Distributed energy storage systems (DESSs) play an important role in maintaining voltage stability in DC microgrids. In order to improve the inertia of DC microgrid and balance the charge/discharge power and the state-of-charge (SOC) of each energy storage unit (ESU), an SOC-based virtual DC machine (VDCM) control strategy for DESSs in DC microgrid is proposed in this paper. By adding SOC to the armature resistance, the improved virtual machine algorithm can dynamically balance power and SOC simultaneously. In this study, the transient stability of DC bus voltage is enhanced using the output inertia of VDCM, and the SOC differences between ESUs are rapidly reduced by introducing the average SOC of ESUs. Small-signal models of both each ESU-interfacing converter and DESS-inclusive DC microgrid with proposed control method are established. The impact of VDCM parameters on voltage stability is analyzed. Finally, a test DC microgrid is built to verify feasibility and effectiveness of the VDCM control strategy.

Characterization of electric loads provides opportunities to incorporate detailed energy usage information into applications such as protection, efficiency certification, demand response, and energy management. This paper proposes a low computational cost, but yet accurate method, to extract signatures for load classification and characterization. Instead of utilizing digital signal processing and frequency-domain analysis, this paper abstracts the similarity of voltage-current (V-I) trajectories between loads and proposes to map V-I trajectories to a grid of cells with binary values. Graphical signatures can then be extracted for many applications. The proposed method significantly reduces the computational cost compared with existing frequency-domain signature extraction methods. Test results show that an average of over 99% of success rate can be achieved using the proposed signatures.

Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management.

Global positioning system (GPS) spoofing attack (GSA) has been shown to be one of the most imminent threats to almost all cyber-physical systems incorporated with the civilian GPS signal. Specifically, for our current agenda of the modernization of the power grid, this may greatly jeopardize the benefits provided by the pervasively installed phasor measurement units (PMUs). In this paper, we consider the case where synchrophasor data from PMUs are compromised due to the presence of a single GSA, and show that it can be corrected by signal processing techniques. In particular, we introduce a statistical model for synchrophasor-based power system state estimation (SE), and then derive the spoofing-matched algorithms for synchrophasor data correction against GSA. Different testing scenarios in IEEE 14-, 30-, 57-, 118-bus systems are simulated to show the proposed algorithms' performance on GSA detection and SE. Numerical results demonstrate that our proposed algorithms can consistently locate and correct the spoofed synchrophasor data with good accuracy as long as the system observability is satisfied. The accuracy of SE is significantly improved compared with the traditional weighted least square method and approaches the performance under the Genie-aided method.

In this article, a systematic assessment of cyber-physical security on the energy management system for connected and automated electric vehicles is proposed, which, to our knowledge, has not been attempted before. The generalized methodology of impact analysis of cyber attacks is developed, including novel evaluation metrics from the perspectives of steady state and transient performance of the energy management system and innovative index-based resilience and security criteria. Specifically, we propose a security criterion in terms of dynamic performance, comfortability, and energy, which are the most critical metrics to evaluate the performance of an electronic control unit (ECU). If an attack does not impact these metrics, it perhaps can be negligible. Based on the statistical results and the proposed evaluation metrics, the impact of cyber attacks on ECU is analyzed comprehensively. The conclusions can serve as guidelines for attack detection, diagnosis, and countermeasures.