Power grid power flow optimization method, system and device, storage medium and product

Abstract

The invention discloses a power grid power flow optimization method, system and device, a storage medium and a product. The method comprises the following steps: acquiring power grid model data and power grid measurement data for a target power grid, and generating a ground state mode according to the power grid model data and the power grid measurement data; wherein the target power grid is a to-be-analyzed power grid with non-convergent load flow calculation, the power grid model data is used for representing the power grid structure of the target power grid, and the power grid measurement data is used for representing the operation state of the target power grid; obtaining a power flow optimization setting item set by a user for the target power grid; and performing power flow optimization calculation on the target power grid according to the ground state mode and the power flow optimization setting item to generate a power grid power flow optimization result. According to the power grid power flow optimization method provided by the embodiment of the invention, rapid power flow convergence optimization calculation can be carried out on a power grid with non-convergent power flow, and a corresponding power grid power flow optimization result is accurately generated to assist a user in carrying out power flow convergence regulation and control on the power grid, so that stable and reliable operation of the power grid is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a power grid flow optimization method, system, equipment, storage medium and product. Background Art

[0002] Power grid flow calculation is a basic calculation to study the steady-state operation of power systems. Its task is to determine the operating status of the entire system based on given operating conditions and network structure, such as the voltage on each bus, the distribution of active power and reactive power in the power grid, etc. In recent years, with the large-scale access of various types of distributed power sources, the uncertainty of the power grid operation status has increased significantly, which has brought great challenges to the stability of power grid flow.

[0003] At present, the convergence regulation of power grid flow mainly relies on manual experience and simple algorithms for optimization and regulation. However, the optimization and regulation of power grid flow is highly complex, and the optimization effect based on manual experience and simple algorithms is not obvious. Summary of the invention

[0004] The present invention provides a power grid flow optimization method, system, device, storage medium and product to realize rapid flow convergence optimization calculation of a power grid with non-convergent flow, accurately generate corresponding power grid flow optimization results, so as to assist users in flow convergence regulation of the power grid, thereby ensuring stable and reliable operation of the power grid, and also helping to improve the voltage quality of the power grid and the level of new energy consumption, reduce network losses, and improve the economic efficiency of distribution network operation.

[0005] According to one aspect of the present invention, a method for optimizing power flow in a power grid is provided, the method comprising:

[0006] Obtaining grid model data and grid measurement data for a target grid, and generating a base state mode according to the grid model data and the grid measurement data; wherein the target grid is a grid to be analyzed for which power flow calculation has not converged, the grid model data is used to characterize the grid structure of the target grid, and the grid measurement data is used to characterize the operating state of the target grid;

[0007] Obtain the power flow optimization setting items set by the user for the target power grid;

[0008] According to the base state mode and power flow optimization setting items, the power flow optimization calculation of the target power grid is performed to generate the power grid power flow optimization result.

[0009] According to another aspect of the present invention, there is provided a power grid flow optimization system, the system comprising:

[0010] A base state mode generation module is used to obtain power grid model data and power grid measurement data for a target power grid, and generate a base state mode according to the power grid model data and the power grid measurement data; wherein the target power grid is a power grid to be analyzed for which power flow calculation does not converge, the power grid model data is used to characterize the power grid structure of the target power grid, and the power grid measurement data is used to characterize the operating state of the target power grid;

[0011] An optimization setting item acquisition module is used to obtain the power flow optimization setting items set by the user for the target power grid;

[0012] The optimization calculation and result output module is used to perform power flow optimization calculation on the target power grid according to the base state mode and power flow optimization setting items, and generate power grid power flow optimization results.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power grid flow optimization method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power grid flow optimization method described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the power grid flow optimization method according to any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention is to obtain the grid model data and grid measurement data for the target grid, and generate a base state mode according to the grid model data and the grid measurement data; wherein the target grid is a grid to be analyzed whose power flow calculation does not converge, the grid model data is used to characterize the grid structure of the target grid, and the grid measurement data is used to characterize the operating state of the target grid; obtain the power flow optimization setting items set by the user for the target grid; perform power flow optimization calculation on the target grid according to the base state mode and the power flow optimization setting items, and generate a grid power flow optimization result. By adopting this technical solution, it is possible to realize fast power flow convergence optimization calculation for a grid whose power flow does not converge, and accurately generate the corresponding grid power flow optimization results, so as to assist the user in power flow convergence regulation of the grid, thereby ensuring the stable and reliable operation of the grid, and at the same time, it is also helpful to improve the voltage quality of the grid and the level of new energy consumption, reduce network losses, and improve the economic efficiency of distribution network operation.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a power grid power flow optimization method provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a power grid flow optimization method provided according to Embodiment 2 of the present invention;

[0024] Figure 3 is a schematic diagram of a display interface of a power grid flow optimization tool provided according to Embodiment 2 of the present invention;

[0025] Figure 4 is a schematic diagram of a display interface of another power grid flow optimization tool provided according to Embodiment 2 of the present invention;

[0026] Figure 5 is a structural schematic diagram of a power grid flow optimization system provided according to Embodiment 3 of the present invention;

[0027] Figure 6 It is a structural schematic diagram of an electronic device for implementing the power grid flow optimization method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 This is a flow chart of a power grid flow optimization method provided in the first embodiment of the present invention. This embodiment can be applied to the situation where the power grid with non-convergent power grid is subjected to power flow convergence optimization calculation and accurately generates the corresponding power grid flow optimization result, i.e., power flow convergence optimization strategy. This method can be executed by a power grid flow optimization system. The power grid flow optimization system can be implemented in the form of hardware and / or software. The power grid flow optimization system can be configured in an electronic device. Figure 1 As shown, a power grid flow optimization method provided in this embodiment includes the following steps:

[0032] S110, acquiring power grid model data and power grid measurement data for a target power grid, and generating a base state mode according to the power grid model data and the power grid measurement data.

[0033] The target power grid may be a power grid to be analyzed for which the power flow calculation does not converge, for example, an electrical island in a certain area.

[0034] The grid model data can be used to characterize the grid structure of the target grid, which may include but is not limited to the following types of information: ① Voltage type information, which is used to identify different voltage levels in the target grid, such as 10kV, 35kV, 110kV, 220kV, 500kV, etc. The voltage type information determines the selection and configuration of equipment such as transmission lines and transformers; ② Regional information, which divides the different geographical or functional areas covered by the target grid. Based on the regional information, the load distribution of the target grid can be understood; ③ Plant and substation information, which records the relevant parameter information of power plants and substations, such as the type of power plant (such as thermal power plant, hydropower plant, wind power plant, photovoltaic power plant, etc.), installed capacity, number of units, etc., as well as the location of the substation, the number of busbars, the number of incoming and outgoing line loops, etc. The plant and station information is a key part of building a grid model, which reflects the production and conversion links of electricity; ④ Circuit breaker information, which describes various attribute information of the circuit breaker, such as the location, model, rated voltage and current, breaking capacity, type of operating mechanism, and current opening and closing status of the circuit breaker; ⑤ Knife switch information ⑥ Busbar information, including the location, type (such as disconnector, earthing switch, etc.), rated voltage and current, operation mode (manual or electric), and opening and closing status of the switch; ⑦ Generator information, including the active and reactive output capacity, rated voltage, and adjustable range of active and reactive power of the generator; ⑧ Load information, recording the power demand and characteristic information of various electrical equipment in the target power grid, such as the location, active power and reactive power, load type, power factor, etc. of the load; ⑨ Line information, describing various parameter information of transmission and distribution lines, such as the name or number of the line, the starting point and end point (two connected nodes or plants), length, installation method (such as overhead line, cable line), resistance, reactance, conductance, susceptance, etc. The line information is an important basis for calculating power grid flow, analyzing voltage distribution and power loss; ⑩ Main transformer information, including the transformation ratio, capacity, loss parameters, etc. of the main transformer. It should be understood that the power grid model data mainly describes the entire power grid structure and basic equipment properties of the target power grid, and these data will not change over time; at the same time, the information of each device in the target power grid can be associated through a unique device identification (ID). Through the device ID, the connection relationship and interaction between different devices in the target power grid can be accurately displayed, making the entire power grid model an organic whole, which is convenient for subsequent analysis and calculation.

[0035] The power grid measurement data can be used to characterize the operating status of the target power grid, which may include but is not limited to the following types of information: circuit breaker remote signal value, switch remote signal value, bus voltage value, generator output value, load value, line active and reactive values, main transformer active and reactive values. It should be understood that the power grid measurement data mainly describes the operating status and equipment status of the target power grid, and these data will change over time.

[0036] The base state method can be used to reflect the initial operating state of the target power grid at a specific moment, which can be generated by superimposing and integrating the aforementioned acquired power grid model data and power grid measurement data.

[0037] In an embodiment of the present invention, for a target power grid whose power flow calculation does not converge, the power grid model data and power grid measurement data for the target power grid can be obtained from a data storage location such as a local or cloud server, and then the above-mentioned power grid model data and power grid measurement data are integrated to generate a corresponding base state mode, which will serve as an important data source for subsequent power flow calculation of the target power grid. Among them, the method of generating the base state mode based on the power grid model data and the power grid measurement data can be: first use the acquired power grid model data to build the topological structure of the target power grid, and then use the device ID as an associated identifier to associate the acquired power grid measurement data with the corresponding devices and nodes in the power grid topological structure, thereby forming a complete data set that reflects the initial operating state of the target power grid at a certain moment, that is, the base state mode. The method of generating the base state mode based on the power grid model data and the power grid measurement data can also be: use professional power system analysis software or tools to process the acquired power grid model data and power grid measurement data, so as to obtain the base state mode corresponding to the target power grid.

[0038] Further, on the basis of the above-mentioned embodiments of the invention, after the base state mode is generated, the device attributes and operation information of the target power grid in the base state mode can also be analyzed, for example, the opening and closing states of the circuit breakers and switches in the target power grid, the current output and the maximum allowable output of the units, the (active / reactive) load distribution, the line operation / shutdown status, etc. can be analyzed. By comprehensively and deeply analyzing the device attributes and operation information of the target power grid in the base state mode, it is helpful to fully understand the operation status of the target power grid, and can provide an accurate and reliable basis for subsequent power flow calculations.

[0039] S120: Obtain power flow optimization setting items set by the user for the target power grid.

[0040] Among them, the flow optimization setting item can be understood as the flow convergence optimization option set by the user for the target power grid where the flow calculation does not converge, which can specifically include: ① Active power flow optimization setting item, which allows the active value of the load node to be optimized and adjusted; ② Reactive power flow optimization setting item, which allows the reactive value of the load node to be optimized and adjusted; ③ Load reactive power flow optimization setting item, which allows the reactive value of the load node to be adjusted according to a certain proportion with the active value; ④ Power supply participation adjustment setting item, which allows the active and reactive power supplies to be optimized and adjusted; ⑤ Considering voltage limit setting item, which means that the voltage limit needs to be considered when optimizing the feasible flow of the power grid, that is, the voltage cannot exceed the upper limit or the lower limit. It should be understood that in the process of optimizing the flow of the target power grid, the user can flexibly select the corresponding flow optimization setting item according to the actual power grid situation, and the options can be checked, that is, multiple options can be selected at the same time for flow optimization calculation. For example, in the case of unbalanced active power in the power grid, active power flow optimization can be adopted in combination with power source participation regulation to enable the power grid to achieve power flow convergence as soon as possible, that is, the user can set ① active power flow optimization setting item and ④ power source participation regulation setting item; for the situation where the power grid flow has just converged but the power grid voltage is obviously unreasonable, only reactive power flow optimization setting and power source participation regulation setting can be adopted to optimize the reactive power and set voltage limit constraints, that is, the user can set ② reactive power flow optimization setting item, ④ power source participation regulation setting item and ⑤ consider voltage limit setting item.

[0041] In an embodiment of the present invention, the power flow optimization setting items set by the user for the target power grid where the power flow calculation does not converge can be obtained, wherein the power flow optimization setting items include one or more of the active power flow optimization setting items, the reactive power flow optimization setting items, the load reactive power flow optimization setting items, the power source participation regulation setting items and the voltage limit consideration setting items.

[0042] S130. Perform power flow optimization calculation on the target power grid according to the base state mode and power flow optimization setting items to generate power grid power flow optimization results.

[0043] In an embodiment of the present invention, based on the initial conditions of the power grid provided by the base state mode and the power flow optimization setting items selected by the user, the power flow convergence optimization calculation of the target power grid can be started, and the corresponding power grid power flow optimization results can be output. For example, the optimization adjustment information such as which equipment is adjusted by the feasible power flow optimization, the active adjustment amount, reactive adjustment amount and voltage adjustment amount of the adjusted equipment, etc. can be included. Subsequently, the user can make corresponding adjustments to the target power grid based on the above-mentioned power grid power flow results to make the target power grid power flow converge, thereby ensuring the stable and reliable operation of the power grid.

[0044] The technical solution of the embodiment of the present invention is to obtain the grid model data and grid measurement data for the target grid, and generate a base state mode according to the grid model data and the grid measurement data; wherein the target grid is a grid to be analyzed whose power flow calculation does not converge, the grid model data is used to characterize the grid structure of the target grid, and the grid measurement data is used to characterize the operating state of the target grid; obtain the power flow optimization setting items set by the user for the target grid; perform power flow optimization calculation on the target grid according to the base state mode and the power flow optimization setting items, and generate a grid power flow optimization result. By adopting this technical solution, it is possible to realize fast power flow convergence optimization calculation for a grid whose power flow does not converge, and accurately generate the corresponding grid power flow optimization results, so as to assist the user in power flow convergence regulation of the grid, thereby ensuring the stable and reliable operation of the grid, and at the same time, it is also helpful to improve the voltage quality of the grid and the level of new energy consumption, reduce network losses, and improve the economic efficiency of distribution network operation.

[0045] Embodiment 2

[0046] Figure 2 This is a flowchart of a power grid flow optimization method provided in the second embodiment of the present invention, which is further optimized and expanded based on the above implementation, and can be combined with various optional technical solutions in the above implementation. Figure 2 As shown, a power grid flow optimization method provided in the second embodiment specifically includes the following steps:

[0047] S210: Find out the grid model data and grid measurement data corresponding to the target grid at the target time in a preset grid service database.

[0048] The preset power grid business database may refer to a database storing various business data of the power system, and the database may store power grid model data and power grid measurement data required for power flow calculation.

[0049] The target time may be the initial power flow optimization time of the target power grid. It should be understood that the target power grid may report power grid measurement data once every preset period (eg, 15 minutes) and store the data together with the corresponding reporting time in the preset power grid service database.

[0050] In an embodiment of the present invention, based on the target power grid and target time to be optimized by the user, the corresponding power grid model data and power grid measurement data can be found in the preset power grid business database. For example, the corresponding power grid model data can be extracted from the voltage type table, area table, plant station table, line table, main transformer table and other database tables in the preset power grid business database, and the corresponding power grid measurement data can be extracted from the circuit breaker telesignaling table, bus voltage table, generator output table, load table and other database tables. In a specific embodiment, the user can input the power grid model data in the preset power grid business database by means of keyboard input, mouse input, voice input and touch input, etc. Figure 3 The target power grid and target time to be optimized are input on the display interface of the power grid flow optimization tool shown, so that the power grid flow optimization tool can respond to the user interface input operation, obtain the corresponding target power grid and target time, and search for the corresponding power grid model data and power grid measurement data in the preset power grid business database based on the target power grid and target time.

[0051] S220, using the grid model data as the basic grid structure of the target grid, associating the grid measurement data with the corresponding nodes in the basic grid structure according to the equipment identification of each power equipment in the target grid, and using the filled basic grid structure as the base state.

[0052] Among them, the basic grid structure can be understood as the corresponding grid topology structure built based on the grid model data of the target grid, which includes the attribute parameter information of the equipment / node, the connection relationship and attribute parameter information of the line, etc.

[0053] In an embodiment of the present invention, the acquired power grid model data can be used to build a basic power grid structure of a target power grid, and then the acquired power grid measurement data can be associated with the corresponding device / node in the basic power grid structure using the device ID as the association identifier, thereby forming a complete data set that reflects the initial operating state of the target power grid at the target time, that is, the base state mode.

[0054] S230: Obtain power flow optimization setting items set by the user for the target power grid.

[0055] In a specific embodiment, the user can Figure 3 Select the required power flow optimization setting items on the display interface of the power grid flow optimization tool shown, and the options can be checked, so that the power grid flow optimization tool can respond to the user interface input operation and obtain the corresponding power flow optimization setting items. Subsequently, the power flow convergence optimization calculation of the target power grid will be performed based on one or more power flow optimization setting items selected by the user.

[0056] S240, determining the target optimization parameter corresponding to the power flow optimization setting item, and extracting the initial parameter value corresponding to the target optimization parameter in the base state mode.

[0057] The target optimization parameters may include the active / reactive forward adjustment amount, the active / reactive reverse adjustment amount of the grid node, the voltage forward adjustment amount / voltage reverse adjustment amount of the PV node, etc.

[0058] In an embodiment of the present invention, the corresponding target optimization parameters can be first determined based on the power flow optimization setting items selected by the user. For example, in a pre-configured mapping table that stores the association relationship between the power flow optimization setting items and the corresponding target optimization parameters, the corresponding associated target optimization parameters can be found in the mapping table according to the identifier of the selected power flow optimization setting item; then, the initial parameter values ​​corresponding to each target optimization parameter are extracted in the above-generated base state method.

[0059] S250. Use a nonlinear programming algorithm to model the power flow convergence optimization of the target power grid and construct a power flow convergence optimization model.

[0060] Among them, nonlinear programming is a method for solving the optimization problem of a nonlinear function in which at least one of the objective function or constraint conditions is a decision variable. The power grid power flow convergence optimization modeling process in this technical solution can be regarded as a nonlinear programming problem. The power flow convergence optimization model may include: objective function and constraint conditions, wherein the objective function may include but is not limited to the following power flow optimization objectives: minimizing network loss, maximizing voltage stability, minimizing the overall adjustment of active-reactive-voltage, etc.; the constraint conditions may include but are not limited to: active balance constraint equation, reactive balance constraint equation, branch power balance constraint equation, voltage constraint, etc.

[0061] In the embodiment of the present invention, a nonlinear programming algorithm can be used to model the power flow convergence optimization process of the target power grid, and active and reactive power problems can be uniformly modeled and solved to construct a corresponding power flow convergence optimization model.

[0062] Further, based on the above-mentioned embodiments of the invention, the objective function of power flow convergence optimization can be expressed as:

[0063]

[0064] This means that the sum of the active adjustment amount multiplied by the penalty coefficient plus the reactive adjustment amount multiplied by the penalty coefficient plus the PV node voltage adjustment amount multiplied by the penalty coefficient is the minimum.

[0065] Constraints can include:

[0066]

[0067] Where i and j are node numbers; P i + , P i - , V i ' + and V i ' - are the target optimization parameters, P i+ is the active positive adjustment of node i, P i - is the active reverse adjustment amount of node i, is the reactive power positive adjustment of node i, is the reactive reverse adjustment of node i, V i ' + is the voltage positive adjustment of the PV node, V i ' - is the voltage reverse adjustment of the PV node; S P is the number set of non-zero injection nodes (including PV nodes and PQ nodes) with active power balance constraints; S Q S is a non-zero injection PQ node number set; V is the PV node number set; ω P ,ω Q and ω V are the penalty coefficients for active power adjustment, reactive power adjustment and PV node voltage set point adjustment respectively; P i ori and are respectively the active power injection and reactive power injection (including output and load) of the original grid node i; P ij (e,f) and Q ij (e, f) represent the active power equation and reactive power equation of branch ij respectively; e and f represent the real part and imaginary part of voltage in rectangular coordinate form respectively; S Z Inject PQ node number set to zero; U i is the square of the voltage amplitude at node i, which is an intermediate variable; V i min and V i max The maximum and minimum voltage settings for the PV nodes are respectively; V i set It is the square of the original voltage setting value of PV node i.

[0068] S260. Based on the initial parameter values, an interior point method is used to solve the power flow convergence optimization model, and corresponding optimization adjustment information is output as a power grid power flow optimization result.

[0069] In an embodiment of the present invention, the optimization model can be iteratively calculated using the interior point method based on the initial parameter values ​​of the power flow convergence optimization model. In each iteration, the objective function value and the satisfaction of the constraint conditions are calculated according to the current variable values, and then the variable values ​​are updated to gradually approach the optimal solution. When it is detected that the iterative process meets the preset convergence conditions, such as the change in the objective function value is less than a certain threshold, the calculation is stopped and the corresponding optimization adjustment information is output as the final power grid power flow optimization result; otherwise, the optimization model continues to be iteratively calculated. It can be understood that the above-mentioned use of the interior point method for model solving is only an example. In actual applications, other power flow calculation methods can also be used, such as the Newton-Raphson method, genetic algorithm, etc., and this embodiment does not impose specific restrictions on this.

[0070] Further, based on the above-mentioned embodiments of the invention, the power grid flow optimization results at least include: a load adjustment information table, a unit voltage adjustment table and a unit active power adjustment table, wherein the load adjustment information table includes at least one of the following fields: load name, reference voltage, active power setting value, active power optimization value, active power adjustment amount, reactive power setting value, reactive power optimization value, reactive power adjustment amount; the unit voltage adjustment table includes at least one of the following fields: unit name, reference voltage, voltage setting value, voltage optimization value, voltage adjustment amount; the unit active power adjustment table includes at least one of the following fields: unit name, reference voltage, active power setting value, active power optimization value, active power adjustment amount.

[0071] Furthermore, based on the above-mentioned embodiments of the invention, after the feasible power flow optimization is performed on the target power grid, the output optimization adjustment information can also be saved to an etext file in a specified directory. Subsequent users can adjust the target power grid accordingly based on the optimization adjustment information recorded in the etext file, so that the target power grid can resume stable and reliable operation.

[0072] In a specific embodiment, Figure 3 As shown, the user can use the power grid flow optimization tool to load the power grid model data and power grid measurement data for the target power grid at the target time, and generate the corresponding base state mode file, wherein the base state mode file may include the following fields: record number, resource number, starting active measurement, starting reactive measurement, starting connection point number, end connection point number, resource alias, starting active, starting terminal ID, starting current, starting reactive, end terminal ID, measurement full range value, starting terminal name, and series resistance.

[0073] Users can Figure 3 Click the "Start Power Flow Calculation" button on the display interface of the power flow optimization tool shown (the power flow optimization setting item is not selected at this time), so that the power flow optimization can respond to the user interface input operation, load the aforementioned generated base state mode file, and perform power flow calculation on the target power grid. Figure 3 From the debugging window in the display interface, it can be seen that the target power grid power flow calculation fails, indicating that the target power grid needs to be optimized for power flow convergence.

[0074] If the target power grid flow calculation fails, the user can Figure 4 Select the power flow optimization setting items on the display interface of the power grid power flow optimization tool shown in the figure. For example, you can directly select all 5 power flow optimization setting items, or you can select the corresponding power flow optimization setting items according to the actual power grid situation; then the user clicks the "Start Power Flow Calculation" button to start the power flow convergence optimization calculation process for the target power grid, and Figure 5 The debugging window in the display interface displays the corresponding process information. After the target power grid flow is calculated successfully, the corresponding power grid flow optimization results can be output, where the optimization adjustment information can be reflected in the unit active power adjustment table, load adjustment information table and unit voltage adjustment table, and the examples are as follows:

[0075] Table 1 Unit active power adjustment table

[0076]

[0077]

[0078] Table 2 Load adjustment information table

[0079]

[0080] Table 3 Unit voltage adjustment table

[0081]

[0082] Through the optimization adjustment information, you can view which devices are specifically adjusted by the feasible power flow optimization and the corresponding adjustment parameters.

[0083] The technical solution of the embodiment of the present invention is to find out the grid model data and grid measurement data corresponding to the target grid at the target time in the preset grid business database; use the grid model data as the basic grid structure of the target grid, and fill the grid measurement data into the corresponding nodes in the basic grid structure according to the equipment identification of each power equipment in the target grid, and use the filled basic grid structure as the base state mode; obtain the flow optimization setting items set by the user for the target grid; determine the target optimization parameters corresponding to the flow optimization setting items, and extract the initial parameter values ​​corresponding to the target optimization parameters in the base state mode; use the nonlinear programming algorithm to model the flow convergence optimization of the target grid, and construct a flow convergence optimization model; based on the initial parameter values, use the interior point method to solve the flow convergence optimization model, and output the corresponding optimization adjustment information as the grid flow optimization result. By adopting this technical solution, it is possible to realize the rapid flow convergence optimization calculation of the grid with non-convergent flow, accurately generate the corresponding grid flow optimization results, so as to assist the user in the flow convergence regulation of the grid, which is helpful to improve the resilience of the distribution network and ensure the safe operation of the distribution network, and also help to improve the voltage quality of the grid and the level of new energy consumption, reduce network losses, and improve the economic efficiency of the distribution network operation.

[0084] Embodiment 3

[0085] Figure 5 This is a schematic diagram of the structure of a power grid flow optimization system provided by Embodiment 3 of the present invention. Figure 5 As shown, the power grid flow optimization system includes:

[0086] The base state mode generating module 31 is used to obtain the power grid model data and the power grid measurement data for the target power grid, and generate the base state mode according to the power grid model data and the power grid measurement data; wherein the target power grid is a power grid to be analyzed for which the power flow calculation does not converge, the power grid model data is used to characterize the power grid structure of the target power grid, and the power grid measurement data is used to characterize the operating state of the target power grid;

[0087] The optimization setting item acquisition module 32 is used to acquire the power flow optimization setting items set by the user for the target power grid;

[0088] The optimization calculation and result output module 33 is used to perform power flow optimization calculation on the target power grid according to the base state mode and power flow optimization setting items, and generate power grid power flow optimization results.

[0089] Further, based on the above-mentioned embodiment of the invention, the base state mode generating module 31 includes:

[0090] A data search unit is used to search for the grid model data and grid measurement data corresponding to the target grid at the target time in a preset grid business database; wherein the target time is the initial power flow optimization time of the target grid;

[0091] The base state mode generating unit is used to use the power grid model data as the basic power grid structure of the target power grid, associate the power grid measurement data with the corresponding nodes in the basic power grid structure according to the equipment identification of each power equipment in the target power grid, and use the filled basic power grid structure as the base state mode.

[0092] Further, based on the above-mentioned embodiments of the invention, the power flow optimization setting items include at least one of the following: active power flow optimization setting items, reactive power flow optimization setting items, load reactive power flow optimization setting items, power source participation regulation setting items, and voltage limit consideration setting items.

[0093] Further, based on the above-mentioned embodiment of the invention, the optimization calculation and result output module 33 includes:

[0094] An initialization unit, used to determine the target optimization parameter corresponding to the power flow optimization setting item, and extract the initial parameter value corresponding to the target optimization parameter in the base state mode;

[0095] An optimization model building unit is used to model the power flow convergence optimization of the target power grid using a nonlinear programming algorithm to build a power flow convergence optimization model;

[0096] The optimization result output unit is used to solve the power flow convergence optimization model based on the initial parameter value by using the interior point method, and output the corresponding optimization adjustment information as the power grid power flow optimization result.

[0097] Further, based on the above-mentioned embodiment of the invention, the power flow convergence optimization model includes an objective function and constraint conditions; the objective function is expressed as:

[0098]

[0099] The constraints include:

[0100]

[0101] Where i and j are node numbers; P i + , P i - , V i ' + and V i ' - are the target optimization parameters, P i + is the active positive adjustment of node i, P i - is the active reverse adjustment amount of node i, is the reactive power positive adjustment of node i, is the reactive reverse adjustment of node i, V i ' + is the voltage positive adjustment of the PV node, V i ' - is the voltage reverse adjustment of the PV node; S P is the set of non-zero injection node numbers for active power balance constraints; S Q S is a non-zero injection PQ node number set; V is the PV node number set; ω P ,ω Q and ω V are the penalty coefficients of active adjustment item, reactive adjustment item and PV node voltage set value adjustment item respectively; Piori and are respectively the active power injection and reactive power injection of the original grid node i; P ij (e,f) and Q ij (e, f) represent the active power equation and reactive power equation of branch ij respectively; e and f represent the real part and imaginary part of voltage in rectangular coordinate form respectively; S Z Inject PQ node number set to zero; U i is the square of the voltage amplitude at node i; V i min and V i max The maximum and minimum voltage settings for the PV nodes are respectively; V i set It is the square of the original voltage setting value of PV node i.

[0102] Further, on the basis of the above-mentioned embodiment of the invention, the power grid flow optimization result at least includes: a load adjustment information table, a unit voltage adjustment table and a unit active power adjustment table, wherein the load adjustment information table includes at least one of the following fields: load name, reference voltage, active power setting value, active power optimization value, active power adjustment amount, reactive power setting value, reactive power optimization value, reactive power adjustment amount;

[0103] The unit voltage adjustment table includes at least one of the following fields: unit name, reference voltage, voltage setting value, voltage optimization value, voltage adjustment amount;

[0104] The unit active power adjustment table includes at least one of the following fields: unit name, reference voltage, active power setting value, active power optimization value, and active power adjustment amount.

[0105] The power grid flow optimization system provided in the embodiment of the present invention can execute the power grid flow optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0106] Embodiment 4

[0107] Figure 6 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0108] like Figure 6 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0109] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0110] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 41 executes the various methods and processes described above, such as a power grid flow optimization method.

[0111] In some embodiments, the power flow optimization method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the power flow optimization method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to perform the power flow optimization method in any other appropriate manner (e.g., by means of firmware).

[0112] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] In some embodiments, the power grid flow optimization method may be implemented as a computer program, which is invisibly included in a computer program product. The computer program implements the power grid flow optimization method of the present invention when executed by a processor. The computer program product can be understood as a software product that implements its solution mainly through a computer program. The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or entirely on a remote machine or server.

[0114] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0115] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0116] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0117] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0118] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0119] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing power flow of a power grid, characterized in that: The method comprises: Obtaining grid model data and grid measurement data for a target grid, and generating a base state mode according to the grid model data and the grid measurement data; wherein the target grid is a grid to be analyzed for which power flow calculation does not converge, the grid model data is used to characterize the grid structure of the target grid, and the grid measurement data is used to characterize the operating state of the target grid; Obtaining power flow optimization setting items set by a user for the target power grid; The target power grid is subjected to power flow optimization calculation according to the base state mode and the power flow optimization setting items to generate a power grid power flow optimization result.

2. The method according to claim 1, characterized in that The acquiring of the grid model data and the grid measurement data for the target grid, and generating a base state mode according to the grid model data and the grid measurement data, includes: In a preset power grid service database, searching for the power grid model data and the power grid measurement data corresponding to the target power grid at a target time; wherein the target time is an initial power flow optimization time of the target power grid; The grid model data is used as the basic grid structure of the target grid, the grid measurement data is associated and filled into the corresponding nodes in the basic grid structure according to the equipment identification of each power equipment in the target grid, and the filled basic grid structure is used as the base state mode.

3. The method according to claim 1, characterized in that The power flow optimization setting items include at least one of the following: an active power flow optimization setting item, a reactive power flow optimization setting item, a load reactive power flow optimization setting item, a power source participation regulation setting item, and a voltage limit consideration setting item.

4. The method according to claim 1, characterized in that The performing power flow optimization calculation on the target power grid according to the base state mode and the power flow optimization setting items to generate a power grid power flow optimization result includes: Determine the target optimization parameter corresponding to the power flow optimization setting item, and extract the initial parameter value corresponding to the target optimization parameter in the base state mode; A nonlinear programming algorithm is used to model the power flow convergence optimization of the target power grid, and a power flow convergence optimization model is constructed; Based on the initial parameter values, the power flow convergence optimization model is solved using an interior point method, and corresponding optimization adjustment information is output as the power grid power flow optimization result.

5. The method according to claim 4, characterized in that The power flow convergence optimization model includes an objective function and constraints; the objective function is expressed as: The constraints include: Among them, i and j are node numbers; and are the target optimization parameters, is the active positive adjustment of node i, is the active reverse adjustment amount of node i, is the reactive power positive adjustment of node i, is the reactive reverse adjustment of node i, is the positive voltage adjustment of the PV node, is the voltage reverse adjustment of the PV node; S P is the set of non-zero injection node numbers for active power balance constraints; S Q S is a non-zero injection PQ node number set; V is the PV node number set; ω P ,ω Q and ω V They are the penalty coefficients of active power adjustment item, reactive power adjustment item and PV node voltage set value adjustment item respectively; and are respectively the active power injection and reactive power injection of the original grid node i; P ij (e,f) and Q ij (e, f) represent the active power equation and reactive power equation of branch ij respectively; e and f represent the real part and imaginary part of voltage in rectangular coordinate form respectively; S Z Inject PQ node number set to zero; U i is the square of the voltage amplitude at node i; and Set the maximum voltage and minimum voltage of the PV node respectively; It is the square of the original voltage setting value of PV node i.

6. The method according to claim 1, characterized in that The power grid flow optimization result at least includes: a load adjustment information table, a unit voltage adjustment table and a unit active power adjustment table, wherein the load adjustment information table includes at least one of the following fields: load name, reference voltage, active power setting value, active power optimization value, active power adjustment amount, reactive power setting value, reactive power optimization value, reactive power adjustment amount; The unit voltage adjustment table includes at least one of the following fields: unit name, reference voltage, voltage setting value, voltage optimization value, voltage adjustment amount; The unit active power adjustment table includes at least one of the following fields: unit name, reference voltage, active power setting value, active power optimization value, and active power adjustment amount.

7. A power grid flow optimization system, characterized in that: The system comprises: A base state mode generation module, used to obtain power grid model data and power grid measurement data for a target power grid, and generate a base state mode according to the power grid model data and the power grid measurement data; wherein the target power grid is a power grid to be analyzed for which power flow calculation does not converge, the power grid model data is used to characterize the power grid structure of the target power grid, and the power grid measurement data is used to characterize the operating state of the target power grid; An optimization setting item acquisition module, used to acquire power flow optimization setting items set by a user for the target power grid; The optimization calculation and result output module is used to perform power flow optimization calculation on the target power grid according to the base state mode and the power flow optimization setting items, and generate power grid power flow optimization results.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power grid power flow optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power grid power flow optimization method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the power grid flow optimization method according to any one of claims 1 to 6.

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