Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOSO) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simultaneously maximize the economic benefits and minimize carbon emissions, and the degradation of the battery energy storage system (BESS) is modeled as a nonlinear function of power throughput. A self-adaptive multi-objective genetic algorithm (SAMOGA) is proposed to solve the SMOSO model, and this algorithm is enhanced by pre-grouped hierarchical selection and self-adaptive probabilities of crossover and mutation. Several case studies are conducted to determine the microgrid size by analyzing Pareto frontiers, and the simulation results validate that the proposed method has superior performance over other algorithms on the solution quality of optimum and diversity.

Lifetime and reliability seriously affect the applications of proton exchange membrane fuel cell (PEMFC). Performance degradation prediction of PEMFC is the basis for improving the lifetime and reliability of PEMFC. To overcome the lower prediction accuracy caused by uncertainty and nonlinearity characteristics of degradation voltage data, this paper proposes a novel deep belief network (DBN) and extreme learning machine (ELM) based performance degradation prediction method for PEMFC. A DBN based fuel cell degradation features extraction model is designed to extract high-quality degradation features in the original degradation data by layer-wise learning. To tackle the issues of overfitting and instability in fuel cell performance degradation prediction, an ELM with good generalization performance is introduced as a nonlinear prediction model, which can get some enhancement of prediction precision and reliability. Based on the designed DBN-ELM model, the particle swarm optimization (PSO) algorithm is used in the model training process to optimize the basic network structure of DBN-ELM further to improve the prediction accuracy of the hybrid neural network. Finally, the proposed prediction method is experimentally validated by using actual data collected from the 5-cells PEMFC stack. The results demonstrate that the proposed approach always has better prediction performance compared with the existing conventional methods, whether in the cases of various training phase or the cases of multi-step-ahead prediction.

The effective region of capacitor-current-feedback active damping (CCF-AD) for suppressing the resonance of the LCL -filter for the grid-following inverter (GFLI) is commonly defined by the positive region of its equivalent resistor in parallel with the filter capacitor. This effective region is related to the delay of the digital control system. Therefore, previous research has focused on minimizing or compensating for the delay in the CCF-AD loop to extend its effective region. However, the effective region of CCF-AD doesn’t serve as a sufficient stability condition for GFLI systems. Instead, the passivity of GFLI systems’ entire equivalent impedance can do it according to the frequency domain passivity theory. From the viewpoint of passivity stability, over-compensation of the delay may lead to deterioration rather than amelioration in the system’s stability. This paper finds out that increasing or decreasing a small amount of delay for the positive or negative CCF-AD will sufficiently contribute to the overall passivity enhancement. Then, the new design guidelines are reformulated accordingly to enhance the overall passivity of GFLI systems. Finally, the theoretical expectations are verified by the experimental results.

The frequency adaptive repetitive controller (FARC) can offer high control accuracy for grid-connected inverters (GCIs) in the presence of grid voltage distortions and frequency variations. The conventional design guidelines for FARC are primarily based on the system's closed-loop transfer function, which is only able to assess the internal stability of GCIs. However, their external stability is uncertain in complex grid impedance conditions, such as a weak inductive grid, a capacitive grid, or a grid comprising multiple inverters. The frequency-domain passivity theory offers a sufficient stability condition for GCIs operated in complex grid impedance conditions and has been widely recognized and applied in the control design. This paper establishes an optimization problem with constraints of internal stability condition, passivity compliance, and grid frequency variation for FARC-controlled LCL -type GCIs involving inverter-side or grid-side current control modes. By solving the established optimization problem, the system can fulfill a full-frequency passive output admittance, simultaneously achieving sufficient stability condition and high control accuracy regardless of grid voltage distortions, impedance and frequency variations. Finally, theoretical findings are verified by experimental results from a laboratory prototype.