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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.

Power-electronic interfacing based devices such as wind generators (WGs) and electrical vehicles (EVs) cause harmonic distortions on the power grid. Higher penetration and uncoordinated operation of WGs and EVs can lead to voltage and current harmonic distortions, which may exceed IEEE limits. It is interesting to note that WGs and EVs have some common harmonic profiles. Therefore, when EVs are connected to the grid, the harmonic pollution EVs impart onto the grid can be reduced to some extent by the amount of wind power injecting into the grid and vice versa. In this context, this work studies the impact of EVs on harmonic distortions and careful utilization of wind power to minimize the distortions in distribution feeders. For this, a harmonic unbalanced distribution feeder model is developed in OpenDSS and interfaced with Genetic Algorithm (GA) based optimization algorithm in MATLAB to solve optimal harmonic power flow (OHPF) problems. The developed OHPF model is first used to study impact of EV penetration on current/voltage total harmonic distortions (THDs) in distribution grids. Next, dispatch of WGs are found at different locations on the distribution grid to demonstrate reduction in the current/voltage THDs when EVs are charging.

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control.

Big data has a potential to unlock novel groundbreaking opportunities in the power grid sector that enhances a multitude of technical, social, and economic gains. The currently untapped potential of applying the science of big data for better planning and operation of the power grid is a very challenging task and needs significant efforts all-around. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data sets from diverse sources. In particular, computational complexity, data security, and operational integration of big data into utility decision frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. Moreover, due to the complex nature of power grids along with the need to balance power in real time, seamless integration of big data into utility operations is very critical. In this context, big data analytics combined with grid 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 electric utility decision framework. Detailed information for utilities looking to apply big data analytics and details insights on how utilities can enhance revenue streams and bring disruptive innovation in the industry are discussed. More importantly, 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.

Power system dynamic modeling involves nonlinear differential and algebraic equations (DAEs). Solving DAEs for large power grid networks by direct implicit numerical methods could be inefficient in terms of solution time; thus, such methods are not preferred when real-time or faster than real-time performance is sought. Hence, this paper revisits Waveform Relaxation (WR) algorithm, as a distributed computational technique to solve power system dynamic simulations. Case studies performed on the IEEE NE 10-generator 39-bus system demonstrate that, for a certain simulation time window, the solve time for WR method is larger than the length of the simulation window; thus, WR lacks the performance needed for real-time simulators, even for a small power network. To achieve real-time performance, then a Windowing technique is applied on top of the WR, for which the solve time was obtained less than the length of a simulation window, that shows the effectiveness of the proposed method for real-time dynamic simulation of power systems.

Incorporating advanced information and communication technologies into smart grids (SGs) offers substantial operational benefits while increasing vulnerability to cyber threats like false data injection (FDI) attacks. Current SG attack analysis tools predominantly employ formal methods or adversarial machine learning (ML) techniques with rule-based bad data detectors to analyze the attack space. However, these attack analytics either generate simplistic attack vectors detectable by the ML-based anomaly detection models (ADMs) or fail to identify critical attack vectors from complex controller dynamics in a feasible time. This paper introduces MISGUIDE, a novel defense-aware attack analytics designed to extract verifiable multi-time slot-based FDI attack vectors from complex SG load frequency control dynamics and ADMs, utilizing the Gurobi optimizer. MISGUIDE can identify optimal (maliciously triggering under/over frequency relays in minimal time) and stealthy attack vectors. Using real-world load data, we validate the MISGUIDE-identified attack vectors through real-time hardware-in-the-loop (OPALRT) simulations of the IEEE 39-bus system.

Small-Signal Stability (SSS) is crucial for the control of power grids. However, False Data Injection (FDI) attacks against SSS can impact the grid stability, hence, the security of SSS needs to be studied. This paper proposes a formal method of synthesizing FDI attack vectors (i.e., a set of measurements to be altered) that can destabilize power systems. We formulate an FDI attack as an optimization problem using AC power flow, SSS model, and stability constraints. The attacker capability is modeled as the accessibility to a limited set of measurements. The solution of the proposed FDI attack model provides a destabilizing attack vector if exists. We implement the proposed mechanism and evaluate its performance by conducting several case studies using the WSCC 3-machine 9-bus system. The case study results showed that the possibility of random FDIs (i.e., with no knowledge of the power system) in launching a destabilizing attack is too low to be successful. However, an intelligent attacker can leverage the grid knowledge to make the system unstable, even with limited access to the measurements.

Load-generation balance and system inertia are essential for maintaining frequency in power systems. Power grids are equipped with Rate-of-Change-of Frequency (ROCOF) and Load Shedding (LS) relays in order to keep load-generation balance. With the increasing penetration of renewables, the inertia of the power grids is declining, which results in a faster drop in system frequency in case of load-generation imbalance. In this context, we analyze the feasibility of launching False Data Injection (FDI) in order to create False Relay Operations (FRO), which we refer to as FRO attack, in the power systems with high renewables. We model the frequency dynamics of the power systems and corresponding FDI attacks, including the impact of parameters, such as synchronous generators inertia, and governors time constant and droop, on the success of FRO attacks. We formalize the FRO attack as a Constraint Satisfaction Problem (CSP) and solve using Satisfiability Modulo Theories (SMT). Our case studies show that power grids with renewables are more susceptible to FRO attacks and the inertia of synchronous generators plays a critical role in reducing the success of FRO attacks in the power grids.

High penetration of wind power with conventional grid following controls for inverter-based wind turbine generators (WTGs) weakens the power grid, challenging the power system stability. Grid-forming (GFM) controls are emerging technologies that can address such stability issues. Numerous methodologies of GFM inverters have been developed in the literature; however, their applications for WTGs have not been thoroughly explored. As WTGs need to incorporate multiple control functions to operate reliably in different operational regions, the GFM control should be appropriately developed for the WTGs. This paper presents a review of GFM controls for WTGs, which covers the latest developments in GFM controls and includes multi-loop and single-loop GFM, virtual synchronous machine-based GFM, and virtual inertia control-based GFM. A comparison study for these GFM-based WTGs regarding normal and abnormal operating conditions together with black-start capability is then performed. The control parameters of these GFM types are properly designed and optimized to enable a fair comparison. In addition, the challenges of applying these GFM controls to wind turbines are discussed, which include the impact of DC-link voltage control strategy and the current saturation algorithm on the GFM control performance, black-start capability, and autonomous operation capability. Finally, recommendations and future developments of GFM-based wind turbines to increase the power system reliability are presented.