In this paper, a planning-oriented resilience assessment framework is proposed that can help to design a more resilient power transmission system against extreme weather-induced events, such as typhoon disasters. It consists of three major models, i) a wind field-based probabilistic typhoon model to determine the level of uncertainty in the intensity and path of the typhoon; ii) a spatiotemporal fragility model of the power transmission corridor to quantify the spatiotemporal impacts of extreme wind speed on the failure probability of transmission corridor, and iii) a resilience assessment model of the planning system that allows us to develop quantitative resilience indices from both the system and component perspectives. The combinatorial enumeration method is introduced into the proposed framework to generate various potential typhoon disasters. Meanwhile, the impact-increment-based state enumeration (IISE) method is embedded within the proposed framework to improve its computational efficiency. The proposed method can provide the key information to identify the weak points of the system as well as inform the design of enhanced resilient planning schemes of transmission system against typhoon disasters. Extensive simulations carried out on the IEEE RTS-79 system considering realistic typhoon scenarios demonstrate the effectiveness of the proposed method.

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.

With the increasing proportion of renewable power generations, the frequency control of microgrid becomes more challenging due to stochastic power generations and dynamic uncertainties. The energy storage system (ESS) is usually used in microgrid since it can provide flexible options to store or release power energy. In this paper, an intelligent control strategy completely based on the adaptive dynamic programming (ADP) is developed for the frequency stability, which is designed to adjust the power outputs of micro-turbine and ESS when photovoltaic (PV) power generation is connected into the microgrid. Further, considering the changes of PV power and load demand in a day, the full utilization of PV power and the recycling of energy storage are realized through the proposed regulation strategy. Numerical simulation results validate the energy-storage-based intelligent frequency control strategy for the microgird with stochastic model uncertainties, and comparative studies based on PID, LQR and fuzzy logic control illustrate the superiority of the proposed control strategy.

This paper proposes a bi-level optimal integration scheme for buildings space heating loads in the integrated community energy system (ICES). The optimal integration scheme consists of an efficient energy management method and a heating pricing method for the ICES with buildings. At the upper level, the ICES operator optimizes the schedules of energy generation and supply, and the heating prices to buildings to maximize its profit. At the lower level, consumers in buildings optimize the water flow rates in the radiators to minimize their heating costs. The thermal dynamics of the building with controllable indoor radiators is modeled using the Resistor-Capacitor thermal network model. Moreover, the model predictive control (MPC) is integrated with the bi-level optimization to achieve economic and reliable scheduling of the ICES and buildings under uncertainties. The bi-level MPC optimization is reformulated as an MPC based mixed-integer linear program using the Karush-Kuhn-Tucker optimality conditions and several linearization techniques. Numerical studies show that the bi-level MPC method can obtain a balanced scheduling scheme between the energy costs of consumers in buildings and the ICES operator's profits. The MPC method can ensure higher profits of the ICES operator and simultaneously, lower energy costs of consumers in buildings.

Pulsed-Power-Loads (PPLs) are commonly incorporated in medium-voltage navy DC microgrids. To mitigate their detrimental effects, energy storage systems (ESSs) can be installed and their converters need to have excellent disturbance rejection capability. To facilitate this objective, a Moving-Discretized-Control-Set Model-Predictive-Control (MDCS-MPC) is proposed in this paper and applied on a Dual-Active-Bridge converter. Fixed switching frequency is maintained, enabling easy passive components design. The proposed MDCS-MPC has a small number of calculating points in each switching period, which enables the implementation in standard commercial control platforms. The operating principle of the MDCS-MPC is introduced in development of a cost function that, on one hand, provides stiff voltage regulation; on the other hand, minimizes current stress. Theoretical claims are verified on a 300V/300V 20kHz 1kW Dual-Active-Bridge.