JP7110624B2 - 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, 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 1). .
Patent document 1 JP 2013-64245 A

When creating an operation plan by dividing a target device that takes a discrete state into partial problems, it is preferable to create an operation plan so as not to result in partial optimization.

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 trip planning method may comprise a problem acquisition step of acquiring an original problem for computing discrete states of each target device that can satisfy predetermined objectives and constraints. 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, solves each subproblem using the distance between the solution of the relaxation problem and the solution of the subproblem, and calculates the discrete state of each target device. There may be a subproblem computing stage for

In the subproblem computation stage, the subproblem solution may be computed such that the distance to the solution of the relaxation problem is minimized.

The objective function indicating the objective of the original problem may include a condition for minimizing a predetermined objective parameter. In the subproblem calculation stage, a function that minimizes the sum of the objective parameter and the distance parameter corresponding to the distance may be used as the objective function of the subproblem.

In the subproblem calculation stage, the original problem is divided into a plurality of subproblems corresponding to a plurality of time sections, and the solution of the subproblem calculated for each time section is used as the initial value of the subproblem in the next time section. , the solutions of multiple subproblems may be computed sequentially.

The original problem constraints may include conditions on multiple time slices. In the partial problem calculation stage, states of the target device in a plurality of time slices may be calculated as one partial problem.

The target device may be a power supply that supplies power to a load. The solution to the mitigation problem may be recomputed when the load prediction is updated.

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 may comprise a problem acquisition unit that acquires an original problem for computing discrete states of each target device that can satisfy predetermined objectives and constraints. 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, solves each subproblem using the solution of the relaxation problem, and has a subproblem calculation unit that calculates the discrete state of each target device. good.

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. It is a figure which shows the outline|summary of the operation planning method demonstrated in FIG. 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. It is a figure which shows an example of the operation plan of several target apparatuses. 5 is a diagram showing changes in the number of operating power supply devices 304 and total power consumption in the example shown in FIG. 4. FIG. FIG. 5 is a diagram showing changes in remaining battery capacity, discharged amount, and charged amount of storage battery 306 in the example shown in FIG. 4 ; FIG. 10 is a diagram showing an example of transition of power consumption when using a strict solution that solves the original problem, a relaxed solution that solves the relaxed problem, and an example solution that solves the partial problem using the relaxed solution. FIG. 10 is a diagram showing an example of transition of the remaining battery charge when using a strict solution that solves the original problem, a relaxed solution that solves the relaxed problem, and an example solution that solves the partial problem using the relaxed solution. FIG. 10 is a diagram showing an example of transition of output power when using a strict solution that solves the original problem, a relaxed solution that solves the relaxed problem, and an example solution that solves the partial problem using the relaxed solution. 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, "start" of the device may be represented by "1" and "stop" by "0" as the state parameter. 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. For example, the constraint conditions may include conditions caused by the performance of the target device, such as the maximum and minimum values of the output power of the power supply device, and may include conditions determined artificially. 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）~（１．４）は運転計画の制約条件を示す。ｉは時間断面（すなわち離散的な時刻）を示しており、ｘ_ｉおよびｙ_ｉは対象装置の時間断面ｉにおける状態を示す状態パラメータである。本明細書では、ｘ、ｙ等の状態パラメータを、単に「状態ｘ」、「状態ｙ」等のように称する場合がある。ｘ_ｉおよびｙ_ｉは、それぞれ異なる対象装置の状態を示してよい。ｆ_ｉは対象装置の状態ｘ_ｉおよびｙ_ｉに応じた所定の目的パラメータを時間断面毎に示す関数である。目的パラメータとは、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、各時間断面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 acquires an original problem for computing the discrete states of each target device that can satisfy the above-mentioned objectives and constraints (problem acquisition step S102). 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) below "st" indicate the constraints of the operation plan. i indicates a time section (that is, a discrete time), and x _i and y _i are state parameters indicating the state of the target device in time section i. State parameters such as x, y, etc. may be simply referred to herein as "state x", "state y", and so on. x _i and y _i may each indicate a different target device state. f _i is a function that indicates a predetermined target parameter corresponding to the states x _i and y _i of the target device for each time section. The target parameter is, for example, the operating cost, etc., as described above. The objective function in this example indicates minimization of the sum of objective parameters in each time section.

g _i is a function that defines the relationship between the predetermined value corresponding to the states x _i and y _i of the target device and the reference value 0 for each time section. In this example, the constraint condition is that the predetermined value is equal to or less than the reference value 0. Also, h is a function that defines the relationship between the predetermined values corresponding to the states x _i and y _i of the target device and the reference value 0 in a plurality of or all time sections. 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 that it takes any real value, and {0, 1} indicates that it takes a discrete value of 0 or 1. As shown in equation (1.4), the state x _i may denote the state of continuous values of n characteristics in one target device, and denote the states of each of the n target devices. good too. State _yi is a state parameter that indicates a discrete binary state of stop (0) and start (1). The state y _i may indicate the state of m characteristics in one target device, or may indicate the respective states of m target devices.

By solving the original problem, the state x, y of the target device at each time slice that satisfies the objective function and constraints can be obtained. However, the original problem given by equations (1.1) and (1.2)-(1.4) is a mixed-integer nonlinear programming problem. The mixed-integer nonlinear programming problem becomes difficult to solve when the number of target devices becomes large, and the amount of calculation becomes enormous. In the operation planning method of this example, instead of solving the original problem, a relaxation problem is solved assuming that each target device can take a continuous state in the original problem (relaxation problem calculation step S104). In S104, while using the objective function and constraint conditions in the original problem, assuming that each target device can take a continuous state under the constraint conditions, the continuous state of the target device is calculated. The objective function and constraints in the relaxed problem and the original problem may be the same, except for whether the state parameters take discrete or continuous values.

式（４）における［０，１］は、０以上１以下の任意の実数を取り得ることを示している。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）～（２．４））は、離散的な状態ｙに代えて、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ｙ'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）～（１．４））と同一である。式（２．１））および制約条件（式（２．２）～（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。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、０から１の間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.

[0, 1] in Equation (4) indicates that it can take any real number from 0 to 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 expressed as Except for using the state y′, which is a continuous variable that can take any value, the objective function (formula (1.1)) and the constraint conditions (formulas (1.2) to (1.4)) of the original problem are are identical. (2.1)) and constraints (Equations (2.2)-(2.4)) are nonlinear programming problems. Nonlinear programming problems can be solved at high speed by techniques such as sequential quadratic programming. In this specification, a solution obtained from a relaxation problem (that is, a solution that is not discrete and can take any value between 0 and 1) is sometimes referred to as a relaxation solution.

Next, it is determined 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, divide the original problem into multiple subproblems and solve them. In this example, an initial value for solving the partial problem is set (initial value setting step S108).

As an example, a partial problem is a problem obtained by dividing the original problem into multiple parts corresponding to multiple time sections. 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 S108, states x and y in any time section may be set. For example, in S108, the states x and y in the first time slice among the multiple time slices corresponding to the multiple partial problems are set. The states x, y in the first time slice may be determined based on current or past states of the target device. As an example, states x, y in the first time slice are the current states of the target device. The initial values for states x and y may be determined based on the current or past state of the target device. For example, in the example of equation (3) below, the current state x _i is defined by the states x _i−1 and y _i−1 one section past. The states x _i-1 and y _i-1 here are the initial values of the current state x _i .

Next, each of the subproblems obtained by dividing the original problem into a plurality of subproblems is solved (subproblem calculation step S110). In the operation planning method of this example, the partial problems in each time section are solved in order. The order of solving the partial problems may be chronological order. Also, in S108, the states x and y obtained by solving the partial problem in S110 may be used as the initial values of the partial problem at the next time.

When solving each subproblem, the relaxed solution of the relaxed problem (that is, state x' that can take any real number value and state y' that can take any value between 0 and 1) and , the distance between the solution of the subproblem (ie, state x and y, a discrete value of 0 or 1). 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 S110, 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、それぞれ時間断面ｉについて、式（４．２）～（４．４）を満たす状態ｘ"_ｉ、ｙ"_ｉを演算する。部分問題における状態ｘ"_ｉは、式（４．４）に示すように任意の実数の値を取る。また状態ｙ"_ｉは、緩和問題で求めた緩和解ｙ'_ｉ（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"_ｉと緩和解ｙ'_ｉ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. In this example, the objective function in the subproblem minimizes a given objective parameter f _i at each time slice. In the partial problem of this example, all states of all target devices included in the original problem are solved in each time section.

In the subproblem of this example, states x″ _i and y″ _i that satisfy equations (4.2) to (4.4) are calculated for each time section i. The states _x''i in the _subproblem take arbitrary real values as shown in equation (4.4 ₎ . can take any value between ) and a predetermined deviation Δ, and takes either a binary value of 0 or 1. The deviation Δ indicates the difference (ie distance) between the state y″ _i and the relaxed solution y′ _i .

In the objective function and constraints of the _subproblem , the _{states y''i} are used in place of the states _yi in the original problem's objective function and constraints. have a difference. In this example, as the objective function of the subproblem, as shown in equation (4.1), the objective is to minimize the sum of the objective parameter defined by the function f _i and the distance parameter d according to the deviation Δ Use functions. Note that d is a scale representing the magnitude of the deviation Δ, and may be arbitrarily set by the user.

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. . By using the states x and y of the target device calculated by the partial problem, the target device can be controlled so as to satisfy the objective and constraint conditions.

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 _y'i and the subproblem solution _y''i , it is possible to quickly obtain a reasonable solution close to the exact solution.

In the example shown in Equation 4, the relaxed solution y' _i was used, but the relaxed solution x' _i may also be used. In this case, x'' _i =x' _i +Δ. Alternatively, both relaxed solutions x' _i and y' _i may be used.

After solving one subproblem, it is determined whether all subproblems have been solved (second determination step S112). If all the subproblems have been solved, the process ends. If there is a partial problem for which no solution has been obtained, the processing from S108 to S112 is repeated for the next partial problem. In this example, the next subproblem is the subproblem of the next time slice (that is, the next time). By solving all subproblems, the states of all target devices can be defined for all time sections.

FIG. 2 is a diagram showing an outline of the operation planning method explained in FIG. The horizontal axis in _FIG . ₂ indicates the value of state y1, and the vertical axis indicates the value of state y2. Discrete values that the target device can actually take in the states y ₁ and y ₂ are plotted with a plurality of diamond-shaped marks 204 . A straight line 202 indicates the boundary between states y ₁ and y ₂ that satisfy the constraint. A region hatched with broken 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. 2 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 computed. 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. 3 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 target devices in this example are one or more power supply devices 304 and one or more storage batteries 306 . The power supply 304 may supply generated power to one or more external devices 302 , and may convert the voltage or the like of power received from the outside and supply it to the external devices 302 .

The storage battery 306 receives and stores electric power from the power supply device 304 or the like. The storage battery 306 functions as a backup power source that supplies power to the external device 302 when the power source device 304 cannot operate, and as an assist power source that supplies power to the external device 302 in cooperation with the power source device 304. may have For example, the storage battery 306 supplies power to the external device 302 even in times other than emergencies, on condition that the storage battery 306 maintains an amount of electricity corresponding to a predetermined backup capacity. By way of example, external device 302 is a plurality of servers, but is not so limited. External device 302 may be a device that consumes more power. The plurality of power supply devices 304 and the plurality of storage batteries 306 may be arranged in the same building, or may be arranged dispersedly in different buildings. A plurality of external devices 302 may also be located in the same building, or may be distributed in different buildings.

FIG. 4 is a diagram illustrating an example of an operation plan for a plurality of target devices; In this example, the solid line indicates the predicted value of the total load of the plurality of external devices 302 in each time section. Also, the power generation amounts of the plurality of power supply devices 304 in each time section are shown by bar graphs. There are five power supply devices 304 in this example. Also, the total amount of discharge from the plurality of storage batteries 306 is indicated by a dashed line, and the total amount of charge to the plurality of storage batteries 306 is indicated by a dashed-dotted line.

The operation plan of this example aims at minimizing the power consumption of the power supply device 304 . The power consumption includes power consumption when the power supply device 304 is started, stopped, and on standby. The power consumption is also obtained by subtracting the power supplied from the storage battery 306 from the total load of the external device 302 and adding the charging power to the storage battery 306 .

Also, the constraints are as follows.
- At least one power supply device 304 is always activated.
- Charging and discharging of the storage battery 306 are not performed at the same time.
- When the SOC (state of charge) of each storage battery 306 is below the specified discharge value, the storage battery 306 cannot be discharged.
- When the SOC (rate of charge) of each storage battery 306 exceeds the specified charging value, the storage battery 306 cannot be charged.
- When the SOC (rate of charge) of each storage battery 306 falls below the specified discharge value, the storage battery 306 cannot be discharged until it exceeds a specified value larger than the specified discharge value.
- The output of the storage battery 306 is within the set upper and lower limits.
- The output of the power supply device 304 is within the set upper and lower limits.
- The total remaining capacity of the storage battery 306 is greater than or equal to the set backup capacity.
- The storage battery 306 is used to reduce the output of the power supply device 304 during peak loads (peak cut).

As shown in FIG. 4, by supplying power from the storage battery 306 when the load reaches a peak, the output of the power supply device 304 can be relaxed. However, the storage battery 306 must be charged in advance before the load peaks. Therefore, if the state of the power supply device 304 is optimized by focusing only on each time slice, the efficiency may decrease at peak load in the future.

According to the operation planning method described in FIG. 1, the relaxation problem is solved for a plurality of time sections to calculate the relaxation solution, and each partial problem is solved using the relaxation solution. Therefore, it is possible to quickly calculate a solution with high validity considering a plurality of time sections.

In the processing from S108 to S112 shown in FIG. 1, the solutions of the partial problems calculated for each time slice are used as the initial values of the partial problems in the next time slice, and the solutions of a plurality of partial problems are sequentially calculated. Therefore, by solving a plurality of subproblems once in order, it is possible to obtain reasonable solutions in each time section.

If the constraints include conditions relating to a plurality of time slices, the state of the target device in the plurality of time slices may be calculated as one partial problem. For example, if each power supply 304 has an upper limit on the change in power generation over a predetermined number of time slices, the original problem may be divided into each predetermined number of time slices. Each subproblem may overlap in some time slices.

Further, in the operation planning method shown in FIG. 1, the processing from S104 may be started when the load prediction as shown in FIG. 4 is updated. That is, the relaxed solution of the relaxed problem may be recalculated, and the division problem after the time may be solved based on the newly acquired relaxed solution. The load prediction may be given to the operation planning device 300 at any time.

また、変数を以下の通りに定義する。

An example objective function and constraints are formulated below. A summary of the objectives and constraints of the operation plan is as described in FIG. For formulation, the following symbols are introduced.

Also, the variables are defined as follows.

An example of the original problem of the operation plan of the storage battery 306 and the power supply device 304 is as follows. The computer may acquire the original question (S102). The original problem becomes a mixed-integer nonlinear programming problem. Therefore, it is difficult to obtain a solution when the time section and the number of devices are large.

In the original problem shown in Equation 5, similarly to the example shown in Equation 2, the state parameters of u _it ^PSU , u _t ^cBCU , and u _t ^dBCU are relaxed to state parameters that take arbitrary values from 0 to 1. By solving the problem, a relaxed solution for each state parameter is obtained (S104, S105).

なお、ｘ''およびu''は、緩和問題を解くことで得られた緩和解を示している。数６に示す問題においては、偏差Δ_x、Δ_uを導入している。また偏差のノルムΔ_x ²、Δ_u ²を距離として用いている。ｗ_x、ｗ_uは重み係数であり、どれだけ距離の最小化を考慮するかを決定するパラメータである。したがって、ここでの距離パラメータｄはｗ_xΔ_x ²とｗ_uΔ_u ²を足し合わせたものに相当する。電源装置３０４の運転台数制御における時間断面の集合の数Ｔ_２が、蓄電池３０６の時間断面の集合の数Ｔ_１よりも十分小さいことで、数６に示した問題の規模が小さくなり、高速に解くことができる。本例では、数６に示した問題を、時間断面毎の部分問題に分割して求解する（Ｓ１０８－Ｓ１１２）。 A relaxed solution in the relaxed problem of the original problem shown in Equation 5 is used. In this case, the problem shown in Equation 5 is transformed into the following equation.

Note that x'' and u'' indicate the relaxed solution obtained by solving the relaxed problem. In the problem shown in Equation 6, deviations Δ _x and Δ _u are introduced. Also, the norms Δ _x ² and Δ _u ² of the deviation are used as distances. w _x , w _u are weighting factors, parameters that determine how much distance minimization is considered. Therefore, the distance parameter d here corresponds to the sum of w _{x Δx} ² and w _u Δ _u ² . Since the number T2 of sets of time slices in the control of the number of operating power supply units 304 is sufficiently smaller than the _number T1 of _sets of time slices of the storage battery 306, the scale of the problem shown in Equation 6 is reduced, and the speed is increased. can be solved. In this example, the problem shown in Equation 6 is divided into partial problems for each time section and solved (S108-S112).

FIG. 5 is a diagram showing changes in the number of operating power supply devices 304 and total power consumption in the example shown in FIG. When the load is low, the number of units in operation is reduced, reducing power consumption.

FIG. 6 is a diagram showing changes in the remaining battery level, the amount of discharge, and the amount of charge of storage battery 306 in the example shown in FIG. It is possible to match the remaining battery levels at the start of operation (time 0) and at the end of operation (time 23).

7, 8, and 9 show the power consumption when using the exact solution that solved the original problem, the relaxed solution that solved the relaxed problem, and the example solution that solved the partial problem using the relaxed solution. , battery level, and output power transition example. Note that the exact solution is the more optimal solution, although it takes much longer to compute. As shown in FIGS. 7 to 9, when the example solution is used, the peak cut effect at the time of load peak is slightly reduced, but at other time cross sections, results similar to the exact solution are calculated. there is

FIG. 10 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 and a partial problem calculation unit 1106 . The relaxation problem calculator 1104 and the partial problem calculator 1106 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 from S108 to S112 shown in FIG. With such a configuration, the operation planning method described with reference to FIGS. 1 to 9 can be executed.

FIG. 11 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 flowchart and block diagram blocks 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 a frame buffer or the like provided in RAM 2214 or itself, 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 into 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 retrieval of information, 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 supply device, 306... storage battery, 1102... problem acquisition Section 1104 Relaxation Problem Calculation Section 1106 Partial Problem Calculation Section 1110 Calculation Section 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 (9)

An operation planning method for generating an operation plan for a plurality of target devices by a computer,
The plurality of target devices include a plurality of power supply devices that supply the power generated by each or the power converted from the power received from the outside to the load, and the power that is received, charged, and supplied to the load. including a plurality of accumulators for supplying
Each of the power supply devices has a discrete value indicating a state parameter indicating whether it is activated or deactivated,
A state parameter indicating whether each storage battery is charging or discharging is indicated by a discrete value,
An objective function that sets objective parameters determined according to the state parameters of the plurality of storage batteries and the plurality of power supply devices to a predetermined state, and a constraint condition regarding the SOC of each of the storage batteries that fluctuates according to the state parameters of the storage batteries. a problem acquisition step of acquiring an original problem for computing the discrete state parameters of each of the storage battery and the power supply , which can satisfy the objective function and the constraints ;
Solving a relaxation problem assuming that the state parameter of each of the batteries in the original problem can take continuous values between the discrete values ; a relaxation problem calculation step of calculating the state parameters ,
dividing the original problem into a plurality of subproblems, solving each subproblem using the distance between the solution of the relaxation problem and the solution of the subproblem, and obtaining the discrete state parameters of each of the target devices; and a subproblem computation stage that computes
In the subproblem calculation step, the original problem is divided into the plurality of subproblems corresponding to a plurality of time sections, and the solutions of the subproblems calculated for the respective time sections are the subproblems in the next time section. and sequentially calculating solutions to the plurality of partial problems . 2. The operation planning method according to claim 1, wherein, in the partial problem calculation step, the solution of the partial problem is calculated such that the distance to the solution of the relaxation problem is minimized. The objective function includes a condition that minimizes the objective parameter,
3. The operation planning method according to claim 2, wherein in the partial problem calculation step, a function that minimizes the sum of the objective parameter and the distance parameter according to the distance is used as the objective function of the partial problem. the constraints of the original problem include conditions on a plurality of time slices;
The operation planning method according to any one of claims 1 to 3 , wherein in the partial problem calculation step, the state parameters of the storage battery in the plurality of time slices are calculated as one partial problem. The operation planning method according to any one of claims 1 to 4 , wherein the solution of the mitigation problem is recalculated when the load prediction is updated. The state parameter of the storage battery in the original problem indicates whether each storage battery is charging or discharging in binary,
The state parameter of the storage battery in the mitigation problem is a parameter that indicates whether the storage battery is in a charged state or a discharged state by a continuous value between the two values,
The constraint conditions include a condition that the SOC of each storage battery fluctuates according to the value obtained by multiplying the state parameter by a predetermined charging efficiency and the value obtained by multiplying the state parameter by the reciprocal of the predetermined discharge efficiency.
The operation planning method according to any one of claims 1 to 5. The objective parameter is the power consumption or operating cost of the power supply
The operation planning method according to any one of claims 1 to 6. A program for causing a computer to execute the operation planning method according to any one of claims 1 to 7 . An operation planning device that generates an operation plan for a plurality of target devices,
The plurality of target devices include a plurality of power supply devices that supply the power generated by each or the power converted from the power received from the outside to the load, and the power that is received, charged, and supplied to the load. including a plurality of accumulators for supplying
Each of the power supply devices has a discrete value indicating a status parameter indicating whether it is activated or deactivated,
A state parameter indicating whether each storage battery is charging or discharging is indicated by a discrete value,
The operation planning device is
An objective function that sets objective parameters determined according to the state parameters of the plurality of storage batteries and the plurality of power supply devices to a predetermined state, and a constraint condition regarding the SOC of each of the storage batteries that fluctuates according to the state parameters of the storage batteries. a problem acquisition unit for acquiring an original problem for computing the discrete state parameters of each of the storage battery and the power supply , which can satisfy the objective function and the constraint ;
Solving a relaxation problem assuming that the state parameter of each of the batteries in the original problem can take continuous values between the discrete values ; a relaxation problem calculation unit for calculating the relevant state parameters ;
dividing the original problem into a plurality of subproblems, solving each subproblem using the distance between the solution of the relaxation problem and the solution of the subproblem, and obtaining the discrete state parameters of each of the target devices; and a subproblem computing unit that computes
The partial problem calculation unit divides the original problem into the plurality of partial problems corresponding to a plurality of time sections, and converts the solutions of the partial problems calculated for the respective time sections into the partial problems in the next time section. An operation planning device that sequentially calculates solutions to the plurality of partial problems .

Images