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To address wind power fluctuations causing curtailment and high costs, this study proposes an integrated method combining wind power forecasting with substation optimization. An enhanced Bidirectional Gated Recurrent Unit (BiGRU) model is developed by incorporating chaotic features (maximum Lyapunov exponent) and sliding-window statistical features (mean, standard deviation), significantly improving short-term prediction accuracy. Based on these high-precision forecasts, a dynamic transformer switching optimization model is established to maximize the wind farm’s net profit. This model finely balances power generation revenue, wind curtailment penalties, and transformer losses (no-load and load) at a 15 min timescale. Experimental results from a wind farm in Xinjiang demonstrate that the proposed method effectively enhances the economic efficiency of wind farm operations. The study provides a valuable framework for optimizing energy storage configuration and improving profitability by leveraging accurate forecasting.

Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method.

To discover novel fungicides with good inhibitory effects on plant fungal diseases, twenty-five 3-indolyl-3-hydroxy oxindole derivatives (3a–3y) were synthesized. These newly derivatives were characterized by NMR and HRMS. Their antifungal activities against five plant pathogenic fungi were assessed in vitro. Most of the compounds exhibited moderate to excellent antifungal activities against the five pathogenic fungi. Notably, compounds 3t, 3u, 3v, and 3w displayed remarkable and broad-spectrum antifungal activities comparable to or superior to those of the fungicides carvacrol (CA) and phenazine-1-carboxylic acid (PCA). Among them, compound 3u displayed the most excellent antifungal activity against Rhizoctonia solani Kühn (R. solani), with an EC50 of 3.44 mg/L, which was superior to CA (7.38 mg/L) and PCA (11.62 mg/L). Preliminary structure–activity relationship (SAR) results indicated that the introduction of I, Cl, or Br substituents at position 5 of the 3-hydroxy-2-oxindole and indole rings is crucial for compounds to exhibit good antifungal activity. The in vivo antifungal activity assay showed that compound 3u has good curative effects against R. solani. The current results suggest that these compounds are capable of serving as promising lead compounds.

In modern electrical power distribution systems, the effective operation of inverter-based arc suppression devices relies on the accuracy of faulty phase selection. In the traditional methods of faulty phase selection for single-phase-to-ground faults (SPGs), power frequency-based amplitude and phase characteristics are used to identify the faulty phase. In the field, when a high-resistance SPG occurs in the system, traditional methods are difficult for accurately identifying the faulty phase because of the weak fault components and complicated process. A novel realizable and effective method of faulty phase selection based on transient current similarity measurements is presented when SPGs occur in resonantly grounded distribution systems in this paper. An optimized Hausdorff distance matrix (MOHD) is proposed and constructed by the transient currents of three phases’ similarity measurements within a certain time window of our method. This MOHD is used to select the sampling time window adaptively, which allows the proposed method to be applied to any scale of distribution systems. Firstly, when a SPG occurs, the expressions for the transient phase current mutation in the faulty and sound phases are analyzed. Then, the sampling process is segmented into several selection units (SUs) to form the MOHD-based faulty phase selection method. Additionally, the Hausdorff distance algorithm (HD) is used to calculate the waveform similarities of the transient phase current mutation among the three phases to form the HD-based faulty phase selection method. Finally, a practical resonant grounded distribution system is modeled in PSCAD/EMTDC, and the effectiveness and performance of the proposed method is compared and verified under different fault resistances, fault inception angles, system topologies, sampling time windows and rates of data missing.

Power loss can influence the accuracy of maximum power point tracking (MPPT) control and the efficiency of a brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). Because power loss is related to both the active power reference and reactive power reference of BDFG, this article proposes active and reactive power optimal control of BDFGWT by considering power loss. Firstly, the mathematical model of BDFGWT, including the wind turbine, BDFG, and back-to-back converter, is established. Then, an active and reactive power optimal control strategy is proposed. In proposed control, the accurate active power reference of power winding (PW) is calculated by considering the active power loss of BDFG; in this way, proposed MPPT control can capture more wind power compared to traditional MPPT control, ignoring the power losses, thus improving the efficiency of BDFGWT. Furthermore, on the basis of the model of BDFG, the relations between reactive power and total active loss are analyzed, and the optimal reactive power control reference to minimize the active power loss is determined. Finally, in order to verify the validity of the proposed control, 2 MW BDFGWT has been constructed, and the proposed method was studied to make a comparison. The results verify that proposed control can maximize the utilization of wind energy, minimize the power loss of the BDFGWT system, and output maximal active power to the power grid.

The optimal resource allocation in the large-scale intelligent device-to-device (D2D) communication system is of great importance for improving system spectrum efficiency and ensuring communication quality. In this study, the D2D resource allocation is modelled as an ultrahigh-dimensional optimization (UHDO) problem with thousands of binary dimensionalities. Then, for efficiently optimizing this UHDO problem, the coupling relationships among those dimensionalities are comprehensively analysed, and several efficient variable-grouping strategies are developed, i.e., cellular user grouping (CU-grouping), D2D pair grouping (DP-grouping), and random grouping (R-grouping). In addition, a novel evolutionary algorithm called the cooperatively coevolving particle swarm optimization with variable-grouping (VGCC-PSO) is developed, in which a novel mutation operation is introduced for ensuring fast satisfaction of constraints. Finally, the proposed UHDO-based allocation model and VGCC-PSO algorithm as well as the grouping and mutation strategies are verified by a comprehensive set of case studies. Simulation results show that the developed VGCC-PSO algorithm performs the best in optimizing the UHDO model with up to 6000 dimensionalities. According to our study, the proposed methodology can effectively overcome the “curse of dimensionality” and optimally allocate the resources with high accuracy and robustness.

Aiming at the problems of frequent voltage overruns in distribution networks and difficulties in centralized optimal dispatch due to the uncertainties of distributed renewable energy sources and bus loads, this paper proposes a dynamic cluster voltage control method considering power deficit and resource allocation in an active distribution network. First, the modularity index is constructed by considering the ability of the bus electrical coupling, and the voltage regulation resources are allocated by balancing power compensation capacity and physical connectivity. This method competes with cluster partitioning and selects pilot buses. Then, an active and reactive power coordinated control model based on non-dominated sorting genetic algorithm II (NSGA-II) is developed. The model aims to minimize voltage violations, distribution network losses, and power consumption costs. Finally, five representative control scenarios are simulated and compared on an enhanced IEEE 51 bus distribution network. The results show that the proposed strategy effectively mitigates node voltage violations, reduces the losses, and enhances resource efficiency.

Shared energy storage (SES) and some photovoltaic prosumers (PVPs) are difficult to aggregate by the virtual power plant (VPP) in the short term. In order to realize the optimal operation of the VPP in the incomplete resource aggregation environment and to promote the mutual benefit of multiple market entities, the energy trading strategy based on the hybrid game of SES–VPP–PVP is proposed. Firstly, the whole system configuration with incomplete resource aggregation is proposed, as well as the preconfigured market rules and the general problem for the optimal energy trading strategy of VPP. Secondly, the novel hybrid game theory-based optimization for the energy trading strategy of VPP is proposed based on the multi-level game theory model. And, the corresponding solving process using Karush–Kuhn–Tucker (KKT), dichotomy, and alternating direction method of multipliers (ADMM) algorithms are also constructed to solve nonconvex nonlinear models. The effectiveness of the proposed strategy is verified through the comparison of a large number of simulation results. The results show that our proposed energy trading strategy can be used for optimal low-carbon operation of VPPs with large-scale renewable energy and some unaggregated electricity consumers and distributed photovoltaic stations, while SES participates as an independent market entity.

Existing short-circuit calculation methods for distribution networks with renewable energy sources ignore the fluctuation of renewable sources and cannot reflect the impact of renewable sources and load changes on short-circuit current in real time at all times of the day and in extreme scenarios. A real-time short-circuit current calculation method is proposed to take into account the stochastic nature of distributed generators (DGs) and electricity loads. Firstly, the continuous power flow of distribution networks is calculated based on the real-time renewable energy output and electricity loads. And then, equivalent DG models with low-voltage ride through (LVRT) strategies are substituted into the iterative calculation method to obtain the short-circuit currents of all main branches in real time. The effects of different renewable energy output curves on distribution network short-circuit currents are quantitatively analyzed during the fluctuation in distributed power output, which can provide an important basis for the setting calculation of distribution network relay protection and the study of new principles of protection.

Based on the operation characteristics of each dispatch unit, a multi-objective hierarchical Microgrid (MG) economic dispatch strategy with load level, source-load level, and source-grid-load level is proposed in this paper. The objective functions considered are to minimize each dispatching unit’s comprehensive operating cost (COC), reduce the power fluctuation between the MG and the main grid connect line, and decrease the remaining net load of the MG after dispatch by way of energy storage (ES) and clean energy. Firstly, the load level takes electric vehicles (EVs) as a means of controlling load to regulate the MG’s load fluctuation using its energy storage characteristics under time-of-use (TOU) price. Then, in order to minimize the remaining net load of the MG and the COC of the ES unit through Multiobjective Particle Swarm Optimization (MPSO), the source-load level adopts clean energy and ES units to absorb the optimized load from the load level. Finally, the remaining net load is absorbed by the main grid and diesel engines (DE), and the remaining clean energy is sold to the main grid to gain benefits at the source-grid-load level. Ultimately, the proposed strategy is simulated and analyzed with a specific example and compared with the EVs’ disorderly charging operation and MG isolated grid operation, which verifies the strategy’s scientificity and effectiveness.