To address the uncertain renewable energy in the day-ahead op-timal dispatch of energy and reserve, a multi-stage stochastic programming model is established in this paper to characterize the uncertainties over the multiple stages by a scenario tree. Most importantly, to deal with the “Curse of Dimensionality” of sto-chastic programming, stochastic dual dynamic programming (SDDP) is employed, which decomposes the original problem into several sub-problems according to the stages. Specifically, the SDDP algorithm performs forward pass and backward pass repeatedly until the convergence criterion is satisfied. At each iteration, the original problem is approximated by creating a lin-ear piecewise function. The results on the IEEE 118-bus system and real-life provincial power grid show the effectiveness of the proposed model and method.

Electric vehicles (EVs) have rapidly developed in recent years and their penetration has also significantly increased, which, however, brings new challenges to power systems. Due to their stochastic behaviors, the improper charging strategies for EVs may violate the voltage security region. To address this problem, an optimal EV charging strategy in a distribution network is proposed to max-imize the profit of the distribution system operators while satisfying all the physical constraints. When dealing with the uncertainties from EVs, a Markov decision process (MDP) model is built to characterize the time series of the uncertainties and then the deep deterministic policy gradient based reinforcement learning tech-nique is utilized to analyze the impact of uncertainties on the charging strategy. Finally, numerical results verify the effectiveness of the proposed method.

In recent years, considerable advances were made in wind power generation. The growing penetration of wind power makes it necessary for wind turbines to maintain continuous operation during voltage dips, which is stated as the low-voltage ride-through (LVRT) capability. Doubly fed induction generator (DFIG)-based wind turbines (DFIG-WTs), which are widely used in wind power generation, are sensitive to disturbances from the power grid. Therefore, several kinds of protection circuits and control methods are applied to DFIG-WTs for LVRT capability enhancement. This paper gives a comprehensive review and evaluation of the proposed LVRT solutions used in DFIG-WTs, including external retrofit methods and internal control techniques. In addition, future trends of LVRT solutions are also discussed in this paper.

A novel approach on robust $H_\infty$ controller design for Doubly Fed Induction Generator (DFIG) based wind generation systems through the State-Dependent Riccati Equation (SDRE) technique is proposed in this paper. The control objective is to mitigate the impact of grid voltage dips to enhance the fault ride through capability of the wind generation system. A robust $H_\infty$ control problem using the fifth-order detailed DFIG model is formulated to mitigate the impact of voltage dips for DFIG riding through the faults. The SDRE technique performs extended linearization of nonlinear systems, and the robust $H_\infty$ control problem can be solved through an algebraic Riccati equation (ARE)-like approach with state dependent coefficient (SDC) matrices. The pointwise stabilizability and controllability of the extended linearized system are guaranteed by using the input-to-state stability (ISS) theory. Accordingly, weighting matrices as well as the SDC matrices are designed with fully studied principles. The notion of controllability Gramian is used to optimize the choice of SDC matrices. The control performance of the proposed robust $H_\infty$ controller is verified through case studies. Comparisons with the PI controllers, the sliding mode control, and the exact linearization based nonlinear control, demonstrate better dynamic performances and disturbance attenuation through the proposed controller.

The high penetration of renewable energy and frequent extreme weather events bring significant uncertainty to the operation and planning of power systems. The extreme scenarios exhibit high impact and low probability characteristics, posing a challenge in providing sufficient and reliable samples for power system decision-making. The existing scenario generation methods struggle to balance the generation efficiency and interpretability. The causality between meteorological factors and power system operation has not provided effective guidance for the extreme scenario generation. In this paper, firstly, the mechanism analysis and sample construction of extreme scenarios were achieved through meteorological factor extraction, meteorological-scenario causality test and scenario clustering. Secondly, an enhanced GAN model considering training stability is established. The physical constraints of power systems are used to ensure the effectiveness of generated scenarios. Finally, a statistical analysis considering the individuals and the whole samples is conducted. The accuracy evaluation method of the generated scenarios is proposed. The robustness of the model is validated based on accuracy. The results indicate that the generated scenario have statistical consistency and high accuracy.