Methods, devices, terminals and media for tiered operation simulation of power grids over long time scales

Abstract

This application discloses a method, device, terminal, and medium for hierarchical operation simulation of power grids over long time scales. The technical solution provided by this application divides the simulation of large power grids into three levels according to hierarchical relationships: grid level, provincial level, and regional level. Through a hierarchical decoupling method, the simulation of ultra-large-scale complex power grid operation is simplified into three stages: grid-level power grid operation simulation, provincial power grid operation simulation, and regional power grid operation simulation. The lower-level simulation model only needs to perform operation simulation based on the key parameters output by the upper-level simulation model. Apart from a small number of key parameter interactions, the calculation processes of each simulation model are independent of each other. This simplifies the computational complexity of power grid operation simulation while taking into account the correlation between the upper-level and lower-level power grids. It can effectively reduce the running time and improve the operating efficiency when facing massive scenario computing requirements.

Description

Technical Field

[0001] This application relates to the field of power grid operation simulation technology, and in particular to a method, device, terminal and medium for stratified operation simulation of power grid over a long time scale. Background Technology

[0002] By conducting long-term operational simulations of the regional power grid, we can identify the extreme operating modes faced by the regional power grid under the high proportion of renewable energy access, analyze and assess the potential risks of regional power grid operation, and provide a reference for the flexible resource planning and scheduling operation of the regional power grid.

[0003] Currently, there are two main calculation methods for long-term operation simulation of regional power grids: The first method ignores the influence of the upstream main grid and directly performs operation simulation calculations of the regional power grid. However, since the regional power grid and the main grid are connected through multiple AC lines and there is power exchange, the operation mode of the main grid will inevitably affect the regional power grid. Therefore, the second method considers the influence of the main grid on the regional power grid, constructs a full-network operation simulation model covering both the main grid and the regional power grid, and performs unified operation simulation calculations for the entire network. This can make up for the lack of accuracy of the first method. However, this method has drawbacks in terms of computational complexity and running time, and the simulation efficiency is low. It cannot support the massive scenario calculation needs of new power systems and does not meet the current engineering application requirements. Summary of the Invention

[0004] This application provides a method, apparatus, terminal, and medium for simulating the layered operation of power grids over long time scales, which addresses the technical problem of low simulation efficiency in existing regional power grid long-term operation simulation technologies.

[0005] To address the aforementioned technical problems, the first aspect of this application provides a method for tiered operation simulation of power grids over long time scales, comprising:

[0006] Historical unit quotations of the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data of the grid-level power grid, are obtained. A regional spot market simulation model is constructed, and the grid-level power grid regional operation simulation is carried out through the regional spot market simulation model to obtain the grid-level power grid regional active power generation data and inter-provincial tie line active power transmission data of the grid-level power grid within a preset time period.

[0007] Based on the topology data of the provincial power grid, and combined with the active power generation data of the entire network area and the active power transmission data of the inter-provincial tie lines as the boundary conditions for the power flow calculation of the provincial power grid, a first power flow calculation model of the provincial power grid is constructed. Based on the first power flow calculation model, power flow calculation is performed on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid within the preset time period.

[0008] Based on the correlation between the provincial power grid topology and the regional power grid topology, common nodes in the provincial power grid and the regional power grid are determined. The power flow parameters corresponding to the common nodes are used as boundary conditions for power flow calculation of the regional power grid. Combined with the nodes of each regional power grid, a second power flow calculation model for each regional power grid is constructed. By solving each second power flow calculation model, the operation simulation results of each regional power grid within the preset time period are obtained.

[0009] Preferably, the process involves acquiring historical unit quotations for the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data for the entire grid-level power grid. A regional spot market simulation model is then constructed. This model is used to simulate the regional operation of the grid-level power grid, yielding the grid-wide active power generation data and inter-provincial tie-line active power transmission data for the entire grid within a preset time period. Specifically, this includes:

[0010] Obtain historical unit quotations for the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data of the grid-level power grid;

[0011] Construct a daily simulation model of the regional spot market for the aforementioned grid-level power grid;

[0012] The daily simulation model of the regional spot market is solved using a preset model solving algorithm, and the daily clearing simulation data of the grid-level power grid is obtained based on the solution results.

[0013] Based on the daily simulation model and model solving algorithm of the regional spot market, and combined with a preset time period, the clearing simulation data of the grid-level power grid within the preset time period is obtained through rolling calculation, and the active power generation data of the entire grid region and the active power transmission data of inter-provincial interconnection lines of the grid-level power grid within the preset time period are determined.

[0014] Preferably, the power flow calculation for each node within the provincial power grid based on the first power flow calculation model, to obtain the power flow parameters for each node within the provincial power grid, specifically includes:

[0015] Based on the first power flow calculation model, the reactive power constraints of the generator nodes in the provincial power grid are set to be unrestricted, and power flow calculations are performed on each node in the provincial power grid to obtain the first power flow calculation results.

[0016] If the power flow convergence of the first power flow calculation result does not meet the preset convergence condition, then the first power flow calculation result is filtered and the reactive power constraints of each generator node are reset.

[0017] If the power flow convergence of the first power flow calculation result meets the preset convergence condition, then the unarranged reactive power of the generator node is determined by statistically analyzing the first power flow calculation result. Combined with the configuration information of the reactive power compensation node, the reactive power compensation value of each generator node is determined according to the reactive voltage sensitivity of each generator node to all reactive power compensation nodes.

[0018] Based on the first power flow calculation model of the generator node with reactive power compensation, power flow calculation is performed on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid.

[0019] Preferably, based on the nodes of the same regional power grid, and using the power flow parameters corresponding to the common nodes as boundary conditions for power flow calculation in the regional power grid, the second power flow calculation model for each regional power grid is constructed, specifically including:

[0020] Using the power flow parameters corresponding to the common nodes as boundary conditions for power flow calculation of the regional power grid, and combining the load data and time series of new energy nodes in the same regional power grid, multiple single-prediction scenario power flow calculation models are constructed according to the type of new energy nodes. Then, combined with multiple preset new energy node operation scenarios, a second power flow calculation model of the regional power grid is formed.

[0021] Preferably, the step of solving each of the second power flow calculation models to obtain the operation simulation results of the power grid in each region within the preset time period specifically includes:

[0022] By using a multi-threaded parallel processing method, the power flow calculation models for each single operating scenario in the second power flow calculation model are solved respectively. Then, the solution results of the power flow calculation models for each single operating scenario are summarized to obtain the operation simulation results of the regional power grid. Then, the power flow calculation models for each single operating scenario in the remaining second power flow calculation models are solved in turn to obtain the operation simulation results of the regional power grid within the preset time period.

[0023] Preferably, solving the power flow calculation model for each single operating scenario in the second power flow calculation model specifically includes:

[0024] Based on the node topology network corresponding to the power flow calculation model for a single operating scenario, the node topology network is divided into several partitions according to the network voltage level.

[0025] Based on the node voltage level, the balancing node is determined from each partition, and the node is searched outward from the balancing node as the center. When the number of search layers reaches the preset layer threshold, multiple open network topologies are output.

[0026] The power flow calculation results of each open network topology are obtained by using the preset open network power flow calculation formula, and are used as the solution results of the power flow calculation model of the single operating scenario.

[0027] Preferably, the power flow parameters specifically include voltage amplitude and phase.

[0028] Meanwhile, a second aspect of this application provides a tiered operation simulation device for a power grid over a long time scale, comprising:

[0029] The grid-level power grid simulation unit is used to acquire historical unit quotations of the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data of the grid-level power grid. It constructs a regional spot market simulation model and uses the regional spot market simulation model to simulate the operation of the grid-level power grid in the region to obtain the active power generation data of the entire grid region and the active power transmission data of inter-provincial tie lines within a preset time period.

[0030] The provincial power grid simulation unit is used to construct a first power flow calculation model of the provincial power grid based on the topology data of the provincial power grid, combined with the active power generation data of the entire network area and the active power transmission data of the inter-provincial tie lines as boundary conditions for the power flow calculation of the provincial power grid. Based on the first power flow calculation model, the power flow calculation is performed on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid within the preset time period.

[0031] The regional power grid simulation unit is used to determine the common nodes in the provincial power grid and the regional power grid based on the correlation between the provincial power grid topology and the regional power grid topology. The power flow parameters corresponding to the common nodes are used as the boundary conditions for the power flow calculation of the regional power grid. Combined with the nodes of each regional power grid, a second power flow calculation model of each regional power grid is constructed. By solving each second power flow calculation model, the operation simulation results of each regional power grid within the preset time period are obtained.

[0032] A third aspect of this application provides a hierarchical operation simulation terminal for a power grid over a long time scale, comprising: a memory and a processor;

[0033] The memory is used to store program code corresponding to the power grid long-term hierarchical operation simulation method provided in the first aspect of this application;

[0034] The processor is used to execute the program code.

[0035] The fourth aspect of this application provides a computer-readable storage medium storing program code corresponding to the long-term hierarchical operation simulation method for power grids provided in the first aspect of this application.

[0036] As can be seen from the above technical solutions, this application has the following advantages:

[0037] The technical solution provided in this application divides the simulation of large power grids into three levels according to hierarchical relationships: grid level, provincial level, and regional level. Through a layered decoupling method, the simulation of ultra-large-scale complex power grid operation is simplified into three stages: grid-level power grid operation simulation, provincial power grid operation simulation, and regional power grid operation simulation. The lower-level simulation model only needs to perform operation simulation based on the key parameters output by the upper-level simulation model. Apart from a few key parameter interactions, the calculation processes of each simulation model are independent of each other. This simplifies the computational complexity of power grid operation simulation while taking into account the correlation between the upper-level and lower-level power grids. In the face of massive scenario computational demands, it can effectively reduce the running time and improve the operating efficiency. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating a long-term, hierarchical operation simulation method for power grids provided in this application;

[0040] Figure 2 A schematic diagram illustrating the specific process of a layered operation simulation method for a power grid over a long time scale, as provided in this application;

[0041] Figure 3 A flowchart illustrating the addition of transmission capacity constraints to a long-term, hierarchical power grid operation simulation method provided in this application;

[0042] Figure 4 A flowchart illustrating the rolling calculation strategy of a layered operation simulation method for a power grid over a long time scale provided in this application;

[0043] Figure 5 A flowchart of parallel computation for regional power grid simulation under massive scenarios in a hierarchical operation simulation method for a power grid over a long time scale provided in this application;

[0044] Figure 6 A schematic diagram of the topology search based on the balance node network in a hierarchical operation simulation method for a power grid over a long time scale provided in this application;

[0045] Figure 7 A schematic diagram of an open network in a long-term, hierarchical operation simulation method for a power grid provided in this application;

[0046] Figure 8 A simplified schematic diagram of an open network in a hierarchical operation simulation method for a power grid over a long time scale, as provided in this application;

[0047] Figure 9 A simplified equivalent circuit diagram of an open network in a hierarchical operation simulation method for a power grid over a long time scale, provided in this application;

[0048] Figure 10 This is a chart showing the predicted monthly electricity volume of western China to Guangdong in 2025.

[0049] Figure 11 This is a chart showing the predicted monthly electricity volume of western China to Guangdong in 2025.

[0050] Figure 12 This is a graph showing the power balance situation in Guangdong Province.

[0051] Figure 13 A comparison chart of voltage amplitude changes between Dieling 500kV substation and Huilong 500kV substation;

[0052] Figure 14 The phase difference prediction diagram for the two nodes of Dieling 500kV station and Huilong 500kV station in 2025;

[0053] Figure 15 A schematic diagram of an embodiment of a power grid long-term hierarchical operation simulation device provided in this application;

[0054] Figure 16 This is a schematic diagram of the structure of an embodiment of a long-term, hierarchical operation simulation terminal for a power grid provided in this application. Detailed Implementation

[0055] This application provides a method, apparatus, terminal, and medium for simulating the layered operation of a power grid over a long time scale, which addresses the technical problem of low simulation efficiency in existing regional power grid long-term operation simulation technologies.

[0056] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] First, this application provides a detailed description of an embodiment of a long-term, hierarchical operation simulation method for power grids.

[0058] Please see Figure 1 The embodiment of the power grid long-term scale hierarchical operation simulation method provided in this application includes:

[0059] Step 101: Obtain historical unit quotations of the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data of the grid-level power grid. Construct a regional spot market simulation model, and use the regional spot market simulation model to simulate the operation of the grid-level power grid in the region to obtain the active power generation data of the entire grid region and the active power transmission data of inter-provincial tie lines within a preset time period.

[0060] It should be noted that the long-term operation simulation problem of regional power grids in the context of massive new energy scenarios is decoupled and divided into three calculation stages: Step 101 in this embodiment corresponds to the first stage of grid-level power grid operation simulation. This stage is based on the load forecast and new energy forecast data of each province and region, the transmission and transformation maintenance plan, the whole network topology data and other power grid model parameters, and generates future unit bidding data based on historical bidding data. It carries out simulation calculation of the southern regional electricity spot market for a future preset time period. Through daily simulation and extrapolation, the active power generation data of the whole network generators and the active power transmission data of inter-provincial tie lines of the grid-level power grid are obtained.

[0061] More specifically, such as Figure 2 As shown, step 101 of this embodiment may include the following steps:

[0062] Step 1011: Obtain historical unit quotations for the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data for the entire grid-level power grid.

[0063] Step 1012: Construct a daily simulation model of the regional spot market for the grid-level power grid;

[0064] Step 1013: Solve the daily simulation model of the regional spot market using a preset model solving algorithm, and obtain the daily clearing simulation data of the grid-level power grid based on the solution results;

[0065] Step 1014: Based on the daily simulation model and model solving algorithm of the regional spot market, and combined with the preset time period, obtain the clearing simulation data of the grid-level power grid within the preset time period through rolling calculation, and determine the active power generation data of the entire grid region and the active power transmission data of inter-provincial interconnection lines within the preset time period.

[0066] It should be noted that the calculation in the first stage of this embodiment can be referred to in the following example:

[0067] A daily simulation model of the regional spot market is established. The objective function of the daily simulation model of the regional spot market is as follows:

[0068]

[0069] The objective function consists of 5 parts:

[0070] Part 1: Generating costs, i.e., the sum of the unit's quoted price and start-up costs; N represents the total number of generating units in the Southern region; T represents the total number of time periods considered; P i,t This represents the output of unit i during time period t; C i,t (P i,t ), Let C be the operating cost, startup cost, and minimum technical output cost of unit i during time period t, respectively. i,t (P i,t () is a multi-segment linear function related to the various output ranges declared by the generating unit and the corresponding energy prices; unit start-up costs It is a function related to the unit's downtime, representing the start-up cost of the unit under different conditions (cold / warm / hot); This is the minimum technical output cost considered only when the unit is in the operating state;

[0071] Part 2: Inter-provincial power transmission costs; n represents the quantity of inter-provincial power transmission components; P L,i,t P represents the transmission power of inter-provincial power transmission component i during time period t. gwf For inter-provincial power transmission fees;

[0072] Part 3: Line constraint relaxation penalty term; M1 represents the network flow constraint relaxation penalty factor used for market clearing optimization; These are the forward and reverse power flow relaxation variables for line l, respectively; NL is the total number of lines.

[0073] Part 4: Section constraint relaxation penalty term; , respectively, represent the forward and reverse current relaxation variables for section s; NS represents the total number of sections;

[0074] Part 5: Penalty for Curtailed Renewable Energy. M2 is the penalty factor for curtailed renewable energy; NH is the total number of renewable energy generating units.

[0075] The constraint equations corresponding to the daily simulation model of the aforementioned regional spot market may include:

[0076] 1) Power balance constraint equations

[0077] Consider the power balance constraint equations to ensure the balance of power supply and demand in each province and region at each time period, as shown below:

[0078]

[0079] in, N represents the output of the intranet trading unit within province a during time period t. a Total number of provinces and regions; NTa represents the planned power of the external connection line j related to province a in time period t (positive for receiving and negative for sending), and NTa is the total number of external connection lines related to province a. NTIa represents the transmission power of transaction component k within the region related to province a during time period t (default reference direction is incoming), and NTIa is the total number of transaction components within the region related to province a. Let t be the system load of province a during time period t. This includes the output of non-market units in provinces and regions.

[0080] 2) Reserve Capacity Constraint Equation

[0081] Considering the reserve capacity constraint equation, the generator is required to reserve a certain capacity for regulation to cope with load and power output fluctuations from new energy sources, thereby improving the reliability of the power grid, as shown below:

[0082]

[0083] In the formula, α i,t α represents the start-up and shutdown status of unit i during time period t. i,t =0 indicates that the unit is shut down, α i,t =1 indicates that the unit is started; This represents the reserve capacity requirement for time period t.

[0084] 3) Line power transmission constraint equations

[0085] Considering line power transmission constraints, i.e., the power transmitted through the lines cannot exceed the power limit, to ensure the safety of power grid operation, as shown below:

[0086]

[0087] In the formula, G l-i G is the generator output power transfer distribution factor from node i to line l; l-j G is the generator output power transfer distribution factor of the node where the external connecting line j is located to line l; l-d The generator output power transfer distribution factor of the node where DC tie line d is located in the region to line l. G represents the transmission power of DC tie line d within the region during time period t; K represents the number of nodes in the system; G l-k D is the generator output power transfer distribution factor from node k to line l; k,t Let be the bus load value of node k in time period t.

[0088] 4) Cross-sectional power transmission constraint equations

[0089] Considering the cross-sectional power transmission constraint, that is, the cross-sectional power transmission power cannot exceed the power limit, to ensure the safety of power grid operation, as shown below:

[0090]

[0091] In the formula, where P s min P s max These represent the power flow transmission limits at section s, respectively; G s-i G is the generator output power transfer distribution factor from node i to section s; s-j G is the generator output power transfer distribution factor from node j of the external connecting line to section s; s-d G is the generator output power transfer distribution factor from the node where DC tie line d is located to section s within the region; s-k Let be the generator output power transfer distribution factor at node k to section s.

[0092] 5) Operating characteristic constraint equations of thermal power units

[0093] Considering the operating characteristic constraint equations of thermal power units, ensure that the operation of thermal power units meets the upper / lower limits, ramp rate, and minimum start-up and shutdown time restrictions, as detailed below:

[0094] ① Upper and lower limits of unit output constraints

[0095] The unit's output should be within its maximum / minimum technical output range, and the constraint can be described as follows:

[0096]

[0097] α represents the minimum technical output of unit i in time period t. If the unit is shut down, α i,t =0, then this constraint condition can limit the unit output to 0; when the unit is started, α i,t =1, this constraint is a conventional upper and lower limit constraint for output.

[0098] ② Unit ramp rate constraint

[0099] When the unit is climbing an incline or descending an incline, the climbing rate requirement must be met. The climbing constraint can be described as follows:

[0100]

[0101]

[0102] In the formula, ΔP i ULet ΔP be the maximum ramp rate of unit i. i D Let be the maximum downhill ramp rate of unit i. The unit's ramp-up / downhill output constraints are determined by several factors: when the unit is in normal operating condition, the range of the unit's ramp-up / downhill output is determined by ΔP. i U ΔP i D The decision; when the unit is in startup, the range of the unit's output increase or decrease is determined by the unit's allowable startup rate (here it is...). The range of the unit's output increase or decrease is determined by the unit's allowable shutdown rate (here, the allowable shutdown rate is the unit's permissible shutdown rate). )Decide.

[0103] ③ Minimum continuous start-up and shutdown time constraints for generating units

[0104] Due to the physical properties and actual operational requirements of thermal power units, they are required to meet a minimum continuous start-up / shutdown time. This minimum continuous start-up / shutdown time constraint can be described as follows:

[0105]

[0106]

[0107] Where, α i,t T represents the start-up and shutdown status of unit i during time period t; U T D These are the minimum continuous start-up time and minimum continuous shutdown time of the unit; The continuous operating time and continuous shutdown time of unit i during time period t can be represented by the state variable α. i,t (i = 1 to N, t = 1 to T) can be used to represent:

[0108]

[0109]

[0110] 6) Constraint equations for the operating characteristics of hydropower units

[0111] Considering the operating characteristic constraint equations of hydropower units, ensure that the operation of hydropower units meets the upper / lower limits and power limits. The upper / lower limit constraints are no different from those of thermal power units. The power constraints for hydropower are as follows:

[0112]

[0113]

[0114] In the formula, P h,t Q represents the output of the hydropower station h during time period t. h,kLet tbgn be the electricity generated by hydropower station h in month k. k ,tend k These represent the start and end times of month k, respectively.

[0115] 7) Constraint equations for the operating characteristics of new energy units

[0116] New energy generating units participate in the market under two models: guaranteed grid connection and market-based bidding. The operational constraints under these two models are as follows:

[0117] ① Guaranteed consumption model: Prioritize clearing out, and the output of new energy projects = the predicted value of new energy.

[0118]

[0119] p w,t The power output of the wth new energy unit in time period t is to be generated by the successful bid. Let be the predicted value of the w-th new energy unit in time period t.

[0120] ② Market-based bidding model: Power output from new energy projects + power curtailment = predicted value of new energy projects

[0121]

[0122] Let represent the amount of renewable energy that the w-th renewable energy unit will forgo during time period t.

[0123] Next, a daily simulation model of the regional spot market is solved. For ultra-large-scale power grids, there are numerous lines and sections, and the safety constraints of these lines and sections are coupled with a large number of unit output variables, resulting in a dense model coefficient matrix that significantly impacts computational efficiency. To address the low clearing calculation efficiency caused by considering massive line and section constraints, such as... Figure 3 As shown, this embodiment proposes a dynamic method for adding massive line and section constraints. Specifically, when solving the optimization model, the unit combination model without considering safety constraints is solved first, then the power flow of the entire network is calculated, and the power flow of each section is checked to see if it exceeds its limit. If a new section exceeds the limit, it is added to the section safety constraint. This process is repeated iteratively until the check is passed.

[0124] The daily simulation model of the regional spot market established by the above steps is a mixed-integer linear programming model, which is solved using a mathematical programming solver, such as CPLEX, Gurobi, COPT and other mathematical programming solvers.

[0125] Next, through a rolling calculation method, the regional simulation calculation results for a preset time period are output. The rolling calculation strategy in this embodiment is as follows: Figure 4 As shown, after the annual simulation calculation of the regional power market is completed, the active power data P of all generating units in the network is output.i,t Active data of inter-provincial communication lines It is used as input for the annual power flow calculation of the provincial power grid in step 102, thereby realizing the decoupled calculation between the regional power grid and the provincial power grid.

[0126] Step 102: Based on the topology data of the provincial power grid, and combined with the active power generation data of the entire network area and the active power transmission data of the inter-provincial tie lines as the boundary conditions for the power flow calculation of the provincial power grid, construct the first power flow calculation model of the provincial power grid. Based on the first power flow calculation model, perform power flow calculation on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid within a preset time period.

[0127] It should be noted that step 102 in this embodiment corresponds to the provincial power grid operation simulation stage in the second stage. In this stage, based on the active power generation plan of the whole grid generators and the active power transmission plan of the inter-provincial tie lines obtained in the previous stage, reactive power is reasonably configured to construct the AC power flow calculation data of the provincial power grid at all times. The power flow parameters of the provincial power grid AC power flow operation in the future preset time period are obtained through AC power flow calculation.

[0128] More specifically, such as Figure 2 As shown, step 102 of this embodiment may include the following steps:

[0129] Step 1021: Based on the topology data of the provincial power grid, and combined with the active power generation data of the entire network area and the active power transmission data of inter-provincial tie lines as the boundary conditions for the power flow calculation of the provincial power grid, construct the first power flow calculation model of the provincial power grid.

[0130] Step 1022: Based on the first power flow calculation model, set the reactive power constraints of the generator nodes in the provincial power grid to be unrestricted, perform power flow calculations on each node in the provincial power grid, and obtain the first power flow calculation results.

[0131] Step 1023: If the power flow convergence of the first power flow calculation result does not meet the preset convergence condition, then filter the first power flow calculation result and reset the reactive power constraints of each generator node. If the power flow convergence of the first power flow calculation result meets the preset convergence condition, then statistically analyze the first power flow calculation result to determine the unarranged reactive power of the generator node. Combined with the configuration information of the reactive power compensation node, determine the reactive power compensation value of each generator node according to the reactive voltage sensitivity of each generator node to all reactive power compensation nodes.

[0132] Step 1024: Based on the first power flow calculation model of the generator node with reactive power compensation, perform power flow calculation on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid.

[0133] It should be noted that the active power plan of the main grid units and the power plan of the inter-provincial tie lines obtained from the regional spot market simulation in step 101 are used as boundary conditions for the annual power flow calculation of the provincial power grid. Through network topology model construction, reactive power and voltage configuration, etc., complete AC power flow calculation data are generated.

[0134] The specific execution process is as follows:

[0135] Read the basic full-start mode data to obtain the network topology model. Based on generator model card, governor card type, and other generator parameter information, distinguish the unit type: hydropower, thermal power, nuclear power, wind power, photovoltaic, etc.

[0136] Read the active power output P of the entire network generator units obtained from the regional spot market simulation. i,t and the active transmission power of inter-provincial connecting lines It obtains data on the output of each major unit, the total output of other units, the total load of each region, the total output of wind power, and the total output of photovoltaic power under various active power operation scenarios.

[0137] For power flow data that does not converge, the power flow calculation is made to converge by relaxing the reactive power limit constraint of the generator, obtaining the unarranged reactive power of the node with relaxed reactive power constraint, and then adjusting the relevant reactive power compensation with the highest sensitivity according to the reactive power voltage sensitivity, and regenerating the mode data after adjusting the reactive power compensation.

[0138] The specific execution process is as follows:

[0139] Read the power flow data (.dat), modify all generator nodes to BE nodes with unrestricted reactive power, generate new data, and perform power flow calculations using power system AC power flow calculation software (such as PSD-BPA software from China Electric Power Research Institute, or DPS software from China Southern Power Grid Research Institute).

[0140] If the power flow does not converge, the method is invalid and should be skipped; if the power flow converges (the majority of data can converge), then proceed with the following steps:

[0141] Read the unallocated reactive power list from the power flow calculation results of the generator with unrestricted reactive power, and obtain the size of the unallocated reactive power of the generator.

[0142] Read the pre-organized table of low-capacity and low-resistance configuration of the power grid to obtain information such as the reactive power compensation nodes, their capacity, and number of groups for the entire network.

[0143] Calculate the reactive voltage sensitivity of each unassigned reactive generator node to all reactive compensation nodes, sort them according to the sensitivity, convert the unassigned reactive value of the generator node into the reactive compensation value of the reactive compensation node with the higher sensitivity, and eliminate the BE node.

[0144] Regenerate the data for the adjusted reactive power compensation method.

[0145] For each active operation scenario (8760 scenarios throughout the year), perform the following steps to generate method data.

[0146] Regional total load splitting to node rules: Based on the initial active load relationship of each node in the basic data, the regional total active load is allocated proportionally; the node reactive load is adjusted according to the principle of keeping the ratio of node reactive load to active load unchanged.

[0147] The power allocated to the nodes is integrated with the network topology model to generate data on the unadjusted reactive power compensation method.

[0148] For data on reactive power compensation without adjustment, perform DC power flow calculations. Read the DC power flow calculation results to obtain the active power flow distribution, and record the sum of active power flow from all outgoing lines at each node, as well as the sum of rated capacity (converted to rated current for transmission lines).

[0149] Read the pre-organized table of low-capacity and low-resistance power grid configuration and the mapping table of reactive power compensation nodes and their control nodes (the control node of reactive power compensation is usually the high-voltage side node of the three-winding transformer). Based on the active power flow load of the reactive power compensation control node (the sum of the absolute values ​​of the active power flow of all outgoing lines / the sum of the rated capacity of all outgoing lines), determine the amount of reactive power compensation.

[0150] Regenerate the data for the adjusted reactive power compensation method.

[0151] Step 103: Based on the correlation between the provincial power grid topology and the regional power grid topology, determine the common nodes in the provincial power grid and the regional power grid. Use the power flow parameters corresponding to the common nodes as the boundary conditions for the power flow calculation of the regional power grid. Combine the nodes of each regional power grid to construct the second power flow calculation model of each regional power grid. By solving each second power flow calculation model, the operation simulation results of each regional power grid within the preset time period can be obtained.

[0152] It should be noted that step 103 in this embodiment corresponds to the third stage of regional power grid operation simulation. This stage is based on the power flow parameters of the provincial power grid AC power flow operation obtained in the previous stage. After generating the wind, solar and load time series of the corresponding region, the proposed method for simulation and analysis evaluation of regional power grid operation under massive scenarios is used for simulation and analysis.

[0153] More specifically, such as Figure 2 As shown, step 103 of this embodiment includes the following steps:

[0154] Step 1031: Based on the correlation between the provincial power grid topology and the regional power grid topology, determine the common nodes in the provincial power grid and the regional power grid;

[0155] Step 1032: Using the power flow parameters corresponding to the common nodes as the boundary conditions for power flow calculation of the regional power grid, and combining the load data and time series of the new energy nodes in the same regional power grid, construct multiple single prediction scenario power flow calculation models according to the type of new energy nodes. Then, combine multiple preset new energy node operation scenarios to form a second power flow calculation model of the regional power grid. By solving each of the second power flow calculation models, the operation simulation results of each regional power grid within the preset time period can be obtained.

[0156] It should be noted that after the annual power flow calculation of the provincial power grid is completed, the voltage and phase of all nodes in the provincial power grid topology are output as power flow parameters. The nodes that coexist in the provincial power grid topology and the regional power grid topology are compared, and the voltage and phase of these nodes are used as inputs for the regional power grid operation simulation in step 103, thereby realizing the decoupled calculation of the regional power grid and the provincial power grid.

[0157] After identifying the nodes that coexist in both the provincial and regional power grid topologies (i.e., common nodes), the active and reactive power flow distributions of the provincial power grid nodes, obtained from the AC power flow operation calculation of the previous provincial power grid, are used as boundary conditions for the regional power grid power flow calculation. Combined with load data and time series from renewable energy nodes within the same regional power grid, multiple single-prediction scenario power flow calculation models are constructed. The load modeling and time series simulation are as follows:

[0158] Active load: Historical data was collected, and normalized load curves were established for four quarters and three different types: weekdays, weekends, and holidays. Simultaneously, the variance of power samples at each hour of the day was calculated to establish corresponding normal distribution models. Monthly statistics of daily maximum load were compiled to generate a continuous load curve, which served as model parameters.

[0159] For reactive load, calculate the load power factor at each hour based on the above typical days to form the mean and variance of the normal distribution model.

[0160] After modeling is complete, node load sequences of arbitrary length can be generated randomly, as follows:

[0161] (1) Generate the daily maximum load and generate the daily active power curve mean sequence according to the normalized curve corresponding to the calendar.

[0162] (2) Sample the active power deviation for each time period according to the corresponding normal distribution parameters and superimpose it with the mean sequence to generate the daily active power load simulation curve.

[0163] (3) Sample the power factor for each time period according to the corresponding power factor normal parameters. Based on the active load simulation value P for the corresponding time period, calculate the reactive power:

[0164] Single-scenario renewable energy output data prediction:

[0165] (1) Wind power modeling

[0166] The original power sample set was divided into segments with a basic time unit of 4 hours. For each small sample, its standard deviation (std) and mean (mean) were calculated, forming an (N,2) feature matrix, where N is the number of small samples. The points in this matrix were then subjected to K-means clustering, with the cluster centers set to 3.

[0167] Based on the clustering results, the joint distribution between each pair of wind power stations is statistically analyzed to obtain the joint distribution matrix. The entropy of each matrix is ​​then calculated using the following formula:

[0168] E = p ij ln∑p ij

[0169] In the formula, E represents the entropy of the matrix, p ij This represents the probability that mode i and mode j occur simultaneously in the current joint distribution matrix.

[0170] The magnitude of this entropy value describes the strength of the correlation between wind farms. A higher entropy indicates a more concentrated pattern and stronger correlation between the two wind farms. Multiple strongly correlated wind farms can form a wind farm cluster for subsequent Markov chain state transition modeling. Wind farms weakly correlated with other wind farms can have their daytime state transition models established independently.

[0171] Based on the k-means clustering results, a power matrix S(N,16) is formed for a specific cluster, where N represents the number of samples in that mode. The average power row M(1,16) is calculated row-wise. Then, the average power row M is subtracted from each row of the power matrix S to obtain the volatility matrix F(N,16). All elements in the volatility matrix are divided into positive and negative volatility databases, resulting in six volatility databases across the three cluster centers.

[0172] The initial state of each wind farm is established based on historical data, and the annual wind power curve can be generated based on the Markov daytime state transition model.

[0173] (2) Photovoltaic modeling

[0174] The average sunrise and sunset times are determined based on the latitude and longitude of the photovoltaic power station's location. The photovoltaic active power output of the area for that month is normalized and categorized for each time point. K-means clustering is used to perform cluster analysis on the data at each time point, and three cluster centers are set to reflect three typical weather conditions: sunny, cloudy, and rainy.

[0175] The fluctuations in each cluster state are calculated based on the cluster centers of the fitting results to simulate the phenomenon of cloud formation, resulting in a fluctuation database of three cluster states. The distribution of these fluctuations is then fitted using kernel density.

[0176] We statistically analyzed the weather transition patterns of all photovoltaic power stations. Each weather mode transition was defined in 1-hour time units. Based on the results of K-means clustering, we calculated the frequency of weather mode transitions between windows according to the cluster of the first data point in each window. This frequency was then used as the probability of the transition.

[0177] The initial weather conditions for the whole year are established based on historical data, and the weather mode sequence for the whole year is generated based on the weather Markov transition matrix, thereby generating the annual photovoltaic power curve.

[0178] The following is an example of generating a new energy node operation scenario:

[0179] The steps for generating massive photovoltaic power output scenarios based on historical data are as follows:

[0180] Input the latitude and longitude of the region and the month to calculate the sunrise and sunset times for that region. Input the duration of the simulation.

[0181] An initial weather mode is randomly selected, and a weather mode sequence is generated based on the time length and the statistically obtained weather Markov transition matrix.

[0182] Based on the weather modal sequence and K-means clustering results, the baseline power curve for this sequence is obtained.

[0183] Based on the data fluctuation library of different weather sequences, fluctuation data is sampled and superimposed on the baseline power curve to complete the scenario simulation.

[0184] The steps for generating massive wind power output scenarios based on historical data are as follows:

[0185] Set the simulation duration and randomly set the initial state of each site in the wind farm cluster.

[0186] Based on the multivariable Markov diurnal transition matrix, the power mode sequence of each station is generated.

[0187] Based on the modal sequence and its corresponding average power curve, a reference power curve for each station is generated.

[0188] Based on the fitted data fluctuation library, fluctuation data of equal length are extracted and superimposed on the benchmark power curve to complete the scenario simulation.

[0189] Based on the data generated in the first two sections, the annual power flow calculation parameters are combined with a large number of renewable energy operation scenarios to form large-scale power flow calculation cases under renewable energy operation scenarios. For example, if there are N renewable energy operation scenarios, then there are 8760 × N power flow calculation cases. Each case includes the following data: regional power grid network topology, node load forecast, generator parameters, renewable energy forecast, and equipment maintenance plan.

[0190] Next, examples of solving and outputting the second power flow calculation model are provided, including: based on the power flow calculation cases under the massive renewable energy operation scenarios generated in the previous section, parallel computing strategies can be used to achieve parallel computing of grid simulation operation under massive scenarios, such as... Figure 5 As shown, the specific process is as follows:

[0191] Data preparation: Acquire power flow calculation case data in a large number of new energy operation scenarios to prepare for power flow calculation in a large number of cases;

[0192] CPU resource allocation: Obtain server parameters and set parallel computing strategies based on the number of CPU threads;

[0193] Assuming there are M CPU threads, M1 threads are needed to maintain the operating system, and N scenarios need to be solved, then the number of power flow calculation cases that need to be run under each CPU thread is:

[0194] Parallel computing: The power flow calculation module for a single scenario is executed in a loop under each thread until all calculation cases assigned under that process are completed.

[0195] The results were statistically analyzed.

[0196] For an example of how to solve the power flow calculation model for a single prediction scenario, please refer to the following example:

[0197] 1) Open-loop decoupling of ring networks:

[0198] In the operation of the regional power grid in the power system, the 110kV power grid operates in an open-loop manner. The constructed system topology diagram shows a ring network phenomenon. To achieve open-loop decoupling in subsequent open-loop power flow calculations, the following method is adopted:

[0199] In accordance with the corresponding zoning plan in the power system operation plan, the corresponding network closed-loop lines are taken out of operation.

[0200] Modify the operating status of the corresponding closed-loop lines in the network according to the changes in the network during real-time operation of the power system.

[0201] Further analysis of the station's overload situation will be conducted, and the operation of the station's connecting lines will be modified by allocating the station's load suspension zones.

[0202] 2) Open network power flow calculation:

[0203] (1) 110KV network partitioning and network tree topology analysis

[0204] Based on the above-mentioned open-loop decoupling process for the ring network, the 110KV network is divided into several partitions, and open-loop power flow calculations are performed on each partition.

[0205] Tree search: Identify the 220kV substation nodes in each zone, and set the 110kV nodes in those substations as balancing nodes. Starting from the balancing nodes, search for substation nodes outwards, such as... Figure 6 As shown, all topology information can be obtained in 3 searches.

[0206] (2) Simplification and Equivalence of Open Networks

[0207] The downstream circuits of node A are b, c, and d. The voltage and power of nodes b, c, and d are known. Starting from the terminal node d, calculate the power loss upwards along the line and add the power to the upstream circuit.

[0208] The network equivalent circuit is simplified by adding half the charging power of that segment to the nodes at the beginning and end of each line segment. This simplification can be achieved as follows: Figure 7 The equivalent circuit shown is simplified to Figure 8 The equivalent circuit is shown. For example, the power at node c becomes...

[0209] Where P LDc and Q LDc To simplify the active and reactive loads of node c before the equivalent line is established, U c S is the voltage at node c, B2 and B3 are the susceptances of the lines on both sides of node c, and S is the voltage at node c. c '、P c and Q c Let C represent the equivalent load, equivalent active load, and equivalent reactive load of node C after the simplification of the equivalent line.

[0210] After simplifying the equivalent circuit, the power distribution calculation of the open network proceeds from the end to the upper-level network, with each line calculated as follows:

[0211] Each line is simplified as described above, as follows: Figure 9 As shown, the line loss is The line start point S1 = S2 + ΔS.

[0212] Where R and X are the resistance and reactance of the line, U2 is the voltage of the lower node of the line, P2 and Q2 are the equivalent active power and reactive power of the lower node of the line, ΔS is the power loss of the line, and S1 and S2 are the equivalent line transmission power of the upper and lower levels of the line.

[0213] (3) Power flow calculation method for open networks

[0214] In each area, the 110kV nodes under the 220kV substation are set as balancing nodes, and the nodes below them are set as PQ nodes. The initial voltage of the lower nodes is set to the reference voltage value (the 110kV balancing node is set to 110kV).

[0215] Starting from the end of the tree network, the line loss and transmission power are calculated progressively upwards. When calculating to the slack node, the voltage drop is calculated downwards from the slack node, and the voltages of all nodes below the slack node are obtained and reassigned. The above forward and backward calculation steps are repeated to improve the accuracy of the system calculation, and finally the power flow calculation results of the open network are obtained.

[0216]

[0217]

[0218] Where U1 and U2 are the voltages of the upstream and downstream nodes of the line, R and X are the resistance and reactance of the line, and P' and Q' are the equivalent line transmission power of the upstream node of the line.

[0219] Based on the above solutions, the following statistical results are obtained:

[0220] (1) Under the statistical forecast scenario, the active power transmission power of power grid lines in all regions throughout the year, and the line operating power status (light load, heavy load, reaching the limit, exceeding the limit);

[0221] (2) Under the statistical forecast scenario, the active power transmission of all regional power grid sections throughout the year, and whether the operating status of the section has reached or exceeded the limit;

[0222] (3) Under the statistical forecast scenario, the voltage of all power grid nodes in all regions throughout the year, and the number of nodes that exceed the power grid's safe operating voltage limit throughout the year;

[0223] (4) Under the statistical forecast scenario, the transformer load rate of the power grid in all regions throughout the year, and the transformer load operation status (light load, heavy load, full load, overload);

[0224] (5) Statistically analyze the probability and magnitude of power grid line overload in all regions under massive scenarios;

[0225] (6) Statistically analyze the probability of all regions' power grid sections reaching the limit, the probability of exceeding the limit, and the magnitude of the limit under massive scenarios;

[0226] (7) Statistically analyze the probability and magnitude of voltage exceedance in all regions of the power grid under massive scenarios;

[0227] (8) Statistical analysis of the probability and magnitude of power grid transformer overload in all regions under massive scenarios.

[0228] The above is a detailed description of an embodiment of a long-term, hierarchical operation simulation method for power grids provided in this application. To more clearly demonstrate the effectiveness of the technical solution of this application, this application also provides experimental explanations based on real-world examples, as follows:

[0229] Based on the 2025 China Southern Power Grid planning data, a simulation calculation of the Yangjiang power grid operation in 2025 was conducted. The calculation was carried out in three stages: grid-level power grid operation simulation, provincial power grid operation simulation, and regional power grid operation simulation. The calculation results are analyzed below:

[0230] 1) Analysis of grid-level power grid operation simulation results:

[0231] (1) Optimization status of Xidian University

[0232] like Figure 10 and Figure 11 As shown, through simulation and calculation, the simulated power transmission volume of the West-to-East Power Transmission Project in 2025 is 246.295 billion kWh, an increase of 24.788 billion kWh compared to the annual plan (the annual plan is 221.507 billion kWh), achieving a completion rate of 111.19%. Among them:

[0233] The simulated annual power transmission volume from western China to Guangdong (receiving end) is 204.93 billion kWh, an increase of 14.098 billion kWh compared to the annual plan (the annual plan is 190.833 billion kWh). The annual power transmission volume from western China to Guangdong has achieved 107.39% of the target.

[0234] The simulated annual power transmission from western China to Guangxi (receiving end) is 38.445 billion kWh, an increase of 7.77 billion kWh compared to the annual plan (the annual plan is 30.674 billion kWh). The annual power transmission rate from western China to Guangxi is 125.33%.

[0235] The simulated annual power generation for the transmission of electricity from western China to Hainan (receiving end) is 2.92 billion kWh, which is 2.92 billion kWh more than the annual planned value (the annual plan was 0 billion kWh).

[0236] Yunnan's simulated annual power transmission volume was 153.822 billion kWh, an increase of 4.16 billion kWh compared to the annual plan (the annual plan was 145.205 billion kWh), and the completion rate of Yunnan's power transmission volume was 105.93%.

[0237] The simulated annual power transmission volume from Guizhou was 41.97 billion kWh, which was 1.243 billion kWh less than the annual plan (the annual plan was 43.031 billion kWh). The power transmission volume of Guizhou achieved 97.54% of the target.

[0238] (2) Power balance on a typical day:

[0239] The peak load across the entire China Southern Power Grid occurred on August 7th. The power balance situation in Guangdong is as follows: Figure 12 As shown.

[0240] 2) Analysis of provincial power grid operation simulation results:

[0241] (1) Power flow distribution of Guangdong power grid in 2025 during period 8760

[0242] Calculation Overview: Of the 8760 generated power flow calculation methods, 8750 achieved direct convergence, resulting in a convergence rate of 99.88%. The remaining 10 methods failed to converge due to insufficient reactive power configuration in the Shenzhen area. After manually adding reactive power compensation, all methods achieved convergence. The average calculation time for each method was approximately 2.5 seconds, with a total calculation time of 6 hours.

[0243] Network loss situation: The average network loss of Guangdong power grid in 2025 is about 1552MW, with the minimum network loss being 289MW (5:00 on January 2) and the maximum network loss being 2733.5MW (11:00 on October 9).

[0244] Power flow distribution under typical conditions in the Guangdong power grid:

[0245] According to the forecast, the peak load of the Guangdong power grid in 2025 is expected to occur at noon on July 13, with the system's active load being approximately 175,000 MW.

[0246] Small-scale data: The minimum load of Guangdong power grid in 2025 is expected to occur at 5:00 AM on January 2, with the system active load at approximately 19,350 MW.

[0247] (2) Operation status of 500kV substations in Yangjiang power grid

[0248] In the 500kV grid, the Yangjiang regional power grid is connected to the Guangdong power grid via the Dieling and Huilong substations. To achieve decoupled calculations between the regional power grid and the main grid, it is necessary to export the voltage amplitude and phase information of the Dieling and Huilong substations as input for the next stage of regional power grid operation simulation.

[0249] From Table 1-1 and Figure 13 and Figure 14 It can be seen that the average voltage amplitude of 8760 modes at Dieling and Huilong throughout the year was 525.9kV and 528.47kV, respectively. The maximum positive and negative voltage deviation rates of Dieling were 6.42% and -5.19%, respectively, while those of Huilong were 6.5% and -3.5%, respectively. The average voltage phase difference between the two stations was 1.7.

[0250]

[0251] Table 1-1 Voltage Amplitude and Phase at Dieling Station and Huilong Station

[0252] 3) Analysis of regional power grid operation simulation results:

[0253] 1) The project involved reading and analyzing the power grid topology in the Yangjiang area. A Python-based program was developed to read the BPA power flow calculation data from the Guangdong power grid and automatically generate the topology, parameters, and open-loop operation modes of each level of the Yangjiang power grid. The resulting 220kV power grid topology map of Yangjiang formed a complex ring network structure.

[0254] After setting the open-loop point according to the operation mode of the Yangjiang power grid, a normally operating open-loop network can be formed.

[0255] 2) Analysis of the simulation results of the Yangjiang power grid operation under the massive new energy scenario: The power flow calculation method of open network was used to perform power flow calculations on eight areas in the Yangjiang region: Yangjiang, Pingdi, Monan, Lingxiao, Denggao, Chuncheng, Bajitou and Qiguling.

[0256] The time points are set to one point per hour, simulating 8760 time points in one year in 2022, with the time set from 0 to 8759.

[0257] (1) Voltage operation status:

[0258] Regarding the operation of the eight zones, the node voltage in each zone is maintained at a level of (108-112KV).

[0259] In the Bajitou area, the voltage amplitude in Shuangyu and Shucun reached a maximum of 112KV in May, while the voltage amplitude in Zhigong, Yong'an and Changxing was the lowest at 109KV. No voltage over-limit situations were found in any of them.

[0260] In the Chuncheng area, the voltage amplitude at Zhonggang reached a maximum of 112KV in April, while the lowest overall voltage amplitude occurred at 109KV, indicating no voltage exceeding the limit.

[0261] Denggao area: The voltage is maintained at 110KV, and the area is operating stably.

[0262] Lingxiao area: The voltage amplitude of Hailuo reached a minimum of 108KV in December.

[0263] In the southern part of the desert: the voltage amplitude in Baisha reached a low of 108KV in June.

[0264] In flat areas: Due to the fact that Leiping Wind Power, Lingnan Wind Power, Xinye Photovoltaic, and Huayu Photovoltaic are power generation stations, the maximum voltage amplitude reaches 114KV.

[0265] Qiguling area: The voltage level is maintained at 110KV.

[0266] In the Yangjiang area, there were numerous voltage anomalies at Yidong, Suidong, and Beiguan substations. The lowest voltage levels at these three substations were 109-110 kV, which is considered normal. The highest voltage amplitude at Beiguan and Suidong substations reached 114.7 kV in the first month, with Yidong substation experiencing the highest amplitude of 119 kV in the first month. Observation of voltage exceedances at Yidong substation (1.05) revealed 538 instances of voltage exceedances at different time points, primarily concentrated in the first three months.

[0267] (2) Heavy overload conditions at 220KV substations:

[0268] Bajitou area: The Bajitou 220kV substation has two sets of three-winding transformers with a main transformer capacity of 360MW. Overload occurred at 7 points in the Bajitou area, and heavy load (exceeding 80%, with subsequent heavy loads being the same) occurred at 112 points. The highest load reached 415kV, occurring at noon on the first day of April. The remaining overload events were concentrated around noon on the following days. Heavy loads occurred in every month, also around noon.

[0269] Chuncheng Area: The 220KV substation in the Chuncheng area has two sets of three-winding transformers with a main transformer capacity of 330MW. No overload occurred in the Chuncheng area. There were 55 instances of heavy load, mainly occurring from October to December, primarily around midnight.

[0270] Denggao Area: The 220kV substation in the Denggao area has a set of three-winding transformers with a main transformer capacity of 180MW. Overload occurred frequently in the Denggao area, with 844 overload times and 2994 heavy load times, indicating a serious situation of severe overload.

[0271] Lingxiao Area: The 220kV substation in the Lingxiao area has two sets of three-winding transformers with a main transformer capacity of 360MW. No severe overload has occurred in the Lingxiao area.

[0272] Monan Area: The Monan area 220kV substation has two sets of three-winding transformers with a main transformer capacity of 360MW. No overload occurred in the Monan area, but there were 125 instances of heavy load, mainly during daytime periods from October to December.

[0273] Pingdi Area: The Pingdi substation has one three-winding transformer in operation and two not in operation. The total capacity of the main transformers in operation is 180MW, and the total capacity of the main transformers not in operation is 360MW. The maximum load in the Pingdi area is 484MW.

[0274] Qiguling area: There is one three-winding transformer in the Qiguling area, with a main transformer capacity of 180MW. The overload situation is relatively serious.

[0275] Yangjiang area: There are three three-winding transformers in Yangjiang area, with a main transformer capacity of 540MW. There are no overload situations in Yangjiang area. There are 461 instances of heavy load, mainly concentrated during daytime hours.

[0276] (3) Overload conditions of 110KV lines:

[0277] In the Bajitou area: the line load rate between Bajitou and Shuangyu reaches a maximum of 53%, the line load rate between Yanziling Photovoltaic and Shuangyu reaches a maximum of 58%, and the line load rate between Nongken Photovoltaic and Weihe reaches a maximum of 64%.

[0278] In the Kunming area, the maximum line load rate is below 30%.

[0279] In high-altitude areas: the line with the highest load rate is the line between Eling and Zhapo, with a maximum load rate of 33%.

[0280] In the Lingxiao area: the line load rate between Lingxiao and Hailuo reaches a maximum of 52%, the line load rate between Guangyan Photovoltaic and Heshui reaches a maximum of 46%, and the maximum load rate of the remaining lines is relatively low.

[0281] In the southern part of the region, the maximum line load rate is less than 40%, with the maximum line load rate between southern and Yinhe being 39%.

[0282] Pingdi area: Some lines in the Pingdi area have excessive load rates. The line between Pingdi and Hailang has the highest load rate, the line between Pingdi and Hailang has the highest load rate at 91.8%, the line between Pingdi and Dagou has the highest load rate at 89%, and the line between Xinye Photovoltaic and Dagou has the highest load rate at 95.8%.

[0283] Qiguling area: The maximum load rate of the lines in the Qiguling area is about 50%, namely Qiguling and Hekou, Jinbao Photovoltaic and Hekou.

[0284] Yangjiang area: The line load rate in Yangjiang area reaches a maximum of 93%, the line load rate between Yangjiang and Suidong reaches a maximum of 93%, and the line load rate between Suidong and Yidong reaches a maximum of 70%.

[0285] The above is a detailed description of an embodiment of a long-term tiered operation simulation method for a power grid provided in this application. The following is a detailed description of an embodiment of a long-term tiered operation simulation device, terminal, and computer-readable storage medium for a power grid provided in this application.

[0286] Please see Figure 15 This application provides an embodiment of a tiered operation simulation device for a power grid over a long time scale, comprising:

[0287] The grid-level power grid simulation unit 201 is used to acquire historical unit quotations of the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data of the grid-level power grid. It constructs a regional spot market simulation model and uses the regional spot market simulation model to simulate the operation of the grid-level power grid in the region to obtain the active power generation data of the entire grid region and the active power transmission data of inter-provincial tie lines within a preset time period.

[0288] The provincial power grid simulation unit 202 is used to construct the first power flow calculation model of the provincial power grid based on the topology data of the provincial power grid, combined with the active power generation data of the whole network area and the active power transmission data of the inter-provincial tie line as the boundary conditions for the power flow calculation of the provincial power grid. Based on the first power flow calculation model, the power flow calculation is performed on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid within a preset time period.

[0289] The regional power grid simulation unit 203 is used to determine the common nodes in the provincial power grid and the regional power grid based on the correlation between the provincial power grid topology and the regional power grid topology. The power flow parameters corresponding to the common nodes are used as the boundary conditions for the power flow calculation of the regional power grid. Combined with the nodes of each regional power grid, a second power flow calculation model of each regional power grid is constructed. By solving each second power flow calculation model, the operation simulation results of each regional power grid within a preset time period can be obtained.

[0290] Please see Figure 16 This application provides an embodiment of a tiered operation simulation terminal for a power grid over a long time scale. The terminal type includes, but is not limited to, personal computers, industrial computers, servers, and embedded intelligent devices. The main components of the terminal include a memory 33 and a processor 31, which can be connected via a communication bus 34.

[0291] The memory is used to store program code corresponding to the tiered operation simulation method for power grids over long time scales provided in the first aspect of this application;

[0292] The processor is used to execute program code stored in memory.

[0293] The fourth aspect of this application provides a computer-readable storage medium storing program code corresponding to the long-term hierarchical operation simulation method for power grids provided in the first aspect of this application.

[0294] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the terminals, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0295] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device 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 an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0296] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0297] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0298] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0299] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0300] If the integrated unit is implemented as a software functional unit 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 the present 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 a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present 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.

[0301] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 this application.

Claims

1. A method for tiered operation simulation of power grids over long time scales, characterized in that, include: Historical unit quotations of the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data of the grid-level power grid, are obtained. A regional spot market simulation model is constructed, and the operation of the grid-level power grid in the region is simulated through the regional spot market simulation model to obtain the active power generation data of the entire grid region and the active power transmission data of inter-provincial tie lines within a preset time period. Based on the topology data of the provincial power grid, and combined with the active power generation data of the entire network area and the active power transmission data of the inter-provincial tie lines as the boundary conditions for the power flow calculation of the provincial power grid, a first power flow calculation model of the provincial power grid is constructed. Based on the first power flow calculation model, power flow calculation is performed on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid within the preset time period. Based on the correlation between the provincial power grid topology and the regional power grid topology, common nodes in the provincial power grid and the regional power grid are determined. The power flow parameters corresponding to the common nodes are used as boundary conditions for power flow calculation of the regional power grid. Combined with the nodes of each regional power grid, a second power flow calculation model for each regional power grid is constructed. By solving each second power flow calculation model, the operation simulation results of each regional power grid within the preset time period are obtained.

2. The method for tiered operation simulation of power grid over a long time scale according to claim 1, characterized in that, The process involves acquiring historical unit quotations for the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data. A regional spot market simulation model is then constructed. This model is used to simulate the regional operation of the grid-level power grid, yielding the grid-wide active power generation data and inter-provincial tie-line active power transmission data for a preset time period. Specifically, this includes: Obtain historical unit quotations for the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data of the grid-level power grid; Construct a daily simulation model of the regional spot market for the aforementioned grid-level power grid; The daily simulation model of the regional spot market is solved using a preset model solving algorithm, and the daily clearing simulation data of the grid-level power grid is obtained based on the solution results. Based on the daily simulation model and model solving algorithm of the regional spot market, and combined with a preset time period, the clearing simulation data of the grid-level power grid within the preset time period is obtained through rolling calculation, and the active power generation data of the entire grid region and the active power transmission data of inter-provincial interconnection lines of the grid-level power grid within the preset time period are determined.

3. The method for tiered operation simulation of power grid over a long time scale according to claim 1, characterized in that, Based on the first power flow calculation model, power flow calculations are performed on each node within the provincial power grid to obtain the power flow parameters of each node within the provincial power grid, specifically including: Based on the first power flow calculation model, the reactive power constraints of the generator nodes in the provincial power grid are set to be unrestricted, and power flow calculations are performed on each node in the provincial power grid to obtain the first power flow calculation results. If the power flow convergence of the first power flow calculation result does not meet the preset convergence condition, then the first power flow calculation result is filtered and the reactive power constraints of each generator node are reset. If the power flow convergence of the first power flow calculation result meets the preset convergence condition, then the unarranged reactive power of the generator node is determined by statistically analyzing the first power flow calculation result. Combined with the configuration information of the reactive power compensation node, the reactive power compensation value of each generator node is determined according to the reactive voltage sensitivity of each generator node to all reactive power compensation nodes. Based on the first power flow calculation model of the generator node with reactive power compensation, power flow calculation is performed on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid.

4. The method for tiered operation simulation of power grid over a long time scale according to claim 1, characterized in that, Based on the nodes of the same regional power grid, and using the power flow parameters corresponding to the common nodes as boundary conditions for power flow calculation in the regional power grid, the second power flow calculation model for each regional power grid is constructed, specifically including: Using the power flow parameters corresponding to the common nodes as boundary conditions for power flow calculation of the regional power grid, and combining the load data and time series of new energy nodes in the same regional power grid, multiple single-prediction scenario power flow calculation models are constructed according to the type of new energy nodes. Then, combined with multiple preset new energy node operation scenarios, a second power flow calculation model of the regional power grid is formed.

5. The method for tiered operation simulation of a power grid over a long time scale according to claim 4, characterized in that, The process of solving each of the second power flow calculation models to obtain the simulation results of the power grid operation in each region within the preset time period specifically includes: By using a multi-threaded parallel processing method, the power flow calculation models for each single operating scenario in the second power flow calculation model are solved respectively. Then, the solution results of the power flow calculation models for each single operating scenario are summarized to obtain the operation simulation results of the regional power grid. Then, the power flow calculation models for each single operating scenario in the remaining second power flow calculation models are solved in turn to obtain the operation simulation results of the regional power grid within the preset time period.

6. The method for tiered operation simulation of a power grid over a long time scale according to claim 5, characterized in that, Solving the power flow calculation model for each single operating scenario in the second power flow calculation model specifically includes: Based on the node topology network corresponding to the power flow calculation model for a single operating scenario, the node topology network is divided into several partitions according to the network voltage level. Based on the node voltage level, the balancing node is determined from each partition, and the node is searched outward from the balancing node as the center. When the number of search layers reaches the preset layer threshold, multiple open network topologies are output. The power flow calculation results of each open network topology are obtained by using the preset open network power flow calculation formula, and are used as the solution results of the power flow calculation model of the single operating scenario.

7. The method for tiered operation simulation of power grid over a long time scale according to claim 1, characterized in that, The power flow parameters specifically include voltage amplitude and phase.

8. A tiered operation simulation device for power grids over a long time scale, characterized in that, include: The grid-level power grid simulation unit is used to acquire historical unit quotations of the grid-level power grid, as well as the grid-wide topology data, transmission and transformation maintenance plan data, load forecast data, and power generation equipment forecast data of the grid-level power grid. It constructs a regional spot market simulation model and uses the regional spot market simulation model to simulate the operation of the grid-level power grid in the region to obtain the active power generation data of the entire grid region and the active power transmission data of inter-provincial tie lines within a preset time period. The provincial power grid simulation unit is used to construct a first power flow calculation model of the provincial power grid based on the topology data of the provincial power grid, combined with the active power generation data of the entire network area and the active power transmission data of the inter-provincial tie lines as boundary conditions for the power flow calculation of the provincial power grid. Based on the first power flow calculation model, the power flow calculation is performed on each node in the provincial power grid to obtain the power flow parameters of each node in the provincial power grid within the preset time period. The regional power grid simulation unit is used to determine the common nodes in the provincial power grid and the regional power grid based on the correlation between the provincial power grid topology and the regional power grid topology. The power flow parameters corresponding to the common nodes are used as the boundary conditions for the power flow calculation of the regional power grid. Combined with the nodes of each regional power grid, a second power flow calculation model of each regional power grid is constructed. By solving each second power flow calculation model, the operation simulation results of each regional power grid within the preset time period are obtained.

9. A tiered operation simulation terminal for power grids over a long time scale, characterized in that, include: Memory and processor; The memory is used to store program code corresponding to the long-term hierarchical operation simulation method for power grids as described in any one of claims 1 to 7; The processor is used to execute the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code corresponding to the long-term, hierarchical operation simulation method for power grids as described in any one of claims 1 to 7.

Images