In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.

The inertia level of microgrids decreases with the access of inverter-based distributed generations. Uncertainty of source loads in islanded microgrids can lead to frequency instability. Considering electric vehicles (EVs) as a typical regulable load, a charging station (CS) virtual synchronous generator (VSG) frequency control (FC) strategy that considers the user charging and grid FC demand is proposed. First, the control architecture of CS based on the VSG is designed. Second, considering the impact of VSG FC on EV user charging demand, the charging power response model considering the charging power FC cost is designed. Furthermore, multiobjective model predictive control algorithm is designed to improve the VSG control strategy, and the FC strategy is obtained. Simulation results show that the proposed method can provide inertial support and frequency regulation capability by the CS with VSG control strategy while satisfying EV charging demand. In addition, the FC cost can be decreased.

A method for evaluating the charging and discharging scheduling potential of electric vehicles in case of the occasional operation of a power grid is proposed, which describes the variations in the users’ responses. Considering the social relationships and mutual learning among users, an evolutionary game model for the optimal response in a complex network is established to study the response rule of the users for the scheduling measures. Wardrop's first traffic equilibrium model is applied to calculate the shortest time for the vehicles to arrive at the charging station from their response sites. Finally, the scheduling potential at a charging station is effectively evaluated considering the influence of the type of activities that the users participate in and their credits. Taking a test traffic network and the traffic network of Beijing third ring road as examples, the responses of the electric vehicle users are studied, and the scheduling potential of electric vehicles in different scenarios is verified. Although an increased incentive can improve the response level of the electric vehicles, it may lead to a reduction in the scheduling capacity, delays in the charging and discharging starting time, and a shortened scheduling duration.

EV charging station is a very important link in urban planning, and the design of the position and capacity of charging stations are not only to meet the public demand for traveling, but also to maintain the stability of operation and management in power grid. Considering the non‐simultaneity and randomness of the EVs arriving at the charging station, the capacity of EV charging stations is calculated by using queuing theory in this study. This study proposes a model which has considered the EVs' charging requirement, economy, and power grid safety to solve the problem of planning EV charging station. Firstly, determine the location of charging station by traffic satisfaction. Secondly, the EV charging stations' charging capacity is optimized with the minimum cost as the optimization target, with the EV user' waiting time patience limit and the distribution network running safety as the constraint condition. Finally, the service area of charging station is divided by the Voronoi diagram. In this study, a 25_nodes traffic network with 24_nodes distribution network underground is modelled in the MATLAB environment to demonstrate the effectiveness of the proposed method. In addition the authors solve the model by the genetic algorithm in MATLAB.

The transient stability assessment (TSA) problem can be mapped into a two‐class classification problem in machine learning, which estimates the dynamic security boundary of the power system by learning from large amount samples. A representational learning approach is proposed to solve the problem based on big data collected from Phasor Measurement Units (PMUs), which includes four stages: (i) Construct original input features by using PMUs data to describe the dynamic characteristics of the power system. (ii) Unsupervised representational feature learning by using the original features. Stacked autoencoders (SAEs) perform representational learning for crucial features. (iii) Supervised classifier training. A powerful deep learning model, convolutional neural network, which is added to SAE, is trained and tested with the learned representation. (iv) Online application, the trained model is applied to the online evaluation for TSA. Simulation on the New England 39‐bus test system shows that the proposed approach has high accuracy, rare misclassification of the unstable sample and excellent robustness with noise in PMUs for TSA.