Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL) by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Robustness of the developed DRL method to different simulation scenarios, model parameter uncertainty and noise in the observations is investigated. Extensive case studies performed in both the two-area, four-machine system and the IEEE 39-bus system have demonstrated excellent performance and robustness of the proposed schemes.

With the ever increasing penetration of renewable energy, smart loads, energy storage, and new market behavior, today's power grid becomes more dynamic and stochastic, which may invalidate traditional study assumptions and pose great operational challenges. Thus, it is of critical importance to maintain good-quality models for secure and economic planning and real-time operation. Following the 1996 Western Systems Coordinating Council system blackout, North American Electric Reliability Corporation (NERC) and Western Electricity Coordinating Council (WECC) in North America enforced a number of policies and standards to guide the power industry to periodically validate power grid models and calibrate poor parameters with the goal of building sufficient confidence in model quality. The PMU-based approach using online measurements without interfering with the operation of generators provides a low-cost alternative to meet NERC standards. This paper presents an innovative procedure and tool suites to validate and calibrate models based on a trajectory sensitivity analysis method and an advanced ensemble Kalman filter algorithm. The developed prototype demonstrates excellent performance in identifying and calibrating bad parameters of a realistic hydro power plant against multiple system events.

The increasing penetration of renewable resources causes some challenges like the electric power demand prediction uncertainty and energy surplus. Energy storage systems (ESS) are promising solutions for these challenges. However, considering the marginal capacity of ESSs according to the installation area and the economic portion of ESSs according to the installation capacity, the use of battery ESSs to reduce surplus energy is not efficient and has practical limitations. To efficiently resolve the challenges, a multi-energy system (MES) that is capable of operating different energy sources, such as natural gas storage (NGS), thermal energy storage (TES), ice energy storage (IES), and hydrogen energy storage (HES) has been proposed. The centerpiece of converting and managing multiple energy sources associated with the MES is the energy hub (EH). In this paper, we reviewed and compared the performance of existing ESSs and the MES, and the results have demonstrated the superiority of the MES. In addition, EHs that include power-to-gas, combined heat power, and combined cooling heat power, have been examined based on their structural characteristics. A review of the methods and the primary purpose of MES is also highlighted in this paper.

This paper proposes a new fault location method for transmission lines in MMC-HVDC grids based on dynamic state estimation (DSE) and gradient descent. The method only requires a short data window of 5 ms after the occurrence of the fault and therefore is applicable for MMC-HVDC grids with high-speed tripping techniques. The method first builds a high-fidelity linear dynamic model of the DC transmission line, which accurately describes physical laws of the transmission line during the fault. Afterwards, the consistency between the measurements and the linear dynamic model is evaluated through the DSE algorithm. Finally, the actual fault location which corresponds to the best consistency is determined via the gradient descent algorithm. Compared to the existing DSE based fault location methods which solve highly nonlinear DSE problems, the proposed method only needs to solve a series of linear DSE problems, which overcomes the issues such as large numerical error and high computational burden especially for transmission lines in MMC-HVDC grids. Numerical experiments validates the effectiveness of the proposed method, with different fault types, resistances and locations. In addition, the method only requires a relatively low sampling rate of 20k samples per second.

High-voltage, direct current (HVDC) transmission lines are increasingly being installed in power systems around the world, and this trend is expected to continue with advancements in power electronics technology. These advancements are also bringing multi-terminal direct current (MTDC) systems closer to practical application. In addition, the continued deployment of phasor measurement units (PMUs) makes dynamic information about a large power system readily available for highly controllable components, such as HVDC lines. All these trends have increased the appeal of modulating HVDC lines and MTDC systems to provide grid services in addition to bulk power transfers. This paper provides a literature survey of HVDC and MTDC damping controllers for interarea oscillations in large interconnected power systems. The literature shows a progression from theoretical research to practical applications. There are already practical implementations of HVDC modulation for lines in point-to-point configuration, although the modulation of MTDC systems is still in the research stage. As a conclusion, this paper identifies and summarizes open questions that remain to be tackled by researchers and engineers.

Accurate pin-pointing of faults on transmission lines minimizes outage time, labor, and costs. This letter proposes a simple yet effective ensemble Kalman filter (EnKF) approach to accurately locating the faults on transmission lines within half-cycle time. The approach is easy to implement and does not require foreknowledge of either the fault type or an approximate guess of the fault location. Extensive results validate the effectiveness and accuracy of the EnKF-based fault location approach.