Simulation method, apparatus and terminal for long-time-scale hierarchical operation of power grid, and medium

Abstract

A simulation method, apparatus and terminal for long-time-scale hierarchical operation of a power grid, and a medium. In the technical solution provided in the present application, on the basis of a hierarchical subordination relationship, simulation of a large-scale power grid is divided into three levels: a national level, a provincial level, and a prefectural level; by means of a hierarchical decoupling method, operation simulation of an ultra-large-scale and complex power grid is simplified into three stages: national-level power grid operation simulation, provincial-level power grid operation simulation, and prefectural-level power grid operation simulation; a lower-level simulation model only needs to perform operation simulation on the basis of key parameters output by an upper-level simulation model, and other than a limited number of key parameter interactions, the operation processes of simulation models at all levels are independent of each other; thus, the computational complexity of operation simulation of the power grid is simplified, and the association between upper-level and lower-level power grids is also taken into consideration, so that when facing massive scenario computation requirements, the operation time can be effectively reduced, and the operation efficiency can be improved.

Description

Power grid long time scale hierarchical operation simulation method and device, terminal and medium

[0001] The present application claims priority to the Chinese patent application No. 2024105279575, filed on April 29, 2024, and entitled "Power grid long time scale hierarchical operation simulation method and device, terminal and medium", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the field of power grid operation simulation, in particular to a power grid long time scale hierarchical operation simulation method and device, terminal and medium. BACKGROUND

[0003] Through the long time scale operation simulation calculation of the regional power grid, the extreme operation mode faced by the regional power grid under high proportion of new energy access is identified, the potential risks of the regional power grid operation are analyzed and judged, and reference is provided for the flexible resource planning and dispatching operation of the regional power grid.

[0004] At present, there are two calculation methods for the long time scale operation simulation of the regional power grid: the first method ignores the influence of the upper main grid and directly performs regional power grid operation simulation calculation, however, since the regional power grid is connected with the main grid through multiple AC lines, there is power exchange, and 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, builds a full grid operation simulation model covering the main grid and the regional power grid, and performs unified operation simulation calculation on the full grid, which can make up for the defects of the first method in accuracy, but this method has drawbacks in calculation complexity and operation time, and the simulation efficiency is low, which cannot meet the massive scenario calculation demand of the new power system and the current engineering application demand.

[0005] SUMMARY

[0006] The present application provides a power grid long time scale hierarchical operation simulation method and device, terminal and medium, which is used to solve the technical problem of low simulation efficiency in the existing long time scale operation simulation technology of the regional power grid.

[0007] To solve the above technical problem, the first aspect of the present application provides a power grid long time scale hierarchical operation simulation method, comprising:

[0008] The historical unit offer of the grid-level power grid and the full grid topology data, the power transmission and transformation maintenance plan data, the load prediction data and the power generation equipment prediction data of the grid-level power grid are obtained, a regional spot market simulation model is constructed, the regional operation simulation of the grid-level power grid is performed through the regional spot market simulation model, and the full grid regional active power generation data and the inter-provincial tie-line active power transmission data of the grid-level power grid in a preset time period are obtained.

[0009] Based on the topology data of the provincial power grid, in combination with the all-region active power generation data and the inter-provincial tie-line active power transmission data as the boundary conditions of the provincial power grid power flow calculation, a first power flow calculation model of the provincial power grid is constructed, so as to perform power flow calculation on each node in the provincial power grid based on the first power flow calculation model, and obtain the power flow electrical parameters of each node in the provincial power grid in the preset time period;

[0010] According to the association relationship between the provincial power grid topology and the regional power grid topology, the common nodes in the provincial power grid and the regional power grid are determined, the power flow electrical parameters corresponding to the common nodes are taken as the boundary conditions of the regional power grid power flow calculation, and in combination with the nodes of each regional power grid, a second power flow calculation model of each regional power grid is constructed, so as to obtain the operation simulation results of each regional power grid in the preset time period by solving each second power flow calculation model.

[0011] Preferably, the historical unit offer of the grid-level power grid and the all-grid topology data, the power transmission and transformation maintenance plan data, the load prediction data and the power generation equipment prediction data of the grid-level power grid are obtained, a regional spot market simulation model is constructed, and the all-grid region active power generation data and the inter-provincial tie-line active power transmission data of the grid-level power grid in the preset time period are obtained by performing grid-level power grid regional operation simulation through the regional spot market simulation model, which specifically includes:

[0012] The historical unit offer of the grid-level power grid and the all-grid topology data, the power transmission and transformation maintenance plan data, the load prediction data and the power generation equipment prediction data of the grid-level power grid are obtained;

[0013] A regional spot market single-day simulation model of the grid-level power grid is constructed;

[0014] A preset model solving algorithm is used to solve the regional spot market single-day simulation model, so as to obtain the single-day clearing simulation data of the grid-level power grid according to the solving result;

[0015] According to the regional spot market single-day simulation model and the model solving algorithm, in combination with the preset time period, the clearing simulation data of the grid-level power grid in the preset time period is obtained through a rolling calculation mode, and the all-grid region active power generation data and the inter-provincial tie-line active power transmission data of the grid-level power grid in the preset time period are determined.

[0016] Preferably, the power flow calculation on each node in the provincial power grid based on the first power flow calculation model obtains the power flow electrical parameters of each node in the provincial power grid, which specifically includes:

[0017] based on the first power flow calculation model, the reactive power constraints of the generator nodes in the provincial power grid are set as unlimited, and power flow calculation is performed on each node in the provincial power grid to obtain a first power flow calculation result;

[0018] If the power flow convergence of the first power flow calculation result does not satisfy the preset convergence condition, the first power flow calculation result is filtered and the reactive power constraints of each generator node are reset.

[0019] If the power flow convergence of the first power flow calculation result satisfies the preset convergence condition, the unarranged reactive power amount of the generator node is determined by statistics of the first power flow calculation result, and the reactive power compensation value of each generator node is determined according to the reactive power voltage sensitivity of each generator node to all reactive power compensation nodes, combined with the configuration information of the reactive power compensation nodes.

[0020] 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 electrical parameters of each node in the provincial power grid.

[0021] Preferably, according to the nodes of the same regional power grid, the power flow electrical parameters corresponding to the common nodes are combined as boundary conditions for regional power grid power flow calculation, and a second power flow calculation model of each regional power grid is constructed, specifically including:

[0022] The power flow electrical parameters corresponding to the common nodes are combined as boundary conditions for regional power grid power flow calculation, and the load data and time sequence of the new energy nodes of the same regional power grid are combined according to the types of the new energy nodes to construct multiple single prediction scenario power flow calculation models, and then combined with the preset multiple new energy node operation scenarios to form the second power flow calculation model of the regional power grid.

[0023] Preferably, the operation simulation result of each regional power grid in the preset time period is obtained by solving each second power flow calculation model, specifically including:

[0024] Each single operation scenario power flow calculation model in the second power flow calculation model is solved by a multi-thread parallel processing mode, and the solution results of each single operation scenario power flow calculation model are summarized to obtain the operation simulation result of the regional power grid, and then each single operation scenario power flow calculation model in the remaining second power flow calculation model is solved in turn to obtain the operation simulation result of each regional power grid in the preset time period.

[0025] Preferably, solving each single operation scenario power flow calculation model in the second power flow calculation model specifically includes:

[0026] According to a node topological network corresponding to a single operation scenario power flow calculation model, the node topological network is divided into a plurality of partitions according to network voltage levels;

[0027] According to node voltage levels, a balance node is determined from each partition, and a node is searched outward from the balance node as a center, and when a search layer reaches a preset layer threshold, a plurality of open network topologies are output.

[0028] Through a preset open network power flow calculation formula, a power flow calculation result of each open network topology is obtained as a solution result of the single operation scenario power flow calculation model.

[0029] Preferably, the power flow electrical parameters specifically include voltage amplitude and phase.

[0030] Meanwhile, the second aspect of the application provides a layered operation simulation device for a long time scale of a power grid, comprising:

[0031] A grid-level power grid simulation unit is configured to obtain historical unit offers of a grid-level power grid, and full-network topological data, power transmission and transformation maintenance plan data, load prediction data, and power generation equipment prediction data of the grid-level power grid, construct a regional spot market simulation model, and perform grid-level power grid regional operation simulation through the regional spot market simulation model to obtain full-network 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.

[0032] A provincial-level power grid simulation unit is configured to construct a first power flow calculation model of the provincial-level power grid based on topological structure data of the provincial-level power grid, in combination with the full-network regional active power generation data and the inter-provincial tie-line active power transmission data as boundary conditions for power flow calculation of the provincial-level power grid, and perform power flow calculation on each node in the provincial-level power grid based on the first power flow calculation model to obtain power flow electrical parameters of each node in the provincial-level power grid within the preset time period.

[0033] A regional power grid simulation unit is configured to determine common nodes in the provincial-level power grid and the regional power grid according to an association relationship between the topological structure of the provincial-level power grid and the topological structure of the regional power grid, take power flow electrical parameters corresponding to the common nodes as boundary conditions for power flow calculation of the regional power grid, construct a second power flow calculation model of each regional power grid in combination with nodes of each regional power grid, and obtain operation simulation results of each regional power grid within the preset time period by solving each second power flow calculation model.

[0034] The third aspect of the application provides a layered operation simulation terminal for a long time scale of a power grid, comprising a memory and a processor.

[0035] The memory is configured to store program code corresponding to the power grid long time scale hierarchical operation simulation method according to the first aspect of the application.

[0036] The processor is configured to execute the program code.

[0037] The fourth aspect of the application provides a computer readable storage medium, which stores program code corresponding to the power grid long time scale hierarchical operation simulation method according to the first aspect of the application.

[0038] From the above technical solutions, the application has the following advantages:

[0039] The technical scheme provided by the application simplifies the operation simulation of the super-large complex power grid into three stages of grid-level power grid operation simulation, provincial power grid operation simulation and regional power grid operation simulation by hierarchical decoupling method. The lower-level simulation model only needs to perform operation simulation according to the key parameters output by the upper-level simulation model. The operation processes of the simulation models at all levels are independent of each other except for a small amount of key parameter interaction. The calculation complexity of the power grid operation simulation is simplified, the correlation between the upper-level power grid and the lower-level power grid is taken into account, the operation time is effectively reduced and the operation efficiency is improved in the face of massive scene calculation demand. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0041] Fig. 1 is a flowchart of a power grid long time scale hierarchical operation simulation method according to the application;

[0042] Fig. 2 is a detailed flowchart of a power grid long time scale hierarchical operation simulation method according to the application;

[0043] Fig. 3 is a flowchart of a power grid long time scale hierarchical operation simulation method according to the application with added transmission capacity constraints;

[0044] Fig. 4 is a flowchart of a power grid long time scale hierarchical operation simulation method according to the application with rolling calculation strategy;

[0045] Fig. 5 is a flowchart of a power grid long time scale hierarchical operation simulation method according to the application with massive scene regional power grid simulation operation parallel operation;

[0046] Fig. 6 is a schematic diagram of searching topology based on a balanced node network in a power grid long-time scale layered operation simulation method provided by the present application;

[0047] Fig. 7 is a schematic diagram of an open network in a power grid long-time scale layered operation simulation method provided by the present application;

[0048] Fig. 8 is a schematic diagram of a simplified open network in a power grid long-time scale layered operation simulation method provided by the present application;

[0049] Fig. 9 is a schematic diagram of a simplified equivalent circuit of an open network in a power grid long-time scale layered operation simulation method provided by the present application;

[0050] Fig. 10 is a diagram of predicted monthly power quantity of Guangdong from west in 2025;

[0051] Fig. 11 is a diagram of predicted monthly power quantity of Guangdong from west in 2025;

[0052] Fig. 12 is a curve diagram of power balance of Guangdong;

[0053] Fig. 13 is a comparison diagram of voltage amplitude variation of Die Ling 500kV station and Hui Long 500kV station;

[0054] Fig. 14 is a prediction diagram of phase difference of Die Ling 500kV station and Hui Long 500kV station in 2025;

[0055] Fig. 15 is a structural schematic diagram of an embodiment of a power grid long-time scale layered operation simulation device provided by the present application;

[0056] Fig. 16 is a structural schematic diagram of an embodiment of a power grid long-time scale layered operation simulation terminal provided by the present application. DETAILED DESCRIPTION

[0057] The embodiments of the present application provide a power grid long-time scale layered operation simulation method, device, terminal and medium, and are used for solving the technical problem of low simulation efficiency of the existing regional power grid long-time scale operation simulation technology.

[0058] In order to make the purposes, characteristics and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the embodiments described below are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0059] First, the detailed description of an embodiment of the power grid long time scale layered operation simulation method provided by the application.

[0060] Referring to FIG. 1, the embodiment of the power grid long time scale layered operation simulation method provided by the application comprises:

[0061] Step 101, obtaining the historical unit offer of the grid-level power grid and the full-network topology data, power transmission and transformation maintenance plan data, load prediction data and power generation equipment prediction data of the grid-level power grid, constructing a regional spot market simulation model, and obtaining the full-network regional active power generation data and inter-provincial tie-line active power transmission data of the grid-level power grid in a preset time period through regional spot market simulation of the grid-level power grid.

[0062] It should be noted that the long time scale operation simulation problem of the regional power grid considering the massive new energy scenario is decoupled and divided into three calculation stages: step 101 of the embodiment corresponds to the grid-level power grid operation simulation stage of the first stage, which is based on the power grid model parameters such as load prediction and new energy prediction data of each province, power transmission and transformation maintenance plan, full-network topology data, and generates future unit offer data according to historical offer, carries out simulation and calculation of the future preset time period of the southern regional power spot market, and obtains the full-network generator active power generation data and inter-provincial tie-line active power transmission data of the grid-level power grid through daily simulation and deduction.

[0063] More specifically, as shown in FIG. 2, step 101 of the embodiment can comprise:

[0064] Step 1011, obtaining the historical unit offer of the grid-level power grid and the full-network topology data, power transmission and transformation maintenance plan data, load prediction data and power generation equipment prediction data of the grid-level power grid;

[0065] Step 1012, constructing a regional spot market single-day simulation model of the grid-level power grid;

[0066] Step 1013, solving the regional spot market single-day simulation model through a preset model solving algorithm, so as to obtain the daily dispatching simulation data of the grid-level power grid according to the solving result;

[0067] Step 1014, obtaining the dispatching simulation data of the grid-level power grid in the preset time period through rolling calculation according to the regional spot market single-day simulation model and the model solving algorithm combined with the preset time period, and determining the full-network regional active power generation data and inter-provincial tie-line active power transmission data of the grid-level power grid in the preset time period.

[0068] It should be noted that the calculation of the first stage of the embodiment can refer to the following examples:

[0069] The target function of the regional spot market one-day simulation model is as follows:

[0070] The target function is divided into five parts:

[0071] The first part: the unit generation cost, that is, the sum of the unit offer and the start-up cost; N represents the total number of units in the South region; T represents the total number of time periods considered; P i,t represents the output of unit i at time period t; C i,t (P i,t ), are the operation cost, start-up cost, and minimum technical output cost of unit i at time period t, respectively, wherein the unit operation cost C i,t (P i,t ) is a multi-segment linear function related to the output interval and corresponding energy price declared by the unit; the unit start-up cost is a function related to the unit downtime to represent the start-up cost of the unit in different states (cold state / warm state / hot state); is the minimum technical output cost considered only when the unit is in the on state;

[0072] The second part: cross-province transmission cost; n represents the number of cross-province transmission components; n represents the number of cross-province transmission components; P L,i,t is the transmission power of cross-province transmission component i at time period t; P gwf is the cross-province transmission fee;

[0073] The third part: line constraint relaxation penalty term; M1 represents the network power flow constraint relaxation penalty factor for market clearing optimization; and are the positive and negative power flow relaxation variables of line l, respectively; NL is the total number of lines;

[0074] The fourth part: cross-section constraint relaxation penalty term; and are the positive and negative power flow relaxation variables of cross-section s, respectively; NS is the total number of cross-sections;

[0075] The fifth part: renewable energy curtailment penalty term. M2 is the renewable energy curtailment power penalty factor; NH is the total number of renewable energy units.

[0076] Among them, the constraint equation corresponding to the above regional spot market one-day simulation model can include:

[0077] 1) Power balance constraint equation

[0078] The power balance constraint equation is considered to ensure the power supply and demand balance of each province at each time period, as shown below:

[0079] where, denotes the power output of the intra-zone transaction unit in zone a at time period t, N a is the total number of zones; denotes the planned power of the inter-zone tie-line j related to zone a at time period t (positive for import, negative for export), NTa is the total number of inter-zone tie-lines related to zone a; denotes the transmission power of the intra-zone transaction component k related to zone a at time period t (default reference direction is import), NTIa is the total number of intra-zone transaction components related to zone a, is the system load of zone a at time period t. includes the power output of non-market units in the zone.

[0080] 2) Reserve capacity constraint equation

[0081] Considering the reserve capacity constraint equation, a certain capacity of the generator is reserved to adjust the output of the load and new energy, so as to improve the reliability of power supply of the power grid, as shown below:

[0082] In the formula, α i,t denotes the start-stop state of unit i at time period t, α i,t = 0 indicates that the unit is stopped, α i,t = 1 indicates that the unit is started; is the reserve capacity requirement at time period t.

[0083] 3) Line power transmission constraint equation

[0084] Considering the line power transmission constraint, that is, the transmission power of the line cannot exceed the power limit, to ensure the safety of the power grid operation, as shown below:

[0085] In the formula, G l-i is the generator output power transfer distribution factor of the node where unit i is located to line l; G l-j is the generator output power transfer distribution factor of the node where inter-zone tie-line j is located to line l; G l-d is the generator output power transfer distribution factor of the node where intra-zone DC tie-line d is located to line l; is the transmission power of intra-zone DC tie-line d at time period t; K is the number of nodes of the system; G l-k is the generator output power transfer distribution factor of node k to line l; D k,t is the bus load value of node k at time period t.

[0086] 4) Cross-section power transmission constraint equation

[0087] The cross-section power transmission constraint, i.e., the cross-section transmission power cannot exceed the power limit, is considered to ensure the safety of power grid operation, as shown below:

[0088] wherein, Pmaxsand Pminsthe power flow transmission limits of cross-section s; G s-i Gis the generator output power transfer distribution factor of node where unit i is located to cross-section s; G s-j Gis the generator output power transfer distribution factor of node where external tie-line j is located to cross-section s; G s-d Gis the generator output power transfer distribution factor of node where internal DC tie-line d is located to cross-section s; G s-k Gis the generator output power transfer distribution factor of node k to cross-section s.

[0089] 5) Thermal power unit operation characteristic constraint equation

[0090] The thermal power unit operation characteristic constraint equation is considered to ensure that the thermal power unit operation meets the upper / lower limit, ramp rate, and minimum start / stop time constraints, as shown below:

[0091] ① Unit output upper and lower limit constraint

[0092] The output of the unit should be within its maximum / minimum technical output range, and the constraint condition can be described as:

[0093] Pmin,i(t) is the minimum technical output of unit i at the tth time period. If the unit is stopped, α i,t = 0, then the unit output can be limited to 0 through this constraint condition; when the unit is started, α i,t = 1, which is the conventional output upper and lower limit constraint.

[0094] ② Unit ramp rate constraint

[0095] When the unit ramps up or down, it should meet the ramp rate requirement. The ramp constraint can be described as:

[0096] wherein, Pmaxup,i is the maximum up-ramp rate of unit i, Pmaxdown,i is the maximum down-ramp rate of unit i. The unit up / down output constraint is determined by several factors: when the unit is in normal operation state, the unit up / down output range is determined by ; when the unit is at the start time, the unit up / down output range is determined by the allowed start rate of the unit (here, P ); when the unit is at the stop time, the unit up / down output range is determined by the allowed stop rate of the unit (here, P ) decision.

[0097] ③ Minimum continuous start-up and shut-down time constraint of generating units

[0098] Due to the physical properties and actual operation needs of thermal power generating units, it is required that the thermal power generating units meet the minimum continuous start-up and shut-down time. The minimum continuous start-up and shut-down time constraint can be described as:

[0099] wherein α i,t is the start-up and shut-down state of the unit i at the time period t; T U and T D are the minimum continuous start-up time and the minimum continuous shut-down time of the unit; is the time that the unit i has been continuously started up and continuously shut down at the time period t, which can be represented by the state variable α i,t (i = 1 ~ N, t = 1 ~ T):

[0100] 6) Water power generating unit operation characteristic constraint equation

[0101] The water power generating unit operation characteristic constraint equation is considered to ensure that the water power generating unit operation meets the upper / lower limit and the power limit, wherein the upper / lower limit constraint is the same as that of the thermal power generating unit, and the water power constraint is as follows:

[0102] wherein P h,t is the output of the water power station h at the time period t, Q h,k is the power of the water power station h in the kth month, tbgn k and tend k are the starting time period and the ending time period of the kth month, respectively.

[0103] 7) New energy generating unit operation characteristic constraint equation

[0104] The new energy generating unit participating in the market is divided into a guaranteeing consumption mode and a market-oriented bidding mode. The operation characteristic constraints in the two modes are as follows:

[0105] ① Guaranteeing consumption mode: priority clearing, new energy winning output = new energy prediction value

[0106] p w,t is the winning output of the wth new energy generating unit at the time period t, and is the prediction value of the wth new energy generating unit at the time period t.

[0107] ② Market-oriented bidding mode: new energy winning output + abandoned power = new energy prediction value

[0108] Let represent the amount of renewable energy that the w-th renewable energy unit will forgo during time period t.

[0109] Next, the 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, as shown in Figure 3, this embodiment proposes a dynamic addition method for 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 constraints. This process is iterated repeatedly until the check passes.

[0110] 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.

[0111] Next, the regional simulation results for a preset time period are output through a rolling calculation method. The rolling calculation strategy in this embodiment is shown in Figure 4. 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.

[0112] 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.

[0113] 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.

[0114] More specifically, as shown in Figure 2, step 102 of this embodiment may include the following steps:

[0115] Step 1021, based on the topology data of the provincial power grid, the active power generation data of the whole network region and the active power transmission data of the inter-provincial tie line are combined as the boundary conditions of the provincial power grid power flow calculation to construct a first power flow calculation model of the provincial power grid;

[0116] Step 1022, based on the first power flow calculation model, the reactive power constraint of the generator node in the provincial power grid is set as unlimited, and the power flow calculation is performed on each node in the provincial power grid to obtain the first power flow calculation result;

[0117] Step 1023, if the power flow convergence of the first power flow calculation result does not satisfy the preset convergence condition, the first power flow calculation result is filtered and the reactive power constraint of each generator node is reset, if the power flow convergence of the first power flow calculation result satisfies the preset convergence condition, the unarranged reactive power amount of the generator node is determined by statistics of the first power flow calculation result, and the reactive power compensation value of each generator node is determined according to the reactive power voltage sensitivity of each generator node to all reactive power compensation nodes in combination with the configuration information of the reactive power compensation nodes;

[0118] Step 1024, based on the first power flow calculation model containing the reactive power compensation of the generator node, the power flow calculation is performed on each node in the provincial power grid to obtain the power flow electrical parameters of each node in the provincial power grid.

[0119] It should be noted that the active power plan of the main grid unit and the inter-provincial tie line power plan obtained by the regional spot market simulation and deduction in step 101 are used as the boundary conditions of the annual power flow calculation of the provincial power grid. Through network topology model construction, reactive voltage configuration and other processes, complete AC power flow calculation data is generated.

[0120] The specific execution process is as follows:

[0121] Read the basic full-on mode data to obtain the network topology model. According to the generator model card, the governor card type and other generator parameter information, the unit type is distinguished: hydroelectric, thermal, nuclear, wind, photovoltaic, etc.

[0122] Read the active power output P i,t of the generator unit in the whole network obtained by the regional spot market simulation and deduction Obtain the output of each main unit, the total output of other units, and the total load of each region, the total output of wind power and photovoltaic power under each active operation scenario.

[0123] For the mode data whose power flow does not converge, the reactive power limit constraint of the generator is released to make the power flow calculation converge, the unarranged reactive power of the released reactive power constraint node is obtained, and then the related reactive power compensation with the highest sensitivity is adjusted according to the size of the reactive power voltage sensitivity to regenerate the mode data after adjusting the reactive power compensation.

[0124] The specific execution process is as follows:

[0125] Read the mode flow data (.dat), modify all generator nodes to reactive power unlimited BE nodes, generate new data, and perform flow calculation through power system alternating current flow calculation software (such as PSD-BPA software of China Electric Power Research Institute and DPS software of South Grid Research Institute).

[0126] If the flow does not converge, the method is invalid, and is skipped; if the flow converges (most data can converge), the following steps are performed:

[0127] Read the unallocated reactive list in the generator reactive power unlimited flow calculation result to obtain the unallocated reactive size of the generator.

[0128] Read the pre-arranged grid low-capacity low-resistance configuration table to obtain the reactive power compensation node and its capacity, group number and other information of the whole network.

[0129] Calculate the reactive voltage sensitivity of each unallocated reactive generator node to all reactive compensation nodes, and sort them according to the sensitivity, convert the unallocated reactive value of the generator node to the reactive compensation value of the reactive compensation node with larger sensitivity, and eliminate the BE node.

[0130] Regenerate the mode data after adjusting the reactive power compensation.

[0131] For each active operation scenario (8760 scenarios per year), the following steps are performed to generate mode data.

[0132] Regional total load splitting to node rules: according to the initial active load size relationship of each node in the basic mode data, allocate the regional total active load in proportion; according to the principle that the size ratio of node reactive load and active load remains unchanged, correct the node reactive load.

[0133] Fuse the power allocated to the node with the network topology model to generate mode data without adjusting the reactive compensation.

[0134] Perform DC flow calculation on the mode data without adjusting the reactive compensation. Read the DC flow calculation result to obtain the active flow distribution, record the sum of all outgoing active flows and the sum of rated capacities (converted by rated current) of each node.

[0135] Read the pre-arranged grid low-capacity low-resistance configuration table and the reactive compensation node and its control node mapping table (the control node of the reactive compensation is usually the high-voltage side node of the three-winding transformer), determine the reactive compensation size according to the active flow load of the reactive compensation control node (the sum of the absolute values of all outgoing active flows / the sum of all outgoing rated capacities).

[0136] Regenerate the mode data after adjusting the reactive compensation.

[0137] In step 103, according to the association relationship 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 electrical parameters corresponding to the common nodes are taken as boundary conditions for the regional power grid power flow calculation, and the second power flow calculation model of each regional power grid is constructed in combination with the nodes of each regional power grid, so as to obtain the operation simulation result of each regional power grid in the preset time period by solving each second power flow calculation model.

[0138] It should be noted that step 103 of the embodiment corresponds to the regional power grid operation simulation stage of the third stage. After generating the wind and light load time sequence of the corresponding region, the proposed regional power grid operation simulation and analysis evaluation method is used for simulation and analysis based on the power flow electrical parameters of the provincial power grid alternating current power flow operation obtained in the last stage.

[0139] More specifically, as shown in FIG. 2, step 103 of the embodiment includes the following steps:

[0140] In step 1031, according to the association relationship 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.

[0141] In step 1032, the power flow electrical parameters corresponding to the common nodes are taken as boundary conditions for the regional power grid power flow calculation, the load data and the time sequence of the new energy nodes of the same regional power grid are combined, a plurality of single prediction scenario power flow calculation models are constructed according to the types of the new energy nodes, and the regional power grid second power flow calculation model is composed in combination with the preset plurality of new energy node operation scenarios, so as to obtain the operation simulation result of each regional power grid in the preset time period by solving each second power flow calculation model.

[0142] 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 electrical parameters, and the nodes commonly existing in the provincial power grid topology and the regional power grid topology are compared, and the voltage and phase of these nodes are taken as the input of the regional power grid operation simulation in step 103, so as to realize the decoupling calculation of the regional power grid and the provincial power grid.

[0143] After the common nodes in the provincial grid topology and the regional grid topology are determined, based on the active and reactive power flow distribution of the nodes in the provincial grid obtained in the previous section, the voltage amplitude and phase of the common coupling nodes in the provincial grid and the regional grid topology are read as the boundary conditions for the regional grid flow calculation, and the load data and time series of the new energy nodes in the same regional grid are combined to construct multiple single prediction scenario flow calculation models. The load modeling and time series simulation are as follows:

[0144] Active load: Statistical historical data, respectively, according to 4 quarters x weekdays / weekends / holidays 3 types to establish normalized load curve. At the same time, the power sample variance of each clock point in a day is calculated, and the corresponding normal model is established. According to the month, the daily maximum load is calculated to form a continuous load curve as a model parameter

[0145] Reactive load, according to the above typical day, the load power factor of each clock point is calculated to form a normal distribution model mean and variance.

[0146] After modeling, any length of node load sequence can be randomly generated, the steps are as follows:

[0147] (1) Generate daily maximum load, and generate daily active power curve mean sequence according to the corresponding normalized curve of the calendar

[0148] (2) For each period, sample the active deviation according to the corresponding normal distribution parameter and superimpose it with the mean sequence to generate the daily active load simulation curve.

[0149] (3) For each period, sample the power factor value according to the corresponding power factor normal parameter, and calculate the reactive power according to the corresponding period active load simulation value P:

[0150] Single scenario new energy output data prediction:

[0151] (1) Wind power modeling

[0152] Cut the original power sample set with 4 hours as the basic time length unit. For each small sample, calculate its standard deviation (std) and mean (means) to form a (N, 2) feature matrix, N is the number of small samples. The points of the matrix are K-means clustered, and the cluster center is set to 3.

[0153] According to the clustering results, the joint distribution between two wind power stations is calculated to obtain a joint distribution matrix, and the entropy of each matrix is calculated according to the following formula: E = p ij lnΣp ij

[0154] where E represents the entropy of the matrix, p ij represents the probability value of the simultaneous occurrence of mode i and mode j in the current joint distribution matrix.

[0155] The size of the entropy value can describe the strength of the correlation between the wind power stations. The greater the entropy, the more concentrated the modes of the two wind power stations are, and the greater the correlation is. Multiple wind power stations with strong correlation can form a wind power station group for subsequent modeling of the Markov chain state transition model. Wind power stations with weak correlation to other wind power stations can be independently statistically established for their daily state transition model.

[0156] According to the k-means clustering result, a power matrix S(N, 16) under a certain class is formed, where N represents the number of samples under the mode. The average power row M(1, 16) is obtained in the row direction, and then each row of the power matrix S is subtracted from the average power row M to obtain the fluctuation matrix F(N, 16). All elements in the fluctuation matrix are divided into positive and negative fluctuation databases according to the positive and negative, and there are 6 fluctuation databases in total for 3 clustering centers.

[0157] According to the historical data, the initial state of each wind power plant is set, and the annual wind power curve can be generated according to the Markov daily state transition model.

[0158] (2) Photovoltaic modeling

[0159] According to the longitude and latitude of the photovoltaic power station, the average sunrise and sunset time is determined. The active power output of the photovoltaic power station in the area in the month is normalized and classified in units of each time point. The K-means clustering algorithm is used for clustering analysis of the data at each time point, and the clustering centers are set to 3 to reflect the sunny, cloudy and rainy weather conditions.

[0160] According to the clustering centers of the fitting results, the fluctuation under each clustering state is calculated to simulate the phenomenon of cloud layer, and the fluctuation databases of the three clustering states are obtained. The distribution is fitted by kernel density.

[0161] The weather transition of all photovoltaic stations is counted. Each weather mode transition takes a 1-hour basic time unit, and according to the K-means clustering result, the weather mode transition frequency between each window is calculated according to the class to which the first data of each window belongs. The frequency is calculated as the transition probability.

[0162] According to the historical data, the annual weather initial state is set, and the annual weather mode sequence is generated according to the weather Markov transition matrix, so as to generate the annual photovoltaic power curve.

[0163] The generation of the new energy node operation scene is as follows:

[0164] The step of generating a large number of photovoltaic output scenes based on historical data is as follows:

[0165] The sunrise and sunset time of the region is calculated by inputting the latitude and longitude of the region and the month. The length of the simulated time is input.

[0166] An initial weather mode is randomly selected, and a weather mode sequence is generated according to the length of time and the statistical weather Markov transition matrix.

[0167] According to the weather mode sequence and the K-means clustering result, the reference power curve under this sequence is obtained.

[0168] According to the data fluctuation library of different weather sequences, fluctuation data is sampled and superimposed on the reference power curve to complete the scene simulation.

[0169] The step of generating a large number of wind power output scenes based on historical data is as follows:

[0170] The length of the simulated time is set, and the initial state of each site in the wind farm group is randomly set

[0171] According to the multivariate Markov interday transition matrix, the power mode sequence of each site is generated.

[0172] According to the mode sequence and its corresponding average power curve, the reference power curve of each site is generated.

[0173] According to the fitted data fluctuation library, fluctuation data of the same length is extracted and superimposed on the reference power curve to complete the scene simulation.

[0174] According to the data generated in the previous two subsections, the annual power flow calculation operation parameters are combined with the large number of new energy operation scenes to form a large number of power flow calculation cases under the new energy operation scenes. For example, if there are N new energy operation scenes, the number of power flow calculation cases is 8760xN. Each case includes the following data: regional power grid network topology, node load prediction, generator parameters, new energy prediction, and equipment maintenance plan.

[0175] Next, the solution and result output example of the second power flow calculation model, including: based on the large number of new energy operation scenes generated in the previous section, through parallel computing strategy, the parallel computing of the simulation operation of the power grid under the large number of scenes can be realized, as shown in FIG. 5, and the specific process is as follows:

[0176] Data preparation: obtain the power flow calculation case data under the large number of new energy operation scenes, and prepare for the power flow calculation of the large number of cases;

[0177] CPU resource allocation: Obtain the parameters of the server, set the parallel computing strategy according to the number of CPU threads;

[0178] Assuming that the number of CPU threads is M, the number of threads required to maintain the operation of the operating system is M1, and the number of scenes to be solved is N, then the number of power flow calculation cases required to run under each CPU thread is

[0179] Parallel computing: Loop the execution of the single-scenario regional power grid power flow calculation module under each thread until all the calculation cases assigned to this process are completed.

[0180] Statistical analysis of the results.

[0181] The solution method of the single prediction scenario power flow calculation model can be referred to the following examples:

[0182] 1) Open loop decoupling processing of ring network:

[0183] The 110KV power grid in the operation of the regional power network of the power system is in open loop operation, and the construction of the system topology graph has a ring network phenomenon. In order to realize the subsequent open network power flow calculation, the following method is adopted to realize open loop decoupling:

[0184] According to the corresponding partition planning in the operation planning of the power system, the corresponding network closed loop line is removed from the operation state.

[0185] According to the changes of the network in the real-time operation of the power system, modify the operation state of the corresponding closed loop line of the network.

[0186] Subsequent analysis of the overloading of the station, through the allocation of the station load hanging partition, modify the operation of the station connection line.

[0187] 2) Open network power flow calculation:

[0188] (1) 110KV network partition and network tree topology analysis

[0189] According to the above ring network open loop decoupling processing, the 110KV network is divided into several partitions, and the open network power flow calculation is performed on each partition.

[0190] Tree search: Determine the 220KV station node in each partition, set the 110KV node in the station as the balance node. Starting from the balance node, gradually find the station node outward, as shown in Figure 6, search 3 times to obtain all the topology information.

[0191] (2) Simplification and equivalence of open network

[0192] The subordinate circuits of node A are b, c, d. The voltage and power of nodes b, c, d are known, and the power loss is calculated from the end node d to the top along the line, and the power is added to the superior line.

[0193] The network equivalent circuit is simplified by adding the charging power of half of each line to the nodes at the beginning and end of the line, and the equivalent circuit shown in Fig. 7 is simplified to the equivalent circuit shown in Fig. 8. For example, the power of node c becomes

[0194] where P LDc and Q LDc are the active and reactive loads of node c before the equivalent line is simplified, U c is the voltage of node c, B2 and B3 are the susceptances of the lines on both sides of node c, S c , P c and Q c are the equivalent load, equivalent active load and equivalent reactive load of node c after the equivalent line is simplified.

[0195] After the equivalent circuit is simplified, the power distribution calculation of the open network is calculated from the end to the superior network, and the calculation of each line is as follows:

[0196] Each line is simplified by the above-mentioned simplification method to the form shown in Fig. 9, and the loss of the line is The transmission power of the line at the beginning S1 = S2 + ΔS.

[0197] 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 powers of the superior and inferior of the line.

[0198] (3) Power flow calculation method of open network

[0199] The 110KV node under the 220KV station in each area is set as a balance node, and the nodes under it are set as PQ nodes. The initial voltage of the lower node is set as the reference voltage value (the 110KV balance node is set as 110KV).

[0200] The line loss and transmission power are calculated from the end of the tree network to the superior level step by step; when the calculation reaches the balance node, the voltage drop is calculated from the balance node to the lower level, and the voltage of all nodes below the balance node is obtained, and the node voltage is revalued. The above forward-backward calculation steps are repeated to improve the calculation accuracy of the system, and finally the power flow calculation result of the open network is obtained.

[0201] Wherein, U1 and U2 are the line upper and lower node voltage, R and X are the resistance and reactance of the line, P' and Q' are the equivalent line transmission power of the line upper node.

[0202] Through the above solving, the following result statistics are obtained based on the solving results:

[0203] (1) Statistically predict the active power transmission of all regional power grid lines throughout the year, as well as the line operating power state (light load, heavy load, limit, overrun) under the prediction scenario;

[0204] (2) Statistically predict the active power transmission of all regional power grid sections throughout the year, and determine whether the section operating state is within the limit or over the limit;

[0205] (3) Statistically predict the voltage of all regional power grid nodes throughout the year, and the number of nodes exceeding the safe operating voltage limit of the power grid throughout the year;

[0206] (4) Statistically predict the transformer load rate of all regional power grids throughout the year, as well as the transformer load operating state (light load, heavy load, full load, overload);

[0207] (5) Statistically predict the overload probability and amplitude of all regional power grid lines under the massive scenario;

[0208] (6) Statistically predict the limit probability, overrun probability and amplitude of all regional power grid sections under the massive scenario;

[0209] (7) Statistically predict the voltage overrun probability and amplitude of all regional power grids under the massive scenario;

[0210] (8) Statistically predict the transformer overload probability and amplitude of all regional power grids under the massive scenario.

[0211] The above is a detailed description of an embodiment of a power grid long-time scale hierarchical operation simulation method provided by the present application. In order to more clearly demonstrate the effect of the technical solution of the present application, the present application also provides a test description based on a real case, as follows:

[0212] Based on the 2025 Southern Power Grid planning data, the 2025 Yangjiang power grid operation simulation calculation is carried out. It is divided into three processes, namely, network-level power grid operation simulation, provincial power grid operation simulation, and regional power grid operation simulation. The calculation results are analyzed as follows:

[0213] 1) Analysis of network-level power grid operation simulation results:

[0214] (1) West electricity optimization

[0215] As shown in FIGS. 10 and 11, through simulation deduction calculation, the simulation power of West-to-East transmission in 2025 is 246.295 billion kWh, which is 24.788 billion kWh more than the annual plan value (2215.07 billion kWh), and the completion rate is 111.19%. Among them:

[0216] The simulation power of West-to-East transmission to Guangdong (the receiving end) is 2049.30 billion kWh, which is 140.98 billion kWh more than the annual plan value (1908.33 billion kWh), and the annual power completion rate of West-to-East transmission to Guangdong is 107.39%.

[0217] The simulation power of West-to-East transmission to Guangxi (the receiving end) is 384.45 billion kWh, which is 77.70 billion kWh more than the annual plan value (306.74 billion kWh), and the annual power completion rate of West-to-East transmission to Guangxi is 125.33%.

[0218] The simulation power of West-to-East transmission to Hainan (the receiving end) is 29.20 billion kWh, which is 29.20 billion kWh more than the annual plan value (0 billion kWh).

[0219] The simulation power of Yunnan transmission (the sending end) is 1538.22 billion kWh, which is 41.60 billion kWh more than the annual plan value (1452.05 billion kWh), and the power completion rate of Yunnan transmission is 105.93%.

[0220] The simulation power of Guizhou transmission (the sending end) is 419.70 billion kWh, which is 12.43 billion kWh less than the annual plan value (430.31 billion kWh), and the power completion rate of Guizhou transmission is 97.54%.

[0221] (2) Power balance of typical day:

[0222] The maximum load of the South China Power Grid appears on August 7, and the power balance of Guangdong is shown in FIG. 12.

[0223] 2) Analysis of provincial power grid operation simulation results:

[0224] (1) 8760-hour period power flow distribution of Guangdong power grid in 2025

[0225] Calculation overview: Among the generated 8760 modes, 8750 modes have direct convergence of alternating current power flow calculation, and the power flow convergence rate is 99.88%, and the remaining 10 modes have power flow non-convergence due to insufficient reactive power configuration in Shenzhen area. After manually increasing the reactive power compensation, all modes of power flow calculation converge. The average time of each mode of power flow calculation is about 2.5 seconds, and the total time is 6 hours.

[0226] Network loss: The average network loss of Guangdong Power Grid in 2025 is about 1552MW, the minimum network loss is 289MW (January 2, 5 o'clock), and the maximum network loss is 2733.5MW (October 9, 11 o'clock).

[0227] Guangdong Power Grid typical mode under the distribution of power flow:

[0228] Large mode: The highest load of Guangdong Power Grid in 2025 is expected to occur at noon on July 13, with a system active load of about 175000MW.

[0229] Small mode: The lowest load of Guangdong Power Grid in 2025 is expected to occur at 5 o'clock on January 2, with a system active load of about 19350MW.

[0230] (2) Yangjiang Power Grid 500kV Station Operation

[0231] In the 500kV network, Yangjiang regional power grid is connected to the main network through the Butterfly Ridge Station, Hui Long Station and Guangdong Power Grid. In order to realize the decoupling calculation of regional power grid and main network, the voltage amplitude and phase information of Butterfly Ridge Station and Hui Long Station need to be exported as input for the next stage of regional power grid operation simulation.

[0232] From Table 1-1 and Figures 13 and 14, the average voltage amplitude of Butterfly Ridge and Hui Long is 525.9kV and 528.47kV respectively. The maximum positive and negative deviation rates of Butterfly Ridge are 6.42% and -5.19% respectively, and the maximum positive and negative deviation rates of Hui Long are 6.5% and -3.5% respectively. The average voltage phase difference of the two stations is 1.7.

[0233] Table 1-1 Voltage amplitude and phase of Butterfly Ridge Station and Hui Long Station

[0234] 3) Analysis of regional power grid operation simulation results:

[0235] 1) Yangjiang regional power grid topology structure reading and analysis: The project is based on Python to read the BPA power flow calculation program of Guangdong Power Grid, and automatically generates the program modules of the topology structure, parameters and open loop operation mode of Yangjiang regional power grid at all levels. The analysis of the Yangjiang power grid 220kV network topology diagram forms a complex ring network structure.

[0236] According to the setting of open loop point of Yangjiang power grid operation mode, the open loop network can be formed under normal operation.

[0237] 2) Analysis of Yangjiang power grid operation simulation results under the scenario of massive new energy, the power flow calculation method of open network is used to calculate the power flow of Yangjiang, Pingdi, Mounan, Lingxiao, Denggao, Chuncheng, Bagijitou and Qiguling eight areas in Yangjiang region.

[0238] The time points set are one point per hour, simulating 8760 time points in 2022, and the time is set to 0-8759.

[0239] (1) Voltage operation:

[0240] For the operation of the eight pieces, the node voltage in each piece is maintained at the level of (108-112KV).

[0241] Dam foundation head area: the voltage amplitude of Shuchun and Shuchun in May reached 112KV, the voltage amplitude of Zhigong, Yong'an and Changxing was the lowest at 109KV, and no voltage overrun occurred.

[0242] Chengcheng area: the voltage amplitude of Zhonggang reached 112KV in April, and the overall voltage amplitude was the lowest at 109KV, which appeared voltage overrun.

[0243] Denggao area: the voltage is maintained at 110KV voltage level, and the area runs stably.

[0244] Lingxia area: the voltage amplitude of Haishua in December was the lowest at 108KV.

[0245] Moen area: the voltage amplitude of Baisha in June was the lowest at 108KV.

[0246] Flat area: Leiping wind power, Lingnan wind power, Xinye photovoltaic, and Huayu photovoltaic are power stations, and the maximum value of voltage amplitude reaches 114KV.

[0247] Qiguoling area: the voltage level is maintained at 110KV voltage level.

[0248] Yangjiang area: Yidong, Suidong and Beigan have more voltage abnormal points. The lowest voltage of the three stations is 109-110KV, which is the normal level. The voltage amplitude of Beigan and Suidong reaches the highest point of 114.7KV in the first month, and the voltage amplitude of Yidong station reaches the maximum value of 119KV in the first month. Observation of voltage overrun of Yidong station (1.05) found that the voltage of 538 time points in Yidong station appeared overrun, which mainly concentrated in the first three months.

[0249] (2) 220KV station overload:

[0250] Dam head area: There are two groups of three-winding transformers in the dam head 220KV station, and the main transformer capacity is 360MW. There are 7 time points of overload in the dam head area, and 112 time points of heavy load (more than 80%, the same for subsequent heavy load). The highest load reaches 415KV, which appears at noon on the first day of April. The remaining overload time points are concentrated in the adjacent days of the noon time. Heavy load occurs in each month and also appears at noon.

[0251] Chun city area: There are two groups of three-winding transformers in the Chun city 220KV station, and the main transformer capacity is 330MW. There is no overload in Chun city area, and there are 55 time points of heavy load, mainly in October to December, mainly around midnight.

[0252] Denggao area: There is a group of three-winding transformers in the Denggao 220KV station, and the main transformer capacity is 180MW. There are many time points of overload in Denggao area, with 844 overload time points and 2994 heavy load time points, and the heavy overload situation is serious.

[0253] Lingxia area: There are two groups of three-winding transformers in the Lingxia 220KV station, and the main transformer capacity is 360MW. There is no heavy overload in Lingxia area.

[0254] Moen area: There are two groups of three-winding transformers in the Moen 220KV station, and the main transformer capacity is 360MW. There is no overload in Moen area, and there are 125 time points of heavy load, mainly in the daytime period from October to December.

[0255] Pingdi area: There is one three-winding transformer in the Pingdi station, and two three-winding transformers are not in operation. The main transformer capacity is 180MW, and the non-operating main transformer capacity is 360MW. The maximum load in Pingdi area is 484MW.

[0256] Qigu area: There is one three-winding transformer in the Qigu area, and the main transformer capacity is 180MW. The heavy overload situation is serious.

[0257] Yangjiang area: There are three three-winding transformers in Yangjiang area, and the main transformer capacity is 540MW. There is no overload in Yangjiang area, and there are 461 time points of heavy load, mainly concentrated in the daytime period.

[0258] (3) 110KV line heavy overload situation:

[0259] Dam head area: The line load rate between Dam head and Shuangyu reaches 53%, the line load rate between Yanziling photovoltaic and Shuangyu reaches 58%, and the line load rate between Nongken photovoltaic and Xuanhe reaches 64%.

[0260] Chuncheng area: the maximum line load rate is less than 30%.

[0261] Climbing area: the line between Eling and Zhapa has the maximum load rate, which is 33%.

[0262] Lingxiao area: the line between Lingxiao and Haishua has the highest load rate of 52%, the line between Guangyan and Heishui has the highest load rate of 46%, and the maximum load rate of the remaining lines is low.

[0263] Moen area: the maximum line load rate is less than 40%, and the maximum load rate between Moen and Yinhui is 39%.

[0264] Pingdi area: some lines in the Pingdi area have high load rates, and the line between He and Haier has the highest load rate of 91.8%, the line between Pingdi and Dagou has the highest load rate of 89%, and the line between Xinye and Dagou has the highest load rate of 95.8%.

[0265] Qigulong area: the maximum load rate of the lines in the Qigulong area is about 50%, which is between Qigulong and Hekou, and between Jinfu and Hekou.

[0266] Yangjiang area: the maximum line load rate in the Yangjiang area is 93%, and the line between Yangjiang and Suandong has the highest load rate of 93%, and the line between Suandong and Yidong has the highest load rate of 70%.

[0267] The above is a detailed description of an embodiment of the power grid long-time scale layered operation simulation method provided by the present application. The following is a detailed description of an embodiment of the power grid long-time scale layered operation simulation device, terminal, and computer readable storage medium provided by the present application.

[0268] Referring to FIG. 15, an embodiment of the power grid long-time scale layered operation simulation device provided by the present application includes:

[0269] The grid-level power grid simulation unit 201 is configured to obtain historical unit bidding of the grid-level power grid, and full-network topology data, power transmission and transformation maintenance plan data, load prediction data, and power generation equipment prediction data of the grid-level power grid, construct a regional spot market simulation model, and perform grid-level power grid regional operation simulation through the regional spot market simulation model to obtain full-network regional active power generation data and inter-provincial tie-line active power transmission data of the grid-level power grid in a preset time period.

[0270] The provincial power grid simulation unit 202 is configured to construct a first power flow calculation model of the provincial power grid based on the topological structure data of the provincial power grid, in combination with the regional active power generation data and the inter-provincial active power transmission data as boundary conditions for power flow calculation of the provincial power grid, to perform power flow calculation on each node in the provincial power grid based on the first power flow calculation model, and to obtain power flow electrical parameters of each node in the provincial power grid in a preset time period.

[0271] The regional power grid simulation unit 203 is configured to determine common nodes in the provincial power grid and the regional power grid according to the association between the topological structure of the provincial power grid and the topological structure of the regional power grid, to take power flow electrical parameters corresponding to the common nodes as boundary conditions for power flow calculation of the regional power grid, to construct a second power flow calculation model of each regional power grid in combination with nodes of each regional power grid, and to obtain operation simulation results of each regional power grid in a preset time period by solving each second power flow calculation model.

[0272] Referring to FIG. 16, an embodiment of a terminal for long-time-scale hierarchical operation simulation of a power grid provided by the present application is provided, and the terminal type includes but is not limited to a personal computer, an industrial computer, a server, and an embedded intelligent device. The main components of the terminal include a memory 33 and a processor 31, which can be connected through a communication bus 34.

[0273] The memory is configured to store program codes corresponding to the method for long-time-scale hierarchical operation simulation of a power grid provided by the first aspect of the present application.

[0274] The processor is configured to execute the program codes stored in the memory.

[0275] The fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores program codes corresponding to the method for long-time-scale hierarchical operation simulation of a power grid provided by the first aspect of the present application.

[0276] Those skilled in the art can clearly understand the specific working processes of the terminal, the device, and the unit described above for the convenience and brevity of description, and the corresponding processes in the foregoing method embodiments can be referred to, which will not be described herein.

[0277] In several embodiments provided in the present application, it should be understood that the disclosed terminal, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is merely a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0278] The terms "first", "second", "third", "fourth" and the like in the description of the specification and the above drawings, if any, are used to distinguish similar objects, and are not necessarily used to describe a particular sequential or chronological order. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in other than the order shown or described herein. In addition, the terms "comprise" and "have" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a list of steps or units as an element does not necessarily limit those steps or units to the clearly listed ones, but can include other steps or units that are not clearly listed or inherent to such processes, methods, products or devices.

[0279] It should be understood that in the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that there are three cases of only A, only B and A and B at the same time, where A and B can be singular or plural. The character " / " generally represents that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions means any combination of these items, including single item or any combination of multiple items. For example, at least one of a, b or c, can mean 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.

[0280] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0281] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0282] When the integrated unit is realized in the form of 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 solutions of the present application, essentially or in part, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods according to the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various other media that can store program codes.

[0283] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the technical solutions thereof; even though the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still make modifications to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features thereof; and these modifications or replacements 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 application.

Claims

1. A hierarchical operation simulation method for long time scale of power grid, characterized in that, The method comprises the following steps: obtaining historical unit offer of a grid-level power grid and full-network topology data, power transmission and transformation maintenance plan data, load prediction data and power generation equipment prediction data of the grid-level power grid, constructing a regional spot market simulation model, and performing regional operation simulation of the grid-level power grid through the regional spot market simulation model to obtain full-network regional active power generation data and inter-provincial tie-line active power transmission data of the grid-level power grid in a preset time period; based on the topology structure data of the provincial power grid, combining the full-network regional active power generation data and the inter-provincial tie-line active power transmission data as boundary conditions for power flow calculation of the provincial power grid, constructing a first power flow calculation model of the provincial power grid, and performing power flow calculation on each node in the provincial power grid based on the first power flow calculation model to obtain power flow electrical parameters of each node in the provincial power grid in the preset time period; determining common nodes in the provincial power grid and the regional power grid according to the correlation between the topology of the provincial power grid and the topology of the regional power grid, taking the power flow electrical parameters corresponding to the common nodes as boundary conditions for power flow calculation of the regional power grid, combining the nodes of each regional power grid, and respectively constructing a second power flow calculation model of each regional power grid to obtain operation simulation results of each regional power grid in the preset time period by solving each second power flow calculation model.

2. The hierarchical operation simulation method of power grid long time scale according to claim 1, characterized in that, The method for obtaining the full-network topology data, power transmission and transformation maintenance plan data, load prediction data and power generation equipment prediction data of the grid-level power grid, constructing a regional spot market simulation model, and performing regional operation simulation of the grid-level power grid to obtain the full-network regional active power generation data and inter-provincial tie-line active power transmission data of the grid-level power grid in a preset time period specifically comprises the following steps: obtaining the historical unit offer of the grid-level power grid and the full-network topology data, power transmission and transformation maintenance plan data, load prediction data and power generation equipment prediction data of the grid-level power grid; constructing a regional spot market single-day simulation model of the grid-level power grid; solving the regional spot market single-day simulation model through a preset model solving algorithm to obtain the daily clearing simulation data of the grid-level power grid according to the solving result; obtaining the clearing simulation data of the grid-level power grid in a preset time period through a rolling calculation mode according to the regional spot market single-day simulation model, the model solving algorithm and the preset time period, and determining the full-network regional active power generation data and inter-provincial tie-line active power transmission data of the grid-level power grid in the preset time period. The method for performing power flow calculation on each node in the provincial power grid based on the first power flow calculation model to obtain the power flow electrical parameters of each node in the provincial power grid specifically comprises the following steps:

3. The hierarchical operation simulation method of power grid long time scale according to claim 1, characterized in that, based on the first power flow calculation model, setting the reactive power constraint of the generator node in the provincial power grid as unlimited, performing power flow calculation on each node in the provincial power grid to obtain a first power flow calculation result; if the power flow convergence of the first power flow calculation result does not satisfy a preset convergence condition, filtering the first power flow calculation result and resetting the reactive power constraint of each generator node; ​ If the power flow convergence of the first power flow calculation result meets the preset convergence condition, the unscheduled reactive power amount of the generator node is determined according to the first power flow calculation result, and the reactive power compensation value of each generator node is determined according to the reactive power voltage sensitivity of each generator node to all reactive power compensation nodes in combination with the configuration information of the reactive power compensation nodes; Based on the first power flow calculation model of the generator node with reactive power compensation, the power flow of each node in the provincial power grid is calculated to obtain the power flow electrical parameters of each node in the provincial power grid.

4. The hierarchical operation simulation method of power grid long time scale according to claim 1, characterized in that, According to the nodes of the same regional power grid, the power flow electrical parameters corresponding to the common nodes are combined as boundary conditions for regional power grid power flow calculation, and the second power flow calculation model of each regional power grid is constructed, specifically including: Taking the power flow electrical parameters corresponding to the common nodes as boundary conditions for regional power grid power flow calculation, combining the load data and time series of the new energy nodes of the same regional power grid, and according to the type of the new energy nodes, a plurality of single prediction scenario power flow calculation models are constructed, and then combined with a plurality of preset new energy node operation scenarios to form the second power flow calculation model of the regional power grid.

5. The hierarchical operation simulation method of power grid long time scale according to claim 4, characterized in that, The solving of each single operation scenario power flow calculation model in the second power flow calculation model is performed through a multi-thread parallel processing mode, and the solving results of each single operation scenario power flow calculation model are summarized to obtain the operation simulation result of the regional power grid, and then each single operation scenario power flow calculation model in the remaining second power flow calculation models is sequentially solved to obtain the operation simulation result of each regional power grid in the preset time period. The solving of each single operation scenario power flow calculation model in the second power flow calculation model is performed through a multi-thread parallel processing mode, and the solving results of each single operation scenario power flow calculation model are summarized to obtain the operation simulation result of the regional power grid, and then each single operation scenario power flow calculation model in the remaining second power flow calculation models is sequentially solved to obtain the operation simulation result of each regional power grid in the preset time period. The solving of each single operation scenario power flow calculation model in the second power flow calculation model is performed through a multi-thread parallel processing mode, and the solving results of each single operation scenario power flow calculation model are summarized to obtain the operation simulation result of the regional power grid, and then each single operation scenario power flow calculation model in the remaining second power flow calculation models is sequentially solved to obtain the operation simulation result of each regional power grid in the preset time period.

6. The hierarchical operation simulation method of a power grid long time scale according to claim 5, characterized in that, The solving of each single operation scenario power flow calculation model in the second power flow calculation model is performed through a multi-thread parallel processing mode, and the solving results of each single operation scenario power flow calculation model are summarized to obtain the operation simulation result of the regional power grid, and then each single operation scenario power flow calculation model in the remaining second power flow calculation models is sequentially solved to obtain the operation simulation result of each regional power grid in the preset time period. The power flow electrical parameters specifically include voltage amplitude and phase. The power flow electrical parameters specifically include voltage amplitude and phase. The grid-level power grid simulation unit is configured to obtain historical unit offers of a grid-level power grid, and full-network topology data, power transmission and transformation maintenance plan data, load prediction data, and power generation equipment prediction data of the grid-level power grid, construct a regional spot market simulation model, and perform grid-level power grid regional operation simulation through the regional spot market simulation model to obtain full-network regional active power generation data and inter-provincial tie-line active power transmission data of the grid-level power grid in a preset time period.

7. The hierarchical operation simulation method of power grid long time scale according to claim 1, characterized in that, ​ 8. A hierarchical operational simulation apparatus for long time scale of power grid, characterized in that, ​ ​ The provincial power grid simulation unit is configured to, based on topology data of the provincial power grid, combine the all-grid regional active power generation data and the inter-provincial tie-line active power transmission data as boundary conditions for provincial power grid power flow calculation, construct a first power flow calculation model of the provincial power grid, and perform power flow calculation on each node in the provincial power grid based on the first power flow calculation model to obtain power flow electrical parameters of each node in the provincial power grid in the preset time period. The regional power grid simulation unit is configured to determine common nodes in the provincial power grid and the regional power grid according to an association between the provincial power grid topology and the regional power grid topology, use power flow electrical parameters corresponding to the common nodes as boundary conditions for regional power grid power flow calculation, combine nodes of each regional power grid, and construct a second power flow calculation model of each regional power grid, respectively, to obtain operation simulation results of each regional power grid in the preset time period by solving each second power flow calculation model. The simulation system comprises:

9. A power grid long-time scale hierarchical operation simulation terminal, characterized in that, a memory and a processor; the memory is configured to store program codes corresponding to the power grid long-time-scale hierarchical operation simulation method according to any one of claims 1 to 7; the processor is configured to execute the program codes. The computer readable storage medium stores program codes corresponding to the power grid long-time-scale hierarchical operation simulation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, ​

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