbed is required. As such, this paper provides the owners and operators with in-depth knowledge about the requirement of different control strategies for the leader and follower nodes in the interconnected network of microgrids. It also emphasizes the need for prioritising resilience under adverse events. The technique is simple and user-friendly for implementation in the already existing control strategies. The real-time simulation results prove its effectiveness so that it can be put into practice.

This work proposes a deep graph learning framework to identify, locate, and classify power, cyber, and cyber power events at the distribution system level. The proposed algorithm jointly exploits spatial, temporal, and node-level cyber and physical data features. The developed graph neural network, together with a deep autoencoder, utilizes physical measurements from distribution level phasor measurement units and cyber data from communication network logs. The spatial structure of the synchrophasor measurements and network is incorporated through a weighted adjacency matrix. The temporal structure is incorporated by defining a spatial operation in the gated recurrent unit. This spatio-temporal learning element resides inside a power event detection, localization, and classification module that provides the degree of confidence for an event label. To accurately pinpoint the location of an event to the nearest bus equipped with a measurement unit, a combination of squared error and proximity score is utilized. Also included is a cyber event detection module that employs heteroskedasticity to analyze the significance of various cyber features during different types of attacks. Finally, a dual-bit cyber-power decision table determines the nature of the event. The proposed method is validated on two distribution systems modeled in OPAL-RT/Hypersim with limited phasor measurement units for different possible physical and cyber events. Further analyses include comparison with other state-of-the-art methods and validation in the presence of measurement noise. Our method outperforms existing approaches and achieves an average detection accuracy of 97.97%, F1-score of 96.88%, precision of 96.53%, and recall of 98.57%.

This work presents a new algorithm for detecting and classifying data anomalies in operational measurements using statistical, clustering, and outlier-based approaches. Base detectors explored in this work includes density-based spatial clustering of applications with noise, K-Means, local outlier factor, feature bagging, and robust random cut forests using real distribution system datasets. An ensemble approach is proposed to achieve high detection accuracy and precision compared with any of the base detector and with less dependency on hyperparameter tuning. Also, developed ensemble architecture can integrate additional base detectors. In addition, a simplistic anomaly classification approach is developed, utilizing the clustering concept, while considering the physics of the power distribution systems. The developed schemes are rigorously tested and validated using data from multiple distribution phasor measurement unit devices in the Bronzeville community microgrid, with a diverse set of events and distributed energy resources at dispersed locations. Performance analysis using three test cases are provided to showcase superiority of the proposed approaches.

Industrial electric power grid operation subject to an extreme event requires decision-making by human operators under stressful conditions. Decision making using system data informatics under adverse dynamic events, especially if forecasted, should be supplemented by intelligent proactive control. Power transmission system operation during wildfires requires resiliency-driven proactive control for load shedding, line switching, and resource allocation considering the dynamics of the wildfire and failure propagation to minimize the impact on the system. However, the possible number of line and load switching in an extensive industrial system during an event make traditional prediction-driven and stochastic approaches computationally intractable, leading operators to often use pre-planned or greedy algorithms. In this work, we model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation. Our approach allows the controller to provide setpoints for all generation fleets in the power grid. We evaluate our approach utilizing the IEEE test system mapped onto a hypothetical terrain. Our results show that the proposed approach can help the operator to reduce load outage during an extreme event. It reduces power flow through lines that are to be de-energized, and adjusts the load demand by increasing power flow through other lines.

Timely and accurate detection of events affecting the stability and reliability of power transmission systems is crucial for safe grid operation. This paper presents an efficient unsupervised machine-learning algorithm for event detection using a combination of discrete wavelet transform (DWT) and convolutional autoencoders (CAE) with synchrophasor phasor measurements. These measurements are collected from a hardware-in-the-loop testbed setup equipped with a digital real-time simulator. Using DWT, the detail coefficients of measurements are obtained. Next, the decomposed data is then fed into the CAE that captures the underlying structure of the transformed data. Anomalies are identified when significant errors are detected between input samples and their reconstructed outputs. We demonstrate our approach on the IEEE-14 bus system considering different events such as generator faults, line-to-line faults, line-to-ground faults, load shedding, and line outages simulated on a real-time digital simulator (RTDS). The proposed implementation achieves a classification accuracy of 97.7%, precision of 98.0%, recall of 99.5%, F1 Score of 98.7%, and proves to be efficient in both time and space requirements compared to baseline approaches.

The electric grid operation is constantly threatened with natural disasters and cyber intrusions. The introduction of Internet of Things (IoT)-based distributed energy resources (DERs) in the distribution system provides opportunities for flexible services to enable efficient, reliable, and resilient operation. At the same time, IoT-based DERs comes with cyber vulnerabilities and requires cyber-power resiliency analysis of the IoT-integrated distribution system. This work focuses on developing metrics for monitoring resiliency of the cyber-power distribution system, while maintaining consumers’ privacy. Here, resiliency refers to the system’s ability to keep providing energy to the critical load even with adverse events. In the developed cyber-power distribution system resiliency (DSR) metric, the IoT trustability score (ITS) considers the effects of IoTs using a neural network with federated learning. ITS and other factors impacting resiliency are integrated into a single metric using fuzzy multiple-criteria decision making (F-MCDM) to compute primary-level node resiliency (PNR). Finally, DSR is computed by aggregating PNR of all primary nodes and attributes of distribution level network topology and vulnerabilities utilizing game-theoretic data envelopment analysis (DEA)-based optimization. The developed metrics will be valuable for: 1) monitoring the DSR considering a holistic cyber-power model; 2) enabling data privacy by not utilizing the raw user data; and 3) enabling better decision making to select the best possible mitigation strategies toward resilient distribution system. The developed ITS, PNR, and DSR metrics have been validated using multiple case studies for the IoTs-integrated IEEE 123 node distribution system with satisfactory results.

Smart meters and the advanced metering infrastructure facilitate distribution system operators (DSOs) to gather information on energy consumption at the customer level. With the increasing penetration of building-level intermittent distributed energy resources (DERs) behind the meter, DER information is not available to DSOs. At the same time, the smart meter enables users to participate in grid, with real-time information. Information for behind the meter is needed by the user to coordinate building-level assets for maximum benefits. The concept of unbundled smart meter (USM) needs agents to decompose smart meter measurements to provide service to DSOs as well as customers. In this article, we propose a spatiotemporal decomposition agent (STDA) for the USM based on artificial intelligence. The STDA can help users optimize their energy usage and help DSOs to utilize building assets for the grid operation. The energy usage strategy developed by the STDA is suitable for different users and can be customized by deep learning (DL) models according to the different energy consumption habits of each user. The power prediction performance results of various DL models and evaluation using a set of data from a Hawaii utility is presented. Also, STDA integration with home energy management systems to manage resources is presented and validated. STDA preprocesses the measurements before model training and provides the spatiotemporal decomposed forecasting.

This Editorial Submission is for SS on Deep Learning and Data Analytics to Support the Smart Grid Operation with Renewable Energy. Out of 81 submissions in SS, only 12 have been finally accepted to be published in this Transactions.

A significant amount of distributed photovoltaic (PV) generation is “invisible” to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total load. This uncertainty directly impacts system reliability, cold load pickup, load behavior modeling, and hence cost of operation. Thus, it is essential to create low-complexity localized models for estimating power generation from these invisible sites behind the meters. This article proposes an adaptive machine learning framework to: a) learn using weather data and a minimal number of BTM PV generation measurement sensors, b) forecast PV generation using weather, location of PV, and trained ML model at location for unmeasured BTM PV; c) use estimated PV and net load measured by smart meter or smart transformer to estimate total true load at each time step; and d) learn the specific load patterns eventually to adapt localized models. The proposed framework's core idea is to transform the data such that: a) the machine learning model can effectively utilize the time dependency of measurements; and b) the measurements are transformed into a lower dimensional space to reduce complexity while maintaining accuracy. The transformed measurements are then used to train the machine learning models for load/PV disaggregation. Machine learning models investigated include linear regression, decision tree, random forest (RF), and multilayer perceptron. The proposed framework's efficacy is demonstrated using two datasets, a real dataset from Hawaii and a simulated dataset using detailed models in GridLab-D. Several test/training split scenarios, including 90-10% split, one-month-out, one-season-out, and panel-independent split are presented to provide a thorough evaluation of the proposed framework. Results on both datasets show that the proposed framework can estimate PV generation with high accuracy using low-complexity methods. The accuracy results are comparable to higher complexity models (e.g., deep architectures), and RF is found to provide superior performance with these specific datasets compared to the other ML models investigated.

We demonstrate Kronos, a framework and system that automatically extracts highly dynamic knowledge for complex event analysis in Cyber-Physical systems. Kronos captures events with anomaly-based event model, and integrates various events by correlating with their temporal associations in realtime, from heterogeneous, continuous cyber-physical measurement data streams. It maintains a lightweight highly dynamic knowledge base, enabled by online, window-based ensemble learning and incremental association analysis for event detection and linkage, respectively. These algorithms incur time costs determined by available memory, independent of the size of streams. Exploiting the highly dynamic knowledge, Kronos supports a rich set of stream event analytical queries including event search (keywords and query-by-example), provenance queries ("which measurements or features are responsible for detected events?"), and root cause analysis. We demonstrate how the GUI of Kronos interacts with users to support both continuous and ad-hoc queries online and enables situational awareness in Cyber-power systems, communication, and traffic networks.

The future power network will be designed to accommodate and integrate all types of distributed renewable energy resources, storage units, and flexible demand response loads in the existing conventional grid. Also it should be able to perform intelligent energy management utilizing advancement in computation and communication to cater the needs of ever growing energy demand in secure manner. This leads the transition of conventional power grid into smart grid. However, performance of the smart grid utilizing automated, intelligent, and integrated functional blocks with widely interconnected distributed energy resources is dependent on advanced communication network, sensors, computing, and information technologies. There is a need for reliable and efficient communication network and computing infrastructure for the robust, affordable, and secure supply of electric power through smart grid operation. This Special Section has accepted altogether seven research manuscripts depending on the novelties and problems that they address. The paper are briefly summarized here.

Fuel cell-based power units have increasingly become an attractive option to provide clean and efficient electricity in certain niche applications. This paper discusses the characteristics of a proton exchange membrane (PEM) fuel cell for a battery extender auxiliary power unit and explains the steps of the design process. A two-leg converter topology is proposed to control the fuel cell output, battery charge and discharge process, and the voltage of the DC link. Different operating modes of the system are analyzed and the functions of energy management system are studied. Sizing for the fuel cell, battery, power electronic converter, and passive components are presented, and the controllers of the power electronic converter are designed. Simulation case studies in both steady state and transient conditions are presented to validate the effectiveness of the presented fuel cell-based battery extender power unit and the proposed design process.