Xinchen Wang’s research while affiliated with Huazhong University of Science and Technology and other places

In order to reduce the economic costs, enhance the efficiency, and improve the structural stability of microgrids, this paper proposes a novel AC/DC hybrid microgrid structure. This structure, based on Silicon Controlled Converters (SCCs) and Polarity Reversal Switches (PRSs), enables bidirectional power flow and provides a low-cost and straightforward control solution. This paper elaborates on the overall control strategy of the microgrid under different states of the PRS and introduces the control logic of the Current Reversible Chopper (CRC) circuit. For typical daily scenarios across the four seasons, where wind and photovoltaic (PV) power generation outputs and load demands vary, this study combines sampled data to investigate the coordinated configuration scheme of wind energy, PV energy, and energy storage within the microgrid, and analyzes the state changes in the PRS. Furthermore, this paper conducts simulation analysis of the microgrid under different states of the PRS and during the switching process of the PRS, verifying the feasibility of the proposed new structure. Finally, this paper compares the proposed structure with traditional microgrid structures in terms of economics, system efficiency, and structural stability, and analyzes the impact of this structure on the frequency, inertia, and multi-energy interaction of the system.

With the rapid development of renewable energy generation in recent years, microgrid technology has increasingly emerged as an effective means to facilitate the integration of renewable energy. To efficiently achieve optimal scheduling for microgrid cluster (MGC) systems while guaranteeing the safe and stable operation of a power grid, this study, drawing on actual electricity-consumption patterns and renewable energy generation in low-latitude coastal areas, proposes an integrated multi-objective coordinated optimization strategy. The objective function includes not only operational costs, environmental costs, and energy storage losses but also introduces penalty terms to comprehensively reflect the operation of the MGC system. To further enhance the efficiency of solving the economic dispatch model, this study combines chaotic mapping and dynamic opposition-based learning with the traditional Grey Wolf Optimization (GWO) algorithm, using the improved GWO (CDGWO) algorithm for optimization. Comparative experiments comprehensively validate the significant advantages of the proposed optimization algorithm in terms of economic benefits and scheduling efficiency. The results indicate that the proposed scheduling strategy, objective model, and solution algorithm can efficiently and effectively achieve multi-objective coordinated optimization scheduling for MGC systems, significantly enhancing the overall economic benefits of the MGC while ensuring a reliable power supply.

To enhance the power supply reliability of the microgrid cluster consisting of AC/DC hybrid microgrids, this paper proposes an innovative structure that enables backup power to be accessed quickly in the event of power source failure. The structure leverages the quick response characteristics of thyristor switches, effectively reducing the power outage time. The corresponding control strategy is introduced in detail in this paper. Furthermore, taking practical considerations into account, two types of AC/DC hybrid microgrid structures are designed for grid-connected and islanded states. These microgrids exhibit strong distributed energy consumption capabilities, simple control strategies, and high power quality. Additionally, the aforementioned structures are constructed within the MATLAB/Simulink R2023a simulation software. Their feasibility is verified, and comparisons with the existing studies are conducted using specific examples. Finally, the cost and efficiency of the application of this study are discussed. Both the above results and analysis indicate that the structures proposed in this paper can reduce costs, improve efficiency, and enhance power supply stability.

In the pursuit of enhancing harmonic detection precision within microgrids, this paper introduces a pioneering algorithm, VMD-DCHHO-HD, which amalgamates Variational Mode Decomposition (VMD) with an advanced Harris Hawk Optimization algorithm characterized by dynamic opposition-based learning and Cauchy mutation (DCHHO). This study establishes a fitness function based on Shannon entropy, thereby minimizing the Local Minimum Entropy (LME) as the optimization objective for DCHHO. Building upon this, the VMD crucial parameters are efficiently identified using the enhanced HHO algorithm (DCHHO), enabling precise decomposition of complex voltage signals. The proposed method effectively addresses issues commonly encountered in traditional Empirical Mode Decomposition (EMD) during harmonic analysis, such as mode mixing, endpoint effects, and significant errors. Notably, it adeptly captures harmonic components spanning diverse frequencies, offering a nuanced solution to common pitfalls in traditional methodologies. In simulation experiments, VMD-DCHHO-HD showcases remarkable proficiency in extracting microgrid voltage signals, excelling at discerning high-order, low-amplitude harmonic components amid noise. The algorithm’s superior precision and heightened reliability, as affirmed by comparative analyses against existing methods, position it as an advanced tool for precise and robust harmonic analysis in microgrid systems.