State transition device, method, and program

Abstract

To provide a state transition device that can make human work more efficient.SOLUTION: The state transition device takes as input a time series of a required operating performance and determines a time series of an operating state of an operating element that satisfies the requirement. The state transition device is configured to determine S2, S3, S4 an operating state at each time by making the state transition device transition from an operating state that has already been determined for a time prior to the time in question such that the operating state of the operating element meets the required operating performance at the time in question at each time.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a state transition device, method, and program that can be used to create an operation plan for facilities such as a power system by using an automaton model.

Background Art

[0002] There are various existing technologies as techniques for creating an operation plan for facilities such as a power system.

[0003] Patent Document 4 discloses "a control system that controls a plurality of electronic devices using a state transition machine having state and state transition information according to a control specification created based on a state transition diagram of the unified modeling language." Patent Document 1 discloses "an operation planning device including a calculation unit that calculates a state continuation time, which is the time during which the state of a device continues, for each device, and a determination unit that determines, for each device, a value of a variable for controlling the operation and stop of the device under the condition that the state continuation time is equal to or longer than a minimum continuation time." Patent Document 2 discloses "a power system supply-demand plan creation device that creates an operation plan for starting and stopping generators by the Lagrange relaxation method and dynamic programming from operation plan information such as predicted total power demand information, power generation facility data, and operation constraints." Patent Document 3 discloses "a generator startup and shutdown plan making method in which, while supplying a pre-calculated predicted load and considering a number of constraints, a plurality of initial solutions are obtained by a priority list method that can obtain a solution at high speed in a generator startup and shutdown plan for determining an operation plan for each generator so as to minimize the operation cost, and then several heuristic methods are applied to the plurality of initial solutions obtained by the priority list method."

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

[0005] The conventional methods for creating schedules and plans, as described above, generally treat them as optimization problems applying optimization theory. This differs from frameworks for automating (mechanizing) human work, and therefore has the drawback of not being able to provide the convenience offered by such frameworks.

[0006] In other words, in addition to automatic calculations using optimization theory, there are also time-consuming and cumbersome scheduling tasks that require human intervention. By providing automation (mechanization) and automation (mechanization) frameworks for such tasks, human work can be made more efficient, leading to liberation from tedious tasks and reductions in labor costs.

[0007] In view of the problems of the prior art described above, the present invention is characterized by providing a state transition device, method, and program that can improve the efficiency of human work. [Means for solving the problem]

[0008] To achieve the above objective, the present invention By using automata, Required Grades The time series is taken as input, and the requirements are met. Multiple targets of situation A state transition device that determines the time series of, The aforementioned state is determined by whether each of the multiple objects is operational at each time point, and is an element of a predetermined finite set. At each time point, The aforementioned target of The aforementioned state is required at that time Grades The time prior to the relevant time has been determined to satisfy the condition. situation By transitioning from this point, at that time situation To decide The time series of the aforementioned states is automatically determined by sequentially repeating this process at each time point in the time series, and in order to cause the transition at each time point, a state transition matrix is ​​referenced according to the state determined for the previous time point and the required performance, which includes at least information on possible states after the transition for the state before the transition.It is characterized by such. Further, it is characterized by being a method or program corresponding to the said apparatus.

Effect of the Invention

[0009] According to the present invention, by automatically determining a time series of operating states that satisfies the operating performance required by the method of determining the state at the next time at each time, manual work can be made more efficient and a time series of operating states can be obtained.

Brief Description of the Drawings

[0010] [Figure 1] It is a functional block diagram of a state transition apparatus according to an embodiment. [Figure 2] It is a diagram showing an example of a total demand curve and a renewable energy curve as a graph. [Figure 3] As an explanatory example, a total of six generators as individual elements are shown in the explanatory column, and the corresponding states, element state vectors, and outputs are shown in a table format. [Figure 4] As an explanatory example, it is a diagram showing an input set, which is a set of numbers, as a graph. [Figure 5] It is a flowchart of state transition according to an embodiment. [Figure 6] It is a diagram showing two state transition matrices in a table format. [Figure 7] It is a diagram showing a first case of plan creation using a state transition apparatus. [Figure 8] It is a diagram showing a second case of plan creation using a state transition apparatus. [Figure 9] It is a diagram showing a modified example of the two state transition matrices in FIG. 6 in a table format. [Figure 10] It is a diagram showing a third case of plan creation using a state transition apparatus. [Figure 11] It is a diagram showing a fourth case of plan creation using a state transition apparatus. [Figure 12] It is a diagram showing the hardware configuration in a general computer.

Modes for Carrying Out the Invention

[0011] <Overall Outline of Device Configuration and Operation> FIG. 1 is a functional block diagram of a state transition device 1 according to an embodiment. The state transition device 1 includes a processing unit 11 and a storage unit 12.

[0012] In this embodiment, as a schedule - plan automatic creation automaton A (automaton model), a Moore machine of a transducer finite automaton (sequential machine) is used. A = {input set I, state set S, output set O, state transition function δ, output function λ} is defined. The state of the state set S of the automaton A is an element - state vector of the number of elements that can grasp the situation (aspect) necessary for the schedule plan for all individual elements to be scheduled in the schedule plan, and the input of the input set I has time - time information.

[0013] Also, as expressed by representing a function as "domain → range" as follows, the state transition function δ outputs state S depending on input I and state S, and the output function λ of the Moore - machine type outputs output O depending only on state S. ​​​​​​​​​​​​

[0016] In other words, an input I belonging to the input set defined by automaton A and possessing time and date is input to the processing unit 11 of the state transition device 1 as an input sequence in the order of time and date. The processing unit 11 refers to the state S and state transition function δ stored in the memory unit 12 and processes the continuation of state S or the transition of state S, which represent the appearance of all individual elements to be scheduled and planned together with input I. Depending on the processed state S, the processing unit 11 processes and outputs using the output function λ stored in the memory unit 12. Input I, state S, and output O are stored in the memory unit 12 as the input sequence, state sequence, and output sequence of time and date, respectively. By obtaining the state S of all individual elements to be scheduled and planned as a state sequence in the order of time and date of input I, a schedule and plan are automatically created.

[0017] In the following explanatory examples, the specific examples of the constituent data of the automaton A, which is defined in this way, will be explained as follows (each described below). Specifically, the specific examples of the input set I are the time-series data of the generator required supply capacity C1 and the required minimum output C2 (the data shown as input S in Figure 4, described later), which are given at each time t=1,2,... Also, states belonging to the state set S (Elements belonging to the state set S) A concrete example of this is state i, which is represented as a state vector v(i), and is automatically determined as a time series {i(t)|t=1,2,…} corresponding to the input time series. Furthermore, the output set is specifically the rated output ST1 and minimum output ST2, which are time series data automatically determined to satisfy the inputs C1 and C2. A concrete example of the output function λ is a function that calculates the corresponding {rated output, minimum output} from the state vector v(i) corresponding to state i, and is given in mathematical form. Also, the state transition function... δ This is a function given as a set of rules that automatically determine the state i(t) at the current time t from the state i(t-1) at the previous time t-1, and is specifically represented by the flowchart in Figure 5, which will be described later.

[0018] <Details of the processing content of state transition device 1 using an explanatory example> The following describes the details of automatic plan creation by the state transition device 1 according to one embodiment. As an example, we will explain the case of automatically creating an operation (operation) plan for a generator when a total electricity demand curve C_total and a renewable energy curve C_renew (hereinafter referred to as the renewable energy curve) for a 24-hour day are given. Figure 2 is a graph showing an example of the total demand curve C_total and the renewable energy curve C_renew.

[0019] In the following explanation, for example, a two-dimensional vector with the first element a and the second element b will be represented using the mathematically conventional notation [ab] in formulas, etc. However, in the text, to ensure the visual clarity of the distinction between elements, the notation {a,b}, which represents a comma-separated set of two elements (ordered pair), will also be used where appropriate. ([ab] and {a,b} are considered to be the same.)

[0020] <About the problem setting> In the example shown in Figure 2, the problem setting for the automatic creation of the operational plan is as follows:

[0021] In other words, the total demand curve C_total at each time point shown in Figure 2, plus reserve capacity, must be supplied by the generator's output within its output range (minimum output 50% to rated output 100%), along with the renewable energy curve. If we denote the total demand curve C_total, reserve capacity, renewable energy curve C_renew, and generator output at time t as C_total(t), Res(t), C_renew(t), and Pow(t), respectively, then conceptually, it is necessary to create an operational plan that obtains a generator output Pow(t) such that equation (1) below holds. (Note that the output Pow(t) is specifically calculated as the required generator supply capacity C1(t) and the required minimum output C2(t) using equations (2) and (3) described later.) C_total(t)+Res(t)=C_renew(t)+Pow(t) …(1)

[0022] <Regarding the individual elements and conditions covered by the operational plan> Furthermore, in this explanatory example, as shown in the schematic explanation column EX of Figure 3, all individual elements subject to the schedule and plan are assumed to be four 6000kW generators ga1, ga2, ga3, ga4 and two 4500kW generators gb1, gb2, with at least two of the 6000kW generators being in continuous operation. For such individual elements and conditions, Table T3 in Figure 3 shows the corresponding states i (i=1,...,9) and their element state vectors v(i), etc. The element state vector v(i) is a two-dimensional vector, with the first component being the number of 6000kW generators in operation and the second component being the number of 4500kW generators in operation. v(i) = {6000kW operating units, 4500kW operating units}

[0023] Table T3 further shows the total rated output and minimum output of the generator as output out(i) which depends on each state i. The set of outputs out(i) which depends on state i is a set of vectors composed of two numerical components: the rated output and the minimum output. out(i) = {rated output in state i, minimum output in state i}

[0024] In other words, the output function for calculating the output out(i) which depends on state i is a calculation formula based on the corresponding generator capacity, number of units, and output range (100% to 50%), as shown by the element state vector v(i) of state i. The output for each state i can be calculated in advance as rated output (output range 100%) and minimum output (output range 50%).

[0025] For example, state i=5 means that v(5)=

[0031] , which means that three 6000kW units are in operation and one 4500kW unit is in operation, and the two elements of output out(5) can be calculated as follows. Rated output = 6000 * 3 + 4500 * 1 = 22500 [kW] Minimum output = 6000 * 3 * 0.5 + 4500 * 1 * 0.5 = 11250 [kW]

[0026] In this embodiment, as illustrated in Table T3 of Figure 3, the following features are observed from the perspective of the user interface (the user interface for the user performing the task of defining automaton A in advance).

[0027] In other words, state i is ordered in descending order (largest to smallest) of the rated output and minimum output, which are state-dependent outputs out(i), and the numerical number in that order is the state. By representing state i as a numerical value i ordered in descending order of state-dependent outputs, it becomes easy to construct the state transition function determined by the input and state used to determine state transitions, and the capacity and number of operating generators can be clearly identified by the state element state vector. Furthermore, since the numerical value i (identifier) ​​of state i directly represents the order of magnitude of the corresponding output out(i), the system is configured so that raising or lowering state i during state transitions can raise or lower the output out(i).

[0028] <About the input set> Figure 4 shows a graph of the input set InputS, which is a set of numbers, as an example. In terms of data format, InputS is given as a series of 288 numbers in two columns, C1 and C2, in chronological order from 0:00 to 23:55 in 5-minute intervals over a 24-hour period. Thus, the input set InputS={C1,C2} consists of inputs that contain time information.

[0029] Regarding the specific details, in Figure 4, the first input sequence C1 of the two input sequences InputS containing time and time information is the total demand curve C_total, which includes reserve capacity (Res(t)), and subtracts the power curve where power generation from renewable energy is guaranteed (the value obtained by multiplying each value of the renewable energy curve C_renew in Figure 2 by a coefficient r of 1 or less) and the battery capacity battry[kW] (a constant here). Expressing the relationship as a function of time t, similar to equation (1) above, we get the following equation (2). C1(t)=C_total(t)+Res(t)-r*C_renew(t)-battry …(2)

[0030] Furthermore, the second input sequence C2 is the curve obtained by subtracting the renewable energy curve C_renew from the total demand curve C_total in Figure 2, and is given by equation (3) below. C2(t)=C_total(t)-C_renew(t) …(3)

[0031] In other words, the input set InputS={C1,C2}, which contains time and date information calculated specifically as shown in equations (2) and (3) above and is input in chronological order, is a set of vectors consisting of two numerical components: the maximum required generator supply capacity and the minimum required output at the same time and date.

[0032] <Regarding state transitions using state transition functions> In creating an operational plan using state transition device 1, it is necessary to ensure power supply by setting the first sequence C1 to the maximum required generator power that can be supplied within the generator's output range, and the second sequence C2 to the minimum required output that can be supplied within the generator's output range. The state transition function is based on these basic conditions.

[0033] Figure 5 is a flowchart of a state transition according to one embodiment. Its overall structure is such that at each time t, a branching decision based on the case distinctions in steps S1 and S2 applies, resulting in the application of one of steps S3, S4, or S5. This process determines the current state i(t) at time t by transitioning from the state i(t-1) already determined at the previous time t-1. This process is repeated for each time t (each time t, specified by an integer number, is 1, 2, 3, ...). (Note that as a result of the transition, the state i(t-1) at the previous time t-1 and the current state i(t) at time t may be the same or different.) For the purpose of explaining each step in the flowchart of Figure 5, let P1(t) be the value of the rated output and P2(t) be the value of the minimum output at each time t. (That is, the output set out(i)=out(i(t)) corresponding to state i(t), as described in Figure 3, is represented as a vector out(i(t))={P1(t),P2(t)}.)

[0034] The basic condition for the state transition function is that, with respect to the rated output and minimum output {P1(t), P2(t)} determined as the output set out(i(t)), the rated output (referred to as the rated condition) is greater than or equal to the generator's required supply capacity, and the minimum output (referred to as the minimum condition) is less than or equal to the required minimum output. The transition to a state that satisfies this basic condition is sought according to the flow in Figure 5. ●The basic condition is that both the rated conditions and the minimum conditions are met. ●The rated conditions are met, that is, "P1(t)≧C1(t)" is true. ● The minimum condition is met, that is, "P2(t)≦C2(t)" is true.

[0035] In step S1, it is determined whether the vectors {P1(t), P2(t)} (rated output and minimum output) output in the current state i(t) satisfy the basic conditions for the input vectors {C1(t), C2(t)} (required generator supply capacity and required minimum output) input at the current time t, which are input in chronological order. If the basic conditions are satisfied, the process proceeds to step S5; otherwise, the process proceeds to step S2. ● If "P1(t)≧C1(t) and P2(t)≦C2(t)" is true, proceed to step S5. ●If the above is false (basic conditions are not met), proceed to step S2.

[0036] In step S1, the state i(t) at the current time t is provisionally assigned the state i(t-1) that was already determined at the previous time t-1 as "i(t)=i(t-1)". Then, the output vectors {P1(t), P2(t)} are calculated to determine whether or not the above basic conditions are met. (That is, at the time of step S1, the state i(t) at the current time t has not yet been determined, and the state i(t) at the current time t will be determined and finalized in one of the steps S3, S4, or S5, which are reached by branching in the subsequent steps.)

[0037] In step S5, since the state i(t) (which is assigned the previous state i(t - 1) as a temporary value) still satisfies the basic conditions at the current time t, it is determined that the state i(t) continues as the same state as the previous state i(t - 1) (the temporary assignment of i(t)=i(t - 1) in step S1 is directly confirmed as the transition result), and then it proceeds to step S6.

[0038] After step S2, if the current state (temporarily assigned as i(t)=i(t - 1)) does not satisfy the rated conditions of the basic conditions for the input vector, it is transitioned to a state with a higher rated output than the current state in step S3, and if it does not satisfy the minimum conditions, it is transitioned to a state with a lower minimum output than the current state in step S4. However, as realized by the arrangement of the determination conditions in step S2 in the flow structure of FIG. 5, the rated conditions take precedence over the minimum conditions.

[0039] That is, in step S2, it is determined as follows whether the current state i(t) = {P1(t), P2(t)} corresponds to a state where the rated conditions of the basic conditions are not satisfied for the input vector {C1(t), C2(t)}. If it corresponds, it proceeds to step S3, and if it does not correspond, it proceeds to step S4. ● If "P1(t)<C1(t)" (corresponding to a state that does not satisfy the rated conditions), it proceeds to step S3 ● If it does not correspond to the above (the rated conditions are satisfied), it proceeds to step S4

[0040] In step S3, the state i(t) at the current time t is determined by transitioning from the state i(t-1) at the previous time t-1 to the state i(t) at the current time t using the first state transition matrix T6a for cases where the rated conditions are not met, and then the process proceeds to step S6. In step S4, the state i(t) at the current time t is determined by transitioning from the state i(t-1) at the previous time t-1 to the state i(t) at the current time t using the second state transition matrix T6b for cases where the minimum output conditions are not met, and then the process proceeds to step S6. (These two matrices T6a and T6b will be described later with reference to Figure 6.) In step S6, the time t is updated to the next time t+1, and then the process returns to step S1, so that the flow shown in Figure 5 is repeatedly executed for each input {C1(t), C2(t)} at each time t=1, 2, ...

[0041] Furthermore, in steps S2, S3, and S4 when the basic conditions are not met (i.e., when at least one of the rated conditions and the minimum output conditions is not met), if the rated conditions are not met, step S3 is applied regardless of whether the minimum output conditions are met or not, thus prioritizing the rated conditions over the minimum output conditions. Step S4 is applied when the basic conditions are not met and the rated conditions are met, and only the minimum output condition of the basic conditions is not met.

[0042] <Regarding the state transition matrix selected by the state transition function> Figure 6 shows the two state transition matrices T6a and T6b used in steps S3 and S4 of Figure 5 in table format. Specifically, Figure 5 shows the first state transition matrix T6a, which is selected by the state transition function when the rated conditions are not met in step S3, and the second state transition matrix T6b, which is selected when the minimum output conditions are not met in step S4. The matrix is ​​number of states × number of states and indicates whether or not a transition occurs from the current state to the next state and its priority. A value of zero indicates that the transition is not possible, and a lower value indicates a higher priority. The priority values ​​here represent the number of units in operation and stopped.

[0043] For example, if the current state is 7 (element state vector v(7) =

[0030] , meaning three 6000kW units are in operation and one 4500kW unit is not in operation) and does not satisfy the rated conditions, then the rated output needs to be increased. Referring to the first state transition matrix T6a, transitions to states with values ​​greater than state 7 (transitions in the direction of reducing the number of operating units) are zero and therefore impossible. The states with higher priority are state 4 (element state vector v(4) =

[0040] ) and state 5 (element state vector v(5) =

[0031] ).

[0044] In other words, in this example, if the state i(t) = 7 at a given time t and the nominal conditions are not met, the following priority order is set in the information of the first state transition matrix T6a for the state to transition to from state 7. (Highest) Priority 1: State 4 (v(4)=

[0040] ), State 5 (v(5)=

[0031] ), State 7 (v(7)=

[0030] ) Priority 2: State 2 (v(2)=

[0041] ), State 3 (v(3)=

[0032] ) (Lowest) Priority 3: State 1 (v(1)=

[0042] ), State 6 (v(6)=

[0022] )

[0045] In this example, the rated output will change as follows before and after the state transition from state 7. (Note that the transition from "state 7 → 7" is intended to allow for the continuation of the current state even if the basic conditions are not met due to constraints such as minimum operating time and maximum shutdown time, and is designed to handle exceptional situations (which will not be explained here).) (Highest) Priority 1, Status 7→4 (Rated output 18000kW→24000kW) Status 7→5 (Rated output 18000kW→22500kW) Status 7 → 7 (Rated output 18000kW → 18000kW) Priority 2, Status 7 → 2 (Rated output 18000kW → 28500kW) Status 7→3 (Rated output 18000kW→27000kW) (Minimum) Priority 3, Status 7→1 (Rated output 18000kW→33000kW) Status 7→6 (Rated output 18000kW→21000kW)

[0046] As shown in the example above, the first state transition matrix T6a, which addresses and resolves the deficiency of rated power, can be designed in advance by taking the following into consideration. ●A transition towards increasing the rated output is necessary. ● If the rated output is increased too much, the margin (operating plan margin) of the minimum output, which is in a trade-off relationship, will decrease. Therefore, transitions where the rated output increases too much should be given a lower priority. ●Even if transitions can increase the rated output to a similar extent, transitions with large changes in the element state vector v(i) are considered to involve large changes in the generator state and thus large control loads, and therefore should be given a lower priority.

[0047] Furthermore, the second state transition matrix T6b, which addresses and resolves the excess of the minimum output, can be pre-designed as follows, taking the same considerations as above. ●A transition is needed that reduces the minimum output. ● If the minimum output is reduced too much, the margin (operating plan margin) of the rated output, which is in a trade-off relationship, will decrease. Therefore, transitions that reduce the minimum output too much should be given a lower priority. ●Even if transitions can reduce the minimum output to a similar extent, transitions with large changes in the element state vector v(i) are considered to have large changes in the generator state and therefore large control loads, so their priority should be lowered.

[0048] Furthermore, rated power and minimum output are proportional and their increase / decrease behaviors coincide, and the generator's required supply capacity and required minimum output, as inputs, are also correlated and their increase / decrease behaviors generally coincide. In creating an operational plan, "rated power ≥ generator's required supply capacity" and "minimum output ≤ required minimum output" are required. Therefore, there is a trade-off relationship between the generator's required supply capacity and required minimum output in the input set, and a corresponding trade-off relationship between rated power and minimum output in the output set.

[0049] Furthermore, in steps S3 and S4, the transition to which state to proceed can be automatically determined by considering various conditions as priorities, including not only the number of operating and stopped units (the priority set in the first and second state transition matrices T6a and T6b in Figure 6), but also operational efficiency, cumulative operating time, minimum operating duration, and maximum downtime.

[0050] For example, in step S3, the priority score (as listed in the first state transition matrix T6a) is set to s1, the operational efficiency score to s2, and the cumulative time score to s3. The weighted sum of these scores is then calculated to obtain a total score w1*s1+w2*s2+w3*s3 (where w1, w2, and w3 are pre-set weights), and the state with the highest total score is automatically determined as the next transition state. (Note that each score should be set so that the higher the priority, the higher the score. For example, for the score s1 in the state transition matrix, the score values ​​s1=3, 2, and 1 should be set for the 1st, 2nd, and 3rd highest priorities (lowest ranks in ascending order), respectively. That is, the highest score s1=3 should be set for the highest rank (1st), and the lowest score s1=1 should be set for the lowest rank (3rd).)

[0051] The operational efficiency score s2 and the cumulative time score s3 can be automatically calculated by setting a predetermined rule (such as a calculation formula) for automatic calculation, either as a function of the candidate transition state i(t), or further, as a function that also includes the determined past states i(t-1), i(t-2), ..., i(ik) (where k≧1 is the length of the period) over a certain period up to the previous time t-1 as arguments.

[0052] In addition to / before judging based on such an overall score, the destination state may be automatically determined by rule-based judgments or the like. For the overall score, only any part of s1, s2, and s3 may be used instead of all of them. (For example, if only s1 is used, the system can automatically transition to the state with the highest priority among the priority levels set in the first and second state transition matrices T6a and T6b, as illustrated in Figure 6. However, in this case, instead of setting multiple highest priority levels as in the specific example in Figure 6, only one should be set.) Furthermore, factors other than s1, s2, and s3 may be considered as additional score elements s4, s5, ... for the overall score.

[0053] In this way, for each time t=1, 2, ..., the state transition from state i(t-1) at the previous time t-1 to state i(t) at the current time t can be automatically determined. Therefore, in creating an operation plan, the state transition device 1 reduces the burden of human work and helps to reduce costs.

[0054] Furthermore, regarding the initial state i(1) at the initial time (for example, t=1), the user may manually select a state that satisfies the basic conditions for the corresponding inputs {C1(1), C2(1)}, or the state transition device 1 may automatically determine it by defining a predetermined rule to automatically determine a state that satisfies the basic conditions.

[0055] <Summary> As described above, the automaton A that automatically generates schedules and plans inputs the time information of the generators, InputS={C1,C2} in Figure 4, as a vector whose components are the required generator supply amount and the minimum required output, in chronological order, into the processing unit 11 of the state transition device 1. The processing unit 11 then refers to the current state i(t)={P1(t),P2(t)} stored in the memory unit 12 (where the determined state i(t-1) from the previous time t-1 is provisionally assigned in step S1) and the output vector out(i) in Figure 3, which consists of the rated output and minimum output for each generator with the number of operating units for each state i. The processing unit 11 then uses the state transition function stored in the memory unit 12 to process the input {C1( If the basic conditions are satisfied for {C1(t), C2(t)}, the current state is continued and i(t) = i(t-1). If the nominal conditions are not met, the first state transition matrix T6a in Figure 6 is selected. If the minimum conditions are not met, the second state transition matrix T6b in Figure 6 is selected. The state transition from state i(t-1) to state i(t) is performed according to the priority of the current state transition destinations of the selected state transition matrix (priority based on the overall score w1*s1 + w2*s2 + w3*s3, etc.) to obtain an output that satisfies the basic conditions for the input {C1(t), C2(t)} at the current time t. The input {C1(t), C2(t)}, the determined state i(t), and its output {P1(t), P2(t)} are stored in the memory unit 12, thus formulating the problem as obtaining an output sequence and a state sequence for the input sequence.

[0056] As mentioned above, the initial state i(1) can be selected as a condition that satisfies the basic conditions in the first input {C1(1), C2(1)}, and the priority of various conditions such as operational efficiency, cumulative operating time, minimum operating duration, and maximum downtime may be considered in the selection.

[0057] The obtained state sequence {i(t)|t=1,2,…,N} (where N is the length of the sequence, and in examples such as Figure 2, N=288) represents the number of operating generators for each time information t contained in the input sequence {{C1(t),C2(t)}|t=1,2,…,N}, i.e., the generator operation schedule / plan, while the output sequence {{P1(t),P2(t)}|t=1,2,…,N} represents the output of the generators when they are operating, as indicated by their states.

[0058] In addition, the state transition device 1 may output the obtained state sequence {i(t)|t=1,2,…,N}, which is an intermediate output, instead of the output sequence {{P1(t),P2(t)}|t=1,2,…,N}, which is the final output, or it may output both of these.

[0059] In contrast to the prior art described in Patent Documents 1-5 and others, which define the start-up and stop-down states of individual devices and show state transitions, making it difficult to grasp the states of all multiple devices, grasp the identical time and timing operations of all devices, and explicitly express time constraints and the possibilities of transitions between states, this embodiment can address these difficulties. For example, state i corresponds to the number of operating units of 6000kW and 4500kW as state vector v(i), encompassing multiple devices. Furthermore, the state transition function explicitly expresses all possible transitions.

[0060] In the following sections, we will describe various examples of plan creation using the state transition device 1 (Examples 1 to 4), and then explain various embodiments of the state transition device 1 (including modified and alternative examples of those described above).

[0061] <Example 1 of plan creation using state transition device 1> Figure 7 shows the first example of plan creation using state transition device 1. In this figure, the input sequence InputS={C1,C2} from the same input set as in Figure 4 is input to state transition device 1 to perform plan creation, and the resulting state sequence {ST11,ST12} and output sequence {P11,P12} are shown as graphs. Initially, state 7 (element state vector v(3) =

[0030] , only 3 x 6000kW units are in operation) is selected. Around 6:00, the rated conditions are not met, and the system transitions to state 4 (element state vector v(4) =

[0040] ) by the first state transition matrix T6a in Figure 6. Around 18:00, the rated conditions are again not met, and the system transitions to state 2 (element state vector v(2) =

[0041] ) by the first state transition matrix T6a. The operation schedule and plan of the generators in the input sequence {C1, C2} can be obtained as the state sequence {ST11, ST12}, and the rated output and minimum output of the generators can be obtained as the output sequence {P11, P12}.

[0062] <Example 2 of plan creation using state transition device 1> Figure 8 shows a second example of plan creation using state transition device 1. In this example, the input sequence InputS={C1,C2} from the same input set as in Figure 4 is input to state transition device 1 to create a plan, and the resulting state sequence {ST21,ST22} and output sequence {P21,P22} are shown as graphs. The difference from the first example in Figure 7 is that the second example in Figure 8 is an additional constraint that the 4500kW generators (both units) can only be started (newly) between 8:00 and 16:00, and the state transition function becomes time-dependent.

[0063] In other words, in the second example, all generators (four 6000kW units and two 4500kW units) can only be selected to be newly started between 8:00 and 16:00. Therefore, as in the first example, the state transition matrices T6a and T6b in Figure 6 are used as the first and second state transition matrices. However, between 0:00 and before 8:00, and between 16:00 and 24:00, the state transition matrices T9a and T9b in Figure 9 are used as the first and second state transition matrices, which are modified versions of the matrices in Figure 6, to reflect the fact that only a portion of the generators (only four 6000kW units) can be selected to be newly started due to the additional constraints mentioned above.

[0064] For example, with respect to the first state transition matrix T9a of the modified example in Figure 9, in the transition of state 7 (element state vector v(7)=

[0030] ), while the matrix T6a in the original Figure 6 allows transitions to states other than states 8 and 9, the first state transition matrix T9a of the modified example allows transitions to state 4 (element state vector v(4)=

[0040] ) other than the current state 7. Thus, the state transition matrix reflects the fact that the 4500kW generator cannot be operated, and this state transition matrix T9a is selected during the relevant time period.

[0065] Furthermore, since the rated conditions cannot be met in state 4 after 16:00 in Figure 8, the generator is pre-activated at 16:00, which is the operating time for 4500kW, to be in state 2 (element state vector v(2)=

[0041] ). Its input is not a vector {C1(16:00), C2(16:00)} consisting of two numerical values: the generator's required supply capacity and the required minimum output at time 16:00, but rather the following matrix M consisting of two numerical sequences: the generator's required supply capacity and the required minimum output for the time period from 16:00 to 24:00.

[0066]

number

[0067] Alternatively, instead of the matrix M described above, the following vector m (a vector m extracted from matrix M representing the worst-case scenario during the period 16:00 to 24:00, specifically the maximum value of the generator supply capacity C1(t) and the minimum required output C2(t) during that period) may be used as input.

[0068]

number

[0069] <Example 3 of plan creation using state transition device 1> Figure 10 shows a third example of plan creation using the state transition device 1. Below, another embodiment of the inputs {C1(t), C2(t)} at each time t used in the state transition device 1 will be described using this third example as a reference.

[0070] In other words, in this alternative embodiment, instead of using the inputs {C1(t), C2(t)} at the same time t as the input for time t, a vector whose components are the required generator power supply and the required minimum output is used as the input {C1(t+α), C2(t+α)} for a future time t+α (in this example, α is equivalent to 1 hour). Alternatively, similar to the matrix M and vector m described in the second example, a range from the current time t to a future time t+α may be used as the input.

[0071] Except for the fact that the input is in the future (or includes the future), the third example in Figure 10 is the same as the first example in Figure 7. In the first example, the state transition occurred around 6:00, while in the third example, the transition occurs around 5:00. In other words, in contrast to the first example, where the transition occurred when the basic conditions were no longer met, the third example allows for a transition in advance by considering an input up to a future time t+α, resulting in a state sequence {ST31,ST32} and output sequence {P31,P32}, i.e., a schedule / plan, that satisfies the basic conditions with more margin.

[0072] <Fourth example of plan creation using state transition device 1> Figure 11 shows the fourth example of plan creation using state transition device 1, where a condition (referred to as the "suppression condition") is added to the state transition function to suppress the number of operating units within the range that satisfies the basic conditions after 20:00. In other words, the fourth example is a modification of the second example, and the time periods in which the state transition matrices are used are the same as in the second example. In the time periods from 0:00 to before 8:00 (excluding 8:00) and from after 16:00 (excluding 16:00) to 24:00, the state transition matrices T9a and T9b in Figure 9, which prohibit new startups, are used, while in the time period from 8:00 to before 16:00, the state transition matrices T6a and T6b in Figure 6, which do not prohibit new startups, are used. The suppression condition mentioned above, which was not imposed in the second example, is added. This setting differs from the second example in that, in the fourth example, around 22:00 after 16:00, the system transitions from state 2 (element state vector v(2)=

[0041] ) to state 4 (element state vector v(4)=

[0040] ) using the second state transition matrix T9b (or equivalent) for the case where the minimum output condition is not met, as shown in the modified example in Figure 9 (which restricts the start of operation of 4500kW after 16:00, but does not restrict continued operation or stopping). As a result, the state sequence {ST41,ST42} and output sequence {P41,P42} shown in Figure 11 are obtained. In addition to the selection of the state transition matrix, it is also possible to make the state transition function dependent on time information in this way.

[0073] <Regarding the setting of the state transition matrix and other settings> The state transition matrix can be constructed based on the operator's empirical rules, taking into account time of day and other conditions, using the operator's past operational state transition data. It can also be learned through on-the-spot operation. Furthermore, by setting various evaluation functions such as operational efficiency, cumulative operating duration, and operating / shutdown costs, and constructing the state transition matrix with a priority order that takes into account operating conditions such as repairs and failures, as well as various constraints, optimized operation becomes possible.

[0074] <Regarding the application of automatic plan creation in state transition device 1> The above explanation uses the example of automatically creating operation and operational plans for a power generator, which is part of the power system, using the state transition device 1. However, the state transition device 1 can be applied similarly to other planning targets.

[0075] For example, in a 24-hour store with peak hours (24 time slots with predicted workloads Y1, Y2, ..., Y24), the scheduling of part-time staff would be as follows: Veteran (Hourly wage B1, Work efficiency K1) N1 person General (Hourly wage B2, Work efficiency K2) N2 people New recruits (hourly wage B3, work efficiency K3): 3 people By having part-time employees continue to perform their duties, and by having them work for more than X1 hours and take more than X2 hours off, the state transition device 1 can automatically determine part-time scheduling that takes into account vacations and the predicted scope of work, thereby reducing labor costs.

[0076] In this case, the following definition can be used for the data of automaton A. • Input set: Work forecast Y1~Y24 and forecast range, Work volume and range with time information (Furthermore, the acceptable labor costs for each time period may also be included in the input set.) • State set: [Number of veterans, Number of general players, Number of new players] • Output set: [Work output, Labor cost output]

[0077] Just as a trade-off relationship could be set between the rated output and minimum output of the output set in the example of a generator, a trade-off relationship can also be set between the work output and labor cost output (and the work and labor costs in the corresponding input set) of the output set in the case of part-time work. That is, by pre-setting a relationship where veterans have higher hourly wages but also higher work efficiency for veterans, general workers, and newcomers, a trade-off relationship can be set between the input set and the output set, based on the following relationship. Regarding hourly wages, B1 > B2 > B3 In terms of work efficiency, K1 > K2 > K3

[0078] Thus, as in the example of generators or part-time workers, the state transition device 1 can be applied to scheduling in general to achieve operational performance (required generator supply capacity and minimum required output or work output and labor cost output) over a given time series by operating the number of operational units for each type of operational element ranked in terms of performance achievement capacity (6000kW, 4500kW generators or veteran, general, and rookie part-time workers).

[0079] <Regarding hardware configuration, etc.> Figure 12 shows an example of the hardware configuration in a typical computer device 70. The state transition device 1 can be implemented as one or more computer devices 70 having such a configuration. When the state transition device 1 is implemented with two or more computer devices 70, information necessary for processing may be sent and received via a network. The computer device 70 includes a CPU (Central Processing Unit) 71 that executes predetermined instructions, a GPU (Graphics Processing Unit) 72 as a dedicated processor that executes some or all of the execution instructions of the CPU 71 on behalf of or in cooperation with the CPU 71, RAM 73 as main memory that provides a work area to the CPU 71 (and GPU 72), ROM 74 as auxiliary memory, a communication interface 75, a display 76, an input interface 77 that accepts user input via a mouse, keyboard, touch panel, etc., and a bus BS for sending and receiving data between these.

[0080] The processing unit 11 of the state transition device 1 can be implemented by a CPU 71 and / or GPU 72 that reads and executes a predetermined program corresponding to the processing content of the processing unit 11 from the ROM 74. Both the CPU 71 and GPU 72 are types of arithmetic units (processors). The storage unit 12 can be implemented by a RAM 73, or by RAM 73 and ROM 74, to store various information necessary for processing by the processing unit 11. Furthermore, when display-related processing is performed, the display 76 operates in conjunction; when communication-related processing related to data transmission and reception is performed, the communication interface 75 operates in conjunction; and when user judgment input is accepted, the input interface 77 operates in conjunction.

[0081] Furthermore, instead of implementing all or part of the processing performed by the processing unit 11 by loading and executing the program on a general-purpose processor such as the CPU 71 and / or GPU 72, it may be implemented by other dedicated processors or circuits specialized for specific processing. The results obtained by the state transition device 1 and related information may be displayed on the display 76 in the format of the graph in Figure 7. The initial state and model information of the automaton A that is defined in advance may be stored in the state transition device 1 by accepting user input via the input interface 77.

[0082] <Regarding the data handled by state transition device 1> In the state transition device 1, the input set InputS may be read in real time to obtain the state and output at each time t as the real-time current time, or the input set InputS may be read from a predetermined data set that spans a certain period (which may include a future period), and the state and output for each time t within that data may be obtained. (Note that once the state and output have been obtained for the entire predetermined data, the flow shown in Figure 5 may be terminated.)

[0083] <Regarding the definition of element state vectors> In the above explanation, for example, in the case of a generator, the state vector v(i) was set to {number of 6000kW generators in operation, number of 4500kW generators in operation}. However, to provide more detailed information, a state vector may be used in which each individual operating element is given a binary value indicating whether it is in operation or not. That is, in the case of the generator example, v(i) = {a1, a2, a3, a4, b1, b2} is a 6-dimensional vector, where elements a1, a2, a3, and a4 represent the state of whether the 1st, 2nd, 3rd, and 4th 6000kW generators are in operation or not, respectively, and elements b1 and b2 represent the state of whether the 1st and 2nd 4500kW generators are in operation or not, respectively. [Explanation of Symbols]

[0084] 1...State transition device, 11...Processing unit, 12...Storage unit, 13...Display unit, 14...Input unit

Claims

1. A state transition device that uses an automaton to take a time series of required performance as input and determine a time series of states of multiple objects that satisfy the requirement, The aforementioned state is determined by whether each of the multiple objects is operational at each time point, and is an element of a predetermined finite set. At each time point, the state of the subject is determined by transitioning from the state determined for the previous time point to such a state that the state of the subject satisfies the required performance at that time point. This process is repeated sequentially at each time point in the time series, thereby automatically determining the time series of the states. A state transition device characterized in that, in order to perform the transition at each time step, it refers to a state transition matrix that is referenced according to the state determined for the previous time step and the required performance, and which includes at least information on possible states after the transition for the state before the transition.

2. The required performance includes a first performance relating to the maximum operating capacity of the plurality of targets, The state transition device according to claim 1, characterized in that, if the state of the time in question is maintained at the next time in each time step, and the first result is not achieved at the next time step, the state at the next time step is determined by referring to a first state transition matrix that is set in advance in relation to the first result, thereby causing the transition.

3. The required performance includes a second performance relating to the minimum operating capacity of the plurality of targets, The state transition device according to claim 2, characterized in that, if the state of that time is maintained in the next time, and the second performance is not achieved in the next time, the state in the next time is determined by referring to a second state transition matrix that is set in advance in relation to the second performance to determine the state in the next time.

4. The state transition device according to claim 3, characterized in that, if the state of the time in question is maintained at the next time in which the first result and the second result are achieved at the next time in which the state of the time in question is maintained at the next time in which case the state at the time in question is determined as if it were maintained at the next time in which case the state at the next time in which the first result and the second result are achieved maintained at the next time in which case the state at the next time in which the first result and the second result are achieved at the next time in which case the state at the next time in which the first result and the second result are achieved at the next time in which case the state at the next time in which the first result and the second result are achieved at the next time in which case the state at the next time in which the

5. The first state transition matrix and the second state transition matrix include priority information for each of the possible transition states, The state that maximizes the overall score calculated as a weighted sum of the score based on the priority and other scores is determined as the transition state. The state transition device according to claim 3 or 4, characterized in that the aforementioned other scores are scores based on at least one of the following: operational efficiency, cumulative operating time, minimum operating duration, and maximum downtime.

6. The state transition device according to any one of claims 3 to 5, characterized in that the state for the initial time among each time in the time series is determined by accepting a manual selection as achieving the first result and the second result, or by being automatically determined by a predetermined rule.

7. The state transition device according to any one of claims 3 to 6, characterized in that each of the plurality of targets is a generator, the first performance is related to the rated output, and the second performance is related to the minimum output.

8. The required performance includes a first performance relating to the amount of work on the plurality of objects and a second performance relating to the work cost on the plurality of objects, At each time step, if the state at that time step is maintained at the next time step, and the first performance is not achieved at the next time step, the state at the next time step is determined by referring to a first state transition matrix that is set in advance in relation to the first performance, thereby causing the transition. At each time step, if the state at that time step is maintained at the next time step, and the second performance is not achieved at the next time step, the state at the next time step is determined by referring to a pre-set second state transition matrix related to the second performance, thereby causing the transition. The state transition device according to claim 1, characterized in that, if the state of the time in question is maintained at the next time in each time step, and the first and second results are achieved at the next time step, the state at the next time step is determined by assuming that the state of the time in question is maintained at the next time step as well.

9. In order to determine the state at each time in time by transitioning from a state that has been determined for the time preceding that time, The state transition device according to any one of claims 1 to 8, characterized in that, in addition to requiring the state of the target to satisfy the performance criteria at that time, it also requires that the performance criteria be satisfied at a certain range of times after that time.

10. The state transition device according to any one of claims 3 to 8, characterized in that, if the state of the time in question is maintained at the next time in the next time in which the first and second results are not achieved, the state at the next time is determined by referring to the first state transition matrix instead of the second state transition matrix to perform the transition.

11. The state transition device according to any one of claims 1 to 10, characterized in that the state transition matrix referenced to perform the transition at each time is set to be dependent on that time.

12. A computer-based state transition method that uses an automaton to take a time series of required performance as input and determine a time series of states of multiple objects that satisfy the requirement, The aforementioned state is determined by whether each of the multiple objects is operational at each time point, and is an element of a predetermined finite set. At each time point, the state of the subject is determined by transitioning from the state determined for the previous time point to such a state that the state of the subject satisfies the required performance at that time point. This process is repeated sequentially at each time point in the time series, thereby automatically determining the time series of the states. A state transition method characterized in that, in order to perform the transition at each time step, it refers to a state transition matrix that is referenced according to the state determined for the previous time step and the required performance, and which includes at least information about possible states after the transition for the state before the transition.

13. A program characterized by causing a computer to function as a state transition device according to any one of claims 1 to 11.

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