Distributed energy cooperative control method facing demand side response
By acquiring node state vectors and coupling information, the operating boundary of distributed energy nodes is dynamically reconstructed, and global security constraints are solved independently and embedded, thus solving the problem of decoupling accuracy degradation in distributed energy collaborative control and realizing reliable traceability and rigid protection of power grid physical security.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-03
- Publication Date
- 2026-03-31
AI Technical Summary
Existing technologies lack effective real-time correction mechanisms in the coordinated control of distributed energy resources, which leads to a decrease in decoupling accuracy and makes it difficult to guarantee the physical safety and reliability of the distribution network operation. In particular, there are problems of convergence oscillation and insufficient safety in the closed-loop verification of global safety constraints.
By acquiring node state vectors and node coupling information, the local operating boundary constraints of distributed energy nodes are dynamically reconstructed, the initial scheduling scheme is solved independently, and a distributed collaborative optimization problem is constructed through the decomposition and embedding of global security constraints. Multiple rounds of iterative calculations are performed to obtain the globally collaborative optimal solution, ensuring reliable traceability of the physical security of the power grid.
It achieves high-quality initial scheduling of distributed energy nodes in real-time operation, ensuring the reliability and rigidity of power grid physical security, avoiding control deviations caused by mismatch between traditional models and real-time conditions, and improving computational efficiency and engineering practicality.
Smart Images
Figure CN121769904A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of smart grid technology, and in particular to a distributed energy collaborative control method oriented towards demand-side response. Background Technology
[0002] With the increasing penetration of renewable energy and the diversification of demand-side resources, the operation mode of power distribution networks is evolving from traditional unidirectional centralized control to a distributed model with multi-level interaction among energy sources, loads, and storage. Against this backdrop, distributed optimization algorithms, particularly those based on the Alternating Directional Multiplier Method (ADMM), have become the mainstream technical approach for coordinating the participation of numerous distributed energy sources in demand-side response. The core idea of this approach is to decompose a complex global optimization problem into multiple sub-problems, which are then solved by each energy node based on local information. Limited neighborhood communication is used to coordinate decisions among nodes, ultimately approximating the optimal operating point of the system.
[0003] However, existing technical solutions lack an effective mechanism for accurately modeling and embedding global power grid security constraints within a distributed computing architecture. This makes it difficult to rigidly guarantee and reliably trace the physical security of distribution network operation during distributed optimization. Specifically, existing methods mainly rely on relaxing global constraints such as line power and node voltage or introducing them as penalty terms into the objective function. This indirect approach severs the intrinsic connection between network physical characteristics and node autonomous decision-making. When the system operating point approaches the safety boundary, this separation can easily lead to the distributed algorithm's solution, while mathematically convergent, potentially violating line thermal stability limits or voltage quality requirements physically. Furthermore, the sensitive setting of the penalty coefficient can cause convergence oscillations, making it impossible to obtain stable and reliable closed-loop verification of the satisfaction of global security constraints throughout the entire distributed decision-making process. Summary of the Invention
[0004] In view of the aforementioned existing problems, the present invention is proposed.
[0005] Therefore, this invention provides a distributed energy collaborative control method oriented towards demand-side response to solve the problem of decreased decoupling accuracy caused by the lack of a real-time correction mechanism in dynamic environments.
[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a distributed energy collaborative control method oriented towards demand-side response, comprising: S1. Based on distributed energy multi-source data and grid parameters, obtain node state vectors and node coupling information; S2. Each distributed energy node obtains its own operating characteristics based on its node state vector; and dynamically reconstructs its local operating boundary constraints and response cost optimization objectives according to its own operating characteristics, and independently solves to generate the initial scheduling scheme for each node. S3. Summarize the initial scheduling schemes of all nodes, perform scheduling conflict detection based on node coupling information, extract global security constraints from the scheduling conflict detection results, decompose the global security constraints and distribute them into the local optimization problems of each node to form a distributed collaborative optimization problem; S4. Input the distributed collaborative optimization problem into the collaborative solver, perform parallel iterative calculations with multiple rounds of limited information interaction, and obtain the converged global collaborative optimal solution; S5. Decode the global collaborative optimal solution and transform it into an optimal global collaborative control scheme oriented towards demand-side response.
[0007] As a preferred embodiment of the distributed energy collaborative control method for demand-side response described in this invention, the method for obtaining node state vectors and node coupling information includes: Establish a power grid topology by using power grid nodes as vertices and power lines as edges in the power grid parameters; map multi-source data of distributed energy resources to the corresponding distributed energy nodes in the power grid topology. Based on the power grid topology, distributed energy multi-source data and power grid parameters are integrated according to the attributes of vertices and edges to form a panoramic power grid dataset; and the operating characteristic parameters of each power grid node are extracted, and node state vectors are generated through time alignment and feature normalization. Based on the node state vector, and combined with the line impedance and admittance parameters in the power grid topology, node coupling information is obtained.
[0008] As a preferred embodiment of the distributed energy collaborative control method oriented towards demand-side response described in this invention, the method for independently solving and generating the initial scheduling scheme for each node includes: Based on the node state vector, operational characteristic parameters reflecting the operational capability and regulation potential of distributed energy nodes are extracted to form the operational characteristics of distributed energy nodes; Based on the operating characteristics of distributed energy nodes, the local operating boundary constraints of distributed energy nodes are dynamically reconstructed to obtain a set of local operating boundary constraints that reflects the real-time operating range. By combining the local operating boundary constraint set with the operating characteristics of distributed energy nodes, a response cost optimization objective is constructed with minimizing operating costs and compensation costs as the core, while simultaneously satisfying power balance constraints and energy dynamic constraints. Based on the optimal response cost objective and the local operating boundary constraint set, the local optimization problem is solved in parallel in the local computing unit of each distributed energy node to generate the initial scheduling scheme for each node.
[0009] As a preferred embodiment of the distributed energy collaborative control method oriented towards demand-side response described in this invention, the method for detecting scheduling conflicts based on node coupling information includes: All initial scheduling schemes of all nodes are aggregated to form a global scheduling scheme set; Based on the power grid topology and node coupling information, a refined power flow calculation is performed on the global scheduling scheme set to obtain the power distribution of each power line and the voltage distribution of each power grid node. The power distribution of each power line is compared with the line thermal stability limit to identify lines that exceed the power limit, and the voltage distribution of each power grid node is compared with the voltage safety range to locate nodes that exceed the voltage limit. Spatially correlate the spatial location and severity of power over-limit lines and voltage over-limit nodes to generate structured scheduling conflict detection results.
[0010] As a preferred embodiment of the distributed energy collaborative control method oriented towards demand-side response described in this invention, the method for forming the distributed collaborative optimization problem includes: The power over-limit lines and voltage over-limit nodes are extracted from the scheduling conflict detection results and quantified into the power over-limit amount of the lines and the voltage over-limit amount of the nodes to be eliminated, thus forming a global security constraint. Based on the grid topology and node coupling information, the active power adjustment sensitivity of each distributed energy node to power over-limit lines is obtained, and the reactive power adjustment sensitivity of each distributed energy node to voltage over-limit nodes is also obtained. Based on the sensitivity of active and reactive power adjustment, the line power exceeding the limit is decomposed into the active power adjustment responsibility of the relevant distributed energy nodes, and the node voltage exceeding the limit is decomposed into the reactive power adjustment obligation of the adjacent distributed energy nodes. The responsibility for active power adjustment is specified as a global security constraint of the power balance equation, and the obligation for reactive power adjustment is specified as a global security constraint of the reactive power regulation inequality. These are then embedded as new decision-making constraints into the local operating boundary constraint sets of each corresponding distributed energy node, forming a collaborative local constraint set. Based on the goal of optimal response cost and the set of coordinated local constraints, a distributed coordinated optimization problem containing this consistency constraint is constructed by defining a globally consistent variable and establishing an equation relationship between it and the local decision variables of each distributed energy node.
[0011] As a preferred embodiment of the distributed energy cooperative control method oriented towards demand-side response described in this invention, the method for obtaining a converged global cooperative optimal solution includes: The distributed collaborative optimization problem is input into the collaborative solver, the local decision variables and global consistency variables of each distributed energy node are initialized, and the first round of limited information interaction is performed to obtain the initial global consistency variable update results. Each distributed energy node solves a local optimization problem that includes a collaborative local constraint set and the objective of optimal response cost based on the updated global consistency variables, generates a local optimal solution and uploads it to the collaborative solver, which then updates the global consistency variables and global residuals. The collaborative solver determines whether the global residual satisfies the global consistency tolerance. If it does, it outputs the global collaborative optimal solution; otherwise, it continues to perform parallel iterative calculations until convergence.
[0012] As a preferred embodiment of the distributed energy collaborative control method oriented towards demand-side response described in this invention, the method for obtaining the optimal global collaborative control scheme includes: Based on the global collaborative optimal solution, the local optimal power command and energy storage charging and discharging strategy of each distributed energy node are extracted to form a global optimal control parameter set; The optimal global control instruction set is distributed to each distributed energy node to form an optimal global collaborative control scheme oriented towards demand-side response.
[0013] As a preferred embodiment of the distributed energy collaborative control method for demand-side response described in this invention, the method for obtaining the power distribution of each power line and the voltage distribution of each grid node includes: Based on the line impedance and admittance parameters in the power grid topology and node coupling information, a node admittance matrix is constructed. The initial scheduling schemes of each node in the global scheduling scheme set are used as node power injection quantities, which together with the node admittance matrix form a set of power flow calculation equations. The power flow calculation equations are solved by iterative algorithm until the active power imbalance and reactive power imbalance of all grid nodes are less than the preset convergence threshold, and the stable solutions of voltage amplitude and phase angle of each grid node are obtained; the stable solutions of voltage amplitude of each grid node constitute the voltage distribution of each grid node. Based on the stable solutions of voltage amplitude and phase angle at each power grid node, the active power distribution and reactive power distribution of each power line are calculated through the physical relationship between the voltage at the power grid node and the power of the branch.
[0014] As a preferred embodiment of the distributed energy collaborative control method for demand-side response described in this invention, the method for obtaining the active power adjustment responsibility and reactive power adjustment obligation includes: Based on the line power over-limit and the active power adjustment sensitivity of each distributed energy node, the active power adjustment amount that each relevant distributed energy node should bear is calculated according to the sensitivity ratio. Based on the node voltage over-limit and the reactive power adjustment sensitivity of each distributed energy node, calculate the reactive power adjustment amount required by each adjacent distributed energy node to eliminate the voltage over-limit. The active power adjustment amount will be formally quantified as the active power adjustment responsibility of the relevant distributed energy nodes; The reactive power adjustment amount is formally quantified as the reactive power adjustment obligation of adjacent distributed energy nodes. As a preferred embodiment of the distributed energy collaborative control method oriented towards demand-side response described in this invention, the method for defining globally consistent variables includes: Based on the power grid topology and node coupling information, the voltage sensitivity amplitude between each distributed energy node is calculated as the electrical distance between nodes, and clustering is performed according to a preset electrical distance threshold to identify distributed energy node clusters with coupling relationships. For each distributed energy node cluster, an auxiliary variable characterizing the overall power balance state of the cluster is defined as a cluster-level globally consistent variable; For power over-limit lines and other important power lines closely related to them, an auxiliary variable characterizing the power safety status of the line is defined as a line-level globally consistent variable; All cluster-level global consistency variables and line-level global consistency variables are merged to form a global consistency variable.
[0015] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the demand-side response-oriented distributed energy cooperative control method as described in the first aspect of the present invention.
[0016] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the demand-side responsive distributed energy coordinated control method as described in the first aspect of the present invention.
[0017] The beneficial effects of this invention are as follows: the dynamic reconfiguration mechanism enables each distributed energy node to autonomously generate a high-quality initial scheduling scheme based on its real-time operating status, providing a precise and reliable decision-making basis for subsequent collaborative optimization, and effectively avoiding control deviations caused by the mismatch between the traditional fixed model and the real-time state; the global security constraint distributed embedding mechanism based on sensitivity analysis quantifies the identified global security conflicts into specific limits and accurately decomposes them into adjustment responsibilities for each node according to the electrical coupling relationship of the nodes. By rigidly embedding security constraints into the local optimization problem, reliable traceability and rigid protection of the physical security of the power grid during the distributed collaborative control process are ensured. Attached Figure Description
[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a flowchart of the distributed energy collaborative control method for demand-side response in this invention.
[0020] Figure 2 This is a flowchart of the process for generating the initial scheduling scheme for each node in this invention.
[0021] Figure 3 This is a flowchart illustrating the formation of the distributed collaborative optimization problem in this invention.
[0022] Figure 4 This is a flowchart for outputting the global cooperative optimal solution in this invention. Detailed Implementation
[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.
[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.
[0026] Reference Figure 1 , Figure 2 , Figure 3 and Figure 4 This is one embodiment of the present invention, which provides a distributed energy collaborative control method oriented towards demand-side response, comprising the following steps: Methods for obtaining node state vectors and node coupling information include: A power grid topology is established by using power grid nodes as vertices and power lines as edges in the power grid parameters; multi-source data of distributed energy resources are then mapped to the corresponding distributed energy nodes in the power grid topology.
[0027] Specifically, the power grid parameters include the coordinates and type information of power grid nodes, the connection relationships of power lines, and impedance parameters; the power grid parameters are parsed to obtain the unique identifiers and location coordinates of all power grid nodes, and the connection relationships between the start and end nodes of power lines are extracted; a power grid topology diagram is constructed with power grid nodes as vertices and power lines as edges; the distributed energy multi-source data includes the distributed energy installation location identifiers, real-time output data, and operating status signals; the distributed energy installation location identifiers are matched with the unique identifiers of power grid nodes, and the successfully matched distributed energy multi-source data is mapped to the corresponding power grid nodes; power grid nodes with distributed energy are marked as distributed energy nodes; and a data association relationship is established between the distributed energy multi-source data and the corresponding distributed energy nodes.
[0028] Based on the power grid topology, distributed energy multi-source data and power grid parameters are integrated according to the attributes of vertices and edges to form a panoramic power grid dataset; and the operating characteristic parameters of each power grid node are extracted, and node state vectors are generated through time alignment and feature normalization.
[0029] Specifically, the installation location identifier, real-time output data, and operating status signals from the multi-source data of distributed energy are assigned to the corresponding power grid node vertices, and the line connection relationships and impedance parameters from the power grid parameters are assigned to the corresponding power line edges, thus completing the attribute integration of vertices and edges; the integrated attribute set constitutes the power grid panoramic dataset. Operational characteristic parameters of each power grid node are extracted from the power grid panoramic dataset, including active and reactive power values in real-time output data, and switch status and fault flags in operation status signals. A unified timestamp is set, and the operational characteristic parameters of different acquisition periods are sorted by timestamp to complete time-series alignment. The active and reactive power values after time-series alignment are normalized to their maximum and minimum values respectively. The normalized values, together with the switch status and fault flags, constitute the node status vector.
[0030] Based on the node state vector, and combined with the line impedance and admittance parameters in the power grid topology, node coupling information is obtained.
[0031] It should be noted that the line admittance parameters are calculated based on the power line impedance parameters in the power grid panoramic dataset. Using the line admittance parameters and the power line connections in the power grid topology diagram, the voltage-power sensitivity coefficient between any two power grid nodes is calculated using the electrical distance formula between power grid nodes; the electrical distance is determined by the cumulative impedance of all lines along the path connecting the power grid nodes. The calculated voltage-power sensitivity coefficients between each power grid node are arranged according to the unique identifier of the power grid node to form a voltage-power sensitivity coefficient matrix. This matrix serves as node coupling information reflecting the degree of power mutual influence between distributed energy nodes.
[0032] Methods for independently solving and generating the initial scheduling scheme for each node include: Existing technologies employ fixed boundary and cost models for distributed optimization, which cannot respond to real-time changes in the state of distributed energy resources. When equipment operating conditions fluctuate, the preset model and the actual adjustment capacity become severely mismatched, causing the initial scheduling scheme to deviate from the actual feasible region. This not only reduces economic efficiency but also threatens the convergence and global security of subsequent collaborative optimization.
[0033] Based on the node state vector, operational characteristic parameters reflecting the operational capability and regulation potential of distributed energy nodes are extracted to form the operational characteristics of distributed energy nodes.
[0034] It should be noted that normalized active power and normalized reactive power values are extracted from the node state vector as the benchmark for power regulation capability; switch status and fault flags are extracted to determine the available regulation time period. The power regulation capability benchmark and the available regulation time period together constitute the operating characteristic parameters reflecting the real-time dispatchability capability; the set of operating characteristic parameters forms the operating characteristics of the distributed energy node. The operating characteristics of the distributed energy node include maximum active power output, minimum active power output, maximum reactive power output, and available operating time window.
[0035] Based on the operating characteristics of distributed energy nodes, the local operating boundary constraints of distributed energy nodes are dynamically reconstructed to obtain a set of local operating boundary constraints that reflects the real-time operating range.
[0036] Specifically, based on the maximum and minimum active power output of distributed energy nodes, upper and lower limits of active power operating boundaries are set; based on the maximum reactive power output of distributed energy nodes, upper limits of reactive power operating boundaries are set; and based on the available operating time window of distributed energy nodes, scheduleable time period operating boundaries are set. These upper and lower limits of active power, upper limits of reactive power, and scheduleable time period operating boundaries together constitute a local operating boundary constraint set reflecting the real-time operating range.
[0037] By combining the local operating boundary constraint set with the operating characteristics of distributed energy nodes, a response cost optimization objective is constructed with minimizing operating costs and compensation costs as the core, while simultaneously satisfying power balance constraints and energy dynamic constraints.
[0038] Within the operating boundary defined by the local operating boundary constraint set, the operating cost is calculated by multiplying the planned active power output of the distributed energy node by the preset operating cost coefficient, and the compensation cost is calculated by multiplying the planned adjustment amount of the distributed energy node by the preset compensation cost coefficient. The objective function is constructed by minimizing the sum of the operating cost and the compensation cost, which is the optimal target for response cost. The objective function must satisfy the power balance constraint that the injected power of the distributed energy node is equal to the load demand power, and satisfy the energy dynamic constraint that the difference between the current capacity and the charging and discharging power of the energy storage unit in the previous period is satisfied.
[0039] Furthermore, a baseline cost coefficient is set according to the equipment type and response protocol, and dynamically adjusted based on the real-time electricity price; for example: the photovoltaic operating cost is 0.05 yuan / kWh, and the interruptible load compensation cost is 0.6 yuan / kWh; when the real-time electricity price exceeds 0.5 yuan / kWh, the cost coefficient increases by 10%-15%.
[0040] Based on the optimal response cost objective and the local operating boundary constraint set, the local optimization problem is solved in parallel in the local computing unit of each distributed energy node to generate the initial scheduling scheme for each node.
[0041] It should be noted that the solution process involves satisfying the active power upper and lower limits, reactive power upper limit, and dispatchable time period limits contained in the local operating boundary constraint set, while also satisfying power balance constraints and energy dynamic constraints. A linear programming algorithm is used to minimize the response cost optimization objective. The optimal solution of the decision variables output by the optimization calculation includes the optimal planned active power output and the optimal planned adjustment amount of the distributed energy nodes. The combination of the optimal planned active power output and the optimal planned adjustment amount constitutes the initial scheduling scheme for each node.
[0042] It should be noted that the expression for minimizing the optimal response cost objective is as follows: ; ; ; in, It is the optimal value of planned effort output. These are the decision variables for the planned work output that need to be optimized. It is the operating cost coefficient. This indicates that the constraints are met. It is the minimum active power output operating boundary. It is the operating boundary of maximum active power output. It is the optimal value of the planned adjustment. These are the decision variables for planned adjustments that need to be optimized. It is the compensation cost coefficient. This is the load demand power for the current period. It is the upper limit of the minimum usable capacity of the energy storage unit. This is the actual energy capacity of the energy storage unit in the previous period. It is the duration of the current optimization period. It is the upper limit of the maximum usable capacity of the energy storage unit.
[0043] By employing a dynamic reconfiguration mechanism, the operating boundary and cost objectives are aligned in real-time with the operating status of distributed energy resources, generating a high-quality initial solution that is both economical and feasible. This solution provides a reliable decision-making starting point for global collaborative optimization, significantly improving computational efficiency and the engineering practicality of the final control strategy.
[0044] Methods for scheduling conflict detection based on node coupling information include: All initial scheduling schemes for each node are aggregated to form a global scheduling scheme set.
[0045] It should be noted that the aggregation process involves centrally transmitting the initial node scheduling schemes generated by the local computing units of each distributed energy node through a data bus; the optimal values of planned active power output and planned adjustment amount contained in the initial node scheduling schemes are indexed and stored according to the unique identifiers of the power grid nodes; the optimal values of planned active power output and planned adjustment amount corresponding to the unique identifiers of all power grid nodes together constitute the global scheduling scheme set.
[0046] Based on the power grid topology and node coupling information, a refined power flow calculation is performed on the global scheduling scheme set to obtain the power distribution of each power line and the voltage distribution of each power grid node.
[0047] By comparing the power distribution of each power line with the line's thermal stability limit, lines exceeding the power limit are identified. By comparing the voltage distribution of each power grid node with the voltage safety range, nodes exceeding the voltage limit are located.
[0048] It should be noted that the comparison process is as follows: read the pre-stored line thermal stability limit value in the power grid parameters, compare the active power value in the active power distribution of each power line with the corresponding line thermal stability limit value, and mark the power line whose active power value exceeds the line thermal stability limit value as a power over-limit line. Read the pre-stored upper and lower limits of the voltage safety range in the power grid parameters, compare the voltage amplitude in the voltage distribution of each power grid node with the upper and lower limits of the voltage safety range, and mark the power grid node with the voltage amplitude below the lower limit of the voltage safety range or above the upper limit of the voltage safety range as a voltage over-limit node.
[0049] Spatially correlate the spatial location and severity of power over-limit lines and voltage over-limit nodes to generate structured scheduling conflict detection results.
[0050] It should be noted that, based on the power grid topology diagram, the starting and ending nodes of the power exceeding the limit line connection are determined, and the coordinate position of the voltage exceeding the limit node in the power grid topology diagram is located; the percentage of active power of the power exceeding the limit line exceeding the thermal stability limit value is calculated as the power exceeding severity, and the percentage of voltage amplitude of the voltage exceeding the upper and lower limits of the voltage safety range is calculated as the voltage exceeding severity; the positions of the power exceeding the limit line, the positions of the voltage exceeding the limit node, the power exceeding severity, and the voltage exceeding severity are associated and mapped according to the unique identifier of the power grid node, forming a structured scheduling conflict detection result containing the location information and severity information of the exceeding element.
[0051] Methods for formulating distributed collaborative optimization problems include: Existing distributed optimization methods introduce global security constraints into the objective function as penalty terms, causing the satisfaction of security constraints to depend on the setting of the penalty coefficient, which easily leads to convergence oscillations. At the same time, the lack of sensitivity analysis based on the physical characteristics of the power grid makes it impossible to accurately decompose global exceedances to each node, resulting in low coordination efficiency and difficulty in rigidly guaranteeing the safety boundary.
[0052] The power over-limit lines and voltage over-limit nodes are extracted from the scheduling conflict detection results and quantified into power over-limit line quantities and voltage over-limit node quantities to be eliminated, thus forming global security constraints.
[0053] It should be noted that the quantification process involves calculating the difference between the current active power value of the power exceeding the limit line and the corresponding thermal stability limit value as the power exceeding the limit line quantity, and calculating the difference between the current voltage amplitude of the voltage exceeding the limit node and the nearest boundary of the voltage safety range as the node voltage exceeding the limit. The set of the power exceeding the limit line quantity and the voltage exceeding the limit node quantity constitutes the global safety constraint that needs to be eliminated in the collaborative optimization.
[0054] Based on the grid topology and node coupling information, the active power adjustment sensitivity of each distributed energy node to power-limit-exceeding lines is obtained, and the reactive power adjustment sensitivity of each distributed energy node to voltage-limit-exceeding nodes is also obtained.
[0055] It should be noted that, from the voltage-power sensitivity coefficient matrix contained in the node coupling information, the first and last nodes corresponding to the power over-limit line are located, and all elements of the column vector corresponding to the power over-limit line are extracted. These element values are the active power adjustment sensitivity of each distributed energy node for the power over-limit line.
[0056] From the voltage-power sensitivity coefficient matrix contained in the node coupling information, locate the row vector corresponding to the voltage over-limit node, extract all elements of the row vector, and these element values are the reactive power adjustment sensitivity of each distributed energy node to the voltage over-limit node.
[0057] Based on the sensitivity of active and reactive power adjustment, the line power exceeding the limit is decomposed into the active power adjustment responsibility of the relevant distributed energy nodes, and the node voltage exceeding the limit is decomposed into the reactive power adjustment obligation of the adjacent distributed energy nodes.
[0058] The responsibility for active power adjustment is specified as a global security constraint of the power balance equation, and the obligation for reactive power adjustment is specified as a global security constraint of the reactive power regulation inequality. These are then distributed and embedded into the local operating boundary constraint set of each corresponding distributed energy node as new decision-making constraints, forming a collaborative local constraint set.
[0059] Specifically, the active power adjustment responsibility value of each relevant distributed energy node is directly assigned as a constant value on the right side of the equation, and the planned active power output variable of the distributed energy node is used as the left side of the equation to establish a mathematical equation relationship that the planned active power output variable is equal to the active power adjustment responsibility value, thus forming a global security constraint for the power balance equation. The absolute value of the reactive power adjustment obligation value of each adjacent distributed energy node is taken as the lower limit constant value. The absolute value of the planned reactive power output variable of the distributed energy node is taken as the left side of the inequality. A mathematical inequality relationship is established that the absolute value of the planned reactive power output variable is greater than or equal to the lower limit constant value, thus forming a global security constraint for reactive power adjustment inequality. The global security constraints of the power balance equation and the global security constraints of the reactive power regulation inequality are added as new decision-making restrictions to the local operating boundary constraint sets of the corresponding distributed energy nodes, forming a collaborative local constraint set that includes the new global security constraints.
[0060] Based on the goal of optimal response cost and the set of coordinated local constraints, a distributed coordinated optimization problem containing this consistency constraint is constructed by defining a globally consistent variable and establishing an equation relationship between it and the local decision variables of each distributed energy node.
[0061] It should be noted that a global consistency variable for line power is created, equal to the number of lines exceeding power limits, and a global consistency variable for node voltage is created, equal to the number of nodes exceeding voltage limits. The equation constraint is set such that the sum of the planned active power output variables of each relevant distributed energy node equals the corresponding line power global consistency variable, and the equation constraint is set such that the sum of the planned reactive power output variables of each adjacent distributed energy node equals the corresponding node voltage global consistency variable. The constructed distributed cooperative optimization problem includes local subproblems in which each distributed energy node optimizes the response cost under the cooperative local constraint set, as well as consistency constraints that couple all local subproblems through global consistency variables.
[0062] By accurately decomposing global security constraints through sensitivity analysis and rigidly embedding them into the local optimization problem, reliable traceability of power grid physical security under a distributed architecture is achieved. Transforming security requirements into specific adjustment responsibilities for distributed energy nodes ensures both the convergence stability of the algorithm and that the final scheduling scheme strictly meets line capacity and voltage safety requirements.
[0063] Methods for obtaining convergent globally cooperative optimal solutions include: The distributed collaborative optimization problem is input into the collaborative solver, the local decision variables and global consistency variables of each distributed energy node are initialized, and the first round of limited information interaction is performed to obtain the initial global consistency variable update results.
[0064] Specifically, the planned active power output variable and planned reactive power output variable of each distributed energy node are set to the optimal values of planned active power output and planned adjustment amount in the initial scheduling scheme of the corresponding node, and the global consistency variables of line power and node voltage are set to zero vectors. Each distributed energy node uploads its initialized local decision variables to the collaborative solver. After collecting the local decision variables of all distributed energy nodes, the collaborative solver calculates the global consistency variable of each line power as the average of the sum of the planned active power output variables of the corresponding related distributed energy nodes, and calculates the global consistency variable of each node voltage as the average of the sum of the planned reactive power output variables of the corresponding adjacent distributed energy nodes, thus obtaining the initial global consistency variable update result.
[0065] Each distributed energy node solves a local optimization problem that includes a collaborative local constraint set and the objective of optimal response cost based on the updated global consistency variables. It generates a local optimal solution and uploads it to the collaborative solver, which then updates the global consistency variables and global residuals.
[0066] Specifically, each distributed energy node uses the received line power global consistency variable and node voltage global consistency variable as fixed parameters, and uses a linear programming algorithm to perform minimum optimization calculation on the response cost optimal objective under the condition of satisfying the cooperative local constraint set, to obtain the local optimal solution including the updated planned active power output variable and planned reactive power output variable. Furthermore, the local optimal solutions uploaded by all distributed energy nodes are collected; the global consistency variable of line power is recalculated, and its value is equal to the average of the sum of the updated planned active power output variables of all relevant distributed energy nodes; at the same time, the global consistency variable of node voltage is recalculated, and its value is equal to the average of the sum of the updated planned reactive power output variables of all adjacent distributed energy nodes; the collaborative solver calculates the sum of squares of the differences between the old and new values of the global consistency variables of line power, and adds the sum of squares of the differences between the old and new values of the global consistency variables of node voltage, and uses the calculation result as the global residual.
[0067] The collaborative solver determines whether the global residual satisfies the global consistency tolerance. If it does, it outputs the global collaborative optimal solution; otherwise, it continues to perform parallel iterative calculations until convergence.
[0068] It should be noted that, based on the accuracy requirements of power grid dispatching and distributed energy regulation, and combined with statistical analysis of historical convergence data, empirical values for energy balance calculation efficiency and solution accuracy are selected. The global consistency tolerance is set to an example value of 0.001.
[0069] Methods for obtaining the optimal global cooperative control scheme include: Based on the global collaborative optimal solution, the local optimal power command and energy storage charging and discharging strategy of each distributed energy node are extracted to form a global optimal control parameter set.
[0070] It should be noted that the extraction process involves reading the optimal planned active power output of each distributed energy node from the global collaborative optimal solution as the active power command, reading the optimal planned adjustment value as the reactive power command, and reading the optimal charging and discharging power of the energy storage unit as the energy storage charging and discharging strategy. The active power command, reactive power command, and energy storage charging and discharging strategy together constitute the global optimal control parameter set.
[0071] The optimal global control instruction set is distributed to each distributed energy node to form an optimal global collaborative control scheme oriented towards demand-side response.
[0072] It should be noted that the active power command, reactive power command, and energy storage charging and discharging strategy of the global optimal control parameter set are transmitted to the local controller of each corresponding distributed energy node through the data communication network. The collection of these commands and strategies constitutes a complete action plan that can be parsed and executed by each distributed energy node. This plan is the optimal global collaborative control scheme for demand-side response.
[0073] Methods for obtaining the power distribution of each power line and the voltage distribution of each power grid node include: Based on the line impedance and admittance parameters in the power grid topology and node coupling information, a node admittance matrix is constructed. It should be noted that the dimension and topology of the node admittance matrix are determined based on the power line connection relationship in the power grid topology diagram, and the line admittance parameters in the node coupling information are filled into the corresponding positions of the node admittance matrix according to Kirchhoff's laws.
[0074] The initial scheduling schemes of each node in the global scheduling scheme set are used as node power injection quantities, which together with the node admittance matrix form a set of power flow calculation equations. It should be noted that the optimal planned active power output of each distributed energy node in the global scheduling scheme set is used as the given value of the active power injected into the corresponding grid node of the distributed energy node; the part of the optimal planned adjustment value that represents reactive power adjustment is used as the given value of the reactive power injected into the corresponding grid node of the distributed energy node. Based on the power flow calculation principle, the node admittance matrix is multiplied by the node voltage phasor of all grid nodes to obtain the calculation formula for the node injected current; then the node injected current is multiplied by the node voltage phasor to obtain the calculation formula for the node injected power. Subtract the injected power calculation formula of each grid node from the given injected power value of that grid node to establish the power balance equation of that grid node; the power balance equations of all grid nodes together constitute a set of nonlinear power flow calculation equations with the voltage amplitude and phase angle of all grid nodes as unknowns.
[0075] The power flow calculation equations are solved by iterative algorithm until the active power imbalance and reactive power imbalance of all grid nodes are less than the preset convergence threshold, and the stable solutions of voltage amplitude and phase angle of each grid node are obtained; the stable solutions of voltage amplitude of each grid node constitute the voltage distribution of each grid node. It should be noted that the solution process involves iteratively solving the power flow calculation equations using the Newton-Raphson method. After each iteration, the active power imbalance and reactive power imbalance at each grid node are calculated. The solution is considered complete when all imbalances are less than the convergence threshold. The iteration stops when the time is right, and the stable solutions for the voltage amplitude and phase angle of each power grid node are output.
[0076] Based on the stable solutions of voltage amplitude and phase angle at each power grid node, the active power distribution and reactive power distribution of each power line are calculated through the physical relationship between the voltage at the power grid node and the power of the branch.
[0077] It should be noted that the calculation process involves using the branch power equation and the voltage amplitude and phase angle stability solution of the power grid nodes to calculate the power transmission values at the beginning and end of each power line, thereby obtaining the active power distribution and reactive power distribution of each power line. It should be noted that the expression for calculating the power transmission value at the beginning and end of each power line is as follows: ; ; in, It is a node connecting the power grid. With grid nodes Power lines at grid nodes Active power transmission value at the side end, It is a node connecting the power grid. With grid nodes Power lines at grid nodes Complex representation of reactive power value at the side end. It is a power grid node The voltage phasor, It is a power grid node The voltage phasor, It is a power grid node voltage amplitude, It is a power grid node voltage phase angle, It is a node connecting the power grid. With grid nodes The impedance of the power line, It is a node connecting the power grid. With grid nodes Power lines at grid nodes Active power transmission value at the side end, It is a node connecting the power grid. With grid nodes Power lines at grid nodes Complex representation of reactive power values at the side terminals. It is the complex conjugate operation. It is the imaginary unit used in electrical engineering.
[0078] Methods for obtaining active power adjustment responsibility and reactive power adjustment obligation include: Based on the line power over-limit and the active power adjustment sensitivity of each distributed energy node, the active power adjustment amount that each relevant distributed energy node should bear is calculated according to the sensitivity ratio.
[0079] It should be noted that the expression for calculating the active power adjustment that each relevant distributed energy node should bear is as follows: ; in, It is a related distributed energy node The amount of active power adjustment that should be borne. It is a power over-limit circuit. For relevant distributed energy nodes Active power adjustment sensitivity, It is the set of all relevant distributed energy nodes. It is a power over-limit circuit. For relevant distributed energy nodes Active power adjustment sensitivity, It is a power over-limit circuit. The more limited the line power, It is the node index in the relevant distributed energy node set; Based on the node voltage over-limit and the reactive power adjustment sensitivity of each distributed energy node, the reactive power adjustment required by each adjacent distributed energy node to eliminate the voltage over-limit is calculated.
[0080] It should be noted that the expression for calculating the reactive power adjustment required by each adjacent distributed energy node to eliminate voltage over-limit is as follows: ; in, Adjacent distributed energy nodes The reactive power adjustment amount that should be provided It is a voltage over-limit node For adjacent distributed energy nodes Reactive power adjustment sensitivity, It is the set of all adjacent distributed energy nodes. It is a voltage over-limit node For adjacent distributed energy nodes Reactive power adjustment sensitivity, It is a voltage over-limit node The more limited the node voltage, The active power adjustment amount will be formally quantified as the active power adjustment responsibility of the relevant distributed energy nodes.
[0081] It should be noted that the quantification process involves directly assigning the active power adjustment value to the corresponding distributed energy node, thus establishing the active power adjustment responsibility that the relevant distributed energy node must fulfill. The reactive power adjustment amount is formally quantified as the reactive power adjustment obligation of adjacent distributed energy nodes.
[0082] It should be noted that the quantification process involves directly assigning the reactive power adjustment value to the corresponding adjacent distributed energy node, thus forming the reactive power adjustment obligation that the adjacent distributed energy node must fulfill.
[0083] Methods for defining globally consistent variables include: Based on the power grid topology and node coupling information, the voltage sensitivity amplitude between each distributed energy node is calculated as the electrical distance between nodes, and clustering is performed according to a preset electrical distance threshold to identify distributed energy node clusters with coupling relationships. It should be noted that the absolute value of the voltage sensitivity coefficient between any two distributed energy nodes is extracted from the voltage-power sensitivity coefficient matrix contained in the node coupling information as the electrical distance. The process of setting the preset electrical distance threshold is as follows: Based on the characteristics of the power grid topology and the distribution density of distributed energy nodes, and combined with the statistical distribution pattern of the voltage sensitivity coefficient between distributed energy nodes in historical operation data, a critical value that can effectively distinguish between strong and weak coupling relationships is selected as the electrical distance threshold; the example value of the electrical distance threshold is 0.15, which means that when the absolute value of the voltage sensitivity coefficient between two distributed energy nodes is greater than 0.15, it is determined to be a strong electrical coupling relationship; distributed energy nodes with an electrical distance less than the preset electrical distance threshold are divided into distributed energy node clusters.
[0084] For each distributed energy node cluster, an auxiliary variable characterizing the overall power balance state of the cluster is defined as a cluster-level globally consistent variable; Specifically, a new real-valued variable is created for each distributed energy node cluster. This variable represents the expected value of the sum of the planned active power output variables of all distributed energy nodes in the cluster. For example, for a cluster containing three distributed energy nodes, a cluster-level global consistency variable Gc is defined. This cluster-level global consistency variable represents the expected value of the sum of the planned active power output variables M1, M2, and M3 of these three distributed energy nodes. That is, Gc corresponds to the target value of M1+M2+M3. The process of creating cluster-level globally consistent variables is as follows: based on the clustering results of distributed energy nodes, a unique cluster identifier is assigned to each distributed energy node cluster; based on the cluster identifier, a corresponding real-valued variable is generated as a cluster-level globally consistent variable.
[0085] For power over-limit lines and other important power lines closely related to them, an auxiliary variable characterizing the power safety status of the line is defined as a line-level globally consistent variable; Specifically, a new real variable is created for each power over-limit line and other important power lines closely related to it. This real variable represents the expected value of the active power transmission value of the corresponding power line. For example, a line-level global consistency variable Hl is defined for the power over-limit line L1. This line-level global consistency variable Hl represents the expected value of the active power transmission value PL1 of line L1. That is, Hl corresponds to the safety target value that ensures that the power of line L1 does not exceed the limit. The process of creating line-level globally consistent variables is as follows: assign a unique line identifier to each line based on the number of power-limited lines and important power lines; generate corresponding real-number variables based on the line identifier as line-level globally consistent variables.
[0086] All cluster-level global consistency variables and line-level global consistency variables are merged to form a global consistency variable.
[0087] It should be noted that, according to the preset sorting rules, the cluster-level global consistency variables corresponding to each distributed energy node cluster and the line-level global consistency variables corresponding to each important power line are arranged in sequence to form a global consistency variable vector. The example sorting rule is to first sort all cluster-level global consistency variables in ascending order by cluster identifier, and then sort all line-level global consistency variables in ascending order by line identifier, and finally form a global consistency variable vector.
[0088] This embodiment also provides a computer device applicable to the distributed energy collaborative control method for demand-side response, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the distributed energy collaborative control method for demand-side response as proposed in the above embodiment.
[0089] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.
[0090] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the distributed energy collaborative control method for demand-side response as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0091] In summary, this invention achieves the following: A dynamic reconfiguration mechanism enables each distributed energy node to autonomously generate a high-quality initial scheduling scheme based on its real-time operating status, providing a precise and reliable decision-making basis for subsequent collaborative optimization and effectively avoiding control deviations caused by the mismatch between traditional fixed models and real-time states; A global security constraint distributed embedding mechanism based on sensitivity analysis quantifies identified global security conflicts into specific limits and accurately decomposes them into adjustment responsibilities for each node based on the electrical coupling relationship between nodes. By rigidly embedding security constraints into the local optimization problem, reliable traceability and rigid protection of the power grid's physical security are ensured during distributed collaborative control.
[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A distributed energy collaborative control method oriented towards demand-side response, characterized in that, include: S1. Based on distributed energy multi-source data and grid parameters, obtain node state vectors and node coupling information; S2. Each distributed energy node obtains its own operating characteristics based on its node state vector; It dynamically reconstructs its local operating boundary constraints and response cost optimization objectives based on its own operating characteristics, and independently solves and generates the initial scheduling scheme for each node; S3. Summarize the initial scheduling schemes of all nodes, perform scheduling conflict detection based on node coupling information, and extract global security constraints from the scheduling conflict detection results; Global security constraints are decomposed and distributed and embedded into the local optimization problems of each node to form a distributed collaborative optimization problem; S4. Input the distributed collaborative optimization problem into the collaborative solver, perform parallel iterative calculations with multiple rounds of limited information interaction, and obtain the converged global collaborative optimal solution; S5. Decode the global collaborative optimal solution and transform it into an optimal global collaborative control scheme oriented towards demand-side response.
2. The distributed energy collaborative control method for demand-side response as described in claim 1, characterized in that: The method for obtaining node state vectors and node coupling information includes: Establish a power grid topology by using power grid nodes as vertices and power lines as edges in the power grid parameters; map multi-source data of distributed energy resources to the corresponding distributed energy nodes in the power grid topology. Based on the power grid topology, distributed energy multi-source data and power grid parameters are integrated according to the attributes of vertices and edges to form a panoramic power grid dataset; and the operating characteristic parameters of each power grid node are extracted, and node state vectors are generated through time alignment and feature normalization. Based on the node state vector, and combined with the line impedance and admittance parameters in the power grid topology, node coupling information is obtained.
3. The distributed energy collaborative control method for demand-side response as described in claim 2, characterized in that: The method for independently solving and generating the initial scheduling scheme for each node includes: Based on the node state vector, operational characteristic parameters reflecting the operational capability and regulation potential of distributed energy nodes are extracted to form the operational characteristics of distributed energy nodes; Based on the operating characteristics of distributed energy nodes, the local operating boundary constraints of distributed energy nodes are dynamically reconstructed to obtain a set of local operating boundary constraints that reflects the real-time operating range. By combining the local operating boundary constraint set with the operating characteristics of distributed energy nodes, a response cost optimization objective is constructed with minimizing operating costs and compensation costs as the core, while simultaneously satisfying power balance constraints and energy dynamic constraints. Based on the optimal response cost objective and the local operating boundary constraint set, the local optimization problem is solved in parallel in the local computing unit of each distributed energy node to generate the initial scheduling scheme for each node.
4. The distributed energy coordinated control method for demand-side response as described in claim 3, characterized in that: The method for scheduling conflict detection based on node coupling information includes: All initial scheduling schemes of all nodes are aggregated to form a global scheduling scheme set; Based on the power grid topology and node coupling information, a refined power flow calculation is performed on the global scheduling scheme set to obtain the power distribution of each power line and the voltage distribution of each power grid node. The power distribution of each power line is compared with the line thermal stability limit to identify lines that exceed the power limit, and the voltage distribution of each power grid node is compared with the voltage safety range to locate nodes that exceed the voltage limit. Spatially correlate the spatial location and severity of power over-limit lines and voltage over-limit nodes to generate structured scheduling conflict detection results.
5. The distributed energy coordinated control method for demand-side response as described in claim 4, characterized in that: The methods for forming distributed collaborative optimization problems include: The power over-limit lines and voltage over-limit nodes are extracted from the scheduling conflict detection results and quantified into the power over-limit amount of the lines and the voltage over-limit amount of the nodes to be eliminated, thus forming a global security constraint. Based on the grid topology and node coupling information, the active power adjustment sensitivity of each distributed energy node to power over-limit lines is obtained, and the reactive power adjustment sensitivity of each distributed energy node to voltage over-limit nodes is also obtained. Based on the sensitivity of active and reactive power adjustment, the line power exceeding the limit is decomposed into the active power adjustment responsibility of the relevant distributed energy nodes, and the node voltage exceeding the limit is decomposed into the reactive power adjustment obligation of the adjacent distributed energy nodes. The responsibility for active power adjustment is specified as a global security constraint of the power balance equation, and the obligation for reactive power adjustment is specified as a global security constraint of the reactive power regulation inequality. These are then embedded as new decision-making constraints into the local operating boundary constraint sets of each corresponding distributed energy node, forming a collaborative local constraint set. Based on the goal of optimal response cost and the set of coordinated local constraints, a distributed coordinated optimization problem containing this consistency constraint is constructed by defining a globally consistent variable and establishing an equation relationship between it and the local decision variables of each distributed energy node.
6. The distributed energy collaborative control method for demand-side response as described in claim 5, characterized in that: The methods for obtaining convergent globally cooperative optimal solutions include: The distributed collaborative optimization problem is input into the collaborative solver, the local decision variables and global consistency variables of each distributed energy node are initialized, and the first round of limited information interaction is performed to obtain the initial global consistency variable update results. Each distributed energy node solves a local optimization problem that includes a collaborative local constraint set and the objective of optimal response cost based on the updated global consistency variables, generates a local optimal solution and uploads it to the collaborative solver, which then updates the global consistency variables and global residuals. The collaborative solver determines whether the global residual satisfies the global consistency tolerance. If it does, it outputs the global collaborative optimal solution; otherwise, it continues to perform parallel iterative calculations until convergence.
7. The distributed energy coordinated control method for demand-side response as described in claim 6, characterized in that: The method for obtaining the optimal global cooperative control scheme includes: Based on the global collaborative optimal solution, the local optimal power command and energy storage charging and discharging strategy of each distributed energy node are extracted to form a global optimal control parameter set; The optimal global control instruction set is distributed to each distributed energy node to form an optimal global collaborative control scheme oriented towards demand-side response.
8. The distributed energy collaborative control method for demand-side response as described in claim 4, characterized in that: The methods for obtaining the power distribution of each power line and the voltage distribution of each power grid node include: Based on the line impedance and admittance parameters in the power grid topology and node coupling information, a node admittance matrix is constructed. The initial scheduling schemes of each node in the global scheduling scheme set are used as node power injection quantities, which together with the node admittance matrix form a set of power flow calculation equations. The power flow calculation equations are solved by iterative algorithm until the active power imbalance and reactive power imbalance of all grid nodes are less than the preset convergence threshold, and the stable solutions of voltage amplitude and phase angle of each grid node are obtained; the stable solutions of voltage amplitude of each grid node constitute the voltage distribution of each grid node. Based on the stable solutions of voltage amplitude and phase angle at each power grid node, the active power distribution and reactive power distribution of each power line are calculated through the physical relationship between the voltage at the power grid node and the power of the branch.
9. The distributed energy coordinated control method for demand-side response as described in claim 5, characterized in that: The methods for obtaining the active power adjustment responsibility and reactive power adjustment obligation include: Based on the line power over-limit and the active power adjustment sensitivity of each distributed energy node, the active power adjustment amount that each relevant distributed energy node should bear is calculated according to the sensitivity ratio. Based on the node voltage over-limit and the reactive power adjustment sensitivity of each distributed energy node, calculate the reactive power adjustment amount required by each adjacent distributed energy node to eliminate the voltage over-limit. The active power adjustment amount will be formally quantified as the active power adjustment responsibility of the relevant distributed energy nodes; The reactive power adjustment amount is formally quantified as the reactive power adjustment obligation of adjacent distributed energy nodes.
10. The distributed energy coordinated control method for demand-side response as described in claim 5, characterized in that: The methods for defining globally consistent variables include: Based on the power grid topology and node coupling information, the voltage sensitivity amplitude between each distributed energy node is calculated as the electrical distance between nodes, and clustering is performed according to a preset electrical distance threshold to identify distributed energy node clusters with coupling relationships. For each distributed energy node cluster, an auxiliary variable characterizing the overall power balance state of the cluster is defined as a cluster-level globally consistent variable; For power over-limit lines and other important power lines closely related to them, an auxiliary variable characterizing the power safety status of the line is defined as a line-level globally consistent variable; All cluster-level global consistency variables and line-level global consistency variables are merged to form a global consistency variable.