Regional power distribution method and system for distributed new energy consumption

Abstract

The invention discloses a regional power distribution method and system for distributed new energy consumption, and relates to the technical field of power systems, and the method comprises the steps: obtaining regional power distribution network data, and constructing a power distribution network analysis model; based on the power distribution network analysis model, dividing the regional power distribution network into a plurality of energy autonomous units by calculating the electrical distance and the power sensitivity; calculating an energy complementation index and a load elastic coefficient of each energy autonomous unit, and constructing a unit feature model; 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; and monitoring the operation state of the energy autonomous units, implementing a cooperative control strategy, and adjusting power exchange between the energy autonomous units. According to the energy autonomous unit division method provided by the invention, the tightly coupled node group can be accurately identified, the nearby consumption and efficient utilization of new energy are realized, and the power transmission and distribution loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a regional power distribution method and system for accommodating distributed renewable energy. Background Art

[0002] With the deepening of my country's power system reform and the comprehensive advancement of energy transformation, distributed renewable energy generation technologies, represented by photovoltaic and wind power generation, have gained widespread adoption. Distributed renewable energy generation offers the advantages of local consumption and clean, environmentally friendly operation. However, its decentralized access and intermittent output also present new challenges for the safe and stable operation of distribution networks. Currently, the rapid growth of distributed renewable energy installed capacity in some regions of my country has led to significant consumption issues during certain periods, resulting in a degree of power rationing and hindering the sustainable and healthy development of distributed renewable energy.

[0003] Existing technologies typically employ methods such as source-grid-load coordinated control and demand-side response to improve the absorption capacity of distributed renewable energy. However, these methods, mostly based on centralized control architectures, suffer from low control efficiency and slow response when faced with large-scale distributed renewable energy integration. Furthermore, due to the uneven load characteristics and distribution of renewable energy across distribution networks, traditional unified control strategies struggle to adapt to the individual needs of different regions, limiting the overall absorption capacity of the system. Summary of the Invention

[0004] The present invention provides a regional power distribution method and system for distributed renewable energy consumption, which is used to solve the technical problems of limited distributed renewable energy consumption, imbalanced supply and demand in various regions, and low coordination and control efficiency in the current distribution network.

[0005] In view of this, a first aspect of the present invention provides a regional power distribution method for accommodating distributed new energy, comprising: Obtain regional distribution network topology data, load distribution data, and distributed renewable energy output characteristics data to build a distribution network analysis model; Based on the distribution network analysis model, the regional 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; Dynamically adjust the active power and reactive power output of distributed renewable energy within each energy autonomous unit based on the unit characteristic model; Monitor the operating status of energy autonomous units, implement collaborative control strategies, and regulate power exchange between energy autonomous units.

[0006] Optionally, building a distribution network analysis model includes: Preprocess the acquired topology data, load distribution data, and renewable energy output characteristic data to generate a standardized data set; Establish a power flow calculation model for the regional distribution network based on a standardized data set; By calculating the voltage sensitivity coefficient and power transfer distribution factor of electrical nodes in the regional distribution network, an electrical characteristic correlation matrix is ​​constructed; The power flow calculation model and the electrical characteristic correlation matrix are integrated to form a distribution network analysis model.

[0007] Optionally, dividing the regional distribution network into a plurality of energy autonomous units by calculating electrical distance and power sensitivity includes: Based on the distribution network analysis model, the electrical distance between each electrical node in the regional distribution network is calculated and the node distance matrix is ​​constructed; Calculate the power sensitivity parameters between electrical nodes in the regional distribution network and construct the node power sensitivity matrix; Determine the node clustering threshold 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 new energy access points and load nodes. If not, adjust the division of the preliminary energy autonomous unit; The power balance within each preliminary energy autonomous unit is calculated, and the boundaries of the preliminary energy autonomous unit are optimized according to the power balance to form multiple energy autonomous units.

[0008] Optionally, constructing the unit feature model includes: Obtain historical output data of distributed renewable energy within each energy autonomous unit and historical power consumption data of load nodes; Based on historical output data and historical electricity consumption data, the temporal correlation between renewable energy output and load demand is analyzed, and the energy complementarity index of each energy autonomous unit is calculated; Based on historical electricity consumption data, identify the adjustable load characteristics within the unit and calculate the load elasticity coefficient of each energy autonomous unit; Combined with the grid constraints, a unit characteristic model is constructed based on the energy complementarity index and load elasticity coefficient.

[0009] Optionally, dynamically adjusting the active power and reactive power output of the distributed renewable energy within each energy autonomous unit includes: Based on the unit characteristic model, a supply and demand forecasting framework for energy autonomous units is established to generate forecast results for load demand and renewable energy output; Based on the prediction results, determine the power balance status of each energy autonomous unit and establish corresponding operation constraints; Combined with the load elasticity coefficient, the objective function of power regulation within the energy autonomous unit is constructed; Based on the objective function and constraints, a distributed optimization algorithm is used to solve the optimal active power and reactive power output values ​​of distributed renewable energy; The optimal active power and reactive power output values ​​are sent to each distributed new energy device to complete the power balance adjustment within the energy autonomous unit.

[0010] Optionally, implementing the collaborative control strategy includes: Monitor the voltage of key nodes of energy autonomous units and the power of interconnection lines between units; Analyze and calculate the monitoring data to obtain the voltage deviation value, power transmission direction and power flow level between units, and determine the area to be adjusted; Based on the area to be regulated and the power regulation capability and voltage constraints of each energy autonomous unit, the combination of energy autonomous units participating in power exchange is determined; According to the voltage deviation of the energy autonomous unit combination, the required active power and reactive power adjustment amount is calculated and the corresponding control instructions are generated; The control instructions are sent to the power regulation equipment of the relevant energy autonomous units to regulate the power exchange between the units.

[0011] A second aspect of the present invention provides a regional power distribution system for accommodating distributed new energy, comprising: The data acquisition module is used to obtain the topological structure data, load distribution data and distributed renewable energy output characteristics data of the regional distribution network and build a distribution network analysis model; The network partitioning module is used to divide the regional distribution network into multiple energy autonomous units by calculating electrical distance and power sensitivity based on the distribution network analysis model; Unit modeling module, used to calculate the energy complementarity index and load elasticity coefficient of each energy autonomous unit and build a unit characteristic model; The power regulation module is used to dynamically adjust the active power and reactive power output of distributed renewable energy 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 power exchange between energy autonomous units.

[0012] The beneficial effects of the present invention are as follows: the energy autonomous unit division method proposed in the present invention can accurately identify tightly coupled node groups, realize the local consumption and efficient utilization of new energy, and reduce transmission and distribution losses; the unit characteristic model constructed based on the energy complementarity index and load elasticity coefficient effectively quantifies the internal supply and demand matching capability and demand-side response potential of the unit, provides a reliable decision-making basis for optimized scheduling, and improves the system operation flexibility; the network analysis model integrating power flow analysis and electrical characteristic matrix enhances the characterization capability of the impact of distributed new energy access, realizes the organic integration of static and dynamic characteristics of the distribution network, and provides an effective analysis tool for improving the new energy consumption capacity of the distribution network; the collaborative adjustment mechanism between energy autonomous units realizes the accurate identification and positioning of abnormal areas of the distribution network through multi-dimensional evaluation of the unit operation status, optimizes the resource allocation efficiency of distributed new energy consumption, and significantly improves the operation flexibility and system stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 The figure is a flow chart of a regional power distribution method for accommodating distributed renewable energy.

[0015] Figure 2 Construct a flow chart for the distribution network analysis model of a regional power distribution method for accommodating distributed renewable energy.

[0016] Figure 3 The present invention provides a flow chart for dividing energy autonomous units in a regional power distribution method for accommodating distributed renewable energy. DETAILED DESCRIPTION

[0017] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] Example 1, with reference to Figures 1 to 3 , which is the first embodiment of the present invention, provides a regional power distribution method for accommodating distributed new energy. The flow chart of the method is as follows Figure 1As shown, the method includes: S1: Collect the topological structure data, load distribution data and distributed renewable energy output characteristic data of the regional distribution network to build a distribution network analysis model.

[0019] In a specific embodiment, the flow chart of constructing the distribution network analysis model is as follows: Figure 2 As shown, including: S1.1: Obtain the topological structure data, load distribution data, and distributed renewable energy output characteristic data of the regional distribution network.

[0020] Among them, topology data includes line parameters, transformer parameters, switch status and node connection relationships; load distribution data includes historical power consumption data of each load node, load type and time-varying characteristics; distributed new energy output characteristic data includes new energy type, installed capacity, historical power generation data and output forecast information.

[0021] Preferably, the acquired data includes topological structure data, load distribution data, and distributed renewable energy output characteristic data. The selection of this data directly serves the goal of distributed renewable energy consumption. Among them, topological structure data reflects the transmission and distribution capacity and flexibility of the distribution network and is the basis for evaluating renewable energy consumption channels; load distribution data reflects the temporal and spatial distribution patterns of electricity load and helps identify demand-side opportunities for renewable energy consumption; distributed renewable energy output characteristic data characterizes the supply characteristics and randomness of renewable energy and is crucial for formulating precise consumption strategies. The coordinated application of these data can accurately grasp the matching relationship between renewable energy supply and load demand, providing a reliable data foundation for optimizing regional distribution plans and improving renewable energy consumption capabilities.

[0022] S1.2: Preprocess the acquired topology data, load distribution data, and renewable energy output characteristic data to generate a standardized data set.

[0023] Optionally, preprocessing includes but is not limited to: data cleaning, validity verification, data standardization and outlier processing.

[0024] S1.3: Establish a power flow calculation model for the regional distribution network based on a standardized data set.

[0025] S1.4: Construct an electrical characteristic correlation matrix by calculating the voltage sensitivity coefficients and power transfer distribution factors of electrical nodes in the regional distribution network.

[0026] In this embodiment, the electrical nodes include at least substation bus nodes, load access point nodes, and distributed new energy access point nodes.

[0027] Specifically, based on a power flow calculation model, with the substation busbar node as a reference, the authors selected load access points and distributed renewable energy access points as injection nodes. A small perturbation was applied, and the voltage responses of all electrical nodes were recorded. The voltage sensitivity coefficients between nodes were calculated. Using the DC power flow equation, a mapping relationship was established between node injection power and branch power, solving for the power transfer distribution factor. The voltage sensitivity coefficients and power transfer distribution factors of each electrical node were combined to construct an electrical characteristics correlation matrix. This electrical characteristics correlation matrix reflects the interactions and coupling characteristics between nodes in the distribution network, providing a quantitative theoretical basis for access assessment and absorption plan development for distributed renewable energy.

[0028] S1.5: Integrate the power flow calculation model and the electrical characteristic correlation matrix to form a distribution network analysis model.

[0029] Furthermore, the network status information of the power flow calculation model is mapped and integrated with the electrical characteristic association matrix to establish a unified network characteristic expression; based on the integrated characteristic expression, a distribution network analysis model including power flow analysis and electrical characteristic calculation is constructed.

[0030] Preferably, the present invention enhances the model's ability to characterize the impact of distributed renewable energy access by combining power flow analysis with the electrical characteristic matrix, realizes the organic integration of static and dynamic characteristics of the distribution network, and provides an effective analysis tool for improving the distribution network's ability to absorb renewable energy.

[0031] S2: Based on the distribution network analysis model, the regional distribution network is divided into multiple energy autonomous units by calculating the electrical distance and power sensitivity.

[0032] In a specific embodiment, the energy autonomous unit division flow chart is as follows: Figure 3 As shown, including: S2.1: Based on the distribution network analysis model, calculate the electrical distance between each electrical node in the regional distribution network and construct a node distance matrix.

[0033] The electrical distance is calculated based on the impedance parameter between nodes. This impedance parameter is extracted from the network parameters of the distribution network analysis model and corrected based on the power flow calculation results. A node distance matrix is ​​constructed, with the matrix elements representing the electrical distance between nodes i and j. The node distance matrix intuitively represents the electrical connection between nodes in the distribution network and provides a spatial correlation basis for the subsequent division of energy autonomous units.

[0034] S2.2: Calculate the power sensitivity parameters between electrical nodes in the regional distribution network and construct a node power sensitivity matrix.

[0035] In this embodiment, power sensitivity parameters include active power-voltage sensitivity and reactive power-voltage sensitivity. These two parameters effectively reflect the impact of node power changes on system voltage. Active power-voltage sensitivity and reactive power-voltage sensitivity are calculated using the partial derivatives of node voltage with respect to injected power. The calculation considers the real-time network status provided by the distribution network analysis model.

[0036] Furthermore, a node power sensitivity matrix is ​​constructed based on the power sensitivity parameters. Matrix elements represent the degree to which a power change at node j affects the voltage at node i. This power sensitivity matrix can be used to identify clusters of nodes with close electrical coupling in the distribution network. The power exchange between these nodes has a strong mutual impact on the system voltage, providing a functional correlation basis for the classification of energy autonomous units.

[0037] S2.3: Determine the node clustering threshold based on the node distance matrix and the node power sensitivity matrix.

[0038] Specifically, based on the node distance matrix, a statistical analysis method is used to calculate the distribution characteristics of the electrical distance and obtain the initial value of the distance threshold. Based on the node power sensitivity matrix, the power coupling strength judgment standard is set to determine the initial value of the sensitivity threshold. Taking the initial values ​​of the distance threshold and the sensitivity threshold into consideration, a weighted method is used to obtain the comprehensive clustering threshold. 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.

[0039] Preferably, the weighted method employs an adaptive weight allocation mechanism, dynamically adjusting the weight coefficients of the distance and sensitivity thresholds based on the voltage stability and power flow distribution characteristics of the nodes in the network. This prioritizes sensitivity characteristics in areas with large voltage fluctuations, while prioritizing electrical distance characteristics in areas with balanced power flow distribution, thereby improving the accuracy and pertinence of clustering thresholds. This threshold determination method, which integrates multiple indicators, comprehensively reflects the physical connection relationships and functional coupling characteristics between nodes, effectively avoiding clustering bias caused by a single indicator. This provides a scientific basis for the precise division of energy autonomous units, thereby improving the efficiency of distributed renewable energy consumption.

[0040] S2.4: Based on the node clustering threshold, a hierarchical clustering algorithm is used to cluster electrical nodes into preliminary energy autonomous units.

[0041] S2.5: Check whether each preliminary energy autonomous unit contains both new energy access points and load nodes. If not, adjust the division of the preliminary energy autonomous unit.

[0042] It should be noted that for preliminary energy autonomous units that do not meet the requirements, based on the principle of closest electrical distance (shortest electrical distance means strong electrical coupling between nodes and high power exchange efficiency), the new energy access points or load nodes in the adjacent units are merged into the unit until the requirements of including both new energy access points and load nodes are met; if the adjustment causes the adjacent units to not meet the requirements, the same method is used to adjust the adjacent units until all units meet the requirements.

[0043] It's important to note that ensuring each autonomous energy unit includes both renewable energy access points and load nodes is a prerequisite for achieving local energy balance and efficient consumption. If a unit contains only renewable energy but no load, the generated electricity will need to be transmitted over long distances to other units, increasing network losses and transmission pressure. If a unit contains only load but no renewable energy, the goal of localizing distributed energy consumption cannot be achieved, hindering the reduction of the main grid's transmission burden and improving energy efficiency.

[0044] Preferably, the energy autonomous unit partitioning method proposed in this invention characterizes node relationships using two dimensions: electrical distance and power sensitivity. Clustering thresholds are determined through an adaptive weighting mechanism, and the resulting unit partitioning is optimized based on new energy-load configuration constraints. This method fully considers the physical and operational characteristics of the distribution network, accurately identifying tightly coupled node clusters within the network and forming rational energy autonomous units. The resulting partitioning facilitates the localized consumption and efficient utilization of new energy, and can be used to guide the zoning management and coordinated control of the distribution network, improving its operational efficiency and reliability.

[0045] S2.6: Calculate the power balance within each preliminary energy autonomous unit, and optimize the boundaries of the preliminary energy autonomous unit based on the power balance to form multiple energy autonomous units.

[0046] Among them, the power balance is determined by the ratio of the output capacity of distributed renewable energy within the unit to the load demand, and the time-varying characteristics of renewable energy output and the volatility of load demand are taken into account.

[0047] Furthermore, optimizing the boundaries of the preliminary energy autonomous units based on the power balance includes comparing the power balance of each preliminary energy autonomous unit with a preset power balance threshold. When the unit power balance does not meet the preset power balance threshold, optimizing the boundaries of the preliminary energy autonomous unit by adjusting the unit affiliation of the corresponding electrical node. The preset power balance threshold includes an upper and lower limit, allowing the unit power balance to fluctuate within a reasonable range.

[0048] In addition, the optimization process adopts an iterative approach, each time reallocating the electrical nodes located at the unit boundary with the best comprehensive electrical distance and power sensitivity until the power balance of all units meets the preset threshold requirements and the adjustment amplitude of adjacent iterations is less than the preset convergence accuracy, or the maximum number of iterations is reached.

[0049] S3: Calculate the energy complementarity index and load elasticity coefficient of each energy autonomous unit and construct a unit characteristic model.

[0050] In a specific embodiment, the implementation process of step S3 includes: S3.1: Obtain the historical output data of distributed renewable energy within each energy autonomous unit and the historical power consumption data of load nodes.

[0051] Among them, historical output data and historical electricity consumption data are extracted from the basic data set of the distribution network analysis model.

[0052] S3.2: Based on historical output data and historical electricity consumption data, analyze the time correlation between renewable energy output and load demand, and calculate the energy complementarity index of each energy autonomous unit.

[0053] Specifically, historical output data and historical electricity consumption data were time-series standardized to make them comparable. The Pearson correlation coefficient between the standardized historical output data and the historical electricity consumption data within each energy autonomous unit was calculated to obtain a time correlation index. The ratio of the average daily output of renewable energy to the average daily load demand, as well as the ratio of the reliable output of renewable energy to the peak load demand, were calculated within each energy autonomous unit to obtain the amplitude matching coefficient. Based on the time correlation index and amplitude matching coefficient, a weighted summation method was used to calculate the energy complementarity index for each energy autonomous unit. The energy complementarity index ranges from [0 to 1], with larger values ​​indicating a higher degree of temporal and amplitude matching between renewable energy output and load demand. This energy complementarity index comprehensively quantifies the supply-demand matching capability within the energy autonomous unit, improving the energy utilization efficiency and system reliability of the regional distribution network.

[0054] S3.3: Based on historical electricity consumption data, identify the adjustable load characteristics within the unit and calculate the load elasticity coefficient of each energy autonomous unit.

[0055] Furthermore, the historical electricity consumption data was decomposed into time series to identify the base load, fluctuating load, and peak load within each energy autonomous unit, and to determine the load composition and adjustable load ratio. The response characteristics of the load to electricity price changes and dispatch instructions in the historical electricity consumption data were analyzed, and the average response time and response amplitude of the load within each energy autonomous unit were calculated. Based on the load composition, adjustable load ratio, average response time and response amplitude, the load elasticity coefficient of each energy autonomous unit was calculated. The load elasticity coefficient ranges from [0 to 1], with larger values ​​indicating faster load response and stronger regulation capability within the unit. This load elasticity coefficient characterizes the demand-side response potential of the energy autonomous unit, enhances the distribution network's ability to adapt to fluctuating energy sources, and effectively improves system operational flexibility.

[0056] S3.4: Combined with grid constraints, construct a unit characteristic model based on energy complementarity index and load elasticity coefficient.

[0057] Furthermore, system operation data of multiple typical time periods are selected, and a grid operation constraint set is established based on load power balance constraints, voltage constraints, line capacity constraints and new energy power constraints, and the data of typical time periods are divided into training sets and test sets according to preset proportions; the energy complementarity index and load elasticity coefficient are feature normalized to construct unit feature vectors; for each typical time period, flow calculations are performed based on the grid operation constraint set to determine the maximum upward power and maximum downward power of each energy autonomous unit; a mapping relationship model between unit feature vectors and power regulation capabilities is established based on the training set data, and the model accuracy is verified using the test set data. When the prediction error meets the requirements, the final unit feature model is obtained.

[0058] Preferably, the unit characteristic model proposed in this invention characterizes the characteristic attributes of energy autonomous units through the energy complementarity index and load elasticity coefficient, establishing a quantitative mapping relationship between unit characteristics and power regulation capabilities. This model organically combines the characteristics of new energy-load matching, load response characteristics, and grid operation constraints, enabling rapid assessment of the regulation potential of energy autonomous units and providing a reliable decision-making basis for subsequent optimized scheduling. The model boasts simple calculations and reliable results, and can be widely applied to distribution network planning and operational optimization, helping to improve the economic efficiency and reliability of distribution networks.

[0059] S4: Based on the unit characteristic model, dynamically adjust the active power and reactive power output of distributed renewable energy within each energy autonomous unit.

[0060] In a specific embodiment, the implementation process of step S4 includes: S4.1: Based on the unit characteristic model, establish a supply and demand forecasting framework for energy autonomous units and generate forecast results for load demand and renewable energy output.

[0061] Specifically, the energy complementarity index and load elasticity coefficient are extracted from the unit feature model as characteristic parameters for supply and demand forecasting; a unified supply and demand feature input layer is constructed, which includes time features (such as hours, date type, season), environmental features (such as temperature, humidity, light intensity) and unit characteristics (energy complementarity index, load elasticity coefficient); the network structure of the unified supply and demand forecasting framework is designed, including the feature extraction layer, the time series analysis layer and the result output layer; the energy complementarity index is embedded in the feature extraction layer as a regulating parameter for supply and demand balance, and the time series correlation of the supply and demand forecast is optimized; the unified supply and demand forecasting framework is trained using historical operation samples to generate short-term load demand forecast values ​​and new energy output forecast values; based on the load elasticity coefficient, the response potential of the adjustable load in the energy autonomous unit is calculated to generate a feasible range for load regulation; the forecast value and the feasible range for load regulation are integrated to form a comprehensive supply and demand forecast result for the energy autonomous unit.

[0062] Through the above design, this solution builds a unified supply and demand forecasting framework. By embedding the energy complementarity index into the feature extraction layer, it improves the forecasting of supply and demand correlation. Furthermore, based on the load elasticity coefficient, it generates a feasible range for load regulation, ensuring that the forecast results are more consistent with actual regulation capabilities. This solution provides both forecast results and load regulation ranges, providing a reliable decision-making basis for subsequent power regulation optimization.

[0063] S4.2: Based on the prediction results, determine the power balance status of each energy autonomous unit and establish corresponding operating constraints.

[0064] Specifically, the power balance state determination process includes: using the load demand forecast value and the new energy output forecast value to calculate the power deficit or surplus of the energy autonomous unit during the forecast period; combining the load regulation feasible interval to calculate the internal balance potential of the unit to obtain the self-balancing power and the power required for external exchange; based on the energy complementarity index, analyzing the impact of new energy fluctuations on the power balance, and dividing the power balance state into surplus state, balance state and deficit state; setting the power regulation direction and regulation priority according to different balance states to form a power balance state sequence.

[0065] Optionally, the operation constraints include, but are not limited to: power balance constraints, voltage limit constraints, line capacity constraints, equipment operation constraints, and safety constraints.

[0066] It should be noted that the determination of the power balance state provides the basic conditions for the subsequent construction of the objective function and the solution of the optimal power output. By clarifying the power regulation requirements and boundary constraints of each unit, the precise guidance of the distributed optimization algorithm is achieved, ensuring the feasibility of power balance regulation within the energy autonomous unit.

[0067] S4.3: Combined with the load elasticity coefficient, construct the objective function of power regulation within the energy autonomous unit.

[0068] Among them, the objective function consists of multiple optimization items, which at least include maximizing new energy consumption, minimizing network losses and minimizing voltage deviations, and each optimization item is weighted by a weight coefficient.

[0069] In addition, the load elasticity coefficient characterizes the response speed and regulation capability of the load within the unit. By affecting the weight coefficients of the two optimization items of maximizing new energy consumption and minimizing voltage deviation, the objective function can adaptively adjust the emphasis of the optimization target according to the load regulation capability.

[0070] S4.4: Based on the objective function and constraints, a distributed optimization algorithm is used to solve the optimal active power and reactive power output values ​​of distributed renewable energy.

[0071] Specifically, the distributed optimization solution process includes: based on the energy complementarity index, the distributed new energy equipment within the energy autonomous unit is divided into multiple collaborative optimization groups; for the equipment within the collaborative optimization group, a local optimization sub-problem is constructed, and the Lagrangian relaxation factor is introduced to deal with the power balance constraint; a distributed iterative solution framework based on ADMM (alternating direction method of multipliers) is designed, and the initial solution and convergence threshold are set; in each round of iteration, the load elasticity coefficient is used to dynamically adjust the variable update step size to accelerate the convergence of the algorithm; the solution is iterated until the convergence condition or the maximum number of iterations is reached, and the optimal active power and reactive power command values ​​are output.

[0072] S4.5: Send the optimal active power and reactive power output values ​​to each distributed new energy device to complete the power balance adjustment within the energy autonomous unit.

[0073] Furthermore, the instruction issuance process includes: determining the priority order of instruction issuance based on the power balance state sequence; establishing an emergency handling mechanism for communication failure to ensure that key equipment can receive adjustment instructions; collecting equipment execution feedback, updating the parameter values ​​in the unit characteristic model, and providing data support for the next cycle adjustment.

[0074] Through the above design, this solution establishes a complete regulation chain from prediction to evaluation to optimization and execution. It leverages the energy complementarity index and load elasticity coefficient from the unit characteristic model throughout each step: improving supply and demand correlation in the prediction phase, assisting in power balance state classification in the evaluation phase, and adaptively adjusting the objective function weights in the optimization phase. This solution makes the regulation strategy more consistent with actual operating characteristics, improves the absorption of distributed renewable energy within the energy autonomous unit, and ensures the safe and economic operation of the distribution network.

[0075] S5: Monitor voltage deviations and power flows between energy autonomous units, implement coordinated control strategies, regulate power exchange between energy autonomous units, and maximize the absorption of distributed renewable energy in regional power grids.

[0076] In a specific embodiment, the implementation process of step S5 includes: S5.1: Monitor the voltage of key nodes of the energy autonomous unit and the power of the interconnection lines between units.

[0077] It should be noted that based on the existing monitoring equipment in the distribution network, the voltage data of each node and the power data of each line are obtained according to the preset sampling period; according to the divided energy autonomous units, the voltage data of key nodes in the unit (such as substation bus, important load access points and distributed new energy access points) and the power data of the interconnection lines between units are screened and sorted from the existing monitoring data; the collected data are quality checked and outliers and missing values ​​are processed.

[0078] 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 area to be adjusted.

[0079] Specifically, the monitoring data processed by S5.1 is obtained; based on the results of the energy autonomous unit division, the interconnection lines between adjacent energy autonomous units and their corresponding boundary nodes are identified; the voltage deviation value of the key nodes of each energy autonomous unit is calculated, and the deviation value is compared with the preset voltage qualified range to determine the energy autonomous unit with abnormal voltage; the power transmission direction and flow level of the interconnection lines between units are calculated to identify the interconnection lines with abnormal flow; the operating status of each energy autonomous unit is evaluated by comprehensively considering the voltage deviation and flow level; based on the operating status evaluation results of each energy autonomous unit, the energy autonomous unit that needs power adjustment is determined to form an area to be adjusted; the electrical connection relationship between each energy autonomous unit in the area to be adjusted is verified to ensure the feasibility of power exchange. This step achieves accurate identification and positioning of abnormal areas in the distribution network by evaluating the operating status of energy autonomous units from multiple dimensions, greatly improving the efficiency and accuracy of problem diagnosis in the process of distributed new energy consumption.

[0080] S5.3: Based on the area to be regulated and in combination with the power regulation capability and voltage constraints of each energy autonomous unit, determine the combination of energy autonomous units participating in the power exchange.

[0081] Furthermore, the operating status information of each energy autonomous unit in the area to be regulated is obtained; the real-time adjustable capacity and load adjustable range of the distributed renewable energy within each energy autonomous unit are retrieved to determine the power regulation capability of each energy autonomous unit; the voltage sensitivity coefficient of each energy autonomous unit in the area to be regulated is obtained to establish a correlation between voltage change and power regulation; the voltage operating constraints of each energy autonomous unit are determined, including the upper and lower voltage limits and voltage regulation margin; the energy autonomous units in the area to be regulated are sorted according to their power regulation capability, and energy autonomous units with sufficient regulation capability are selected as candidate units; based on the electrical connection relationship in the area to be regulated, the possible power exchange paths between the candidate units are determined; and the power regulation capability, voltage constraints, and power exchange paths of the candidate units are comprehensively considered to determine the final combination of energy autonomous units participating in the power exchange. This step innovatively establishes a coordinated regulation mechanism between energy autonomous units, optimizes the resource allocation efficiency of distributed renewable energy consumption, and significantly improves the operational flexibility and system stability of the distribution network.

[0082] S5.4: Calculate the required active power and reactive power adjustment amounts based on the voltage deviation of the energy autonomous unit combination and generate corresponding control instructions.

[0083] S5.5: Send control instructions to the power regulation equipment of the relevant energy autonomous units, adjust the power exchange between the units, and verify the voltage recovery and power transmission status after adjustment.

[0084] Furthermore, this embodiment also provides a regional distribution system for accommodating distributed new energy, including: a data acquisition module, used to obtain topological structure data, load distribution data and distributed new energy output characteristic data of the regional distribution network, and construct a distribution network analysis model; a network partitioning module, used to divide the regional distribution network into multiple energy autonomous units based on the distribution network analysis model by calculating electrical distance and power sensitivity; a unit modeling module, used to calculate the energy complementarity index and load elasticity coefficient of each energy autonomous unit, and construct a unit characteristic model; a power regulation module, used to dynamically adjust the active power and reactive power output of the distributed new energy in each energy autonomous unit based on the unit characteristic model; a collaborative control module, used to monitor the operating status of the energy autonomous unit, implement collaborative control strategies, and regulate power exchange between energy autonomous units.

[0085] In summary, the energy autonomous unit division method proposed in the present invention can accurately identify tightly coupled node groups, realize the local consumption and efficient utilization of new energy, and reduce transmission and distribution losses; the unit characteristic model constructed based on the energy complementarity index and load elasticity coefficient effectively quantifies the internal supply and demand matching capability and demand-side response potential of the unit, provides a reliable decision-making basis for optimized scheduling, and improves the system operation flexibility; the network analysis model integrating power flow analysis and electrical characteristic matrix enhances the characterization ability of the impact of distributed new energy access, realizes the organic integration of static and dynamic characteristics of the distribution network, and provides an effective analysis tool for improving the new energy consumption capacity of the distribution network; the collaborative adjustment mechanism between energy autonomous units realizes the accurate identification and positioning of abnormal areas of the distribution network through multi-dimensional evaluation of the unit operation status, optimizes the resource allocation efficiency of distributed new energy consumption, and significantly improves the operation flexibility and system stability of the distribution network.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A regional power distribution method for accommodating distributed new energy, characterized in that: include: Obtain regional distribution network topology data, load distribution data, and distributed renewable energy output characteristics data to build a distribution network analysis model; Based on the power distribution network analysis model, the regional power distribution network is divided into a plurality of 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; Dynamically adjust the active power and reactive power output of distributed renewable energy within each energy autonomous unit based on the unit characteristic model; The operating status of the energy autonomous units is monitored, a coordinated control strategy is implemented, and power exchange between the energy autonomous units is regulated.

2. The regional power distribution method for accommodating distributed new energy according to claim 1, characterized in that: The construction of the power distribution network analysis model includes: Preprocess the acquired topology data, load distribution data, and renewable energy output characteristic data to generate a standardized data set; Based on the standardized data set, a power flow calculation model of the regional distribution network is established; By calculating the voltage sensitivity coefficient and power transfer distribution factor of electrical nodes in the regional distribution network, an electrical characteristic correlation matrix is ​​constructed; The power flow calculation model and the electrical characteristic correlation matrix are integrated to form a distribution network analysis model.

3. The regional power distribution method for accommodating distributed new energy according to claim 1, characterized in that: The dividing the regional power distribution network into a plurality of energy autonomous units by calculating electrical distance and power sensitivity comprises: Based on the distribution network analysis model, the electrical distance between each electrical node in the regional distribution network is calculated to construct a node distance matrix; Calculate the power sensitivity parameters between electrical nodes in the regional distribution network and construct the node power sensitivity matrix; Determining a node clustering threshold according to 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. The power balance within each preliminary energy autonomous unit is calculated, and the boundary of the preliminary energy autonomous unit is optimized according to the power balance to form a plurality of energy autonomous units.

4. The regional power distribution method for accommodating distributed new energy according to claim 1, characterized in that: The building block feature model includes: Obtain historical output data of distributed renewable energy within each energy autonomous unit and historical power consumption data of load nodes; Based on the historical output data and the historical electricity consumption data, analyzing the time correlation between the output of new energy and the load demand, and calculating the energy complementarity index of each energy autonomous unit; Based on the historical electricity consumption data, identifying the adjustable load characteristics within the unit and calculating the load elasticity coefficient of each energy autonomous unit; In combination with grid constraints, a unit characteristic model is constructed based on the energy complementarity index and the load elasticity coefficient.

5. The regional power distribution method for accommodating distributed new energy according to claim 1, characterized in that: The dynamic adjustment of the active power and reactive power output of the distributed renewable energy in each autonomous energy unit includes: Based on the unit characteristic model, a supply and demand forecasting framework for energy autonomous units is established to generate forecast results of load demand and new energy output; Based on the prediction results, determine the power balance state of each energy autonomous unit and establish corresponding operation constraints; Combined with the load elasticity coefficient, the objective function of power regulation within the energy autonomous unit is constructed; Based on the objective function and constraints, a distributed optimization algorithm is used to solve the optimal active power and reactive power output values ​​of distributed renewable energy; The optimal active power and reactive power output values ​​are sent to each distributed new energy device to complete the power balance adjustment within the energy autonomous unit.

6. The regional power distribution method for accommodating distributed new energy according to claim 1, characterized in that: The implementation of the collaborative control strategy includes: Monitor the voltage of key nodes of energy autonomous units and the power of interconnection lines between units; Analyze and calculate the monitoring data to obtain the voltage deviation value, power transmission direction and power flow level between units, and determine the area to be adjusted; Based on the area to be regulated and in combination with the power regulation capability and voltage constraints of each energy autonomous unit, determining a combination of energy autonomous units participating in power exchange; Calculate the required active power and reactive power adjustment amounts based on the voltage deviation of the energy autonomous unit combination and generate corresponding control instructions; The control instructions are sent to the power regulation devices of the relevant energy autonomous units to regulate the power exchange between the units.

7. A regional power distribution system for accommodating distributed new energy, characterized in that: include: The data acquisition module is used to obtain the topological structure data, load distribution data and distributed renewable energy output characteristics data of the regional distribution network and build a distribution network analysis model; A network partitioning module, configured to divide the regional power distribution network into a plurality of energy autonomous units by calculating electrical distance and power sensitivity based on the power distribution network analysis model; Unit modeling module, used to calculate the energy complementarity index and load elasticity coefficient of each energy autonomous unit and build a unit characteristic model; A power regulation module, configured to dynamically adjust the active power and reactive power output of the distributed renewable energy 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 power exchange between the energy autonomous units.

Images