Mobile emergency generators (MEGs) can effec-tively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boost-ing the distribution system resilience. Therefore, MEGs are re-quired to be optimally allocated and utilized. For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS). Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties. Specif-ically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation. Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU. It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints. Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads. The proposed planning model can be large-scale due to multiple sce-narios. Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden. The simulation results in 13 and 123 node distribution systems show the effec-tiveness and superiority of the proposed three-stage MEG plan-ning model over the traditional two-stage model.

To address the system operability challenges due to the continuous reduction of system inertia by increasing penetration of renewable power generation, this paper proposes a new synthetic inertia control (SIC) strategy for doubly-fed induction generator (DFIG) based wind turbine generators (WTG). The proposed SIC scheme enables individual DFIG generators to contribute an effective inertial response which helps to stabilise the rate of change of frequency (RoCoF) and minimise the maximum frequency deviations in particular under severe disturbance conditions. To achieve the proposed SIC, Lithium-ion supercapacitors are required in the DC link of the DFIG power electronic conversion system to increase extra energy storage capability. The DFIG's DC voltage operates at various levels following the derived SIC algorithms for energy exchange with the connected AC grid, and the conventional control system for DFIG is modified to implement the algorithms. The functions and system benefits of SIC scheme is verified using a DFIG model connected to a power system and compared with the simulations of conventional DFIG control scheme. Practical issues such as size, cost, weight, connection configuration and charging/discharging rate of lithium-ion capacitors for the SIC are discussed and relevant recommendations are presented.

Electric vehicles (EVs) have been rapidly developed during the last few years due to the low‐carbon industry and smart grid initiatives. Meanwhile, the impact of large‐scale EVs' integration on the reliability and safety of power grids is becoming increasingly prominent. To address and solve these problems, challenges on EV charging control have been presented. Besides, the EV charging load modelling with improved accuracy and rationality is required. To investigate the influencing factors of EV charging load, this survey summarises the existing EV charging load modelling methods. In addition, a new research framework for a scale EV evolution model of charging load is proposed, with an emphasis on reducing the deficiencies of the existing research in dealing with the EV scale development. Moreover, the future research prospect of EV charging load modelling on power system planning, operation, and market design has also been discussed.