Junjian Qi

Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian

In this paper, the empirical observability Gramian calculated around the operating region of a power system is used to quantify the degree of observability of the system states under specific phasor measurement unit (PMU) placement. An optimal PMU placement method for power system dynamic state estimation is further formulated as an optimization problem which maximizes the determinant of the empirical observability Gramian and is efficiently solved by the NOMAD solver, which implements the Mesh Adaptive Direct Search algorithm. The implementation, validation, and the robustness to load fluctuations and contingencies of the proposed method are carefully discussed. The proposed method is tested on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system by performing dynamic state estimation with square-root unscented Kalman filter. The simulation results show that the determined optimal PMU placements by the proposed method can guarantee good observability of the system states, which further leads to smaller estimation errors and larger number of convergent states for dynamic state estimation compared with random PMU placements. Under optimal PMU placements an obvious observability transition can be observed. The proposed method is also validated to be very robust to both load fluctuations and contingencies.

An Interaction Model for Simulation and Mitigation of Cascading Failures

In this paper, the interactions between component failures are quantified and the interaction matrix and interaction network are obtained. The quantified interactions can capture the general propagation patterns of the cascades from utilities or simulation, thus helping to better understand how cascading failures propagate and to identify key links and key components that are crucial for cascading failure propagation. By utilizing these interactions a high-level probabilistic model called interaction model is proposed to study the influence of interactions on cascading failure risk and to support online decision-making. It is much more time-efficient to first quantify the interactions between component failures with fewer original cascades from a more detailed cascading failure model and then perform the interaction model simulation than it is to directly simulate a large number of cascades with a more detailed model. Interaction-based mitigation measures are suggested to mitigate cascading failure risk by weakening key links, which can be achieved in real systems by wide-area protection such as blocking of some specific protective relays. The proposed interaction quantifying method and interaction model are validated with line outage data generated by the AC OPA cascading simulations on the IEEE 118-bus system.

Blackout Model Considering Slow Process

In this paper a blackout model that considers the slow process at the beginning of blackouts is proposed based on the improved ORNL-PSerc-Alaska (OPA) model. It contains two layers of iteration. The inner iteration, which describes the fast dynamics of the system, simulates the power system cascading failure, including the tree contact and failure of lines caused by heating. The simulation of protective relays and the dispatching center is also improved to make it closer to practical conditions. The outer iteration, which describes the long-term slow dynamics of the system, adds the simulation of tree growth and utility vegetation management (UVM). Typical blackout process with slow process is simulated on the Northeast Power Grid of China and self-organized criticality (SOC) characteristic of this system is analyzed with the proposed model. The effectiveness of the proposed model is verified by the simulation results.

Towards Estimating the Statistics of Simulated Cascades of Outages With Branching Processes

Branching processes can be applied to simulated cascading data to describe the statistics of the cascades and quickly predict the distribution of blackout sizes. We improve the procedures for discretizing load shed data so that a Galton-Watson branching process may be applied. The branching process parameters such as average propagation are estimated from simulated cascades and the branching process is then used to estimate the distribution of blackout size. We test the estimated distributions with line outage and load shed data generated by the improved OPA and AC OPA cascading simulations on the IEEE 118-bus system and the Northeast Power Grid of China.

Robust State Estimator Based on Maximum Normal Measurement Rate

In this paper, the concept of normal measurement rate (NMR) is defined based on the theory of uncertainty in measurement and a robust state estimator named maximum normal measurement rate (MNMR) estimator is proposed. Comparison is made between the MNMR estimator and other robust estimators. The robustness and precision of this estimator is tested with the IEEE 14-bus, 30-bus, and 118-bus systems. Simulation results show that the MNMR estimator is effective in identifying bad data and is also time efficient.

Optimal distributed generation planning in active distribution networks considering integration of energy storage

A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the ‘best’ compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG&E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60%.

Risk Mitigation for Dynamic State Estimation Against Cyber Attacks and Unknown Inputs

Phasor measurement units (PMUs) can be effectively utilized for the monitoring and control of the power grid. As the cyber-world becomes increasingly embedded into power grids, the risks of this inevitable evolution become serious. In this paper, we present a risk mitigation strategy, based on dynamic state estimation, to eliminate threat levels from the grid’s unknown inputs and potential cyber-attacks. The strategy requires: 1) the potentially incomplete knowledge of power system models and parameters and 2) real-time PMU measurements. First, we utilize a dynamic state estimator for higher order depictions of power system dynamics for simultaneous state and unknown inputs estimation. Second, estimates of cyber-attacks are obtained through an attack detection algorithm. Third, the estimation and detection components are seamlessly utilized in an optimization framework to determine the most impacted PMU measurements. Finally, a risk mitigation strategy is proposed to guarantee the elimination of threats from attacks, ensuring the observability of the power system through available, safe measurements. Case studies are included to validate the proposed approach. Insightful suggestions, extensions, and open problems are also posed.

Self-Healing Attack-Resilient PMU Network for Power System Operation

In this paper, we propose a self-healing phasor measurement unit (PMU) network that exploits the features of dynamic and programmable configuration in a software-defined networking infrastructure to achieve resiliency against cyber-attacks. After a cyber-attack, the configuration of network switches is changed to isolate the compromised PMUs/phasor data concentrators to prevent further propagation of the attack; meanwhile, the disconnected yet uncompromised PMUs will be reconnected to the network to “self-heal” and thus restore the observability of the power system. Specifically, we formulate an integer linear programming model to minimize the overhead of the self-healing process (e.g., the recovery latency), while considering the constraints of power system observability, hardware resources, and network topology. We also propose a heuristic algorithm to decrease the computational complexity. Case studies of a PMU network based on the IEEE 30-bus and 118-bus systems are used to validate the effectiveness of the self-healing mechanism.

Estimating the Propagation of Interdependent Cascading Outages With Multi-Type Branching Processes

In this paper, the multitype branching process is applied to describe the statistics and interdependencies of line outages, the load shed, and isolated buses. The offspring mean matrix of the multitype branching process is estimated by the Expectation Maximization (EM) algorithm and can quantify the extent of outage propagation. The joint distribution of two types of outages is estimated by the multitype branching process via the Lagrange-Good inversion. The proposed model is tested with data generated by the AC OPA cascading simulations on the IEEE 118-bus system. The largest eigenvalues of the offspring mean matrix indicate that the system is closer to criticality when considering the interdependence of different types of outages. Compared with empirically estimating the joint distribution of the total outages, good estimate is obtained by using the multitype branching process with a much smaller number of cascades, thus greatly improving the efficiency. It is shown that the multitype branching process can effectively predict the distribution of the load shed and isolated buses and their conditional largest possible total outages even when there are no data of them.

Cybersecurity for distributed energy resources and smart inverters

The increased penetration of distributed energy resources (DER) will significantly increase the number of devices that are owned and controlled by consumers and third-parties. These devices have a significant dependency on digital communication and control, which presents a growing risk from cyber-attacks. This study proposes a holistic attack-resilient framework to protect the integrated DER and the critical power grid infrastructure from malicious cyber-attacks, helping ensure the secure integration of DER without harming the grid reliability and stability. Specifically, the authors discuss the architecture of the cyber-physical power system with a high penetration of DER and analyse the unique cybersecurity challenges introduced by DER integration. Next, they summarise important attack scenarios against DER, propose a systematic DER resilience analysis methodology, and develop effective and quantifiable resilience metrics and design principles. Finally, they introduce attack prevention, detection, and response measures specifically designed for DER integration across cyber, physical device, and utility layers of the future smart grid.