Power grid day-ahead optimization scheduling method based on partition reactive power constraint and related equipment

Abstract

The application discloses a power grid day-ahead optimal scheduling method based on partition reactive power constraint and related equipment, and relates to the technical field of power grid operation and dispatching.

Description

Technical Field

[0001] This invention relates to the field of power grid operation and dispatching technology, and in particular to a day-ahead optimization dispatching method and related equipment for power grids based on regional reactive power constraints. Background Technology

[0002] As the proportion of inverter-connected grid-connected new energy power units continues to increase, conventional synchronous units with inertia support and voltage regulation capabilities are gradually being replaced. The voltage stability and frequency safety operation characteristics of the power grid are undergoing profound changes, and sufficient reactive power reserves are the core foundation for ensuring the safety and stability of the power grid after a fault.

[0003] The existing day-ahead dispatch schemes of the power grid are mainly divided into two categories. One is a pure active power dispatch plan, which optimizes the start-up and shutdown and output curves of units based on frequency security and active power reserve constraints. The other is an integrated active and reactive power plan, which combines AC power flow and voltage stability constraints to simultaneously formulate active and reactive power dispatch schemes.

[0004] However, purely active power planning cannot provide effective voltage support and has limited fault tolerance. Meanwhile, integrated active and reactive power planning relies on time-domain simulation, resulting in excessive computational costs and low engineering usability. Summary of the Invention

[0005] This invention provides a day-ahead optimization scheduling method and related equipment for power grids based on partitioned reactive power constraints, which solves the technical problems of insufficient voltage support and excessive computational cost in day-ahead scheduling of high-proportion renewable energy power grids.

[0006] In a first aspect, the present invention provides a day-ahead optimization scheduling method for power grids based on regional reactive power constraints, comprising:

[0007] Obtain the power grid topology and equipment parameters, as well as the power grid source-load operating condition parameters predicted for the next day;

[0008] Based on the power grid topology and equipment parameters, reactive power support characteristics are calculated, and hierarchical clustering is used to divide the power grid into multiple zones based on the reactive power support characteristics.

[0009] Based on the power grid source and load operating parameters, calculate the minimum reactive power reserve requirement for each zone during the entire next day.

[0010] Obtain the output boundaries of various types of reactive power sources in each partition;

[0011] By combining the output boundaries of the various types of reactive power sources and the minimum reactive power reserve requirement, a zoned reactive power reserve constraint is constructed.

[0012] The reactive power reserve constraints of each region are embedded into a pre-built day-ahead optimization model. The day-ahead optimization model that incorporates the reactive power reserve constraints of each region is solved to obtain the active and reactive power dispatch instructions of the power grid for the entire next day.

[0013] In some embodiments, the reactive power support characteristics include reactive power voltage sensitivity characteristics, electrical distance characteristics, and reactive power source distribution distance characteristics. The calculation of the reactive power support characteristics based on the power grid topology and equipment parameters includes:

[0014] Extract the power grid operating status parameters, power grid topology parameters, and power grid node equipment attribute parameters from the power grid topology and equipment parameters;

[0015] The reactive voltage sensitivity characteristics are calculated based on the power grid operating status parameters and power grid topology parameters.

[0016] The electrical distance characteristics are calculated based on the power grid topology parameters;

[0017] Based on the attribute parameters of the power grid node equipment, the distribution distance characteristics of the reactive power source are calculated.

[0018] In some embodiments, calculating the minimum reactive power reserve requirement for each zone for the entire next day based on the power grid source-load operating parameters includes:

[0019] The power grid source-load operating condition parameters are mapped to each region to obtain the regional source-load operating condition parameters of each region; the regional source-load operating condition parameters include at least one of the following: parameters characterizing the output characteristics of distributed power sources, parameters characterizing the power demand characteristics of loads, and parameters characterizing the dynamic response characteristics of loads.

[0020] Based on the regional source-load operating parameters, calculate the minimum reactive power reserve requirement for each zone throughout the next day.

[0021] In some embodiments, the parameters characterizing the output characteristics of distributed power sources include the predicted renewable energy output of each time zone node on the next day; the parameters characterizing the load power demand characteristics include the predicted active and reactive power levels of each time zone node; the parameters characterizing the load dynamic response characteristics include the proportion of induction motors in each time zone node; and the minimum reactive power reserve requirement is specifically calculated using the following formula:

[0022] ;

[0023] in, Number the nodes. For time numbering, For partition numbering, This represents the total number of power grid time periods. This represents the total number of power grid zones. For partitioning The total number of nodes, for Time-sharing Minimum reactive power reserve requirement for Time-sharing node The predicted output of new energy sources for the next day for Time-sharing node Predicted active power load level, for Time-sharing node Predicted reactive power load; for Time-sharing node The proportion of induction motors, and All of these are preset normalized configuration parameters.

[0024] In some embodiments, the multiple types of reactive power sources include at least one of conventional generating units, synchronous condensers, static var generators, and grid-connected renewable energy generating units, and the zoned reactive power reserve constraint is specifically expressed by the following formula:

[0025] ;

[0026] in, Number the nodes. For time numbering, For partition numbering; This represents the total number of power grid time periods. This represents the total number of power grid zones. , , , The partitions are represented in sequence. The total number of conventional generating units, synchronous condensers, static var generators, and grid-connected new energy generating units. for Time-sharing Minimum reactive power reserve requirement , , , The partitions are represented in sequence. Located at node The upper limit of reactive power output of conventional generating units, synchronous condensers, static var generators, and new energy generating units; , , , They represent respectively Time-sharing Located at node The planned reactive power output of conventional generating units, synchronous condensers, static var generators, and new energy generating units.

[0027] In some embodiments, the pre-built day-ahead optimization model includes an objective function and constraints. Embedding the regional reactive power reserve constraints into the pre-built day-ahead optimization model, solving the day-ahead optimization model incorporating the regional reactive power reserve constraints, and obtaining the active and reactive power dispatch instructions for the power grid throughout the next day includes:

[0028] The partitioned reactive power reserve constraint is embedded as an additional constraint into the constraints of the day-ahead optimization model to obtain the fused constraint.

[0029] The objective function is solved using a mathematical programming solver based on the fusion constraints to obtain the optimal control variables for the entire next day.

[0030] Based on the optimal control variables, active and reactive power dispatch instructions for the power grid are generated for the entire next day.

[0031] In some embodiments, the objective function is specifically represented by the following formula:

[0032] ;

[0033] in, Number the nodes. For time numbering, This represents the total number of power grid time periods. This represents the total number of conventional generating units. This represents the total number of new energy generating units. for The time period is located at the node The start-up and shutdown status of conventional generating units. for The time period is located at the node The active power output of conventional generating units, for The time period is located at the node The active power output of the new energy units for Periodic New Energy Predicted positive contributions; For the node The power generation cost of conventional generating units, and They represent the nodes respectively. The start-up and shutdown costs of conventional generating units, Sacrifice punitive costs for the sake of new energy.

[0034] Secondly, the present invention provides a day-ahead optimization scheduling device for power grids based on partitioned reactive power constraints, used to execute the aforementioned day-ahead optimization scheduling method for power grids based on partitioned reactive power constraints, comprising:

[0035] The data acquisition module is used to acquire power grid topology and equipment parameters, as well as the power grid source and load operating condition parameters predicted for the next day.

[0036] The feature partitioning module is used to calculate the reactive power support characteristics based on the power grid topology and equipment parameters, and to divide the power grid into multiple partitions based on the reactive power support characteristics using hierarchical clustering.

[0037] The demand calculation module is used to calculate the minimum reactive power reserve demand of each zone for the entire next day based on the power grid source and load operating condition parameters.

[0038] The boundary acquisition module is used to acquire the output boundaries of multiple types of reactive power sources in each partition;

[0039] The constraint construction module is used to construct partitioned reactive power reserve constraints by combining the output boundaries of the multiple types of reactive power sources and the minimum reactive power reserve requirement.

[0040] The model solving module is used to embed the regional reactive power reserve constraints into the pre-built day-ahead optimization model, solve the day-ahead optimization model that integrates the regional reactive power reserve constraints, and obtain the active and reactive power dispatch instructions of the power grid for the entire period of the next day.

[0041] Thirdly, the present invention provides an electronic device including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the above-described grid day-ahead optimization scheduling method based on partitioned reactive power constraints.

[0042] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the above-described power grid day-ahead optimization scheduling method based on partitioned reactive power constraints.

[0043] As can be seen from the above technical solutions, the present invention has the following advantages: The present invention provides a day-ahead optimization scheduling method and related equipment for power grids based on partitioned reactive power constraints. By acquiring the power grid topology and equipment parameters and calculating the reactive power support characteristics, and combining hierarchical clustering methods to scientifically partition the power grid, the reactive power sources and load characteristics within each partition are highly correlated, avoiding the problem of excessive complexity caused by unified modeling of the entire network. Based on the source and load operating conditions of the next day, the minimum reactive power reserve demand is calculated by partition and time period, and the output boundaries of multiple types of reactive power sources are obtained, so that the reactive power reserve demand is closely coupled with the actual operating state of the system. This overcomes the defects of traditional methods in that the reactive power demand estimation is coarse and cannot reflect the time-series fluctuation characteristics. By constructing partitioned reactive power reserve constraints and embedding them into the day-ahead optimization model for solution, the active and reactive power scheduling instructions of the power grid for the next day are obtained without relying on time-domain simulation. While improving the voltage support capability of the system, it effectively reduces the computational cost and improves the practicality of engineering. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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.

[0045] Figure 1 A flowchart of a day-ahead optimization scheduling method for power grids based on partitioned reactive power constraints provided by the present invention;

[0046] Figure 2 A schematic diagram of a day-ahead optimization dispatching device for a power grid based on partitioned reactive power constraints provided by the present invention;

[0047] Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided by the present invention. Detailed Implementation

[0048] This invention provides a day-ahead optimization scheduling method and related equipment for power grids based on partitioned reactive power constraints, which is used to solve the technical problems of insufficient voltage support and excessive computational cost in day-ahead scheduling of high-proportion renewable energy power grids.

[0049] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0050] Please see Figure 1 , Figure 1 An optional flowchart of a day-ahead optimization scheduling method for power grid based on partitioned reactive power constraints provided in an embodiment of the present invention includes steps 101 to 106.

[0051] Step 101: Obtain the power grid topology and equipment parameters, as well as the predicted power grid source-load operating condition parameters for the next day;

[0052] Power grid topology and equipment parameters refer to the set of basic parameters that characterize the physical structure and equipment attributes of the power grid. These include node topology connections, line impedance parameters, node impedance matrices, and the output boundary parameters of various types of reactive power sources configured at each node, such as conventional generating units, synchronous condensers, static var generators (SVG), and grid-connected renewable energy generating units.

[0053] The grid source and load operation parameters predicted for the next day refer to the operation prediction data of the grid power source and load for the entire period of the next day, including the predicted value of active power output of new energy sources at each node, the predicted value of active / reactive power of load, and the load ratio parameters of induction motors, etc., which provide dynamic input for subsequent reactive power reserve demand calculation.

[0054] Step 102: Based on the power grid topology and equipment parameters, calculate the reactive power support characteristics, and use hierarchical clustering based on the reactive power support characteristics to divide the power grid into multiple zones;

[0055] Hierarchical Clustering (HC) is an unsupervised clustering algorithm that achieves clustering by merging samples layer by layer from bottom to top. In this embodiment, it is used to divide power grid nodes into several partition units with similar electrical attributes.

[0056] Optionally, the reactive power support characteristics include reactive power voltage sensitivity characteristics, electrical distance characteristics, and reactive power source distribution distance characteristics. First, based on the reactive power voltage sensitivity characteristics, electrical distance characteristics, and reactive power source distribution distance characteristics, the three types of characteristic values ​​of each node pair in the power grid are weighted and summed to form a comprehensive distance matrix. This comprehensive distance reflects the voltage support relationship, electrical coupling strength, and similarity of reactive power resource allocation between nodes. Then, a hierarchical clustering method is adopted, using the comprehensive distance as a similarity measure between clusters. By gradually merging the clusters with the smallest comprehensive distance, the power grid nodes are divided into a predetermined number of partitions.

[0057] Hierarchical clustering is used to divide the power grid into several zones based on reactive power voltage sensitivity characteristics, electrical distance characteristics, and reactive power source distribution distance characteristics. The specific formula is as follows:

[0058] ;

[0059] ;

[0060] in, For nodes and nodes The combined distance between them For the resulting set of power grid partitions, For hierarchical clustering methods, These all represent different power grid zones. This represents the total number of power grid zones. For nodes and nodes Reactive voltage sensitivity between For nodes and nodes Electrical distance between them For nodes and nodes The distance between reactive power source distributions.

[0061] In some embodiments, the calculation of reactive power support characteristics based on power grid topology and equipment parameters includes:

[0062] Extract power grid operating status parameters, power grid topology parameters, and power grid node equipment attribute parameters from the power grid topology and equipment parameters;

[0063] Based on the power grid operating status parameters and power grid topology parameters, the reactive voltage sensitivity characteristics are calculated.

[0064] Calculate electrical distance characteristics based on power grid topology parameters;

[0065] Based on the attribute parameters of power grid node equipment, the distribution distance characteristics of reactive power sources are calculated.

[0066] The reactive voltage sensitivity feature refers to a two-dimensional matrix with dimensions of the total number of grid nodes × the total number of grid nodes. The matrix elements quantify the degree of influence of reactive power injection changes at a single node on the voltage amplitude of other nodes, reflecting the reactive power-voltage coupling strength and voltage support capability between nodes. In this embodiment, based on grid operating state parameters (including the node voltage amplitude and phase angle at the current operating point) and grid topology parameters, the partial derivative of node voltage with respect to reactive power injection is calculated, which is the sensitivity submatrix of voltage amplitude with respect to reactive power injection in the Jacobian matrix, thus obtaining a reactive voltage sensitivity feature matrix with dimension N×N. The physical meaning of the element in the i-th row and j-th column of this matrix is: the change in voltage amplitude of node i when node j injects a unit of reactive power. The larger the value, the stronger the voltage support effect of node j on node i.

[0067] The electrical distance characteristic refers to the distance matrix between nodes derived from the node impedance matrix, which is used to characterize the tightness of electrical coupling between nodes. The smaller the value, the stronger the electrical connection between nodes. The node impedance matrix is ​​formed based on the power grid topology parameters.

[0068] The reactive power source distribution distance feature refers to the similarity matrix obtained by calculating the Euclidean distance based on the adjustable capacity distribution of multiple types of reactive power sources at each node. It is used to characterize the similarity of the configuration of reactive power regulation resources between different nodes. In this embodiment, based on the attribute parameters of grid node equipment, the adjustable reactive power capacity of conventional units, synchronous condensers, SVG and grid-connected new energy units connected to each node is counted. These capacity values ​​constitute the reactive power source distribution vector of the node. Then, the difference between the reactive power source distribution vectors of any two nodes is calculated using Euclidean distance to obtain the reactive power source distribution distance feature matrix. The smaller the distance, the more similar the two nodes are in terms of reactive power resource composition.

[0069] Optionally, based on power grid operating state parameters and power grid topology parameters, the calculation dimension is... reactive voltage sensitivity characteristic matrix The specific formula is as follows:

[0070] ;

[0071] in, The total number of power grid nodes. For nodes voltage amplitude, For nodes reactive power injection for The Middle Line 1 Column elements are used to represent nodes. Reactive power injection changes on nodes The degree of influence of voltage amplitude, The larger the value, the more likely it is to be a node. For nodes The stronger the voltage support, the better.

[0072] Based on power grid topology parameters, the calculation dimension is: Electrical distance feature matrix The specific formula is as follows:

[0073] ;

[0074] in, Here is the node impedance matrix; For nodes Self-impedance, For nodes Self-impedance; For nodes and nodes Mutual impedance between them; for The Middle Line 1 Column elements represent nodes and nodes Electrical distance between them The smaller the value, the stronger the electrical coupling between the two nodes.

[0075] Based on the attribute parameters of grid node equipment, the adjustable reactive power capacity of conventional units, synchronous condensers, SVG, and grid-connected renewable energy units located at each node is determined, and a reactive power source distribution vector for each node is constructed. The Euclidean distance was used to calculate the difference in reactive power source distribution between nodes, resulting in a dimension of The reactive power source distribution distance characteristic matrix is ​​given by the following formula:

[0076] ;

[0077] ;

[0078] in, , , ,and They are located at the nodes in order. The upper limit of reactive power output of conventional generating units, synchronous condensers, SVG, and grid-type new energy generating units; For nodes The reactive power source distribution vector, For nodes The reactive power source distribution vector; The first element in the reactive power source distribution distance characteristic matrix is... Line 1 Column elements represent nodes and nodes Distribution of reactive power sources between them The smaller the value, the more similar the reactive resource configuration between the two nodes.

[0079] Step 103: Based on the power grid source and load operating parameters, calculate the minimum reactive power reserve requirement for each zone during the entire next day.

[0080] The minimum reactive power reserve requirement refers to the minimum adjustable reactive power capacity that all reactive power sources (including conventional units, synchronous condensers, SVG, grid-connected new energy sources, etc.) in a zone must reserve to ensure that the voltage of each node in the zone remains within the acceptable range during normal operation and after anticipated fault disturbances. This requirement is the minimum threshold value for reactive power voltage safety in the zone, and its value is related to the load level in the zone, the power output fluctuation characteristics of new energy sources, and the dynamic response characteristics of the load.

[0081] In some embodiments, step 103 includes the following sub-steps:

[0082] The power grid source and load operating condition parameters are mapped to each region to obtain the regional source and load operating condition parameters of each region. The regional source and load operating condition parameters include at least one of the following: parameters characterizing the output characteristics of distributed power sources, parameters characterizing the power demand characteristics of loads, and parameters characterizing the dynamic response characteristics of loads.

[0083] Based on the regional source-load operating parameters, calculate the minimum reactive power reserve requirement for each zone throughout the next day.

[0084] Regional source-load operating condition parameters refer to the set of parameters used to characterize the operating characteristics of power sources and loads in each region after mapping the power grid source-load operating condition parameters to each region based on the zoning results. Specifically, these parameters may include: parameters characterizing the output characteristics of distributed power sources (such as the predicted output curves and time series change rates of wind power and photovoltaic power), parameters characterizing the load power demand characteristics (such as the predicted curves of active and reactive loads at each node), and parameters characterizing the dynamic response characteristics of the load (such as the voltage static characteristic coefficient and frequency static characteristic coefficient of the load, which are used to describe the self-regulation capability of the load power as voltage and frequency change).

[0085] Optionally, the parameters characterizing the output characteristics of distributed power sources include the predicted renewable energy output of the partition nodes for each time period on the next day; the parameters characterizing the load power demand characteristics include the predicted active and reactive power levels of the load at the partition nodes for each time period; and the parameters characterizing the load dynamic response characteristics include the proportion of induction motors at the partition nodes for each time period. The minimum reactive power reserve requirement is specifically calculated using the following formula:

[0086] ;

[0087] in, Number the nodes. For time numbering, For partition numbering, This represents the total number of power grid time periods. This represents the total number of power grid zones. For partitioning The total number of nodes, for Time-sharing Minimum reactive power reserve requirement for Time-sharing node The predicted output of new energy sources for the next day for Time-sharing node Predicted active power load level, for Time-sharing node Predicted reactive power load; for Time-sharing node The proportion of induction motors, and All of these are preset normalized configuration parameters.

[0088] Step 104: Obtain the output boundaries of multiple types of reactive power sources in each partition;

[0089] Multiple types of reactive power sources refer to various equipment types in the distribution network that have the ability to output or absorb reactive power, including conventional synchronous generator sets, synchronous condensers, static var generators (SVG), and grid-forming renewable energy units (referring to wind power, photovoltaic, or energy storage inverters that adopt grid-forming control strategies and can autonomously establish and support grid connection point voltage). Different types of reactive power sources have significant differences in response speed, regulation range, and operating characteristics.

[0090] The output boundary refers to the upper and lower limits of reactive power that a reactive power source can output within its safe operating range. It is characterized by the minimum adjustable reactive power output (usually a negative value, indicating reactive power absorption) and the maximum adjustable reactive power output (a positive value, indicating reactive power generation). The output boundary is constrained by multiple factors such as the equipment's own capacity, the current active power output level, the terminal voltage limit, and the operating temperature.

[0091] Step 105: Combine the output boundaries of multiple types of reactive power sources with the minimum reactive power reserve requirement to construct a zoned reactive power reserve constraint.

[0092] The reactive power reserve constraint of a region refers to the mathematical inequality constraint constructed for each power grid region. It requires that, on the basis of satisfying the reactive power balance at the current operating point, the sum of the remaining adjustable reactive power capacity of all online reactive power sources in the region is not less than the minimum reactive power reserve requirement of the region in that time period. This constraint is the core link connecting the reactive power demand of the region with the reactive power resource allocation of the whole network. After being embedded in the day-ahead optimization model, the scheduling scheme output by the model has sufficient dynamic reactive power support capability at each region level.

[0093] Online reactive power sources refer to equipment that is in grid-connected operation and has reactive power regulation capabilities in the daily dispatch plan, including conventional synchronous generator sets, synchronous condensers, static var generators (SVG), and grid-connected new energy units.

[0094] In some embodiments, the partitioned reactive power reserve constraint is specifically expressed by the following formula:

[0095] ;

[0096] in, Number the nodes. For time numbering, For partition numbering; This represents the total number of power grid time periods. This represents the total number of power grid zones. , , , The partitions are represented in sequence. The total number of conventional generating units, synchronous condensers, static var generators, and grid-connected new energy generating units. for Time-sharing Minimum reactive power reserve requirement , , , The partitions are represented in sequence. Located at node The upper limit of reactive power output of conventional generating units, synchronous condensers, static var generators, and new energy generating units; , , , They represent respectively Time-sharing Located at node The planned reactive power output of conventional generating units, synchronous condensers, static var generators, and new energy generating units.

[0097] Step 106: Embed the zoned reactive power reserve constraints into the pre-built day-ahead optimization model, solve the day-ahead optimization model that incorporates the zoned reactive power reserve constraints, and obtain the active and reactive power dispatch instructions of the power grid for the entire next day.

[0098] A day-ahead optimization model refers to an optimization model constructed during the day-ahead scheduling phase (e.g., 24 hours in advance) to determine the operating plans of each controllable device in the power grid during each scheduling period of the following day. This model uses minimizing the total system operating cost as the objective function, with the physical operating characteristics of the power grid and the regulating capacity of the equipment as constraints, and obtains the optimal scheduling scheme through mathematical programming methods. The model's control variables include the start-up and shutdown status and output plan of conventional units, the output plan of renewable energy units, the output plan of various reactive power sources, and the switching scheme of capacitors, etc.

[0099] Control variables refer to the unknown variables in the current-day optimization model that are decided by the solver, and their values ​​directly determine the final scheduling scheme. In this embodiment, the control variables include: the start-up and shutdown status of conventional generating units (binary variables, taking values ​​of 0 or 1), the active and reactive power output of conventional generating units (continuous variables), the active and reactive power output of renewable energy generating units (continuous variables), the reactive power output of synchronous condensers (continuous variables), the reactive power output of static var generators (SVG) (continuous variables), the reactive power output of grid-connected renewable energy generating units (continuous variables), and the number of capacitor banks switched on and off (integer variables), etc.

[0100] The active and reactive power dispatch instructions refer to the set of control instructions for each power generation unit and reactive power compensation equipment, output after solving the day-ahead optimization model, covering all time periods. These instructions include the start-up and shutdown status of each conventional unit in each time period, the active and reactive power output setpoints, the active and reactive power output setpoints of each new energy power station, the reactive power output setpoints of each synchronous condenser and SVG, and the switching schemes of each capacitor bank, etc. The dispatch instructions cover 24 time periods from 00:00 to 24:00 of the next day at an hourly granularity.

[0101] In some embodiments, the pre-built day-ahead optimization model includes an objective function and constraints, and step 106 includes the following sub-steps:

[0102] By embedding the partitioned reactive power reserve constraint as an additional constraint into the constraints of the day-ahead optimization model, the fused constraint is obtained.

[0103] A mathematical programming solver is used to solve the objective function based on fused constraints, and the optimal control variables for the entire next day are obtained.

[0104] Based on the optimal control variables, active and reactive power dispatch instructions for the power grid are generated for the entire next day.

[0105] A mathematical programming solver is a specialized computer software tool used to solve mathematical optimization problems. This embodiment uses the CPLEX solver (IBM ILOG CPLEX Optimizer, a high-performance mixed-integer linear programming and quadratic programming solver). This solver can efficiently handle optimization problems containing continuous variables, integer variables, and a large number of linear constraints, and return the global optimal solution or a proven high-quality feasible solution.

[0106] The fusion constraint condition refers to the complete set of constraints formed by merging the constructed partitioned reactive power reserve constraint as a new constraint item with the various physical constraints (including power flow constraints, equipment output constraints, reserve capacity constraints, unit start-up and shutdown time constraints, ramping constraints, frequency security constraints, and voltage security constraints) in the current optimization model.

[0107] Specifically, the objective function aims to minimize the total system operating cost, which includes the start-up and shutdown costs of conventional units (reflecting fuel consumption and equipment wear and tear costs incurred during each start-up and shutdown), the output costs of conventional units (reflecting fuel consumption costs at a specific active power output level), and the penalty cost of abandoning renewable energy (reflecting the renewable energy generation forced to be abandoned due to system peak shaving or security constraints; the penalty coefficient is usually set higher than the generation cost of conventional units to encourage the priority consumption of renewable energy). The objective function is expressed by the following formula:

[0108] ;

[0109] in, Number the nodes. For time numbering, This represents the total number of power grid time periods. This represents the total number of conventional generating units. This represents the total number of new energy generating units. for The time period is located at the node The start-up and shutdown status of conventional generating units. for The time period is located at the node The active power output of conventional generating units, for The time period is located at the node The active power output of the new energy units for Periodic New Energy Predicted positive contributions; For the node The power generation cost of conventional generating units, and They represent the nodes respectively. The start-up and shutdown costs of conventional generating units, Sacrifice punitive costs for the sake of new energy.

[0110] Optionally, the constraints specifically include: linear AC power flow constraints (based on line parameters describing the balance between active and reactive power at each node; this embodiment uses linearization approximation to reduce solution complexity); multi-type equipment output constraints (limiting the reactive power output of each conventional unit, synchronous condenser, SVG, renewable energy unit, and grid-connected renewable energy unit within their respective output boundaries); active power reserve capacity constraints for conventional units and renewable energy (ensuring the system has sufficient upward and downward adjustment capabilities to cope with load forecast deviations and renewable energy output fluctuations); minimum continuous start-up and shutdown time constraints for conventional units (reflecting the actual limitation that units cannot frequently start and stop within a short period due to thermodynamic and mechanical characteristics); ramp-up constraints for conventional units (limiting the rate of change of active power output between adjacent time periods); frequency safety constraints (limiting the system inertia, maximum frequency change rate, and maximum frequency deviation to prevent frequency collapse after large disturbances); and voltage safety constraints (limiting the voltage amplitude deviation at each node within the allowable range), as detailed below:

[0111] 1) Linear AC power flow constraints, specifically expressed by the following formula:

[0112] ;

[0113] ;

[0114] in, For nodes and Inter-conductance, For nodes and Mutual susceptance between them; For nodes voltage amplitude offset For nodes The offset of the voltage phase angle; for The time period is located at the node The active power output of conventional generating units, for The time period is located at the node The active power output of the new energy unit with the configuration; , , and In order to be The time period is located at the node The planned reactive power output of conventional generating units, synchronous condensers, SVG and configuration-based new energy generating units; for Time period nodes The active load level, for Time period nodes The reactive load level; for The time period is located at the node The number of capacitor banks switched on, for The time period is located at the node The switching capacity of the capacitor bank;

[0115] 2) Output constraints for multiple types of equipment, specifically expressed by the following formula:

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] in, , , , and The numbers, in order, are conventional generating units, synchronous condensers, SVG, new energy generating units, and the total number of grid-connected new energy generating units. for The time period is located at the node The start-up and shutdown status of conventional generating units; for The time period is located at the node The active power output of conventional generating units, For The time period is located at the node The active power output of the new energy generating units; , , ,and In order to be The time period is located at the node The planned reactive power output of conventional generating units, synchronous condensers, SVG, and grid-connected new energy generating units; and They are located at the nodes respectively The upper and lower limits of active power output of conventional generating units; for The time period is located at the node The predicted active power output of the new energy generating units; and They are located at the nodes respectively The upper and lower limits of reactive power output of conventional generating units; and They are located at the nodes respectively The upper limit and lower limit of the reactive power output of the synchronous condenser; and They are located at the nodes respectively The upper and lower limits of reactive power output of the SVG; and They are located at the nodes respectively The upper and lower limits of reactive power output of grid-connected new energy generating units; For nodes The upper limit of capacitor bank input;

[0124] 3) The active power reserve capacity constraints for conventional units and new energy sources are specifically expressed by the following formula:

[0125] ;

[0126] ;

[0127] in, For the power grid Maximum backup capacity requirement for a given period For the power grid The lower limit of the standby capacity requirement for a given time period;

[0128] 4) Minimum continuous start-up and shutdown time constraint for conventional units, specifically expressed by the following formula:

[0129] ;

[0130] ;

[0131] in, for The time period is located at the node The continuous operating time of conventional generating units for The time period is located at the node The continuous downtime of conventional generating units; For the node The upper limit of the start-up and shutdown time of conventional units. For the node The lower limit of the start-up and shutdown time of conventional generating units; For time period t-1, the node is located The start-up and shutdown status of conventional generating units;

[0132] 5) Ramp-up constraints for conventional units, specifically expressed by the following formula:

[0133] ;

[0134] ;

[0135] in, For the node The maximum ramp rate for conventional generating units in a single time period. For the node The lower limit of the ramp rate for conventional generating units in a single time period. For time period t-1, the node is located The active power output of conventional generating units;

[0136] 6) Frequency security constraints, specifically expressed by the following formula:

[0137] ;

[0138] ;

[0139] ;

[0140] in, The rated frequency of the power grid; For power grid system disturbance power, For power grid Total inertia over a period of time; This is the maximum allowable rate of frequency change limit for the power grid; This represents the maximum permissible frequency deviation of the power grid when a disturbance occurs. For the node The complete response time of a single frequency regulation in a conventional unit, For the node The frequency regulation response delay of conventional generating units; For the node The maximum primary frequency regulation capacity provided by conventional generating units; For the node The primary frequency regulation capacity provided by conventional generating units;

[0141] 7) Voltage safety constraints, specifically expressed by the following formula:

[0142] ;

[0143] in, For nodes voltage amplitude offset; For nodes Upper and lower limits of voltage amplitude offset For nodes The lower limit of voltage amplitude offset.

[0144] In step 106, the reactive power reserve constraint of each zone is first embedded into the above constraint conditions as an additional constraint condition. Specifically, the reactive power reserve constraint inequalities of each zone in each time period are added one by one to the original constraint set to form a fused constraint condition. This newly added constraint, together with the original constraint, constitutes a complete feasible region, so that the final solved scheduling scheme has sufficient dynamic reactive power voltage support margin at each zone level while meeting the traditional economic and safety requirements.

[0145] Subsequently, the CPLEX solver is used to solve the day-ahead optimization model after merging the reactive power reserve constraints of the partition. Specifically, the CPLEX solver receives the mathematical expression of the objective function, the coefficient matrix of all equality and inequality in the merging constraints, and the boundary definitions of the variables. It iterative optimization is performed using mixed integer programming algorithms such as the branch and bound method and the simplex method to search for the combination of control variables that minimizes the objective function within the feasible region. After the solution is completed, the optimal values ​​of each control variable are output, i.e., the optimal control variables.

[0146] Finally, based on the optimal control variables, active and reactive power dispatch instructions are generated for the entire next day. These instructions are output in the form of a dispatch table, listing the operating setpoints of each controllable device for each time period, and are sent to the automatic control systems of each power generation unit and reactive power compensation device in the power grid for execution.

[0147] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0148] The following describes the day-ahead power grid optimization scheduling device based on partitioned reactive power constraints provided in the embodiments of this application. The day-ahead power grid optimization scheduling device based on partitioned reactive power constraints described below can be referred to in correspondence with the day-ahead power grid optimization scheduling method based on partitioned reactive power constraints described above.

[0149] Reference Figure 2 , Figure 2 This is an optional structural diagram of a day-ahead optimal scheduling device for power grids based on partitioned reactive power constraints, provided in an embodiment of the present invention. This device is used to execute the aforementioned day-ahead optimal scheduling method for power grids based on partitioned reactive power constraints, and includes:

[0150] Data acquisition module 01 is used to acquire power grid topology and equipment parameters and the power grid source and load operating condition parameters predicted for the next day;

[0151] Feature partitioning module 02 is used to calculate reactive power support characteristics based on power grid topology and equipment parameters, and to divide the power grid into multiple partitions based on the reactive power support characteristics using hierarchical clustering.

[0152] Demand calculation module 03 is used to calculate the minimum reactive power reserve demand of each zone for the entire next day based on the power grid source and load operating parameters.

[0153] Boundary acquisition module 04 is used to acquire the output boundaries of multiple types of reactive power sources in each partition;

[0154] Constraint Construction Module 05 is used to construct zoned reactive power reserve constraints by combining the output boundaries of multiple types of reactive power sources and the minimum reactive power reserve requirement.

[0155] Model Solver Module 06 is used to embed the regional reactive power reserve constraints into the pre-built day-ahead optimization model, solve the day-ahead optimization model that integrates the regional reactive power reserve constraints, and obtain the active and reactive power dispatch instructions of the power grid for the entire next day.

[0156] In some embodiments, the feature partitioning module 02 is configured as follows:

[0157] Extract power grid operating status parameters, power grid topology parameters, and power grid node equipment attribute parameters from the power grid topology and equipment parameters;

[0158] Based on the power grid operating status parameters and power grid topology parameters, the reactive voltage sensitivity characteristics are calculated.

[0159] Calculate electrical distance characteristics based on power grid topology parameters;

[0160] Based on the attribute parameters of power grid node equipment, the distribution distance characteristics of reactive power sources are calculated.

[0161] In some embodiments, the demand calculation module 03 is configured as follows:

[0162] The power grid source and load operating condition parameters are mapped to each region to obtain the regional source and load operating condition parameters of each region. The regional source and load operating condition parameters include at least one of the following: parameters characterizing the output characteristics of distributed power sources, parameters characterizing the power demand characteristics of loads, and parameters characterizing the dynamic response characteristics of loads.

[0163] Based on the regional source-load operating parameters, calculate the minimum reactive power reserve requirement for each zone throughout the next day.

[0164] In some embodiments, the model solving module 06 is configured as follows:

[0165] By embedding the partitioned reactive power reserve constraint as an additional constraint into the constraints of the day-ahead optimization model, the fused constraint is obtained.

[0166] A mathematical programming solver is used to solve the objective function based on fused constraints, and the optimal control variables for the entire next day are obtained.

[0167] Based on the optimal control variables, active and reactive power dispatch instructions for the power grid are generated for the entire next day.

[0168] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0169] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules 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; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0170] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0171] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0172] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0173] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned day-ahead optimal scheduling method for power grids based on zoned reactive power constraints. This electronic device can be any smart terminal, including a tablet computer.

[0174] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0175] Please see Figure 3 , Figure 3 The hardware structure of an electronic device according to another embodiment is illustrated, including:

[0176] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0177] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to execute the day-ahead optimal scheduling method for power grid based on partitioned reactive power constraints according to the embodiments of this invention.

[0178] The input / output interface 903 is used to implement information input and output;

[0179] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0180] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0181] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0182] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described power grid day-ahead optimization scheduling method based on partitioned reactive power constraints.

[0183] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0184] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0185] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A day-ahead optimal scheduling method for power grids based on zoned reactive power constraints, characterized in that, include: Obtain the power grid topology and equipment parameters, as well as the power grid source-load operating condition parameters predicted for the next day; Based on the power grid topology and equipment parameters, reactive power support characteristics are calculated, and hierarchical clustering is used to divide the power grid into multiple zones based on the reactive power support characteristics. Based on the power grid source and load operating parameters, calculate the minimum reactive power reserve requirement for each zone during the entire next day. Obtain the output boundaries of various types of reactive power sources in each partition; By combining the output boundaries of the various types of reactive power sources and the minimum reactive power reserve requirement, a zoned reactive power reserve constraint is constructed. The reactive power reserve constraints of each region are embedded into a pre-built day-ahead optimization model. The day-ahead optimization model that incorporates the reactive power reserve constraints of each region is solved to obtain the active and reactive power dispatch instructions of the power grid for the entire next day.

2. The day-ahead optimal scheduling method for power grids based on regional reactive power constraints according to claim 1, characterized in that, The reactive power support characteristics include reactive power voltage sensitivity characteristics, electrical distance characteristics, and reactive power source distribution distance characteristics. The calculation of reactive power support characteristics based on the power grid topology and equipment parameters includes: Extract the power grid operating status parameters, power grid topology parameters, and power grid node equipment attribute parameters from the power grid topology and equipment parameters; The reactive voltage sensitivity characteristics are calculated based on the power grid operating status parameters and power grid topology parameters. The electrical distance characteristics are calculated based on the power grid topology parameters; Based on the attribute parameters of the power grid node equipment, the distribution distance characteristics of the reactive power source are calculated.

3. The day-ahead optimal scheduling method for power grids based on regional reactive power constraints according to claim 1, characterized in that, The calculation of the minimum reactive power reserve requirement for each zone during the entire next day, based on the power grid source-load operating parameters, includes: The power grid source-load operating condition parameters are mapped to each region to obtain the regional source-load operating condition parameters of each region; the regional source-load operating condition parameters include at least one of the following: parameters characterizing the output characteristics of distributed power sources, parameters characterizing the power demand characteristics of loads, and parameters characterizing the dynamic response characteristics of loads. Based on the regional source-load operating parameters, calculate the minimum reactive power reserve requirement for each zone throughout the next day.

4. The day-ahead optimal scheduling method for power grids based on partitioned reactive power constraints according to claim 3, characterized in that, The parameters characterizing the output characteristics of distributed power sources include the predicted renewable energy output of each time zone node on the next day; the parameters characterizing the load power demand characteristics include the predicted active and reactive power levels of each time zone node; the parameters characterizing the load dynamic response characteristics include the proportion of induction motors in each time zone node; and the minimum reactive power reserve requirement is specifically calculated using the following formula: ； in, Number the nodes. For time numbering, For partition numbering, This represents the total number of power grid time periods. This represents the total number of power grid zones. For partitioning The total number of nodes, for Time-sharing Minimum reactive power reserve requirement for Time-sharing node The predicted output of new energy sources for the next day for Time-sharing node Predicted active power load level, for Time-sharing node Predicted reactive power load; for Time-sharing node The proportion of induction motors, and All of these are preset normalized configuration parameters.

5. The day-ahead optimal scheduling method for power grids based on partitioned reactive power constraints according to claim 1, characterized in that, The various types of reactive power sources include at least one of conventional generating units, synchronous condensers, static var generators, and grid-connected renewable energy generating units. The zoned reactive power reserve constraint is specifically expressed by the following formula: ； in, Number the nodes. For time numbering, For partition numbering; This represents the total number of power grid time periods. This represents the total number of power grid zones. , , , The partitions are represented in sequence. The total number of conventional generating units, synchronous condensers, static var generators, and grid-connected new energy generating units. for Time-sharing Minimum reactive power reserve requirement , , , The partitions are represented in sequence. Located at node The upper limit of reactive power output of conventional generating units, synchronous condensers, static var generators, and new energy generating units; , , , They represent respectively Time-sharing Located at node The planned reactive power output of conventional generating units, synchronous condensers, static var generators, and new energy generating units.

6. The day-ahead optimal scheduling method for power grids based on partitioned reactive power constraints according to claim 1, characterized in that, The pre-built day-ahead optimization model includes an objective function and constraints. The step of embedding the regional reactive power reserve constraints into the pre-built day-ahead optimization model, solving the day-ahead optimization model that incorporates the regional reactive power reserve constraints, and obtaining the active and reactive power dispatch instructions for the power grid throughout the next day includes: The partitioned reactive power reserve constraint is embedded as an additional constraint into the constraints of the day-ahead optimization model to obtain the fused constraint. The objective function is solved using a mathematical programming solver based on the fusion constraints to obtain the optimal control variables for the entire next day. Based on the optimal control variables, active and reactive power dispatch instructions for the power grid are generated for the entire next day.

7. The day-ahead optimal scheduling method for power grids based on regional reactive power constraints according to claim 6, characterized in that, The objective function is specifically expressed by the following formula: ； in, Number the nodes. For time numbering, This represents the total number of power grid time periods. This represents the total number of conventional generating units. This represents the total number of new energy generating units. for The time period is located at the node The start-up and shutdown status of conventional generating units. for The time period is located at the node The active power output of conventional generating units, for The time period is located at the node The active power output of the new energy generating units for Periodic New Energy Predicted positive contributions; For the node The power generation cost of conventional generating units, and They represent the nodes respectively. The start-up and shutdown costs of conventional generating units, Sacrifice punitive costs for the sake of new energy.

8. A power grid day-ahead optimization dispatching device based on zoned reactive power constraints, characterized in that, A method for performing day-ahead grid optimization scheduling based on partitioned reactive power constraints as described in any one of claims 1 to 7, comprising: The data acquisition module is used to acquire power grid topology and equipment parameters, as well as the power grid source and load operating condition parameters predicted for the next day. The feature partitioning module is used to calculate the reactive power support characteristics based on the power grid topology and equipment parameters, and to divide the power grid into multiple partitions based on the reactive power support characteristics using hierarchical clustering. The demand calculation module is used to calculate the minimum reactive power reserve demand of each zone for the entire next day based on the power grid source and load operating condition parameters. The boundary acquisition module is used to acquire the output boundaries of multiple types of reactive power sources in each partition; The constraint construction module is used to construct partitioned reactive power reserve constraints by combining the output boundaries of the multiple types of reactive power sources and the minimum reactive power reserve requirement. The model solving module is used to embed the regional reactive power reserve constraints into the pre-built day-ahead optimization model, solve the day-ahead optimization model that integrates the regional reactive power reserve constraints, and obtain the active and reactive power dispatch instructions of the power grid for the entire period of the next day.

9. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the day-ahead optimal scheduling method for power grid based on partitioned reactive power constraints as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the day-ahead optimal scheduling method for power grid based on partitioned reactive power constraints as described in any one of claims 1 to 7.

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