To address the issue of significant unpredictability and intermittent nature of renewable energy sources, particularly wind and solar power, this paper introduces a novel optimization model based on online reinforcement learning. Initially, an energy management optimization model is designed to achieve plan adherence and minimize energy storage (ES) operation costs, taking into account the inherent challenges of wind power-photovoltaic energy storage systems (WPESS). An online reinforcement learning framework is employed, which defines various state variables, action variables, and reward functions for the energy management optimization model. The state-action-reward-state-action (SARSA) algorithm is applied to learn the joint scheduling strategy for the microgrid system, utilizing its iterative exploration mechanisms and interaction with the environment. This strategy aims to accomplish the goals of effective power tracking and reduction of storage charging and discharging. The proposed method's effectiveness is validated using a residential community with electric vehicle (EV) charging loads as a test case. Numerical analyses demonstrate that the approach is not reliant on traditional mathematical models and adapts well to the uncertainties and complex constraints of the WPESS, maintaining a low-cost requirement while achieving computational efficiency significantly higher than that of the model predictive control (MPC) and deep Q-network (DQN) algorithm.

Compared with a power system under centralized control, a power system under fully distributed control has stronger interdependence between its physical side and cyber side. Such a system is a typical cyber-physical system (CPS). The time delay problem is one of the most critical issues in the application of distributed control for power systems. First, a distributed information flow model is established considering multiple sources of time delay, especially the communication delay between neighboring agents. Second, combining the dynamics of physical power systems, a general model of time-delayed CPS under fully distributed control is proposed, especially for inverter-based microgrids. Third, the stability of the time-delayed CPS is analyzed based on the proposed model with a critical eigenvalue tracking algorithm. A real-world microgrid with DGs under fully distributed frequency control is tested for small-signal stability analysis. The findings show that the stability of the distributed CPS with multiple sources of time delay depends on not only the absolute value of time delays but also the coordination of time delays and that the delay merging property in centralized mode is not relevant in a distributed CPS. The accuracy of the model and the obtained stability margin are verified by Simulink simulations.

The high-precision parameters in distribution networks are difficult to obtain, which brings difficulties to the model-based methods and analysis. With the widespread deployment of high-precision measurement units, data-driven methods have greater advantages in practice. In addition, with massive integration of distributed renewable generation and fast electric vehicle chargers, the fluctuations of net load increase significantly. The data-driven power flow calculation method based on the linear model becomes difficult to obtain accurate results for the low nonlinear adaptability. To improve the data-driven power flow calculation accuracy under high penetration of renewable distribution generation, this paper proposed an approach with high adaptability to the nonlinearity of power flow. Based on the thought of Koopman operator theory, the nonlinear relationship in power flow calculation is converted into a linear mapping in a higher dimension state space, which can significantly improve the calculation accuracy. Case studies on different IEEE cases have demonstrated that the proposed method can realize higher accuracy in power flow calculation with large-scale power fluctuations, compared to the existing data-driven method, in both mesh and radial networks. Finally, measurement data of a practical 10kV distribution network has been further used to verify the effectiveness of the proposed method in practical applications.

This paper mainly focuses on transient stability issue of phase-locked-loop synchronization (PLL-Syn) in a weak-grid connected VSC (WG-VSC) system. First, the linear matrix inequality (LMI) optimization method has been applied to construct the largest estimated domain of attraction (LEDA) of the WG-VSC system for the first time. Compared with the frequently-used methods such as direct energy function method, Takagi-Sugeno (TS) method and Sum-of-Square Programming (SOS) method, the proposed method has the lowest conservation in constructing LEDA. Second, to ensure transient stability of PLL-Syn in severe faults, the concept of the feasible region of active/reactive current reference is proposed. Furthermore, based on the LEDA obtained by LMI optimization method, a simple algorithm to obtain this feasible region is presented. And, the influence of typical parameters on this feasible region is analyzed. Third, for transient instability scenarios, a simple variable step size iterative algorithm has been proposed to search the estimated critical action time with the derived LEDA. Moreover, two associated emergency control methods including adjusting the current reference values back to the feasible region and clearing grid fault have been considered to verify the estimated critical action time. Time-domain simulation results based on detailed switching model in PSCAD/EMTDC have been provided to verify the above theoretical analysis.

With the rapid growth of wind power penetration in power grid, the system operators need to implement the grid specification of wind farm participating in primary frequency regulation (PFR). In this paper, a double-layer control framework based on permanent magnet synchronous generator (PMSG) is proposed, which can simulate the inertia and droop characteristics similar to thermal units. The top-layer control adopts centralized model predictive control (MPC), while the bottom layer carries out distributed real-time correction control in case of inaccurate MPC prediction. In order to realize fast distributed correction among wind turbines (WTs), a distributed Newton method is proposed, which only requires WTs to exchange limited information with their neighbors on sparse communication network, and has a super-linear convergence rate. By introducing an index of state of energy, this method adequately exploits the kinetic energy of all WTs without loss of security. Compared with the centralized schemes, the dependence on prediction accuracy and data synchronization is greatly reduced. Compared with the distributed schemes, the overall characteristics and computing efficiency can be guaranteed. The simulation results show that the method has satisfactory dynamic performance and reliability, and the system frequency stability is significantly improved under the synchronized control method.