Operation plan creation device and program

Abstract

To provide an operation plan creation apparatus and program for creating a plan including output curtailment of renewable energy, while considering impartiality of renewable energy output curtailment.SOLUTION: A first uncertainty set generation unit generates a first uncertainty set based on demand past result data. A second uncertainty set generation unit generates a second uncertainty set based on power generation past result data. A renewable energy power generation variability expression generation unit calculates a variability expression for calculating an index value that indicates variability of power generated using renewable energy on the basis of the second uncertainty set. A problem generation unit generates an objective function and constraints regarding power generator cost and renewable energy curtailment cost, using power generator data and system data and the variability expression. A plan creation unit generates plan data of a power generator by solving an optimization problem to approximate the objective function to a desired value under the constraints, to obtain an optimal solution.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] An embodiment of the present invention relates to an operation plan creation device and a program. [Background technology]

[0002] In recent years, the introduction of renewable energy sources such as solar and wind power has progressed. The amount of power generated by renewable energy sources is difficult to predict because it depends on the weather. In addition, there have been cases where the amount of power generated by renewable energy sources exceeds demand, resulting in requests to renewable energy operators to curtail output. Traditionally, generator operation plans to meet power demand for each time period have been created in advance by solving optimization calculations using constant input data or by relying on the operator's experience. However, the large-scale introduction of renewable energy sources has made calculations and predictions difficult.

[0003] Here, the ability of a transmission and distribution operator (TSO) to control frequency and supply and demand in its supply area is called balancing power. TSOs are responsible for matching supply and demand, taking into account the stability of the power system. If supply and demand are not balanced, the power system frequency will fluctuate, and in the worst case scenario, this could lead to power outages.

[0004] The exchange of fees between operators regarding adjustment capacity is, for example, as follows: If the TSO adjusts to increase generator output to match supply and demand, it pays the power generation company an amount equivalent to the fuel costs of that increase. If the TSO adjusts to decrease generator output, the power generation company pays the TSO an amount equivalent to the unnecessary fuel costs. Furthermore, if the TSO curbs renewable energy, it will damage the power generation company's power generation opportunities, and the TSO is expected to pay the power generation company an amount equivalent to the opportunity loss. Under these conditions, it is desirable for the TSO to create a plan that minimizes costs. Regarding this issue, Patent Document 2 discloses a technology for creating a plan that minimizes costs even under uncertainty about demand and renewable energy.

[0005] On the other hand, from the perspective of the owners of renewable energy sources (specifically, owners of solar panels) that are subject to output curtailment, output curtailment means a loss of power generation opportunities, so it is undesirable in terms of fairness for curtailment to be concentrated on owners of specific renewable energy sources. Also, from the perspective of equipment maintenance, it is undesirable for operation to be concentrated on specific renewable energy facilities. In this regard, the technology described in Patent Document 2 may not be able to eliminate solutions in which output commands to renewable energy sources are concentrated on specific renewable energy sources. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2020-65368 [Patent Document 2] Patent Publication No. 2021-33625 [Non-patent literature]

[0007] [Non-Patent Document 1] Jun Hasegawa et al., "Institute of Electrical Engineers University Lecture: Power System Engineering", Institute of Electrical Engineers, pp. 92-103 (2002) [Non-patent document 2] Benders, JF, "Partitioning procedures for solving mixed-variables programming problems", Numerische Mathematik Vol.4, No.3, 238-252 Summary of the Invention [Problem to be solved by the invention]

[0008] The problem to be solved by the present invention is to provide an operation plan creation device and a program that can create a plan that includes suppression of renewable energy output while taking into consideration the fairness of the suppression of renewable energy output. [Means for solving the problem]

[0009] An operation plan creation device according to an embodiment includes an acquisition unit, a first uncertainty set generation unit, a second uncertainty set generation unit, a renewable energy power generation amount variation formula generation unit, a problem generation unit, and a plan creation unit. The acquisition unit acquires data related to a target power system, including actual power demand data, actual renewable energy power generation amount data, generator data indicating generator specifications, and system data indicating the state of the power system. The first uncertainty set generation unit creates a first uncertainty set based on the actual demand data. The second uncertainty set generation unit creates a second uncertainty set based on the actual power generation amount data. The renewable energy power generation amount variation formula generation unit calculates a variation formula for calculating an index value indicating the variation of the renewable energy power generation amount based on the second uncertainty set. The problem generation unit uses the generator data, the system data, and the power generation amount variation formula to create an objective function and constraints related to the generator cost and the renewable energy suppression cost. The plan creation unit solves an optimization problem that brings the objective function closer to a desired value under the constraint conditions, and creates plan data for the generator by obtaining an optimal solution. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a configuration diagram of an operation plan creation device 1 according to an embodiment. [Figure 2] FIG. 1 is a diagram showing a list of definitions of each term in equation (1) and other equations. [Figure 3] FIG. 4 is a diagram showing an example of an interface screen displayed by a selection unit 70. [Figure 4] Diagram (part 1) showing an image of the solution when there are two generators that do not use renewable energy, two generators that use renewable energy, and the number of time frames is 1 to 24. [Figure 5] Diagram (part 2) showing an image of the solution when there are two generators that do not use renewable energy, two generators that use renewable energy, and the number of time frames is 1 to 24. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an operation plan creation device and a program according to an embodiment will be described with reference to the drawings.

[0012] First Embodiment [composition] FIG. 1 is a configuration diagram of an operation plan creation device 1 according to an embodiment. The operation plan creation device 1 includes, for example, an acquisition unit 10, a first uncertainty set generation unit 20, a second uncertainty set generation unit 22, a renewable energy power generation amount variation formula generation unit 24, a problem generation unit 30, a preprocessing unit 40, a plan creation unit 50, an evaluation calculation unit 60, a selection unit 70, a renewable energy suppression command unit 80, a generator operation command unit 90, and a storage unit 100. The components other than the storage unit 100 are realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by a combination of software and hardware. The program may be stored in advance in a storage device (a storage device with a non-transitory storage medium) such as an HDD (Hard Disk Drive) or flash memory, or may be stored in a removable storage medium (a non-transitory storage medium) such as a DVD or CD-ROM, and installed by inserting the storage medium into a drive device.

[0013] The storage unit 100 includes, for example, a RAM (Random Access Memory), a HDD, a flash memory, etc. The storage unit 100 stores, for example, first performance data 110, second performance data 112, generator data 114, system data 116, problem data 120, and plan data 130. Furthermore, the storage unit 100 may store the above-mentioned programs.

[0014] The acquisition unit 10 acquires first actual data 110, second actual data 112, generator data 114, and system data 116, and stores them in the storage unit 100. The acquisition unit 10 acquires these data from other devices, for example, via a network. Networks include wide area networks (WANs), local area networks (LANs), the Internet, cellular networks, etc. In this case, the acquisition unit 10 may include a communication interface such as a network card. The acquisition unit 10 may also acquire the above data via a keyboard, a mouse, a touch panel, a portable storage device, a drive device, etc.

[0015] The first actual data (demand actual data) 110 is actual data of power consumption by consumers connected to the power grid that is the processing target of the operation plan creation device 1. The first actual data 110 is a collection of power consumption measured by a power meter or the like installed in the consumers. The first actual data 110 may be the sum of the power consumption of each consumer, or may be the sum of the power consumption of multiple consumers.

[0016] The first uncertainty set generation unit 20 generates demand forecast data based on the first actual result data 110. The demand forecast data sets, for example, an expected value (predicted value, median) of demand and a fluctuation range for the future power consumption of a consumer that includes uncertainty. For example, when the future power consumption of a consumer has a probability distribution, the fluctuation range may be selected so that the range is ±1σ (68.27[%]) of the predicted value, or may be defined based on ±2σ or ±3σ of the predicted value.

[0017] The second actual data (renewable energy power generation actual data) 112 is actual data on the amount of power generated by a generator using renewable energy (renewable energy) in the power system that is the processing target of the operation plan creation device 1. Renewable energy includes solar power, wind power, geothermal power, hydroelectric power, etc. Hydroelectric power may be classified as non-renewable energy power generation. The second actual data 112 is data measured by a power meter or the like installed in a generator that generates power using renewable energy. The second actual data 112 may also be data measured for an aggregate of many generators, such as a mega solar power plant or a wind farm. Hereinafter, a generator that uses renewable energy may be referred to as a renewable energy generator.

[0018] The second uncertainty set generation unit 22 generates renewable energy power generation forecast data based on the second actual data. The renewable energy power generation forecast data is data that sets, for example, an expected value (predicted value, median) of the future renewable energy power generation amount, a fluctuation range, etc., for the future renewable energy power generation amount that includes uncertainty. Regarding the fluctuation range, for example, if the future renewable energy power generation amount has a probability distribution, a fluctuation range that is ±1σ of the predicted value may be selected, or may be defined based on ±2σ or ±3σ of the predicted value. The fluctuation range in the demand forecast data and the fluctuation range in the renewable energy power generation forecast data do not need to follow the same probability distribution, and may be set separately.

[0019] The renewable energy power generation amount variation formula generating unit 24 generates (calculates) a variation formula for calculating an index value indicating the variation in the power generation amount of the renewable energy generator, based on the renewable energy power generation amount prediction data.

[0020] The generator data 114 is a collection of various information (specification data) related to generators that do not use renewable energy, organized by generator (or by generator group). Generators that do not use renewable energy include, for example, thermal power generators, nuclear power generators, gas power generators, and fuel cells, which are capable of adjusting the amount of power generated with some degree of flexibility. Generators may also include those that are not strictly defined as generators but have similar functions, such as battery storage systems. The generator data 114 includes, for example, the minimum operating time, minimum stop time, output change rate, output increase rate, output decrease rate, maximum output and minimum output, and reserve constraints for each generator. Reserve capacity is the excess output capacity of a generator that is assumed to be immediately output to the grid in order to suppress tidal flow fluctuations.

[0021] The system data 116 includes the amount of reserved reserve power of the power system processed by the operation plan creation device 1, the upper limit of the reserved reserve power, and data on the upper and lower limits of the power that can be passed through the transmission lines. The amount of reserved reserve power is the total value of the reserve power required for generators in the entire system. The upper limit of the reserved power is the upper limit of the reserve power set for each generator, and the reserve power of the generator needs to be set smaller than this value.

[0022] The problem generator 30 generates an objective function and a constraint equation for an optimization calculation using as input values ​​various data included in the demand forecast data, renewable energy power generation forecast data, the variation equation for the renewable energy generator, the generator data 114, and the system data 116. Note that "generating an equation" refers to, for example, a process of determining which item of the various data is to be assigned to an input value in a predetermined mathematical equation.

[0023] [Objective function] The objective function is expressed by equation (1). The definitions of each term in equation (1) are as shown in Figure 2. Figure 2 is a diagram showing a list of definitions of each term in equation (1) and the equations explained below. The part of the above equation excluding Γ*D(S) represents the cost as seen by the TSO. D(S j ) is an index value that indicates the variance of the power generation amount per unit time of renewable energy generator j (in the example below, the variance of the power generation amount per unit time of renewable energy generator j). D(Sj ) is the calculation result of the variation formula generated by the renewable energy power generation amount variation formula generation unit 24. In the upper formula of formula (1), D(S) and in the lower formula, D(S j ), but they are the same thing. A start variable is a signal that rises when start-up first begins and then falls. A stop variable is a signal that rises when stop-up first begins and then falls.

[0024]

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[0025] The constraint equations are expressed by equations (2) to (15). Each of equations (2) and (3) indicates a constraint on starting and stopping the generator.

[0026]

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[0027] Equations (4) and (5) are equations that indicate constraints on the minimum operation time and the minimum stop time, respectively.

[0028]

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[0029] Equation (6) represents the balance between supply and demand in a power grid. Demand is the amount of electricity consumed, and supply is the sum of the amount of electricity generated by generators and renewable energy sources. These must match. For example, if the output of renewable energy sources is curtailed (when r = 1), the total amount of electricity generated will decrease. Equation (7) expresses demand as the expected value d (bar) j t and fluctuation range d (hat) j t This expected value and fluctuation range are included in the demand forecast data generated by the first uncertainty set generation unit 20. Equation (8) defines the future amount of renewable energy power generation as the expected value z (bar)j t and fluctuation range z (hat) j t This expected value and fluctuation range are included in the renewable energy power generation amount prediction data generated by the second uncertainty set generation unit 22.

[0030]

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[0031] Equation (9) defines the generator output change rate. i t is the generator ramp-up rate (maximum speed of power increase), and RD i t is the generator ramp-down rate (maximum rate of output reduction).

[0032]

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[0033] Equation (10) shows the power flow constraint when the transmission line is healthy. Equation (11) shows the power flow constraint when a part or all of the transmission line is broken. f l max is the upper limit of the active power that can flow through the transmission line, and f l,i max is the upper limit of the active power that can flow through the transmission line when the transmission line i is disconnected. l ' and a l,i ' is the sensitivity coefficient of the transmission line. When a transmission line falls off, generally a l,i The elements of the vector of ' are a l ' vector elements, the amount of power that can flow through the transmission line is limited. The range of change in the elements themselves may be calculated in advance by power flow calculations or the like.

[0034]

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[0035] Equation (12) shows the upper and lower limit constraints on the generator output. When the generator is stopped, x i t = 0, so p i t = 0. p i min is the lower limit of the output of generator i, and p i max is the upper limit of the output of generator i.

[0036]

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[0037] Equation (13) specifies that the sum of the generator output and reserve power must be within the upper and lower limits of the generator output. i,a t is the generator reserve. The argument a belongs to set A, where A is a set consisting of combinations of TMSR, TMNSR, and TMOR.

[0038]

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[0039] Equation (14) defines the amount of reserve power required for the entire power system. q(bar)kt is the reserve power required for the entire power system, and Nr is the type of required reserve power. The types of reserve power are TMSR, T10, and T30. TMSR stands for Ten-Minute Spinning Reserve and refers to reserve power that can be supplied within 10 minutes (generators operating at partial load). Ak is one of ATMSR = {TMSR}, AT10 = {TMSR, TMNSR}, and AT30 = {TMSR, TMNSR, TMOR}. TMNSR stands for Ten-Minute Non-Spinning Reserve and refers to reserve power that can be started within 10 minutes (hydroelectric and thermal power plants on standby). TMOR stands for Thirty-Minute Operating Reserve and refers to reserve power that can be supplied to loads within 30 minutes.

[0040]

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[0041] Equation (15) defines the upper limit of the reserve capacity of each generator. q(bar) i,K t is the upper limit of the reserve capacity of generator i. For a generator, the sum of the components of the generator's reserve capacity, TMSR, TMNSR, and TMOR, must be less than the upper limit of the reserve capacity.

[0042]

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[0043] The mathematical expressions (objective functions and constraint expressions) created by the problem generator 30 according to the above concept are stored in the storage unit 100 as problem data 120.

[0044] The preprocessing unit 40 transforms the equations to suit the processing method used by the plan creation unit 50. For example, when the plan creation unit 50 uses stochastic programming, it creates multiple scenarios. Below, an example of using a robust optimization method will be described.

[0045] For example, the preprocessing unit 40 may generate a new variable ζ j t and η j t Using the formula, formula (7) is transformed into formula (16), and formula (8) is transformed into formula (17).

[0046]

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[0047]

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[0048] Furthermore, the pre-processing unit 40 transforms equation (1) into equation (18), equations (2) to (5) into equation (19), equations (9), (13) to (15) into equation (20), equations (10) to (12) into equation (21), and equation (6) into equation (22).

[0049]

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[0050] Equation (18) is equivalent to equation (1), where x is a vector related to generator start / stop and output curtailment, y is a vector related to generator output and reserve capacity, and Φ(ζ,η) is a quadratic function of the uncertainty parameters ζ and η, and corresponds to Γ*D(s) in equation (1).

[0051] [Preprocessing and planning] The preprocessing unit 40 transforms the terms including uncertain parameters in order to obtain an operation plan by applying a robust optimization method. In equation (18), x is a function of x and r, y is a function of d and z (while x is not a function of y), and the demand d j t and renewable energy output z j t Since are uncertain parameters that include fluctuations, in order to satisfy the solution regardless of the range of fluctuations, first, the cost-maximizing b T Since it is necessary to find y, it can be transformed into a MinMax problem shown in equation (23). Furthermore, since y is also a variable of x, ζ, and η, in order to find the minimum cost when x is fixed, it is sufficient to solve the second term of equation (23). Note that equation (24) summarizes the constraint equations.

[0052]

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[0053] The plan creation unit 50 creates plan data 130 using a method such as a robust optimization method, a stochastic programming method, or a genetic algorithm, and stores the plan data 130 in the storage unit 100. The plan data 130 is a set of parameters that minimize an objective function.

[0054] Hereinafter, it is assumed that the plan creation unit 50 creates the plan data 130 using a robust optimization method. The optimal solution related to equation (23) is defined by equation (25). Equation (25) represents the economic dispatch cost when w, ζ, and η are fixed. If the amount of renewable energy power generation is large, the amount of power generated by the generator will decrease and the power generation cost will be small, and if demand is low, the power generation cost will also be small, so these cases are not considered to be the worst case. Therefore, it can be assumed that the worst case occurs when ζ and η are both greater than or equal to zero. Therefore, the uncertainty set can be simplified as in equation (26).

[0055]

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[0056]

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[0057] By applying the duality theorem to equation (26), we obtain equation (27). a (small circle) b is a vector with the same number of elements as a and b, and for the i-th element of each vector, a i ·b i This can be calculated by calculating the product of elements.

[0058]

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[0059] The variability of the power generation amount of the renewable energy generator is expressed as Φ(ζ,η) ζ,η If the maximum value (optimum value) of the sum of S(w, ζ, η) is R(x), then R(x) is expressed by equation (28).

[0060]

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[0061] Since equation (28) is a bilinear optimization problem, the preprocessing unit 40 transforms equation (28) into an integer linear programming problem, taking the following points into consideration. Since the set D×Z is a convex set, it can be proven that optimal solutions ζ*, η* exist on the endpoints of the set D×Z, and therefore, for each x, optimal solutions ζ*, η* exist, which are expressed as binary vectors. In particular, the multilinear terms in the objective function R(x) contain a portion expressed as the product of a continuous variable and a binary variable (ζ or η), and can be rewritten as κβ, which is the product of a positive continuous variable κ and a binary variable β. This can be linearized by introducing a new variable ν=κβ that satisfies the linear constraint shown in equation (29). However, κ m is the upper limit of κ. In this way, by substituting ν = κβ for S, R(x) can be treated as an integer linear problem. Based on the assumptions above, the added first term Φ(ζ,η) is a continuous convex function, so R(x) as a whole can be obtained by solving a mixed integer quadratic programming problem with linear constraints. This can be solved using commonly used mathematical programming solvers.

[0062]

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[0063] The plan creation unit 50 finds a solution using a technique such as Benders decomposition, and creates the plan data 130. Benders decomposition is a classical algorithm proposed as an iterative solution method for nonlinear problems involving a mixture of continuous variables and integers (Non-Patent Document 2). Below, an example of an algorithm when Benders decomposition is applied to the present invention is given.

[0064] 1. First, as an initialization process, set U = ∞ and k = 1. Next, 2. and the following steps are repeated until convergence is achieved.

[0065] 2. Solve the robust problem RP expressed by equation (30) and define the optimal solution as x k The optimal value is L. The argument k indicates the number of cycles of the process to be repeatedly executed.

[0066]

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[0067] 3. Recourse Problem R(x k ) and find the optimal value v(R(x k ))The optimal solution is Φ k , λ k , μ k , η k , ζ k Let's say.

[0068] 4.U k =c T x k ++v(R(x k )) to update U.

[0069] 5. If the convergence criteria expressed by equation (31) are satisfied, then x k and Φ k , λ k , μ k , η k , ζ k is the optimal solution and the process is terminated. If the convergence criteria are not satisfied, the constraint equation expressed by equation (32) is added to the robust problem RP.

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[0070] [Rating / Selection] Hereinafter, it is assumed that multiple sets of optimal solutions are calculated by the plan creation unit 50. If only one optimal solution is calculated by the plan creation unit 50, the evaluation calculation unit 60 and the selection unit 70 may be omitted, and a renewable energy suppression command and a generator operation command according to the one optimal solution may be output to the generator to be controlled.

[0071] When multiple sets of optimal solutions are calculated by the plan creation unit 50, the evaluation calculation unit 60 acquires the decision variable values ​​resulting from the optimization calculation by the plan creation unit 50, and calculates, for each optimal solution, a first index related to the cost from the TSO's perspective and a second index related to fairness for renewable energy generators based on the acquired values. The first index is, for example, a value obtained by removing the term D(S) from the objective function of Equation (1). The second index is, for example, the Gini coefficient. The Gini coefficient is used in the field of economic statistics as an index for quantifying income imbalances, etc. When the Gini coefficient is applied to the present invention, the Gini coefficient Gini is expressed by Equation (33). Here, j' represents the argument j sorted in ascending order. A larger value of the Gini coefficient Gini indicates greater unfairness. If the power generation amount of each renewable energy generator is completely fair, that is, if the power generation amount of all renewable energy generators is the same value, the Gini coefficient is 0. Conversely, in the most unfair case, if power generation is concentrated in one renewable energy generator, the Gini coefficient is 1. In reality, the scale of individual renewable energy generators varies, and there are cases where calculating the Gini coefficient directly from the amount of power generated by individual renewable energy generators does not necessarily reflect inequality. For example, when mega solar power plants and residential PV-scale systems are mixed. In such cases, fairness can be expressed by normalizing the amount of power generated by each individual renewable energy generator by its rated value and calculating it as a power generation ratio.

[0072]

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[0073] The selection unit 70 selects one optimal solution by performing processing based on the first index and the second index calculated by the evaluation calculation unit 60. The selection unit 70 may automatically select one optimal solution by performing some kind of calculation processing on the first index and the second index, or may select one optimal solution by presenting information to an operator (e.g., a member of the TSO management) and accepting the operator's selection operation, as will be described below.

[0074] For example, the selection unit 70 displays an image in which the first index and the second index for each optimal solution are plotted in a two-dimensional graph on a display device (not shown). The display device may be a dedicated monitor connected to the operation plan creation device 1, or may be a smartphone, a tablet terminal, or a personal computer. FIG. 3 is a diagram showing an example of an interface screen displayed by the selection unit 70. On this interface screen, for example, cost (first index) is set on the vertical axis and Gini coefficient (second index) is set on the horizontal axis, and the coordinates of each optimal solution are shown in a two-dimensional graph. After displaying this interface screen on the display device, the selection unit 70 accepts an operation by the operator to specify which optimal solution to select, and selects the operated optimal solution.

[0075] The renewable energy suppression command unit 80 outputs a renewable energy suppression command to a renewable energy generator or its control device based on the selected optimal solution. Also, the generator operation command unit 90 outputs an operation command to a non-renewable energy generator or its control device based on the selected optimal solution.

[0076] [Example solution] Figures 4 and 5 show an example of a solution when there are two non-renewable energy generators, two renewable energy generators, and the number of time frames ranges from 1 to 24. In the figures, x1 and x2 are renewable energy generators, and p1 and p2 are generators that do not use renewable energy. Calculating the Gini coefficient (Gini) based on the example shown gives S1 = 120 and S2 = 360. The cumulative relative frequency of the first renewable energy generators is 0.25, and the cumulative relative frequency of the number of renewable energy generators is 1 / 2 = 0.5. The cumulative relative frequency of the second renewable energy generators is 1, and the cumulative relative frequency of the number of renewable energy generators is 1. In this case, the Gini coefficient is 2*{(0.5 - 0.25) + (1 - 1)} = 0.5.

[0077] Second Embodiment In the first embodiment described above, the index value D(s) (or D(sj)) indicating the variation in the amount of power generated by renewable energy is assumed to be a variance, but it may also be the sum of absolute values ​​of differences from the average value, as shown in equation (34). Equation (34) is a variation equation for finding the sum of absolute values ​​of differences from the average value. Even in this case, the same effects as in the first embodiment can be achieved.

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[0079] Third Embodiment Furthermore, a positive continuous variable K may be introduced as a variation equation for determining an index value that indicates the variation in the amount of power generated by renewable energy. In this case, the index value D(s) is expressed by equation (35), which adjusts the total upper limit of s and adds a linear constraint to the problem as shown in equation (36). If the upper limit can be kept low, the number of cases where an overwhelmingly large allocation occurs will decrease, and even in this case, the same effect as in the first embodiment can be achieved.

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[0081] According to at least one of the embodiments described above, there is provided an acquisition unit 10 that acquires data related to a target power system, including actual power demand data, actual data on power generation amount by renewable energy, generator data indicating the specifications of the generator, and system data indicating the state of the power system; a first uncertainty set generation unit 20 that creates a first uncertainty set based on the actual demand data; a second uncertainty set generation unit 22 that creates a second uncertainty set based on the actual power generation amount data; a renewable energy power generation amount variation formula generation unit 24 that calculates a variation formula for power generation amount by renewable energy based on the second uncertainty set; a problem generation unit 30 that uses the generator data and system data to create an objective function and constraint conditions related to the generator cost and the renewable energy suppression cost; and a plan creation unit 50 that solves an optimization problem of approximating the objective function to a desired value under the constraint conditions and creates an optimal solution to create generator plan data, thereby making it possible to create a plan that includes renewable energy output suppression while taking into consideration the fairness of the renewable energy output suppression.

[0082] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention described in the claims and their equivalents. [Explanation of symbols]

[0083] 1 Operation plan creation device 10 Acquisition Department 20 First Uncertainty Set Generation Unit 22 Second uncertainty set generation unit 24 Renewable energy power generation variation equation generation unit 30 Problem generation section 40 Pretreatment section 50 Planning Department 60 Evaluation Calculation Department 70 Selection Section 100 Storage section 110 First Actual Data 112 Second Actual Data 114 Generator Data 116 System Data 130 Planning Data

Claims

1. an acquisition unit that acquires data related to a target power system, including actual power demand data, actual renewable energy power generation data, generator data indicating the specifications of the generator, and system data indicating the state of the power system; a first uncertainty set generation unit that generates a first uncertainty set based on the demand result data; a second uncertainty set generation unit that generates a second uncertainty set based on the power generation amount actual data; a renewable energy power generation amount variation formula generation unit that calculates a variation formula for calculating an index value indicating the variation of the power generation amount by the renewable energy based on the second uncertainty set; a problem generator that uses the generator data, the system data, and the power generation amount variation equation to generate an objective function and constraint conditions related to the generator cost and the renewable energy suppression cost; a plan creation unit that solves an optimization problem that brings the objective function closer to a desired value under the constraint conditions, obtains an optimal solution, and creates plan data for the generator; An operation plan creation device comprising:

2. a pre-processing unit that performs equation transformation for applying the objective function and the constraints to a robust optimization method; the plan creation unit creates the plan data based on a robust optimization method for the transformed equation. The operation plan creating device according to claim 1.

3. an evaluation calculation unit that acquires decision variable values ​​that are the results of the optimization calculation by the plan creation unit, and calculates, for each optimal solution, a first index related to cost and a second index related to fairness for the renewable energy power generators based on the acquired values; The operation plan creation device according to claim 1 , further comprising: a selection unit that performs processing based on the first index and the second index to select one optimal solution.

4. the selection unit displays an image on a display device in which the first index and the second index for each optimal solution are plotted on a two-dimensional graph, and then selects one optimal solution in response to an operation by an operator to select an optimal solution. The operation plan creating device according to claim 3.

5. On the computer, Acquiring data relating to a target power system, including actual power demand data, actual renewable energy power generation data, generator data indicating the specifications of the generator, and system data indicating the state of the power system; creating a first uncertainty set based on the demand performance data; creating a second uncertainty set based on the actual power generation amount data; Calculating a variation formula for calculating an index value indicating the variation of the amount of power generation by the renewable energy based on the second uncertainty set; creating an objective function and constraints related to generator costs and renewable energy suppression costs using the generator data, the system data, and the power generation amount variation equation; solving an optimization problem that brings the objective function closer to a desired value under the constraint conditions, and obtaining an optimal solution to create planning data for the generator; A program to execute.

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