A Spatial–Temporal model (STM) was developed to evaluate the impact of large scale deployment of plug-in electric vehicles (EVs) on urban distribution networks. The STM runs based on the integration of power system analysis and transportation analysis. Origin–Destination (OD) analysis from intelligent transportation research was used to model the EV mobility. Based on the EV technical and market information provided by the EU MERGE project and the output of OD analysis, a Monte Carlo simulation method was developed within the STM to obtain the EV charging load of each load busbar over time. The STM aims to facilitate power system evaluation and planning, and is able to provide both average values and probabilities of nodal bus voltages and branch loadings. The STM is able to identify the critical network components that will require to be upgraded. A high customer density urban network from the United Kingdom Generic Distribution System combined with geographic information was used as a test system. Two EV charging strategies, “dumb” charging and “smart” charging, were simulated and compared under different EV penetration levels (0%, 25% and 50%) to verify the effectiveness of STM

Modular multilevel converters (MMCs) have attracted extensive research interests in various ac and dc conversion applications due to their modular structure and excellent harmonic performance. However, the large number of power switches increases the potential risk of submodule (SM) failure, which greatly challenges the safe and reliable operation of the MMC. This article presents a detailed review of fault diagnosis and fault-tolerant control methods of the MMC under SM failures. On this basis, comprehensive comparisons are conducted among different fault diagnosis methods, and verification results are provided to analyze the advantages and disadvantages of the popular fault-tolerant control methods. Finally, the review is concluded, and future trends and research opportunities are discussed.

High penetration of renewable energy may cause considerable system frequency deviations. Electric vehicles (EVs) offer alternative potentials for frequency regulation with rapid responding speed. However, for many EVs under centralized control, existing modeling methods may achieve accurate control results at the cost of high computational complexity, a high real-time communication requirement due to many individual control signals, and possible disturbances to charging preferences. This paper focuses on solving these crucial issues. At the system level, we use the state-space method to further simplify the model for an EV aggregator (EVA) with limited data measurements from EVs. During frequency regulation, the reduced EVA model predicts the EVA's regulation capacity and generates a simplified global control signal for the probabilistic control of individual EVs. In cooperation with the ramp rate of conventional generation, the EVA implements a progressive state recovery strategy to reduce the disturbance of regulation service to EVs' charging preferences. The EVA decentralizes partial control authorities to individual EVs by applying response functions at the user side. Based on operating states and laxities, an individual EV adjusts the response process to ensure its charging preference under probabilistic control. Comparative simulations validate the effectiveness of this EVA modeling and control method.

Existing models featuring numerous electric vehicles (EVs) for centralized frequency regulation achieved high accuracy at the expense of heavy computational workloads and a high real-time communication requirement. This paper develops a state space model (SSM) that provides a probability to realize the real-time power control of aggregated EVs with high accuracy and computational efficiency but a low real-time communication requirement. The SSM, a reduced model based on the state space method, accurately describes aggregated EVs with different connecting states and various state-of-charge (SOC) states. Considering heterogeneous charging characteristics and random traveling behaviors of EVs, the SSM realizes the state transition prediction and the regulation capacity estimation with the Markov state transition method, which has a much higher computational efficiency than the existing models. The SSM is used for the frequency regulation, and the SOC adaptive coefficient is implemented to derive the identical control signal and improve the prediction accuracy. The SSM lowers the real-time communication requirement by replacing some real-time processes with offline processes. Meanwhile, the identical control signal is more suitable for real-time dispatching because it broadcasts the control signal to individual EVs globally. Simulation results indicate that the SSM achieves the high prediction accuracy with much higher computational efficiency. Comparison results are conducted to validate the effectiveness of SSM for real-time frequency regulation.

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.

The multi-energy microgrid (MEMG) improves the energy supply economy through a multi-energy coupling operation. However, due to faults or maintenance, outages may occur in the main grid, forcing the MEMG switch to the islanded mode, which heavily impacts the energy supply reliability. To solve this problem, a multi-objective stochastic optimization approach for planning the MEMG considering unscheduled islanded operation is proposed in this paper. This method aims to optimize the planning decision, improve the operation economy in the grid-connected mode and enhance the energy supply reliability in the islanded mode. A planning-operation co-optimization model is established with two objective functions: the minimum total planning cost and the minimum expected economic loss caused by islanded operation. The model incorporates equipment capacity allocation constraints, operation constraints in two operation modes and load curtailment constraints in the islanded mode. In addition to considering the uncertainties of load and renewable energy, the model of the uncertain islanded mode is established using four correlated random variables, and the scenario tree is used for islanded scenario generation. The adaptive weighted sum (AWS) method is used to solve the multi-objective model and obtain an optimal and evenly distributed Pareto frontier. On this basis, the technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine the final planning scheme. Numerical results illustrate the effectiveness of the proposed method, which makes a trade-off between planning economy and energy supply reliability.