Power system stabilization calculation system, comparative evaluation system, and power system stabilization calculation method

Abstract

To provide a power system stabilization calculation system and a power system stabilization calculation method that can perform calculations for various problem types.SOLUTION: A power system stabilization calculation system that calculates the control amount to control a controlled object that makes up a power system for system stabilization includes a problem generation unit that generates an optimization problem by selecting an objective function, a constraint, and a decision variable on the basis of a power system model regarding the configuration of the power system, and selectable item data that includes the relationship between the objective function, constraint, and decision variable that can be selected for system stabilization. Further, the power system stabilization calculation system includes a solution generation unit that generates a solution to the optimization problem. The solution generation unit is configured to generate a solution for the optimization problem using a calculation function linked to the constraint of the optimization problem, on the basis of management information that manages a calculation function linked to each of the various constraints.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a power system stabilization calculation system, a comparative evaluation system, and a power system stabilization calculation method. [Background technology]

[0002] In recent years, with the large-scale introduction of renewable energy sources into power systems, methods for maximizing the utilization of power system resources have been attracting attention. In this regard, Patent Document 1 describes the use of artificial intelligence to calculate the optimal operating point of a power system. Furthermore, Patent Document 2 describes the use of a dual interior point method to calculate the optimal operating point of a power system.

[0003] Furthermore, Non-Patent Document 1 describes the use of Benders decomposition to calculate the optimal operating point of a power system. Non-Patent Document 2 describes the use of two-level optimization to calculate the optimal operating point of a power system. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] U.S. Patent Application Publication No. 2020-0302325 [Patent Document 2] U.S. Patent Application Publication No. 2012-0150504 [Non-patent literature]

[0005] [Non-Patent Document 1] Q. Wang, J.D. McCalley, T. Zheng and E. Litvinov, "A Computational Strategy to Solve Preventive Risk-Based Security-Constrained OPF," [online], May 2013, in IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1666-1675, [Retrieved August 16, 2022], Internet<doi:10.1109 / TPWRS.2012.2219080.> [Non-patent document 2] S. Fliscounakis, P. Panciatici, F. Capitanescu and L. Wehenkel, "Contingency Ranking With Respect to Overloads in Very Large Power Systems Taking Into Account Uncertainty, Preventive, and Corrective Actions," [online], Nov. 2013, in IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, [Retrieved August 16, 2022], Internet <doi:10.1109 / TPWRS.2013.2251015. Summary of the Invention [Problem to be solved by the invention]

[0006] However, the above-mentioned conventional techniques can only handle problem formats that are pre-specified when calculating the optimal operating point of a power system. While preparing a method suited to a specific problem format is useful for specific problem formats, there are concerns that it lacks scalability as the control object and constraints change. Furthermore, there are concerns that it is difficult to prepare a fully verified and appropriate method for each optimization calculation target and constraint condition related to the power system. Under these circumstances, users who require optimization calculations cannot perform the calculations, making it difficult to perform new optimization calculations in response to changes in regulations and operations, which could lead to various adverse effects such as increased operational losses, lost stabilization opportunities, and delays in planning.

[0007] The present invention has been made in consideration of the above points, and has as its object to provide a power system stabilization calculation system and a power system stabilization calculation method that can perform calculations for various problem formats. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems, one aspect of the present invention is a power system stabilization calculation system that calculates a control amount for controlling a control object that constitutes a power system for system stabilization, the system comprising: a problem generation unit that generates an optimization problem by selecting an objective function, a constraint condition, and a decision variable that are selectable for system stabilization, based on a power system model related to the configuration of the power system and selectable item data including relationships between the objective function, the constraint condition, and the decision variable; and a solution generation unit that generates a solution to the optimization problem, wherein the solution generation unit generates a solution for solving the optimization problem using a calculation function linked to the constraint condition of the optimization problem, based on management information that manages calculation functions linked to each of various constraint conditions. [Effects of the Invention]

[0009] According to the present invention, for example, it is possible to solve stabilization problems corresponding to various problems without preparing solution methods according to the problem format. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram showing an example of a power system in which a significant overload occurs during a fault. [Figure 2] FIG. 1 is a diagram showing an example of the functional configuration of a power system stabilization calculation system according to a first embodiment. [Figure 3] FIG. 10 is a diagram showing an example of a network structure of selectable item data. [Figure 4] FIG. 10 is a diagram showing an example of detailed data of selectable item data. [Figure 5] FIG. 10 is a diagram showing an example of solution list data. [Figure 6] FIG. 2 is a diagram showing an example of the configuration of a basic function unit. [Figure 7] FIG. 1 is a diagram showing an example of a hardware configuration of a power system stabilization calculation system. [Figure 8] 1 is a flowchart showing an example of the overall processing of a power system stabilization calculation system. [Figure 9] 10 is a flowchart showing an example of details of an optimization model generation process. [Figure 10] 10 is a flowchart showing an example of a network structure in which the selection range is narrowed down. [Figure 11] FIG. 10 is a diagram showing an example of a display during optimization model generation processing. [Figure 12] 10 is a flowchart showing an example of details of a solution generation process. [Figure 13] FIG. 10 is a diagram showing an example of a solution (solution-finding process) constructed by the solution generation process. [Figure 14] 10 is a flowchart showing an example of details of a solution-finding process. [Figure 15] FIG. 4 is a diagram showing an example of a display of the results of a power system stabilization calculation according to the first embodiment. [Figure 16] FIG. 10 is a diagram showing an example of a solution calculation result of the power system stabilization calculation system. [Figure 17] FIG. 10 is a diagram showing an example of the analysis results of a power system in which no significant overload occurs during a fault. [Figure 18] FIG. 10 is a diagram showing an example of the configuration of a comparison evaluation system according to a second embodiment. [Figure 19] 10 is a flowchart showing an example of a comparative evaluation process performed by a comparative evaluation unit. [Figure 20] FIG. 10 is a diagram showing an example of a comparative evaluation display in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments according to the disclosure of the present application will be described with reference to the drawings. The embodiments, including the drawings, are examples for explaining the present application. In the embodiments, for clarity of explanation, appropriate omissions and simplifications have been made. Unless otherwise specified, each component of the embodiments may be singular or plural.

[0012] The same or similar components are given the same reference numerals, and in later embodiments, the description may be omitted or the description may focus on the differences. The embodiments disclosed in this application also include an embodiment in which part or all of another embodiment or modified example is integrated into another embodiment.

[0013] When there are multiple identical or similar components, they may be distinguished by adding different subscripts to the same reference numeral. When there is no need to distinguish between these multiple components, the subscripts may be omitted.

[0014] In the following embodiments, various types of information are described in table format, but the various types of information may be in a data format other than a table format. Furthermore, various names such as "XX information," "XX table," "XX list," and "XX queue" are interchangeable. For example, "XX information" may be called "XX table." Furthermore, when describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are interchangeable.

[0015] (Background of embodiment 1) In the first embodiment, a case where the present disclosure is applied to preventive control of a power system will be described.

[0016] First, optimization calculations in the field of power systems will be described. In the field of power systems, optimization calculations are used at each stage of planning, protection, and operation. In this embodiment, a case will be described in which the present disclosure is applied to a preventive control function of a power system used in the protection and operation of the power system.

[0017] First, let us explain the stable operation of a power grid. A power grid is a type of system that consists of generators, power transmission equipment, loads, etc., and the following three requirements for stable operation must be met: (1) AC voltage is within the specified range (appropriate reactive power is being supplied) (2) The frequency of the AC voltage is within a specified range (power supply and demand is balanced). (3) The capacity of the power transmission equipment is within the specified range (the power transmission route is appropriate).

[0018] The three requirements for stable operation exist to ensure that the equipment that makes up the power system operates normally, and if even one of the requirements is not met, equipment failure or a protective device designed to prevent equipment failure will be activated, cutting off the power supply to the loads in the power system and causing a blackout.The three requirements for stable operation must be met not only during normal operation when there are no failures or accidents in the power system, but also during failures within the expected range.

[0019] In the operation of a general power system, power flow calculations (OPF (Optimal Power Flow)) are known that determine the operating points (generator output and equipment on / off status) of generators and power transmission equipment that satisfy the three requirements for stable operation based on predicted system conditions. When calculating the operating point based on power flow calculations, for example, if there are significant fluctuations in renewable energy output or predicted system conditions, the calculated operating point may deviate from the actual position, and the three requirements for stable operation may not be met.

[0020] It is known that when the three requirements for stable operation partially deviate from the range of stable operation, the power system stabilization system will be activated, preventing a major blackout in the event of a failure.

[0021] On the other hand, if the deviation from the predicted grid state is too large, there is a possibility that the range of stable operation will be exceeded before the power grid stabilization system can be activated in response to a minor fault.

[0022] FIG. 1 is a diagram showing an example of a power system PS in which a serious overload occurs during a fault. The power system PS is configured by connecting a renewable energy power generation facility 101 (e.g., a wind power generator (WF (Wind Fan))), a generator 101g, and a power load (not shown) via a transmission line 102 and a bus 103. FIG. 1 shows that a power transmission fault 104 near the center of the power system PS causes an overload 105 in the transmission line 102 around the fault. In this situation, it is necessary to use preventive control to shift to a more stable operating point.

[0023] In the state shown in Figure 1, for example, the following equation (1) is known as a problem for finding an operating point that satisfies (3) of the three requirements for stable operation mentioned above. In equation (1), P is a decision variable, f(P) is an objective function, and it includes a constraint equation for the decision variable P.

number

[0024] The most difficult part here is calculating the state of the power transmission equipment after a fault. The steady-state state of the power system after a fault typically requires calculations such as power flow calculations, which essentially involve many nonlinear elements. Therefore, systems that handle this type of problem are known to require a significant computational load, including quality assurance, in order to ensure stable operation.

[0025] (Outline of this embodiment) In this embodiment, when systematically solving an optimization problem for preventive control of a power system, in order to reduce the computational load, a new solution method is not introduced, but rather a combination of existing problems for which a solution method has been established is used to calculate alternative control variables. That is, a better solution that is not necessarily optimal but can sufficiently stabilize the power system in preventive control of the power system is calculated within a reasonable computation time for solving the optimization problem. From this perspective, in this embodiment, "optimization of the power system" is a concept that includes "stabilization of the power system" based on a better solution that is not necessarily optimal.

[0026] (Configuration of Power System Stabilization Calculation System 1) 2 is a diagram showing an example of the functional configuration of the power system stabilization calculation system 1. The power system stabilization calculation system 1 includes, as processing functions, a problem generation unit 2, a solution generation unit 3, a solution-finding unit 4, a basic function unit 5, and a display unit 10.

[0027] The data handled by the power system stabilization calculation system 1 includes power system model data DB1, selectable item data DB2, optimization model data DB3, solution list data DB4, solution data DB5, and calculated solution data DB6.

[0028] The power system model data DB1, the selectable item data DB2, the optimization model data DB3, the solution list data DB4, the solution data DB5, and the calculated solution data DB6 are stored in a database constructed in a storage device. In this embodiment, the expression "XXX data DB" refers to "XXX data" or "a database storing XXX data" depending on the situation.

[0029] The power system model data DB1 stores a system model that models the power system PS. The selectable item data DB2 stores items related to stabilization calculations for the power system PS. The optimization model data DB3 is an optimization model generated by the problem generation unit 2 and is composed of an objective function, decision variables, and constraint conditions. The solution list data DB4 stores existing solutions corresponding to the optimization model data DB3. The solution data DB5 stores solutions generated using basic functions. The calculated solution data DB6 is a solution calculated by the solution finding unit 4.

[0030] The problem generator 2 receives power system model data DB1 and selectable item data DB2 as inputs and generates optimization model data DB3. The solution generator 3 receives optimization model data DB3 and solution list data DB4 as inputs and outputs solution data DB5. The solution finder 4 appropriately calls basic functions from the basic function unit 5 based on the solution data DB5, performs calculations using the basic functions, and outputs calculated solution data DB6.

[0031] The basic function unit 5 is an environment for executing basic functions related to the power system stabilization calculation system 1, and calculates the execution results of the basic functions. The basic functions are a type of existing calculation library for which solution methods have been established, and provide calculation functions using existing methods.

[0032] Display unit 10 outputs the processing results of problem generator 2 and solution generator 3, and calculated solution data DB6, which is the calculation result of solution finder 4.

[0033] Next, examples of various types of input data will be described with reference to FIGS.

[0034] (Power system model data DB1) First, the power system model data DB1 will be described. Although not shown in the figure, the power system model data DB1 stores information about the configuration of the power system PS, such as the network configuration of the power system PS, impedance, demand patterns, upper and lower output limits of the generators, load characteristics, and generator parameters of the generator model. Depending on the contents of the power system model data DB1, it is possible to set in advance the ranges of various parameters that can be used in optimization calculations.

[0035] (Selectable item data DB2) Next, the selectable item data DB2 will be described. Fig. 3 is a diagram showing an example of a network structure DB21 of the selectable item data DB2. Fig. 4 is a diagram showing an example of detailed data DB22 of the selectable item data DB2. The selectable item data DB2 is made up of two parts: a network structure DB21 that represents the relationship between the objective function, constraint conditions, and decision variables in the optimization calculation of the power system PS, and detailed data DB22 that shows details of the selectable item data DB2.

[0036] (Network Structure DB21) First, the network structure DB21 of the selectable item data DB2 will be described with reference to FIG. 3. As shown in FIG. 3, the network structure DB21 represents the relationship between each "element" and a selectable "objective function," "constraint," and "decision variable." The network structure DB21 can represent the relationship between the "objective function," "constraint," and "decision variable" by, for example, dividing the relationship between the "objective function," "constraint," and "decision variable" into categories: a high relevance indicated by a solid line, a low relevance indicated by a dashed line, and no relevance indicated by no line. When one or more elements of the "objective function," "constraint," and "decision variable" are selected, other elements necessary for the optimization calculation are automatically selected or are recommended and displayed for selection.

[0037] (Detailed data DB22) Next, the detailed data DB22 of the selectable item data DB2 will be described with reference to Fig. 4. The detailed data DB22 is an example of management information that manages calculation functions linked to each of various constraint conditions and related constraint conditions, which are other constraint conditions related to each of the various constraint conditions.

[0038] The detailed data DB22 stores detailed information associated with each "element" (objective function, constraint, decision variable) in the selectable item data DB2. At this time, each "element" stores a "name," a "standard formula," a "related constraint," a "related basic function," and an "attribute." A "related constraint" is another "constraint" that can replace the corresponding "constraint." A "related basic function" indicates a "basic function" linked to the corresponding "objective function" or "constraint." An "attribute" represents the attribute of each element, such as a linear objective function for an "objective function," a linear constraint or a nonlinear constraint for a "constraint," or a continuous value, integer, or binary value for a "decision variable."

[0039] By referring to the detailed data DB 22, it is possible to generate a standard optimization model (optimization problem) from the items of the selected elements, and to call up basic functions linked to the optimization model.

[0040] For example, the entry for row number 1 in Figure 4 has an "element" of "objective function," a "name" of "control variable minimization," a "standard formula" of "maxΣf(t)," and an "associated basic function" of "linear programming." In other words, when "control variable minimization" is selected as the "objective function," the objective function is "maxΣf(t)," "linear programming" is used as the "associated basic function," and the "attribute" is a "linear objective function."

[0041] For example, the entry on line number 6 in Figure 4 has an "element" of "constraint," a "name" of "N-1 overload prevention," a "standard formula" of "P_l(t)after≦P_l^max," a "related constraint" of "N-1 overload prevention (DC)," and an "attribute" of "nonlinear constraint." In other words, the "constraint" of "N-1 overload prevention (DC)" is a "nonlinear constraint," which means that it can be replaced by "N-1 overload prevention (DC)," which is a "linear constraint."

[0042] For example, the entry in line number 11 in Figure 4 has an "element" of "decision variable," a "name" of "WF control," a "standard formula" (decision variable) of "plow(t)," and an "attribute" of "continuous value." In other words, when "WF control" is selected as the "decision variable," the decision variable is "plow(t)" and the "attribute" is "continuous value."

[0043] The optimization model data DB3 is data that represents an optimization problem including an objective function, constraint conditions, and decision variables that are the objects of calculation by the power system stabilization calculation system 1.

[0044] (Solution list data DB4) Next, the solution list data DB4 will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of the solution list data DB4. The solution list data DB4 stores existing established solutions prepared in advance corresponding to various optimization problems (objective functions, constraint conditions, and decision variables (control target devices)).

[0045] The solution list data DB4 is checked when an optimization problem (objective function, constraints, and decision variables (devices to be controlled)) is solved using an already established existing method. The optimization problem in this case is a problem created by selecting elements from the optimization model data DB3 generated by the problem generator 2 based on the network structure DB21. If a corresponding existing method exists in the solution list data DB4, this existing method is provided as the method for solving the optimization problem.

[0046] The solution list data DB4 has the following items: "Objective function," "Constraints," "Decision variables (controlled equipment)," and "Routine." "Name" is the specific name of the "Objective function." "Standard formula" is a specific mathematical expression of the "Objective function." "Routine" is an established existing method for optimizing the "Objective function" related to the "Decision variables (controlled equipment)."

[0047] Next, a description will be given of the configuration of the basic function unit 5 of the power system stabilization calculation system 1. FIG.

[0048] The basic function unit 5 has basic calculation functions related to the power system PS in addition to the optimization calculation of the power system PS. The basic function unit 5 is a type of calculation library, including, for example, optimization-specialized functions such as linear programming and functions used in power system analysis such as power flow calculation. These basic functions are appropriately called as needed to solve various basic problems. In this embodiment, the target optimization problem can be approximately (or roughly) solved by replacing the target optimization problem with a combination of one or more basic problems and solving each basic problem using the basic functions. An approximate solution to an optimization problem is not necessarily the best solution, but it is a better solution.

[0049] As shown in FIG. 6 , the basic function unit 5 includes a linear programming execution unit 51, a power flow calculation execution unit 52, an LODF (Line Outage Distribution Factor) calculation execution unit 53, and a DC power flow calculation execution unit 54. LODF stands for line outage distribution factor. Each of the linear programming execution unit 51, power flow calculation execution unit 52, LODF calculation execution unit 53, and DC power flow calculation execution unit 54 corresponds to a basic function. The linear programming execution unit 51 is a basic function that executes linear programming calculations. The power flow calculation execution unit 52 is a basic function that executes power flow calculations. The LODF calculation execution unit 53 is a basic function that executes LODF calculations. The DC power flow calculation execution unit 54 is an execution unit that executes DC power flow calculations.

[0050] <Hardware configuration> Next, a description will be given of the hardware configuration of the power system stabilization calculation system 1. Fig. 7 is a diagram showing an example of the hardware configuration of the power system stabilization calculation system.

[0051] The power system stabilization calculation system 1 is a system implemented on a computer. The power system stabilization calculation system 1 is composed of a memory 91, a database DB, a calculation means 92, an input means 93, an output means 94, and a communication means 95, all connected to a bus B1.

[0052] The memory 91 stores, in a short-term manner, programs and intermediate results required for the operation of the power system stabilization calculation system 1. The program database DB is a device having a computer-readable, non-transitory storage medium that stores programs and output results that realize the power system stabilization calculation system 1. The program database DB may be a media reading device that can read the contents recorded on the computer-readable, non-transitory storage medium.

[0053] The calculation means 92 is configured with, for example, a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), etc., and executes a predetermined program to perform power system stabilization calculations.

[0054] The input means 93 is, for example, a human-machine interface such as a keyboard or a mouse, or a data linking means between systems such as a LAN (local area network) port or Wi-Fi (Wireless Fidelity, registered trademark (hereinafter the same)).

[0055] The output means 94 is an information display means such as a monitor or a sound output means such as a speaker. The communication means 95 is an information input / output means such as a LAN port or Wi-Fi, which transmits and receives information to and from an external device.

[0056] (Overall processing of power system stabilization calculation system 1) Next, a description will be given of the overall processing of the power system stabilization calculation system 1. Fig. 8 is a flowchart showing an example of the overall processing of the power system stabilization calculation system 1.

[0057] First, in step S101, the power system stabilization calculation system 1 reads the power system model data DB1, the selectable item data DB2, and the solution list data DB4. Next, in step S102, the problem generator 2 generates an optimization model (optimization model data DB3) based on the power system model data DB1 and the selectable item data DB2. Details of the optimization model generation process in step S102 will be described later with reference to FIG. 9.

[0058] Next, in step S103, the solution generating unit 3 reads the optimization model data DB3 and the solution list data DB4, and generates a solution for the optimization model data DB3 generated in step S102. Details of the solution generating process in step S103 will be described later with reference to FIG.

[0059] Next, in step S104, the solution finding unit 4 finds a solution to the optimization problem. Details of the solution finding process in step S104 will be described later with reference to FIG.

[0060] Next, in step S105, the solution finding unit 4 outputs the solution found in step S104.

[0061] (Details of the optimization model generation process) 9 is a flowchart showing an example of details of the optimization model generation process (step S102 (FIG. 8)). The optimization model generation process aims to output an objective function, constraint conditions, and target devices.

[0062] First, in step S1021, the question generator 2 reads the power system model data DB1 and the selectable item data DB2.

[0063] Next, in step S1022, the problem generator 2 narrows down the selectable items (elements) from the power system model data DB1. That is, in step S1022, the problem generator 2 narrows down the selectable elements of the network structure DB21 based on data such as the network configuration, demand pattern, and generator parameters of the power system PS indicated in the power system model data DB1. For example, if the control parameters of the generators cannot be acquired, the optimization calculations that require the generator parameters cannot be performed, so the corresponding constraints are disabled or made unselectable in the network structure DB21. An example of this is shown in FIG. 10. In this way, by using the power system model data DB1, the selection range of the network structure DB21 can be narrowed down.

[0064] Next, in step S1203, the problem generator 2 displays the selectable items (elements) of the network structure DB 21 narrowed down in step S1022 on the display unit 10. Next, in step S1024, the problem generator 2 waits for the user to input a selection item (element) of the network structure DB 21. The selection item (element) here is at least one of "objective function," "constraint condition," and "control target."

[0065] If the question generator 2 has received a user input for a selection item (element) (YES in step S1024), it proceeds to step S1025, and if the user input for a selection item (element) has not yet been received (NO in step S1024), it repeats step S1024.

[0066] In step S1025, the question generator 2 selects a related item in accordance with the user input received in step S1024. The related item here is an element in the network structure DB 21 (FIG. 3) that is connected by a solid or dashed line to the selected item (element) for which selection input was received in step S1024.

[0067] Next, in step S1026, the question generator 2 displays on the display unit 10 the selected items (elements) selected in step S1024 and the related items (elements) selected in step S1025.

[0068] Next, in step S1027, the question generator 2 checks whether the user input has ended. If the user input has ended (step S1027 YES), the question generator 2 proceeds to step S1028, and if the user input has ended (step S1027 NO), the question generator 2 returns to step S1024.

[0069] In step S1028, the problem generator 2 outputs the optimization model data DB3 (objective function, constraint conditions, and controlled device) displayed in step S1026.

[0070] Steps S1024 to S1027 will now be described with reference to Fig. 11. In step S1024, the user selects one or more of the objective function, constraint conditions, and controlled devices from the menu display 10D1 of the objective function, constraint conditions, and controlled devices displayed on the display unit 10 of the output means 94. Then, the problem generator 2 can automatically select related items or recommend less related items via the network structure DB 21.

[0071] 11 is a diagram showing an example of a menu display 10D1 during the optimization model generation process. For example, in this embodiment, assume that the user selects "minimize control cost" under "objective function" in step S1024 (see display 10D11). In response to this selection, "prevent N-1 serious overload" under "constraint condition" is selected as a related item in step S1025 (see display 10D12). In response to this, "control target device (decision variable)" and "WF output" are recommended (see display 10D13). In this way, the optimization calculation is performed after the necessary items have been input and selected.

[0072] As shown in FIG. 11, once required or selectable items are selected (in the example of FIG. 11, "Minimize Control Cost" for "Objective Function" and "Constraint Condition" "N-1 Critical Overload Prevention" are selected), other items required for the calculation are automatically recommended. In the example of FIG. 11, "Controlled Equipment" and "Decision Variable" are recommended. In addition, to perform more advanced calculations, further items may be recommended (in the example of FIG. 11, the hatched areas in the solid circle include "Control Condition," "Frequency Stability," "Normal Voltage Upper and Lower Limits," "Transient Stability (Synchronization)," "Charging Constraint," "Controlled Equipment (Decision Variable)," "Emergency Tap," "Normal Tap," "HVDC (High Voltage Direct Current)," "EV (Electric Vehicle)," and "Storage Battery").

[0073] (Details of the solution generation process) Next, the detailed flow of step S103 will be described with reference to Fig. 12. Fig. 12 is a flowchart showing an example of the details of the solution generation process.

[0074] First, in step S1031, the solution generating unit 3 reads the optimization model data DB3 and the solution list data DB4.

[0075] Next, in step S1032, the solution generation unit 3 performs problem feature matching. Problem feature matching means calling a routine described in the solution list data DB4 that corresponds to the objective function, constraint conditions, and decision variables (controlled equipment) described in the optimization model data DB3. The "objective function," "control variable minimization," "minΣf(t)," "constraint conditions," and "decision variables (controlled equipment)" described in the optimization model data DB3 match the entries in the first row of the solution list data DB4.

[0076] In step S1033, if the solution list data DB4 contains a corresponding item described in the optimized model data DB3 (step S1033 YES), the solution generating unit 3 proceeds to step S1035.

[0077] In step S1034, the solution generating unit 3 selects a solution (routine) for the relevant item from the solution list data DB4.

[0078] Meanwhile, in step S1035, the solution generating unit 3 identifies a "basic function for embedding constraint conditions" for embedding the current constraint conditions in the current objective function from the constraint conditions and the decision constant (control target device). The "basic function for embedding constraint conditions" is a "related basic function" linked to the current "constraint conditions" in the detailed data DB 22 (FIG. 4). If there is no "related basic function" linked to the current "constraint conditions" in the detailed data DB 22 (FIG. 4), the "basic function for embedding constraint conditions" is a "related basic function" linked to the "related constraint conditions" corresponding to the current "constraint conditions."

[0079] Next, in step S1036, the solution generating unit 3 identifies the "basic function of the objective function after embedding constraint conditions" from the current objective function, the "basic function for embedding constraint conditions" identified in step S1035, and the decision constant (control target device). The "basic function of the objective function after embedding constraint conditions" is the "related basic function" linked to the "objective function" after the current constraint conditions have been embedded by the "basic function for embedding constraint conditions" in the detailed data DB 22 (FIG. 4).

[0080] Next, in step S1037, the solution generating unit 3 identifies a "basic function for checking compliance with constraint conditions" from the constraint conditions and the decision constants (control target equipment) to check whether the decision constants (control target equipment) comply with the constraint conditions. The constraint conditions here are "related constraint conditions" corresponding to the current constraint conditions described above. The correspondence between the combinations of constraint conditions and decision constants and the "basic function for checking compliance with constraint conditions" is stored in a predetermined database (not shown, which may be the detailed data DB 22 (FIG. 4)).

[0081] Next, in step S1038, the solution generator 3 generates a solution (solution-finding process). The generated solution repeatedly solves the optimization problem using the "basic functions of the objective function after embedding constraint conditions" identified in step S1036, and checks constraint compliance using the "basic functions for checking compliance with constraint conditions" identified in step S1037.

[0082] In step S1039, the solution generating unit 3 outputs the solution identified in step S1034 or generated in step S1038.

[0083] Here, step S103 will be described in detail. In this embodiment, the user has included a combination of "objective function" "minimize control cost," "constraint condition" "prevent N-1 serious overload," and "decision variable (control target device)" "limit WF output." It is assumed that this combination is not included in the solution list data DB4. In this case, the optimization problem cannot normally be solved.

[0084] On the other hand, using the "standard formula" stored in the detailed data DB 22, it is possible to generate a solution process for a combination of the "objective function" "minimization of control cost," the "constraint condition" "prevention of N-1 serious overload," and the "decision variable (control target equipment)" "WF output limit." An example of this will be explained below.

[0085] In step S1035, the format of the main problem is first identified. In this embodiment, a standard formula is called from the database. It is found that the main problem is as shown in formula (2).

number

[0086] In step S1036, a basic function for solving this equation (2) is identified. The basic procedure is as follows. (Step 1) Determine the problem format as an optimization problem. (Step 2) Determine whether the optimization calculation can be solved in the current problem format (corresponding to step S1033 (FIG. 12)). If the optimization calculation can be solved in the current problem format, solve it. (Step 3) If a solution cannot be found in the current problem format, identify the relevant basic functions of the constraints. If the relevant basic functions of the constraints do not exist, identify the relevant constraints and the relevant basic functions of the relevant constraints. (Step 4) Return to step 1 for a new problem format of the optimization problem using the constraints or related basic functions of the related constraints. When using related basic functions of the related constraints, provide a margin to the related constraints.

[0087] By (Step 1), it can be determined from the standard formula shown in the detailed data DB22 in Figure 4 that the optimization problem of equation (2) has a "linear objective function" as the "objective function," a "linear constraint" as the "constraint condition," and a "continuous value" as the "decision variable."

[0088] On the other hand, the basic functions included in this embodiment do not include a routine that handles such nonlinear optimization problems of continuous values, so the procedure moves to (Procedure 2).

[0089] In addition, although "N-1 Serious Overload" is not linked to a basic function, it has "N-1 Serious Overload (DC)" as a "Related Constraint." In the case of "N-1 Serious Overload (DC)," the LODF function is linked as a basic function.

[0090] Therefore, by using "N-1 critical overload (DC)" as a constraint instead of "calculation of N-1 critical overload", the objective function of the optimization problem can be expressed by linear programming, which is included in the basic function, using the LODF function.

[0091] In this way, each constraint is replaced with a related constraint and the related basic function associated with the replaced related constraint is used until the optimization problem can be expressed in a predetermined problem format (basic function such as "linear programming").

[0092] On the other hand, by using the "N-1 critical overload (DC)" as a constraint, the DC method is used instead, so it is necessary to compensate for the calculation by including a margin, and equation (2) is rewritten as equation (3).

number

[0093] In equation (3), "M" is a margin used to make the constraints more stringent in order to replace the nonlinear N-1 severe overload calculation with the linear DC N-1 severe overload calculation. The margin "M" is determined appropriately depending on the combination of calculation types before and after the replacement (in this example, the combination of "nonlinear N-1 severe overload calculation" and "linear DC N-1 severe overload calculation").

[0094] In step S1037, a predetermined database (not shown) is referenced to confirm that the "basic function for checking compliance with constraints" is the (AC) power flow calculation function, and this is confirmed by executing the power flow calculation execution unit 52 (Fig. 6). In step S1038, the solution-finding and confirmation phases are repeated a predetermined number of times while adjusting the margins.

[0095] Here, an example of a solution constructed by the solution generation process of Fig. 12 will be described with reference to Fig. 13. Fig. 13 is a diagram showing an example of a solution (solution-finding process) So1 constructed by the solution generation process. In Fig. 13, the solution (solution-finding process) So1 illustrates a solution for solving the optimization problem of the above-mentioned equation (2).

[0096] The constructed solution method (solution-finding process) So1 shown in Fig. 13 includes a basic process Pr1. The basic process Pr1 is composed of a calculation preparation Pr11, an LODF calculation Pr12, a linear problem construction Pr13, a problem solution calculation Pr14, and a solution confirmation Pr15. The solution method (solution-finding process) So1 can easily substitute for optimization functions that are not originally provided by calling each basic function from the basic function unit 5.

[0097] First, in calculation preparation Pr11, the basic functions of power flow calculation are called, and power flow calculation execution unit 52 is executed. Next, in LODF calculation Pr12, the basic functions of LODF that replace the nonlinear constraints are called, and LODF calculation execution unit 53 is executed. Next, in linear problem construction Dr13, the nonlinear constraints are replaced by the execution of LODF calculation execution unit 53, and a linear programming problem of equation (3) is constructed from the original linear programming problem of equation (2). Next, in problem solution calculation Pr14, the basic functions of linear programming are called, and linear programming execution unit 51 is executed, and the linear programming problem of equation (3) is solved. Next, in solution confirmation Pr15, a specified database is referenced to confirm that the ``basic functions for constraint compliance confirmation'' are (AC) power flow calculation functions, and the basic functions of power flow calculation are called, and power flow calculation execution unit 52 is executed to confirm that the solution complies with the constraints.

[0098] FIG. 14 is a flowchart showing an example of the details of the solution-finding process (step S104 (FIG. 8)).

[0099] First, in step S1041, the solution finding unit 4 reads the solution data DB 5 and optimization-related parameters. Next, in step S1042, the solution finding unit 4 calls the necessary basic functions based on the solution data DB 5. In the example shown in Fig. 13, the necessary basic functions are the power flow calculation execution unit 52 corresponding to "power flow calculation," the LODF calculation execution unit 53 corresponding to "LODF," and the linear programming execution unit 51 corresponding to "linear programming."

[0100] Next, in step S1043, the solution finding unit 4 executes the solution (solution finding process) indicated in the solution data DB5 using the basic function read out in step S1042. Next, in step S1044, the solution finding unit 4 determines whether the solution was executed normally in step S1043. If the solution was executed normally in step S1043 (YES in step S1044), the solution finding unit 4 proceeds to step S1045, and if the solution was not executed normally (NO in step S1044), the solution finding unit 4 proceeds to step S1046. In step S1045, the solution finding unit 4 outputs the solution found in step S1043. On the other hand, in step S1046, the solution finding unit 4 outputs an error.

[0101] (Power system stabilization calculation result display 10D2) 15 is a diagram showing an example of a result display 10D2 of the power system stabilization calculation in embodiment 1. In step S1045 (FIG. 14), the solution finding unit 4 displays, on the display unit 10, a result display 10D2 including a solution (solution finding process) So1 and a solution calculation result Re1 indicating the power generation amount and the control amount before and after control of a controlled object (e.g., a generator).

[0102] (Solution calculation result Re1) 16 is a diagram showing an example of a solution calculation result Re1 of the power system stabilization calculation system 1. A user can avoid serious overload by controlling the power system PS using the solution (e.g., the control amount for each generator) calculated by the power system stabilization calculation system 1.

[0103] FIG. 17 is a diagram showing an example of the analysis results of a power system PS in which no serious overload occurs in the event of a fault. FIG. 17 shows an example of executing a solution calculated by the power system stabilization calculation system 1 of this embodiment. As can be seen from FIG. 17, before control, the bus voltage in the contingency at bus number 23 exceeded the threshold (indicated by a single cross mark exceeding the dashed threshold). However, after control, it can be confirmed that serious overloads were avoided in all fault cases.

[0104] (Effects of the First Embodiment) By using the power system stabilization calculation system 1 of this embodiment, the operator of the power system PS can solve the power system stabilization problem and avoid serious overloads by substituting a combination of existing methods for calculations without introducing a new method.

[0105] This embodiment can be applied to other optimization calculations in the field of power systems. For example, consider optimization problems that can be modeled but for which it is difficult to prepare an existing dedicated solution, such as a voltage reactive control function that keeps voltage within an appropriate range, a transmission line shutdown plan, generator parameter tuning, and identifying reinforcement locations. This embodiment can obtain a solution to such optimization problems that is not a mathematically rigorous solution but is sufficiently meaningful from an operational standpoint. Furthermore, this embodiment can be applied not only to the field of power systems, but also to solving optimization problems that can be modeled but for which it is difficult to prepare an existing established dedicated solution.

[0106] (Embodiment 2) 18 is a diagram illustrating an example of the configuration of a comparative evaluation system 11 according to embodiment 2. In this embodiment, a comparative evaluation system 11 that compares and evaluates a solution newly generated in the power system stabilization calculation system 1 of embodiment 1 with solutions previously calculated in the power system stabilization calculation system 1 will be described.

[0107] The comparative evaluation system 11 has an execution archive DB7, as shown in Fig. 18. The optimization model data DB3, solution data DB5, and calculated solution data DB6 output by the power system stabilization calculation system 1 are stored in an optimization model archive DB3b, a solution archive DB5b, and a calculated solution archive DB6b, respectively, within the execution archive DB7.

[0108] Furthermore, the power system stabilization calculation system 1 performs a solution search on the new solution method list DB8 to obtain a new solution method calculated solution DB9. The comparison evaluation unit 12 receives the solution archive DB5b, the calculated solution archive DB6b, the new solution method list DB8, and the new solution method calculated solution DB9 as inputs, and compares and evaluates the past generating solution method and calculated solution with the new generating solution method and calculated solution. The comparison evaluation result is output to the comparison evaluation result DB10.

[0109] The comparative evaluation unit 12 compares the new solution calculation solution DB9 with the calculation solution archive DB6b of the execution archive DB7, and evaluates the superiority of the new solution calculation solution DB9 by the new solution method. The comparative evaluation unit 12 compares, for example, one or more of the calculated values ​​of the objective function, the calculated values ​​of the decision variables, the calculation cost, and the calculation time for each solution method.

[0110] That is, the calculation solution archive DB6b is a storage unit that stores past calculation solutions previously obtained by the power system stabilization calculation system 1. The comparison evaluation unit compares the new solution method calculation solution DB9 with the past calculation solutions. The new solution method calculation solution DB9 is a solution obtained by the power system stabilization calculation system 1 using a new solution method managed in the new solution method list DB8 that manages new solution methods for solving various optimization problems. The past calculation solutions are solutions previously obtained by the power system stabilization calculation system 1 without using the new solution method stored in the calculation solution archive DB6b.

[0111] The comparative evaluation system 11 and the comparative evaluation unit 12 have the same hardware configuration as the power system stabilization calculation system 1 shown in FIG.

[0112] (Comparative evaluation process by comparative evaluation unit 12) FIG. 19 is a flowchart showing an example of the comparative evaluation process performed by the comparative evaluation unit 12.

[0113] First, in step S1901, the comparison evaluation unit 12 reads the new solution calculation DB9, the solution archive DB5b, and the calculated solution archive DB6b.

[0114] Next, in step S1902, the comparative evaluation unit 12 calculates the difference in objective function between the new solution calculation solution DB9 and the calculation solution archive DB6b. Next, in step S1903, the comparative evaluation unit 12 calculates the difference in decision variables between the new solution calculation solution DB9 and the calculation solution archive DB6b. Next, in step S1904, the comparative evaluation unit 12 calculates the difference in calculation cost of the decision variables between the new solution calculation solution DB9 and the calculation solution archive DB6b. Next, in step S1905, the comparative evaluation unit 12 outputs each difference for each optimization model (optimization problem) calculated in steps S1902 to S1903 as the comparative evaluation result DB10.

[0115] (Comparative evaluation in embodiment 2) Fig. 20 is a diagram showing an example of a comparative evaluation display 10D3 in embodiment 2. As an example, the comparative evaluation result DB 10 is displayed in the display 10D3. As shown in Fig. 20, the computational costs of the past generating solution method and the new generating solution method are compared for each optimization model item number, and the difference is displayed. Note that while computational costs are used as an example here, required computation time may also be used.

[0116] (Effects of the second embodiment) According to this embodiment, operators of power systems PS can quantitatively check the relative merits of new generative solution methods and calculated solutions compared to past generative solution methods and calculated solutions. Therefore, when developing a module for a new solution method, the superiority of the new solution method can be evaluated by comparing the solution obtained by the new solution method with a solution obtained in the past by replacing the constraints of the corresponding optimization problem with existing basic functions, and the results can be used as reference information for module development.

[0117] In the above-described embodiments, functions and means not specifically mentioned in relation to the configuration may be realized by electric circuits, electronic circuits, logic circuits, and integrated circuits incorporating these.

[0118] Furthermore, the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0119] 1: Power system stabilization calculation system, 2: Problem generation unit, 3: Solution generation unit, 4: Solution finding unit, 5: Basic function unit, 10: Display unit, 11: Comparative evaluation system, 12: Comparative evaluation unit, 91: Memory, 92: Calculation means.

Claims

1. A power system stabilization calculation system for calculating a control amount for controlling a control object constituting a power system for system stabilization, comprising: a problem generator that generates an optimization problem by selecting an objective function, a constraint, and a decision variable that can be selected for stabilizing the power system, based on a power system model relating to the configuration of the power system and selectable item data including relationships between the objective function, the constraint, and a decision variable; a solution generator for generating a solution to the optimization problem, The solution generation unit selecting a solution to the optimization problem when an existing solution to the optimization problem exists in a solution method list that manages existing solution methods for solving various optimization problems; and if an existing solution to the optimization problem does not exist in the solution list, a solution for solving the optimization problem is generated using a computation function linked to the constraint condition of the optimization problem in management information that manages computation functions linked to each of various constraint conditions.

2. 2. The power system stabilization calculation system according to claim 1, The management information further manages related constraints, which are other constraints related to each of the various constraints; The solution generation unit when there is no existing solution to the optimization problem in the solution list and no computation function linked to a constraint condition of the optimization problem in the management information, the power system stabilization computation system identifies associated constraint conditions related to the constraint condition, and generates the solution using the computation function linked to the identified associated constraint condition.

3. 3. The power system stabilization calculation system according to claim 2, The solution generation unit a power system stabilization calculation system, characterized in that, when generating the solution, a calculation function linked to a related constraint condition related to each of the constraint conditions is used until the optimization problem can be expressed in a predetermined problem format.

4. 4. The power system stabilization calculation system according to claim 3, The solution generation unit a computational function for constraint compliance confirmation that confirms whether the constraint conditions are complied with in accordance with a combination of the constraint conditions and decision variables of the optimization problem, and generates the solution using the specified computational function.

5. 3. The power system stabilization calculation system according to claim 2, The solution generation unit A power system stabilization calculation system, characterized in that, when generating the solution using a calculation function linked to the related constraint condition, a predetermined margin is provided for the related constraint condition.

6. 2. The power system stabilization calculation system according to claim 1, The question generator When generating the optimization problem, generating selectable combinations of the objective function, the constraint conditions, and the decision variables corresponding to the optimization problem based on the power system model and the selectable item data, selecting constraint conditions for the optimization problem based on the selectable combinations, and recommending to a user related constraint conditions or decision variables associated with the selected constraint conditions.

7. The power system stabilization calculation system according to any one of claims 1 to 6, a solution generating unit that invokes the calculation function and executes the solution of the optimization problem in accordance with the solution of the optimization problem generated by the solution generating unit.

8. The power system stabilization calculation system according to claim 7; a storage unit for storing past calculation solutions previously obtained by the power system stabilization calculation system; a comparison evaluation unit that compares and evaluates a new-method calculated solution of the optimization problem solved by the power system stabilization calculation system using a new solution managed in a new solution method list that manages new solution methods for solving various optimization problems, with a previous calculated solution that was previously obtained by the power system stabilization calculation system without using the new solution method stored in the storage unit.

9. 9. The comparative evaluation system according to claim 8, the storage unit stores a past solution generated by the solution generation unit and used when calculating the past calculated solution; The comparison evaluation unit A comparative evaluation system characterized by calculating the computational cost when calculating the new solution using the new solution, or the computed value of an objective function or decision variable when using the new solution, and the computational cost when calculating the past computed solution using the past solution, or the computed value of an objective function or decision variable when using the past solution, and comparing the computational costs or the computed values.

10. 10. The comparative evaluation system according to claim 9, The comparison evaluation unit A comparative evaluation system that outputs a comparison result of the calculation cost or the calculated value.

11. A power system stabilization calculation method executed by a power system stabilization calculation system that calculates a control amount for controlling a control object that constitutes a power system for system stabilization, comprising: a problem generation step of generating an optimization problem by selecting an objective function, a constraint, and a decision variable selectable for stabilizing the power system based on a power system model relating to the configuration of the power system and selectable item data including relationships between the objective function, the constraint, and a decision variable; a solution generation step of generating a solution to the optimization problem, In the solution generation step, selecting a solution to the optimization problem when an existing solution to the optimization problem exists in a solution method list that manages existing solution methods for solving various optimization problems; if an existing solution to the optimization problem does not exist in the solution list, a solution for solving the optimization problem is generated using a computational function linked to the constraint conditions of the optimization problem in management information that manages computational functions linked to each of various constraint conditions.

12. The power system stabilization calculation method according to claim 11, The management information further manages related constraints, which are other constraints related to each of the various constraints; In the solution generation step, when there is no existing solution to the optimization problem in the solution list and no computation function linked to a constraint condition of the optimization problem in the management information, identifying an associated constraint condition related to the constraint condition, and generating the solution using the computation function linked to the identified associated constraint condition.

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