[Objective] To improve the reactive response of photovoltaics and limit the problem of voltage overlimit，a subdivision-based reactive voltage adjustment coefficient optimization method is proposed considering a large number of distributed photovoltaics connected to the grid. [Methods] To limit the voltage fluctuations of each node under various scenarios，an optimization model of nodes with reactive voltage differential coefficients based on partitions is proposed. The control model is simplified by using the characteristics of "strong coupling within the interval and weak coupling between partitions" to realize that photovoltaics can independently adjust the reactive output according to external voltage fluctuations and make full use of the reactive residual. [Results] The simulation results of the IEEE 33 node standard example show that the partition-based reactive voltage adjustment coefficient optimization method can effectively use the reactive to regulate node voltage，which is more conducive to meeting the requirements of accuracy and speed of reactive optimization. [Conclusions] The method proposed in this study can effectively improve the voltage quality of distribution grid nodes under various scenarios，effectively utilizing the photovoltaic reactive residual.

[Objective] Multi-energy microgrids（MEMGs）can integrate multiple energy carriers to improve energy efficiency，thereby contributing to the achievement of "dual carbon" goals. [Methods] This study proposes a data-driven distributionally robust optimization scheduling method for MEMGs that considers uncertainty correlations and outlier data. First，an ambiguity set incorporating uncertainty correlations is introduced using the copula function. A distributionally robust optimization scheduling model with opportunity constraints is then formulated，integrating the ambiguity set and opportunity constraints to address the uncertainty correlations. Second，because the distributionally robust optimization model cannot be solved directly，a worst-case transformation method is derived for the proposed ambiguity set using dual theory，McCormick relaxation，and conditional value-at-risk approximation. This transforms the distributionally robust model into a linear deterministic model，thereby enabling an efficient solution using optimization solvers. Finally，a sample-pruning algorithm is proposed，which iteratively generates subsamples from the original dataset by removing the outliers and extreme data points. This approach mitigates the adverse effects of such data on distributionally robust opportunity constraint scheduling results. [Results] Case simulations demonstrate that the proposed distributionally robust model effectively eliminates unrealistic distributions in the ambiguity set，resulting in an 8.16% reduction in out-of-sample costs. The proposed sample-pruning algorithm further reduces the out-of-sample costs by 3.33%. [Conclusions] The proposed method enhances the scheduling efficiency and ensures reliability，which collectively demonstrates its superiority.

[Objective] With the integration of large-scale distributed photovoltaics，issues such as reverse power flow and overvoltage arise，posing challenges to the safe and stable operation of power systems. To address this issue，both domestically and internationally，regulations mandate that PV systems must have grid support capabilities，such as voltage-reactive control，which actively regulates reactive power based on voltage deviations. However，such regulations are typically designed for balanced voltage scenarios and unbalanced voltage conditions are rarely considered. [Methods] Therefore，using voltage-reactive power control（volt-var control，VVC）as an example，this study analyzes the influence of voltage imbalance on the reactive power output and proposes an improved voltage-support control method aimed at minimizing three-phase voltage deviation. Based on instantaneous power theory，the mathematical relationship between voltage imbalance and active/reactive power is derived，demonstrating the voltage regulation effect of reactive power and the existence of the minimum voltage point. Then，with the three-phase voltage deviation as an index，the positive sequence voltage and reactive power corresponding to such point are calculated，and dynamic adjustments to the voltage-support control curve are made using this point，voltage limits，and reactive power capacity of PV inverters. [Results] Matlab/Simulink simulation results demonstrate that the proposed improved voltage-support control method ensures that three-phase voltages remain within limits while achieving minimal deviation from the nominal voltage under the lowest reactive power output. This ensures economical and efficient regulation of the three-phase voltage in unbalanced scenarios. [Conclusions] The proposed control strategy effectively coordinates the traditional VVC control with negative-sequence current/power control. It maintains three-phase voltages within grid-connected standard limits despite changes in irradiance，temperature，or other factors causing photovoltaic power fluctuations or voltage-unbalance variations，significantly enhancing the adaptability of conventional VVC control functions.