168 results about "Pv power" patented technology

The invention provides a photovoltaic output scene clustering method. High-frequency noise of an original photovoltaic output time sequence is suppressed through self-adaptive smoothing processing, fluctuation characteristics of the high-frequency noise are extracted, it is ensured that target time sequence data accurately reflect photovoltaic power generation working conditions, and high-quality input is provided for subsequent clustering; the target clustering number is determined based on joint optimization parameters (fusing local dynamic characteristics and global distribution characteristics), and scene division deviation caused by subjective setting of the clustering number is avoided; the clustering center is updated by iteratively optimizing the clustering objective function, and it is ensured that the typical scene center truly reflects the mainstream output mode. Therefore, scene representativeness is improved in the aspects of data preprocessing, clustering number decision making and central optimization of a whole link, and generalization ability of a power grid dispatching strategy is remarkably enhanced.

The invention relates to a water-light-storage integrated micro-grid system and a control method thereof. The system comprises a distributed hydroelectric power supply, a distributed photovoltaic power supply, an energy storage system, a medium-voltage AC-DC flexible interconnection device, a micro-grid energy management system and a load. The medium-voltage AC / DC flexible interconnection device is connected in series between a grid-connected point of the micro-grid and a superior power distribution network, an AC port of the medium-voltage AC / DC flexible interconnection device is used for realizing flexible power flow mutual aid between the micro-grid and the superior power distribution network, and a DC port of the medium-voltage AC / DC flexible interconnection device is used for being connected with an energy storage system; and the micro-grid energy management system is in communication connection with the distributed hydroelectric power supply, the distributed photovoltaic power supply, the energy storage system and the medium-voltage AC / DC flexible interconnection device, and is used for collecting system operation data, executing an optimization control strategy and realizing autonomous operation of the micro-grid and cooperative control of the micro-grid and a superior power distribution network. The system can effectively cope with seasonal load peaks, greatly improve the local consumption rate of distributed water and light resources, and enhance the power supply reliability and operation flexibility of a local power grid.

The invention relates to the technical field of photovoltaic power generation, in particular to a regional autonomous regulation and control method and system for distributed photovoltaic groups and user side energy storage. The method comprises the following steps: obtaining photovoltaic group output data and user side load data; performing energy balance evaluation according to the photovoltaic group output data and the user side load data to determine a regional energy gap; performing energy storage autonomous adjustment analysis based on the regional energy gap to obtain energy storage autonomous adjustment data; performing peak clipping and valley filling identification based on the energy storage autonomous adjustment data to obtain peak clipping and valley filling data; positioning an energy mutual aid path by using peak clipping and valley filling data; performing power grid stability detection based on the energy mutual aid path to obtain power grid stability data; predicting a critical instability state according to the power grid stability data; and adjusting energy storage parameters according to the critical instability state to obtain energy storage adjustment parameters. Based on the photovoltaic power generation technology, the energy dispatching precision and the power grid operation stability of the regional photovoltaic and energy storage system are improved.

A DC microgrid has increased DC bus voltage inertia without adding large physical capacitors. A Virtual Inertia Enhancement Controller (VIEC) of a DC-DC converter emulates a DC machine's mechanical inertia characteristics through a virtual capacitor which compensates for a power imbalance causing DC bus voltage fluctuations. When a battery charges and generated photovoltaic (PV) power is more than the load power, VIEC controls the PV's DC-DC converter to the DC bus. When the battery discharges and generated PV power is less than load power, VIEC controls the battery's DC-DC converter to the DC bus. VIEC controls both the PV's DC-DC converter and battery's DC-DC converter to the DC bus when generated PV power is more than the load power and battery is fully charged. VIEC emulates an additional virtual capacitance in parallel with DC link capacitors. DC bus voltage fluctuations are significantly reduced by the VIEC emulating virtual capacitors.

The invention discloses an ultra-short-term photovoltaic output prediction method based on multi-modal data and adaptive weights, and belongs to the technical field of electric power and artificial intelligence. Historical photovoltaic output data of a photovoltaic power station and sky image data at corresponding moments are acquired through measurement equipment, and a multi-modal photovoltaic prediction data set is constructed; respectively constructing an original sequence, a primary difference sequence and a secondary difference sequence for the historical photovoltaic output data, carrying out feature extraction, and carrying out feature extraction on the sky image sequence; a cross-modal attention mechanism is introduced, and interactive fusion between photovoltaic time sequence features and sky image features is realized; a fusion weight is generated based on sky image features, and adaptive weighted fusion is carried out on the multi-path features; and training an ultra-short-term photovoltaic output prediction model by using the fusion features, and predicting the photovoltaic output in real time. The method can improve the accuracy and stability of photovoltaic output ultra-short-term prediction, and is especially suitable for a novel power system operation scene with high-proportion photovoltaic access.

A state-enhanced method and system for large-scale distributed photovoltaic (PV) power shaving includes: establishing a deep reinforcement learning model for distributed PV power shaving; establishing a multi-timescale reward function as a composite reward function for the deep reinforcement learning model; during training the deep reinforcement learning model, calculating the transformer overload performance index in each iteration, calculating adaptive noise based on the transformer overload performance index, generating a perturbation term, and superimposing the perturbation term on the current policy network; using the deep reinforcement learning model for distributed PV power shaving to perform peak shaving, establishing a state enhancement space based on state transmission delay and weather conditions during peak shaving, predicting the state delay compensation amount at each time step using the state enhancement space, and correcting the current state with the compensation amount. This invention improves the accuracy and timeliness of peak shaving decisions through multi-timescale reward functions, adaptive noise, and state enhancement.

This invention provides a method for detecting anomalies in low-voltage residential photovoltaic (PV) power generation. The method includes acquiring low-voltage PV power generation user profile information and identifying user data for each user; calculating the electricity consumption data for each user based on their user data; establishing preliminary anomaly screening rules correlated with daily power generation and installed capacity; and filtering all PV users in the low-voltage PV user profile information by combining their user data and corresponding electricity consumption data to obtain PV users that meet the preliminary anomaly screening rules; and using a support vector machine (SVM) algorithm to perform cluster analysis on the PV users that meet the preliminary anomaly screening rules to further select anomalous PV users. This invention also provides a low-voltage residential PV power generation anomaly detection system. Implementing this invention overcomes the problems of complex operation, low efficiency, and low accuracy in existing low-voltage residential PV power generation anomaly detection methods.

The invention discloses a direct-current bus harmonic suppression method and system for photovoltaic grid connection in an industrial park, and relates to the technical field of harmonic suppression of a direct-current bus of a photovoltaic power supply and improvement of the power quality of a power distribution network, and the method comprises the steps: generating required direct-current output through series voltage boosting and parallel current increasing, constructing a photovoltaic power generation unit, and adding a DC-DC converter, based on MPPT, a three-layer control method is adopted to track the maximum power point and control a DC-DC converter switching device; for a DC-DC converter and a DC-AC inverter, system mathematical modeling and theoretical analysis are carried out, and a DC side current is deduced; and based on the collected DC side current, extracting a specific harmonic component and generating a reverse compensation signal, and injecting the signal into a corresponding DC bus control link for harmonic suppression. According to the method, the DC bus voltage ripple coefficient is reduced, harmonic waves generated by MPPT, phase-locked loop disturbance and switch modulation are suppressed, the total harmonic distortion rate of the output current of the inverter can be reduced, and the stability margin of the system under the weak power grid condition is improved.

The invention discloses a base station power distribution system which comprises at least one of a PV / DC photovoltaic power module and a DC / DC bidirectional stacked storage power module, a stacked storage battery, a system centralized monitoring module, a load primary power-off port, a load secondary power-off port and a load power-off and data acquisition control module. The system can be flexibly configured as a pure light system, a stack storage system or a light storage integrated system, facilitates the subsequent increase, decrease and change of the configuration according to the needs, is high in expansibility, and enables the resource utilization rate to be maximized. The integrated design is integrated to complete system operation and work scheduling required by functions, high reliability and stability are achieved, meanwhile, system operation state information is integrated to be in butt joint with a data platform, and remote inspection operation and maintenance are facilitated.

The invention provides a photovoltaic inspection method and device, electronic equipment and a storage medium, and relates to the technical field of unmanned aerial vehicle photovoltaic inspection, and the method comprises the steps: obtaining the geographic information, photovoltaic array parameters and inspection task parameters of a photovoltaic station; wherein the inspection task parameters comprise inspection time and an expected sampling distance; determining a mirror reflection area of the photovoltaic array according to the geographic information, the photovoltaic array parameters and the inspection time; and by using a heuristic algorithm, based on at least one of the specular reflection area, the photovoltaic array parameters and the expected sampling distance, a determined constraint condition and a cost function, calculating a target shooting point and a target holder attitude of the unmanned aerial vehicle. The accuracy of a photovoltaic inspection result can be improved, and meanwhile, the overall efficiency of photovoltaic inspection is improved.

The invention relates to the technical field of power systems, and provides a system short-circuit current calculation method after large-scale photovoltaic grid connection and electronic equipment. Comprising the steps that a power grid positive-sequence, negative-sequence and zero-sequence equivalent circuit of a photovoltaic power source is established, and the photovoltaic power source is represented as a controlled current source in a positive-sequence network and is represented as disconnected in a negative-sequence and zero-sequence network; establishing a positive sequence node voltage equation, a negative sequence node voltage equation and a zero sequence node voltage equation based on an equivalent circuit; establishing a boundary condition equation of a fault point according to the fault type; simultaneously solving the node voltage equation and the boundary condition equation by adopting an iteration method, calculating each sequence voltage of each node, in each iteration, judging whether to enter a low-voltage ride-through state, and correcting the output current of the photovoltaic power supply based on a low-voltage ride-through control strategy; and when the iterative calculation converges, calculating the short-circuit current based on the final sequence voltage of each node. The method is suitable for calculation of actual short-circuit current of engineering, and the calculation accuracy is improved.

The invention relates to a photovoltaic power prediction method based on a full-connection space-time diagram and an adaptive attenuation matrix, belongs to the technical field of new energy power generation and intelligent power grids, and particularly relates to multi-station photovoltaic short-term and medium-term power prediction. The method solves the problems that time-space modeling of a traditional multivariable time sequence prediction model is disjointed, far-time information is lost too fast due to attenuation mechanism rigidity, gradient disappears easily, and multi-site data adaptability is poor. According to the technical scheme, the method comprises the steps of multi-source data acquisition and standardized preprocessing, construction of a model integrating 1D-CNN feature coding, a full-connection space-time adjacency matrix, adaptive attenuation matrix fusion, MPNN space-time feature aggregation and a lightweight MLP prediction layer, and generation of a prediction result through training. According to the method, the photovoltaic power prediction precision, stability and engineering deployment adaptability can be remarkably improved, and the method is effectively suitable for complex scenes such as alpine all climate.

The invention relates to a photovoltaic power short-step prediction method and device. The method comprises the following steps: respectively constructing a time convolutional network (TCN) core module based on a preset convolutional neural network architecture and a Transform core module based on an encoder-decoder architecture, and completing the construction of a basic module of a photovoltaic power prediction model; the time convolution network core module and the Transform core module are fused on the basis of a preset serial framework, and a short-term photovoltaic power prediction model of TCN-Transform is generated; preset photovoltaic data parameters are collected and preprocessed, the photovoltaic data parameters are used as a training set and a test set, and training of a short-term photovoltaic power prediction model of TCN-Transform is completed; and through a short-term photovoltaic power prediction model of the trained TCN-Transform, realizing photovoltaic power short-step prediction. According to the invention, through the photovoltaic power prediction model of deep learning, the prediction precision of the short-term photovoltaic power is significantly improved.

This invention discloses a method for low-carbon management of building energy consumption by tracking grid carbon emission factors. The method includes: collecting data on photovoltaic (PV) power generation equipment and air conditioning systems of the target building, as well as real-time power generation, air conditioning load, grid electricity purchase and sale data, and electric vehicle power consumption; modeling the output power of the PV power generation equipment and the air conditioning power; obtaining the dynamic carbon emission factor curve of the target building; constructing the operating cost and carbon emissions of the target building; constructing the objective function and constraints accordingly, including electric vehicle capacity constraints, tie-line power transmission constraints, power balance constraints, and carbon emission constraints; and using a particle swarm optimization algorithm to iteratively seek the optimal solution. This invention enables buildings to rationally utilize resources, thereby effectively reducing carbon emissions from building electricity consumption and improving the direct economic benefits of buildings.

This invention provides a grid-connected control method and system for a distributed photovoltaic (PV) power station. It includes: acquiring the electricity consumption of an electrical device; acquiring the effective power of the PV power station; when the effective power is greater than the electricity consumption, using the PV power station to supply power to the electrical device; and when the effective power is less than the electricity consumption, connecting the PV power station to the grid and using mains power to supply power to the electrical device. Because the effective power of the PV power station can be acquired, and power is supplied to the electrical device only when the effective power exceeds the electricity consumption, and otherwise using mains power, the power supply equipment for the electrical device is a single device, preventing the simultaneous use of two power supply devices, reducing the complexity of power supply control, improving power supply stability, and reducing power supply costs.

This invention discloses a method for detecting and classifying the structural state of photovoltaic (PV) support structures. The method includes: using a Swing Transformer-modified Detectron2 instance segmentation model to obtain pixel-level masks and target regions of the PV support; performing mask reconstruction and anomaly scoring using the ViT-AE autoencoder anomaly detection model to obtain an anomaly severity index; introducing an attention gating mechanism into the Swing Transformer classification network and constructing an SPDG constraint loss with anomaly severity as auxiliary supervision to achieve robust classification of the support structure state. This invention solves the problems of false positives and false negatives caused by overlapping and shading of slender components, strong background interference, and diverse anomaly patterns in PV support structures. It can achieve automated, high-precision, and highly generalizable detection and classification of rail-mounted and flat single-axis supports and their columns, and is suitable for unmanned inspection and intelligent operation and maintenance of PV power plants in complex scenarios such as deserts and mountains.

The invention provides a mountain photovoltaic single unit power abnormity diagnosis method and device, and relates to the technical field of mountain photovoltaic power generation, and the method comprises the steps: judging whether a target unit has a power abnormal reduction condition or not according to a current-day period-by-period power sequence and a historical same-period sunny day power sequence of the target unit of a mountain photovoltaic power station; and if yes, executing the subsequent process. And then power-irradiance synchronism is judged, and the fault condition of the target unit is accurately judged based on a synchronism judgment result. In the scene with high synchronism, the fault reason of the target unit is judged by combining the conditions of other units in the mountain photovoltaic power station and weather factors; and in a scene with low synchronism, based on the current-day power curve and the current-day irradiance curve, a DTW algorithm is utilized to judge a fault abnormity reason and quantify a fault degree. The method accurately adapts to the problem of asynchronous power of the mountain photovoltaic units, and can accurately and reliably diagnose the power abnormality of the single photovoltaic unit in the mountain terrain.

This invention discloses a training method, data processing method, apparatus, equipment, medium, and product for a distributed photovoltaic (PV) power generation prediction model. The method includes: training a central model for meteorological and power generation prediction using sample data; the central model includes a pre-trained self-attention backbone network, a spatiotemporal graph neural network, and an adapter; distributing the central model parameters of the trained central model to at least one distributed PV site, enabling the site to incrementally train its own model based on these parameters; the central model parameters include self-attention backbone network parameters and adapter parameters, with the self-attention backbone network parameters in a frozen state and the adapter parameters in an updatable state. This invention solves the problem of data silos in distributed PV power generation prediction. By using a backbone parameter freezing and adapter incremental training strategy, it significantly improves the collaborative prediction accuracy of PV clusters while reducing communication and computational overhead.

This invention relates to a bifacial photovoltaic (PV) power generation module and a PV power generation system. The bifacial PV power generation module includes a mounting frame, photovoltaic cells, a first reflective mirror, and a second reflective mirror. A first mounting plate and a second mounting plate are connected to form a V-shape. n rows of first photovoltaic cells are mounted on the first mounting plate to form a first photovoltaic cell group, and n-1 rows of second photovoltaic cells are mounted on the second mounting plate to form a second photovoltaic cell group. n-1 rows of first reflective mirrors are mounted on the first mounting plate, and n rows of second reflective mirrors are mounted on the second mounting plate. The first reflective mirrors face the second cell group, and the second reflective mirrors face the first cell group. This invention's bifacial PV power generation module increases the amount of sunlight received by the photovoltaic cells, fully utilizing the power generation characteristics of both reflected and direct sunlight from the bifacial cells. It can generate electricity throughout the sunshine period, thus achieving higher power output and allowing users to obtain a higher return on investment.

This invention discloses a method and system for static reconfiguration of photovoltaic (PV) modules based on energy storage unit compensation, relating to the field of PV power generation technology. The method includes: statically reconfiguring an initial PV array with rows and columns of PV module strings according to column-cycle rules to obtain a fixed electrical connection topology; acquiring the output power data of each row of PV module strings based on this topology and calculating the power difference between rows; determining a target compensation row based on the power difference and a preset distribution level, and determining the corresponding number of distributed energy storage units using an indicator function, where , , and are positive integers; setting a number of distributed energy storage units at the corresponding positions of the target compensation row; and controlling the discharge of the distributed energy storage units at the corresponding positions during operation to compensate for the power difference. This invention optimizes the electrical connection layout through column-cycle static reconfiguration under a TCT structure, combined with precise compensation from energy storage units, effectively solving the maximum power point tracking failure problem.

This invention provides a method and system for locating photovoltaic (PV) modules in a photovoltaic power plant based on average power change rate. The method includes the following steps: First, historical data of the PV power plant is acquired. After removing known fault data segments, an accurate power prediction model is trained for each PV string by combining environmental data and cloud imagery. During real-time operation, a performance deviation feature spectrum is generated by comparing the actual power of the string with the theoretical power predicted by the model. Subsequently, wavelet transform is applied to decompose this feature spectrum to extract multi-dimensional fault feature vectors. The core step is to use a dynamic time warping algorithm to determine whether the fault is an independent fault of the string itself or a coordinated fault caused by the inverter, etc., by measuring the morphological similarity of the performance deviation spectra of each string under the same inverter. Finally, precise location is performed based on the judgment result. This invention achieves accurate fault location for PV modules.

This invention relates to the field of data management technology, and discloses an Internet of Things (IoT) terminal management method and system. By receiving the total power generated by each IoT terminal in a photovoltaic (PV) power plant, the total output power of the PV power plant is determined. When the active power value generated by the PV power plant or the actual value of the high-voltage busbar of the PV power plant's main transformer is compared with the target value issued by the dispatch center, the active power limit of the inverter is automatically adjusted to bring the current active power or voltage generated by the PV power plant closer to the target value, improving accuracy and control speed, meeting grid demand, enhancing the peak-shaving capability of the IoT system, ensuring that the operation of the new energy power plant and the grid is within a safe and controllable range, and calling upon the planned total output power of the PV power plant, thereby realizing active power control and reactive power control of the PV power plant. It tracks the active power adjustment target value and reactive power adjustment target value issued by the dispatch center, achieving the grid connection technology requirements for high-precision peak-shaving of the PV power plant.

The invention discloses a photovoltaic transformer area optimization regulation and control method, system and device and a medium, and belongs to the technical field of power distribution networks, and the method comprises a transformer area intelligent fusion terminal which fuses multi-source real-time data to diagnose abnormity online and executes a hierarchical collaborative optimization regulation and control strategy; the energy storage device is used for storing and releasing electric energy of the transformer area according to the regulation and control instruction; the photovoltaic grid-connected intelligent switch is used for controlling the rigid grid-connected / off-grid state of the photovoltaic power supply; monitoring the state of the transformer area in real time; when abnormity occurs, the energy storage device is adjusted preferentially; if the energy storage adjustment is insufficient, flexible power adjustment is carried out on the photovoltaic inverter through the photovoltaic information acquisition unit; and if the flexible adjustment is invalid or the condition is emergency, the intelligent switch executes rapid rigid off-network through local pre-decision of the fusion terminal. According to the invention, through a cooperative regulation and control strategy of multiple intelligent devices, local rapid response independent of a master station is realized, and the operation safety and economy of the photovoltaic transformer area and the absorption capability of new energy are improved.

This invention belongs to the field of distribution network relay protection technology, and particularly relates to a method for improving the accuracy of relay protection devices in medium-voltage distribution networks containing photovoltaic (PV) power sources. The invention includes: determining the location where distributed PV power sources are connected to the medium-voltage distribution network, and configuring a deeply integrated primary and secondary pole-mounted circuit breaker at the point of common coupling (PCC); co-commissioning the PCC-configured PCC-configured circuit breaker with the main substation; and configuring setting parameters for each relay protection device on the grid according to the location of the distributed PV power source connected to the medium-voltage distribution network. This invention addresses the increasing number and capacity of distributed PV power sources connected to medium-voltage distribution networks, effectively improving the accuracy of relay protection at all levels of the medium-voltage distribution network, ensuring accurate operation of the distribution network during faults, and significantly improving the reliability of the distribution network.

The invention discloses a distributed photovoltaic power station support and a support system, and relates to the technical field of photovoltaic supports. The mounting frame is hinged to the top of the rack body and is used for mounting the photovoltaic panel body; the upper end of the inclined strut column is hinged to the mounting frame, and the lower end is slidably arranged at the top of the rack body through a U-shaped seat; the adjusting seat is driven by external force and drives the U-shaped seat to slide to adjust the inclination angle of the photovoltaic panel body; the first damping part is mounted between the U-shaped seat and the adjusting seat; the lock control mechanism comprises a side plate, a long rod and a short rod, and the side plate and the adjusting seat move synchronously; the long rod is used for being connected with the adjusting base and a limiting hole in the frame body in an inserted mode to lock the adjusting base. According to the distributed photovoltaic power station support and the support system, by arranging the U-shaped base, the adjusting base and other structures, photovoltaic panel body supporting, inclination angle adjusting and damping protection are synchronously achieved, state switching of the damping system is flexibly conducted, and the use efficiency of the distributed photovoltaic power station support is improved.

This application relates to the field of power grid security management technology, specifically to an active integration management system for distribution network power grids with high photovoltaic (PV) integration. This system includes: a power grid dynamic monitoring module: acquiring power flow distribution maps of the distribution network power grid at various times, collecting three-phase voltages in the power grid lines at various times, and the output power of each PV power generation system at various times; a power flow calculation module: determining the power flow increment from each PV power generation system to each power grid line; determining the power flow coordination coefficient between each PV power generation system and each power grid line; and acquiring the power flow radiation lines of each PV power generation system; and a distribution network power grid integration management module: acquiring the power flow disorder of each power flow radiation line; determining the three-phase imbalance of each power flow radiation line; comprehensively evaluating each power flow radiation line belonging to the trunk line; acquiring the key sections of the distribution network power grid; and actively integrating and managing the distribution network power grid. This application improves the security, stability, and power supply quality of the distribution network power grid.

This invention relates to the field of short-term photovoltaic (PV) power prediction, specifically to a short-term PV power prediction method based on Patching-TCN-Transformer. The scheme includes: acquiring and preprocessing historical output data of PV power plants; extracting local features using a TCN module; processing the input sequence through causal convolution and introducing causal constraints; the TCN module, through a stacked residual block structure, achieves deep extraction of local nonlinear fluctuation features of PV output while maintaining temporal order; performing temporal patching processing on the features output by the TCN module; modeling global long-range dependencies using Transformer; and performing multi-step prediction output and inverse normalization to output the prediction result. This invention is applicable to short-term PV power prediction.

The utility model discloses a photovoltaic power supply lightning protection device, and particularly relates to the technical field of photovoltaic power supply lightning protection, the photovoltaic power supply lightning protection device comprises a mounting base, the upper end of the mounting base is provided with a photovoltaic power supply lightning protection device, and the photovoltaic power supply lightning protection device comprises an adjusting assembly and a positioning assembly. Compared with the prior art, the photovoltaic power supply lightning protection device further ensures the installation stability of the power supply lightning protection device body, and when the power supply lightning protection device body needs to be overhauled, the driving motor can be started to drive the power supply lightning protection device body to move downwards to return to the original position. The power supply lightning protection device body needs to be moved out of the slot through the positioning insertion block arranged at the rear end before moving downwards, and the power supply lightning protection device body is adjusted through simple and convenient up-down movement, so that the equipment is ensured to be installed at a certain height; the problem that the maintenance safety of equipment is reduced due to the fact that a worker needs to climb to a high place during the maintenance of the power supply lightning protection device body is avoided.

The invention discloses a rural power distribution network photovoltaic access capacity improvement method based on hybrid energy storage system dual-time scale adjustment capability. The method comprises the following steps: step 1, analyzing rural power consumption demands and seasonal distribution characteristics of illumination resources; step 2, establishing a random scene generation method for generating load and illumination intensity based on a Latin hypercube sampling method; step 3, establishing a hybrid energy storage system dual-time scale operation model; step 4, establishing a two-stage optimization model with the goal of maximizing the photovoltaic absorption capability; 5, solving the two-stage stochastic optimization model by using an improved step-by-step hedging algorithm; and 6, outputting a two-stage optimization decision: determining the access capacity and position of the distributed photovoltaic power supply and the hybrid energy storage system, and making a dual-time-scale optimal operation decision of the hybrid energy storage system. According to the method, the rural load demand and the photovoltaic resource mismatch problem can be reduced to the maximum extent, and the renewable energy utilization efficiency and the operation economy of the rural power distribution network are improved.

This invention belongs to the field of power system modeling and simulation technology, and discloses a method for multi-machine equivalent modeling of distributed photovoltaic (PV) clusters. The method includes: S1, building a model of a single PV system and establishing mathematical models for each sub-module; constructing a simulation environment and building a distributed PV simulation scenario; S2, calculating node voltage sensitivity and constructing an electrical distance matrix; constructing a dynamic characteristic matrix based on the waveforms of active / reactive power after state changes at each PV power station; S3, constructing a comprehensive distance matrix using the weights of the electrical distance matrix and the dynamic characteristic matrix; determining the optimal number of clusters to divide the PV cluster; and S4, aggregating the parameters of PV power stations within the same cluster to construct a multi-machine equivalent model of the distributed PV cluster. This method scientifically considers both electrical distribution characteristics and dynamic characteristics, objectively determines indicator weights, and possesses good engineering applicability.