JP7234566B2 - Operation planning method, operation planning device and program - Google Patents

Description

The present invention relates to an operation planning method, an operation planning device, and a program.

Conventionally, there is known a technique of creating a plan for starting and stopping a generator using an iterative method called the Lagrangian relaxation method (see, for example, Patent Literature 1). The Lagrangian relaxation method in Patent Literature 1 divides a problem into subproblems for each target device and calculates a solution. When calculating a solution by the Lagrangian relaxation method, the number of iterations of processing increases, and the calculation time may become longer. As a related technique, a control device is known that includes a relaxed problem optimization unit that solves a relaxed problem with relaxed discrete constraints and a subproblem optimization unit that solves a subproblem (see, for example, Patent Document 2).
[Patent Literature]
[Patent Document 1] JP-A-2002-300720 [Patent Document 2] JP-A-2013-64245

When creating an operation plan for a target device that takes discrete states, it is preferable to shorten the time required to compute feasible solutions.

In order to solve the above problems, a first aspect of the present invention provides an operation planning method for generating operation plans for a plurality of target devices by a computer. The operation planning method is a problem of obtaining an original problem for computing a plurality of state values, including discrete state values representing discrete states of each target device, capable of satisfying predetermined objectives and constraints. An acquisition step may be provided. The operation planning method may include a relaxation problem calculation step of solving a relaxation problem assuming that each target device can assume a continuous state in the original problem, and calculating a continuous state of each target device. The operation planning method divides the original problem into a plurality of subproblems, and solves the subproblems using the distance between the solution of the relaxation problem and the solution of the subproblem, thereby discretizing at least some of the plurality of state values. and a subproblem computing stage of computing values including state values. In the operation planning method, some of the plurality of state values are fixed to the values calculated in the subproblem calculation stage, and the slack problem using the constraints of the original problem is solved to find out of the plurality of state values There may be a slack problem computation stage that computes the remaining state values.

The plurality of state values may include a state value indicative of an output quantity of the target device. In the partial problem calculation stage, a state value indicating the output quantity of at least some of the target devices may be calculated. In the slack problem calculation step, the output amounts of at least some of the target devices may be fixed, and a state value indicating the output amounts of the remaining target devices may be calculated.

A target device for calculating a state value in the slack problem calculation stage may be selected based on at least one characteristic of output capacity, output efficiency and responsiveness of the target device.

The plurality of target devices may include one or more power generation devices and one or more power storage devices. In the partial problem calculation stage, a state value indicating the output quantity of the generator may be calculated. In the slack problem calculation stage, the output amount of the power generation device may be fixed, and a state value indicating the output amount of the power storage device may be calculated.

The plurality of state values may include a state value indicating an output amount of the target device and a state value indicating an active state of the target device. In the partial problem calculation stage, state values indicating the activation states of a plurality of target devices may be calculated. In the slack problem calculation stage, the state values indicating the activation states of the plurality of target devices may be fixed, and the state values indicating the output amounts of the plurality of target devices may be calculated.

The plurality of state values may include a state value indicating an output amount of the target device and a state value indicating an active state of the target device. In the first slack problem calculation step, the output quantity for some target devices may be fixed to the value obtained in the partial problem calculation step, and a state value indicating the output quantity of the remaining target devices may be calculated. . In the first slack problem calculation stage, if there is no state value that satisfies the constraint conditions, in the second slack problem calculation stage, the state values indicating the activation states of the plurality of target devices are the values obtained in the partial problem calculation stage. , and a state value indicating the output amount of a plurality of target devices may be calculated.

The plurality of state values may include a state value indicating an output amount of the target device and a state value indicating an active state of the target device. In the first slack problem calculation step, the state values indicating the activation states of the plurality of target devices are fixed to the values obtained in the partial problem calculation step, and the state values indicating the output amounts of the plurality of target devices are calculated. you can In the first slack problem calculation stage, if there is no state value that satisfies the constraint conditions, in the second slack problem calculation stage, the output quantity for some target devices is fixed to the value obtained in the partial problem calculation stage. and a state value indicative of the output amount of the remaining target device may be calculated.

A second aspect of the present invention provides a program for causing a computer to execute the operation planning method according to the first aspect.

A third aspect of the present invention provides an operation planning device that generates operation plans for a plurality of target devices. The operation planning device is a problem of obtaining an original problem for computing a plurality of state values, including discrete state values representing discrete states of each target device, capable of satisfying predetermined objectives and constraints. An acquisition unit may be provided. The operation planning device may include a relaxation problem calculation unit that solves a relaxation problem that assumes that each target device can assume a continuous state in the original problem, and calculates the continuous state of each target device. The operation planning device divides the original problem into a plurality of subproblems, and solves the subproblems using the distance between the solution of the relaxation problem and the solution of the subproblem, thereby discretizing at least some of the plurality of state values. A partial problem calculation unit for calculating a value including a state value may be provided. The operation planning device fixes some of the plurality of state values to the values calculated by the partial problem calculation unit, solves the slack problem using the constraint conditions of the original problem, and determines one of the plurality of state values. A slack problem calculator may be provided for calculating discrete values of the remaining state values.

It should be noted that the above summary of the invention does not list all the necessary features of the invention. Subcombinations of these feature groups can also be inventions.

It is a flow chart which shows an example of the operation planning method concerning one embodiment of the present invention. FIG. 2 is a flow chart showing the contents of a slack problem calculation step in FIG. 1; FIG. FIG. 2 is a diagram showing an overview of a partial problem solving process in the operation planning method described in FIG. 1; FIG. 2 is a flow chart showing a first example of a slack problem calculation stage in FIG. 1; FIG. It is a figure which shows the outline|summary of the operation planning method demonstrated in FIG. It is an example of each solution of the mitigation problem, the partial problem, and the slack problem in the operation planning method explained in FIG. 10 is a flow chart showing an example of a process of selecting a target device to be calculated in a slack problem calculation stage; It is a figure which shows an example of the target apparatus which the operation planning apparatus 300 which concerns on one embodiment of this invention controls. FIG. 2 is a flow chart showing a second example of a slack problem calculation step in FIG. 1; FIG. FIG. 3 is a flow chart showing a third example of the slack problem calculation stage in FIG. 1; FIG. FIG. 2 is a diagram showing a configuration example of an operation planning device 300; FIG. An example computer 2200 is shown in which aspects of the present invention may be embodied in whole or in part.

Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential for the solution of the invention.

FIG. 1 is a flow chart showing an example of an operation planning method according to one embodiment of the present invention. In the operation planning method, a computer generates an operation plan for a plurality of target devices. The computer may be a general-purpose computer or a dedicated computer for generating the operation plan.

A target device is a device that assumes discrete states. For example, the target device is a power supply device, and the discrete states are "start" and "stop". In this specification, the "start" of the device as the state value may be represented by "1" as the discrete state value, and the "stop" of the device may be represented by "0" as the discrete state value. The target device takes on a plurality of state values including discrete state values indicating discrete states. The state value may include the output amount of the target device as well as the state value indicating the activation state described above. The output amount of the target device is, for example, the value of the output power. Note that the target device is not limited to the power supply device. Also, the discrete states are not limited to "start" and "stop". Also, the target device may be a device that takes three or more discrete states.

The operation plan of the target device is, for example, a plan of the timing to "start" or "stop" each of the plurality of power supply devices. As an example, the operation plan of the target device sets the states of a plurality of target devices at each time so as to satisfy a predetermined purpose while satisfying predetermined constraints. In one example, the constraints may include conditions due to the performance of the target device, such as the maximum and minimum values of the output power of the power supply, and may include artificially negotiated conditions. The objective of the operation plan may be the minimization of certain parameters, such as minimizing power consumption, minimizing operating costs, and the like. The purpose of the operation plan may be maximization of a given parameter, or may bring the given parameter closer to a reference value.

式（１．１）は運転計画の目的を示す目的関数であり、「ｓ．ｔ．」以下に示される式（１．２）から（１．４）は運転計画の制約条件を示す。ａは分割される部分を示しており、例えば、各ノードを示している。ａは、例えば各対象装置の番号を示している。一例において、ｘ_ａおよびｙ_ａは、ａ番目のノード、例えば、ａ番目の対象装置における状態を示す状態値である。本明細書では、ｘ、ｙ等の状態値を、単に「状態ｘ」、「状態ｙ」等のように称する場合がある。ｆ_ａはａ番目のノードの状態ｘ_ａおよびｙ_ａに応じた所定の目的パラメータをノード毎に示す関数である。目的パラメータは、例えば上述したように運転コスト等である。本例の目的関数は、各ノードにおける目的パラメータの総和を最小化することを示している。 First, the computer obtains an original problem for calculating a plurality of state values indicating the state of each target device that can satisfy the above-described objective and constraint conditions (question obtaining step S102). The plurality of state values includes discrete state values indicating discrete states of the target device. The original problem can be expressed by the following formula as an example.

Formula (1.1) is an objective function indicating the purpose of the operation plan, and formulas (1.2) to (1.4) shown below "st" indicate constraints of the operation plan. a indicates a divided portion, for example, each node. a indicates, for example, the number of each target device. In one example, x _a and y _a are state values that indicate the state at the ath node, eg, the ath target device. State values such as x, y, etc. may be simply referred to herein as "state x", "state y", and so on. f _a is a function that indicates, for each node, a predetermined objective parameter according to the states x _a and y _a of the a-th node. The target parameter is, for example, operating cost, etc., as described above. The objective function in this example indicates minimizing the sum of objective parameters at each node.

_Ga is called an independent constraint. _Ga is a function that defines the relationship between a predetermined value corresponding to the states xa _{and ya} of the target device and the reference value 0 for each node. In this example, the constraint condition is that the predetermined value is equal to or less than the reference value 0. The independent constraint G _a may be a power supply output constraint or a voltage constraint at each node. H is a function that defines the relationship between a predetermined value corresponding to the states _xa and _ya of the target device and the reference value 0 at a plurality of or all nodes. For example, H is a constraint over variables x, y. H includes supply and demand balance constraints such that the sum of the output amounts of all target devices is equal to the load, network constraints, and the like. Note that the constraint is not limited to specifying that the predetermined value is equal to or less than the reference value. Constraints that constrain various characteristic values of the target device to predetermined conditions can be used.

R in equation (1.4) indicates an arbitrary real value, and Z indicates a plurality of discrete values. As shown in equation (1.4), the state value x _a is a continuous variable of size n. In one example, the state value _xa may indicate the state of continuous values of n characteristics in one target device, and may indicate the respective states of n target devices. For example, _xa is a state value indicating power output or the like for each time series. The state value y _a is a discrete variable of size m. In one example, the state value y _a is a state value that indicates a discrete binary state of stop (0) and start (1). The state value y _a may be a state value indicative of stepped operation, such as a transformer tap. The status value y _a may indicate the status of m characteristics in one target device, or may indicate the status of each of the m target devices.

When solving the original problem, the states x, y of the target device at each time slice that satisfy the objective function and constraints can be solved. However, the original problem given by equations (1.1) and (1.2)-(1.4) is a mixed-integer nonlinear programming problem. Mixed-integer nonlinear programming problems belong to a class of problems called NP-hard. In the mixed-integer nonlinear programming problem, when the number of target devices becomes large, the amount of computation becomes enormous, and it becomes difficult to obtain a solution.

In the operation planning method of this example, instead of solving the original problem, the computer solves a relaxation problem assuming that each target device can take a continuous state in the original problem (relaxation problem calculation step S104). In S104, the computer uses the objective function and constraints in the original problem, assumes that each target device can take a continuous state under the constraints, and calculates the continuous states of the target devices. The objective function and constraints in the relaxed problem and the original problem may be the same, except for whether the state values are discrete or continuous.

式（２．４）におけるｙ_ｉハットは、特定の範囲の任意の実数を取り得ることを示している。例えば、ｙ_ｉハットは、０以上１以下の任意の実数を取り得る。本例の緩和問題の目的関数（式（２．１））および制約条件（式（２．２）から（２．４））は、離散的な状態値ｙに代えて、式（２．４）における所定の範囲の任意の値（たとえば、０から１の間の任意の値）を取り得る連続変数である状態値ｙハットを用いること以外は、元問題の目的関数（式（１．１））および制約条件（式（１．２）から（１．４））と同一である。式（２．１）および制約条件（式（２．２）から（２．４））で示される緩和問題は、非線形計画問題である。非線形計画問題は、逐次二次計画法等の手法で高速に解くことができる。たとえば、非線形計画問題である緩和問題は、目的関数が凸性の仮定の下において，解を演算することができる。本明細書では、緩和問題で求めた解（すなわち離散的でなく、所定の範囲の任意の値を取り得る解）を緩和解と称する場合がある。 The mitigation problem can be illustrated by the following equation as an example.

The y _i hats in equation (2.4) indicate that they can take any real number within a certain range. For example, y _i can take any real number between 0 and 1 inclusive. The objective function (formula (2.1)) and the constraints (formulas (2.2) to (2.4)) of the relaxation problem in this example are replaced by the discrete state value y in formula (2.4) ), the objective function of the original problem (equation (1.1 )) and the constraints (equations (1.2) to (1.4)). The relaxation problem given by equation (2.1) and the constraints (equations (2.2) to (2.4)) is a nonlinear programming problem. Nonlinear programming problems can be solved at high speed by techniques such as sequential quadratic programming. For example, a relaxation problem, which is a nonlinear programming problem, can be solved under the assumption that the objective function is convex. In this specification, a solution obtained from a relaxation problem (that is, a solution that is not discrete and can take any value within a predetermined range) is sometimes referred to as a relaxation solution.

Next, the computer determines whether there exists a feasible solution (ie states x, y that satisfy the constraints) in the relaxation problem (first decision step S106). If no feasible solution exists, the process ends. If a feasible solution exists, the computer solves a plurality of subproblems obtained by dividing the original problem (subproblem calculation step S108).

As an example, the partial problem is a problem obtained by dividing the original problem into multiple parts corresponding to multiple nodes (target devices). Each subproblem may contain one time slice or may contain multiple time slices. The number of time slices included in each subproblem may be the same, or may differ between subproblems.

In solving each subproblem, the computer computes the relaxed solution of the relaxed problem (i.e., state y, which can take any real value, and state y, which can take any value between 0 and 1, for example). hat) and the solution of the subproblem (ie state x and y, a discrete value of 0 or 1). The subproblem computing step S108 computes a value including at least some discrete state values among the plurality of state values by solving the subproblem using the solution distance. Solution distance refers to a measure of how far apart two points corresponding to each solution are in their respective state parameter (x and y in this example) space. Types of distance include Euclidean distance, Chebyshev distance, and the like. Alternatively, indices such as similarity and proximity may be used in addition to the distance. As an example, in S108, the solution of the subproblem may be computed such that the Euclidean distance to the solution of the relaxation problem is minimized.

本例の部分問題では、それぞれノードａについて、式（３．２）から（３．４）を満たす状態ｘ´_ａ、ｙ´_ａを演算する。部分問題における状態ｘ´_ａは、式（３．４）に示すように任意の実数の値を取る。また状態ｙ´_ａは、緩和問題で求めた緩和解ｙ_ａハット（すなわち、０から１の間の任意の値を取り得る）に所定の偏差Δを加算したものであり、０または１の２値のいずれかの値を取る。偏差Δは、状態ｙ´_ａと緩和解ｙ_ａハットとの差分（すなわち距離）を示している。 A subproblem can be shown by the following formula as an example. Here, the variables other than the subproblems x' _{a and} y _{' a} , that is, x ₁ hat, y ₁ hat _{, ...} x _a-1 hat, y _a-1 hat, x _{a + 1} hat, y _{a + 1} hat, ... . . x _A hat and y _A hat are treated as fixed variables using the relaxation solution. In this example, the objective function in the subproblem minimizes a given objective parameter _fa at each node.

In the subproblem of this example, states x'a and _y'a that satisfy equations (3.2) to (3.4 ₎ are computed for each node a. The state _x'a in the subproblem takes any real value as shown in equation (3.4). State _y'a is obtained by adding a predetermined _deviation .DELTA. take any value. The deviation Δ indicates the difference (or distance) between the state _y'a and the relaxed solution _ya hat.

In the objective function and constraint of the subproblem, the state _y'a is used instead of the state _ya in the objective function and constraint of the original problem. The state _y'a has a deviation .DELTA. difference from the relaxed solution _ya . In this example, the objective function of the subproblem is to minimize the sum of the objective parameter defined by the function _fa and the distance parameter d according to the deviation Δ, as shown in equation (3.1). Use functions. Note that d is a scale representing the magnitude of the deviation Δ, and may be arbitrarily set by the user. d may be a proximity index that evaluates the distance (proximity) between y and the feasible modified solution _y'a . d may be the square norm or the like as the simplest index.

By solving such a subproblem, the original problem can be divided into multiple subproblems, and each subproblem can be solved using the distance between the relaxed solution of the relaxed problem and the discrete value solution of the subproblem. . In a subproblem, the original problem is divided into multiple parts, so the scale of one problem becomes smaller. Therefore, even if a discrete state y is included, it can be calculated at high speed. The calculation of the partial problem can be executed at high speed by using, for example, the branch-and-bound method.

Then, by introducing a distance parameter (d in this example) into the objective function and solving the subproblem, the solution of the subproblem is prevented from becoming a suboptimal solution optimized by considering only one subproblem. be able to. In particular, by setting the objective function so as to minimize the distance between the relaxed solution _ya hat and the partial problem solution _y'a , a reasonable solution close to the exact solution can be obtained at high speed.

In the examples shown in formulas (3.1) to (3.4), the relaxed solution y _a hat is used, but the relaxed solution x _a hat may be used. In this case, x′ _a =x _a hat+Δ. Alternatively, both relaxed solutions x _a and y _a may be used.

After solving one subproblem, it is determined whether or not a predetermined number of subproblems have been solved (second determination step S110). If the predetermined number of subproblems have not been solved, the process from S108 to S110 is repeated for the next subproblem. However, the subproblems a=1, . . . A may be solved in parallel. When solutions have been calculated for the predetermined number of subproblems, the computer solves the slack problem (slack problem calculation step S112).

FIG. 2 is a flow chart showing the content of the slack problem calculation stage in FIG. The computer fixes some of the plurality of state values to the values calculated in the partial problem calculation step S108 (S202). Then, the computer solves the slack problem using the constraints of the original problem and calculates the remaining state values among the plurality of state values (S204).

The slack problem can be represented by the following equation as an example. In this example, x _{' 1, y} ' ₁ , x' _2, y' _{2 , . . .} , x _{' s−1,} y _{' s−1,} x' _s+1, y _{′ s+1} , . . . x _{′ A,} y′ _1, x _{′ A} may be fixed as part of the plurality of state values.

In the objective function and constraint of the slack problem, the state y _'s is used instead of the state _ya in the objective function and constraint of the original problem. The state y _{' s} has a deviation Δ from the relaxed solution y _s hat. In this example, as the objective function for the slack problem, an objective function that minimizes the objective parameter defined by the function _fs is used as shown in Equation (4.1).

In solving the slack problem, some of the plurality of state values are fixed to the values calculated in the subproblem calculation stage, and the rest of the state values are calculated. The computer may solve all the partial problems in the partial problem calculation step S108, and then re-solve at least some of the partial problems as slack problems in the slack problem calculation step (S112).

Then, except for the state values to be solved as the slack problem, the computer fixes the plurality of state values to the values calculated in the subproblem calculation step S108, solves the slack problem using the constraints of the original problem, Compute remaining state values of the plurality of state values. Therefore, the occurrence of a state in which there is no state value that satisfies the constraint conditions is suppressed, compared to the case where there is no slack problem calculation stage. That is, it becomes possible to adjust the solution at the slack problem calculation stage so that a feasible solution can be obtained. Therefore, it is possible to shorten the time for computing a feasible solution.

FIG. 3 is a diagram showing the outline of the partial problem solving process in the operation planning method described in FIG. The horizontal axis in FIG. 3 indicates the state value _y1 , and the vertical axis indicates the state value _y2 . Discrete values that the target device can actually take for the state values y ₁ and y ₂ are plotted with a plurality of diamond-shaped marks 204 . A straight line 202 indicates the boundary between state values y ₁ and y ₂ that satisfy the constraint. A region hatched with dashed lines outside the straight line 202 is a region that does not satisfy the constraint, and a non-hatched region is a region that satisfies the constraint. FIG. 3 shows a straight line 202 in a certain time section.

Also, the positions corresponding to the relaxation solutions are plotted with circular marks 206 . A relaxed solution is the solution that best fits the objective function within a region that satisfies the constraints. On the other hand, in the partial problem, among the discrete solutions (mark 204) within the region that satisfies the constraint conditions, the solution (mark 208) that minimizes the deviation Δ from the relaxed solution is calculated. Through such processing, a solution that does not necessarily match the exact solution obtained from the original problem, but that is close to the relaxed solution and has high validity can be solved at high speed.

FIG. 4 is a flow chart showing a first example of the slack problem calculation stage in FIG. The plurality of state values may include not only the state value indicating the activated state of the target device, but also the output amount of the target device. Then, in the partial problem calculation step S108 of FIG. 1, not only the state value indicating the activation state of at least some of the target devices but also the output amount of the target device is calculated.

As shown in FIG. 4, the state value indicating the output amount and the state value indicating the active state are fixed for some target devices among the plurality of target devices. The state value indicating the output quantity and the state value indicating the activation state for some target devices are fixed to the values obtained by solving the subproblem using the distance between the solution of the relaxation problem and the solution of the subproblem. (S302). For example, if there are a target device (A), a target device (B), a target device (C), and a target device (D), the target device (A), the target device (B), and the target device (D ), the state value indicating the output quantity and the state value indicating the activation state are fixed to the values obtained by solving the partial problem using the solution distance.

Next, the computer calculates a state value indicating an output amount and a state value indicating an activation state for the remaining target devices among the plurality of target devices (S304). According to the above example, the computer calculates the state value indicating the output amount and the state value indicating the activated state for the remaining target devices (C). The slack problem calculation process is executed after the partial problem calculation process, fixing each state value of the partial problem other than the problem to be solved as the slack problem, and calculating each unfixed state value.

FIG. 5 is a diagram showing an outline of the operation planning method explained in FIG. First, the computer solves the relaxation problem assuming that each target device can assume a continuous state in the original problem, and calculates the continuous state of each target device. Then, the computer solves the partial problem with the power supply G1 and the power supply G2 as target devices. In particular, the computer uses the distance between the solution of the relaxation problem and the solution of the subproblem to fix the state values indicating the amount of power output and the state values indicating the activation state for power supplies G1 and G2. The computer then solves the slack problem with power supply G3 as the remaining target device. It should be noted that the partial problems may be solved for all target devices, that is, the power supply G1, the power supply G2, and the power supply G3, and then the slack problem for the power supply 3 may be solved again. On the other hand, for the power source 3 to be solved as the slack problem, the partial problem arithmetic processing may be omitted.

FIG. 6 is an example of each solution of the mitigation problem, the partial problem, and the slack problem in the operation planning method explained in FIG. State values x1, x2, and x3 respectively indicating the output amounts of the power source G1, the power source G2, and the power source G3 are exemplified. First, the relaxed solutions x1hat, x2hat, and x3hat are calculated under the restricted state in the relaxed problem. Then, by solving the subproblems using the distances between the relaxed solution and the solutions of the subproblems, solutions x' ₁ , x' ₂ , and x' ₃ of the subproblems are calculated. However, as shown in FIG. 6, there may be a case where the solution obtained in the partial problem calculation stage does not satisfy the limiting conditions. For example, the sum of the solutions of each subproblem may exceed the load. In this case, no feasible solution is obtained. Therefore, by solving the slack problem for some of the plurality of partial problems, processing is performed so that a feasible solution can be obtained.

A process of selecting a target device to be a calculation target in the slack problem calculation stage from among a plurality of target devices will be described. FIG. 7 is a flow chart showing an example of a process of selecting a target device to be calculated in the slack problem calculation stage. The operation planning device acquires characteristic information about a plurality of target devices and stores the acquired characteristic information in a database. The characteristic information may include at least one of output capacity, output efficiency, and responsiveness of the target device. Each time a new target device is provided, the new target device may transmit characteristic information to the operation planning device via the communication network.

If the operation plan is to be executed (S404: YES), the computer functioning as the operation planning device refers to the database and determines the output capacity, output efficiency, and responsiveness of the target device based on at least one characteristic of the target device. to select a target device to be calculated in the slack problem calculation stage (S406). Specifically, a target device whose output capacity is greater than a predetermined value, in particular, a target device with the maximum output capacity may be selected. By choosing the target device with the largest output capacity, the adjustment range is widened in the slack problem calculation stage.

Also, a target device with an output efficiency lower than a predetermined value, in particular, a target device with the lowest output efficiency may be selected. Output efficiency is output efficiency with respect to consumed energy. As shown in FIG. 6, there is a possibility that the target device that is the object of calculation in the slack problem calculation stage will be the farthest solution from the optimum solution. Therefore, by setting a target device with low output efficiency as a computation target in the slack problem computation stage, it is possible to reduce the influence caused by the possibility of a solution far from the optimal solution.

Further, a target device having a response higher than a predetermined value, particularly a target device having the fastest response among a plurality of target devices, may be selected as a target device to be calculated in the slack problem calculation stage. Responsiveness may be a follow-up speed with respect to a control command. In addition, after S406 in FIG. 7, the same processing as in FIG. 1 may be executed.

FIG. 8 is a diagram showing an example of target devices controlled by the operation planning device 300 according to one embodiment of the present invention. The multiple target devices in this example are multiple power sources. Specifically, the plurality of target devices ( 304 , 306 ) includes one or more power generation devices 304 and one or more power storage devices 306 . Power generation device 304 and power storage device 306 supply power to external device 302 . External device 302 may be a server.

The operation planning device 300 calculates state values indicating the output amounts of the plurality of power generators 304 in the partial problem calculation step S108. The operation planning device 300 may fix the output amount of the power generation device 304 and calculate the state value indicating the output amount of the power storage device 306 in the slack problem calculation step S112. When there are a plurality of power storage devices 306, one power storage device 306 may be selected as a target device to be calculated in the slack problem calculation stage. In this case, the output amounts of the power storage devices other than the power storage device 306 to be calculated in the slack problem calculation stage may be fixed in the slack problem calculation stage. The responsiveness of the power storage device 306 is faster than the responsiveness of the power generation device 304 .

As described above, in FIGS. 4 to 8, in a plurality of nodes, that is, in a plurality of target devices, after fixing the output amount and activation state of the target device other than the target device for calculation in the slack problem calculation stage, In the slack problem calculation stage, a state value indicating the output amount of the target device to be calculated and a state value indicating the activation state were calculated. However, the present invention is not limited to the process of selecting the computation target in the slack problem computation stage according to the node.

FIG. 9 is a flow chart showing a second example of the slack problem calculation stage in FIG. The state value of the number includes a state value indicating the output amount of the target device and a state value indicating the activation state of the target device. Then, in the partial problem calculation step S108 of FIG. 1, at least the state value y indicating the activation state of the plurality of target devices is calculated. In particular, at least the state value y indicating each activated state in all the target devices is calculated. The formula calculates the output quantity _{xa of} each target device after fixing the state value _ya indicating the activation state of a plurality of target devices to the value ya calculated in the partial problem calculation stage.

As shown in FIG. 9, the status value _ya bar indicating the activation status of a plurality of target devices is fixed. A state value _ya bar indicating each activation state for all target devices may be fixed. A state value indicating the activation state of a plurality of target devices is fixed to a value obtained by solving the partial problem using the distance between the solution of the relaxation problem and the solution of the partial problem (step S502). For example, if there are target device (A), target device (B), target device (C), and target device (D), target device (A), target device (B), and target device (C ), and the target device (D), the state value _ya indicating the activated state is fixed to the value obtained by solving the partial problem using the solution distance.

Next, among the plurality of state values, the remaining state values, that is, the state values indicating the output amount of the target device are calculated (S504). According to the above example, for all of the plurality of target devices (A), target device (B), target device (C), and target device (D), the output quantity _xa of each target device is calculated. In the slack problem calculation processing, a plurality of state values other than the state value indicating the output amount to be solved as the slack problem may be fixed, and the solution may be calculated using the state values indicating the output amount to be solved as the slack problem.

As described above, as described with reference to FIGS. 4 to 8, the process of selecting the calculation target in the slack problem calculation stage according to the node, and as described with reference to FIG. There is a process of selecting a computation target in the problem computation stage. These two types of processing may differ in the possibility of obtaining a feasible solution. Therefore, if one process does not yield a feasible solution to the slack problem, the other process may be performed.

FIG. 10 is a flow chart showing a third example of the slack problem calculation stage in FIG. The state value of the number includes a state value indicating the output amount of the target device and a state value indicating the activation state of the target device. First, the first slack problem solving process is executed (S602), and if there is a feasible solution (S604: YES), the slack problem solving process is completed and the process returns as it is. On the other hand, if there is no feasible solution, the second slack problem solving process is executed (step S606).

The first slack problem-solving process S602 may be the processes of S302 and S304 in FIG. 4, and the second slack problem-solving process S606 may be the processes of S502 and S504 in FIG. In this case, in the first slack problem calculation step (S602), the output quantity for some target devices is fixed to the value obtained in the partial problem calculation step, and the output quantity for the remaining target devices is indicated. Calculate the value (S302 and S304 in FIG. 4). Then, in the first slack problem calculation step, if there is no state value that satisfies the constraint conditions (S604: NO), in the second slack problem calculation step (S606), the state value indicating the activation state of the plurality of target devices. is fixed to the value obtained in the partial problem calculation stage, and state values indicating output amounts of a plurality of target devices are calculated (S502 and S504 in FIG. 9).

On the other hand, the first slack problem-solving process S602 may be the processes of S502 and S504 of FIG. 9, and the second slack problem-solving process S606 may be the processes of S302 and S304 of FIG. In this case, in the first slack problem calculation step (S602), the state values indicating the activation states of the plurality of target devices are fixed to the values obtained in the partial problem calculation step, and the output amounts of the plurality of target devices are fixed. The indicated state value is calculated (S502 and S504 in FIG. 9). Then, in the first slack problem calculation stage, if there is no state value that satisfies the constraint conditions (S604: NO), in the second slack problem calculation stage (S606), the output amount for some of the target devices is partially reduced. Fix the value obtained in the problem calculation stage and calculate the state value indicating the remaining output amount of the target device (S302 and S304 in FIG. 4).

According to such processing, even if a state value that satisfies the constraint conditions cannot be obtained by one type of slack problem calculation, a state value that satisfies the constraint conditions can be obtained by another type of slack problem calculation. Sometimes. Therefore, it becomes easier to obtain a feasible solution.

FIG. 11 is a diagram showing a configuration example of the operation planning device 300. As shown in FIG. The operation planning device 300 includes a problem acquisition unit 1102 , a relaxation problem calculation unit 1104 , a partial problem calculation unit 1106 and a slack problem calculation unit 1108 . The relaxation problem calculator 1104 , the partial problem calculator 1106 , and the slack problem calculator 1108 may be a common calculator 1110 .

The question acquisition unit 1102 executes the question acquisition step S102 shown in FIG. The mitigation problem calculation unit 1104 executes the mitigation problem calculation step S104 and the first determination step S106 shown in FIG. The partial problem calculation unit 1106 executes the processing up to S1108 and S1110 shown in FIG. With such a configuration, the operation planning method described with reference to FIGS. 1 to 10 can be executed.

FIG. 12 illustrates an example computer 2200 upon which aspects of the invention may be implemented in whole or in part. Programs installed on the computer 2200 may cause the computer 2200 to function as one or more sections of an operation or apparatus associated with an apparatus according to embodiments of the invention, or may Sections can be executed and/or computer 2200 can be caused to perform methods or steps of methods according to embodiments of the invention. Such programs may be executed by CPU 2212 to cause computer 2200 to perform certain operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

Computer 2200 according to this embodiment includes CPU 2212 , RAM 2214 , graphics controller 2216 , and display device 2218 , which are interconnected by host controller 2210 . Computer 2200 also includes input/output units such as communication interface 2222, hard disk drive 2224, DVD-ROM drive 2226, and IC card drive, which are connected to host controller 2210 via input/output controller 2220. there is The computer also includes legacy input/output units such as ROM 2230 and keyboard 2242 , which are connected to input/output controller 2220 through input/output chip 2240 .

CPU 2212 operates according to programs stored in ROM 2230 and RAM 2214, thereby controlling each unit. Graphics controller 2216 retrieves image data generated by CPU 2212 into itself, such as a frame buffer provided in RAM 2214 , and causes the image data to be displayed on display device 2218 .

Communication interface 2222 communicates with other electronic devices over a network. Hard disk drive 2224 stores programs and data used by CPU 2212 within computer 2200 . DVD-ROM drive 2226 reads programs or data from DVD-ROM 2201 and provides programs or data to hard disk drive 2224 via RAM 2214 . The IC card drive reads programs and data from IC cards and/or writes programs and data to IC cards.

ROM 2230 stores therein programs that are dependent on the hardware of computer 2200, such as a boot program that is executed by computer 2200 upon activation. Input/output chip 2240 may also connect various input/output units to input/output controller 2220 via parallel ports, serial ports, keyboard ports, mouse ports, and the like.

A program is provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The program is read from a computer-readable medium, installed in hard disk drive 2224 , RAM 2214 , or ROM 2230 , which are also examples of computer-readable medium, and executed by CPU 2212 . The information processing described within these programs is read by computer 2200 to provide coordination between the programs and the various types of hardware resources described above. An apparatus or method may be configured by implementing the manipulation or processing of information in accordance with the use of computer 2200 .

For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 executes a communication program loaded in the RAM 2214 and sends communication processing to the communication interface 2222 based on the processing described in the communication program. you can command. The communication interface 2222 reads transmission data stored in a transmission buffer processing area provided in a recording medium such as the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or an IC card under the control of the CPU 2212, and transmits the read transmission data. Data is transmitted to the network, or received data received from the network is written to a receive buffer processing area or the like provided on the recording medium.

In addition, the CPU 2212 causes the RAM 2214 to read all or necessary portions of files or databases stored in external recording media such as a hard disk drive 2224, a DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. Various types of processing may be performed on the data in RAM 2214 . CPU 2212 then writes back the processed data to the external recording medium.

Various types of information, such as various types of programs, data, tables, and databases, may be stored on recording media and subjected to information processing. CPU 2212 performs various types of operations on data read from RAM 2214, information processing, conditional decision making, conditional branching, unconditional branching, and information retrieval, as specified throughout this disclosure and by instruction sequences of programs. Various types of processing may be performed, including /replace, etc., and the results written back to RAM 2214 . In addition, the CPU 2212 may search for information in a file in a recording medium, a database, or the like. For example, if a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 2212 determines that the attribute value of the first attribute is specified. search the plurality of entries for an entry that matches the condition, read the attribute value of the second attribute stored in the entry, and thereby associate it with the first attribute that satisfies the predetermined condition. an attribute value of the second attribute obtained.

The programs or software modules described above may be stored in a computer readable medium on or near computer 2200 . Also, a recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the program to the computer 2200 via the network. do.

Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the description of the scope of claims that forms with such modifications or improvements can also be included in the technical scope of the present invention.

The execution order of each process such as actions, procedures, steps, and stages in the devices, systems, programs, and methods shown in the claims, the specification, and the drawings is particularly "before", "before etc., and it should be noted that they can be implemented in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the specification, and the drawings, even if the description is made using "first," "next," etc. for the sake of convenience, it means that it is essential to carry out in this order. not a thing

202... straight line, 204, 206, 208... mark, 300... operation planning device, 302... external device, 304... power generation device, 306... power storage device, 1102... problem Acquisition unit 1104 Relaxation problem computation unit 1106 Partial problem computation unit 1108 Slack problem computation unit 1110 Computation unit 2200 Computer 2201 DVD-ROM , 2210... host controller, 2212... CPU, 2214... AM, 2216... graphic controller, 2218... display device, 2220... input/output controller, 2222... communication interface, 2224 hard disk drive, 2226 DVD-ROM drive, 2230 ROM, 2240 input/output chip, 2242 keyboard

Claims (8)

An operation planning method for generating an operation plan for a plurality of target devices by a computer,
a problem acquisition step of acquiring an original problem for computing a plurality of state values, including discrete state values indicative of discrete states of each target device, capable of satisfying predetermined objectives and constraints;
a relaxation problem calculation step of solving a relaxation problem assuming that each target device can take a continuous state in the original problem, and calculating a continuous state of each target device;
Discrete states of at least some of the plurality of state values by dividing the original problem into a plurality of subproblems and solving the subproblems using a distance between a solution of the relaxation problem and a solution of the subproblem. a subproblem computing stage of computing a value containing a value;
fixing some of the plurality of state values to the values calculated in the partial problem calculation stage, and solving the slack problem using the constraint conditions of the original problem, a slack problem computation stage that computes the remaining state values , and
Selecting the target device for calculating the state value in the slack problem calculation step based on at least one characteristic of the target device, output capacity, output efficiency and responsiveness.
Operation planning method. An operation planning method for generating an operation plan for a plurality of target devices by a computer,
a problem acquisition step of acquiring an original problem for computing a plurality of state values, including discrete state values indicative of discrete states of each target device, capable of satisfying predetermined objectives and constraints;
a relaxation problem calculation step of solving a relaxation problem assuming that each target device can take a continuous state in the original problem, and calculating a continuous state of each target device;
Discrete states of at least some of the plurality of state values by dividing the original problem into a plurality of subproblems and solving the subproblems using a distance between a solution of the relaxation problem and a solution of the subproblem. a subproblem computing stage of computing a value containing a value;
fixing some of the plurality of state values to the values calculated in the partial problem calculation stage, and solving the slack problem using the constraint conditions of the original problem, a slack problem computation stage that computes the remaining state values;
with
the plurality of target devices include one or more power generation devices and one or more power storage devices;
calculating a state value indicating an output amount of the power generation device in the partial problem calculation step;
The operation planning method, wherein in the slack problem calculation step, the output amount of the power generation device is fixed, and a state value indicating the output amount of the power storage device is calculated. the plurality of state values include a state value indicating an output amount of the target device;
in the partial problem calculation step, calculating a state value indicating an output amount of at least a part of the target device;
3. The operation planning method according to claim 1 , wherein in said slack problem calculation step, the output amounts of at least some of said target devices are fixed, and state values indicating the output amounts of the remaining target devices are calculated. An operation planning method for generating an operation plan for a plurality of target devices by a computer,
a problem acquisition step of acquiring an original problem for computing a plurality of state values, including discrete state values indicative of discrete states of each target device, capable of satisfying predetermined objectives and constraints;
a relaxation problem calculation step of solving a relaxation problem assuming that each target device can take a continuous state in the original problem, and calculating a continuous state of each target device;
Discrete states of at least some of the plurality of state values by dividing the original problem into a plurality of subproblems and solving the subproblems using a distance between a solution of the relaxation problem and a solution of the subproblem. a subproblem computing stage of computing a value containing a value;
fixing some of the plurality of state values to the values calculated in the partial problem calculation stage, and solving the slack problem using the constraint conditions of the original problem, a slack problem computation stage that computes the remaining state values;
with
the plurality of state values include a state value indicating an output amount of the target device and a state value indicating an activation state of the target device;
in the partial problem calculation step, calculating a state value indicating the activation state of the plurality of target devices;
The operation planning method, wherein, in the slack problem calculation step, state values indicating activation states of the plurality of target devices are fixed, and state values indicating output amounts of the plurality of target devices are calculated. An operation planning method for generating an operation plan for a plurality of target devices by a computer,
a problem acquisition step of acquiring an original problem for computing a plurality of state values, including discrete state values indicative of discrete states of each target device, capable of satisfying predetermined objectives and constraints;
a relaxation problem calculation step of solving a relaxation problem assuming that each target device can take a continuous state in the original problem, and calculating a continuous state of each target device;
Discrete states of at least some of the plurality of state values by dividing the original problem into a plurality of subproblems and solving the subproblems using a distance between a solution of the relaxation problem and a solution of the subproblem. a subproblem computing stage of computing a value containing a value;
fixing some of the plurality of state values to the values calculated in the partial problem calculation stage, and solving the slack problem using the constraint conditions of the original problem, a slack problem computation stage that computes the remaining state values;
with
the plurality of state values include a state value indicating an output amount of the target device and a state value indicating an activation state of the target device;
In the first slack problem calculation step, the output amounts for some of the target devices are fixed to the values obtained in the partial problem calculation step, and state values indicating the output values of the remaining target devices are calculated. death,
In the first slack problem calculation stage, if the state value satisfying the constraint condition does not exist,
In the second slack problem calculation step, the state values indicating the activation states of the plurality of target devices are fixed to the values obtained in the partial problem calculation step, and the state values indicating the output amounts of the plurality of target devices. operation planning method. An operation planning method for generating an operation plan for a plurality of target devices by a computer,
a problem acquisition step of acquiring an original problem for computing a plurality of state values, including discrete state values indicative of discrete states of each target device, capable of satisfying predetermined objectives and constraints;
a relaxation problem calculation step of solving a relaxation problem assuming that each target device can take a continuous state in the original problem, and calculating a continuous state of each target device;
Discrete states of at least some of the plurality of state values by dividing the original problem into a plurality of subproblems and solving the subproblems using a distance between a solution of the relaxation problem and a solution of the subproblem. a subproblem computing stage of computing a value containing a value;
fixing some of the plurality of state values to the values calculated in the partial problem calculation stage, and solving the slack problem using the constraint conditions of the original problem, a slack problem computation stage that computes the remaining state values;
with
the plurality of state values include a state value indicating an output amount of the target device and a state value indicating an activation state of the target device;
In the first slack problem calculation step, the state values indicating the activation states of the plurality of target devices are fixed to the values obtained in the partial problem calculation step, and the state values indicating the output amounts of the plurality of target devices. and
In the first slack problem calculation stage, if the state value satisfying the constraint condition does not exist,
In the second slack problem calculation stage, the output quantity for some of the target devices is fixed to the value obtained in the partial problem calculation step, and a state value indicating the output quantity of the remaining target devices is calculated. Operation planning method. A program for causing a computer to execute the operation planning method according to any one of claims 1 to 6 . An operation planning device that generates an operation plan for a plurality of target devices,
a problem acquisition unit for acquiring an original problem for computing a plurality of state values including discrete state values representing discrete states of respective target devices capable of satisfying predetermined objectives and constraints;
a relaxation problem calculation unit that solves a relaxation problem assuming that each target device can assume a continuous state in the original problem, and calculates a continuous state of each target device;
Discrete states of at least some of the plurality of state values by dividing the original problem into a plurality of subproblems and solving the subproblems using a distance between a solution of the relaxation problem and a solution of the subproblem. a subproblem calculation unit that calculates a value including a value;
Fixing some of the plurality of state values to values calculated by the partial problem calculation unit, and solving the slack problem using the constraint conditions of the original problem, a slack problem calculator that calculates discrete values of the remaining state values;
with
selecting the target device for which the state value is to be calculated in the slack problem calculation unit based on at least one characteristic of the target device's output capacity, output efficiency, and responsiveness;
Operation planning device.

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