A distributed new energy consumption regional power distribution method and system

Abstract

The application discloses a kind of distributed new energy consumption's regional power distribution method and system, it is related to electric power system technical field, including obtaining regional power distribution network data, constructs power distribution network analysis model;Based on power distribution network analysis model, by calculating electrical distance and power sensitivity, regional power distribution network is divided into multiple energy autonomous units;The energy complementary index and load elasticity coefficient of each energy autonomous unit are calculated, and a unit feature model is constructed;According to unit feature model, the active power and reactive power output of distributed new energy in each energy autonomous unit are dynamically adjusted;The operating state of energy autonomous unit is monitored, and a coordinated control strategy is implemented to adjust the power exchange between energy autonomous units.The energy autonomous unit division method proposed by the application can accurately identify closely coupled node groups, enabling the local consumption and efficient use of new energy, and reducing transmission and distribution losses.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a regional power distribution method and system for distributed new energy consumption. BACKGROUND

[0002] With the deepening of China's power system reform and the comprehensive promotion of energy transformation, distributed new energy generation technologies represented by photovoltaic power generation and wind power generation have been widely used. Distributed new energy generation has the advantages of local consumption and clean and environmental protection, but due to its scattered access mode and strong intermittent output, it also brings new challenges to the safe and stable operation of the distribution network. At present, the installed capacity of distributed new energy in some areas of China is growing rapidly, and there are prominent consumption problems in local time periods, causing a certain degree of power rationing, which is not conducive to the sustainable and healthy development of distributed new energy.

[0003] In the prior art, methods such as source-grid-load coordinated control and demand side response are usually used to improve the consumption capacity of distributed new energy. However, these methods are mostly based on centralized control architecture, and when facing large-scale distributed new energy access, there are problems such as low regulation efficiency and slow response speed. At the same time, due to the unevenness of load characteristics and new energy distribution in each region of the distribution network, traditional unified regulation strategies are difficult to adapt to the individual needs of different regions, which restricts the overall consumption level of the system. SUMMARY

[0004] The present application provides a regional power distribution method and system for distributed new energy consumption, which is used to solve the technical problems of limited distributed new energy consumption, unbalanced supply and demand in each region, and low coordination control efficiency in the current distribution network.

[0005] Therefore, the first aspect of the present application provides a regional power distribution method for distributed new energy consumption, comprising:

[0006] Obtaining the topological structure data, load distribution data and distributed new energy output characteristic data of the regional power distribution network, and constructing a power distribution network analysis model;

[0007] Based on the power distribution network analysis model, the regional power distribution network is divided into multiple energy autonomous units by calculating the electrical distance and power sensitivity;

[0008] Calculating the energy complementary index and load elasticity coefficient of each energy autonomous unit, and constructing a unit characteristic model;

[0009] According to the unit characteristic model, the active power and reactive power output of the distributed new energy in each energy autonomous unit is dynamically adjusted;

[0010] Monitoring the operating state of the energy autonomous unit, implementing a coordinated control strategy, and adjusting the power exchange between the energy autonomous units.

[0011] Optionally, the constructing the power distribution network analysis model comprises:

[0012] Preprocessing the acquired topological structure data, load distribution data and new energy output characteristic data to generate a standardized data set;

[0013] Based on the standardized data set, a power flow calculation model of the regional power distribution network is established;

[0014] By calculating the voltage sensitivity coefficient and power transmission distribution factor of the electrical nodes in the regional power distribution network, an electrical characteristic correlation matrix is constructed;

[0015] Integrating the power flow calculation model and the electrical characteristic correlation matrix, a power distribution network analysis model is formed.

[0016] Optionally, the dividing the regional power distribution network into multiple energy autonomous units by calculating the electrical distance and power sensitivity comprises:

[0017] Based on the power distribution network analysis model, the electrical distance between each electrical node in the regional power distribution network is calculated to construct a node distance matrix;

[0018] The power sensitivity parameters between each electrical node in the regional power distribution network are calculated to construct a node power sensitivity matrix;

[0019] According to the node distance matrix and the node power sensitivity matrix, a node clustering threshold is determined;

[0020] Based on the node clustering threshold, a hierarchical clustering algorithm is used to cluster the electrical nodes to form preliminary energy autonomous units;

[0021] Checking whether each preliminary energy autonomous unit contains both a new energy access point and a load node, if not, adjusting the division of the preliminary energy autonomous unit;

[0022] Calculating the power balance degree of each preliminary energy autonomous unit, and optimizing the boundary of the preliminary energy autonomous unit according to the power balance degree to form multiple energy autonomous units.

[0023] Optionally, the constructing the unit characteristic model comprises:

[0024] Acquiring historical output data of distributed new energy and historical electricity consumption data of load nodes in each energy autonomous unit;

[0025] Based on the historical output data and the historical electricity consumption data, the time correlation between new energy output and load demand is analyzed, and the energy complementary index of each energy autonomous unit is calculated;

[0026] Based on the historical electricity consumption data, the adjustable load characteristics in the unit are identified, and the load elasticity coefficient of each energy autonomous unit is calculated;

[0027] The unit characteristic model is constructed based on the energy complementation index and the load elasticity coefficient in combination with the power grid constraint condition.

[0028] Optionally, the dynamic adjustment of the active power and the reactive power output of the distributed new energy in each energy autonomous unit comprises:

[0029] Based on the unit characteristic model, a supply-demand prediction framework of the energy autonomous unit is established to generate a prediction result of the load demand and the new energy output;

[0030] According to the prediction result, the power balance state of each energy autonomous unit is determined, and a corresponding operation constraint condition is established;

[0031] In combination with the load elasticity coefficient, a target function of the power regulation in the energy autonomous unit is constructed;

[0032] Based on the target function and the constraint condition, a distributed optimization algorithm is used to solve the optimal active power and the optimal reactive power output value of the distributed new energy;

[0033] The optimal active power and the optimal reactive power output value are sent to each distributed new energy device to complete the power balance regulation in the energy autonomous unit.

[0034] Optionally, the implementation of the cooperative control strategy comprises:

[0035] The key node voltage of the energy autonomous unit and the tie-line power between units are monitored;

[0036] The monitoring data are analyzed and calculated to obtain the voltage deviation value, the power transmission direction and the power flow level between units, and the region to be adjusted is determined;

[0037] Based on the region to be adjusted, in combination with the power regulation capacity and the voltage constraint of each energy autonomous unit, a combination of energy autonomous units participating in power exchange is determined;

[0038] According to the voltage deviation of the combination of energy autonomous units, the required active power and reactive power regulation amount are calculated to generate a corresponding control instruction;

[0039] The control instruction is sent to the power regulation device of the related energy autonomous unit to regulate the power exchange between units.

[0040] The second aspect of the present application provides a regional power distribution system for distributed new energy consumption, comprising:

[0041] A data acquisition module is configured to acquire topological structure data, load distribution data and distributed new energy output characteristic data of the regional power distribution network, and construct a power distribution network analysis model;

[0042] a network partitioning module, configured to divide the regional power distribution network into a plurality of energy autonomous units based on the power distribution network analysis model by calculating electrical distances and power sensitivities;

[0043] a unit modeling module, configured to calculate energy complementation indexes and load elasticity coefficients of the energy autonomous units and construct unit characteristic models;

[0044] a power regulation module, configured to dynamically regulate active power and reactive power outputs of the distributed new energy in each energy autonomous unit according to the unit characteristic models;

[0045] a collaborative control module, configured to monitor operating states of the energy autonomous units, implement a collaborative control strategy, and regulate power exchange between the energy autonomous units.

[0046] The energy autonomous unit division method provided by the application can accurately identify closely coupled node groups, realize nearby consumption and efficient utilization of new energy, and reduce power transmission and distribution losses. The unit characteristic models constructed based on the energy complementation indexes and the load elasticity coefficients effectively quantify the internal supply-demand matching capability and demand-side response potential of the units, provide reliable decision-making basis for optimal scheduling, and improve system operation flexibility. The network analysis model integrating power flow analysis and electrical characteristic matrices enhances the characterization capability of the influence of distributed new energy access, realizes the organic integration of static characteristics and dynamic characteristics of the power distribution network, and provides an effective analysis tool for improving the new energy consumption capacity of the power distribution network. The collaborative regulation mechanism between the energy autonomous units realizes accurate identification and positioning of abnormal areas of the power distribution network through multi-dimensional evaluation of unit operating states, optimizes resource allocation efficiency of distributed new energy consumption, and significantly improves operation flexibility and system stability of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0048] Fig. 1 A flowchart of a regional power distribution method for distributed new energy consumption.

[0049] Fig. 2 A power distribution network analysis model construction flowchart of a regional power distribution method for distributed new energy consumption.

[0050] Fig. 3 An energy autonomous unit division flowchart of a regional power distribution method for distributed new energy consumption. DETAILED DESCRIPTION

[0051] In order to make the application purposes, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0052] Embodiment 1, Reference Figs. 1-3 For the first embodiment of the present application, the embodiment provides a regional power distribution method for distributed new energy consumption. A flowchart of the method is shown in Fig. 1 The method comprises the following steps.

[0053] S1: Collecting topological structure data, load distribution data and distributed new energy output characteristic data of the regional power distribution network, and constructing a power distribution network analysis model.

[0054] In one specific embodiment, a flowchart of the power distribution network analysis model construction is shown in Fig. 2 The method comprises the following steps.

[0055] S1.1: Obtaining topological structure data, load distribution data and distributed new energy output characteristic data of the regional power distribution network.

[0056] The topological structure data includes line parameters, transformer parameters, switch state and node connection relationship; the load distribution data includes historical power consumption data, load type and time-varying characteristics of each load node; and the distributed new energy output characteristic data includes new energy type, installed capacity, historical power generation data and output prediction information.

[0057] Preferably, the obtained data includes topological structure data, load distribution data and distributed new energy output characteristic data, and the selection of these data directly serves the distributed new energy consumption target. The topological structure data reflects the transmission and distribution capacity and flexibility of the power distribution network, and is the basis for evaluating new energy consumption channels; the load distribution data reflects the spatial and temporal distribution law of the power load, and helps to identify demand-side opportunities for new energy consumption; and the distributed new energy output characteristic data describes the supply characteristics and randomness of new energy, which is crucial for formulating accurate consumption strategies. The coordinated application of these data can accurately grasp the matching relationship between new energy supply and load demand, and provide a reliable data basis for optimizing regional power distribution schemes and improving new energy consumption capacity.

[0058] S1.2: Preprocessing the obtained topological structure data, load distribution data and new energy output characteristic data to generate a standardized data set.

[0059] Optionally, the preprocessing includes, but is not limited to, data cleaning, validity verification, data standardization and outlier processing.

[0060] S1.3: Based on the standardized data set, a power flow calculation model of the regional power distribution network is established.

[0061] S1.4: By calculating the voltage sensitivity coefficient and the power transmission distribution factor of the electrical nodes in the regional power distribution network, an electrical characteristic correlation matrix is constructed.

[0062] In the embodiment, the electrical nodes at least include transformer bus nodes, load access point nodes and distributed new energy access point nodes.

[0063] Specifically, based on the power flow calculation model, the transformer bus nodes are taken as references, the load access point nodes and the distributed new energy access point nodes are selected as injection nodes respectively, a small perturbation is applied, the voltage responses of all electrical nodes are recorded, the voltage sensitivity coefficients between nodes are calculated, the mapping relationship between node injection power and branch power is established by using the direct current power flow equation, the power transmission distribution factor is solved, and the voltage sensitivity coefficients and the power transmission distribution factor of each electrical node are combined to construct the electrical characteristic correlation matrix. Through the electrical characteristic correlation matrix, the interaction relationship and coupling characteristics between nodes in the power distribution network can be reflected, and a quantitative theoretical basis is provided for the access evaluation and consumption scheme of the distributed new energy.

[0064] S1.5: The power flow calculation model and the electrical characteristic correlation matrix are integrated to form a power distribution network analysis model.

[0065] Further, the network state information of the power flow calculation model and the electrical characteristic correlation matrix are mapped and integrated to establish a unified network characteristic expression; based on the integrated characteristic expression, a power distribution network analysis model including power flow analysis and electrical characteristic calculation is constructed.

[0066] Preferably, by combining the power flow analysis and the electrical characteristic matrix, the model enhances the characterization ability of the influence of the distributed new energy access, realizes the organic integration of the static characteristics and the dynamic characteristics of the power distribution network, and provides an effective analysis tool for improving the new energy consumption capacity of the power distribution network.

[0067] S2: Based on the power distribution network analysis model, the regional power distribution network is divided into multiple energy autonomous units by calculating the electrical distance and the power sensitivity.

[0068] In one specific embodiment, the energy autonomous unit division flow chart is as shown in Fig. 3 .

[0069] S2.1: Based on the distribution network analysis model, the electrical distance between each electrical node in the regional distribution network is calculated, and a node distance matrix is constructed.

[0070] Wherein, the electrical distance is calculated based on the impedance parameter between nodes, which is extracted from the network parameters of the distribution network analysis model and corrected in combination with the power flow calculation result; the node distance matrix is constructed, and the matrix elements are the electrical distance between node i and node j. The node distance matrix directly represents the electrical connection tightness between each node in the distribution network, and provides a spatial correlation basis for subsequent energy autonomous unit division.

[0071] S2.2: Calculate the power sensitivity parameters between each electrical node in the regional distribution network, and construct a node power sensitivity matrix.

[0072] In this embodiment, the power sensitivity parameters include active power-voltage sensitivity and reactive power-voltage sensitivity, which can effectively reflect the influence degree of node power change on system voltage. Wherein, the active power-voltage sensitivity and the reactive power-voltage sensitivity are calculated by the partial derivative of node voltage to power injection, and the real-time network state provided by the distribution network analysis model is considered in the calculation process.

[0073] Further, based on the power sensitivity parameters, a node power sensitivity matrix is constructed, and the matrix elements represent the influence degree of power change at node j on the voltage of node i. Through the power sensitivity matrix, the node group with close electrical coupling relationship in the distribution network can be identified, and the power exchange between these nodes has strong mutual influence on system voltage, which provides a functional correlation basis for the division of energy autonomous unit.

[0074] S2.3: Determine the node clustering threshold according to the node distance matrix and the node power sensitivity matrix.

[0075] Specifically, based on the node distance matrix, the distribution characteristics of the electrical distance are calculated by using statistical analysis method to obtain the distance threshold initial value; based on the node power sensitivity matrix, the power coupling strength judgment standard is set to determine the sensitivity threshold initial value; the comprehensive clustering threshold is obtained by using the weighting method by comprehensively considering the distance threshold initial value and the sensitivity threshold initial value; according to the actual scale and topological characteristics of the regional distribution network, the comprehensive clustering threshold is corrected to obtain the final node clustering threshold.

[0076] Preferably, the weighting method adopts an adaptive weight distribution mechanism, dynamically adjusts the weight coefficients of the distance threshold and the sensitivity threshold according to the voltage stability and power flow distribution characteristics of the nodes in the network, so that the sensitivity characteristics are given priority in the area with large voltage fluctuations, and the electrical distance characteristics are emphasized in the area with balanced power flow distribution, thereby improving the accuracy and pertinence of the clustering threshold. This multi-dimensional index fusion threshold determination method can comprehensively reflect the physical connection relationship and functional coupling characteristics between nodes, effectively avoid clustering deviation caused by a single index, provide a scientific basis for accurate division of energy autonomous units, and further improve the consumption efficiency of distributed new energy.

[0077] S2.4: Based on the node clustering threshold, a hierarchical clustering algorithm is used to cluster the electrical nodes to form preliminary energy autonomous units.

[0078] S2.5: Check whether each preliminary energy autonomous unit contains new energy access points and load nodes at the same time, if not, adjust the division of the preliminary energy autonomous unit.

[0079] It should be noted that for the preliminary energy autonomous unit that does not meet the requirements, based on the principle of electrical distance nearest (electrical distance nearest means strong electrical coupling between nodes and high power exchange efficiency), the new energy access point or load node in the adjacent unit is integrated into the unit until the requirement of containing new energy access point and load node at the same time is met; if adjustment leads to adjacent units not meeting the requirements, the same method is used to adjust the adjacent units until all units meet the requirements.

[0080] It should be noted that ensuring that each energy autonomous unit contains new energy access points and load nodes at the same time is a necessary prerequisite to achieve energy balance and efficient consumption on site. If there is only new energy in the unit without load, the generated electric energy needs to be transmitted to other units over a long distance, increasing network loss and power transmission pressure; if there is only load in the unit without new energy, the goal of local consumption of distributed energy cannot be achieved, which is not conducive to reducing the burden of main grid power transmission and improving energy utilization efficiency.

[0081] Preferably, the energy autonomous unit division method proposed by the present application represents the node relationship through two dimensions of electrical distance and power sensitivity, determines the clustering threshold through an adaptive weight mechanism, and optimizes the unit division result based on the new energy-load configuration constraint. This method fully considers the physical characteristics and operating characteristics of the distribution network, can accurately identify the closely coupled node group in the network, and form reasonable energy autonomous units. The division result helps to achieve local consumption and efficient use of new energy, and can be used to guide the partition management and coordinated control of the distribution network, and improve the operating efficiency and reliability of the distribution network.

[0082] S2.6: Calculate the power balance degree of each preliminary energy autonomous unit, optimize the boundary of the preliminary energy autonomous unit according to the power balance degree, and form a plurality of energy autonomous units.

[0083] The power balance degree is determined by the ratio of the output capacity of the distributed new energy in the unit to the load demand, and the time-varying characteristics of the new energy output and the volatility of the load demand are considered.

[0084] Further, the optimization of the boundary of the preliminary energy autonomous unit according to the power balance degree includes: comparing the power balance degree of each preliminary energy autonomous unit with a preset power balance degree threshold, and when the unit power balance degree does not meet the preset power balance degree threshold requirement, the boundary of the preliminary energy autonomous unit is optimized by adjusting the unit belonging of the corresponding electrical node. The preset power balance degree threshold includes an upper and lower limit interval, and the unit power balance degree is allowed to fluctuate within a reasonable range.

[0085] In addition, the optimization process adopts an iterative manner, and each time the electrical node located at the unit boundary and optimal in terms of comprehensive electrical distance and power sensitivity is redistributed until the power balance degree of all units meets the preset threshold requirement and the adjustment amplitude of adjacent iterations is less than the preset convergence precision, or the maximum iteration number is reached.

[0086] S3: Calculate the energy complementarity index and load elasticity coefficient of each energy autonomous unit, and construct a unit feature model.

[0087] In one specific embodiment, the implementation process of step S3 includes:

[0088] S3.1: Obtain historical output data of distributed new energy and historical electricity consumption data of load nodes in each energy autonomous unit.

[0089] The historical output data and historical electricity consumption data are extracted from the basic data set of the power distribution network analysis model.

[0090] S3.2: Based on the historical output data and historical electricity consumption data, analyze the time correlation between new energy output and load demand, and calculate the energy complementarity index of each energy autonomous unit.

[0091] Specifically, the historical output data and the historical power consumption data are subjected to time series standardization processing to make the data comparable; the Pearson correlation coefficients of the standardized historical output data and the historical power consumption data in each energy autonomous unit are calculated to obtain a time correlation index; the ratios of the daily average output of new energy to the daily average demand of load and the ratios of the reliable output of new energy to the peak demand of load in each energy autonomous unit are calculated to obtain an amplitude matching coefficient; based on the time correlation index and the amplitude matching coefficient, a weighted summation method is used to calculate the energy complementation index of each energy autonomous unit, and the energy complementation index has a value range of [0, 1], and the greater the value, the higher the matching degree of the new energy output and the load demand in time and amplitude. The energy complementation index comprehensively quantifies the supply-demand matching capability of the energy autonomous unit, and improves the energy utilization efficiency and system reliability of the regional power distribution network.

[0092] S3.3: Based on the historical power consumption data, the characteristics of the adjustable load in the unit are identified, and the load elasticity coefficient of each energy autonomous unit is calculated.

[0093] Further, the historical power consumption data is subjected to time series decomposition to identify the base load, fluctuating load and peak load in each energy autonomous unit, determine the load composition structure and the adjustable load proportion, analyze the response characteristics of the load to the price change and dispatching instructions in the historical power consumption data, calculate the average response time and response amplitude of the load in each energy autonomous unit, and calculate the load elasticity coefficient of each energy autonomous unit according to the load composition structure, the adjustable load proportion, the average response time and the response amplitude. The load elasticity coefficient has a value range of [0, 1], and the greater the value, the faster the response speed and the stronger the adjustment capability of the load in the unit. The load elasticity coefficient describes the demand side response potential of the energy autonomous unit, enhances the adaptability of the power distribution network to fluctuating energy sources, and effectively improves the system operation flexibility.

[0094] S3.4: A unit feature model is constructed based on the energy complementation index and the load elasticity coefficient in combination with the grid constraint conditions.

[0095] Further, the system operation data of multiple typical time periods are selected, a grid operation constraint set is established based on the load power balance constraint, the voltage constraint, the line capacity constraint and the new energy power constraint, and the data of the typical time periods are divided into a training set and a test set according to a preset proportion; the energy complementation index and the load elasticity coefficient are subjected to feature normalization processing to construct a unit feature vector; for each typical time period, the maximum upward power and the maximum downward power of each energy autonomous unit are determined based on the grid operation constraint set; a mapping relationship model of the unit feature vector and the power regulation capability is established based on the training set data, and the model accuracy is verified by using the test set data. When the prediction error meets the requirements, the final unit feature model is obtained.

[0096] Preferably, the unit characteristic model proposed by the present application represents the characteristic properties of the energy autonomous unit through the energy complementarity index and the load elasticity coefficient, and establishes a quantitative mapping relationship between the unit characteristics and the power regulation capability. The model organically combines the new energy-load matching characteristics, the load response characteristics and the grid operation constraints, can quickly evaluate the regulation potential of the energy autonomous unit, and provides reliable decision basis for subsequent optimization scheduling. The model has the characteristics of simple calculation and reliable results, and can be widely applied to distribution network planning and operation optimization, which helps to improve the economy and reliability of the distribution network.

[0097] S4: dynamically adjusting the active power and reactive power output of the distributed new energy in each energy autonomous unit according to the unit characteristic model.

[0098] In one specific embodiment, the implementation process of step S4 includes:

[0099] S4.1: based on the unit characteristic model, establishing a supply and demand prediction framework for the energy autonomous unit to generate predicted results of load demand and new energy output.

[0100] Specifically, the energy complementarity index and the load elasticity coefficient are extracted from the unit characteristic model as characteristic parameters for supply and demand prediction; a unified supply and demand characteristic input layer is constructed, including time characteristics (such as hours, date types, seasons), environmental characteristics (such as temperature, humidity, and light intensity), and unit characteristics (energy complementarity index and load elasticity coefficient); the network structure of the unified supply and demand prediction framework is designed, including a feature extraction layer, a time series analysis layer, and a result output layer; the energy complementarity index is embedded into the feature extraction layer as an adjustment parameter for supply and demand balance, optimizing the time series correlation of supply and demand prediction; the unified supply and demand prediction framework is trained using historical operation samples to generate short-term load demand prediction values and new energy output prediction values; based on the load elasticity coefficient, the response potential of the adjustable load in the energy autonomous unit is calculated to generate a load adjustment feasible interval; the prediction values and the load adjustment feasible interval are integrated to form the comprehensive supply and demand prediction results of the energy autonomous unit.

[0101] Through the above design, the present scheme constructs a unified supply and demand prediction framework, improves the prediction effect of supply and demand correlation by embedding the energy complementarity index into the feature extraction layer. At the same time, based on the load elasticity coefficient, the load adjustment feasible interval is generated, so that the prediction result is more in line with the actual adjustment capability. The scheme provides the prediction result and the load adjustment range at the same time, which provides a reliable decision basis for subsequent power regulation optimization.

[0102] S4.2: determining the power balance state of each energy autonomous unit according to the prediction results, and establishing the corresponding operation constraint conditions.

[0103] Specifically, the power balance state determination process comprises: calculating the power shortage or surplus of the energy autonomous unit in the prediction period by using the load demand prediction value and the new energy output prediction value; combining the load adjustment feasible interval, calculating the internal balance potential of the unit to obtain the self-balancing power and the external exchange power; based on the energy complementary index, analyzing the influence degree of new energy fluctuation on power balance, and dividing the power balance state into surplus state, balance state and shortage state; setting the power adjustment direction and adjustment priority according to different balance states to form the power balance state sequence.

[0104] Optionally, the operation constraints include but are not limited to: power balance constraint, voltage limit constraint, line capacity constraint, device operation constraint, safety constraint.

[0105] It should be noted that the determination of the power balance state provides a basic condition for the subsequent construction of the objective function and the solution of the optimal power output, and through the explicit power adjustment demand and boundary constraint of each unit, the precise guidance of the distributed optimization algorithm is realized, and the feasibility of the internal power balance adjustment of the energy autonomous unit is guaranteed.

[0106] S4.3: Combining the load elasticity coefficient, constructing the objective function of the internal power adjustment of the energy autonomous unit.

[0107] The objective function is composed of multiple optimization items, which at least include new energy consumption maximization, network loss minimization and voltage deviation minimization, and each optimization item is weighted by a weight coefficient.

[0108] In addition, the load elasticity coefficient represents the response speed and adjustment capacity of the load in the unit, and by affecting the weight coefficients of the new energy consumption maximization and voltage deviation minimization two optimization items, the objective function can adaptively adjust the emphasis degree of the optimization target according to the load adjustment capacity.

[0109] S4.4: Based on the objective function and the constraint condition, the optimal active power and reactive power output values of the distributed new energy are solved by using the distributed optimization algorithm.

[0110] Specifically, the distributed optimization solving process comprises: based on the energy complementary index, dividing the distributed new energy devices in the energy autonomous unit into multiple collaborative optimization groups; for the devices in the collaborative optimization group, constructing a local optimization sub-problem, and introducing a Lagrange relaxation factor to process the power balance constraint; designing a distributed iterative solving framework based on ADMM (Alternating Direction Method of Multipliers), setting an initial solution and a convergence threshold; in each iteration, the load elasticity coefficient is used to dynamically adjust the variable update step size to accelerate the algorithm convergence; iteratively solving until the convergence condition or the maximum iteration number is reached, and outputting the optimal active power and reactive power instruction values.

[0111] S4.5: The optimal active power and reactive power output values are issued to each distributed new energy device, and the power balance adjustment within the energy autonomous unit is completed.

[0112] Further, the instruction issuing process includes: determining the priority order of instruction issuing according to the power balance state sequence; establishing an emergency handling mechanism for communication failure to ensure that critical devices can receive adjustment instructions; collecting device execution feedback, updating parameter values in the unit characteristic model, and providing data support for the next period of adjustment.

[0113] Through the above design, the present scheme establishes a complete adjustment chain of prediction-evaluation-optimization-execution, and uses the energy complementary index and load elasticity coefficient in the unit characteristic model throughout each link: improves the supply-demand correlation in the prediction link, assists in power balance state division in the evaluation link, and adaptively adjusts the target function weight in the optimization link. The scheme makes the adjustment strategy more in line with the actual operation characteristics, improves the consumption level of distributed new energy in the energy autonomous unit, and at the same time ensures the safe and economic operation of the distribution network.

[0114] S5: Monitor the voltage deviation and power flow between energy autonomous units, implement a coordinated control strategy, adjust the power exchange between energy autonomous units, and realize the maximum consumption of distributed new energy in the regional power grid.

[0115] In one specific embodiment, the implementation process of step S5 includes:

[0116] S5.1: Monitor the key node voltage of the energy autonomous unit and the inter-unit tie line power.

[0117] It should be noted that based on the existing monitoring devices in the distribution network, the voltage data of each node and the power data of each line are obtained according to a preset sampling period; according to the divided energy autonomous unit, the voltage data of the key nodes (such as transformer bus, important load access point and distributed new energy access point) within the unit and the power data of the inter-unit tie line are selected and sorted from the existing monitoring data; the collected data is subjected to quality inspection, and abnormal values and missing values are processed.

[0118] S5.2: Analyze and calculate the monitoring data to obtain the voltage deviation value, power transmission direction and power flow level between units, and determine the region to be adjusted.

[0119] Specifically, the processed monitoring data is acquired S5.1; based on the energy autonomous unit division result, the tie lines between adjacent energy autonomous units and their corresponding boundary nodes are identified; the voltage deviation values of the key nodes of each energy autonomous unit are calculated, and the deviation values are compared with the preset voltage qualified range to determine the energy autonomous units with voltage abnormalities; the power transmission direction and the power flow level of the tie lines between units are calculated to identify the tie lines with abnormal power flow; the operation states of each energy autonomous unit are evaluated by comprehensively considering the voltage deviation and the power flow level; the energy autonomous units that need to be adjusted are determined according to the operation state evaluation results of each energy autonomous unit, and the region to be adjusted is formed; the electrical connection relationship between each energy autonomous unit in the region to be adjusted is verified to ensure the feasibility of power exchange. This step realizes the accurate identification and positioning of the abnormal region of the distribution network by evaluating the operation state of the energy autonomous unit in multiple dimensions, greatly improving the problem diagnosis efficiency and accuracy in the process of distributed new energy consumption.

[0120] S5.3: Based on the region to be adjusted, the combination of energy autonomous units participating in power exchange is determined in combination with the power regulation capacity and voltage constraints of each energy autonomous unit.

[0121] Further, the operation state information of each energy autonomous unit in the region to be adjusted is acquired; the real-time adjustable capacity of the distributed new energy and the adjustable range of the load in each energy autonomous unit are retrieved to determine the power regulation capacity of each energy autonomous unit; the voltage sensitivity coefficient of each energy autonomous unit in the region to be adjusted is acquired to establish the correlation between voltage change and power regulation; the voltage operation constraints of each energy autonomous unit are determined, including the upper and lower limits of voltage and the voltage regulation margin; the energy autonomous units in the region to be adjusted are sorted according to the power regulation capacity, and the energy autonomous units with sufficient regulation capacity are selected as candidate units; the possible power exchange paths between the candidate units are determined according to the electrical connection relationship of the region to be adjusted; the combination of energy autonomous units finally participating in power exchange is determined by comprehensively considering the power regulation capacity, voltage constraints and power exchange paths of the candidate units. This step innovatively establishes a collaborative regulation mechanism between energy autonomous units, optimizes the resource allocation efficiency of distributed new energy consumption, and significantly improves the operation flexibility and system stability of the distribution network.

[0122] S5.4: According to the voltage deviation of the combination of energy autonomous units, the required active power and reactive power regulation amounts are calculated, and the corresponding control instructions are generated.

[0123] S5.5: The control instructions are issued to the power regulation devices of the related energy autonomous units, the power exchange between units is adjusted, and the voltage recovery situation and power transmission state after adjustment are verified.

[0124] Further, the embodiment also provides a distributed new energy consumption regional power distribution system, comprising: a data acquisition module, configured to acquire topological structure data, load distribution data and distributed new energy output characteristic data of a regional power distribution network, and construct a power distribution network analysis model; a network partition module, configured to divide the regional power distribution network into a plurality of energy autonomous units based on the power distribution network analysis model by calculating electrical distance and power sensitivity; a unit modeling module, configured to calculate energy complementary indexes and load elasticity coefficients of the energy autonomous units, and construct a unit characteristic model; a power regulation module, configured to dynamically regulate active power and reactive power output of the distributed new energy in each energy autonomous unit according to the unit characteristic model; and a collaborative control module, configured to monitor operation states of the energy autonomous units, implement a collaborative control strategy, and regulate power exchange between the energy autonomous units.

[0125] To sum up, the energy autonomous unit division method provided by the embodiment can accurately identify closely coupled node groups, realize nearby consumption and efficient utilization of new energy, and reduce power transmission and distribution loss; the unit characteristic model constructed based on the energy complementary indexes and the load elasticity coefficients effectively quantifies internal supply-demand matching capability and demand-side response potential of the unit, provides reliable decision basis for optimal scheduling, and improves system operation flexibility; the network analysis model integrating power flow analysis and electrical characteristic matrix enhances the characterization capability of the influence of the distributed new energy access, realizes organic integration of static characteristics and dynamic characteristics of the power distribution network, and provides an effective analysis tool for improving new energy consumption capability of the power distribution network; the collaborative regulation mechanism between the energy autonomous units realizes accurate identification and positioning of abnormal areas of the power distribution network through multi-dimensional evaluation of unit operation states, optimizes resource allocation efficiency of distributed new energy consumption, and significantly improves operation flexibility and system stability of the power distribution network.

[0126] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A regional power distribution method for distributed renewable energy consumption, characterized in that, include: Acquire topology data, load distribution data, and distributed renewable energy output characteristic data of the regional power distribution network, and construct a power distribution network analysis model; Based on the aforementioned power distribution network analysis model, the regional power distribution network is divided into multiple energy autonomous units by calculating electrical distance and power sensitivity; Calculate the energy complementarity index and load elasticity coefficient of each energy autonomous unit, and construct a unit characteristic model; Based on the aforementioned unit characteristic model, the active and reactive power outputs of distributed new energy sources within each energy autonomous unit are dynamically adjusted. Monitor the operating status of the energy autonomous units, implement a coordinated control strategy, and regulate the power exchange between the energy autonomous units; The method of dividing the regional power distribution network into multiple energy autonomous units by calculating electrical distance and power sensitivity includes: Based on the power distribution network analysis model, the electrical distances between electrical nodes in the regional power distribution network are calculated, and a node distance matrix is ​​constructed. Calculate the power sensitivity parameters among electrical nodes in the regional power distribution network and construct the node power sensitivity matrix; The node clustering threshold is determined based on the node distance matrix and the node power sensitivity matrix; Based on the node clustering threshold, a hierarchical clustering algorithm is used to cluster electrical nodes into preliminary energy autonomous units; Check whether each preliminary energy autonomous unit contains both a new energy access point and a load node. If not, adjust the division of the preliminary energy autonomous unit. Calculate the power balance degree within each preliminary energy autonomous unit, and optimize the boundary of the preliminary energy autonomous unit based on the power balance degree to form multiple energy autonomous units; The feature model of the building unit includes: Acquire historical power output data of distributed renewable energy sources and historical electricity consumption data of load nodes within each energy autonomous unit; Based on the historical power output data and the historical electricity consumption data, the time correlation between new energy power output and load demand is analyzed, and the energy complementarity index of each energy autonomous unit is calculated. Based on the historical electricity consumption data, the adjustable load characteristics within the unit are identified, and the load elasticity coefficient of each energy autonomous unit is calculated. Based on the energy complementarity index and the load elasticity coefficient, a unit characteristic model is constructed in conjunction with the power grid constraints.

2. The regional power distribution method for distributed renewable energy consumption according to claim 1, characterized in that, The construction of the power distribution network analysis model includes: The acquired topology data, load distribution data, and renewable energy output characteristic data are preprocessed to generate a standardized dataset; Based on the standardized dataset, a power flow calculation model for the regional power distribution network is established. An electrical characteristic correlation matrix is ​​constructed by calculating the voltage sensitivity coefficient and power transmission distribution factor of electrical nodes in the regional power distribution network. By integrating the power flow calculation model with the electrical characteristic correlation matrix, a power distribution network analysis model is formed.

3. The regional power distribution method for distributed renewable energy consumption according to claim 1, characterized in that, The dynamic adjustment of the active and reactive power output of distributed new energy sources within each energy autonomous unit includes: Based on the aforementioned unit characteristic model, a supply and demand forecasting framework for energy autonomous units is established, generating forecast results for load demand and new energy output. Based on the prediction results, the power balance state of each energy autonomous unit is determined, and corresponding operating constraints are established. By combining the load elasticity coefficient, an objective function for power regulation within the energy autonomous unit is constructed; Based on the objective function and constraints, a distributed optimization algorithm is used to solve for the optimal active and reactive power output values ​​of distributed new energy sources. The optimal active and reactive power output values ​​are distributed to each distributed new energy device to complete the power balance regulation within the energy self-governing unit.

4. The regional power distribution method for distributed renewable energy consumption according to claim 1, characterized in that, The implementation of the collaborative control strategy includes: Monitor the voltage of critical nodes and the power of inter-unit interconnects within the energy autonomous unit; The monitoring data is analyzed and calculated to obtain the voltage deviation value between units, the power transmission direction and the power flow level, and to determine the area to be adjusted; Based on the region to be regulated, and in combination with the power regulation capability and voltage constraints of each energy autonomous unit, the combination of energy autonomous units participating in power exchange is determined. Based on the voltage deviation of the energy autonomous unit combination, the required active and reactive power adjustment amounts are calculated, and corresponding control commands are generated. The control commands are sent to the power regulation devices of the relevant energy autonomous units, and power is exchanged between the regulation units.

5. A regional power distribution system for distributed renewable energy consumption, characterized in that, include: The data acquisition module is used to acquire topology data, load distribution data, and distributed renewable energy output characteristic data of the regional power distribution network, and to build a power distribution network analysis model. The network partitioning module is used to divide the regional power distribution network into multiple energy autonomous units based on the power distribution network analysis model by calculating electrical distance and power sensitivity; The unit modeling module is used to calculate the energy complementarity index and load elasticity coefficient of each energy autonomous unit and construct the unit characteristic model; The power regulation module is used to dynamically adjust the active and reactive power output of distributed new energy sources within each energy autonomous unit based on the unit characteristic model. The collaborative control module is used to monitor the operating status of the energy autonomous units, implement collaborative control strategies, and regulate the power exchange between the energy autonomous units. The method of dividing the regional power distribution network into multiple energy autonomous units by calculating electrical distance and power sensitivity includes: Based on the power distribution network analysis model, the electrical distances between electrical nodes in the regional power distribution network are calculated, and a node distance matrix is ​​constructed. Calculate the power sensitivity parameters among electrical nodes in the regional power distribution network and construct the node power sensitivity matrix; The node clustering threshold is determined based on the node distance matrix and the node power sensitivity matrix; Based on the node clustering threshold, a hierarchical clustering algorithm is used to cluster electrical nodes into preliminary energy autonomous units; Check whether each preliminary energy autonomous unit contains both a new energy access point and a load node. If not, adjust the division of the preliminary energy autonomous unit. Calculate the power balance degree within each preliminary energy autonomous unit, and optimize the boundary of the preliminary energy autonomous unit based on the power balance degree to form multiple energy autonomous units; The feature model of the building unit includes: Acquire historical power output data of distributed renewable energy sources and historical electricity consumption data of load nodes within each energy autonomous unit; Based on the historical power output data and the historical electricity consumption data, the time correlation between new energy power output and load demand is analyzed, and the energy complementarity index of each energy autonomous unit is calculated. Based on the historical electricity consumption data, the adjustable load characteristics within the unit are identified, and the load elasticity coefficient of each energy autonomous unit is calculated. Based on the energy complementarity index and the load elasticity coefficient, a unit characteristic model is constructed in conjunction with the power grid constraints.

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