JP7808531B2 - Power system operation plan generation device and power system operation plan generation method - Google Patents

Description

The present invention relates to a power system operation plan generating device and a power system operation plan generating method .

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.

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 could have an impact on the power grid. Grid operation is required to take into account uncertain parameters (hereinafter referred to as uncertain parameters), such as renewable energy output and EV charging demand.

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 DERs) such as private generators, storage batteries, and EVs.

Furthermore, examples of control variables for the day include the activation command amount of the adjustment margin of the SVC (Static Var Compensator), DERs (e.g., grid storage batteries) owned by the grid operator, and DERs owned by reserved consumers.

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

For this reason, it is necessary to determine operational planning variables so that system operation KPIs (e.g., voltage tolerances, line capacity violations, and response costs) do not deteriorate excessively (hereinafter referred to as "robust") even when predictions of uncertain parameters fluctuate.

The simplest way to create 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 for customer-owned DERs. While this method certainly ensures robustness, it is not realistic because it increases the costs of grid operation.

For this reason, in order to apply robust system operation plans to actual system operations, a method for formulating system operation plans that minimizes system operation costs while ensuring robustness against misforecasts is required.

One known technique for determining operational planning variables aimed at robust grid operation is the technology described in Patent Document 1. Patent Document 1 describes a "power distribution device, power distribution method, and program."

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.

Japanese Patent Application Laid-Open No. 2022-77459

The optimization method described in Patent Document 1 is generally known as robust optimization. In the solution obtained through robust optimization, the balance between the objective function (e.g., system operation cost) and robustness (e.g., the safety of the power system against uncertain fluctuations) is determined by the size of the fluctuation range of the uncertain parameters.

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.

In contrast, Patent Document 1 describes a method for setting the fluctuation range, stating that "an estimation may be made based on the standard deviation of the amount of change in output from distributed power sources 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.

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

However, Patent Document 1 does not take these temporal and spatial fluctuation characteristics into consideration, so even if the output is actually within the range of 100kW to 300kW, it sets the fluctuation range to 0kW to 500kW. As a result, it outputs an operation plan that places more emphasis on robustness than necessary, which could increase system operation costs.

The objective of this invention is to generate a power system operation plan that is robust to mispredictions and minimizes system operation costs in a power system operation plan generation device.

An electric power system operation plan generating device according to one aspect of the present invention is 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 an uncertain data fluctuation range relating 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 uncertain data fluctuation range, wherein the uncertain data fluctuation range creation unit uses the processor to draw a scatter plot based on the uncertain data, finds multiple vertices of a polygon with the smallest area that surrounds all of the uncertain data, divides the multiple vertices into those above and below a center line connecting the bottom left point and the top right point of the multiple vertices as a reference, and creates the constraint equation by converting equal signs in the linear equation of the 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 .

According to one aspect of the present invention, a power system operation plan generation device can generate a power system operation plan that is robust to mispredictions and minimizes system operation costs.

1 is a block diagram showing a configuration of a power system operation plan generating device according to a first embodiment; FIG. 10 is a diagram illustrating an example of uncertain data past performance. FIG. 10 is a diagram illustrating an example of an uncertain data fluctuation range. FIG. 10 is a diagram illustrating an example of a system operation plan. FIG. 10 is a diagram illustrating an example of a processing flow according to the first embodiment. FIG. 10 is a diagram illustrating an example of vertex movement using a robustness parameter. FIG. 10 is a block diagram showing a configuration of a power system operation plan generating device according to a second embodiment. FIG. 10 is a diagram illustrating an example of a processing flow according to a second embodiment. FIG. 2 is a diagram illustrating a hardware configuration of a power system operation plan generating device.

Below, examples of a power system operation plan generation device and a power system operation plan generation method are described in detail with reference to the drawings. Note that the present invention is not limited to these examples. Furthermore, each example can be combined as appropriate to the extent that the processing content is not inconsistent.

In Example 1, the application example is a distribution system including consumer-owned DERs, grid operator-owned DERs, SVRs, LRTs, and SVCs. The power system operation plan generation device of Example 1 targets photovoltaic power generation output as an uncertain parameter and formulates a robust operation plan taking into account the temporal fluctuation characteristics of photovoltaic power generation output.

The configuration of the power system operation plan generation device of Example 1 will be described with reference to Figure 1. Figure 1 is a diagram showing the processing content of the calculation unit of the power system operation plan generation device, which is realized using a computer device, as its representative processing function unit.

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 for inputting input data, a communication interface unit for communicating with other terminals, etc.

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 uses a processor to create an uncertain data fluctuation range 310 related to the amount of fluctuation per time or the amount of fluctuation per space of the uncertain data based on past performance data 110 of uncertain data having uncertainty and a robustness parameter 120, and generates constraint equation information 230 for the uncertain data fluctuation range 310. The system operation plan generation unit 220 uses a processor to generate a system operation plan 320 for the power system by referencing the constraint equation information 230. The calculation result storage unit 30 also has a database for storing information, and stores the uncertain data fluctuation range 310, the system operation plan 320, and a future system state prediction 330.

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, grid equipment information 140, operational constraint information 150, power market price information 160, renewable energy forecast information 170, and consumer-owned DER information 180.

The uncertain data past performance 110, one of the external inputs used in this case, will be explained using Figure 2.

The first line from the top of Figure 2 represents the ID 111 of each solar power generation system, the second line represents the north latitude of the installation location of each solar power generation system, the third line represents the east longitude of the installation location of each solar power generation system, and each line from the fourth line onwards represents the output of each solar power generation system at each time step. As an example, the time step interval in Figure 2 is set to 5 minutes.

Furthermore, the robustness parameter 120 is numerical data, and if the robustness parameter 120 is denoted by p, then it is numerical data that satisfies, for example, 0≦p≦1.

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 daily tap adjustment limit, the equipment cost, and the installation cost.

In addition, operational constraint information 150 indicates the voltage range and power flow range that must be observed during system operation.

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

Returning to Figure 1, the uncertain data fluctuation range creation unit 210 obtains uncertain data past performance data 110 and robustness parameters 120 as external input data, 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 for the uncertain data fluctuation range to the system operation plan generation unit 220.

The uncertain data fluctuation range 310 of the output data is explained using Figure 3.

The uncertain data fluctuation range 310 includes a plot 311 and an outer fluctuation range frame 312 when the solar power generation output at time t is taken on the horizontal axis and the solar power generation output at time t+1 is taken on the vertical axis.

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

In Figure 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, for example. Furthermore, the fluctuation range outer frame 312 is the edge that constitutes the smallest-area polyhedron that encloses the entire set of plots 311, i.e., the convex hull.

In Figure 3, 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, to take into account spatial fluctuation characteristics, the horizontal axis represents the output of a given solar power generation system and the vertical axis represents the output of a nearby solar power generation system at the same time.

The uncertain data variation range constraint equation information 230 is obtained by converting the equal sign into an inequality sign in the equation for the straight line of the variation range outer frame 312, y = ax + b. x is the value on the horizontal axis of Figure 3, y is the value on the vertical axis of Figure 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. Also, when the variation range outer frame 312 is below the plot 311, y≧ax + b.

Returning to Figure 1, 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 consumer-owned DER information 180 as external input data, solves a robust optimization problem based on this information, and outputs a system operation plan 320 and a future forecast of the system state 330 to the calculation result storage unit 30.

Of the output data, the system operation plan 320 will be explained using Figure 4.

Each row in Figure 4 represents a time step 321, and each column represents the tap adjustment device ID 322 and the 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 adjustment margin 225 for each time step is stored. Since the reserved amount of adjustment 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.

Returning to Figure 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 consumer-owned DER 520, the system operator-owned DER 530, the SVR 540, the LRT 550, and the SVC 560. This controls the above equipment groups, realizing system operation that ensures robustness while reducing system operation costs.

Next, the processing flow of Example 1 will be explained. The explanation will be made according to the flowchart in the processing unit 20 shown in Figure 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.

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

In process 212, a line (hereafter referred to as the center line) is drawn connecting the bottom-left and top-right vertices of the convex hull. In process 213, the distance d between the center line and each convex hull vertex is calculated.

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

Figure 6 illustrates process 214. The light gray plot 601 in Figure 6 is the same as plot 311 in Figure 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. The target convex hull vertex 603 is then moved along the perpendicular line 605 so that the distance from the center line 604 becomes d'. As an example, Figure 6 shows the moved convex hull vertex 606 when the robustness parameter p is 1.3.

Here, we will 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 roughly the same area. If this area where many plots are concentrated were to be placed outside the convex hull, it would result in a fluctuation pattern that is not taken into account in the robust optimization described below, and as a result, the operation plan may violate system operation constraints. For these reasons, in this invention, steps 213 and 214 are performed using the distance d between the center line and each convex hull vertex.

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 to an inequality sign. The direction of the inequality sign is such that if the line is above plot 311, y≦ax + b. Also, if the line is below plot 311, y≧ax + b.

In this way, the uncertain data variation range creation unit 210 draws a scatter plot based on the uncertain data and determines the multiple vertices of the polygon with the smallest area that encloses all of the uncertain data.

Using the center line connecting the bottom-left and top-right points of the multiple vertices as a reference point, 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 for the outer frame of the uncertain data fluctuation range into inequality signs.

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

In addition, 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.

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 robustness parameters, which are then added to the constraint equations of the mathematical programming problem and solved.

Finally, in process 221, a robust optimization problem is formulated and solved. If 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 will be expressed as shown in the following equation (Equation 1).

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

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

A feature of the above robust optimization problem 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 uncertainty parameter u (most severe scenario) that maximizes (worsens) the system operation KPI, and a part that determines the daily control y that minimizes the system operation KPI.

Due to the three-layer structure described above, this robust optimization problem cannot be solved as is. Therefore, it is divided into three optimization problems using a decomposition method such as Benders decomposition and then solved. After division, the optimization problem is reduced to a mixed integer programming (hereinafter referred to as MILP) or a mixed integer second-order cone programming (hereinafter referred to as MISOCP), which can be solved using a mathematical programming solver such as Gurobii or CPLEX.

In this way, by solving the above robust optimization, an operation plan x that minimizes (optimizes) the system operation KPI over the planning period T is obtained.

The configuration of the power system operation plan generation device of Example 2 will be described with reference to Figure 7.

The difference between Figure 7 showing Example 2 and Figure 1 showing Example 1 is that a noise data removal number 720 has been added as input data for the uncertain data fluctuation range 710 in the processing unit 20. The other functions and data are the same as those in Figure 1, so a description thereof will be omitted.

The noise data removal number 720 is a value expressed as an integer greater than or equal to 0, and represents the number of pieces of data that are removed as noise data from the past performance of uncertain data and do not contribute to creating the variation range. Note that the noise data removal number 720 may also be given as a ratio of the number of pieces of data in the past performance of uncertain data.

Next, the processing flow for Example 2 will be explained with reference to Figure 8.

Of the processes in the flowchart, processes 211 and 212 through 221 are the same as those shown in Figure 5, while processes 811, 812, and 813 are processes added from Figure 5. Furthermore, at the start of the flowchart, numerical data a is initialized to 0 and kept in that state.

In process 811, when the number of noise data removed 720 is x, it is checked whether a<x is satisfied. If it is satisfied, the process moves to process 812; if it is not satisfied, the process moves to process 212.

In process 812, the number of vertices calculated in process 211 is set to b, and the value of a is updated to a + b.

In process 813, the values of b and x - a are compared, and the smaller one is assigned to c. The value c calculated in this way indicates the number of vertices to be removed from the uncertain data. Note that the vertices to be removed can be selected randomly, or in descending order of distance to the nearest point, etc.

After process 813, the process returns to process 211. By repeating the above process, 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 removed x.

Figure 9 shows the hardware configuration of the power system operation plan generation device 10.

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

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

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

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

According to the above embodiment, the power system operation plan generation device can generate a system operation plan that is robust to mispredictions and minimizes system operation costs.

10 Power system operation plan generating device 20 Calculation unit 30 Calculation result storage unit 210 Uncertain data fluctuation range creation unit 220 System operation plan generating unit 410 Calculation result display unit 510 Controller 520 Customer-owned DER
530 Grid operator-owned DER
540 SVR
550 LRT
560 SVC
710 Uncertain data fluctuation range creation unit

Claims (6)

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; 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. 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. 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, the method 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. 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. 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.