Node load adjustment instruction baseline dynamic updating method and system for power demand response

Abstract

The application relates to the technical field of power system optimization scheduling, and particularly provides a node load regulation instruction baseline dynamic updating method and system for power demand response, which comprises the following steps: determining a unified load regulation instruction baseline of a current power system, calculating a load regulation instruction baseline network component, generating and publishing node load regulation instruction baselines of all power grid nodes at present; analyzing marginal influence degrees of power grid operation constraints on the node load regulation instruction baselines, and generating line sensitivity analysis results; collecting node net power deviations of all nodes in real time, combining the line sensitivity analysis results, the node load regulation instruction baselines and a real-time identified congestion line set, iteratively optimizing and solving to obtain optimized node load regulation instruction baselines, and completing closed-loop updating of the node load regulation instruction baselines. Through real-time and self-adaptive correction of the load regulation instruction baseline, the actual effect of the demand response when facing power system operation state fluctuation can be improved.

Description

Technical Field

[0001] This application relates to the field of power system optimization and dispatching technology, and in particular to a method and system for dynamic updating of nodal load regulation command guidelines for power demand response. Background Technology

[0002] Currently, in existing solutions that use nodal load baseline adjustment command baselines for demand response, the load adjustment command baseline mechanism relies on centralized optimization models, which are difficult to meet the needs of a transparent power grid. Furthermore, the parameter selection of the optimization model is highly subjective and lacks transparency, which is not conducive to the power grid disclosing data to the outside world. Users can only respond passively and cannot realize the connection between their own load adjustment targets and the power grid's control needs, which limits its potential for large-scale and routine application.

[0003] Furthermore, the rolling updates of load regulation command guidelines in existing related schemes usually rely on centralized optimization models, which require complex boundary data processing and solution processes, rather than closed algebraic models. The cycle is usually in the form of hours or several hours, without considering finer time granularity. Such a long cycle makes it difficult to effectively track the minute-level rapid fluctuations in renewable energy output and load demand, and thus adapt to the needs of real-time operation and adjustment of the power system. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method and system for dynamically updating the nodal load regulation command baseline for power demand response. By performing real-time and adaptive corrections to the load regulation command baseline, the actual effectiveness of demand response in the face of fluctuations in the power system's operating status can be improved.

[0005] To achieve the objectives of this application, the following technical solution is provided: Firstly, this application provides a method for dynamically updating the nodal load regulation command baseline for power demand response, applied to a power system, including: Based on the current preset scheduling time granularity and the boundary information of power system scheduling operation, the current unified load adjustment command baseline of the power system is determined; the preset scheduling time granularity is a time period that is not an integer hour. By combining the nodal load regulation command baseline decomposition model and power demand response user information, the network components of the load regulation command baseline are calculated, and the nodal load regulation command baselines of each power grid node are generated and published. The decomposition model of the nodal load regulation command guideline is reconstructed into a matrix operation form. The marginal impact of power grid operation constraints on the nodal load regulation command guideline is analyzed using the corresponding reconstruction structure to generate line sensitivity analysis results. The line sensitivity analysis results are the sensitivity of the network components of the nodal load regulation command guideline to the upper limit of line capacity. The net power deviation of each node is collected in real time. Combined with the line sensitivity analysis results, the node load adjustment command guideline, and the real-time identified set of blocked lines, the optimized node load adjustment command guideline is obtained through iterative optimization, thus completing the closed-loop update of the node load adjustment command guideline.

[0006] A further improvement in this application is that, based on the current preset scheduling time granularity and the boundary information of power system scheduling operation, determining the current unified load adjustment command baseline of the power system includes: obtaining generator set information and load information corresponding to the power grid in the power system; the generator set information includes a set of conventional generator sets, a planned output of conventional generator sets, a set of renewable energy generator sets, and the upper limit of output of renewable energy generator sets; the load information includes a set of adjustable loads, a set of rigid loads, and rigid load output information; determining the current preset scheduling time granularity within the scheduling cycle; the scheduling time granularity includes the number of scheduling time periods within the scheduling cycle and the scheduling duration corresponding to each scheduling time period; and, for the current preset scheduling time granularity within the scheduling cycle, calculating the current unified load adjustment command baseline of the power system based on the number of scheduling time periods, the scheduling duration, the generator set information, and the load information. ; In the formula, To unify the standard for load regulation commands, , , , They are respectively a collection of conventional units Adjustable load set rigid load set New energy unit collection Index; , , , They are respectively , , , The total number of elements in; For conventional units Total power generation over all scheduling cycles; For adjustable load Total electricity consumption across all scheduling cycles; rigid load The output curve, For new energy units Maximum output curve, This represents the number of scheduling time periods within the scheduling cycle. This represents the scheduling duration for each scheduling time segment.

[0007] A further improvement in this application is that the step of combining the nodal load regulation command baseline decomposition model and electricity demand response user information to calculate the load regulation command baseline network components, and generating and publishing the nodal load regulation command baselines for each current power grid node, includes: obtaining the adjustable load capacity of electricity demand response users; constructing a nodal load regulation command baseline decomposition model, which includes power grid operation constraints, including normalization constraints for the regulation command baseline, shape constraints for the regulation command baseline, power system energy balance constraints, and power grid network constraints; calculating the load regulation command baseline network components based on the nodal load regulation command baseline decomposition model, the adjustable load capacity of electricity demand response users, the upper limit of line capacity, the planned output of conventional units, and the current unified load regulation command baseline; determining the nodal load regulation command baselines corresponding to each current power grid node based on the load regulation command baseline network components and the current unified load regulation command baseline, and performing KKT condition analysis on the nodal load regulation command baseline decomposition model to determine the data to be disclosed and publish it externally.

[0008] A further improvement in this application is that the step of performing KKT conditional analysis on the decomposition model of the nodal load regulation command line to determine the data to be disclosed and then publishing it externally includes: determining a mathematical composition analysis method for the nodal load regulation command line to represent the compositional relationship of the nodal load regulation command line based on coefficient calculation information and the decomposition model of the nodal load regulation command line; the coefficient calculation information is the time-varying regulation demand coefficient of the power grid calculated based on the Lagrange multipliers corresponding to the power system energy balance constraints and the power grid network constraints in the decomposition model of the nodal load regulation command line; determining the data to be disclosed based on the mathematical composition analysis method of the nodal load regulation command line, and performing information disclosure operations on the power grid user terminals corresponding to the data to be disclosed.

[0009] A further improvement in this application is that the step of performing KKT conditional analysis on the decomposition model of the nodal load regulation command line to determine the data to be disclosed and then publishing it externally includes: determining a mathematical composition analysis method for the nodal load regulation command line to represent the compositional relationship of the nodal load regulation command line based on coefficient calculation information and the decomposition model of the nodal load regulation command line; the coefficient calculation information is the time-varying regulation demand coefficient of the power grid calculated based on the Lagrange multipliers corresponding to the power system energy balance constraints and the power grid network constraints in the decomposition model of the nodal load regulation command line; the mathematical composition analysis method for the nodal load regulation command line is used to algebraically represent the mathematical compositional relationship of the nodal load regulation command line; and based on the mathematical composition analysis method for the nodal load regulation command line, determining the data to be disclosed and performing information disclosure operations on the power grid user terminals corresponding to the data to be disclosed.

[0010] A further improvement of this application is that the step of determining the data to be disclosed based on the mathematical composition analysis method of the node load regulation command guideline includes: comparing the node load regulation command guidelines corresponding to different power grid nodes using the mathematical composition analysis method of the node load regulation command guideline to obtain the regulation command guideline comparison analysis results; and determining the data to be disclosed based on the regulation command guideline comparison analysis results and the mathematical composition analysis method of the node load regulation command guideline.

[0011] A further improvement in this application is that the step of reconstructing the nodal load regulation command guideline decomposition model into a matrix operation form, and using the corresponding reconstruction structure to analyze the marginal influence of power grid operation constraints on the nodal load regulation command guideline, and generating line sensitivity analysis results, includes: reconstructing the nodal load regulation command guideline decomposition model into a matrix form to determine the reconstructed model; based on preset relaxation variables, treating the reconstructed model as a parametric programming problem corresponding to the power grid operation variables, and obtaining the corresponding mathematical mapping relationship; the mathematical mapping relationship is a mathematical mapping relationship in which the reconstructed model is regarded as an input power grid operation variable and outputs nodal load regulation command guideline components; based on the mathematical mapping relationship, performing Kuhn-Tak condition analysis on the reconstructed model; solving the derivatives of each condition with respect to the power grid operation variables, and simultaneously solving the conditions to determine the equilibrium equation; and performing pseudo-inverse solving based on the equilibrium equation to obtain the line sensitivity analysis results.

[0012] A further improvement of this application is that the equilibrium equation is: ; In the formula, It is the identity matrix. This is the transpose of the coefficient matrix of the reconstructed equality constraints. This is the transpose of the coefficient matrix corresponding to the reconstructed inequality constraints. To solve the diagonal matrix, Preset slack variables; The sensitivity of the equality-constrained Lagrange multipliers to the upper limit of line capacity. The sensitivity of the network component of the node load adjustment command guideline to the upper limit of the line capacity. , The sensitivity of the Lagrange multipliers constrained by inequality to the upper limit of line capacity. constraint function Partial derivative with respect to the upper limit of line capacity, constraint function Partial derivative with respect to the upper limit of line capacity; The step of solving the pseudo-inverse based on the equilibrium equation and constructing an analytical sensitivity analysis model in the reconstructed form includes: simplifying the equilibrium equation as follows: ,in, The coefficient matrix, The sensitivity solution matrix is... Let be a constant matrix; solve the pseudo-inverse of the equilibrium equation to calculate the sensitivity of the network component of the node load adjustment command guideline to the upper limit of the line capacity: ; in, The sensitivity of the network component of the node load adjustment command guideline to the upper limit of the line capacity; in When the change is small, the change in the upper limit of line capacity The variation of the load adjustment command guideline at the node The following relationship still holds: .

[0013] A further improvement in this application is that the real-time acquisition of the node net power deviation of each node, combined with the line sensitivity analysis results, the node load adjustment command guideline, and the real-time identified set of blocked lines, iteratively optimizes and solves to obtain the optimized node load adjustment command guideline, completing the closed-loop update of the node load adjustment command guideline. This includes: obtaining the set of blocked lines and the real-time monitored node net power deviation, wherein the set of blocked lines is a set of blocked lines obtained based on the Lagrange multipliers corresponding to the shape constraints of the adjustment command guideline; and based on the line sensitivity analysis results, under the condition of selecting line capacity as a parameter planning variable, solving the equilibrium equation to determine the relationship between the node load adjustment command guideline and the line capacity. The line sensitivity is determined; based on the node net power deviation, the set of blocked lines, and the power transmission distribution factor, the additional transmission power corresponding to the blocked lines is analyzed to determine the power analysis result; wherein, the power transmission distribution factor is the power transmission distribution factor of the power grid node for each line in the set of blocked lines; based on the power analysis result, the line sensitivity, and the line blocking direction information corresponding to the set of blocked lines, the node load adjustment command guideline network component is corrected to obtain the corrected node load adjustment command guideline network component; based on the corrected node load adjustment command guideline network component and the current unified load adjustment command guideline, the corrected node load adjustment command guideline is determined.

[0014] A further improvement of this application is that the step of analyzing the additional transmission power corresponding to the blocked lines based on the node net power deviation, the set of blocked lines, and the power transmission distribution factor to determine the power analysis result includes: after extracting the power transmission distribution factor, using the node net power deviation as input, calculating the additional transmission power that each blocked line needs to carry. ;in, For nodes For blocked lines The power transmission distribution factor, For nodes During the period The deviation of net power injection from the predicted value, of which, Net power injection to nodes; the additional transmission power is assessed, and if the direction and magnitude of the additional transmission power align with the blocking direction, the net power injection deviation is equivalent to a passive tightening of the line power flow constraints; a constraint correlation function is constructed. The power analysis results are generated as follows: ; in, To block the line Additional transmission power required.

[0015] A further improvement in this application is that the step of correcting the node load regulation command guideline network component based on the power analysis results, the line sensitivity, and the line blocking direction information corresponding to the blocked line set, to obtain the corrected node load regulation command guideline network component, includes: using the additional transmission power and the line blocking direction information corresponding to the blocked line set as the correction basis, introducing the line sensitivity, calculating the optimal deviation correction amount of the node load regulation command guideline node by node using the algebraic expression of the adjustment command guideline deviation, and obtaining the corrected node load regulation command guideline network component by superimposing the optimal deviation correction amount; the algebraic expression of the adjustment command guideline deviation is specifically: ; ; in, Represents network components For blocked lines Sensitivity of line capacity The network components representing the corrected node load adjustment command baseline. To constrain the tightening function, This is the optimal deviation correction amount.

[0016] Secondly, this application provides a dynamic update system for nodal load regulation command guidelines oriented towards power demand response, used to implement the aforementioned dynamic update method for nodal load regulation command guidelines oriented towards power demand response, including: The unified baseline determination module is used to determine the current unified load adjustment command baseline of the power system based on the current preset scheduling time granularity and the boundary information of power system scheduling operation; the preset scheduling time granularity is a time period that is not an integer hour. The node guideline determination module is used to combine the node load regulation command guideline decomposition model and power demand response user information to calculate the load regulation command guideline network components, generate and publish the node load regulation command guidelines for each power grid node at present. The sensitivity analysis module is used to reconstruct the decomposition model of the node load regulation command guideline into a matrix operation form, and use the corresponding reconstruction structure to analyze the marginal impact of power grid operation constraints on the node load regulation command guideline, and generate line sensitivity analysis results; wherein, the line sensitivity analysis results are the sensitivity of the network components of the node load regulation command guideline to the upper limit of line capacity; The dynamic correction module for the reference line is used to collect the net power deviation of each node in real time. Combining the line sensitivity analysis results, the node load adjustment command reference line, and the real-time identified set of blocked lines, iteratively optimizes and solves to obtain the optimized node load adjustment command reference line, thus completing the closed-loop update of the node load adjustment command reference line.

[0017] Thirdly, this application provides a terminal, the terminal including a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing the dynamic update method for node load regulation command guidelines for power demand response as described above; the processor is used to execute the programs.

[0018] Compared with the prior art, this application has the following beneficial effects: The method and system for dynamically updating the nodal load regulation command baseline for power demand response provided in this application firstly determines the current unified load regulation command baseline of the power system based on the preset scheduling time granularity and the boundary information of power system scheduling operation. Then, combined with the nodal load regulation command baseline decomposition model and power demand response user information, the nodal load regulation command baseline of each power grid node is determined. Next, the nodal load regulation command baseline decomposition model is reconstructed, and the marginal impact of power grid operation constraints on the nodal load regulation command baseline is analyzed using the corresponding reconstruction results to construct an analytical sensitivity analysis model in the reconstruction form. Afterward, after the power system enters the operation phase, the corrected nodal load regulation command baseline is determined based on the sensitivity analysis model, the current unified load regulation command baseline and the set of congested lines, as well as the real-time monitored node net power deviation. This allows the load regulation command baseline to track minute changes in the system state in near real-time. When prediction deviations or emergencies occur, the control targets can be corrected in a timely manner, solving the problems of excessively long rolling optimization cycles and lack of real-time correction in existing related schemes. The node load regulation command baseline is updated at a higher frequency, thereby enabling real-time and adaptive correction of the load regulation command baseline. This improves the actual effectiveness of demand response in the face of fluctuations in the power system's operating state and enhances the transparency of demand response based on the node load regulation command baseline, thus better serving the power demand-side response based on the node load regulation command baseline. Attached Figure Description

[0019] The accompanying drawings are provided to further understand this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. Figure 1 A schematic flowchart of an optional method for dynamically updating the nodal load regulation command guideline for power demand response provided in this application embodiment; Figure 2 This is another optional flowchart illustrating the dynamic update method for the nodal load regulation command baseline for power demand response provided in the embodiments of this application. Figure 3 A schematic diagram of the structure of a dynamic update system for nodal load regulation command guidelines for power demand response provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of this application, unless otherwise stated, "multiple" means two or more.

[0022] Unified load regulation command baseline: refers to the standardized target load curve that the power system expects the distribution network or load aggregation to present under the objectives of safe operation, maximum consumption of new energy sources and optimal economic operation.

[0023] Adjustable load capacity for electricity demand response users: The average total electricity consumption of electricity demand response users within a day.

[0024] Node net power deviation: The deviation between the node net load curve and the day-ahead node net load forecast curve under intraday, real-time renewable energy fluctuations.

[0025] Planned output of conventional generating units: The dispatching side pre-plans and determines the time-series base power generation of each conventional generating unit based on load level, grid operation constraints and unit operating characteristics.

[0026] Currently, in existing demand response schemes using nodal load regulation command guidelines, the load regulation command guideline mechanism relies on optimized centralized clearing models, which are highly subjective and computationally complex, making it difficult to meet the needs of transparent power grids and real-time power system operation adjustments. Furthermore, the solution process lacks transparency, hindering the grid's ability to disclose data, leaving users with only passive responses and unable to correlate their own electricity consumption characteristics with the system's global demand. This limits its potential for large-scale, routine application. In addition, the rolling optimization cycle of the load regulation command guideline in existing schemes is typically on the order of hours or several hours, making it difficult to effectively track the minute-level rapid fluctuations in renewable energy output and load demand. To address the above technical problems, this application proposes the following technical solutions and corresponding embodiments.

[0027] The following is combined with Figures 1 to 4 The embodiments shown illustrate the technical solutions of this application: Example 1 This application provides an embodiment of a method for dynamically updating the load regulation command baseline of power grid nodes for power demand response, referring to... Figure 1 As shown, the process includes the following steps S101 to S104: Step S101: Based on the current preset scheduling time granularity and the boundary information of power system scheduling operation, determine the current unified load adjustment command baseline of the power system; the preset scheduling time granularity is a time period that is not an integer hour.

[0028] In this embodiment, the boundary information for power system dispatch operation refers to the boundary information required for power system dispatch. This information characterizes the set of limit conditions for various operating variables that must be met to maintain the safe and stable operation of the power system. This includes power line connection relationships, electrical parameters of power lines and equipment, parameters and output plans of conventional generating units, total system load and its distribution location, the system's predicted load curve for the next day, parameters and predicted output curves for wind, solar, and other renewable energy units for the next day, demand response load capacity, and their distribution location. The preset dispatch time granularity in this embodiment is a non-integer hour time period.

[0029] In this embodiment of the application, during the day-ahead phase or every few hours within a day, the ideal load curve (i.e., the Unified Load Regulation Directive CDL, Customer Directrix Load) is calculated using the following formula (1): Specifically, the set of conventional generating units, the output plan of conventional generating units, the set of new energy generating units, and the upper limit of output of new energy generating units corresponding to the power grid in the power system are obtained to obtain generator unit information; the set of adjustable loads, the set of rigid loads, and the output information of rigid loads corresponding to the power grid are obtained to obtain load information; the current preset scheduling time granularity within the scheduling cycle is determined to determine the number of scheduling time periods within the scheduling cycle; the time length corresponding to the preset scheduling time granularity is obtained to determine the scheduling duration corresponding to each scheduling time period; and then, for any preset scheduling time granularity within the scheduling cycle, the current ideal load curve of the power system is calculated based on the obtained target number of segments, scheduling duration, generator unit information, and load information.

[0030] Among them, the proposed unified load regulation command guideline based on existing research Based on the theory, this embodiment further considers the case where a single scheduling period is not an integer hour, and proposes a unified load adjustment command guideline. The calculation method is as follows: Equation (1); In the formula, For indexes of conventional units, Index for adjustable load users, For rigid load index, An index for new energy generating units (renewable energy sources), , , , The corresponding sets are respectively the conventional unit sets. Adjustable load set rigid load set New energy unit collection ; , , , These respectively represent the aforementioned set of conventional generating units. Adjustable load set rigid load set New energy unit collection The total number of elements in; Indicates the first The total power generation of the conventional generating units participating in regulation throughout all dispatch cycles; Indicates the first The total electricity consumption of an adjustable load during all scheduling cycles; It is the first The output curve of a rigid load. Indicates the first The maximum output curve of Taiwan's new energy power units. The number of scheduling time periods within a scheduling cycle is the total number of time periods obtained by evenly dividing the full runtime of a day, i.e., the resolution of daily scheduling decisions. This refers to the scheduling duration corresponding to each scheduling time period. It should be noted that the unified load adjustment command baseline here... It is a vector that indicates the ideal electricity consumption proportion of the load at each time period.

[0031] Step S102: Combining the node load regulation command baseline decomposition model and power demand response user information, calculate the load regulation command baseline network components, generate and publish the node load regulation command baselines for each power grid node.

[0032] Reference Figure 2 The load regulation command guideline network components are calculated using the node load regulation command guideline decomposition model shown in equations (2) to (6) to form the current node load regulation command guideline for each node. Specifically, the adjustable load capacity of power demand response users is obtained; the node load regulation command guideline decomposition model includes grid operation constraints, which include regulation command guideline normalization constraints, regulation command guideline shape constraints, power system energy balance constraints, and grid network constraints; the line set and power transmission distribution factor information corresponding to the grid are obtained, which includes the power transmission distribution factor for each line in the line set, representing the nodes where flexible units (conventional units), power demand response users, new energy units, and rigid loads are located; based on the node load regulation command guideline decomposition model, the adjustable load capacity of power demand response users, the upper limit of line capacity, the planned output of conventional units, and the current unified load regulation command guideline, the load regulation command guideline network components are calculated; and then, based on the load regulation command guideline network components and the current unified load regulation command guideline, the node load regulation command guideline corresponding to each grid node is determined.

[0033] In this embodiment, the node load adjustment command baseline is composed of a unified load adjustment command baseline and load adjustment command baseline network components, therefore: ; In the formula, Indicates demand response load exist The baseline value of the node load adjustment command at any given time. Indicates the load adjustment command baseline for each node. Deviation from the uniform load adjustment command line The degree of this can be referred to as the load regulation command guideline network component; The decomposition model of the nodal load adjustment command guideline is as follows: Equation (2); Equation (3); Equation (4); Equation (5); Equation (6); In the formula, For the index of the preset scheduling time, The set of times within the scheduling period; the first The set corresponding to each line is The total number of lines is ; , , , These represent conventional units. Adjustable load users New energy units Rigid load For the line The power transmission distribution factor; in this embodiment, the Lagrange multipliers corresponding to the constraints are marked after the colons on the right side of the constraints in the above equations (3) to (6), such as: The Lagrange multiplier represents the normalization constraint (3) of the adjustment command guideline. This normalization constraint ensures that the sum of the network components in each scheduling cycle is 0, without affecting the shape of the node load adjustment command guideline. and These are the Lagrange multipliers of the lower and upper limits of the shape constraint (4) of the adjustment command guideline, respectively. This shape constraint of the adjustment command guideline ensures that the value of the load adjustment command guideline at each scheduling cycle node is between 0 and 1. Equation (5) represents the Lagrange multiplier corresponding to the power system energy balance constraint, which ensures that the load energy called upon is consistent when using the unified load regulation command baseline and the nodal load regulation command baseline; Equation (6) represents the power grid network constraint. and These are the Lagrange multipliers for the lower and upper limits of the power grid constraint, respectively. This power grid constraint ensures that the line power flow does not exceed the limits when the load fully tracks the load regulation command baseline; among them... For the first Line capacity limit For the first Taiwan conventional units Ideal power output at any given moment (planned power output). For the first Taiwan's new energy power units Maximum output at any moment For the first A rigid load in Maximum output at any given moment.

[0034] In this embodiment, a node load regulation command baseline decomposition model is used to decompose the unified load regulation command baseline into node load regulation command baselines, i.e., the target load curve of each node. At the same time, the power grid operation constraints are taken into account to avoid the problem of the total system load meeting the target but local lines being overloaded.

[0035] In this embodiment, based on the node load adjustment command guideline decomposition model, the adjustable load capacity of power demand response users, the upper limit of line capacity, the planned output of conventional units, and the current unified load adjustment command guideline calculated by formula (1), the network components of the load adjustment command guideline are calculated to determine the network component calculation results; then, based on the network component calculation results and the current unified load adjustment command guideline, the node load adjustment command guidelines corresponding to each current power grid node are determined.

[0036] Furthermore, after determining the nodal load regulation command baselines for each current power grid node, this embodiment can analyze the KKT conditions (Karush-Kuhn-Tucker) of the nodal load regulation command baseline decomposition model to obtain an expression characterizing the mathematical components of the nodal load regulation command baseline (refer to Equation 11), and disclose its composition to the user to reflect the transparency of the mechanism. Specifically, firstly, the Kuhn-Tucker conditions corresponding to the nodal load regulation command baseline decomposition model are determined. Then, for the preset quasi-static conditions in the Kuhn-Tucker conditions, the Lagrange multipliers corresponding to the shape constraints of the regulation command baseline in the nodal load regulation command baseline decomposition model are analyzed based on the nodal load regulation command baseline decomposition model and the coefficient calculation information. The mathematical composition analysis method of the nodal load regulation command baseline is used to algebraically represent the mathematical composition relationship of the nodal load regulation command baseline. The coefficient calculation information is the time-varying regulation demand coefficient of the power grid.

[0037] Specifically, when determining the mathematical composition analysis method for the nodal load regulation command guideline, the Lagrange multipliers corresponding to the shape constraints of the regulation command guideline in the nodal load regulation command guideline decomposition model are processed based on the preset quasi-static conditions in the Kuhn-Tak condition, yielding the processing results. Then, by simultaneously solving the regulation command guideline normalization constraints and the processing results, the Lagrange multipliers related to the user's own cross-time period coupling constraints are eliminated to obtain the multiplier elimination results. Simultaneously, based on the Lagrange multipliers corresponding to the power system energy balance constraints and the power grid network constraints in the nodal load regulation command guideline decomposition model, the time-varying regulation demand coefficient of the power grid is calculated, obtaining coefficient calculation information. Therefore, based on this coefficient calculation information and the mathematical composition analysis method for the nodal load regulation command guideline, the key factors affecting the network components of the load regulation command guideline are analyzed.

[0038] This embodiment aims to analyze the load regulation command guideline network components. To determine the influence of Lagrange multipliers from other constraints of the power system, write out the KKT conditions for the above optimization problem (3) to (6), where a quasi-static condition is preset. It can be represented as: Equation (7); When the system's adjustment capacity is sufficient, it can be assumed that constraint (7) is unlikely to reach the boundary, therefore approximation is used. We can obtain: Equation (8); By combining equations (8) and (3), the aim is to eliminate variables related to the user's own cross-time coupling constraints. The result of multiplier elimination is as follows: Equation (9); Substituting back into expression (8), we can obtain the mathematical representation of the load regulation command guideline network components: Equation (10); because The corresponding constraints characterize the real-time power balance of the power grid. and The corresponding constraints characterize the power grid network constraints, are independent of the user, and are defined as follows: Let be the time-varying demand coefficient for power grid regulation, and let its average value over the dispatching period be denoted as . Where the two components are the load regulation responsibility redistribution factor and the network constraint balance factor, then equation (10) can be expressed as equation (11): Equation (11); Therefore, equations (10) and (11) constitute the mathematical composition analysis method for the node load regulation command guideline. Equation (11) can fully demonstrate the compositional relationship of the node load regulation command guideline, that is, the regulation demand is related to network constraints and the redistribution of regulation responsibility under network constraints, and is also closely related to the load size of the node. By comparing the node load regulation command guidelines of different nodes using this expression, it can be clearly seen that the load regulation command guideline of this node is different from that of other nodes due to factors such as network congestion and demand response load capacity.

[0039] Thus, in this embodiment, after decoupling the nodal load regulation command guideline into a unified load regulation command guideline part and a load regulation command guideline network component part, KKT conditional analysis is carried out on the nodal load regulation command guideline decomposition model using the physical meaning of the Lagrange multipliers in the optimization model for the network component part of the load regulation command guideline. Finally, the relationship between this component and the shape of the nodal load regulation command guideline is derived through reasonable approximation. This separation and decoupling method can quantify the effect of each specific grid constraint on the user node, while eliminating the influence of the regulation command guideline's own related constraints on theoretical analysis. This helps the grid disclose demand response-related data, enabling users to understand the relationship between the overall system control demand and the local control demand. In this way, the ambiguous control commands are transformed into guiding signals with clear physical meaning, greatly improving the transparency and user acceptance of demand response based on the nodal load regulation command guideline, and effectively overcoming the drawbacks of traditional methods such as subjective parameter setting and weak physical interpretability.

[0040] As a feasible implementation method, based on the aforementioned mathematical composition analysis method for nodal load regulation command guidelines, the nodal load regulation command guidelines corresponding to different power grid nodes are compared to obtain the comparison analysis results of the regulation command guidelines. Then, based on these comparison analysis results and the mathematical composition analysis method for nodal load regulation command guidelines, data to be disclosed is determined, and information disclosure operations are performed on the corresponding power grid user terminals based on this data. Here, the components and technical information of the nodal load regulation command guidelines are published to the outside world. This technical information is used to explain the reasons for the different shapes of the load regulation command guidelines at each node, including at least load capacity and the degree of network constraint violation.

[0041] Step S103: Reconstruct the decomposition model of the node load regulation command guideline into a matrix operation form, and use the corresponding reconstruction structure to analyze the marginal influence of power grid operation constraints on the node load regulation command guideline, and generate line sensitivity analysis results; wherein, the line sensitivity analysis results are the sensitivity of the network components of the node load regulation command guideline to the upper limit of line capacity.

[0042] In this application embodiment, the decomposition model of the node load adjustment command guideline shown in equations (2) to (6) will be post-processed to form the optimization problem shown in equations (12) to (14) below. Then, sensitivity analysis will be performed on the problem, and the marginal effect of network constraints on the node load adjustment command guideline will be obtained by pseudo-inverse, i.e., equation (36).

[0043] Specifically, in this embodiment, the decomposition model of the nodal load regulation command guideline is reconstructed into a matrix form to obtain the reconstructed model, which includes the objective Lagrange multipliers corresponding to the grid operation constraints. Then, based on preset relaxation variables, the reconstructed model is treated as a parametric programming problem corresponding to the grid operation variables to obtain the corresponding mathematical mapping relationship. This mathematical mapping relationship is a mathematical mapping relationship in which the reconstructed model is regarded as an input grid operation variable and an output nodal load regulation command guideline component. Subsequently, based on this mathematical mapping relationship, Kuhn-Tak condition analysis is performed on the reconstructed model to solve the derivatives of each condition with respect to the grid operation variables, and the equilibrium equation is determined by solving the equation simultaneously. Finally, based on the equilibrium equation, a pseudo-inverse solution is performed to construct an analytical sensitivity analysis model in the reconstructed form.

[0044] In this embodiment of the application, for the convenience of sensitivity analysis, the decomposition model of the nodal load regulation command guideline shown in equations (2) to (6) is reconstructed into matrix form, and the Lagrange multipliers of each constraint are listed, specifically: Equation (12); Equation (13); Equation (14); In the formula, A matrix representing all loads over all time periods. for The transpose of , The coefficient matrix of the reconstructed equality constraints. This is the constant vector corresponding to the reconstructed equality constraints. This is the coefficient matrix corresponding to the reconstructed inequality constraints. This is the constant vector corresponding to the upper bound of the reconstructed inequality constraints. The constant vector corresponding to the lower bound of the reconstructed inequality constraints is given by equation (6), which contains the upper limit parameter of the line capacity. Therefore, this vector can also be regarded as a function of this parameter; These are Lagrange multipliers for inequality constraints; Let be the Lagrange multiplier vector corresponding to the equality constraint. Equation (13) is reconstructed from the aforementioned equations (3) and (5), and equation (14) is reconstructed from the aforementioned equations (4) and (6). Each parameter, coefficient matrix, or vector satisfies the contents of equations (15) to (25) as follows: Equation (15); Equation (16); Equations (15) and (16) provide a method for reconstructing the decision variable vector, which involves reconstructing the network components of the load adjustment command baselines for each load and each time period node. Integrate into a decision variable vector , where intermediate variables For the first A vector of decision variables corresponding to each flexible load. It is the first Flexible load at different times The vector formed It is a matrix consisting of all loads over all time periods, regarding and The composition comparison is illustrated in Table 1 below: Table 1

[0045] As shown in Table 1 above, assuming a total time period of 3, the adjustable load... When there are 2, and The difference between them.

[0046] Equation (17); Equation (18); Equation (19); Equations (17) to (19) above give the calculation methods for each coefficient matrix, where, The power transmission distribution matrix in the power system. For adjustable load capacity matrix, The reordering matrix for decision variables can be any matrix that satisfies equation (21): Equation (20); Equation (21); Equation (22); In the above mathematical expression of matrices, the matrix subscripts represent the number of rows and columns of the matrix, such as... Indicates 1 row A matrix of all 1s in each column. Indicates 1 row A matrix of all zeros, and the subscripts of the matrix. This indicates the transpose; Represents the identity matrix. This represents solving for a diagonal matrix; in the above formula, This represents the total energy that flexible load 1 participates in regulation across all scheduling cycles. Indicates the first The total energy of a flexible load participating in regulation across all scheduling cycles; This is the scripture The mapped decision variable vector, in addition to the intermediate variables For time period The corresponding decision variable vector. Furthermore... and The structures are different, as shown in Table 2: Table 2

[0047] As shown in Table 2 above, assuming a total of 3 time periods, the adjustable load... When there are 2, and The difference between them necessitates the construction of a matrix. Switch between the two.

[0048] Equation (23); Equation (24); Equation (25); in, represent The injected power at each node of the power system at any given time satisfies subscript Represents the power system node number. This represents the total number of nodes in the system. Represents the power transmission distribution matrix in a power system. , , , The matrix and the symbol respectively mark the conventional units. New energy units Adjustable load Rigid load The node locations where power is injected into the power system.

[0049] Next, the KKT conditions of the problem are analyzed, and sensitivity analysis is conducted. Specifically, first, slack variables are defined. , After eliminating the inequality constraints, the KKT conditions satisfy the following relationship: Equation (26); Equation (27); Equation (28); Equation (29); Equation (30); Equation (31); Equation (32); Among them, equations (26) to (28) are the original feasible constraints, equation (29) is the dual feasible constraint, equations (30) and (31) are the complementary relaxation constraints, and equation (32) is the quasi-static constraint. Then, the equations (27) and (28) are... , Substitute equations (30) and (31); then, for equations (26) to (32) and the adjusted equations (30) and (31), regarding the line capacity Finding the absolute value, we can form the KKT equilibrium equation as shown in equation (33). The coefficient matrix of this equation can be denoted as: The sensitivity solution matrix is ​​denoted as The constant matrix is ​​denoted as Then the KKT equilibrium equation can be simplified as follows: : Equation (33); Among them, matrix and The elements in can be determined. Specifically, for The partial derivatives in the equation are: Equation (34); Equation (35); in, All are identity matrices, representing the first... Travel indivual The identity matrices are stacked vertically. express OK, A matrix of all zeros in each column; due to the matrix The network components are relatively large and sparse, and may exhibit ill-conditioned behavior. Therefore, a pseudo-inverse is calculated to solve for sensitivity. The sensitivity of the network components of the node load adjustment command guideline to the upper limit of line capacity is defined. Line sensitivity for load regulation command guideline: (36); Its physical meaning is the impact of relaxing unit network constraints on the shape of the adjustment command guideline at each node and at each time period. Although this model is not linear, in When the change is small, the change in the upper limit of line capacity Change in the guideline of load adjustment command at the same node The following relationship still holds: (37); Equation (37) clearly expresses the marginal impact of network constraints on the load adjustment command baseline of nodes, and can be further used for short-term correction of the load adjustment command baseline.

[0050] In this way, through model reconstruction, listing KKT conditions, variable substitution, and differential analysis, the marginal impact of operational constraints such as network constraints on the morphology of the nodal load regulation command guideline is accurately quantified. The marginal impact calculation method in this embodiment enables real-time perception of the grid's operational status by the load side, facilitating rapid identification of regulation needs by the power system.

[0051] Step S104: Collect the net power deviation of each node in real time, combine the line sensitivity analysis results, the node load adjustment command guideline, and the real-time identified set of blocked lines, and iteratively optimize the solution to obtain the optimized node load adjustment command guideline, thus completing the closed-loop update of the node load adjustment command guideline.

[0052] In the embodiments of this application, combined with Figure 2 As shown, during the operation phase of the power system, the net power deviation of each node is monitored in real time, and the load adjustment command line of each node is updated according to Equation (39) and Equation (40) to realize the short-term correction of the load adjustment command line. Then, the next calculation of the optimization model Equation (2) to Equation (6) is performed and the above steps S102 to S103 are re-executed.

[0053] In this embodiment, under the condition that line capacity is selected as the power grid operation variable, the equilibrium equation is solved to determine the line sensitivity of the node load regulation command guideline to the line capacity. Then, based on the line sensitivity, the line blocking direction information corresponding to the blocked line set, and the power analysis results determined by the real-time monitored node net power deviation, the network components of the node load regulation command guideline are corrected to obtain the corrected node load regulation command guideline network components. Then, based on the corrected node load regulation command guideline network components and the current unified load regulation command guideline, the corrected node load regulation command guideline is determined. Here, the blocked line set is the set of blocked lines obtained based on the Lagrange multipliers corresponding to the shape constraints of the regulation command guideline; the power analysis results are determined by analyzing the additional transmission power corresponding to the blocked lines, wherein the additional transmission power corresponding to the blocked lines is analyzed based on the node net power deviation, the blocked line set, and the power transmission distribution factor, and the power transmission distribution factor is the power transmission distribution factor of the power grid node to the blocked lines.

[0054] In this embodiment of the application, regarding the correction of the load regulation command guideline, firstly, the optimization problem of the optimization expressions (2) to (6) is solved in the day-ahead or intraday phase to obtain the node injection power transmission distribution matrix. And the Lagrange multipliers corresponding to network constraints Then, using Lagrange multipliers, the congestion status and direction of the lines are recorded and included in the set of congested lines. :like Then the line exist If it is always in reverse blocking, Then the line exist The system is constantly in a positive blocking state; subsequently, it enters a real-time operation phase, where the system continuously monitors the nodes. Net power injection deviation compared to forecasts And calculate the additional transmission power that the blocked line needs to handle. ,in, Represents a node For blocked lines The power transmission distribution factor. If this additional transmission power is in the same direction as the blockage, it is equivalent to a tightening of the power flow constraint on the line; therefore, a constraint correlation function is constructed. have: Equation (38); in, To block the line The additional transmission power required is calculated using the sensitivity analysis model determined in step S13, and the network components of the load adjustment command guideline for each node are then corrected and solved (i.e., Figure 2 (Corresponding algebraic expression for the deviation of the adjustment command line) Equation (39); Equation (40); in, express For blocked lines Sensitivity of line capacity The network components representing the corrected node load adjustment command baseline will With unified load adjustment command line After addition, the corrected nodal load regulation command guideline is formed. This completes the short-term correction method for the nodal load regulation command guideline based on sensitivity analysis under demand response conditions.

[0055] In this embodiment, the unified load adjustment command baseline, the mathematical composition analysis method of the node load adjustment command baseline, and the sensitivity analysis model are updated periodically based on the preset scheduling time granularity within the scheduling cycle. This allows the intraday dynamic adjustment mechanism based on ultra-short-term forecasting to enable users to update the node load adjustment command baseline at a high frequency (e.g., every 15 minutes or 5 minutes) based on real-time monitoring of renewable energy output and load, thereby dynamically and adaptively correcting the load adjustment command baseline. This mechanism enables the load adjustment command baseline to track minute changes in system status almost in real time. In the event of forecast deviations or emergencies, it can promptly correct the control targets, transforming the lagging adjustment command baseline into a forward-looking and sensitive tool, significantly improving the actual effectiveness of demand response in the face of system operating state fluctuations.

[0056] The dynamic update method for node load regulation command guidelines for power demand response provided in this embodiment first determines the current unified load regulation command guidelines of the power system based on the preset scheduling time granularity and the boundary information of power system scheduling operation. Then, combining the node load regulation command guideline decomposition model and power demand response user information, the node load regulation command guidelines for each grid node are determined. Next, the Kuhn-Tak condition of the node load regulation command guideline decomposition model is analyzed to determine the mathematical composition analysis method for the node load regulation command guidelines. This method is then used to disclose information to the corresponding grid user terminals. Afterward, the node load regulation command guideline decomposition model is reconstructed, and the reconstruction results are used to analyze the marginal impact of grid operation constraints on the node load regulation command guidelines, thereby constructing an analytical sensitivity analysis model in the reconstructed form. Finally, after the power system enters the operation phase, the corrected node load regulation command guidelines are determined based on the sensitivity analysis model, the current unified load regulation command guidelines, the set of congested lines, and the real-time monitored node net power deviation. This allows the load regulation command baseline to track minute changes in the system state in near real-time. When prediction deviations or emergencies occur, the control targets can be corrected in a timely manner, solving the problems of excessively long rolling optimization cycles and lack of real-time correction in existing related schemes. The node load regulation command baseline is updated at a higher frequency, thereby enabling real-time and adaptive correction of the load regulation command baseline. This improves the actual effectiveness of demand response in the face of fluctuations in the power system's operating state and enhances the transparency of demand response based on the node load regulation command baseline, thus better serving the power demand-side response based on the node load regulation command baseline.

[0057] Example 2 Based on the above embodiments, see Figure 3 As shown, this embodiment also provides a dynamic update system for nodal load regulation command guidelines oriented towards power demand response, used to implement the above-mentioned dynamic update method for nodal load regulation command guidelines oriented towards power demand response. The system of this embodiment includes: The unified baseline determination module 11 is used to determine the current unified load adjustment command baseline of the power system based on the current preset scheduling time granularity and the boundary information of power system scheduling operation; the preset scheduling time granularity is a time period that is not an integer hour. The node guideline determination module 12 is used to combine the node load regulation command guideline decomposition model and power demand response user information to calculate the load regulation command guideline network components, generate and publish the node load regulation command guidelines of each power grid node at present. Sensitivity analysis module 13 is used to reconstruct the decomposition model of the node load regulation command guideline into a matrix operation form, and use the corresponding reconstruction structure to analyze the marginal influence of power grid operation constraints on the node load regulation command guideline, and generate line sensitivity analysis results; wherein, the line sensitivity analysis results are the sensitivity of the network components of the node load regulation command guideline to the upper limit of line capacity; The dynamic correction module 14 is used to collect the net power deviation of each node in real time, and combine the line sensitivity analysis results, the node load adjustment command guideline and the real-time identified blocked line set to iteratively optimize and solve for the optimized node load adjustment command guideline, thus completing the closed-loop update of the node load adjustment command guideline.

[0058] Furthermore, embodiments of this application also disclose an electronic device, Figure 4 This is a structural diagram of an electronic device according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0059] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of this application. The electronic device 20 specifically includes: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the dynamic update method for node load regulation command guidelines for power demand response disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment can specifically be a computer.

[0060] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0061] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0062] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the dynamic update method for the node load regulation command baseline for power demand response executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0063] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method in any of the embodiments of this application. Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the above embodiments is stored, and the computer (or CPU (Central Processing Unit) or MPU (Microprocessor Unit) of the system or apparatus may read and execute the program code stored in the storage medium.

[0064] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined above in the system of this application.

[0065] It should be noted that the computer-readable storage medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF (Radio Frequency), etc., or any suitable combination thereof.

[0066] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0067] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0068] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed systems, modules, and methods can be implemented in other ways. For example, the module embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules or units, and may be electrical, mechanical, or other forms.

[0070] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. This application is not limited to the exact structures described above and illustrated in the accompanying drawings, and it should not be considered that the specific implementation of this application is limited to these descriptions. For those skilled in the art, various changes and modifications made without departing from the concept of this application should be considered to fall within the protection scope of this application.

Claims

1. A method for dynamically updating the nodal load regulation command baseline for power demand response, characterized in that, include: Based on the current preset scheduling time granularity and the boundary information of power system scheduling operation, the current unified load regulation command baseline of the power system is determined; The preset scheduling time granularity is a time period that is not a whole hour. By combining the nodal load regulation command baseline decomposition model and power demand response user information, the network components of the load regulation command baseline are calculated, and the nodal load regulation command baselines of each power grid node are generated and published. The decomposition model of the nodal load regulation command guideline is reconstructed into a matrix operation form. The marginal impact of power grid operation constraints on the nodal load regulation command guideline is analyzed using the corresponding reconstruction structure to generate line sensitivity analysis results. The line sensitivity analysis results are the sensitivity of the network components of the nodal load regulation command guideline to the upper limit of line capacity. The net power deviation of each node is collected in real time. Combined with the line sensitivity analysis results, the node load adjustment command guideline, and the real-time identified set of blocked lines, the optimized node load adjustment command guideline is obtained through iterative optimization, thus completing the closed-loop update of the node load adjustment command guideline.

2. The method for dynamically updating the nodal load regulation command baseline for power demand response according to claim 1, characterized in that, The process of determining the current unified load regulation command baseline of the power system based on the current preset scheduling time granularity and the boundary information of power system scheduling operation includes: The generator set information and load information corresponding to the power grid in the power system are obtained. The generator set information includes a set of conventional generator sets, a planned output of conventional generator sets, a set of new energy generator sets, and the upper limit of output of new energy generator sets. The load information includes a set of adjustable loads, a set of rigid loads, and the output information of rigid loads. Determine the current preset scheduling time granularity within the scheduling period. The scheduling time granularity includes the number of scheduling time periods within the scheduling period and the scheduling duration corresponding to each scheduling time period. For the current preset scheduling time granularity within the scheduling cycle, the current unified load adjustment command baseline of the power system is calculated based on the number of scheduling time periods, the scheduling duration, the generator set information, and the load information.

3. The method for dynamically updating the nodal load regulation command baseline for power demand response according to claim 2, characterized in that, The process of combining the nodal load regulation command baseline decomposition model and power demand response user information to calculate the network components of the load regulation command baseline, generating and publishing the nodal load regulation command baselines for each power grid node, includes: Obtain adjustable load capacity for users in the power demand response; A decomposition model of the nodal load regulation command guideline is constructed. The decomposition model of the nodal load regulation command guideline includes power grid operation constraints, which include regulation command guideline normalization constraints, regulation command guideline shape constraints, power system energy balance constraints, and power grid network constraints. Based on the node load regulation command guideline decomposition model, the adjustable load capacity of the power demand response user, the upper limit of the line capacity, the planned output of conventional units, and the current unified load regulation command guideline, calculate the load regulation command guideline network components. Based on the network components of the load regulation command guideline and the current unified load regulation command guideline, the node load regulation command guideline corresponding to each power grid node is determined, and KKT condition analysis is performed on the decomposition model of the node load regulation command guideline to determine the data to be disclosed and then released to the outside world.

4. The method for dynamically updating the nodal load regulation command baseline for power demand response according to claim 3, characterized in that, The process of performing KKT conditional analysis on the decomposition model of the node load adjustment command guideline to determine the data to be disclosed and then releasing it externally includes: Based on the coefficient calculation information and the decomposition model of the nodal load regulation command guideline, a mathematical composition analysis method for representing the compositional relationship of the nodal load regulation command guideline is determined; the coefficient calculation information is the time-varying regulation demand coefficient of the power grid calculated based on the Lagrange multipliers corresponding to the power system energy balance constraints and the power grid network constraints in the nodal load regulation command guideline decomposition model; the mathematical composition analysis method for the nodal load regulation command guideline is used to algebraically represent the mathematical compositional relationship of the nodal load regulation command guideline. Based on the mathematical composition analysis method of the node load regulation command guideline, the data to be disclosed is determined, and the information disclosure operation is performed on the power grid user terminals corresponding to the data to be disclosed.

5. The method for dynamically updating the nodal load regulation command baseline for power demand response according to claim 4, characterized in that, The method for analyzing the mathematical composition of the node load regulation command guideline determines the data to be disclosed, including: By using the mathematical composition analysis method of the node load regulation command guideline, the node load regulation command guidelines corresponding to different power grid nodes are compared to obtain the comparison analysis results of the regulation command guidelines. Based on the comparative analysis results of the regulation command guideline and the mathematical composition analysis method of the node load regulation command guideline, the data to be disclosed is determined.

6. The method for dynamically updating the nodal load regulation command baseline for power demand response according to claim 3, characterized in that, The process of reconstructing the nodal load regulation command guideline decomposition model into a matrix operation form, using the corresponding reconstructed structure to analyze the marginal impact of power grid operation constraints on the nodal load regulation command guideline, and generating line sensitivity analysis results includes: The decomposition model of the node load adjustment command guideline is reconstructed into matrix form, and the reconstructed model is determined. Based on preset relaxation variables, the reconstructed model is treated as a parametric programming problem corresponding to the power grid operation variables, and the corresponding mathematical mapping relationship is obtained; the mathematical mapping relationship is a mathematical mapping relationship in which the reconstructed model is regarded as an input power grid operation variable and an output node load regulation command guideline component. Based on the mathematical mapping relationship, a Kuhn-Tak condition analysis is performed on the reconstructed model; the derivatives of each condition with respect to the power grid operation variables are solved, and the equilibrium equations are determined by combining the conditions. The line sensitivity analysis results are obtained by performing a pseudo-inverse solution based on the equilibrium equation.

7. The method for dynamically updating the nodal load regulation command baseline for power demand response according to claim 6, characterized in that, The process involves combining the line sensitivity analysis results, the node load adjustment command guideline, and the real-time identified congested line set, iteratively optimizing the solution to obtain the optimized node load adjustment command guideline, and completing the closed-loop update of the node load adjustment command guideline, including: Obtain the set of blocked lines and the real-time monitored net power deviation of the nodes. The set of blocked lines is a set of blocked lines obtained based on the Lagrange multipliers corresponding to the shape constraints of the adjustment command guideline. Based on the line sensitivity analysis results, and under the condition that the line capacity is selected as the parameter planning variable, the equilibrium equation is solved to determine the line sensitivity of the node load adjustment command guideline and the line capacity. Based on the node net power deviation, the set of blocked lines, and the power transmission distribution factor, the additional transmission power corresponding to the blocked lines is analyzed to determine the power analysis results; wherein, the power transmission distribution factor is the power transmission distribution factor of the power grid node for each line in the set of blocked lines. Based on the power analysis results, the line sensitivity, and the line blocking direction information corresponding to the blocked line set, the node load adjustment command guideline network component is corrected to obtain the corrected node load adjustment command guideline network component. Based on the modified node load adjustment command baseline network components and the current unified load adjustment command baseline, the modified node load adjustment command baseline is determined.

8. The method for dynamically updating the nodal load regulation command baseline for power demand response according to claim 7, characterized in that, The process of analyzing the additional transmission power corresponding to the blocked lines based on the node net power deviation, the set of blocked lines, and the power transmission distribution factor to determine the power analysis results includes: After extracting the power transmission distribution factor, the additional transmission power required for each congested line is calculated using the net power deviation of the nodes as input. ;in, For nodes For blocked lines The power transmission distribution factor, For nodes During the period The deviation of net power injection from the predicted value, of which, Indicates net node injection; The additional transmission power is judged, and if the direction and magnitude of the additional transmission power are consistent with the direction of blockage, the net power injection deviation is equivalent to tightening the line power flow constraint. Construct constraint correlation functions to generate power analysis results.

9. The method for dynamically updating the nodal load regulation command baseline for power demand response according to claim 8, characterized in that, Based on the power analysis results, the line sensitivity, and the line blocking direction information corresponding to the blocked line set, the node load regulation command guideline network component is corrected to obtain the corrected node load regulation command guideline network component, including: Based on the additional transmission power and the line blocking direction information corresponding to the blocked line set, the line sensitivity is introduced. The optimal deviation correction amount of the node load adjustment command guideline is calculated node by node using an algebraic expression for the deviation of the adjustment command guideline. The corrected node load adjustment command guideline network components are obtained by superimposing the optimal deviation correction amounts. Specifically, the algebraic expression for the adjustment command guideline deviation is: ； ； in, Represents network components For blocked lines Sensitivity of line capacity The network components representing the corrected node load adjustment command baseline. To constrain the tightening function, This is the optimal deviation correction amount.

10. A dynamic update system for nodal load regulation command guidelines oriented towards power demand response, used to implement the method described in any one of claims 1-9, characterized in that, include: The unified baseline determination module is used to determine the current unified load regulation command baseline of the power system based on the current preset scheduling time granularity and the boundary information of power system scheduling operation; The preset scheduling time granularity is a time period that is not a whole hour. The node guideline determination module is used to combine the node load regulation command guideline decomposition model and power demand response user information to calculate the load regulation command guideline network components, generate and publish the node load regulation command guidelines for each power grid node at present. The sensitivity analysis module is used to reconstruct the decomposition model of the node load regulation command guideline into a matrix operation form, and use the corresponding reconstruction structure to analyze the marginal impact of power grid operation constraints on the node load regulation command guideline, and generate line sensitivity analysis results; wherein, the line sensitivity analysis results are the sensitivity of the network components of the node load regulation command guideline to the upper limit of line capacity; The dynamic correction module for the reference line is used to collect the net power deviation of each node in real time. Combining the line sensitivity analysis results, the node load adjustment command reference line, and the real-time identified set of blocked lines, iteratively optimizes and solves to obtain the optimized node load adjustment command reference line, thus completing the closed-loop update of the node load adjustment command reference line.

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