JP2017010343A - Power equipment variable calculation device and method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a power equipment variable calculation device and method that optimize a discrete state variable and a consecutive state variable of power equipment in a power system.SOLUTION: A power equipment variable calculation device includes: a state variable setting unit 11 that determines a consecutive state variable and a discrete state variable; an optimization problem setting unit 12 that sets an optimization problem including the consecutive state variable and a plurality of deemed consecutive state variables set for each of values which the discrete state variable can take, and integrating a plurality of cases where the values of the plurality of deemed consecutive state variables become a prescribed combination; and an optimization problem solution unit 13 that solves the optimization problem under a constraint condition that the values of the plurality of deemed consecutive state variables become a combination.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a technique for optimizing variables indicated by power facilities of a power system including discrete state variables and continuous state variables.

  In the power supply to the consumer by the power system, the voltage is adjusted by controlling various parameters. Among the parameters that can be controlled in the power equipment, there are a parameter that takes a continuous value and a parameter that takes a discrete value. The discrete parameters include, for example, the electrical connection configuration of the line, the tap position of the voltage regulator such as LRT (Load Ratio Transformer) and SVR (Step Voltage Regulator), and the operation / stop state of the generator. In the control of these parameters, an operation that solves an optimization problem is used in order to minimize cost or loss.

  In the estimation or determination of the state of the current power system, the electrical connection configuration of the line, the winding ratio of the primary and secondary sides of the transformer, the operation / stop state of the generator, etc. Sometimes you want to treat a state as a variable.

  As an example, a state estimation calculation, which is an optimization method for estimating a power flow state in an electric power system, is taken as an example. In the state estimation calculation, the connection configuration information such as the switching state of the switch is treated as a discrete value, and the tidal current measurement information is treated as a continuous value. As described above, it is difficult to obtain a solution in the optimization calculation in which both discrete values and continuous values are defined as variables. Therefore, many state estimation methods have been developed in which discrete values that change less frequently are regarded as constants, and optimization calculations are performed using only continuous power flow conditions as variables.

  These state estimation methods have the following problems. In other words, the open / close state of the switch that is regarded as a constant may change when the calculation is executed. For this reason, the estimation error may be excessive or a solution may not be obtained.

  In order to solve such a problem, there is a method of performing state estimation calculation for a plurality of combinations of energization / cutoff states of the line or a part of the combinations. In each calculation, the calculation can be performed by considering the energization / cutoff state of the line as a constant, and optimization in consideration of various combinations of states can be performed. However, in this method, it is necessary to repeatedly perform optimization calculation for the number of combinations of the energization / cutoff states of the line, and the calculation amount becomes enormous.

JP 2008-253076 A JP 2003-235180 A

  Various methods for simultaneously optimizing discrete state variables and continuous state variables have been proposed in the power system optimization problem, including the state estimation calculation method exemplified above. These methods can be broadly classified into two types: a method that solves discrete state variables as constants, and a method that performs operations on combinations of values that can be taken by discrete state variables or some of them. The These two types have their merits and demerits, and each has its own problems.

  There is a problem that a true optimal solution may not be obtained when a discrete state variable is regarded as a constant as a problem of a method of performing computation by regarding a discrete state variable as a constant.

  In addition, the calculation method for combinations of values that can be taken by discrete state variables has the problem that the amount of calculation becomes enormous and the time required for calculation becomes long, and the optimal solution among all calculated combinations. There is a problem that may not be obtained.

  As described above, it is not easy to simultaneously optimize the discrete state variables of the power equipment and the continuous state variables of the power equipment such as measurement error and generator output.

  The objective of this invention is providing the suitable technique which optimizes the discrete state variable and continuous state variable of the electric power installation in an electric power grid | system.

  A power equipment variable calculation device according to one embodiment of the present invention is a power equipment variable calculation device that performs optimization calculation of a state variable of a power equipment, and a state variable setting unit that determines a continuous state variable and a discrete state variable And a plurality of continuous state variables set for each possible value of the discrete state variable, and a plurality of the values of the plurality of continuous state variables are a predetermined combination. An optimization problem setting unit that sets an optimization problem that integrates the above cases, and an optimization problem solution that solves the optimization problem under the constraint that the values of only the plurality of continuous state variables are the combination Part.

  According to the present invention, for a power facility having a discrete state variable and a continuous state variable, the continuous state variable is virtually regarded as a discrete state variable, which is similar to the optimization problem of only the continuous state variable. Can be calculated.

It is a block diagram which shows the hardware constitutions of the calculation apparatus which manages the state of an electric power grid | system. It is a block diagram which shows the functional structure of the calculation apparatus by this embodiment. It is a block diagram which shows schematic structure of the electric power grid | system in this embodiment. It is a flowchart which shows the flow of the process by the calculation apparatus of this embodiment. It is a figure which shows the equivalent circuit model of the track | line of an electricity supply state. It is a figure which shows the equivalent circuit model of the track | line in which switching between an electricity supply state and a interruption | blocking state is performed. It is a figure which shows the equivalent circuit model of the track | line in which the transformer which can switch a tap position exists.

  Embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a block diagram showing a hardware configuration of a computing device that manages the state of the power system. Referring to FIG. 1, a computing device 10 is connected to a power system 20 that includes power facilities such as power generation, power transmission, power transformation, and consumer.

  The calculation device 10 includes a communication unit 102, a storage unit 103, a calculation unit 104, and an output unit 105. In this embodiment, the calculation apparatus 10 which is an apparatus which estimates the state of the electric power grid | system 20 is illustrated.

  The electric power system 20 includes various facilities (not shown), and measuring devices (not shown) for measuring the state are installed in these facilities. These measuring instruments acquire measurement information in an arbitrary time section such as a fixed time interval, for example, and notify the communication unit 102 of the computing device 10. The measurement information is, for example, the voltage value, current value, phase, etc. of each part. The measurement information is sent from the communication unit 102 to the storage unit 103 and stored therein.

  In addition to the acquired measurement information, the storage unit 103 also stores pre-stored equipment setting information for the power system 20 and the like. The setting information of the facilities of the electric power system 20 is, for example, line impedance, transformer tap position, and the like. Both the measurement information and the setting information stored in the storage unit 103 are sent to the calculation unit 104.

  The calculation unit 104 performs optimization calculation for optimizing the discrete state variable and the continuous state variable of the facility, records the calculation result in the storage unit 103, and sends the calculation result to the output unit 105. The discrete state variable is, for example, the setting information described above. The continuous state variables are, for example, the voltage value, current value, and phase of the power flow.

  The calculation unit 104 performs optimization calculation that optimizes the discrete state variable of the facility and the continuous state variable of the power flow state, records the calculation result in the storage unit 103, and sends the calculation result to the output unit 105. The output unit 105 then reflects, for example, the optimum value of the setting information on the equipment of the power system 20.

  The optimization calculation by the calculation unit 104 as described above is formulated as shown in Expression (1).

  The function f is an objective function for state estimation calculation, and the function K represents a constraint condition. The calculation unit 104 estimates the values of some variables and calculates the optimum values of other variables so as to minimize a desired index represented by the objective function f, using Equation (1). A constraint K is imposed on the calculation. The objective function can be a continuous state variable in the optimization calculation of the power system 20, and some or all of the vectors of voltage V, current I, active power P, reactive power Q, and continuous variable vectors F0, F1,. , FN. Hereinafter, F0, F1,... FN are referred to as flag variables. In the present embodiment, a method is used in which a discrete state variable is assumed to be a continuous state variable and its solution is obtained.

  Hereinafter, the assumed continuous state variable may be referred to as an assumed continuous state variable.

  Flag variables F0, F1,..., FN each represent a possible value of the discrete state variable by a flag. Therefore, the number N of flag variables F0, F1,..., FN is the maximum number of states that the discrete variable can take. Only one of the flag variables F0, F1,..., FN (for example, the kth element) converges to a possible state, and the remaining flag variables converge to 0. Examples of constraint conditions regarding the flag variable are shown in Expression (2) and Expression (3). Under this constraint, only one of F0, F1, F2,... FN converges to 1, and the rest converges to 0.

  FIG. 2 is a block diagram showing a functional configuration of the computing device according to the present embodiment. The computing device 10 includes a state variable setting unit 11, an optimization problem setting unit 12, and an optimization problem solving unit 13. The main parts of the state variable setting unit 11, the optimization problem setting unit 12, and the optimization problem solving unit 13 are realized by the calculation unit 104 executing a software program stored in the storage unit 103.

  The state variable setting unit 11 determines a continuous state variable and a discrete state variable to be calculated from among the state variables of the power system 20.

  The optimization problem setting unit 12 includes a continuous state variable and a plurality of continuous state variables set for each possible value of the discrete state variable. Set an optimization problem that integrates multiple cases. As an example, the predetermined combinations are all zero except for any one of a plurality of continuous state variables.

  The optimization problem solving unit 13 solves the optimization problem under the constraint that the values of only the plurality of continuous state variables are combinations thereof.

  According to this, for power facilities having discrete state variables and continuous state variables, the continuous state variable is virtually regarded as a discrete state variable, and as with the optimization problem of only the continuous state variable. It becomes possible to calculate.

  At that time, the optimization problem setting unit 12 integrates all the cases in which the objective function is fixed to a value that can take the discrete state variables, and there are only a plurality of continuous state variables except for any one of them. To improve. According to this, first, an objective function in which discrete state variables are fixed to possible values is determined, and these objective functions are integrated, so that a minimization problem can be easily set.

  In addition, the optimization problem setting unit 12 regards a plurality of N flag variables F1 to FN corresponding to each of a plurality of N states as a continuous state for a discrete state variable that can take a plurality of N states. The optimization problem is set so as to include each case where all but one of the flag variables F1 to FN are all zero. By introducing a flag variable, a discrete state variable that can take a plurality of N states is considered, an optimization problem is set as a continuous state variable, and a constraint condition is imposed so that the optimization problem converges to a discrete value. It is possible.

  The constraint conditions for the flag variables F1 to FN are expressed by the above-described expressions (2) and (3). According to this, since a constraint condition expressed by a simple expression is used, an optimization problem can be calculated by applying a simple expression to a deemed continuous state variable.

  FIG. 3 is a block diagram showing a schematic configuration of the power system in the present embodiment. The power system 20 includes a switch 21, a transformer 22, a generator 23, and a line 24. The switch 21 is installed on a track (not shown), and is opened and closed by an external operation. When the switch 21 is open, the line is cut off. When the switch 21 is closed, the line is energized. The transformer 22 can select a plurality of tap positions having different winding ratios on the primary side and the secondary side. The generator 23 generates electricity using hydropower, thermal power, nuclear power, or the like. The generator 23 can be started and stopped, and the amount of power generation in the activated state can be controlled. The line 24 is, for example, a distribution line that can be energized and cut off.

  The optimization problem setting unit 12 has (F1, F2) = (0, 1) or (1) as possible continuous state variables for the discrete state variables indicating the energized and disconnected states of the line 24. , 0), a flag variable F1 corresponding to the shut-off state and a flag variable F2 corresponding to the energized state are set, and an optimization problem is set using the flag variables F1 and F2. In setting the optimization problem, for example, an objective function and constraint conditions are determined. According to this, each state of the line 24 that can take two states of energization and interruption is represented by the flag variables F1 and F2, and the optimization problem is set. Therefore, it is easy to optimize the state of the power equipment including the line 24 Can be done.

  The optimization problem setting unit 12 integrates the power equation in the energized state of the line 24 and the power equation in the interrupted state, and sets an objective function including these power equations. According to this, the optimization problem can be easily set by using power equations in each state of the line 24 and integrating them.

  Further, the power equation in the energized state of the line 24 and the power equation in the interrupted state are equations included in the objective function of the power equipment including the line 24. The objective function is a function in which the minimization problem is expressed by a Jacobian matrix in which elements discretely change due to changes in the power equation that changes depending on whether the line 24 is energized or interrupted.

  In addition, the optimization problem setting unit 12 takes the primary and secondary winding ratios as assumed continuous state variables for discrete state variables indicating the tap positions of the transformer 22 that can be changed by the tap positions. The value obtained is (F1, F2, F3) = (0, 0, 1), (0, 1, 0), or (1, 0, 0), and F2 corresponding to the current tap position and the current F1 and F3 respectively corresponding to the tap positions on both sides of the tap position may be set, and the optimization problem may be set based on the flag variables F1, F2, and F3. According to this, since the state that can be taken next to the transformer 22 that can take three or more states is represented by the three flag variables F1, F2, and F3, the optimization problem is set. The state can be easily optimized.

  Here, flag variables are set for the three tap positions that can be taken next to the current tap position, but as another example, flag variables are set for all tap positions of the transformer 22. Also good. For example, if there are six tap positions, six flag variables F0, F1,..., F5 may be set.

  Further, the optimization problem setting unit 12 assumes that the possible values as the assumed continuous state variables for the discrete state variables indicating the closed state and the open state of the switch 21 are (F1, F2) = (0, 1) or The flag variable F1 corresponding to the open state and the flag variable F2 corresponding to the closed state, which are (1, 0), may be set, and the optimization problem may be set using the flag variables F1 and F2. According to this, since each state of the switch 21 that can take two states of the closed state and the open state is represented by the flag variables F1 and F2 and the optimization problem is set, the state of the power equipment including the switch 21 is determined. It can be easily optimized.

  Further, the optimization problem setting unit 12 applies the optimization problem so that one or both of the continuous state measurement value and the discrete state setting value acquired from the power equipment are integrated as a constant. According to this, by applying the values obtained from power equipment for discrete state variables and some continuous state variables to the optimization problem, only the values of the state variables that have not been obtained are subject to estimation. Can do.

  Moreover, the memory | storage part 103 collects the measured value of the continuous state acquired from the power equipment, and memorize | stores it with the information of the time when they were acquired. The optimization problem setting unit 12 applies the measurement values in the same time section to the optimization problem. Measured values are stored together with time information, and the same time section information is applied to the optimization problem, so even if there are variables with different timings to be acquired, it is possible to perform better calculations with time-equipped information Is possible.

  Further, the optimization problem setting unit 12 may determine which state value is a constant and which state value is a variable based on the frequency of change of data acquired in the past for the state value of the power equipment. Good. By making a state value with little state change a constant, variables can be reduced and calculation can be facilitated.

  FIG. 4 is a flowchart showing the flow of processing by the computing device of this embodiment. Each process shown in FIG. 4 is mainly executed by the calculation unit 104.

  First, the computing device 10 extracts discrete state variables based on the information of the power system 20 power equipment (step S101). Next, the computing device 10 similarly extracts continuous state variables based on the information on the power equipment of the power system 20 (step S102). Here, the objective function can be determined based on the information on the power equipment of the power system 20 including the discrete state variable and the continuous state variable.

  Subsequently, the computing device 10 virtually sets the discrete state variables as continuous state variables, sets a plurality of flag variables corresponding to values that the discrete state variables can take, and values that the plurality of flag variables can take. A constraint condition based on the combination of the above is set (step S103). Furthermore, the computing device 10 sets an improved objective function (step S104). This improved objective function has been improved to include constraints. Finally, the calculation apparatus 10 calculates the improved objective function (step S105).

Although the embodiment of the present invention has been outlined so far, the objective function and the like will be specifically described below by taking some examples.
(First embodiment)

  The configuration of the system and apparatus in the first example is the same as that of the above-described embodiment.

  The calculation apparatus 10 according to the present embodiment performs calculation to solve the measurement error minimization problem. In addition, the calculation apparatus 10 of this embodiment solves the optimization of the continuous state variables of voltage V, current I, active power P, and reactive power Q, and the discrete state variables of line energization / cutoff states. State estimation. Since the discrete state variable of the energized / interrupted state of the line has two states, the energized state and the interrupted state, flag variables F0 and F1 are defined in order to consider it as a continuous state variable. One of the flag variables F0 and F1 converges to 1 and the other converges to 0 according to the equation (2).

  Here, if (F0, F1) = (1, 0), it is in the cut-off state, and if (F0, F1) = (0, 1), it is in the energized state, and the continuous state variables P, Q, V , I, F0, and F1 are optimized.

  The optimization problem setting unit 12 uses an existing objective function as the objective function of the state estimation calculation, and improves the objective function using the flag variables F0 and F1 described above. As examples of existing objective functions, there are objective functions for state estimation calculation described in Ali Abur, Antonio Gomez Exposure, “Power System Estimation”, CRC Press, 2004, pp9-29.

  The objective function before improvement in the present embodiment uses the equation (4).

  Here, H is a Jacobian matrix. R is a vector whose elements are errors of continuous state variables of voltage V, active power P, and reactive power Q.

  In the objective function of Equation (4), when the energization / cutoff state of the line between any two points of the power system 20 which is a discrete state variable changes, each element of the Jacobian matrix of Equation (4) becomes discrete. To change. This is because the equation of the line (power circuit) between any two points, that is, the power equation changes. Therefore, in this embodiment, the power equation portion in the existing objective function is improved using flag variables F0 and F1. Details will be described below.

  The power equation of the line is considered separately when it is energized and when it is interrupted.

First, the power equation when the line is energized will be described. FIG. 5 is a diagram showing an equivalent circuit model of a line in an energized state. The power circuit between the node #i 202 and the node #j 205 in FIG. 5 is a general line power equation. According to the distributed constant model of the transmission line, the equivalent circuit of this line includes a resistance component 203 (g _ij ), an inductance component 204 (jb _ij ), a capacitance component 207 (g _si + jb _si ), 208 (g _sj + jb _sj ) Is included. This equivalent circuit is represented by a power equation of Formula (5) indicating active power and a power equation of Formula (6) indicating reactive power.

Each parameter in the equations (5) and (6) is as follows.
P _ij : Active power flowing from node #i toward node #j Q _ij : Reactive power flowing from node #i toward node #j V _i : Voltage at node #i V _j : Voltage θ _{ij at} node #j : Phase difference angle g _ij between node #i and node #j: conductance b _ij between node #i and node #j: susceptance g _si between node #i and node #j: of node #i and ground Conductance b _si between: susceptance between node #i and the ground

  On the other hand, when the line is in the cut-off state, the active power and reactive power between the two nodes are 0 as shown in Expression (7). Therefore, in the cut-off state, all conductances and susceptances in the power equations of Equation (5) and Equation (6) are treated as zero. A state in which the conductance and susceptance are 0 when the line is in a cut-off state is expressed as in Expression (8).

  In order to use the discrete state variable of the energized / interrupted state of the line as the variable of the objective function, the equation (5) which is the power equation of the active power in the energized state and the equation (6) which is the power equation of the reactive power, It is necessary to formulate to include equations (7) and (8) representing the shut-off state. In this embodiment, Equation (9) is defined as a power equation of active power including the energized state and the interrupted state, and Equation (10) is defined as a power equation of reactive power including the energized state and the interrupted state.

  Here, (F0, F1) is a continuous variable that converges to (1, 0) or (0, 1) according to the constraint condition of Expression (2).

  FIG. 6 is a diagram illustrating an equivalent circuit model of a line that is switched between an energized state and a cut-off state. The equivalent circuit of the line including the flag variables F0 and F1 is a circuit in which two equivalent circuits 301 and 302 in an energized state and a cut-off state are connected in parallel as shown in FIG. Since the flag variables F0 and F1 represent the energized state and the interrupted state of the line, (F0, F1) converges to either (0, 1) or (1, 0).

  When (F0, F1) becomes (0, 1), the equivalent circuit on the F1 side in FIG. 6 is eliminated, and the energized state equations (5) and (6) become the power equation of the line. On the other hand, if (F0, F1) becomes (1, 0), there is no equivalent circuit on the F1 side, and the equations (7) and (8) in the cut-off state become the power equation of the line. Equations (9) and (10) have been improved so as to express the change in the line state in which the equivalent circuit on the F1 side and the equivalent circuit on the F2 side in FIG. Yes.

  The optimization problem solving unit 13 simultaneously estimates the current supply / cutoff state of the line by solving the optimization problem of the objective function including the improved power equation shown in the equations (9) and (10). Realize measurement error minimization.

As described above, according to the present embodiment, the line in the objective function that minimizes the measurement error in the calculation of the optimization problem of the power system 20 including the line that can take the discrete states of the energized state and the cut-off state. Since the objective function is improved by assuming that the power equation part of the above is virtually continuous using the flag variable and the optimization problem is calculated using the improved objective function, the estimation of the discrete state variable Measurement error can be minimized at the same time.
(Second embodiment)

  The configuration of the system and apparatus in the second example is the same as that of the above-described embodiment.

  Similar to the first embodiment, the calculation apparatus 10 according to the present embodiment performs calculation to solve the minimization problem using the measurement error as an objective function. As in the first embodiment, the objective function is a vector whose elements are respective errors of continuous state variables such as voltage V, active power P, and reactive power Q. Further, the calculation device 10 of this embodiment optimizes the continuous state variable and the discrete state variable as in the first embodiment, but includes the tap position of the transformer as the discrete state variable. .

  The tap position of the transformer in this embodiment usually has three or more possible states, unlike the line energization / cutoff state shown in the first embodiment. FIG. 7 is a diagram illustrating an equivalent circuit model of a line in which a transformer capable of switching the tap position exists. To consider transformer tap positions, which are discrete state variables, as continuous state variables. For example, three flag variables F0, F1, and F2 corresponding to three tap positions of the transformer are defined.

  As shown in FIG. 7, the equivalent circuit of the transformer including the flag variables F0, F1, and F2 is a circuit in which three equivalent circuits 401, 402, and 403 that rub against each tap position are connected in parallel. Since the tap position of the transformer is alternatively selected, one of the flag variables F0, F1, and F2 converges to 1 and the other two converge to 0 according to Equation (2).

  Using the flag variables F0, F1, and F2, the objective function indicating the measurement error is improved. In that case, flag variables F0, F1. F2 is introduced so that the flag variable converges to (F0, F1, F2) = (1, 0, 0) or (0, 1, 0) or (0, 0, 1) Improve.

  Further, a calculation for optimizing the continuous state variables P, Q, V, I, F0, F1, and F2 is performed using the improved objective function. This calculation minimizes measurement errors and estimates discrete state variables.

As described above, according to the present embodiment, the line in the objective function that minimizes the measurement error in the calculation of the optimization problem of the power system 20 including the equipment that can take a discrete state such as the tap position of the transformer. The objective function is improved using the flag equation as a virtual continuous state variable using the flag variable, and the optimization problem is calculated using the improved objective function. Can be simultaneously minimized.
(Third embodiment)

  The configuration of the system and apparatus in the third example is the same as that of the above-described embodiment. In the third embodiment, a plurality of facilities are estimated by treating flag variables as vectors. The power system 20 of the present embodiment is configured to include both the line that takes the energization / cutoff state shown in the first embodiment and the transformer that can change the tap position shown in the second embodiment.

  In addition, it is assumed that the power system 20 of the present embodiment includes equipment that can acquire the set state by measurement. For example, it is assumed that switches are installed on a plurality of tracks, and that some of the switches have their switching states measured. In that case, it is only necessary to use the acquired state for the line whose open / closed state can be acquired by measurement, and to set the state of the line from which the state cannot be acquired as an estimation target using the flag variable. For example, the flag variables corresponding to the switch #A are F0 (A) and F1 (A). For the line for which the state of the switch #A is acquired, if the switch is open, the line is considered to be in the cut-off state shown in the first embodiment, and (F0 (A), F1 (A)) = ( It is only necessary to calculate 0, 1) as a constant instead of a variable. If the switch is in the closed state, it is regarded as the energized state shown in the first embodiment, and the calculation is performed with (F0 (A), F1 (A)) = (1, 0) as a constant instead of a variable. Just do it.

  In addition, it is assumed that the power system 20 of this embodiment includes a transformer that can acquire the set tap position by measurement. For example, it is assumed that transformers are installed on a plurality of lines, and the tap positions of some of the transformers are measured. In that case, it is only necessary to use the acquired state for a transformer whose state of the tap position can be acquired by measurement, and to set the state of the transformer whose state cannot be acquired as an estimation target using the flag variable. For the line from which the tap position state is acquired, for example, if the tap position selected in the transformer #B is 1, (F0 (B), F1 (B), F2 (B)) = (1, 0 , 0). Similarly, if the selected tap position is 2, it can be converged to (F0 (B), F1 (B), F2 (B)) = (0, 1, 0). If the selected tap position is 3, it can be converged to (F0 (B), F1 (B), F2 (B)) = (0, 0, 1).

Further, which line state and which transformer tap state are to be estimated and which line state and which transformer tap state are constants can be determined by utilizing big data. For example, from the data of the switching state of the switch or the setting state of the tap position of the transformer measured and accumulated in the past predetermined period, equipment in a state similar to the installation such as the line and transformer in the power system 20, The state of equipment whose state frequently changes in past data may be used as a variable, and the state of equipment with little change may be handled as a constant. By doing so, it is possible to efficiently and satisfactorily perform the state estimation calculation of the electric power system 20 including various facilities that differ in how the state changes.
(Fourth embodiment)

  The fourth embodiment exemplifies a start / stop problem that determines states such as start, stop, and output of each generator in a system that supplies power by a plurality of generators.

  In the start / stop problem, the supply and demand balance, the upper and lower limits of the generator output, the time required to stop the generator, the reserve capacity, and the like are the limiting conditions. In the start / stop problem, the state of each generator is determined so that the power generation cost is minimized.

  There is an equal λ method as a general method for solving the start / stop problem. The method of determining the state of the generator in the present embodiment improves the solution of this λ method by assuming that the discrete state variables of starting and stopping the generator are continuous state variables.

  Assuming that all the generators are always in the starting state, it is not necessary to consider the discrete state variables of starting and stopping the generator. First, the case will be described before the present embodiment.

  Equation (11) is the most general objective function for the problem of starting and stopping the (N + 1) generators.

Each parameter included in Equation (11) is as follows.
F _i (Pi): Fuel cost in generator #i λ: Grid incremental cost P _i : Active power output in generator #i P: Sum of load power

In general, in the equal λ method, when each generator output is determined so that the incremental cost of each generator is equal, the power generation cost is minimized. Therefore, the optimal solution of the objective function shown in Expression (11) is expressed by Expression (12). ) Will be satisfied. That is, the output of each generator may be determined by Equation (12) so that the incremental fuel costs dF _i / dP _i of each generator are equal.

  The above is the solution by the equal λ method when all the generators are assumed to be in the activated state.

  On the other hand, in this embodiment, the optimum output of each generator is determined in consideration of the start and stop of the generator. In this embodiment, a continuous state variable called a generator output and a discrete state variable called starting and stopping of a generator, that is, whether or not a generator output is present, are determined simultaneously.

  At this time, the above-described solution of the equal λ method is improved by assuming that the discrete state variable is a continuous state variable. A discrete state variable such as the presence or absence of the output of the generator is added to the equation (11), and the discrete state variable is assumed to be a continuous state variable by using a flag variable. Then, the objective function of Expression (13) can be defined for the start / stop problem.

In the present embodiment, to avoid confusion with the function F representing the fuel cost of the generator, it describes a flag variable in lowercase f _i in equation (13). f _i1 is a flag variable that is true in the activated state, and f _i2 is a flag variable that is true in the deactivated state.

  Since the partial differentiation of the flag variable is 0, if the condition of the equal λ method is used, the objective function of Expression (13) satisfies Expression (14).

  Here, if the number of generators that need to be started is K, the objective function shown in equation (13) can be replaced by equation (15).

  By solving the objective function of the equation (15) together with the constraint condition, it is possible to simultaneously determine the presence / absence of output of each generator and the active power output.

According to the present embodiment, it is assumed that the discrete state variable of the start and stop state of the generator is a continuous state variable and is solved by imposing a constraint condition, thereby generating power in the start / stop problem of a plurality of generators. The start and stop of the machine and the output can be optimized simultaneously.
(Fifth embodiment)

  The fifth embodiment exemplifies the problem of optimizing the switching state of each switch in a power distribution system in which switches are provided between nodes. The problem of optimizing the switching state of the switch in this type of distribution system is to solve the objective function that minimizes the distribution line loss.

  Hereinafter, the distribution line loss of the distribution system can be expressed by Expression (16).

In Expression (16), P _ij indicates a distribution line loss between the node #i and the node #j. P _ij is expressed by Expression (5) when the switch between the node #i and the node #j is closed, and is expressed by Expression (7) when the switch is open.

  Therefore, in the same way as in the first embodiment, assuming that the open / close state of the switch, which is a discrete state variable, is a continuous variable using flag variables, instead of Equation (5) and Equation (7) The expression (9) expressed by the flag variable F can be used.

  Using Equation (9), the objective function of Equation (16) can define the distribution line loss minimization problem of Equation (17).

  By solving the minimization problem of Expression (17) together with the constraint condition, the switching state of the switch that minimizes the distribution line loss can be obtained.

  As described above, according to each of the embodiments and examples, in the optimization calculation such as the state estimation calculation, the generator output optimization calculation, and the loss minimization calculation, the line energization / cutoff state, the transformer tap By treating discrete state variables such as position and switching state of switches as continuous state variables, it is possible to obtain an optimal solution among all combinations of solutions that can be taken by the defined objective function. Further, since the number of flag variables to be used can be arbitrarily determined as shown in the third embodiment, the number of values that can be taken by any discrete state variable is not limited, The calculation can be performed at a higher speed than when the optimization calculation is attempted for a certain number of combinations. Therefore, it can contribute to the improvement of the calculation speed of the optimization calculation in the power system.

  The above-described embodiments and examples of the present invention are merely examples for explaining the present invention, and the scope of the present invention is not intended to be limited only to those embodiments and examples. Those skilled in the art can implement the present invention in various other modes without departing from the gist of the present invention.

DESCRIPTION OF SYMBOLS 10 ... Calculation apparatus, 102 ... Communication part, 103 ... Memory | storage part, 104 ... Calculation part, 105 ... Output part, 11 ... State variable setting part, 12 ... Optimization problem setting part, 13 ... Optimization problem solution part, 20 ... Power system, 21 ... switch, 22 ... transformer, 23 ... generator, 24 ... line, 202, 205 ... node, 203 ... resistance component, 204 ... inductance component, 207, 208 ... capacitance component, 301, 302 ... equivalent Circuit, 401, 402, 403 ... equivalent circuit

Claims (15)

A power equipment variable calculation device that performs optimization calculation of state variables of power equipment,
A state variable setting unit for determining continuous state variables and discrete state variables;
A plurality of cases where the continuous state variable includes a plurality of continuous state variables set for each possible value of the discrete state variable, and the values of the plurality of continuous state variables are a predetermined combination. An optimization problem setting unit that sets an optimization problem that integrates
An electric power equipment variable calculation apparatus comprising: an optimization problem solving unit that solves the optimization problem under a constraint that values of the plurality of continuous state variables are the combination.   The electric power equipment variable calculation device according to claim 1, wherein the combination is a combination in which all but one of the plurality of continuous state variables is all zero. The optimization problem setting unit integrates the objective function in which the discrete state variables are fixed at the possible values, and integrates all cases where the plurality of none and only one of the continuous state variables is all zero. To improve,
The power equipment variable calculation apparatus according to claim 2. The optimization problem setting unit regards a plurality of N flag variables F1 to FN corresponding to each of the plurality of N states as the continuous state for discrete state variables that can take a plurality of N states. A variable, and the optimization problem is set to include each case where all but one of the flag variables F1 to FN are all zero.
The power equipment variable calculation apparatus according to claim 2. The electric power equipment variable calculation apparatus of Claim 4 with which the said constraint condition about the said flag variables F1-FN is represented by Formula (A) and Formula (B).
  The optimization problem setting unit has (F1, F2) = (0,) as possible continuous state variables for the discrete state variables indicating the energized and interrupted states of the lines included in the power equipment. The electric power equipment variable calculation according to claim 4, wherein the optimization problem is set based on a flag variable F1 corresponding to a cut-off state and a flag variable F2 corresponding to an energized state, which is 1) or (1, 0). apparatus.   The power equipment variable calculation device according to claim 6, wherein the optimization problem setting unit integrates a power equation in the energized state of the line and a power equation in the cut-off state, and sets an objective function including the power equation. .   The power equipment according to claim 7, wherein the objective function is a function in which a minimization problem is expressed by a Jacobian matrix in which elements discretely change due to a change in a power equation that changes depending on whether the line is energized or interrupted. Variable calculator.   The optimization problem setting unit includes a value that can be taken as an assumed continuous state variable for a discrete state variable indicating each tap position for a transformer that can change a winding ratio by a tap position, included in the power equipment. (F1, F2, F3) = (0, 0, 1), (0, 1, 0), or (1, 0, 0), F2 corresponding to the current tap position and the current tap position The electric power equipment variable calculation device according to claim 4, wherein the optimization problem is set based on F1 and F3 respectively corresponding to the tap positions on both sides.   The optimization problem setting unit has (F1, F2) = (0,) as possible continuous state variables for the discrete state variables indicating the closed state and the open state of the switches included in the power equipment. The power equipment variable calculation according to claim 4, wherein the optimization problem is set based on a flag variable F1 corresponding to an open state and a flag variable F2 corresponding to a closed state, which is 1) or (1, 0). apparatus. The optimization problem setting unit is applied to the optimization problem so as to integrate one or both of a continuous state measurement value and a discrete state setting value acquired from the power equipment as a constant,
The power equipment variable calculation device according to claim 1. It further includes a storage unit that collects continuous state measurement values acquired from the power equipment and stores them together with information on the time at which they were acquired;
The optimization problem setting unit applies the measurement value in the same time section to the optimization problem.
The power equipment variable calculation device according to claim 11.   The optimization problem setting unit determines which state value is a constant and which state value is a variable based on a frequency of change of data acquired in the past for the state value of the power equipment. The power equipment variable calculation device described in 1. A power equipment variable calculation method for performing optimization calculation of state variables of power equipment,
The state variable setting means determines continuous state variables and discrete state variables,
The optimization problem setting means includes the continuous state variable and a plurality of continuous state variables set for each possible value of the discrete state variable, and the values of the plurality of continuous state variables are predetermined. Set up an optimization problem that integrates multiple cases that are a combination of
A power equipment variable calculation method in which an optimization problem solving means solves the optimization problem under a constraint that values of the plurality of continuous state variables are the combination.   The electric power equipment variable calculation method according to claim 14, wherein the combination is a combination in which any one of the plurality of continuous state variables other than one is all zero.