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The smart grid (SG), regarded as the complex cyber-physical ecosystem of infrastructures, orchestrates advanced communication, computation, and control technologies to interact with the physical environment. Due to the high rewards that threats to the grid can realize, adversaries can mount complex cyber-attacks such as advanced persistent threats-based and coordinated attacks to cause operational malfunctions and power outages in the worst scenarios: The latter of which was reflected in the Ukrainian power grid attack. Despite widespread research on smart grid security, the impact of targeted attacks on control and power systems is anecdotal. This article reviews the smart grid security from collaborative factors, emphasizing the situational awareness (SA). Specifically, we propose a threat modeling framework and review the nature of cyber-physical attacks to understand their characteristics and impacts on the smart grid’s control and physical systems. We examine the existing threats detection and defense capabilities, such as intrusion detection systems (IDSs), moving target defense (MTD), and co-simulation techniques, along with discussing the impact of attacks through situational awareness and power system metrics. We discuss the human factor aspects for power system operators in analyzing the impacts of cyber-attacks. Finally, we investigate the research challenges with key research gaps to shed light on future research directions. [ABSTRACT FROM AUTHOR]

The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate knowledge of these settings to be difficult to obtain in practice. This paper addresses the problem of determining inverter reactive power control settings from net load advanced metering infrastructure (AMI) data. The estimation is first cast as fitting parameterized control curves. We argue for an intuitive and practical approach to preprocess the AMI data, which exposes the setting to be extracted. We then develop a more general approach with a data-driven reactive power disaggregation algorithm, reframing the problem as a maximum likelihood estimation for the native load reactive power. These methods form the first approach for reconstructing reactive power control settings of solar PV inverters from net load data. The constrained curve fitting algorithm is tested on 701 loads with behind-the-meter (BTM) PV systems with identical control settings. The settings are accurately reconstructed with mean absolute percentage errors between 0.425% and 2.870%. The disaggregation-based approach is then tested on 451 loads with variable BTM PV control settings. Different configurations of this algorithm reconstruct the PV inverter reactive power timeseries with root mean squared errors between 0.173 and 0.198 kVAR. [ABSTRACT FROM AUTHOR]

This article proposes a supervised learning approach for centralized localization of faults in microgrids with radial configuration that can operate in grid-connected or islanded mode. The key concept is to identify faults by learning and analyzing the features of voltage and current disturbances detected by a microgrid central protection unit. Two algorithms are developed that determine fault indices and transient features for simulation-based training, classification, and backup protection purposes. The proposed data-driven approach is effective under high penetration of distributed energy resources (DERs), and is robust under microgrid topology variations. Moreover, it requires few sensors (two for radial structure) and is easy to implement, yet provides high degree of reliability. The performance and accuracy of the developed approach are verified through extensive simulations of low-voltage microgrids that incorporate different types of DERs, including wind and solar photovoltaic systems. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to assess large-scale wind generation projects that considers their effect on network security. The proposed method is based on contingency analysis, including temporal study. Inputs to the simulation are grid model, forecasted load, conventional generation profiles, and wind variability of proposed projects. A time-step simulation is run for the time horizon to produce benefit indices for every location (bus) in the system. The congested transmission elements that require expansion are identified and ranked as part of the simulation. Each wind project in the proposed portfolio can result in benefits or costs for grid security. Policy makers can then use the method to design policies that ensure preservation of long-term system security. Developers could use the tool to identify security effects and assess their wind portfolios. Measuring network security and determining benefits of large-scale wind projects is a complex planning task that involves several aspects: temporal wind variability, spatial distribution of flows, multiple load and generation profiles, and numerous possible contingencies. All these wind project development aspects must be isolated to identify and correctly assign security costs and benefits. Index Terms--Generation Planning, transmission loading relief (TLR), wind integration, wind variability.

This paper presents a comprehensive methodology to determine the locational value of new generation in enhancing system security. The method is based on contingency analysis postprocessing and a novel three-dimensional weighted transmission loading relief (WTLR) calculation. A locational value that can be visualized on a geo-referenced map is assigned to each bus in the system. The spatial representation of the locational value can be overlaid with energy resource maps in order to prioritize siting projects to achieve adequacy and benefit overall system security. Index Terms--Distributed generation, generation planning, transmission loading relief (TLR).

This paper deals with static transfer stability limits (STSL) of network branches and their relation with singularity of the power flow Jacobian. It describes the effect of large transfers on the branch complex flows and shows that when a transfer takes place in the system, at least one branch must reach its STSL before the singularity is encountered. Numerical examples confirm this necessary condition and show that the first STSL appears close to the singularity. Therefore, it is possible to monitor the proximity to the power flow Jacobian singularity by supervising the complex flow in individual transmission lines. Index Terms--Available transfer capability (ATC), distribution factors, static collapse, voltage stability.

Fast, accurate algorithms to compute network capabilities are indispensable for transfer-based electricity markets. Most transfer studies involve contingencies and multipattern scenarios that often can only be performed in reasonable time with the use of linear methods. One of the limitations of linear ATC is the error produced by neglecting the effect of reactive power flows in line loading. This paper describes a fast algorithm to incorporate this effect. The estimation of the line post-transfer complex flow is based on circle equations and a megavar-corrected megawatt limit. The method can be easily integrated into existing linear ATC software because the computation remains based on active power distribution factors. The algorithm is illustrated in a small example and the error correction demonstrated for transfers in larges systems. Index Terms--Linear ATC, reactive power.

We propose a multi-case, multi-period AC model to coordinate the procurement of grid services from an aggregation of connected distributed energy resources and microgrids. The proposed model aggregates the capabilities of all resources in the system to manage the network’s power flow and provide services to the power grid at the point of common coupling. The modeling approach enables incorporating a number of services (e.g., reserve, regulation, and reactive power capacity) with simple addition of service defining equations. We also propose a solution methodology for the resultant mixed-integer quadratically-constrained programming (MICQP) problem based on successive linear programming (SLP). The SLP is augmented with a fix-binary algorithm that promises efficient handling of discrete variables. The paper also provides a decentralized formulation that decomposes the problem into an aggregator problem and multiple small $\mu $ Gs’ problems. We integrate the SLP method with the analytical target cascading (ATC) algorithm to optimize the overall system with minimal shared information between the aggregator and the $\mu $ Gs. Case studies analyze the convergence of the centralized and the decentralized approaches, present the optimal power and service schedules of the tested system, and study the network conditions under service calls. [ABSTRACT FROM AUTHOR]

This article is the second part of a two-part article on communication-failure-resilient architecture for distributed operation and control in smart grids with hybrid producer/consumer (prosumer) agents. Part I of this article proposed the distributed architecture and algorithmic development, while this Part II provides the corresponding algorithmic implementation, technical requirements, practical applications, and simulations. We propose a general and scalable framework for implementing resilient distributed frequency regulation (DFR) on large-scale prosumer-based power grids, such as utilities, where each prosumer represents a frequency control area. We first develop a method to obtain quasi-steady state models of prosumers from the dynamic models of generators, loads, and power lines. This model allows demonstrating the dynamic evolution of prosumers under normal conditions and during communication failures. Next, we propose a framework to implement the resilient DFR algorithm on prosumer power grids. We show that under normal conditions prosumers need to solely communicate with their neighbors to obtain optimal control actions. But, during communication failures, prosumers need to extend the communication hops to maintain system-wide stability as well as fair power sharing. The resilient DFR algorithm is demonstrated on two real-world, large-scale power grids and the results show that it can indeed complement and enhance today's Automatic Generation Control systems. [ABSTRACT FROM AUTHOR]

Distributed algorithms have been proposed as options to scale control propositions to the massive number of intelligent energy devices, sub-systems, and distributed energy resources being integrated into the electricity grid. Distributed algorithms rely on the communication network for exchanging information. Failures in the communication network can jeopardize distributed decision-making and in the worst-case scenario can lead to system-level stability problems. This paper proposes a communication-failure-resilient architecture for distributed operation and control in smart grids with hybrid producer/consumer (prosumer) agents. We describe the relations between system-wide performance and communication failures and identify topological conditions on the cyber-physical network, under which prosumers can perform key operating tasks, such as distributed frequency regulation through an imperfect communication network. [ABSTRACT FROM AUTHOR]

The increasing power/load demand is becoming a new concern for the grid operators as it can potentially overload transmission lines. Among advantages of a multi-terminal dc (MTDC) grid, its high controlability makes it a promising asset for ac line overload alleviation. This paper proposes a model predictive control based strategy, which regularly updates the power setpoints of the MTDC converter stations and ac generators to alleviate ac line overloads. To compute and update the power setpoints, necessary measurements are received by the controller within regular time intervals. The proposed controller ensures that subsequent to any ac line overload, changing the power setpoints of the MTDC converter station does not cause any operation violation in the MTDC grid. Furthermore, it ensures that the ac voltage limits and voltage stability criteria are met to avoid voltage instability. Performance and effectiveness of the proposed controller are evaluated by simulation studies on two systems, i.e., the 39-bus New England system integrated with a 5-bus MTDC system and the IEEE 118-bus system connected to a 6-bus MTDC system. Simulation results confirm that the MTDC grid can play a major role in controlling active powers of most of the ac transmission lines. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Deploying microgrids ($\boldsymbol {\mu G} \text{s}$) with the capability of islanding and self-supply can considerably enhance power system resilience in coping with weather events. To this end, practical methods are needed in order to operationally position and manage the $\boldsymbol {\mu G}$ to optimally control its resources and minimize the risk in the face of such disturbances. This paper proposes a two-stage adaptive robust formulation for $\boldsymbol {\mu G}$ predisturbance scheduling to minimize the damaging consequences of islanding events. The approach considers different uncertainties and obtains the best day-ahead schedule for the $\boldsymbol {\mu G}$ , which is robust against realization of uncertain parameters. An appropriate decomposition strategy, called column-and-constraint generation algorithm, is adopted to efficiently solve the problem, and “budget of uncertainty” parameters are introduced to control the conservatism of the robust solution. The effectiveness of the novel framework is evaluated and discussed on a $\boldsymbol {\mu G}$ using a set of illustrative case studies. The simulations show that the framework mitigates the huge cost of load shedding at the expense of a small increase in the predisturbance preparation cost. The advantages of our model are particularly remarkable when a weather event will come through, and a significant level of uncertainty exists. [ABSTRACT FROM AUTHOR]

Understanding the impact of distributed photovoltaic (PV) resources on various elements of the distribution feeder is imperative for their cost effective integration. A year-long quasi-static time series (QSTS) simulation at 1-second granularity is often necessary to fully study these impacts. However, the significant computational burden associated with running QSTS simulations is a major challenge to their adoption. In this paper, we propose a fast scalable QSTS simulation algorithm that is based on a linear sensitivity model for estimating voltage-related PV impact metrics of a three-phase unbalanced, nonradial distribution system with various discrete step control elements including tap changing transformers and capacitor banks. The algorithm relies on computing voltage sensitivities while taking into account all the effects of discrete controllable elements in the circuit. Consequently, the proposed sensitivity model can accurately estimate the state of controllers at each time step and the number of control actions throughout the year. For the test case of a real distribution feeder with 2969 buses (5469 nodes), 6 load/PV time series power profiles, and 9 voltage regulating elements including controller delays, the proposed algorithm demonstrates a dramatic time reduction, more than 180 times faster than traditional QSTS techniques. [ABSTRACT FROM AUTHOR]

Enabling secure delivery of control commands and alert messages is one of the critical requirements of smart grid communications. If adversaries were to gain access, they could alter or send malicious commands from the Control Center (CC), intended to the Remote Terminal Units (RTUs) and Intelligent Electronic Devices (IEDs). These malicious commands could disrupt power system operations and cause damage to power devices. In this paper, we propose an efficient signature scheme for ensuring the delivery of only legitimate commands over the communication network. We propose efficient signatures using hash that are sent by the CC along with commands for protecting command alteration over the network, which also provide sender authenticity by verifying the signatures at the RTU/IED. By using the proposed scheme, both random and malicious commands sent by an adversary are discarded. The security analysis shows that the scheme is secure against replay, repudiation and impersonation attacks. The performance evaluation and prototype simulation show that our signature scheme using SHA1, SHA256 and SHA384 is feasible, and can be used for communications from the CC to the field. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multiagent based distributed algorithm for enhanced frequency response in large-scale multiarea power systems. Control nodes associated with these areas preemptively agree on their relative contributions in supporting the frequency response in a completely distributed manner. Consequently, in case of system contingencies, each area knows the total change in generation required to facilitate faster frequency restoration during primary frequency control. Within an area, local controllers associated with generators execute a distributed power agreement via sparse information exchange to achieve this required change in generation. As a result of these coordinated control actions, frequency deviation from the nominal remains minimal, thereby reducing the dependency on secondary frequency control. Inspired from graph theoretic techniques in networked controls, the proposed architecture requires nodes with minimalistic computation and communication capabilities, thereby increasing its applicability. Simulations performed on a large test case demonstrate the efficacy of the proposed algorithm in improving the transient performance of the frequency response. [ABSTRACT FROM AUTHOR]

Smart vehicle-to-grid (V2G) involves intelligent charge and discharge decisions based on user operational energy requirements, such as desired levels of charging and waiting time. V2G is also supported by information management capabilities enabled by a secure network, such as a reliable privacy-preserving payment system. In this article, we describe the network security and privacy requirements and challenges of V2G applications. We present a new network security architecture to support V2G. We propose a scheme with the following security and privacy-preserving features: anonymous authentication, fine-grained access control, anonymous signatures, information confidentiality, message integrity, remote attestation, and a payment system. This article is oriented toward practitioners interested in designing and implementing secure and privacy-preserving networks for smart V2G applications. [ABSTRACT FROM PUBLISHER]

Hierarchical control/protection applications in smart microgrids require knowledge of real-time status of distributed generation (DG) systems. Lack or failure of communications with the microgrid central controller (MGCC) can significantly undermine performance of such applications since the MGCC cannot determine the number of operational energy sources. To overcome these challenges, the MGCC needs a secondary mechanism in order to track presence or absence of DG systems. This paper proposes a new monitoring approach that empowers the MGCC to estimate the number of operational DG systems and thus determine the total generation capacity of the microgrid. A parameter estimator is developed to extract an autoregressive model for the synchrophasors of current symmetrical components (CSC) of the main point of common coupling (PCC). The extracted model is used by an adaptive algorithm that identifies abrupt changes in DG by evaluating the norm of forward prediction error. The proposed approach uses real-time synchrophasor data to dynamically update the criterion for event detection and is very robust against abrupt load changes. The performance is verified using extensive simulations of the IEEE 13-Bus benchmark with four photovoltaic (PV) units. [ABSTRACT FROM AUTHOR]

The Evolved Packet System-based Authentication and Key Agreement (EPS-AKA) protocol of the longterm evolution (LTE) network does not support Internet of Things (IoT) objects and has several security limitations, including transmission of the object's (user/device) identity and key set identifier in plaintext over the network, synchronization, large overhead, limited identity privacy, and security attack vulnerabilities. In this article, we propose a new secure and efficient AKA protocol for the LTE network that supports secure and efficient communications among various IoT devices as well as among the users. Analysis shows that our protocol is secure, efficient, and privacy preserved, and reduces bandwidth consumption during authentication. [ABSTRACT FROM AUTHOR]