To address weak infrastructure， poor voltage stability， and low renewable-energy utilization in rural areas， this paper proposes a siting-and-sizing model for distributed generation （DG） that simultaneously optimizes voltage quality and economic performance. One objective aims to minimize voltage deviations caused by DG integration， thereby enhancing distribution-network power quality； the other seeks to minimize the levelized cost of energy （LCOE） over the full life cycle of the DG portfolio， accounting for investment， operation and maintenance expenses， and energy yield. The model is solved with a double deep Q-network （DDQN）， yielding a configuration that balances voltage stability and cost. Simulation on a modified IEEE 33-bus rural feeder shows that the DDQN-based scheme markedly improves voltage profiles while reducing upgrade costs. Furthermore， comparative analyses with the deep Q-network （DQN）， non-dominated sorting genetic algorithm II （NSGA-II）， and multi-objective particle swarm optimization （MOPSO） methods verify the superiority of the proposed approach， highlighting the efficiency， adaptability， and robustness of reinforcement learning for complex energy-system optimization.

With the continuous increase in the grid-connected capacity of distributed photovoltaics in rural distribution networks， it has become extremely urgent to achieve the flexible transformation of distributed photovoltaics， enabling them to be measurable， adjustable， and controllable. Based on this backdrop， this paper proposes a rolling approximate dynamic programming （ADP） model for rural distribution networks under the process of distributed photovoltaic flexible transformation. Firstly， taking into account the process of distributed photovoltaic flexible transformation， based on the concept of “multi-stage planning and first-stage implementation”， a rolling multi-stage stochastic programming （MSSP） model for rural distribution networks is established. The MSSP model comprehensively considers various factors such as the construction progress of distributed photovoltaics and the dynamic changes in power demand in rural areas at different stages. Secondly， using the ADP algorithm based on the Markov decision process （MDP） as the core， a rolling ADP algorithm is developed. This algorithm can iteratively optimize the decision-making process in each stage， thereby realizing the rolling solution of the proposed planning model. Through this approach， the model can adapt to the changing scenarios in the process of rural distribution network planning and distributed photovoltaic development. Finally， the improved IEEE 33-bus typical system is employed to validate the proposed model and algorithm. The simulation results demonstrate that the proposed model can effectively address the challenges in the process of distributed photovoltaic flexible transformation in rural distribution networks. It can obtain an optimal configuration plan that not only offers better economic benefits but also exhibits a stronger ability to handle the continuously emerging long-term uncertainties.

In recent years，typhoons have occurred frequently in coastal cities，and the vulnerability of distribution network lines and loads affected by typhoons has been increasing day by day. Therefore，an optimal scheduling model of mobile emergency power supply vehicle based on hierarchical sequence method was proposed. Firstly，the failure rate model under extreme disaster conditions was constructed，and the Monte Carlo method was used to determine the line vulnerability model. At the same time，the synergy of multiple flexible resources such as distributed power sources，energy storage systems and mobile emergency power vehicles under different spatial and temporal scales was considered to formulate a reliable distribution network resilience assessment model combining islanding and reconfiguration. Finally，the original nonlinear problem was convexized into a standard mixed integer second-order cone problem that was easy to solve by the second-order cone relaxation technique. In this study，the hierarchical sequential solution algorithm is used to take the supply rate of important loads as the main goal，while reducing the overall load loss of the distribution network as much as possible as a sub-goal. The effectiveness of the strategy is verified by the example results. Compared with other scheduling methods，the accuracy of the algorithm are further proved.

In low-voltage distribution networks with large-scale integration of distributed photovoltaic （PV） systems， existing residual current devices （RCDs） cannot distinguish between abnormal PV leakage currents and electric shock currents in residual current circuits， leading to frequent misoperations. This poses risks to electrical safety and power supply reliability. To solve this problem， this study proposes a leakage fault identification method based on support vector machine （SVM） and an electric shock current detection method based on extreme gradient boosting （XGBoost）. Firstly， variational mode decomposition （VMD） is used to extract components of residual current signals under different leakage scenarios， establishing a fault feature dataset. Then， using these features as input， an SVM model optimized by the sparrow search algorithm （SSA） is developed to identify leakage fault types. For cases where residual currents fail to reflect real electric shock conditions， an XGBoost regression model optimized by grid search and cross-validation （GSCV） is built to accurately extract electric shock currents from residual currents. Test results show that compared to standard SVM and kernel extreme learning machine （KELM） models， the SSA-SVM model achieves the highest leakage fault identification accuracy， with an average of 99.28%. The GSCV-XGBoost model accurately fits the extracted electric shock currents to real values. This work provides a theoretical basis for developing new RCDs with leakage fault identification and electric shock current detection capabilities.

The integration of high proportion of new energy into distribution networks poses significant challenges for voltage control of distribution network. Traditional voltage control methods cannot ensure satisfactory control performance in scenarios with high penetration of new energy. Additionally， traditional voltage control relies on mechanical voltage regulation equipment， and frequent operation of these equipment significantly affects their lifespan. To address these issues， this paper proposes a multilayer voltage control method based on model predictive control (MPC). In the proposed method， mechanical voltage regulation equipment， such as transformers and shunt capacitors， is controlled in the upper layer with a longer control period， while active and reactive power outputs of distributed generations are rapidly adjusted in the lower layer with a shorter timescale. The objective of the upper-layer control is to reduce the number of operation of mechanical voltage regulation equipment， while the lower-layer control aims to minimize network losses and active power curtailment， ensuring the economic operation of the distribution network. The proposed method considers both the current and future states of the distribution network， ensuring that voltage operates within allowed limits under high penetration of new energy. Simulation results show that the proposed method can effectively address the voltage violation issues introduced by the high penetration of new energy in the distribution network， decreasing the system voltage mean square error from 3.9% to 0.97%. Additionally， the proposed method significantly reduces operation number of mechanical voltage regulation equipment， which is only 40% and 16.4% of those under traditional voltage control methods， respectively.

A microgrid model is established to address the optimization and scheduling of microgrid in the context of new energy generation access and demand response. The objective function is constructed to consider the user's electricity discomfort caused by demand response and the operating cost of the system， and the user's transferable load is adjusted. Based on the randomness and volatility of wind and solar power output， the fuzzy K-means algorithm is used to cluster the wind and solar power output data and obtain typical wind and solar power output curves. Next， this paper improves the Harris hawks optimization (HHO) algorithm to address the issues of uneven population distribution and susceptibility to local optima. Firstly， Tent mapping is introduced in the initialization stage of the population to make the initial population coverage more comprehensive and avoid falling into local optima in the early stage. Then， Levy flight function is introduced in the search stage to enhance the global search ability of the algorithm. Finally， improved HHO (IHHO) algorithm is applied to optimization and compared with classical algorithms. The final results validate the effectiveness of the proposed strategy and the superiority of the IHHO algorithm.

Under the background of the rapid development of green economy and new energy, renewable energy power supply has been developed rapidly. In order to deal with the problems of high volatility and weak anti-interference ability of distributed energy, it is of great significance to explore the operation strategy of independent power supply microgrid group dominated by renewable energy. According to the planning and construction of microgrid and the corresponding control mode, the independent operation and interconnected operation strategies of microgrid are proposed. In order to reduce the phenomenon of abandoned wind and light in the independent operation of microgrid and improve its economic benefits, this paper established a bi-level optimization model covering the internal and inter-microgrid scheduling with the goal of minimizing the independent operation cost of microgrid and maximizing the social benefits of microgrid interconnected operation. The alternating direction multiplier (ADM) method is used to solve the problem iteratively to obtain the renewable energy output power, the energy storage output power, and the electricity purchase and sale strategy that take into account the overall operation efficiency of each micro-network body and its group. Finally, through the simulation analysis of 14-node independent power supply microgrid group, the rationality of the proposed model and the effectiveness of the method are verified.

Due to the random variation of load, the participation of demand response, the fluctuation of distributed power supply, and the variety of measurement devices, the measurement data of distribution network is prone to abnormal values, which leads to the decline of dynamic state estimation accuracy. In order to improve the accuracy of distribution network state estimation, this paper proposed a dynamic state estimation method for distribution network based on adaptive H_∞ cubature Kalman filter. Firstly, based on the cubature Kalman filter, the adaptive factor and H_∞ filter were combined to deal with and limit the model error. Secondly, combined with the noise estimator, the parameters in the process noise were estimated online to reduce the influence of noise on the prediction error. Finally, a typical distribution network system with 69 nodes was simulated. The simulation results show that the estimation accuracy of the proposed method is improved by more than 10% under three scenarios: system normal operation, demand response participating in peak load shaving and load mutation, maintaining a relatively high estimation accuracy.

When discharging, electric vehicles can serve as distributed energy storage units of the power grid to alleviate the power supply pressure of microgrids with high proportions of new energy integration. Capitalizing on the characteristics of time-of-use tariffs across multi-time scales, this study proposes a multi-time scale optimization scheduling method for microgrids that takes into account clusters of electric vehicles. In day-ahead scheduling phase, the equipment output such as internal energy storage, interruptible loads and transferable loads in the microgrid is optimized based on time of use tariffs; During intra-day optimization scheduling phase, electric vehicle clusters will be included in the energy scheduling of microgrids, and reasonable charging and discharging can be achieved by analyzing the scheduling potential of each electric vehicle cluster. To verify the effectiveness of the proposed scheme, electric vehicle clusters are selected to participate in microgrid energy scheduling based on variable time of use tariffs during peak, flat, and valley periods. The results show that the multi-time scale optimization scheduling for microgrids considering the participation of electric vehicle clusters can make full use of the energy storage resources of electric vehicle clusters and improve the flexibility and economy of microgrid scheduling operation.

To evaluate the reliability of islanded microgrid and analyze the impact of energy storage devices on system reliability, a reliability evaluation scheme of islanded microgrid based on sequential Monte Carlo simulation method was proposed. Firstly, the output model of key equipment such as distributed power supply is built by taking the island type wind turbine/diesel generator/energy storage hybrid microgrid system as the research object. Secondly, the reliability evaluation index system, different energy storage operation strategies and load reduction strategies were formulated. On this basis, the reliability evaluation algorithm of islanded wind turbine/diesel generator/energy storage microgrid based on sequential Monte Carlo simulation method was proposed. Finally, through the simulation of the improved RBTS Bus 6 F4 islanding system, the influence of the operation strategy, storage capacity and capacity configuration of the energy storage device on the reliability level of the system was quantitatively analyzed. The results show that reasonable energy storage strategy, appropriate increase of energy storage capacity and appropriate storage system configuration scheme can improve the system reliability.

In order to build a new energy system and a new power system with new energy as the main body, different types of new energy have joined the power system, and the influence of the addition of a high proportion of new energy on the power system has become increasingly apparent. Based on the era background of national efforts to develop new energy, combined with the location advantages of small hydropower and wind power in the mountainous area of Guangdong province, using theory, simulation and other research methods, the frequency variation law of multi-energy complementary microgrid island composed of different kinds of new energy such as photovoltaic, wind power, small hydropower was studied in the initial stage. Firstly, the variation of the amplitude and frequency of small hydropower, wind power and photovoltaic power generation as separate distributed energy sources were respectively connected to the microgrid island operation. It was found that only the photovoltaic microgrid had better island operation stability. Then, the influence of the addition of small hydropower and photovoltaic on the amplitude and frequency change of wind power microgrid in the initial island was discussed. It was found that photovoltaic had a supporting effect on the whole working condition of wind power microgrid, while small hydropower had a greater supporting effect under specific working conditions. Finally, the influence of amplitude and frequency changes in the initial phase of the wind-light-water multi-energy complementary microgrid islanding was analyzed. The results showed that the wind-light-water multi-energy new energy had better operation stability under a certain ratio.

A hierarchical optimization scheduling model considering the coordination between demand response, the photothermal power stations, and electric heaters is proposed for the optimization scheduling problem of a cogeneration microgrid for a photothermal power station. In the upper-level model, the load curve is divided using the moving boundary method, and electricity prices are solved at different time periods with the goal of minimizing the difference between renewable energy and load. The lower-level model aims to minimize the scheduling cost of the microgrid. Considering the inability of wind and photovoltaic power alone to meet load demands, etc, the model also includes the regulation scheduling of the photothermal power station and controllable power sources. An economic dispatch optimization model based on mixed-integer linear programming is established, incorporating the coordinated scheduling of the photothermal power stations, demand response, and electric heaters. The effectiveness and rationality of the proposed method are verified through a practical case.

Ignoring the load characteristics of distribution network operation will lead to insufficient optimization of battery state and power exchange. In order to solve the problems of distribution network node topology and load uncertainty, a hierarchical coordination control method for distribution network considering load uncertainty is proposed. The topological structure of distribution network including load node, transformer, circuit breaker, isolation switch and other nodes is analyzed, and the energy relationship under grid-connected mode and island mode is calculated respectively, and the hierarchical coordination control model of source, network, load and storage is established. In addition, Atin hypercube sampling method is used to generate uncertain scenes, and parameters such as inertia weight and learning factor are introduced to improve the particle swarm optimization algorithm, and the regulation process of multi-objective layered particle swarm optimization is designed to realize the layered coordinated control of load and storage in the source network of the distribution network. The experimental results show that the energy state of the battery is better and the exchange power is higher after the method is applied. The effect of hierarchical coordination control is better, and the load state of distribution network can be adjusted adaptively according to the actual demand.

Aiming at the problem of synchrophasor measurement with harmonics, out-of-band interference, and other dynamic interferences, a new frequency measurement algorithm combining a finite impulse response filter with M-class synchrophasor measurement algorithm (FIR-MSMA) is proposed for distribution networks. Firstly, the least squares method is utilized to design a FIR-1 filter for extracting the low-frequency signals, and to design a FIR-2 filter for measuring frequency. Then, the parameter setting principles for two types of filters are analyzed. Finally, simulation examples in Matlab software are performed to verify the dynamic measurement performance of the proposed algorithm. The results show that the FIR-MSMA algorithm has stronger anti-interference ability and higher measurement accuracy than the discrete Fourier transform algorithm and the sliding average filter algorithm.

Through researching the power consumption strategy of microgrid, a bi-layer optimization model for microgrid is proposed in response to the situation that the power supply capacity of industrial parks cannot meet the demand for electricity loads. In the outer layer of the optimization model, an economic benefit model of photovoltaic-energy storage system is established, and the model uses payback period and internal rate of return as evaluation indicators of economic benefits. In the inner layer, the operating states of the microgrid, load, and energy storage system are taken as constraint conditions, and the cooperation between distributed photovoltaic, energy storage system and load is considered synchronously, and the model uses power supply reliability as an evaluation index to set up charging and discharging operation strategies of battery storage system. This paper selects an industrial park in the northwest region as the application scenario, and solves the optimization model to determine the optimal capacity configuration of the microgrid system. At the same time, by comparing and analyzing between the improved optimization scheme and the energy storage capacity configuration scheme that meets continuous power supply, it is found that the proposed energy storage capacity configuration can achieve optimal economic benefits, and its effectiveness and feasibility of the model are verified in practice.

This article proposes a two-stage strategy for improving the elastic resilience of distribution network systems under extreme weather conditions, with a long and short time scale. After a disaster occurs, the distribution network is isolated and reconstructed to restore short-term power supply by arranging distributed power sources and interconnection switches in a short period of time. Considering the impact of extreme weather on the transportation network after an extreme disaster occurs, the maintenance team needs to complete maintenance in the shortest possible time to minimize load reduction costs. For analyzing the evolution of long and short-term faults, a robust optimization (RO) model is established through collaborative optimization, which is solved using column and constraint generation (C&CG) algorithm. Finally, case studies are conducted to verify the effectiveness of the proposed model in improving the elastic resilience of distribution networks.

In order to improve the fault diagnosis accuracy of three-phase microgrid lines, a fault diagnosis and classification method based on adaptive variational mode decomposition (AVMD) and convolutional neural network (CNN) is proposed in this paper. Firstly, a microgrid radial structure model including wind, light and water system is established. AVMD is used to decompose the original fault signal into multiple modal components, where the parameters of variational mode decomposition (VMD) are optimized by aquila optimizer (AO). Only a few of the modes retain the fault signal information. Effective weighted peak relevance index (EWPR) is used to select the modal components, and the three modes that can best retain the fault information are selected as sensitive modes. The influence of noise and other irrelevant modes is eliminated, and CNN is used to diagnose and classify the circuit faults of the microgrid. 110 groups of fault data are generated for training and verification of neural network. The results show that 21 groups of data in 22 groups of verification data sets are classified correctly, and the fault diagnosis accuracy of this method reaches 95.46%.

Under the aggregator management model, efficient utilization of the regulation capacity of large-scale electric vehicles (EV) can reduce system operating costs and facilitate the consumption of renewable energy resources (RES). At the same time, with the popularization of EV fast charging facilities, compared with slow charging, there may be more flexibility for EV to participate in grid auxiliary services under fast charging mode, but it is undoubted that the affordability of the power system is tested. To give full play to the advantages of resource flexibility on the demand side, this paper considers applying the fast charging concept to vehicle to grid (V2G) scenario i. e. considering the two-way fast power, and a day-ahead economic scheduling method for distribution networks considering the flexibility of EV charging and discharging modes is proposed. It includes two models: the EV aggregation model considering charging and discharging modes and the distribution network economic dispatch model. The results show that the strategy improves the flexibility of the dispatchable domain of EV and brings considerable benefits to the economic scheduling of the distribution network.

The high proportion of wind-photovoltaic power supply connected to microgrid system introduces more uncertain factors, which brings challenges to the optimal dispatching of power grid. In order to improve the economic and environmental performance of the system, rationally allocate the installed capacity of the wind and photovoltaic units, and promote the consumption of the wind-photovoltaic energy, this paper establishes a high-proportion clean energy microgrid model, defines the economic and environmental objective functions, proposes a multi-objective optimal scheduling strategy, and uses particle swarm optimization algorithm to solve the problem. The influence of clean energy permeability on the economic and technical characteristics of microgrid and the consumption rate of clean energy is explored through the analysis of examples. With the increase of the permeability of clean energy, the absorption rate generally shows a downward trend, and there is a critical permeability value. Increasing the permeability within the critical range can effectively improve the comprehensive operation efficiency of microgrid. The results show that the proposed scheduling strategy is correct and effective, the algorithm convergence is reliable, and it can provide an important reference for the rational allocation of the installed capacity of wind and photovoltaic units.