High voltage transmission lines, especially high-voltage direct-current (HVDC) lines, have been built around the world linking the populated load centers with major green energy resources at a far distance or offshore. Any major faults on these long-distance point-to-point connections may lead to large power unbalances and cause frequency security problems in the regional grid at the energy receiving end. In this work, an emergence frequency control strategy is proposed, utilizing available frequency response resources at the receiving end, including synchronous generators, local wind farms, battery-based energy storage systems, pumped hydro storages, etc. First, a system frequency response model consisting of multiple resources is formulated, including the secondary frequency drop caused by wind farms. Then, the order of the model is reduced to derive the analytical expressions of the frequency nadirs and the quasi-steady-state frequency. Finally, the emergency frequency control decision method is proposed with the consideration of the system frequency security constraints. The proposed strategy is studied and analyzed with simulations on a generalized model of Huadong regional grid in eastern China.

This paper discusses a simulation-based generator-level stability index and demonstrates the use of the index to quantify the impact of different load scenarios, transmission line reactances, and generator parameters on transient stability. A Basin Stability concept is used that is based on recent works in the physics community on complex systems and power systems, which estimates the regions of attraction through time-domain simulation. The quantitative results generated by Basin Stability can provide a general ranking metric to reflect which generator or a group of generators is likely to become unstable after disturbances. A detailed sub-transient model with sixth-order sub-transient generators, second-order exciters, and third-order power system stabilizers has been used for discussion. Additionally, a multi-node basin stability study on a 16-machine 68-bus system with the swing-equation model is also included to demonstrate the replicability. The state-of-the-art parallel computing techniques can significantly reduce the simulation time of the proposed method.

Distribution networks have complex topologies and limited number of monitoring devices. Because quick and accurate fault location is critical for the operation quality and reliability of distribution systems, a lot of methods have been proposed, including installation of additional monitoring equipment. Although accurate fault location is possible by dividing the complex topologies into simple ones with fewer branches, the cost of installing additional devices is uneconomic, which makes it hard to be widely applied in practical systems. Based on the theoretical analysis of the relationship between electrical measurements and fault points, an intelligent location method with limited measurement information for multi-branch distribution networks is proposed. Logical reasoning and machine learning are combined to locate fault points with limited measurement information. The relationship between the fault point and unknown distribution of uncontrollable factors in fault equivalents are fitted by machine learning model which can preserve time-related features of electrical waveforms. An adaptive migration strategy by transfer learning is proposed to deal with the problem of small samples in practical application. The proposed method is tested in different topologies. Simulation results prove the effectiveness and generalization capability of the proposed method under complex operating conditions.

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems. Conventional offline time-domain simulation-based stability assessment methods may no longer be able to face changing operating conditions. In this work, a graph neural network-based online transient stability assessment framework is proposed, which can interactively work with conventional methods to provide assessment results. The proposed framework consists of a feature preprocessing module, multiple physics-informed neural networks, and an online updating scheme with transfer learning and central moment discrepancy. The t-distributed stochastic neighbor embedding is used to virtualize the effectiveness of the proposed framework. The IEEE 16-machine 68-bus system is used for case studies. The results show that the proposed method can achieve accurate online transient stability assessment under changing operating conditions of power systems.

With the increasing number of controllable devices in the grid, devices owned by different entities are supposed to cooperatively support the grid operation. To balance the various interests of the customers and the utility, this paper proposes a two-level scheduling algorithm to address voltage regulation by coordinating the customer-owned battery energy storage system (BESS) and utility-owned load tap changers (LTCs) in power distribution systems. The objective is to find the optimal operation scheduling for both BESSs and LTCs that simultaneously maximize the economic payback and satisfy system operation rules. For BESSs, a local optimization problem is formulated in a chance-constrained manner in major consideration of the economic performance. For the utility interest, a Markov decision process (MDP) and a computationally efficient reinforcement learning (RL) algorithm are utilized to solve the optimal tap setting problem. To coordinate BESSs and LTCs, a global voltage adjustment is applied. The effectiveness of the proposed algorithm is validated via simulations on the IEEE 13-node and 34-node distribution test feeders.

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary for grid operators to develop an advanced AGC strategy to handle fluctuations and uncertainties. AGC dynamic optimization is a sequential decision problem that can be formulated as a discrete-time Markov decision process. Therefore, this article proposes a novel framework based on proximal policy optimization (PPO) reinforcement learning algorithm to optimize power regulation among each AGC generator in advance. Then, the detailed modeling process of reward functions and state and action space designing is presented. The application of the proposed PPO-based AGC dynamic optimization framework is simulated on a modified IEEE 39-bus system and compared with the classical proportional−integral (PI) control strategy and other reinforcement learning algorithms. The results of the case study show that the framework proposed in this article can make the frequency characteristic better satisfy the control performance standard (CPS) under the scenario of large fluctuations in power systems.