• Energy Research

To mitigate forced oscillation and avoid confusion with modal oscillation, fundamentals of forced oscillation in multimachine power system are investigated. First, the explicit formulation of the oscillation is formulated in terms of forced disturbances and system mode shapes. Then, forced oscillation amplitude, components and envelope are intensively studied. Consequently, measures for mitigating the oscillation are obtained. Forced oscillation can also be effectively detected and discriminated from modal oscillation by utilizing its uniqueness of components properties and envelope shapes. Study results of the 10-machine 39-bus New England test system and a real-life power system demonstrate the correctness of theoretical analyses and effectiveness of detection methods for forced oscillation.

This letter devises a distributed microgrid control allowing for time-varying networks utilizing a continuous-time push-sum algorithm. The novelty of the push-sum-based approach lies in: 1) the ability to deal with unbalanced cyber network which represents unreliable communication among distributed energy resources (DERs); 2) the capability of switching among communication networks so as to provide non-interrupted operation; and 3) resiliency against cyber attacks. Case studies verify the efficacy of the new distributed microgrid control strategy and its capability of providing communication robustness while ensuring resilient frequency regulation and active power sharing.

A salient feature of a renewable energy power supply system (REPSS) on islands is the high level of uncertainties caused by high penetration of volatile power sources, such as wind and solar photovoltaic farms. This creates large forecast errors under some conditions and makes the fixed time-scale dispatch impotent in maintaining system reliability. To tackle this challenge, a time-scale adaptive dispatch method is developed in this paper. The time-scale for dispatch REPSS on islands is adjusted online according to the confidence interval of forecast error predicted by neural network. Extensive tests have validated the effectiveness of the presented method in offsetting the uncertainties in the system and improving the system reliability.

Abstract Cyberattacks in power systems that alter the input data of a load forecasting model have serious, potentially devastating consequences. Existing cyberattack-resilient work focuses mainly on enhancing attack detection. Although some outliers can be easily identified, more carefully designed attacks can escape detection and impact load forecasting. Here, a cyberattack-resilient load forecasting approach based on an adaptive robust regression method is proposed, where the observations are trimmed based on their residuals and the proportion of the trim is adaptively determined by an estimation of the contaminated data proportion. An extensive comparison study shows that the proposed method outperforms the standard robust regression in various settings.

Quantum key distribution (QKD) provides a potent solution to securely distribute keys for two parties. However, QKD itself is vulnerable to denial of service (DoS) attacks. A flexible and resilient QKD-enabled networked microgrids (NMs) architecture is needed but does not yet exist. In this article, we present a programmable quantum NMs (PQNMs) architecture. It is a novel framework that integrates both QKD and software-defined networking (SDN) techniques capable of enabling scalable, programmable, quantum-engineered, and ultra-resilient NMs. Equipped with a software-defined adaptive post-processing approach, a two-level key pool sharing strategy and an SDN-enabled event-triggered communication scheme, these PQNMs mitigate the impact of DoS attacks through programmable post-processing and secure key sharing among QKD links, a capability unattainable using existing technologies. Through comprehensive evaluations, we validate the benefits of PQNMs and demonstrate the efficacy of the presented strategies under various circumstances. Extensive results provide insightful resources for building QKD-enabled NMs in practice.

Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA challenge. We devise a quantum TSA (qTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A low-depth, high expressibility quantum neural network for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm for efficient qTSA training; 3) A systematical analysis on qTSA's performance under various quantum factors. qTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of qTSA.