Power system operation plan generation device and power system operation plan generation method

Abstract

To determine a system operation plan that has both robustness to erroneous prediction and minimization of system operation cost, in a power system operation plan generation device.SOLUTION: A power system operation plan generation device has: an uncertain data fluctuation range preparation unit for preparing an uncertain data fluctuation range on a fluctuation amount of uncertain data per time or a fluctuation amount of uncertain data per space on the basis of past results of uncertain data having uncertainty and a robustness parameter, to generate constraint formula information in the uncertain data fluctuation range; and a system operation plan generation unit that refers to the constraint formula information to generate the system operation plan of a power system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention provides Power system operation plan generation device and power system operation plan generation method Regarding. [Background technology]

[0002] The introduction of renewable energy sources (hereafter referred to as renewable energy) that do not emit greenhouse gases is progressing in the power grid. The output of typical renewable energy sources, such as solar power generation (hereafter referred to as PV) and wind power generation, fluctuates depending on weather conditions.

[0003] In addition, with the spread of electric vehicles (hereinafter referred to as EVs), the number of fast charging stations is increasing and their capacity is increasing, and this power demand may affect the power grid. There is a need for grid operation that takes into account parameters with uncertainty (hereinafter referred to as uncertain parameters), such as renewable energy output and EV charging demand.

[0004] Meanwhile, there are two types of variables in system operation: variables that must be determined in advance, such as the day before (hereinafter referred to as operation planning variables), and variables that can be determined in real time (hereinafter referred to as intraday control variables).For example, operation planning variables in a distribution system include the tap position of tap adjustment devices (e.g., LRT: Load Ratio Control Transformer, SVR: Step Voltage Regulator), and the reserved amount of adjustment margin for consumer-owned distributed energy resources (hereinafter referred to as DER) such as private generators, storage batteries, and EVs.

[0005] In addition, examples of control variables for the day include the activation command amount of the adjustment margin of an SVC (Static Var Compensator), a DER (e.g., a grid storage battery) owned by a grid operator, and a DER owned by a reserved consumer.

[0006] The first method that comes to mind for determining operational planning variables is to assume that the predictions of uncertain parameters will be correct and select the combination of values ​​that will optimize the system operation KPIs (for example, violations of voltage tolerances or line capacity, and response costs).This method does not take into account cases where the predictions are incorrect, and depending on the fluctuation pattern of the uncertain parameters, there is a possibility that the system operation KPIs will deteriorate drastically (for example, violations of constraints such as voltage tolerances or line capacity, or extreme increases in costs due to additional responses, etc.).

[0007] Therefore, even if the prediction of uncertain parameters fluctuates, it is necessary to determine operational planning variables so that system operation KPIs (e.g., voltage tolerance range, line capacity violations, response costs) do not deteriorate excessively (hereinafter referred to as "robust").

[0008] The simplest method for creating a robust grid operation plan is to make a conservative plan to deal with uncertain fluctuations, for example by reserving as much adjustment margin as possible from consumer-owned DERs. While this method certainly ensures robustness, it is not realistic because it increases the cost of grid operation.

[0009] Therefore, in order to apply a robust system operation plan to actual system operation, a method for formulating a system operation plan that minimizes system operation costs while ensuring robustness against misforecasts is required.

[0010] One known technique for determining operation plan variables aimed at robust system operation is that described in Patent Document 1. Patent Document 1 describes a "power distribution device, power distribution method, and program."

[0011] In Patent Document 1, the fluctuation range of power that can be output in the future is estimated based on the past operating history of each distributed power source, and the active power and reactive power of each distributed power source that can satisfy predetermined target conditions and constraint conditions is calculated based on this fluctuation range. [Prior art documents] [Patent documents]

[0012] [Patent Document 1] Japanese Patent Publication No. 2022-77459 Summary of the Invention [Problem to be solved by the invention]

[0013] The optimization method in Patent Document 1 is generally called robust optimization. In a solution obtained by robust optimization, the balance between the objective function (e.g., system operation cost) and robustness (e.g., safety of the power system against uncertain fluctuations) is determined by the magnitude of the fluctuation range of the uncertain parameters.

[0014] In other words, if the variation range is large, robustness increases but the objective function deteriorates. For this reason, it is essential to set the variation range appropriately in robust optimization.

[0015] In contrast, Patent Document 1 describes a method for setting the fluctuation range as "an estimation may be made based on the standard deviation of the amount of change in output from a distributed power source over a predetermined period." However, estimation based on the standard deviation results in setting the fluctuation range without taking into account the temporal and spatial fluctuation characteristics of the uncertain data.

[0016] Here, we will explain the temporal and spatial fluctuation characteristics of uncertain data. Taking the example of a renewable energy device (hereafter referred to as "renewable energy device") with an output range of 0kW to 500kW, if the output at a certain time is 200kW, the output at the next time step may fall within the range of 100kW to 300kW. Furthermore, if the output of a nearby renewable energy device is 200kW, the output of this renewable energy device at the same time may fall within the range of 100kW to 300kW.

[0017] However, Patent Document 1 does not take into account these temporal and spatial fluctuation characteristics, and therefore sets the fluctuation range to 0kW to 500kW even when the output would normally fall within the range of 100kW to 300kW. As a result, an operation plan that places more emphasis on robustness than necessary is output, which may increase system operation costs.

[0018] An object of the present invention is to obtain a power system operation plan that is robust against prediction errors and minimizes system operation costs in a power system operation plan generating device. [Means for solving the problem]

[0019] A power system operation plan generation device according to one aspect of the present invention comprises: An electric power system operation plan generating device that takes into consideration uncertainty parameters in an electric power system, determines operation plan variables that should be determined in advance and current day control variables that can be determined in real time, and generates a system operation plan for the electric power system, the electric power system operation plan generating device having an arithmetic unit that executes predetermined calculations using a processor, the arithmetic unit having an uncertain data fluctuation range creating unit that creates, using the processor, an uncertain data fluctuation range related to the fluctuation amount per time of the uncertain data or the fluctuation amount per space of the uncertain data based on past performance of the uncertain data having uncertainty and a robustness parameter, and generates a constraint equation for the uncertain data fluctuation range; and a system operation plan generation unit that generates the system operation plan for the power system by referring to the constraint equation of the data fluctuation range, wherein the uncertain data fluctuation range creation unit uses the processor to draw a scatter plot based on the uncertain data, find multiple vertices of a polygon with the smallest area that surrounds all of the uncertain data, divide the multiple vertices into upper and lower sides based on a center line connecting the lower left point and the upper right point of the multiple vertices, and create the constraint equation by converting equal signs in a linear equation of an outer frame of the uncertain data fluctuation range into inequality signs above and below the center line, and the system operation plan generation unit uses the processor to generate the system operation plan for the power system by referring to the constraint equation. It is characterized by: [Effects of the Invention]

[0020] According to one aspect of the present invention, a power system operation plan generating device can generate a power system operation plan that is robust against prediction errors and minimizes system operation costs. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a block diagram showing a configuration of a power system operation plan generating device according to a first embodiment. [Figure 2] FIG. 10 is a diagram illustrating an example of uncertain data past performance. [Figure 3] FIG. 10 is a diagram illustrating an example of an uncertain data fluctuation range. [Figure 4] FIG. 10 is a diagram illustrating an example of a system operation plan. [Figure 5] FIG. 2 is a diagram illustrating an example of a processing flow according to the first embodiment. [Figure 6] FIG. 10 is a diagram illustrating an example of vertex movement using a robustness parameter. [Figure 7] FIG. 10 is a block diagram showing a configuration of a power system operation plan generating device according to a second embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of a processing flow according to a second embodiment. [Figure 9]FIG. 2 is a diagram illustrating a hardware configuration of a power system operation plan generating device. DETAILED DESCRIPTION OF THE INVENTION

[0022] Hereinafter, embodiments of a power system operation plan generating device and a power system operation plan generating method will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments. Each embodiment can be appropriately combined within the scope of not causing any contradiction in the processing contents. [Example]

[0023] In the first embodiment, an application example is directed to a power distribution system including consumer-owned DERs, grid operator-owned DERs, SVRs, LRTs, and SVCs. The power system operation plan generation device of the first embodiment targets photovoltaic power generation output as an uncertain parameter and formulates a robust operation plan in consideration of the temporal fluctuation characteristics of the photovoltaic power generation output.

[0024] The configuration of a power system operation plan generating device according to a first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the processing contents of a calculation unit of the power system operation plan generating device realized using a computer device, expressed as a representative processing function unit.

[0025] As shown in Fig. 1, the power system operation plan generation device 10 has a calculation unit 20 and a calculation result storage unit 30. For example, the calculation unit 20 is a calculation device that executes predetermined calculations using a processor. However, in addition to the functional units shown in Fig. 1, the operation plan development device 10 may also have, for example, an input unit that inputs input data, a communication interface unit that communicates with other terminals, and the like.

[0026] The calculation unit 20 has an uncertain data fluctuation range creation unit 210 and a system operation plan generation unit 220. The uncertain data fluctuation range creation unit 210 creates, by a processor, an uncertain data fluctuation range 310 relating to the amount of fluctuation per time of the uncertain data or the amount of fluctuation per space of the uncertain data based on past performance data 110 of the uncertain data having uncertainty and a robustness parameter 120, and generates constraint equation information 230 of the uncertain data fluctuation range 310. The system operation plan generation unit 220 generates, by a processor, a system operation plan 320 of the power system by referring to the constraint equation information 230. Furthermore, the calculation result storage unit 30 has a database for storing information and stores the uncertain data fluctuation range 310, the system operation plan 320, and a future prediction 330 of the system state.

[0027] To carry out its processing, the calculation unit 20 receives as external inputs the following: uncertain data past performance 110, robustness parameters 120, power demand information 130, system equipment information 140, operational constraint information 150, power market price information 160, renewable energy forecast information 170, and customer-owned DER information 180.

[0028] Among the external inputs used in this case, the uncertain data past performance 110 will be described with reference to FIG.

[0029] The first line from the top of Figure 2 shows the ID111 of each solar power generation system, the second line shows the north latitude of the installation location of each solar power generation system, the third line shows the east longitude of the installation location of each solar power generation system, and each line from the fourth line onwards shows the output of each solar power generation system at each time step. In Figure 2, the time step interval is set to 5 minutes as an example.

[0030] The robustness parameter 120 is numerical data, and when the robustness parameter 120 is set to p, the robustness parameter 120 is numerical data that satisfies, for example, 0≦p≦1.

[0031] In addition, the system equipment information 140 includes, for example, the equipment type of each tap adjustment device (e.g., LRT, SVR, etc.), the installed bus ID, the number of tap stages, the tap width per stage, the tap reference position, the response speed to control commands, the limit on the number of tap adjustments per day, the equipment cost, the installation cost, etc.

[0032] The operational constraint information 150 is the range of voltage and power flow that must be observed during system operation.

[0033] The consumer-owned DER information 180 may include, for example, the type of equipment (e.g., private power generation, electric vehicle, etc.), the installed bus ID, the facility capacity, the response speed to control commands, etc. The consumer-owned DER information 180 may be input individually for each distributed energy resource DER, or may be input as a combined value of multiple distributed energy resource DERs.

[0034] Returning to Figure 1, the uncertain data fluctuation range creation unit 210 obtains uncertain data past performance data 110 and robustness parameters 120 as input data from outside, and based on these, generates the fluctuation range of the uncertain data and its constraint equation, outputs the uncertain data fluctuation range 310 to the calculation result storage unit 30, and outputs constraint equation information 230 of the uncertain data fluctuation range to the system operation plan creation unit 220.

[0035] The uncertain data fluctuation range 310 of the output data will be explained with reference to FIG.

[0036] The uncertain data fluctuation range 310 includes a plot 311 and a fluctuation range outer frame 312 when the horizontal axis represents the photovoltaic power generation output at time t and the vertical axis represents the photovoltaic power generation output at time t+1.

[0037] The plot 311 is scattered based on the uncertain data past performance 110. For example, if the output, which was 20 kW at 12:00, changes to 40 kW at 12:05, the plot 311 is plotted at the point where the horizontal axis is 20 kW and the vertical axis is 40 kW.

[0038] 3, past performance data for all time periods is plotted on the same scatter plot, but the scatter plot may be divided into morning, afternoon, and evening plots, for example. The fluctuation range outer frame 312 is the polyhedron with the smallest area that encloses the entire set of plots 311, i.e., the edges that make up the convex hull.

[0039] In Figure 3, in order to take into account the temporal fluctuation characteristics of solar power generation output, the horizontal axis represents the solar power generation output at time t and the vertical axis represents the solar power generation output at time t+1. However, if spatial fluctuation characteristics are to be taken into account, the horizontal axis should represent the output of a certain solar power generation system, and the vertical axis should represent the output of a nearby solar power generation system at the same time.

[0040] Furthermore, the constraint equation information 230 for the uncertain data variation range is obtained by converting the equal sign in the equation for the straight line of the variation range outer frame 312, y=ax+b, to an inequality sign. x is the value on the horizontal axis of FIG. 3, y is the value on the vertical axis of FIG. 3, and a and b are constants. The direction of the inequality sign is y≦ax+b when the variation range outer frame 312 is above the plot 311. Furthermore, when the variation range outer frame 312 is below the plot 311, y≧ax+b.

[0041] Returning to FIG. 1 again, the system operation plan generation unit 220 obtains power demand information 130, system equipment information 140, operational constraint information 150, power market price information 160, renewable energy forecast information 170, and customer-owned DER information 180 as input data from outside, solves a robust optimization problem based on these, and outputs a system operation plan 320 and a future forecast of the system state 330 to the calculation result storage unit 30.

[0042] Of the output data, the system operation plan 320 will be described with reference to FIG.

[0043] Each row in Figure 4 represents a time step 321, and each column represents a tap adjustment device ID 322 and a customer-owned DER ID 323. For each tap adjustment device ID 322, the tap position 324 at each time step 321 is stored. Furthermore, for each customer-owned DER ID 323, the reserved amount of regulation margin 225 at each time step is stored. Because the reserved amount of regulation margin 325 can take values ​​for both an increase and decrease in demand, it can also take a negative value as shown in Figure 4.

[0044] Returning to Fig. 1 again, the information stored in the calculation result storage unit 30 is displayed to the operator / planner 420 via the calculation result display unit 410. Furthermore, in accordance with the system operation plan 320 stored in the calculation result storage unit 30, control commands are sent from the controller 510 to the customer-owned DER 520, the system operator-owned DER 530, the SVR 540, the LRT 550, and the SVC 560. In this way, the above-mentioned equipment groups are controlled, and system operation that ensures robustness and reduces system operation costs is realized.

[0045] Next, a processing flow of the first embodiment will be described. The description will be made according to the flowchart in the processing unit 20 shown in Fig. 5. Of the processes in the flowchart, processes 211 to 216 are executed by the uncertain data fluctuation range creation unit 210. Furthermore, process 221 is executed by the system operation plan generation unit 220.

[0046] In process 211, the scatter plot shown in Fig. 3 is drawn based on the uncertain data past performance 110, and the polygon with the smallest area that encloses all the plots, i.e., the vertices of the convex hull, are found. This can be found by applying the gift wrapping method or the like.

[0047] In process 212, a line (hereinafter referred to as the center line) is drawn connecting the lower left point and the upper right point of the convex hull vertices. In process 213, the distance d between the center line and each convex hull vertex is calculated.

[0048] In process 214, a value (hereinafter referred to as d') is calculated by multiplying the robustness parameter 120 (hereinafter referred to as p) by the distance d. Then, the convex hull vertex is moved in the direction of the perpendicular to the center line so that it is on the perpendicular to the center line passing through the convex hull vertex and the distance from the center line becomes d'. The above process is performed for all convex hull vertices.

[0049] FIG. 6 illustrates process 214. The light gray plot 601 in FIG. 6 is the same as plot 311 in FIG. 3. Of the plots 601, the dark gray plots 602 are the vertices of the convex hull calculated in process 211. Of these, a perpendicular line 605 is drawn from the target convex hull vertex 603 toward the center line 604. Then, the target convex hull vertex 603 is moved along the perpendicular line 605 so that the distance from the center line 604 becomes d'. As an example, FIG. 6 shows the moved convex hull vertex 606 when the robustness parameter p is 1.3.

[0050] Here, we explain why the distance d between the center line and each convex hull vertex is used in steps 213 and 214. Another option is to use the distance between each convex hull vertex and the center point of all plots as the reference point. However, in this case, when the robustness parameter p is less than 1, the plots near the lower left and upper right of the center line may be outside the updated convex hull. Many plots are concentrated near the center line because the outputs at time t and time t+1 are almost the same area. If this area where many plots are concentrated were placed outside the convex hull, it would result in a fluctuation pattern that is not considered in the robust optimization described below, and as a result, the operation plan may violate the system operation constraints. For these reasons, in the present invention, steps 213 and 214 are performed using the distance d between the center line and each convex hull vertex.

[0051] In process 215, the equation y=ax+b of the line connecting the convex hull vertices after being moved in process 214 is found. Then, in process 216, the equality sign of the line equation is converted into an inequality sign. The direction of the inequality sign is y≦ax+b when the line is above the plot 311. Also, when the line is below the plot 311, the direction is y≧ax+b.

[0052] In this way, the uncertain data fluctuation range creation unit 210 draws a scatter diagram based on the uncertain data, and finds multiple vertices of a polygon with the smallest area that encloses all of the uncertain data.

[0053] Using the center line connecting the bottom-left and top-right points of the multiple vertices as a reference, the multiple vertices are divided into those above and below the center line. Then, for the above and below the center line, constraint equations are created by converting the equality signs in the linear equation of the outer frame of the uncertain data fluctuation range into inequality signs.

[0054] If the line is above the center line, the equality sign of the linear equation of the outer frame of the uncertain data fluctuation range is converted to a less-than or equal inequality sign (≦).If the line is below the center line, the equality sign of the linear equation of the outer frame of the uncertain data fluctuation range is converted to a greater-than or equal inequality sign (≧).

[0055] Furthermore, the uncertain data variation range creation unit 210 adjusts the area of ​​the uncertain data variation range by expanding or contracting it based on the distance between the vertex and the center line, based on the robustness parameter.

[0056] In this way, the lines that make up the convex hull are converted into constraint equations, and the range of the convex hull is adjusted using a robustness parameter, which is then added to the constraint equations of the mathematical programming problem and solved.

[0057] Finally, a robust optimization problem is formulated and solved in process 221. When a system operation KPI defined as a weighted sum of system operation costs, transmission losses, etc. is used as the objective function of this optimization problem, the objective function is expressed as the following equation (Equation 1).

[0058]

number

[0059] Φ KPIis the system operation KPI, t is the time step, and T is the set of t, i.e., the planning period. The subscripts of min and max represent decision variables, x is a vector of operation planning variables (e.g., the amount of tap position control of the SVR, the amount of regulation margin reserved by the consumer-owned DER), and y is a vector of control variables for the day (e.g., the SVC output, the amount of command to activate the reserved regulation margin).

[0060] The constraint conditions for the robust optimization problem include the allowable ranges of voltage and power flow, the operable range of the system equipment, the upper and lower limits of the adjustable amount of the consumer DER, and the constraint equation information 230 for the uncertain data fluctuation range created by the uncertain data fluctuation range creation unit 210.

[0061] A feature of the robust optimization problem described above is that the objective function has a three-layer structure of min and max. Starting from the outside of (Equation 1), there is a part that determines the operation plan x that minimizes (optimizes) the system operation KPI, a part that determines the value of the uncertain parameter u (the most severe scenario) that maximizes (worsens) the system operation KPI, and a part that determines the intraday control y that minimizes the system operation KPI.

[0062] Due to the three-layer structure described above, this robust optimization problem cannot be solved as is. Therefore, a decomposition method such as Benders decomposition is used to split the problem into three optimization problems for solution. After splitting, the optimization problem is reduced to a mixed integer programming (hereafter referred to as MILP) or a mixed integer second-order cone programming (hereafter referred to as MISOCP), which can be solved using a mathematical programming solver such as Gurobi or CPLEX.

[0063] In this way, by solving the above robust optimization, an operation plan x that minimizes (bests) the system operation KPI during the planning period T is obtained. [Example]

[0064] The configuration of a power system operation plan generating device according to the second embodiment will be described with reference to FIG.

[0065] The difference between FIG. 7 showing Example 2 and FIG. 1 showing Example 1 is that the number of noise data removals 720 is added as input data for the range of uncertain data variation 710 in the processing unit 20. Since other functions and data are the same as those in FIG. 1, the description thereof is omitted.

[0066] The number of noise data removals 720 is a value represented by an integer of 0 or more, and represents the number of data that is removed as noise data from the past record of uncertain data and does not contribute to the creation of the variation range. Note that the number of noise data removals 720 may be given as a ratio of the number of data in the past record of uncertain data.

[0067] Subsequently, referring to FIG. 8, the processing flow of Example 2 will be described.

[0068] Among the processes in the flowchart, the processes from process 211 and process 212 to process 221 are the same as the processes shown in FIG. 5, and processes 811, 812, and 813 are the processes added from FIG. 5. Also, at the start of the flowchart, numerical data a is held in an initialized state of 0.

[0069] In process 811, when the number of noise data removals 720 is set as x, it is checked whether a < x is satisfied. If satisfied, the process moves to process 812, and if not satisfied, the process moves to process 212.

[0070] In process 812, the number of vertices obtained in process 211 is set as b, and the value of a is updated to a + b.

[0071] In process 813, the magnitudes of b and x - a are compared, and the smaller value is substituted into c. The calculated c indicates the number of vertices to be removed from the uncertain data. Note that the method of selecting the vertices to be removed may include random selection or selection in descending order of the distance from the nearest neighbor points.

[0072] After process 813, the process moves back to process 211 again. By repeating the above processes, the vertices of the convex hull are repeatedly removed until the number of data a removed from the uncertain data reaches the number of noise data removals x.

[0073] FIG. 9 is a diagram showing a hardware configuration of the power system operation plan generating device 10. As shown in FIG.

[0074] The power system operation plan generating device 10 shown in FIG. 1 is configured, for example, as shown in FIG. 9, by a computer having an input device 910, an output device 920, a memory 930, a storage device 940, and a CPU 950.

[0075] Furthermore, the functions of the "units" shown in FIGS. 1 and 7 are realized by, for example, executing a program by a processor (such as the CPU 950).

[0076] For example, the uncertain data fluctuation range creating units 210 and 710 shown in FIGS. 1 and 7 realize the uncertain data fluctuation range creating function by executing a program using a processor (such as the CPU 950).

[0077] In addition, the system operation plan generating unit 220 shown in FIGS. 1 and 7 realizes a system operation plan generating function by executing a program using a processor (such as the CPU 950).

[0078] According to the above embodiment, the power system operation plan generating device can obtain a power system operation plan that is robust against prediction errors and minimizes the system operation cost. [Explanation of symbols]

[0079] 10. Power system operation plan generator 20 Arithmetic section 30 Operation result storage section 210 Uncertain Data Variation Range Creation Unit 220 System Operation Plan Generation Unit 410 Calculation result display section 510 Controller 520 Consumer-owned DER 530 Grid Operator Owned DER 540 SVR 550 LRT 560 SVC 710 Uncertain Data Variation Range Creation Unit

Claims

1. A power system operation plan generation device that takes into consideration uncertainty parameters in a power system, determines operation plan variables that should be determined in advance and current day control variables that can be determined in real time, and generates a system operation plan for the power system, The power system operation plan generation device includes: a computing device that executes predetermined operations using a processor; The computing device an uncertain data variation range creation unit that creates an uncertain data variation range related to the fluctuation amount per time of the uncertain data or the fluctuation amount per space of the uncertain data based on the past performance of the uncertain data having uncertainty and a robustness parameter, and generates a constraint equation for the uncertain data variation range by using the processor; a system operation plan generating unit configured to generate the system operation plan of the power system by referring to the constraint equation of the uncertain data fluctuation range, The uncertain data fluctuation range creation unit, by the processor, Draw a scatter plot based on the uncertain data, and find multiple vertices of a polygon with the smallest area that encloses all of the uncertain data; Using a center line connecting the leftmost bottom point and the rightmost top point of the plurality of vertices as a reference, the plurality of vertices are divided into an upper side and a lower side of the center line, creating the constraint equation by converting the equality sign of the linear equation of the outer frame of the uncertain data fluctuation range to an inequality sign on the upper and lower sides of the center line; The system operation plan generation unit, by the processor, a power system operation plan generating device that generates the power system operation plan by referring to the constraint equations;

2. The uncertain data fluctuation range creation unit, by the processor, In the case of the upper side of the center line, the equal sign of the linear equation of the outer frame of the uncertain data fluctuation range is converted into an inequality sign of less than or equal to; The power system operation plan generating device according to claim 1, characterized in that, in the case of the lower side of the center line, the equal sign of the linear equation of the outer frame of the uncertain data fluctuation range is converted into an inequality sign of greater than or equal to.

3. The uncertain data fluctuation range creation unit The processor adjusts the area of ​​the uncertain data fluctuation range by expanding and contracting it based on the distance between the vertex and the center line based on the robustness parameter; The system operation plan generation unit 3. The power system operation plan generating device according to claim 2, wherein the processor formulates and solves a robust optimization problem.

4. A power system operation plan generation method for generating a power system operation plan for a power system by taking into consideration uncertainty parameters in the power system, determining operation plan variables to be determined in advance and current day control variables that can be determined in real time, and generating a power system operation plan for the power system, comprising: an uncertain data variation range creation step in which a processor creates an uncertain data variation range relating to the amount of fluctuation per time of the uncertain data or the amount of fluctuation per space of the uncertain data based on past performance of the uncertain data having uncertainty and a robustness parameter, and generates a constraint equation for the uncertain data variation range; a system operation plan generating step of generating, by the processor, the system operation plan for the power system by referring to the constraint equation for the uncertain data fluctuation range, The uncertain data fluctuation range creating step is performed by the processor. Draw a scatter plot based on the uncertain data, and find multiple vertices of a polygon with the smallest area that encloses all of the uncertain data; a center line connecting the leftmost and rightmost points of the plurality of vertices is used as a reference to divide the plurality of vertices into upper and lower sides of the center line; creating the constraint equation by converting the equality sign of the linear equation of the outer frame of the uncertain data fluctuation range to an inequality sign on the upper and lower sides of the center line; The system operation plan generating step is performed by the processor. A power system operation plan generating method, comprising: generating the power system operation plan for the power system by referring to the constraint equation.

5. The uncertain data fluctuation range creating step is performed by the processor. In the case of the upper side of the center line, the equal sign of the linear equation of the outer frame of the uncertain data fluctuation range is converted into an inequality sign of less than or equal to; The power system operation plan generation method according to claim 4, characterized in that, in the case of the lower side of the center line, the equal sign of the linear equation of the outer frame of the uncertain data fluctuation range is converted into an inequality sign of greater than or equal to.

6. The uncertain data fluctuation range creating step is performed by the processor. Based on the robustness parameter, expand and contract the area of ​​the uncertain data fluctuation range based on the distance between the vertex and the center line; The system operation plan generating step is performed by the processor.

5. The power system operation plan generating method according to claim 4, wherein a robust optimization problem is formulated and solved.

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