Power system planning support method and power system planning support system

Abstract

To provides a method and a system for supporting power grid planning.SOLUTION: A power grid planning support method includes the steps of: (i) receiving an objective function, the objective function representing a quantity or parameter related to a power grid; (ii) receiving one or more constraint functions, the constraint functions representing constraints on the power grid, the objective function and the constraint functions together representing a model of the power grid; (iii) receiving input data related to the power grid; (iv) performing an optimization calculation defined by the model of the power grid and the input data; and (v) displaying a result of the optimization calculation.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a power system planning support method and a power system planning support system. [Background technology]

[0002] As energy production and supply transition to carbon neutrality, the demand for and adoption of variable renewable energy sources (VRE) is increasing. Intermittent generation by VRE, different characteristics between VRE and conventional synchronous generators (SG), and long-distance transmission due to the location of VRE and the location of demand are expected to reduce the reliability of the power grid.

[0003] Therefore, it is desirable to apply optimization calculations to general grid planning to provide the lowest-cost solution within planning-related constraints such as asset capacity, bus voltage range, and reliability criteria.

[0004] However, due to the numerous reliability issues posed by the energy transition and the need to consider many scenarios depending on the grid planning uncertainties, the optimization problem is generally too complex to solve efficiently. Without a solution, grid planners cannot continue planning the grid.

[0005] Patent Document 1 proposes an apparatus and method for optimizing cost-effectiveness in grid expansion projects, including the conversion of retired generators to synchronous condensers. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2022-142649 Summary of the Invention [Problem to be solved by the invention]

[0007] The proposed method has an iterative process that includes contingency analysis, countermeasure consideration, benefit analysis, cost analysis, cost-benefit analysis, and determination of a conversion plan to identify the optimal solution.

[0008] However, as described above, in order to ensure that the obtained solution is the best, a vast pattern of the power grid situation must be considered. When combined with the complex constraints related to power flow and reliability and the large number of variables, problems occur in convergence.

[0009] This disclosure has been made based on the above considerations.

Means for Solving the Problem

[0010] Therefore, in a first aspect, an embodiment of the present invention provides a power system planning support method implemented on a computer, the method including the following steps: (i) receiving an objective function, where the objective function represents a quantity or parameter related to the power grid, (ii) receiving one or more constraint functions, where the constraint functions represent constraints on the power grid, and when the objective function and the constraint functions together represent a model of the power grid, (iii) receiving input data related to the power grid, and (iv) performing an optimization calculation defined by the model of the power grid and the input data, and (v) displaying the result of the optimization calculation.

[0011] By such a method, a transmission network planner can robustly obtain a solution to the optimization problem of the transmission network.

[0012] Steps (i) to (iii) can be executed in any order.

[0013] Steps (i)-(iii) and (v) may be performed on or using a graphical user interface (GUI).

[0014] The method may further include calculating one or more intermediate results if execution of the optimization calculation returns an indication that the optimization calculation cannot be completed. The method may further include indicating to a user that the optimization calculation cannot be completed and providing the calculated intermediate results to the user.

[0015] The method may further include receiving one or more overrides and / or edits to the constraint(s) received in step (ii), such that step (iv) is performed without the overriden constraint(s) and / or with the edited constraint(s).

[0016] The method may further include applying a decomposition technique to the objective function to generate a plurality of sub-problems representing the objective function, the plurality of sub-problems and the constraint functions representing a model of the power grid.

[0017] Step (ii) may further include receiving a plurality of constraint functions, and the method may further include receiving an order in which the constraint functions are to be applied.

[0018] The method may further include applying a constraint generation method to one or more of the constraint functions, the constraint generation method applying a subset of the constraints and sequentially adding additional constraints. The method may include receiving a priority for the subset of constraints and / or a maximum number of constraints to generate at one time.

[0019] Step (i) includes receiving definitions of one or more variables, definitions of one or more constraints, and definitions of one or more datasets.

[0020] A reinforcement learning system can provide one or more constraint functions.

[0021] The method may further include a step of receiving a request to perform a trial calculation before performing the optimization calculation, and the method may further include performing the trial calculation, where the trial calculation indicates whether a complete solution is expected from the optimization calculation defined by the power grid model and the input data.

[0022] In a second aspect, an embodiment of the present invention provides a power system planning support system, which includes one or more processors and a memory, and the memory includes machine-executable instructions that, when executed on the processor, cause the processor to perform the following: (i) Receive an objective function, where the objective function represents a quantity or parameter related to the power grid. (ii) Receive one or more constraint functions, where each constraint function represents a constraint on the power grid, and the objective function and the constraint functions together represent a model of the power grid. (iii) Receive input data related to the transmission grid. (iv) Perform an optimization calculation defined by the power grid model and the input data. (v) Display the result of the optimization calculation. Steps (i) to (iii) may be performed in any order.

[0023] The machine-executable instructions may cause the processor to calculate one or more intermediate results if the execution of the optimization calculation returns an indication that the optimization calculation cannot be completed. The machine-executable instructions may cause the processor to indicate to the user that the optimization calculation cannot be completed and provide the user with the calculated intermediate results.

[0024] The machine-executable instructions may cause the processor to receive one or more invalidations and / or edits to the received constraint(s) in step (ii) such that step (iv) is performed without the invalidated constraint(s) and / or with the edited constraint(s).

[0025] The machine-executable instructions can cause a processor to apply a decomposition method to an objective function and a constraint function so as to reach a plurality of sub-problems representing the objective function, and the plurality of sub-problems having the decomposed objective function and constraint function represent a model of a power grid.

[0026] Step (ii) can include receiving a plurality of constraint functions, and the method can further include receiving an order in which the constraint functions are applied.

[0027] The machine-executable instructions can cause a processor to apply a constraint generation method to one or more of the constraint functions, and the constraint generation method applies a subset of the constraints and sequentially adds additional constraints. The machine-executable instructions can cause a processor to receive a priority for a subset of the constraints and / or a maximum number of constraints generated at one time.

[0028] Step (i) can include receiving a definition of one or more variables, a definition of one or more constants, and a definition of one or more data sets.

[0029] The reinforcement learning system can provide one or more of the constraint functions and / or constraint application methods.

[0030] The machine-executable instructions can cause a processor to receive a request to perform a trial calculation before performing an optimization calculation, and the method can further include performing the trial calculation, where the trial calculation indicates whether a complete solution is expected from the optimization calculation defined by the model of the power grid and the input data.

[0031] The present invention includes combinations of the described aspects and preferred features, except where such combinations are clearly not permitted or are explicitly avoided.

[0032] Further aspects of the present invention provide a computer program comprising code which, when executed on a computer, causes the computer to perform the method of the first aspect, a computer readable medium storing a computer program comprising code which, when executed on a computer, causes the computer to perform the method of the first aspect, and a computer system programmed to perform the method of the first aspect. [Effects of the Invention]

[0033] The present invention allows power grid planners to obtain robust solutions to power grid optimization problems.

[0034] In addition, the user or system can define the decomposition of the original optimization problem, including the constraint application method, and the process flow for collaboratively solving the decomposed problem, allowing for a collaborative and robust optimal solution to be obtained from the decomposed problem instead of directly solving the original problem. Even when a final result cannot be obtained (such as in a numerical problem), the output interface for intermediate calculation results and constraint violations, as well as the database of intermediate calculation results, allow the user or an external system to make decisions about the next step based on the intermediate results.

[0035] In addition, users or external systems can edit the decomposition definition of the original problem based on the intermediate results of the output calculation, then re-run the optimization calculation to obtain a better solution in the next calculation. Therefore, system planners can continue their planning work because the decomposition definition allows them to robustly obtain the optimal solution for power system planning within the expected value.

[0036] Even if an optimal solution cannot be obtained, it is possible to make a judgment based on intermediate calculation results, change the decomposition definition and re-execute the optimization calculation, or attempt to obtain an optimal solution. [Brief description of the drawings]

[0037]

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[0038] Aspects and embodiments of the present invention will now be described with reference to the accompanying figures. Further aspects and embodiments will be apparent to those skilled in the art.

[0039] Generally, it is composed of multiple modules including a calculation model database module, a calculation input database module, a calculation final result database module, as well as a model management module, a constraint application method database module, and a calculation intermediate result database module.

[0040] It also has an interface for inputting constraint conditions from grid planners (users) or external systems, and for outputting intermediate calculation results and constraint violations to the planners or external systems.

[0041] The constraint application method input interface, model management module, and constraint application method database module allow a user or external system to edit how constraints are applied to solve an optimization problem.

[0042] Using these modules, users or systems can define the decomposition of the original optimization problem, including the constraint application method, and the process flow for collaboratively solving the decomposed problem. Instead of directly solving the original problem, the decomposed problem can be used to collaboratively and robustly obtain an optimal solution. Robust optimization is a modeling technique and solution method for optimization problems that returns reliable results even when the data defining the problem is inaccurate or uncertain. The output interface for intermediate calculation results and constraint violations, as well as the database of intermediate calculation results, allow users or external systems to make decisions about the next step based on the intermediate results, even when final results cannot be obtained (such as for numerical problems).

[0043] In addition, users or external systems can edit the decomposition definition of the original problem based on the intermediate results of the output calculation, then re-run the optimization calculation to obtain a better solution in the next calculation. Therefore, system planners can continue their planning work because the decomposition definition allows them to robustly obtain the optimal solution for power system planning within the expected value.

[0044] Even if an optimal solution cannot be obtained, it is possible to make a judgment based on intermediate calculation results, change the decomposition definition and re-execute the optimization calculation, or attempt to obtain an optimal solution.

[0045] In the present embodiment, explanations may be given using mathematical expressions, but the symbols in the mathematical expressions may be simplified to represent the symbols in the drawings that are correctly written.

[0046] FIG. 1 shows an example of a power system planning support system 1. The power system planning support system may also be called a power system planning support tool. In this example, the tool 1 is provided as a general computer having a central control unit 11, an arithmetic and logic unit 12, an input device 13, an output device 14, a main memory device 15, and an auxiliary memory device 16. These units are connected to each other via a computer bus 17. The auxiliary memory device 16 includes one or more databases 18 and one or more programs 19.

[0047] 2 is realized by the central control unit 11 loading a program stored in the secondary storage 16 into the main storage 15. The output unit 14 may consist of a display device or may be a component that provides output to a display device or other user terminal (to be displayed by the terminal).

[0048] 2 is a diagram showing an example of a processing module of the power grid planning support system. As described above, the data flow, processing modules, and processing flow are determined by the program 19. The power grid planning support tool 1 has an input module 201, an output module 202, a model management module 203, a calculation module 204, a calculation model database module 210, a calculation input database module 211, a constraint application method database module 212, a calculation intermediate result database module 213, and a calculation final result database module 214. When the power grid planning support system 1 is started, the output module 202 displays an initial screen 301.

[0049] 3 is an example of an initial screen 301. The initial screen 301 includes a new project button 310, a project load button 311, a project save button 312, an execution calculation button 313, a model management button 314, a data management button 315, and a trial calculation button 316. The new project button 310 is used to create a new project. Here, a "project" refers to a collection of models and input / output data related to optimization calculations associated with power system planning work.

[0050] The Load Project button 311 is used to load a previously saved project. The Save Project button 312 is used to save the current working project. The Run Calculation button 313 is used to run an optimization calculation defined by a model and data. The Model Management button 314 is used to open the Model Management screen 401 as shown in FIG. 4. The Data Management button 315 is used to open the Data Management screen 2301 as shown in FIG. 23, which is used to set parameters and load data. The Trial Calculation button 316 is used to check the validity of a user-defined model by calculating the initial few steps of the optimization problem.

[0051] FIG. 4 shows an example of a model management screen 401. The model management screen 401 includes a model load button 410, a model save button 411, a model details button 412, a close button 413, an objective function edit button 414, a constraint edit button 415, a disable button 416, a constraint add button 417, an add constraint application method drop-down button 418, an objective function table 420, and a constraint table 421. The model load button 410 is used to load a template model or a previously saved model. The model save button 411 is used to save the current working model. The model details button 412 is used to open a model detail settings screen 1801 for setting model details related to the constraint application method.

[0052] The close button 413 is used to close the model management screen 401. The objective function editing button 414 is used to open the objective function editing screen 501 shown in FIG. 5 and edit the model shown in the objective function table 420.

[0053] The constraint editing button 415 is used to open the constraint editing screen 601 shown in FIGS. 6 - 17 and edit the model shown in the constraint table 421. The invalidation button 416 is used to invalidate one or more constraints shown in the constraint table 421. The constraint addition button 417 is used to add a row to the constraint table 421. The constraint application method drop-down button 418 is used to open a candidate list of constraint application methods. The objective function table 420 shows the name of the objective function of the current working model, and the constraint table 421 shows a list of the names of the constraints of the current working model.

[0054] As an example, an optimization problem regarding the placement of reactive power sources is formulated as follows. The objective function of this problem includes the total cost obtained from the power generation cost and the asset placement cost. The total cost is formulated according to Equation (1).

[0055]

Equation

[0056] Here, a, b, and c are real constant numbers indicating the power generation cost coefficients. P_g is a real variable indicating the active power generation of the generator. G and S are the sets of generators and reactive power sources, respectively. C_dep is a real constant number indicating the depreciation cost of the asset. u_s is an integer variable indicating the placement state of the asset. In this example, us is u_sr and usyc:

[0057]

Equation

[0058] Here, \(u_{sr}\) and \(u_{syc}\) are integer variables indicating the arrangement states of the STATCOM (Static Synchronous Compensator) and the synchronous condenser, respectively. The constraint conditions of this problem include power flow equations, power flow limits, reactive reserve requirements, short-circuit level requirements, asset capacity constraints, asset placement lead-time constraints, asset removal prohibition constraints, variable ranges and definitions. The power flow is calculated according to Equation (3).

[0059]

Equation

[0060] Here, it is a complex variable indicating the bus voltage. Y is a complex constant indicating the bus admittance of the power system. \(Y^*\) is the complex conjugate of Y. \(P_g\) and \(Q_g\) are real constants indicating the active power and reactive power demands of the bus, respectively. \(P_g\) is a real variable related to \(Q_g\), indicating the active power and reactive power generation from the generator, respectively (\(g_{bus}=m\)). \(Q_s\) is a real variable indicating the reactive power generation from the reactive power source. \(Q_s\) includes the real variable \(Q_{st}\) indicating the reactive power generation from the STATCOM, and \(Q_{syc}\) is a real variable indicating the reactive power from the synchronous condenser calculated using Equation (4):

[0061]

Equation

[0062] \((S_{bus})\) is a subset of the reactive power sources connected to bus m. BUS is a set of buses, and TIME is a set of times. The set of reactive power sources is the sum of the sets of STATCOM (ST) and synchronous condenser (SyC) as follows.

[0063]

Equation

[0064] The flow limit is formulated as a plurality of equations as follows.

[0065]

Number

[0066]

Number

[0067]

Number

[0068]

Number

[0069] Here, Yf and Yt are complex constants indicating the branch admittance of the power system. Fmax is a real constant indicating the flow limit of the branch. BRANCH is the set of branches, and (l, m, n) is an element of BRANCH, indicating that branch l is connected from bus m to bus n. The requirement for reactive power reserve is formulated as follows:

[0070]

Number

[0071]

Number

[0072] Here, Qg max and Qg min are real constants indicating the maximum reactive power capacity and the minimum reactive power capacity of the generator, respectively. Qs max and Qs min are real constants indicating the maximum and minimum reactive power capacities of the reactive power source, respectively. Qres+ and Q-res- are real constants indicating the required amounts of reactive power reserves in the forward and reverse directions within the area, respectively. AREA is a set of areas. {garea=a} means a subset of generators located in the area of.Qres+. {Sarea=a} means a subset of reactive power sources within the area. The short-circuit level requirement is formulated as a set of multiple equations as follows:

[0073]

Number

[0074]

Number

[0075]

Number

[0076]

Number

[0077] Here, Z_ext means a complex variable indicating the extended impedance of the power grid. SCR_lim means a real constant indicating the limit of the short-circuit ratio. Y_ext means a complex variable indicating the extended bus admittance of the power system. I means the identity matrix. x_(d_g)^' and x‘_(d_SyC) mean real constants indicating the transient reactances of the generator and synchronous condenser, respectively. {SyC_bus=m} means a subset of synchronous condensers connected to bus m. The asset capacity constraint is formulated as a system of simultaneous equations as follows:

[0078]

Number

[0079]

Number

[0080]

Number

[0081]

Number

[0082] And \(P_g^{max}\) and \(P_g^{min}\) respectively represent real constant numbers indicating the maximum and minimum active power capacities of the generator. \(Q_{ST}^{max}\) and \(Q_{ST}^{min}\) are average real constant numbers respectively indicating the maximum reactive power capacity and the minimum reactive power capacity of the STATCOM, \(Q_{SyC}^{max}\) and \(Q_{SyC}^{min}\) are average real constant numbers respectively indicating the maximum reactive power capacity and the minimum reactive power capacity of the synchronous condenser. The lead time constraint for asset allocation is formulated as follows.

[0083]

Number

[0084] Here, \(C_{LT}\) represents an integer constant indicating the coefficient of the lead time condition of the reactive power source. For the reactive power source in case \(i\) and when \(t \leq T_{LT}\), the corresponding element is 1. When \(t > T_{LT}\), the corresponding element is 0. The asset withdrawal prohibition constraint is formulated as follows.

[0085]

number

[0086] The ranges and definitions of the variables are as follows:

[0087]

number

[0088]

number

[0089]

number

[0090]

number

[0091] Numbers 2 and 4 are also included in the variable range and definition. Here, VM and theta are real variables that indicate the magnitude and angle of the bus voltage, respectively. are average real constants that indicate the minimum and maximum values of Vmin and Vmax, respectively. Vm, theta min, and theta max are constants that indicate the minimum and maximum values of theta, respectively. Us min and Us max are real constants that indicate the minimum and maximum values of Us, respectively.

[0092] These equations can be confirmed and edited on the objective function edit screen 501 and the constraint edit screen 601. When the user presses the objective function edit button 414 on the model management screen, the objective function edit screen 501 opens.

[0093] FIG. 5 is an example of an objective function editing screen 501, which has a close button 413, an objective function confirmation button 510, an add variable row button 511, a delete variable row button 512, an add constraint row button 513, a delete constraint row button 514, an add set row button 515, a delete set row button 516, an objective function name field 520, an objective function formulation field 521, a variable table 522, a constant table 523, and a set table 524.

[0094] The Objective Function Confirmation button 510 is used to overwrite the objective function model with the objective function name edited in the Objective Function Name field 520, the formulation edited in the Objective Function Formulation field 521, the variables edited in the Variables table 522, the constants edited in the Constants table 523, and the sets edited in the Sets table 524.

[0095] The add variable row button 511, add constraint row button 513, and add set row button 515 are used to add rows to the variable table 522, constant table 523, and set table 524, respectively.

[0096] The delete variable row button 512, delete constant row button 514, and delete set row button 515 are used to delete rows from the variable table 522, constant table 523, and set table 524, respectively.

[0097] An objective function name field 520 indicates the name of the objective function, and an objective function formulation field 521 indicates the formulation of the objective function (for example, as described in [Mathematical Expression 1]). A variable table 522, a constant table 523, and a set table 524 indicate the variables, constants, and sets included in the objective function. The objective function name in the objective function name field 520, the formulation in the objective function formulation field 521, the variables in the variable table 522, the constants in the constant table 523, and the sets in the set table 524 are editable.

[0098] Similarly, when the user presses the Edit Constraint button 415 on the model management screen, the Edit Constraint screen 601 for the corresponding constraint opens.

[0099] Figures 6 - 16 are examples of the constraint editing screens for each constraint. Figure 6 is an example of the constraint editing screen 601 for the power flow equation. On the constraint editing screen 601, there are a close button 413, a variable row addition button 511, a variable row deletion button 512, a constraint row addition button 513, a constant row deletion button 514, a set row addition button 515, a set row deletion button 516, a constraint confirmation button 610, a variable table 522, a constant table 523, a set table 524, a constraint name field 620, and a constraint formulation table 621.

[0100] The constraint confirmation button 610 is used to overwrite the current constraint model with the edited constraint name on the constraint name field 620, the edited formulation on the constraint formulation table 621, the edited variables on the variable table 522, the edited constants on the constant table 523, and the edited sets on the set table 524. The constraint name field 620 indicates the corresponding constraint name. The constraint formulation table 621 indicates the formulation of the equation as described, for example, in [Equation 3]. The variable table 522, the constant table 523, and the set table 524 indicate the variables, constants, and sets included in the constraint. The constraint name on the constraint name field 520, the formulation on the constraint formulation field 621, the variables on the variable table 522, the constants on the constant table 523, and the sets on the set table 524 are editable.

[0101] Figures 7 - 17 show example constraint editing screens 601 for power flow limit, reactive power reserve requirement, short - circuit level requirement, asset capacity constraint, asset placement lead - time constraint, asset removal prohibition constraint, and variable range / definition. Figures 7 and 8, Figures 10 and 11, Figures 12 and 13, Figures 16 and 17 are the scroll parts of the same screen respectively.

[0102] When the user presses the model details button 412 on the model management screen 401, the model details setting screen 1801 opens.

[0103] 18 is an example of a model detail setting screen, which includes several common buttons and a subscreen area 1802. The common buttons are a variable management subscreen button 1811, a constant management subscreen button 1812, a set management subscreen button 1813, a constraint management subscreen button 1814, and a close button 413.

[0104] The variable management subscreen button 1811, the fixed management subscreen button 1912, the set management subscreen button 1813, and the constraint management subscreen button 1814 are used to display the variable management subscreen 1901, the fixed management subscreen 2001, the set management subscreen 2101, and the constraint management subscreen 2201, respectively, in the subscreen area 1802.

[0105] 19 is an example of a variable management subscreen 1901 that includes a confirm button 1910 and a variable definition table 1920. The confirm button 1910 is used to confirm the variable definition edited on the variable definition table 1920 and update the current working model. The variable definition table 1920 has variable rows and type rows. The variable row lists the variables included in the current working model, and the type row lists the variable types, such as integer, real, and complex. The elements in this row can be edited to set the variable type.

[0106] 20 is an example of a constant management subscreen 2001, which includes a confirm button 1910, a constant right move button 2010, a constant left move button 2011, a constant table (input from data) 2020, and a constant table (user defined) 2021. The constant right move button 2010 is used to move a selected constant from the constant table (input from data) 2020 to the constant table (user defined) 2021. The constant left move button 2011 is used to move a selected constant from the constant table (user defined) 2021 to the constant table (input from data) 2020. The constant table (input from data) 2020 shows a list of constants defined from input data. The constant table (input from data) 2021 has constant rows and definition rows. The constant table (input from data) 2021's constant rows show a list of constants, and their values are defined from other constants. The definition rows of the constant table (input from data) 2021 show the definitions of the constants, and the elements of this row are editable.

[0107] Figure 21 is an example of the set management sub-screen 2101, including the confirmation button 1910, the set table (input from data) 2120, and the set table (user-defined) 2121. The set table (input from data) 2120 is a list showing the elements of the set defined from the input data. The set table (user-defined) 2121 has set rows and definition rows. The set rows of the set table (user-defined) 2121 show a list of sets, and the elements of those sets are defined by other sets. The definition rows of the set table (user-defined) 2121 show the definitions of the sets, and the elements of this row are editable.

[0108] Figure 22 is an example of the constraint management sub-screen 2201, including the constraint application method drop-down button 418, the constraint management category drop-down button 2210, and the constraint management category field 2220. The constraint management category drop-down button 2210 is used to select the category of constraint management. The constraint management category field 2220 shows the selected category of constraint management. Figure 22 shows an example when the order of the constraint application method is selected as the category of constraint management. In this case, the constraint application method order table 22221 used to set the order of the constraint application method is displayed. Hereinafter, the detailed usage method of this sub-screen 2201 will be described.

[0109] FIG. 23 shows an example of a data management screen 2301 , which includes a dataset management subscreen 2302 , an import button 2310 , a close button 413 , a dataset drop-down button 2311 , an add dataset button 2312 , and a dataset name field 2321 . The import button 2310 is used to import data from a file specified in the file name file 2324 based on the mapping determined in the data mapping table 2325 . The dataset drop-down button 2311 is used to open a drop-down list for selecting a dataset for mapping. The add dataset button 2312 is used to add a new dataset. The dataset name field 2321 shows the name of the dataset selected by the drop-down list opened by the dataset drop-down button 2311. The Data Set Management sub-screen 2302 includes a Constant and Set Assignment button 2313 , an Open Explorer button 2313 , a Constants table 2322 , a Sets table 2323 , a Filename field 2324 , and a Data Mapping table 2325 . The constant and set assignment button 2313 is utilized to assign the selected constant and set in the constant table 2322 and set table 2323 . The constant table 2322 and the set table 2323 respectively list constants and sets, and the elements of these tables are selectable. The open explorer button 2314 is used to open a file system explorer window for selecting an input file. The file name field 2324 shows the file name selected in the explorer window opened by the open explorer button 2314 . The data mapping table 2325 has set & constraint columns and input file columns. After the user selects a constant in the constant table 2322 or a set in the set table 2323 and presses the constant / set assignment button, the selected constant or set is displayed in the set / constant column of the data mapping table 2325. For each constant or set shown in the data mapping table 2325, the data placed in the input file should be specified at the location of the input file in the data mapping table 2325. The user can input a column number or a row number as an element of the location column in the input file.

[0110] Figure 24 is an example of the data management screen 2301 after data mapping. In this example, as shown in Figure 25, GenData2501 in the input file is determined.

[0111] After setting all the data sets, when the user presses the import button 2310, data is imported from the file specified in the file name field 2324 based on the mapping determined in the data mapping table 2325. The confirmed model and the imported data set are stored in the calculation model database module 210 and the calculation input database module 211 respectively. After the user sets the model and data via the model management screen 401, the objective function editing screen 501, the constraint condition editing screen 601, the model detailed setting screen 1801, and the data management screen 2301, when the user presses the calculation execution button 313, the calculation module 204 uses the model from the calculation model database module 210 and the data from the calculation input database module 211 to execute the calculation using a solver. When the calculation is completed, the calculation result is displayed on the output screen.

[0112] Examples of the final result screen are shown in FIGS. 26 to 28. FIG. 26 shows an example of the final result screen 2601, which has a final result sub-screen area 2602, a placement result button 2611, an operation result button 2612, an export button 2613, and a close button 413.

[0113] The placement result button 2611 is used to open the placement result sub-screen 2701. The operation result button 2612 is used to open the operation result sub-screen 2801, and the export button 2613 is used to export the result data for further analysis.

[0114] FIG. 27 shows an example of the placement result sub-screen 2701. The placement result sub-screen 2701 has a calendar button 2711, a total cost field 2721, a total placement cost field 2722, a date field 2723, a network diagram field 2724, and an asset placement plan table 2725. The total cost field 27211 shows the comparison of the total cost with and without asset intervention. The total placement cost file 2722 shows the comparison of the total placement cost with and without asset intervention. The calendar button 2711 is used to select a date. The date field 2723 shows the selected data. The network diagram field 2724 shows the network diagram in which assets are placed based on the calculation result of the date specified in the date field 2723, and the asset placement plan table 2725 shows the asset placement plan including information on date, location, and capacity from the calculation result.

[0115] FIG. 28 shows the operation of the operation result sub-screen 2801. The operation result sub-screen 2801 has a total cost file 2721, a total power generation cost field 2821, and a time-series power generation cost field s2822. The total power generation cost field 2821 shows the comparison of the total power generation cost with and without asset intervention. The time-series power generation cost field 2822 shows the comparison of the time-series power generation cost with and without asset intervention. If no constraint application method is specified on the model management screen 401, the defined problem is calculated as a single problem. That is, when the calculation is completed, the user can obtain the calculation result. Conversely, if the calculation is not completed (for example, the calculation times out due to the numerical difficulty of the calculation), the calculation result cannot be obtained.

[0116] To avoid such a situation, the user can disable, edit, or apply one or more constraint application methods. If some constraints have a lower priority than other constraints, the possibility of completing the calculation by disabling the constraints is improved.

[0117] Figure 29 shows the model management screen 401 when the user disables the reactive power reserve requirement. Since it has been disabled, the constraint condition is not used in the calculation. When the constraint condition is strict or complex, the possibility of completing the calculation is improved by modifying the constraint condition.

[0118] Figure 30 is a further example of the constraint editing screen 601, in which the reactive power reserve requirement has been modified. As shown in the constraint formulation table, the user added the slack variables s_+ and s_- to the left side of the equation and variable table 522. The user can also modify the objective function, variable range and definition, and data mapping to match this modification. With this disabling / modifying function, the user can flexibly try and error without directly editing the source code of the program and obtain favorable results. The model management module 203 provides an automatic function to support such trial and error based on the constraint application method.

[0119] Figure 31 is a diagram showing an example of the drop-down list 3101 on the model management screen 401. When the user presses the constraint application method drop-down button 418 on the model management screen 401, the drop-down list 3101 of constraint application methods opens, and the constraint application method can be selected. In this example, "Benders decomposition" and "Constraint generation" are shown as candidates. Benders decomposition is a method used to solve complex optimization problems by decomposing the original problem into a master problem and one or more sub-problems.

[0120] Also, constraint generation starts from a subset of constraints and sequentially adds constraints, and is a method used to solve large-scale optimization problems.

[0121] Figure 32 is an example of the model management screen 401 after selecting the constraint application method. In this example, the constraint generation method is applied to the power flow limit and the reactive power margin requirement, and the Benders decomposition method is applied to the short-circuit level requirement. Since no constraint application method is applied to other constraints, these constraints are handled as usual in the optimization calculation.

[0122] After selecting the constraint application method, the user can define the order of the constraint application methods via the constraint management sub-screen 2201 of the model detailed settings screen 1801.

[0123] Figure 33 shows an example of the constraint management sub-screen 2201 after setting the order of the constraint application methods. According to this order, the optimization calculation proceeds in the following three steps. In the first step, an optimization problem that does not include the constraints specified by the constraint application method is calculated. In the second step, the constraint generation method is applied to the optimization calculation. In the last step, the Benders decomposition method is applied to the optimization calculation. Depending on the conditions, iterative calculations may be performed between the second step and the final step.

[0124] On the model detailed settings screen 1801, the user can set the properties of each constraint application method. As an example of the properties of the constraint condition generation method, the user can set the properties of the priority criteria and the maximum number of constraint conditions to be generated at one time.

[0125] Figure 34 shows an example of the constraint generation property setting sub-screen 3401 of the model detailed settings screen 1801. The constraint generation property setting sub-screen 3401 has a priority criteria drop-down button 3411, a priority criteria field 3421, a maximum constraint generation number field 3422, and a priority criteria drop-down list 3423.

[0126] The priority criteria drop-down button 3411 is used to open the priority criteria drop-down list 3423 to select the priority criteria for determining which constraints should be generated in the algorithm. When the user selects a priority criteria, it is displayed in the priority criteria field 3421. The maximum constraint generation number field indicates the maximum number of constraints generated by the algorithm at one time, and this field is editable. As an example of the properties of the Benders decomposition method, the details of the decomposition property can be set.

[0127] Figure 35 is a diagram showing an example of the Benders decomposition property setting sub-screen 3501, which includes a master problem definition button 3511, a sub-problem definition button 3512, a connector 3513, an interface definition button 3514, a layer addition button 3515, and a sub-problem addition button 3516.

[0128] The master problem definition button 3511 is used to check the definition of the master problem for applying the Benders decomposition. The sub-problem definition button 3512 is used to define the sub-problems for applying the Benders decomposition method. The connector 3513 is used to link between the master problem and the sub-problems of different layers or between sub-problems. The interface definition button (top to bottom) 3514 is used to define variables provided from the upper layer problem to the lower layer problem and additional constraints from the lower layer problem to the upper layer problem. The add layer button 3515 is used to add a lower layer. The add sub-question button 3516 is used to add a sub-question to the layer.

[0129] When the user presses the master question definition button 3511, a master question definition screen 3601 for checking the definition of the master question is displayed, as shown in FIG.

[0130] The master problem definition screen 3601 includes a close button 413 , an objective function table (original problem) 3620 , a constraint table (original problem) 3621 , an objective function table (master problem) 3622 , and a constraint table (master problem) 3623 . An objective function table (original problem) 3620 and a constraint table (original problem) 3621 indicate the objective function and constraints in the original problem, respectively. The objective function table (master problem) 3622 and the constraint table (master problem) 3623 also show the objective function and constraints of the master problem, respectively. Objective functions and constraints that are not managed by Benders decomposition are shaded based on the specification on the model management screen 401. In this example, the short-circuit level requirement is included in the original problem, but is not included in the master problem because the user selected Benders decomposition as the constraint application method for this constraint.

[0131] When the user presses the Define Sub-Problem button 3512, a Define Sub-Problem screen 3701 is displayed as shown in FIG. 37, allowing the user to assign constraints from the original problem to the sub-problem. The sub-problem definition screen 3701 includes a confirm button 1901, a reset button 3710, a close button 413, a reset button 3710, an assignment button (from original problem to sub-problem) 3711, an edit objective function button (sub-problem) 33712, a delete button 3713, an objective function table (original problem) 3620, a constraint table (original problem) 3621, an objective function table (sub-problem) 3720, and a constraint table (sub-problem) 3721. The objective function table (subproblems) 3720 and the constraint table (subproblems) 3721 indicate the objective functions and constraints in the subproblems, respectively. Objective functions and constraints that are not managed by the Benders decomposition are shown with diagonal lines. In this example, the total cost and short-circuit level requirements are managed by the Benders decomposition. If the user wants to add a total cost and short-circuit level requirement to this subproblem, they can be allocated by pressing the allocate button (from original problem to subproblem) 3711.

[0132] 38 is an example of the sub-problem definition screen 3710 after allocation. If it is desired to delete a constraint after allocation, the constraint can be deleted from the sub-problem by pressing the remove button 3713. If you want to start over with the assignments, use the reset button 3710 to reinitialize the assignments. The Edit Objective Function button (Sub-Problem) is used to open the Edit Objective Function screen (Sub-Problem) 3901, allowing the user to edit the objective function of the sub-problem.

[0133] When the user presses the objective function edit button (subproblem) 3717, an objective function edit screen (subproblem) 3901 is displayed, as shown in Figure 39. The objective function edit screen (subproblem) 3901 has a confirm button 510, a close button 413, a reset button 3710, an add variable row button 511, an add constant row button 513, an add set row button 515, a move button (from variable to constant) 3910, a variable definition button 3911, a constant definition button 3912, a set definition button 3913, an objective function name field 520, an objective function formulation field 521, a variable table 522, a constant table 523, and a set table 524.

[0134] The move button (variable to constant) 3910 is used to change a variable to a constant. The define variable button 3911, define constant button 3912, and define set button 3913 are used to define the properties of an additional variable, constant, and set, respectively.

[0135] If the user wants to modify the objective function, they can edit it directly in the objective function formulation field 521. In this example, the following penalty terms have been added to the objective function to avoid infeasible conditions:

[0136]

number

[0137] and w_SCR and s_SCR are real constants representing the penalty price and slack variable, respectively. These additional constants and variables are also added to constant table 523 and variable table 522, respectively. Additionally, by using the move button 3910 (from variable to constant), u can be treated as a constant rather than a variable, leading to a simpler subproblem. The user can confirm the edited results by pressing the confirmation button 1910.

[0138] When the user presses the interface definition button (upper → lower) 3514, an interface definition screen 4001 opens as shown in FIG. 40, and the interface between the upper-layer problem and the lower-layer problem is defined. The interface definition screen 4001 has added thereto a constant definition button 4011, a variable table (from higher to lower) 4020, a constraint table (from lower to higher) 4021, and a constant table 4022. The variable table (from top to bottom) 4020 is editable and defines the variables to be provided from the higher-level problem to the lower-level problem. The constraints table (lower to higher) 4021 is editable to define constraints that are added to higher layer problems based on lower layer problems, and the additional constants table 4022 is editable to define additional constants required by the constraints. The define additional constants button 4011 is used to define the properties of additional constants.

[0139] Once the problem correction is complete, all models, data, and constraint application methods are stored in the calculation model database 210, calculation input database 211, and constraint application method database 212, respectively. Then, a trial calculation can be performed by pressing the trial calculation button 316 on the initial screen 301, and the feasibility and validity of the problem can be confirmed. If no errors are found in the trial calculation, the calculation can be executed by pressing the execute calculation button 313 on the initial screen 301. As described above, the optimization calculation in this embodiment proceeds to the execution of three steps, as shown in Figure 41.

[0140] The first step, denoted as S4101, solves the unconstrained optimization problem specified by any constraint application method. In the second step, shown as step S4102, a constraint generation technique is applied to the optimization calculation. In the third step, shown as step S4103, the Benders decomposition method is applied to the optimization calculation. After the third step, as shown in S3104, constraint violations are checked, and if a constraint violation is confirmed, the process returns to S4102, and the calculation proceeds with the updated constraints. If there is no constraint violation, the calculation is terminated. When the calculation is completed, the calculation results are stored in the calculation final result database 214 and output to the user.

[0141] Figure 42 shows a plot of the calculation steps versus the tolerance when no constraint enforcement method is applied. Due to the size and complexity of the original problem, the tolerance does not reach the threshold. On the other hand, Figure 43 shows a plot of the calculation steps versus the tolerance when two constraint enforcement methods are applied. Because the optimization problem at each step is smaller and simpler than the original problem, the calculated tolerance eventually reaches the tolerance value. Furthermore, the calculation results at steps S4191, S4102, and S4103 are stored in the calculation intermediate result database 213 during the calculation and are output to the user if the calculation cannot be completed due to numerical difficulties. For example, if the calculation of the substep S4103 is not completed but the calculation of the previous substep was possible, the calculation intermediate result of S4103, including the violation check result, can be output to the user.

[0142] Furthermore, if the calculation of the sub-step S4103 has not been completed and there are no calculation results for the previous sub-step S4103, the intermediate calculation results of S4102 can be output to the user together with the violation check results.

[0143] FIG. 44 shows an example of an intermediate result screen 4401, which includes an intermediate result sub-screen area 4402, an arrangement result button 2611, an operation result button 2612, a constraints & violations button 4411, an export button (intermediate results) 4412, and a close button 413.

[0144] The constraints & violations button 4414 is used to open a constraints & violations subscreen 4701 as shown in Fig. 47. The export button (intermediate results) 4413 is used to export intermediate results.

[0145] Figures 45 and 46 are examples of sub - screens of the layout result 2701 and the operation result 2801 respectively, which are displayed on the intermediate result screen 4401. The configuration of these sub - screens on the intermediate result screen 4401 is the same as that on the final result screen 2601.

[0146] The constraint & violation sub - screen 4701 includes a detail button 4711 and a constraint & violation status table 4721. The constraint & violation status table 4721 shows a list of constraints, the application status of the constraints, and the violations against the constraints. When the user wants to check the applied status and the details of the violations, the detail button 4711 is used to open the constraint & violation detail screen 4801 shown in Figure 48.

[0147] The constraint & violation detail screen 4801 has a constraint & violation detail table 4821, which shows the application status of the constraints and the amount of violations at the object level. If all calculations are not completed, the user can make a judgment based on this intermediate result, and can also change the model or the application method of the constraints to try the next calculation.

[0148] Therefore, the proposed power system planning support tool and method enable the user to continue the planning work and provide a flexible environment for obtaining a planning solution.

[0149] FIG. 49 shows a further system configuration example in which the power system planning support tool 1 cooperates with the reinforcement learning system 4903 via the communication network 4902. In this example, instead of the user selecting the method of applying the constraint conditions, the reinforcement learning system 4902 selects and outputs the calculation results to the reinforcement learning system 4903. The modeling data input interface from the reinforcement learning system 4903 includes the priority criterion field 3421, the maximum number constraint generation field 3422, the objective function table (sub-problem) 3720, the constraint condition table (sub-problem) 3721, the variable table (from top to bottom) 4020, the constraint condition table (from bottom to top) 4021, the additional constraint condition table 4022 in the embodiment, and data including the constraint condition table 421, the objective function formulation field 521, the variable table 522, the constraint condition table 523, the set table 524, and the constraint condition application method order table 2221. By connecting the power system planning support tool 1 to the reinforcement learning system 4903, a solution can be automatically obtained based on the input from the reinforcement learning system.

[0150] The systems and methods of the above embodiments can be implemented in a computer system (in particular, computer hardware or computer software) in addition to the described structural components and user interactions.

[0151] The term "computer system" includes the hardware, software, and data storage devices for embodying the system according to the above-described embodiments or for executing the method. For example, a computer system may be composed of a central processing unit (CPU), input means, output means, and a data storage device. The computer system can have a monitor that provides a visual output display. The data storage may be composed of RAM, a disk drive, or other computer-readable media. The computer system may include a plurality of computing devices connected by a network and capable of communicating with each other via the network.

[0152] The method of the above-described embodiment can be provided as a computer program, or as a computer program product or a computer-readable medium carrying a computer program arranged to execute the above method when executed on a computer.

[0153] The term "computer-readable medium" includes, but is not limited to, any non-transitory medium or media that can be directly read and accessed by a computer or computer system. Media include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media, magnetic tapes, optical storage media such as optical disks and CD-ROMs, electrical storage media such as memories like RAM, ROM, flash memory, and hybrids or combinations such as magnetic / optical storage media.

[0154] Although the present disclosure has been described in conjunction with the above-described exemplary embodiments, many equivalent modifications and variations will be apparent to those skilled in the art given the present disclosure. Accordingly, the above-described exemplary embodiments of the present disclosure are considered to be illustrative and not limiting. Various changes can be made to the described embodiments without departing from the spirit and scope of the present disclosure.

[0155] In particular, although the method of the above embodiment has been described as being implemented on the system of the described embodiment, the method and system of the present disclosure need not be implemented in relation to each other and can be implemented on alternative systems or using alternative methods, respectively.

[0156] The features disclosed in this specification, the following claims, or the accompanying drawings can be expressed in their specific forms, or from the perspective of means for performing the disclosed functions, or methods or processes for obtaining the disclosed results, and can be used in various forms, separately or in any combination of such features, to implement the present disclosure.

[0157] Although the present disclosure has been described in conjunction with the exemplary embodiments described above, many equivalent modifications and variations will be apparent to those skilled in the art given the present disclosure. Accordingly, the exemplary embodiments of the present disclosure described above are considered to be illustrative and not restrictive. Various changes can be made to the described embodiments without departing from the spirit and scope of the present disclosure.

[0158] To avoid doubt, the theoretical explanations provided herein are provided for the purpose of enhancing the reader's understanding. The inventors do not wish to be bound by these theoretical explanations.

[0159] All section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0160] Throughout this specification, including the claims that follow, unless the context requires otherwise, the words "comprise" and "include", and variations such as "comprises", "comprising", and "including", are to be understood to mean including the stated integer or step or group of integers or steps but not to mean excluding other integers or steps or group of integers or steps.

[0161] It should be noted that, as used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. In this specification, ranges may be expressed as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from one particular value and / or to another particular value. Similarly, when values are expressed as approximations, by use of the antecedent "about", it is understood that the particular value forms another embodiment. The term "about" with respect to a numerical value is optional and means, for example, ±10%.

Explanation of Reference Numerals

[0162] 1: Power system planning support system, 11: CPU, 12: ALU, 13: Input device, 14: Output device, 15: Main memory device, 16: External memory device, 18: Database, 19 Program, 201: Input module, 202: Output module, 203: Model management module 204: Calculation module, 211: Calculation input database module, 212: Constraint condition application method database module, 213: Calculation intermediate result database module, 214: Calculation final result database module.

Claims

1. A power system planning support method executed on a computer, comprising: (i) receiving an objective function, the objective function representing a quantity or parameter related to a power grid; (ii) receiving one or more constraint functions, the constraint functions representing constraints on the power grid, and the objective function and the constraint functions together representing a model of the power grid; (iii) receiving input data related to the power grid; (iv) performing an optimization calculation of the total cost based on the model of the power grid and the input data; (v) displaying the result of the optimization calculation, and further comprising, when the execution of the optimization calculation returns an indication that the optimization calculation cannot be completed, calculating intermediate results of one or more of the plurality of methods applied to the optimization calculation Power system planning support method.

2. The power system planning support method according to claim 1, further comprising: indicating to the user that the optimization calculation cannot be completed; and providing the user with the calculated intermediate results. Power system planning support method.

3. The power system planning support method according to claim 2, further comprising: invalidating and / or editing the constraints of one or more of the constraint functions received in step (ii) so that the optimization calculation in step (iv) is executed. Power system planning support method.

4. The power system planning support method according to claim 3, further comprising applying a decomposition method to the objective function and the constraint functions to reach a plurality of sub-problems representing the objective function, and the plurality of sub-problems having the decomposed objective function and constraint functions represent a model of the power grid. Power system planning support method.

5. The power system planning support method according to claim 4, wherein step (ii) includes receiving a plurality of constraint functions, and the power system planning support method further includes receiving the order in which the constraint functions are applied. Power system planning support method.

6. The power system planning support method according to claim 5, further comprising applying a constraint generation method to one or more of the constraint functions, the constraint generation method starting from a subset of the constraint conditions and sequentially adding constraint conditions. Power system planning support method.

7. The power system planning support method according to claim 6, ​ ​ ​ ​ ​ The constraint generation method further includes receiving a priority criterion for a subset of constraints and / or a maximum number of constraint conditions generated at one time. Power system planning support method. Claim 8 The power system planning support method according to claim 7, The step (i) includes receiving a definition of one or more variables, a definition of one or more constants, and a definition of one or more data sets. Power system planning support method. Claim 9 The power system planning support method according to claim 8, A reinforcement learning system that cooperates with a power system planning support tool via a communication network provides a method for applying constraint conditions to the power system planning support tool to obtain a calculation result. Power system planning support method. Claim 10 The power system planning support method according to claim 9, The method further includes receiving a request to perform a trial calculation before performing the optimization calculation, and the method further includes performing the trial calculation, where the trial calculation indicates whether a complete solution is expected from the optimization calculation defined by the power grid model and input data. Power system planning support method. Claim 11 A power system planning support system, comprising one or more processors and a memory, wherein the memory, when executed on the processor, (i) receives an objective function, which represents a quantity or parameter related to the power grid, (ii) receives one or more constraint functions, each of which represents a constraint on the power grid, and the objective function and the constraint functions together represent a model of the power grid, (iii) receives input data related to the power grid, (iv) performs an optimization calculation of the total cost based on the power grid model and the input data, (v) displays the result of the optimization calculation, and includes machine-executable instructions. The memory, when executed on the processor, further includes machine-executable instructions for calculating intermediate results of one or more of a plurality of methods applied to the optimization calculation in the processor when the execution of the optimization calculation results in an instruction to the effect that the optimization calculation cannot be completed. Power system planning support system. Claim 12 In the power system planning support system according to claim 11, the memory, when executed on the processor, further includes machine-executable instructions for indicating to the user that the optimization calculation cannot be completed and for providing the user with the calculated intermediate results. Power system planning support system. Claim 13 A non-transitory computer-readable storage medium that, when executed on one or more processors, causes the processors to execute the method according to any one of claims 1 to 10, comprising machine-executable instructions.

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