Microgrids (MGs) are becoming active segments of future power systems. Frequency regulation inislanded MGs is of high importance to guarantee stable and reliable operation under different loading conditions. This paper proposes a decentralized frequency regulation of AC MGs, which relies on the local information. Comparing to the conventional approaches, the proposed scheme does not require a communication system for frequency regulation, resulting in higher reliability and lower costs. Simulation results verify the effectiveness of the proposed frequency control strategy.@en

Distributed model predictive control (DMPC) has become a focus in the energy management of shipboard power systems due to its capabilities for privacy preservation, robustness, and distributing computing burdens to local processors. DMPC determines control actions in a distributed manner based on the predictions of system statuses. However, the performance of DMPC is affected by inaccurate predictions resulting from uncertain parameters in nominal prediction models. Particularly, these inaccuracies in predicting propulsion loads and solar panel generation powers can lead to power imbalances when implementing the control actions determined by DMPC. To address this challenge, this paper proposed a novel reinforcement learning compensated DMPC (RL-C-DMPC) to distributively compensate for the control actions determined by DMPC baseline control, thereby rectifying the power imbalances caused by uncertain parameters in nominal prediction models. A value-decomposition-network-based training and distributed testing mechanism is designed for our proposed RL-C-DMPC. Furthermore, a method for range selection of compensation rate is specifically proposed for the energy management of shipboard power systems. To validate the effectiveness of our proposed RL-C-DMPC, we conduct a comprehensive case study utilizing real-life voyage data and historical solar power generation data in the area of the voyage to build the environment for training and testing. By comparing power imbalances between DMPC and RL-C-DMPC, our results indicate significant reductions in power imbalances so that frequency stability can be better ensured. Furthermore, via the case study, we also evaluate the communication robustness of RL-C-DMPC.@en

Predictive maintenance scheduling of modular-multilevel-converters (MMCs) requires estimations of the failure probabilities of MMCs during a period of time in the future. Particularly, the predicted future failure probabilities are influenced by two main factors, the mission profiles of the MMCs and the maintenance decisions on the MMCs during the prediction period. This paper proposes a failure probability prediction model (FPM) for MMCs by considering these two factors. First, the expectations of the failure probabilities of the components for all the scenarios of mission profiles are obtained. Second, in predictive maintenance scheduling problems, the decisions to perform the maintenance actions are represented by binary variables. When the number of submodules is very large, using the binomial probability form currently used in reliability engineering to express the 'r-out-of-n' failure probability of arms of the MMCs is intractable. Thus, this paper proposes a tractable form (T-form) in FPM by observing that the submodules on one arm are homogeneous. Furthermore, an approximation method, i.e., clustering and assignment (C&A), is proposed to reduce the computation times for calculating the parameters needed by the proposed T-form. Then, we perform a case study that assesses the accuracy and computation time of the C&A approach. The results show that the accuracy of the C&A approach is high and that the computation time is reduced significantly compared with the accurate method. We also show that the computation time for solving the predictive maintenance scheduling problem can be reduced hugely by using the T-form instead of the binomial probability form.@en