Power system monitoring device and monitoring method

Abstract

To provide a device and method for monitoring a power system capable of extracting a future tidal current state that will be severe in reliability on the basis of a reliability index designated by a system operator.SOLUTION: A power system monitoring device 1 that assumes a tidal current state of a power system, comprises: an input part 201 that receives a reliability index; a system parameter database DB2 that stores a system parameter; a state estimation cross section database DB3 that stores a state estimation cross section; a prediction information database DB4 that stores prediction information on power demand / supply; a variable / constant type database DB5 that stores a variable / constant type for setting whether factors of the power system are fluctuated or constant; a severity forecast tidal current cross section generation part 203 that generates a severity forecast tidal current cross section and a reliability index value; a severity forecast tidal current cross section database DB6 that stores the severity forecast tidal current cross section and the reliability index value; a display part 204 that displays the severity forecast tidal current cross section and the reliability index value; and an output part 205 that outputs the severity forecast tidal current cross section and the reliability index value.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a power system monitoring device and a monitoring method for monitoring a power system. [Background technology]

[0002] In order to properly plan operation policies for a power system, it is necessary to understand and predict the current and future state of the power system. Specifically, the state of the power system includes the bus voltage, the active and reactive power flowing in and out of the bus, and the power flow in the transmission line, such as the active and reactive power flow. Operation policies for the stable operation of the power system are planned based on the power flow state and the results of stability analysis based on the power flow state. The current power flow state of a power system is determined based on measurements collected from measuring instruments installed at various locations in the power system. However, the collected measurements contain various errors due to measurement errors, discrepancies in measurement time, and losses and transmission delays due to communication failures. State estimation is used to determine a plausible power flow state from a group of inconsistent measurements. In general, state estimation is described as an optimization problem that minimizes the residual between the estimated values ​​obtained by state estimation and the measured values ​​while satisfying the power flow equations of the power system.

[0003] On the other hand, future current conditions are calculated based on the current current conditions, future expected demand, and expected output of renewable energy (hereinafter also referred to as "RE") power generation. How the current state will change from the present to the future is very important information for determining the operation policy of the power system. However, since the system operator cannot control the demand or the output of renewable energy generation, there is a certain degree of uncertainty, and it is necessary to assume the future current state within this range of uncertainty.

[0004] As a technique related to the above-mentioned problem, for example, Patent Document 1 is known. The [Abstract] of Patent Document 1 discloses the technology for power system monitoring systems, saying, "[Problem] To provide a power system monitoring system, power system monitoring method, and program that are capable of evaluating reliability taking into account uncertain factors included in a power system. [Solution] The power system monitoring system of an embodiment has an estimation unit, a reliability evaluation unit, a determination unit, and a control content determination unit. The reliability evaluation unit derives the reliability of power supply from the power system equipment based on a plurality of states estimated by the estimation unit. The determination unit determines whether a predetermined condition related to reliability is satisfied based on the reliability derived for each of the plurality of states by the reliability evaluation unit. The control content determination unit determines the control content for the power system equipment to improve reliability when the determination unit determines that the predetermined condition is not satisfied." In this way, Patent Document 1 discloses a power system monitoring system, a power system monitoring method, and a program that create multiple future power system flow states using combination patterns of predicted values ​​based on the probability distribution of renewable energy power generation output, and determine control content based on a reliability evaluation of the multiple flow states. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2019-216534 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, the power system monitoring system, power system monitoring method, and program disclosed in Patent Document 1 require a comprehensive evaluation of combinations of anticipated uncertain factors, which poses the challenge (problem) of having to evaluate a huge number of combinations when taking into account N-1 anticipated failures of a single unit of equipment.

[0007] In order to address the above-mentioned problems, the present invention has an objective (purpose) to provide a power system monitoring device and a monitoring method thereof that anticipate and extract future power flow conditions that will be severe in terms of the reliability of the power system, based on a reliability index specified by a user (system operator). [Means for solving the problem]

[0008] In order to solve the above problems, the present invention is configured as follows. That is, the power system monitoring device of the present invention is a power system monitoring device which estimates the future power flow state of the power system, and includes an input unit for inputting a reliability index which is an index showing reliability when estimating the state of the power system, a system parameter database which stores system parameters of the power system, a state estimation cross section database which stores state estimation cross sections generated by a state estimation calculation of the power system, a prediction information database which stores prediction information of power supply and demand in the power system, a variable and constant type database which stores variable and constant types which set whether factors of the power system fluctuate or are constant, and a severe forecast power flow cross section generation unit which generates a severe forecast power flow cross section and a reliability index value which is an index value for the reliability index using the reliability index, the system parameters, the state estimation cross section, the prediction information, and the variable and constant types. The severe forecast power flow cross section generating unit sets a provisional power flow cross section and includes a reliability evaluation unit that uses the provisional power flow cross section to obtain a provisional index value, and the reliability evaluation unit evaluates the provisional index value and displays the provisional index value that satisfies a judgment condition in the evaluation on a display unit or outputs it to the outside. Reliability index value Let's say It is characterized by:

[0009] Other means will be described in the description of the embodiment of the invention. Effect of the Invention

[0010] According to the present invention, it is possible to provide a power system monitoring device and a monitoring method thereof that estimate and extract future power flow conditions that will be severe in terms of the reliability of the power system, based on a reliability index specified by a user (system operator). [Brief description of the drawings]

[0011] [Figure 1] 1 is a diagram showing a configuration example of a power system monitoring device according to a first embodiment of the present invention and its relationship with a power system. [Diagram 2] FIG. 1 is a diagram showing the processing contents of the power system monitoring device 1 according to the first embodiment of the present invention, organized by processing functions implemented by a computer. [Diagram 3] FIG. 1 is a diagram illustrating an example of the configuration of a power system. [Figure 4A] 13 is a diagram showing a list of node numbers, voltage magnitudes, active powers, and reactive powers for nodes at measurement cross section D2. [Figure 4B] 13 is a diagram showing a list of node numbers, active power flow, and reactive power flow at the sending and receiving ends of the branches at measurement cross section D2. [Figure 5A] 13 is a diagram showing a list of node numbers, voltage magnitudes, voltage phases, active powers, and reactive powers for nodes in a state estimation cross section D4. [Figure 5B] This figure shows a list of node numbers at the transmitting end and receiving end of the branch, active power flow, and reactive power flow for the branch of state estimation cross section D4. [Figure 6A] FIG. 13 is a diagram showing a list of residuals at nodes (node ​​residuals) for each item. [Figure 6B] FIG. 13 is a diagram showing a list of residuals in branches (branch residuals) by item. [Figure 7A] FIG. 13 is a diagram showing an example of setting a flag for a reliability index used in the severe forecast power flow profile generating unit from among a plurality of reliability indexes. [Figure 7B] FIG. 13 is a diagram showing an example of setting weighting coefficients for reliability indexes used in the severe forecast power flow profile generator from among a plurality of reliability indexes. [Figure 7C] FIG. 13 is a diagram showing an example of setting a flag for a reliability index used in the severe forecast power flow profile generating unit from among a plurality of reliability indexes. [Figure 7D] FIG. 13 is a diagram showing an example of setting weighting coefficients for reliability indexes used in the severe forecast power flow profile generator from among a plurality of reliability indexes. [Figure 8]FIG. 13 is a diagram showing power-related prediction information for a time ahead of the date and time of the measured cross section. [Figure 9] This is a diagram showing a list of the "facilities" used in the severe forecast current cross section generation unit, the "types" that stipulate the variables and constants of the characteristics of the facilities, and their "constraints." [Figure 10] FIG. 3 is a diagram illustrating an example of a flowchart of a strict forecast power flow cross section generation process of the strict forecast power flow cross section generation unit of the power system monitoring device according to the first embodiment of the present invention. [Figure 11A] FIG. 13 is a diagram showing an example of setting a "constant" used in a search. [Figure 11B] FIG. 13 is a diagram showing an example of setting “variables” used in a search. [Figure 12A] This is a diagram showing an example of a severe forecast current cross section D8 (node) of the obtained solution output by "Data Output". [Figure 12B] This is a diagram showing an example of the severe forecast current cross section D8 (branch) of the obtained solution output by "Data Output". [Figure 13] FIG. 13 is a diagram showing an example of a reliability index value D9 of an obtained solution output by "Data Output." [Figure 14] FIG. 11 is a diagram showing a configuration example of a power system monitoring device according to a second embodiment of the present invention and its relationship with a power system. [Figure 15] FIG. 11 is a diagram illustrating an example of a flowchart of a strict forecast power flow cross section generation process of a strict forecast power flow cross section generation unit of a power system monitoring device according to a second embodiment of the present invention. [Figure 16] FIG. 13 is a diagram showing a configuration example of a power system monitoring device according to a fourth embodiment of the present invention and its relationship with a power system. [Figure 17] FIG. 13 is a diagram illustrating an example of a flowchart of the process flow of a severe forecast power flow profile generating unit and a reliability evaluating unit of a power system monitoring device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, a mode for carrying out the present invention (hereinafter referred to as an "embodiment") will be described with reference to the drawings as appropriate. Note that the embodiment described below is an example, and the invention itself is not limited to the specific content described.

[0013] First Embodiment The configuration of a power system monitoring device according to a first embodiment of the present invention will be described with reference to Figures 1 to 13. Note that, although the power system monitoring device will be mainly described below, a power system monitoring method using the power system monitoring device will also be described.

[0014] <Configuration and functions of power system monitoring device> FIG. 1 is a diagram showing an example of the configuration of a power system monitoring device 1 according to a first embodiment of the present invention and its relationship with a power system 103. In FIG. 1, a power system monitoring device 1 is configured to include a computer. The power system monitoring device 1 also includes a central control device 11 , an input device 12 , an output device 13 , a display device 14 , a main memory device 15 , and an auxiliary memory device 16 . The display device 14 includes a display. The auxiliary storage device 16 includes various databases 17 and stores various programs 18 . The central control unit 11, the input device 12, the output device 13, the display device 14, the main memory device 15, and the auxiliary memory device 16 are interconnected by a bus 19, and transmit and receive signals therebetween.

[0015] In the following explanation, when the subject of an operation is described as "the XX unit", it means that the central control unit 11 reads the processing contents of the XX unit, which is a program, from the auxiliary memory device 16, loads it into the main memory device 15, and then realizes the function of the XX unit (described in detail below).

[0016] In addition, in FIG. 1, the power system monitoring device 1 can be connected to a plurality of measuring instruments 102 via a communication line 101 . A plurality of measuring instruments 102 measure the output state of generators installed in a power system 103, the power flow state of power transmission and transformation facilities, and the facility states of devices to be controlled, such as phase modifying facilities and switches.

[0017] FIG. 2 is a diagram showing the processing contents of the power system monitoring device 1 according to the first embodiment of the present invention, organized by processing functions configured with a computer. In FIG. 2, the power system monitoring device 1 includes an input unit 201 , a state estimation unit 202 , a severe forecast power flow profile generation unit 203 , a display unit 204 , and an output unit 205 . The power system monitoring device 1 also includes a measurement value database DB1, a system parameter database DB2, a state estimation section database DB3, a prediction information database DB4, a variable / constant type database DB5, and a severe forecast current section database DB6. In FIG. 2, for example, "measurement value database" is written as "measurement value DB", and "database" is written as "DB".

[0018] The function of each program (XX unit) and the flow of each data in the power system monitoring device 1 will be described with reference to FIG. In addition, while the "XX" device in FIG. 1 indicates hardware, the "XX unit" in FIG. 2 indicates a program (software).

[0019] In FIG. 2, the power system monitoring device 1 acquires a measurement value in a power system 103 measured by a measuring instrument 102 from an input unit 201 via a communication line 101 . The measurement value D1 acquired from the input unit 201 is stored in a measurement value database DB1. When the measurement values ​​at each point of the power system 103 have been sufficiently stored in the measurement value database DB1, or when the measurement values ​​stored at a predetermined period are output to the state estimation unit 202 as a measurement cross section D2, which is measurement data for each time cross section.

[0020] The state estimation unit 202 performs a state estimation calculation based on the measurement cross section D2 and system parameters D3 stored in the system parameter database DB2. One example of the state estimation calculation is a method using the weighted least squares method. A state estimation cross section D4 is generated by the state estimation calculation of the state estimation unit 202. The state estimation cross section D4 is stored in a state estimation cross section database DB3.

[0021] The severe forecast current cross section generation unit 203 uses a state estimation cross section D4, system parameters D3 obtained from a system parameter database DB2, a reliability index D5 obtained from the input unit 201, prediction information D6 obtained from a prediction information database DB4, and variable / constant types D7 obtained from a variable / constant type database DB5. Furthermore, the severe forecast power flow cross section generating unit 203 generates a forecast power flow cross section in which the index value corresponding to the reliability index D5 becomes severe within the range of the prediction information D6 (hereinafter, the severe forecast power flow cross section).

[0022] The reliability index D5 is specified by a user (a power system operator). The reliability index D5 is the maximum voltage phase difference (Δθ max For example, there is the Voltage Stability Margin (VSM) for voltage stability. Another example is the L-index, which is also related to voltage stability. In principle, publicly known indices and methods are used for these reliability indices D5.

[0023] The forecast information D6 is composed of forecast values ​​of future demand and renewable energy power generation output, and the uncertainty regarding the forecast values. As mentioned above, the term "renewable energy" in "renewable energy power output" is a simplified version of the term. In the following description, we will use the term "renewable energy" where appropriate. Furthermore, the uncertainty of a predicted value may be, for example, a range indicating how far the predicted value may deviate, or may be something like a probability distribution. The prediction information D6 is generated outside the power system monitoring device 1 and stored in the prediction information database DB4. However, the prediction information D6 may be generated inside the power system monitoring device 1 using known means.

[0024] The variable / constant type D7 specifies the type (variable or constant) of the state quantity treated as a variable or constant when the severe forecast power flow cross section generating unit 203 generates a severe forecast power flow cross section. Details of the variable / constant type D7 will be described later, but for example, uncertain demand and renewable energy power output are specified as variables. Conversely, the outputs of various control devices are specified as constants, as will be described later.

[0025] The severe forecast power flow cross section generating unit 203 generates a severe forecast power flow cross section D8 and a reliability index value D9 for that cross section. The "reliability index value D9" corresponds to the "reliability index D5" specified by the user obtained from the input unit 201, and is calculated as a stricter index value for reliability in the strict forecast current profile generating unit 203. The severe forecast power flow cross section D8 generated by the severe forecast power flow cross section generation unit 203 and the reliability index value D9 for that cross section are stored in the severe forecast power flow cross section database DB6, and are also displayed to the user via the display unit 204. Furthermore, the severe forecast power flow cross section D8 and the reliability index value D9 are output from the output unit 205 to the outside of the power system monitoring device 1.

[0026] <Example of power system configuration> FIG. 3 is a diagram showing an example of the configuration of the power system 103. 3, power system 103 is configured to include generators G1, G2, G3, loads L1, L2, L3, transformers 311-313, and power transmission lines 345, 349, 356, 367, 378, 389. In addition, nodes #1-#9 are shown as nodes to which each piece of equipment in power system 103 is connected. Hereinafter, specific examples and effects of each data and each process will be described for the power system shown in Fig. 3. As described above, the power system 103 is composed of nodes #1 to #9 to which each piece of equipment is connected, and branches that connect the nodes.

[0027] The nodes are connected to generators G1 to G3 and loads L1 to L3. Note that a node to which a generator is connected is called a generator node, and a node to which a load is connected is called a load node, as appropriate. Moreover, the branches are mainly composed of transformers (311, 312, 313) and transmission lines (345, 349, 356, 367, 378, 389). Active power and reactive power flow in and out of the power grid 103 from the nodes to which the generators (G1 to G3) and the loads (L1 to L3) are connected, and are transmitted to other nodes through the branches. The generator G3 is a renewable energy generator (renewable energy power generation, renewable energy generator).

[0028] Measuring instruments (not shown) are installed at each node and both ends of each branch in FIG. In the case of a node, the active power, reactive power, and voltage magnitude flowing in and out of the node are measured. In the case of a branch, the active and reactive power flows passing through the branch are measured at both ends of the branch. After these measurements are collected, they are given a time stamp for the measurement and stored as measurement values ​​D1 (Fig. 2) in the measurement value database DB1 (Fig. 2).

[0029] <Measurement section D2> An example of the measurement cross section D2 (FIG. 2) stored in the measurement value database DB1 (FIG. 2) is shown in FIG. 4A and FIG. 4B. The date and time of measurement of the measurement cross section D2 is 11:30 a.m. (time stamp) on February 16, 2021 in both Figures 4A and 4B.

[0030] FIG. 4A is a diagram showing a list of node numbers, voltage magnitudes, active powers, and reactive powers for nodes at measurement cross section D2. FIG. 4A shows the magnitude of voltage at each node (#) relative to the reference unit (pu: Per Unit) of the power system. 4A also shows the active power (MW) and reactive power (Mvar) at each node (#). Note that since node #1, node #2, and node #3 are connected to generators G1, G2, and G3, respectively, the active powers of each node show high positive values ​​that indicate inflow into the power system. Moreover, since node #5, node #7, and node #9 are connected to loads L1, L2, and L3, respectively, they show negative values ​​indicating outflow from the power system.

[0031] Figure 4B is a diagram showing a list of the node numbers (From, To) at the sending end and receiving end of the branch, the active power flow at the sending end (MW) (From), the reactive power flow at the sending end (Mvar) (From), the active power flow at the receiving end (MW) (To), and the reactive power flow at the receiving end (Mvar) (To) for the branch at measurement cross section D2. In Fig. 4B, (From) and (To) indicate the direction of the power flow. Also, at the nodes (From, To) at the sending and receiving ends of the branch, the active power flow (MW) (From) and the active power flow (MW) (To) do not necessarily have the same absolute positive and negative values ​​because of the inclusion of transmission losses and measurement errors.

[0032] <System parameter D3> The system parameters D3 (FIG. 2) are composed of constants that represent the characteristics of each facility (transmission and transformation facilities and generators) in the power system. It should be noted that the system parameter D3 is used in general state estimation and system analysis, and therefore a detailed description thereof will be omitted here.

[0033] <Condition estimation section D4> The state estimation unit 202 (Figure 2) performs a state estimation calculation using a measurement cross section D2 (Figure 2) obtained from the measurement value database DB1 (Figure 2) and system parameters D3 (Figure 2) obtained from the system parameter database DB2 (Figure 2), and generates a state estimation cross section D4 (Figure 2).

[0034] An example of the state estimation cross section D4 is shown in FIG. 5A and FIG. 5B. The date and time of condition estimation for condition estimation cross section D4 is 11:30 a.m. on February 16, 2021 in both Figures 5A and 5B.

[0035] FIG. 5A is a diagram showing a list of node numbers, voltage magnitudes, voltage phases, active powers, and reactive powers for nodes in a state estimation cross section D4. In Fig. 5A, voltage phase (°) is added to the items in Fig. 4A. As the result of state estimation, voltage phase is also obtained, so in Fig. 5A, the item of voltage phase is added. It should be noted that the items in FIG. 5A are merely an example, and the state estimation cross section D4 does not need to include all of these data, and only some of the data may be sufficient.

[0036] Figure 5B is a diagram showing a list of the node numbers (From, To) at the transmitting end and receiving end of the branch, the active power flow (MW) (From) at the transmitting end, the reactive power flow (Mvar) (From) at the transmitting end, the active power flow (MW) (To) at the receiving end, and the reactive power flow (Mvar) (To) at the receiving end, for the branches of state estimation cross section D4. In FIG. 5B, for each branch (#1 to #9), the active power flow and reactive power flow are indicated with power values ​​(active power flow, reactive power flow) as “From” and “To,” which indicate the direction in which power is transmitted (power flow). In FIG. 5B, the numerical values ​​in the table for "From" and "To" may differ due to transmission loss, measurement error, and the like.

[0037] The items in Fig. 5B are the same as the items in Fig. 4B. Note that the state estimation cross section D4 (branch) shown in Fig. 5B is a result of calculation performed by the state estimation unit 202 using the measurement cross section D2 (Fig. 2) and the system parameters D3 (Fig. 2), and therefore values ​​slightly different from those of the measurement cross section D2 (branch) shown in Fig. 4B are shown as a list.

[0038] <Residual of state estimation calculation> Furthermore, when performing a state estimation calculation, a residual error occurs in the calculation result. Examples of the residual error are shown in Figures 6A and 6B. FIG. 6A is a diagram showing a list of residuals at nodes (node ​​residuals) for each item. The residuals (node ​​residuals) shown in FIG. 6A are composed of the same items as those in FIG. 4A. The values ​​in each column of Fig. 6A are obtained by subtracting the values ​​in Fig. 5A from the values ​​in Fig. 4A. These residuals are included as part of the state estimation cross section D4.

[0039] FIG. 6B is a diagram showing a list of residuals in the branches (branch residuals) for each item. The residuals (branch residuals) shown in FIG. 6B are composed of the same items as in FIG. 4B. The values ​​in each column of Fig. 6B are obtained by subtracting the values ​​in Fig. 5B from the values ​​in Fig. 4B. These residuals are included as part of the state estimation cross section D4.

[0040] Generally, the objective of state estimation is to minimize the residual error, so in Figures 6A and 6B, each item has a small value close to the minimum state.

[0041] <Reliability Index D5> Examples of the reliability index D5 are shown in Figures 7A and 7B, and further shown in Figures 7C and 7D. FIG. 7A is a diagram showing an example of setting a flag for a reliability index used in the severe forecast power flow profile generating unit 203 (FIG. 2) from among a plurality of reliability indexes. In Fig. 7A, the reliability indices are VSM, L-index, Δθ maxis shown as an example. And the maximum voltage phase difference (Δθ max ) is selected and the flag is set to "1." For the other items, voltage stability margin (VSM) and voltage stability (L-index), the flag is set to "0." The input method for setting the flag may be selection from a pull-down menu or selection using a check box using the display unit 204 and input unit 201 in FIG.

[0042] FIG. 7B is a diagram showing an example of setting weighting coefficients for reliability indexes used in the severe forecast power flow profile generating unit 203 (FIG. 2) from among a plurality of reliability indexes. As shown in FIG. 7B, the weighting coefficients may be set separately from the flags (FIG. 7A) or in addition to the flags.

[0043] FIG. 7C is a diagram showing an example of setting a flag for a reliability index used in the severe forecast power flow profile generating unit 203 (FIG. 2) from among a plurality of reliability indexes. In Fig. 7C, as the reliability index, the flags of "VSM" and "L-index" are set to 1. That is, this shows a case where two reliability indexes are used.

[0044] FIG. 7D is a diagram showing an example of setting weighting coefficients for reliability indexes used in the severe forecast power flow profile generating unit 203 (FIG. 2) from among a plurality of reliability indexes. In Fig. 7D, "VSM" and "L-index" are selected as the reliability indices, and their respective weighting factors are set to "0.7" and "0.3." In addition, all other reliability indices are set to 0. According to the weighting coefficients of the plurality of reliability indexes thus set, calculations are performed in the severe forecast power flow profile generating unit 203 (FIG. 2).

[0045] <Prediction Information D6> An example of the prediction information D6 is shown in FIG. FIG. 8 is a diagram showing power-related prediction information for a time ahead of the date and time of the measured cross section. In Fig. 8, there are items for "target date and time", "equipment", "type", "active power (MW)", and "reactive power (Mvar)". In addition, for "active power (MW)" and "reactive power (Mvar)", there are items for "predicted value" and "uncertainty range".

[0046] The date and time of the measurement cross sections in Figures 4A, 4B, 5A, and 5B is 11:30 a.m. on February 16, 2021. In Figure 8, the target dates and times for future predictions 10 minutes, 20 minutes, and 30 minutes from the above time are recorded as "11:40," "11:50," and "12:00" on February 16, 2021. Note that the time interval of the prediction information is merely an example and is not limited to this.

[0047] Let us focus on the forecast information for 11:40 on February 16, 2021, shown in Figure 8. In FIG. 8, the prediction information D6 includes a predicted value and an uncertainty range for the equipment requiring prediction. Facilities that require prediction are those that the grid operator cannot directly control, such as renewable energy power generation (operated by a different business than the grid operator) and demand (electricity demand). In Fig. 8, the generator G3 and the loads L1 to L3 in the power system shown in Fig. 3 are the targets. If necessary, the type of each facility is specified as shown in the figure. In addition, the generator G3 is classified as renewable energy generation, and the loads L1 to L3 are classified as demand.

[0048] In the case of renewable energy generation, the generator's active power output varies depending on the weather, so a predicted value and uncertainty range are specified. The uncertainty range may be given as a plus or minus (±) range as shown in FIG. 8, or may be derived using variance, etc., when the predicted value is expressed as a probability distribution. On the other hand, it is assumed that some control is performed on the reactive power output, and it is excluded from the prediction in Fig. 8. The some control is constant reactive power output control, constant power factor control, constant voltage control, etc.

[0049] When the reactive power output is constant or correlated with other state quantities, there is no need to predict it independently, and it is therefore excluded from the scope of prediction. In Fig. 8, it is assumed that the reactive power output is always a constant value due to constant reactive power output control. For that reason, both the "predicted value" and "uncertainty range" in the reactive power column in Fig. 8 are marked with "-", and it is excluded from the scope of prediction.

[0050] In addition, since the loads (equipment) L1 to L3 are demands, they have predicted values ​​and uncertainty ranges for both active power and reactive power. If reactive power has a strong correlation with other state quantities such as active power and prediction is unnecessary, it may be excluded from the prediction. Note that in Fig. 8, the case where the power flows into the power system is considered positive, but other expressions may be used.

[0051] <Variable / constant type D7> An example of the variable / constant type D7 is shown in Figure 9. FIG. 9 is a diagram showing a list of the "facility" used in the severe forecast current profile generating unit 203, the "type" that specifies the variables and constants of the characteristics of the facility, and its "constraints." In Figure 9, it is specified whether the voltage magnitude of each node and the active power and reactive power of each piece of equipment connected to the node are variables or constants. Note that line constants and other items that are clearly used as constants do not need to be included in the items of Figure 9.

[0052] We will further explain the "constant" and "variable" items in the "Type" section of Figure 9. In electrical characteristics, a specified value is treated as a "constant", and a variable is treated as a "variable". For example, the "voltage magnitude" of node #1 and node #2 is set as a constant. This is because non-renewable energy generators are connected to node #1 and node #2. The "voltage magnitude" of the node to which the generator is connected is kept constant by voltage control by the generator, so it is treated as a constant. On the other hand, the "voltage magnitude" in other nodes is set as a variable. The reason is that, unlike node #1 and node #2, the "voltage magnitude" fluctuates with fluctuations in demand and renewable energy output.

[0053] For active power, the generators G1 to G3 and the loads L1 to L3 are all treated as variables. The generator G3, which is a renewable energy generator, and the loads L1 to L3 contain uncertainties, and are therefore treated as variables to explore severe power flow cross sections within the range of uncertainty. On the other hand, generators G1 and G2 are generators that do not generate renewable energy, and since their output can be controlled in normal system operation, there is no need to predict it. Nevertheless, they are treated as variables in Figure 9 because when a supply-demand imbalance occurs when generating the severe forecast current cross section, the imbalance needs to be compensated for by these controllable generators.

[0054] Regarding reactive power, only generator G3 is treated as a constant, and all others are treated as variables. The reason why loads L1 to L3 are treated as variables is the same as for active power. The generators G1 to G2 are treated as variables because the reactive power output changes in order to keep constant the voltage magnitude of the bus to which the generator is connected. The generator G3 is treated as a constant because the output is a constant value due to the constant reactive power output control. If the control is not the constant reactive power output control but the constant power factor control or other control that has a correlation with other state quantities, it is treated as a variable, and the correlation is added as a constraint.

[0055] The item "constraint" in FIG. 9 will be explained. The output of non-renewable energy generators is controlled based on certain rules, and these rules must be considered as constraints. Examples of such rules include economic load dispatch and proportional allocation (Pro rata) that is proportional to the output change rate. Here, we assume that the proportional division (Pro rata) is used, and the output change amount of the generator G2 is always half the output change amount of the generator G1. In other words, the constraint of the equipment G2 in FIG. 9 is "ΔG2=0.5×ΔG1". Furthermore, in the case of economical load dispatch rather than proportional distribution (pro rata), a constraint that the incremental fuel costs of generators G1 and G2 are equal is required in place of the above constraint.

[0056] <Specific example of processing in the severe forecast current profile generator> The severe forecast power flow cross section generating unit 203 in FIG. 2 generates a severe forecast power flow cross section D8 using system parameters D3, state estimation cross section D4, reliability index D5, prediction information D6, and variable / constant type D7. A specific example of the processing in the severe forecast current profile generating unit 203 is shown in FIG.

[0057] <Flowchart of processing in the severe forecast current profile generator> Next, the processing in the severe forecast power flow profile generating unit 203 shown in FIG. 2 will be described with reference to the flowchart of FIG. FIG. 10 is a diagram showing an example of a flowchart of the strict forecast power flow cross section generation process of the strict forecast power flow cross section generation unit 203 of the power system monitoring device 1 according to the first embodiment of the present invention.

[0058] <Outline of the flowchart for generating severe forecast current profiles> First, an outline of a flowchart of the severe forecast current profile generation process will be described with reference to Fig. 10. Details of each step shown in Fig. 10 will be described later with reference to Figs. 11A, 11B, 12A, 12B, and 13.

[0059] <Step S1001> In step S1001 denoted as "read data" in FIG. 10, each data of the system parameters D3, the state estimation cross section D4, the reliability index D5, the prediction information D6, and the variable / constant type D7 is read.

[0060] <Step S1002> Next, in step S1002 marked "variable and constant setting," variables and constants to be used in the search are set based on the data that has been read in. Details of "variable and constant setting" will be described later.

[0061] <Step S1003> Next, constraints are set in step S1003, which is described as "constraint setting." Details of "constraint setting" will be described later.

[0062] <Step S1004> Next, an objective function is set in step S1004, which is denoted as "objective function setting." Details of the "objective function setting" will be described later.

[0063] <Step S1005> Next, in step S1005, which is indicated as "search execution", a solution that achieves the objective is searched for using the objective function, variables, constants, and constraints that have been set up to this point. Details of "search execution" will be described later.

[0064] <Step S1006> Finally, in step S1006, the solution obtained in step S1005 is set as the severe forecast current cross section D8, and the value of the reliability index at that time is output as a reliability index value D9.

[0065] <Details of "Step S1002" "Variable / Constant Setting"> An example of setting variables and constants in the "variable / constant setting" in step S1002 (FIG. 10) will be described in detail with reference to FIGS. 11A and 11B.

[0066] FIG. 11A is a diagram showing an example of setting the "constants" used in the search. The "constants" include, first, the impedance of the system equipment included in the system parameters D3, and the values ​​thereof are the same as those specified in the system parameters D3. For example, the parameter r in FIG. 14 , x 14 , b 14 etc. In addition, "r 14 ", "x 14 ", "b 14 " are the resistance, reactance, and susceptance between nodes #1 and #4, respectively. Note that each unit is the standard unit (pu).

[0067] Furthermore, parameters set as constants in the variable / constant type D7 are also included. For example, “|V1|”, “|V2|”, and “Q G3 " " and "|V1|" and "|V2|" are the absolute values ​​of the voltages at nodes #1 and #2, respectively. G3 " is the reactive power of renewable energy power generation (renewable energy generator) G3. For these constants, the values ​​of the state estimation cross section D4 are used.

[0068] FIG. 11B is a diagram showing an example of setting the "variables" used in the search. The "variables" include the index value specified by the reliability index D5. For example, Δθ max It is. Furthermore, the parameters set as variables in the variable / constant type D7 are also included. For example, |V3| (pu) related to the voltage of the renewable energy power generation G3.

[0069] Furthermore, among the parameters set as variables, if a predicted value and an uncertainty range are specified in the prediction information D6, these are used to specify the initial value and upper and lower limits of the variable. The initial value may not be a predicted value, but may be a value of the state estimation cross section D4, or may be determined by another method. In FIG. 11B, for example, P L1is a parameter related to the active power demand in the load L1, and is set as a variable. For this variable, the initial value is set to -105, the upper limit value is set to -102, and the lower limit value is set to -108 using the predicted value.

[0070] For variables that are not specified in the prediction information D6, for example, the value of the state estimation section D4 is used as the initial value. As the upper and lower limits, the upper and lower limits of the equipment itself are set. For example, P G1 is a parameter related to the active power output of the generator G1, which is set as a variable. For this variable, the predicted value is set to 72.8 using the value of the state estimation section D4, and the upper and lower limits are set to 100 and 20 using the upper and lower output limits included in the system parameter D3.

[0071] <Details of "Step S1003" "Constraint Setting"> Next, constraints are set in step S1003 (FIG. 10). For variables for which upper or lower limits have been set in step S1002, first, a constraint equation that defines the upper and lower limits of the variable is set, as in the following equation (1).

[0072]

number

[0073] where a is a variable, a min is the lower limit of the variable, a max represents the upper limit of the variable. Furthermore, the constraints must include a power flow equation for the power system and an equation that specifies the index value specified as the reliability index D5. Moreover, the power flow equation of the power system is set, for example, as shown in the following equation (2).

[0074]

number

[0075] Here, "dot over V" is a complex vector that represents the voltage of each bus in the forecast power flow section, and "dot over Y" is a complex vector that represents the voltage of each bus in the forecast power flow section. bus " is the admittance matrix in the power system, and "dot-over-S" is a complex vector representing the amount of current flowing in and out of each bus in the predicted power flow cross section. Note that "[dot over V]" is a diagonal matrix with "dot over V" as the physique element. Also, "dot over Y bus and the upper bar of the product of the dot over V' is the dot over Y bus and represents the complex conjugate of the product of dot over V'.

[0076] The constraints on the reliability index are as follows: Maximum voltage phase difference (Δθ max ) is set as shown in the following equation (3).

[0077]

number

[0078] In equation (3), θ is a real vector representing the voltage phase at each bus. max(θ) is the maximum value of the bus voltage phase, and min(θ) is the minimum value of the bus voltage phase. By taking the absolute value (abs) of these differences, the maximum voltage phase difference (Δθ max ) can be obtained. The squared value may be used instead of the absolute value. The above formula (3) is merely an example, and it is not necessary to follow this formula.

[0079] <Details of "Step S1004" "Objective function setting"> Next, in step S1004 (FIG. 10), an objective function is set. In this example, the maximum voltage phase difference is used as a reliability index, so the maximum voltage phase difference (Δθ max ) is the objective function.

[0080]

number

[0081] <Details of "Step S1005" "Search execution"> In step S1005 (FIG. 10), a solution that achieves the objective is searched for using the objective function, variables, constants, and constraints set in each process (step) above. The search algorithm may be a mathematical programming interior point method, or a highly versatile (meta-heuristic) method such as Particle Swarm Optimization (PSO).

[0082] <Details of "Step S1006" "Data Output"> An example of the output of the solution obtained in the "data output" of step S1006 (FIG. 10) will be described in detail with reference to FIGS. 12A, 12B and 13. FIG. In the final step S1006, the solution obtained in step S1005 is output as the severe forecast current cross section D8, and the value of the reliability index at that time is output as the reliability index value D9. Fig. 12A shows an example of the severe forecast power flow cross section D8 (node), Fig. 12B shows an example of the severe forecast power flow cross section D8 (branch), and Fig. 13 shows an example of the reliability index value D9.

[0083] FIG. 12A is a diagram showing an example of severe forecast current cross section D8 (node) of the obtained solution output in "Data output" (FIG. 10, S1006). The severe forecast power flow cross section D8 (node) in Figure 12A includes the items "node," "voltage magnitude," "voltage phase," "active power," and "reactive power," which are the same items as the state estimation cross section D4 (node) shown in Figure 5A. However, while the "date and time" in Figure 5A is "2021 / 2 / 16 11:30," the "date and time" in Figure 12A is "2021 / 2 / 16 11:40," and the value predicted (forecasted) for 10 minutes later is listed.

[0084] FIG. 12B is a diagram showing an example of the severe forecast current cross section D8 (branch) of the obtained solution output in "Data output" (FIG. 10, S1006). The severe forecast power flow cross section D8 (branch) in Figure 12B includes the items "Node (From, To)", "Active power flow (From, To)", and "Reactive power flow (From, To)", which are the same items as the state estimation cross section D4 (branch) shown in Figure 5B. However, while the "Date and Time" in Figure 5B is "2021 / 2 / 16 11:30," the "Date and Time" in Figure 12B is "2021 / 2 / 16 11:40," and the value shown is a forecast (prediction) of the value 10 minutes later.

[0085] As shown in Figures 12A and 12B, the severe forecast current cross section D8 (nodes, branches) includes the same items as the state estimation cross section D4 (nodes, branches). However, it is not necessary to include all of these items; at the very least, it is sufficient to include enough information to reproduce this power flow cross section by power flow calculation.

[0086] FIG. 13 is a diagram showing an example of the reliability index value D9 of the obtained solution output in "Data output" (FIG. 10, S1006). The reliability index value D9 includes the values ​​of the reliability indexes (FIGS. 7A, 7B, 7C, and 7D) in the severe forecast tidal flow cross section. In the example shown in FIG. 7A and FIG. 7B, the maximum voltage phase (Δθ max ) was specified as a reliability index with the "flag" set to "1" and the "weighting factor" set to "1.0". max ) is included in the reliability index value D9.

[0087] In FIG. 7A, the reliability index is calculated based on the maximum voltage phase (Δθ max ) is set to "1", but the present invention is not limited to this example. For example, if multiple reliability indices are set and weighting coefficients are set for each of the multiple items, the index value after weighting and summing may be included, as well as individual index values ​​before weighting.

[0088] As explained above, when generating a forecast power flow cross section, by adding conditions that are severe in terms of the reliability index specified by the user (system operator), it is possible to narrow down in advance the cross sections that should be considered when planning operation measures for the power system.

[0089] <Summary of the First Embodiment> In the power system monitoring device of the first embodiment, when generating a predicted power flow cross section, a condition that is severe from the viewpoint of a reliability index specified by a user (system operator) is added, thereby making it possible to narrow down in advance the cross sections to be considered when planning operational measures assuming future power flow conditions in the power system. Furthermore, when considering combinations with "anticipated faults" explained in a second embodiment described later, it is sufficient to verify a relatively small number of cases, and therefore control can be performed in shorter cycles than in the past. Furthermore, when the system operator determines the operation policy, the burden on the system operator can be reduced because there are fewer power flow cross sections to be considered.

[0090] <Advantages of the First Embodiment> According to the first embodiment of the present invention, it is possible to provide a power system monitoring device and a monitoring method thereof that estimate and extract future power flow conditions that will be severe in terms of the reliability of the power system based on a reliability index specified by a user (system operator).

[0091] Second Embodiment The configuration of a power system monitoring device according to a second embodiment of the present invention will be described with reference to Fig. 14 and Fig. 15. The second embodiment shows a case in which contingent faults are also taken into consideration in generating a severe forecast power flow cross section.

[0092] FIG. 14 is a diagram showing a configuration example of a power system monitoring device 1B according to the second embodiment of the present invention and its relationship with a power system 103. The difference between the power system monitoring device 1B of the second embodiment shown in FIG. 14 and the power system monitoring device 1 of the first embodiment shown in FIG. 2 is that the power system monitoring device 1B in FIG. 14 is provided with a contingent fault case database DB7. Except for the provision of the contingent fault case database DB7, the power system monitoring device 1B in FIG. 14 has the same configuration as the power system monitoring device 1 in FIG. 2, so a duplicated description will be omitted. However, in FIG. 14, the respective reference numerals are newly written as power system monitoring device 1B and severe forecast power flow profile generating unit 203B.

[0093] As described above, in FIG. 14, the power system monitoring device 1B is provided with the contingent fault case database DB7. The contingency case database DB7 stores a contingency case D10. The stored contingency case D10 is an additional input to the severe foreseeable power flow cross section generator 203B, which also references the contingency case D10 to generate the severe foreseeable power flow cross section. The contingency case D10 includes, for example, a list of conditions assuming a case where one of the facilities on the power system stops for some reason, which is generally called an N-1 contingency.

[0094] It is not necessary to be limited to N-1 anticipated faults, but may include all anticipated faults that need to be considered in system operation. In the first embodiment, only the power system under normal conditions is the subject of the study, but in the second embodiment, the power system under normal conditions and when a contingent fault occurs are also taken into consideration.

[0095] <Flowchart of processing in the severe forecast power flow profile generator when a contingency occurs> Next, the process of generating a severe forecast power flow cross section including a contingent fault by the severe forecast power flow cross section generating unit 203B shown in FIG. 14 will be described with reference to the flowchart in FIG. In the example shown in the flowchart in Figure 15, a severe predicted power flow cross section is generated for each system state in which a fault is assumed to have occurred. Finally, a method is considered for extracting the severest cross section from among these.

[0096] FIG. 15 is a diagram showing an example of a flowchart of strict forecast power flow cross section generation processing by the strict forecast power flow cross section generation unit 203B of the power system monitoring device 1B according to the second embodiment of the present invention.

[0097] The flowchart of the process of generating a severe forecast power flow cross section including a contingent fault in FIG. 15 includes steps S1001 to S1006, step S1012, step S1056, and step S1057. Steps S1001 to S1006 in FIG. 15 are generally the same as steps S1001 to S1006 in FIG. 10 described above, and therefore overlapping descriptions will be omitted where appropriate. 15 differs from the example of processing shown in Fig. 10 in that "setting of contingent fault cases" is provided as step S1012 between steps S1001 and S1002, and that a determination step of "have all contingent fault cases been performed?" as step S1056 and a process (step) of "extracting severe sections" as step S1057 are provided between steps S1005 and S1006. This added step is described next.

[0098] <Step S1012> 15, a contingent fault case is set as "setting of contingent fault case". That is, since there are various contingent fault cases, a predetermined contingent fault case is selected from a plurality of contingent fault cases and set. Then, the process proceeds to the next step S1002 for this set assumed fault case. Explanation of step S1002 will be omitted to avoid duplication.

[0099] <Step S1056> In step S1056 of FIG. 15, following "search execution" in step S1005, it is determined whether "all contingent fault cases have been executed?". If all the assumed fault cases have been performed (Yes), the process proceeds to step S1057. If all the contingency cases have not been performed (No), the process returns to step S1012, "setting of contingency cases." Incidentally, by providing step S1012 and step S1056, the processes from step S1002 to step S1005 are performed multiple times for each contingent fault case.

[0100] <Step S1057> Step S1057 in FIG. 15 is a process (step) of "harsh cross section extraction." In steps S1002 to S1005, a severe forecast power flow section is generated for each contingency case, and in step S1057, the most severe forecast power flow section among all the contingency cases executed is extracted. This process (step) makes it possible to generate a severe forecast power flow section that takes contingency cases into account. After step S1057, the process proceeds to step S1006. As described above, step S1006 is the data output process (step). A duplicated description will be omitted.

[0101] <Effects of the second embodiment> In the first embodiment, only the power system under normal conditions is the subject of the study, but in the second embodiment, the power system under normal conditions and when a contingent fault occurs are also taken into consideration. Therefore, in the second embodiment, it is possible to provide a power system monitoring and control device that can handle even severer conditions, taking into consideration the occurrence of anticipated faults.

[0102] Third Embodiment A method of generating a strict forecast power flow cross section that takes into account both uncertainties and contingent faults in the strict forecast power flow cross section generating unit 203B (FIG. 14) of the power system monitoring device according to the third embodiment of the present invention will be described.

[0103] In the second embodiment, a case was described in which contingent faults are also taken into account when generating the severe foreseeable power flow cross section. In the third embodiment, however, a method is implemented in which a severe foreseeable power flow cross section that takes into account both uncertainties and contingent faults is generated at once using the configuration shown in Figure 14 used in the second embodiment.

[0104] For example, consider a parameter b, which takes the value b0 under normal circumstances and the value b1 in the event of an anticipated fault. In this case, the value of the parameter b is the binary variable x b Using the above, it can be expressed as the following formula (5). In formula (5), x is a binary variable x b It supports.

[0105]

number

[0106] Binary variable x b can only take on the value 0 or 1. Therefore, in equation (5), the value of the parameter b is determined by the binary variable x b When (=x) is 0, normal b0, binary variable x b If is 1, the value of b1 at the time of the contingency is applied. In this way, for each parameter, we set variables and constraints that represent the values ​​during normal operation and during expected failures, and then we set the binary variable x b By imposing the constraint of the following equation (6) on the total, it is possible to prevent multiple contingency faults from being taken into account.

[0107]

number

[0108] In this way, by solving a problem in which binary variables and constraints related to expected faults are added, it is possible to generate a severe forecast power flow cross section that takes expected faults into account. In this way, by generating severe forecast power flow cross sections that take into account both uncertainties and anticipated faults at once, the burden on system operators in formulating operational strategies can be further reduced.

[0109] <Effects of the third embodiment> It is possible to provide a power system monitoring and control device that generates a severe predictive power flow cross section that takes into account both uncertainties and anticipated faults. In addition, the burden on system operators in formulating operational strategies can be reduced.

[0110] Fourth Embodiment The configuration of a power system monitoring device according to a fourth embodiment of the present invention will be described with reference to Fig. 16 and Fig. 17. In the fourth embodiment, a case will be described in which a reliability index that is difficult to directly calculate in the severe forecast power flow cross section generating unit is used.

[0111] FIG. 16 is a diagram showing a configuration example of a power system monitoring device 1C according to a fourth embodiment of the present invention and its relationship with a power system 103. The difference between the power system monitoring device 1C of the fourth embodiment shown in FIG. 16 and the power system monitoring device 1B of the second embodiment shown in FIG. 14 is that the power system monitoring device 1C in FIG. 16 is provided with a reliability evaluation unit 1601. A configuration will be described for the case where a reliability index that is difficult to directly calculate in the severe forecast power flow profile generator is used. When an index value for a reliability index cannot be directly calculated in the severe forecast power flow cross section generator, a reliability evaluation unit 1601 for evaluating reliability is additionally provided in the power system monitoring device 1C. Then, the evaluation of the reliability evaluation unit 1601 is incorporated into the severe forecast power flow cross section generator 203C.

[0112] In this case, the constraint equation regarding the reliability index is not directly taken into consideration in the power flow cross section search in the severe forecast power flow cross section generation unit 203C. Instead, the reliability evaluation unit 1601 evaluates the index value (provisional) D12 for the current cross section (provisional) D11, which is the search point generated by the severe forecast current cross section generation unit 203C, and executes a process of returning the index value (provisional) D12 to the severe forecast current cross section generation unit 203C.

[0113] <Flowchart of processing using reliability evaluation in the severe forecast power flow profile generator> Next, the processing in the severe forecast power flow profile generating unit 203C shown in FIG. 16 will be described with reference to the flowchart in FIG. FIG. 17 is a diagram showing an example of a flowchart of the process flow of the severe forecast power flow profile generating unit 203C and the reliability evaluating unit 1601 of the power system monitoring device 1C according to the fourth embodiment of the present invention.

[0114] <Steps S1001 to S1004> In Fig. 17, steps S1001 to S1004 for setting an objective function are similar to the steps of the first embodiment shown in Fig. 10 or the steps of the second embodiment shown in Fig. 15. Duplicate explanations will be omitted.

[0115] <Steps S1701 to S1703> Steps S1701 to S1703 in FIG. 17 will be described in order.

[0116] <Step S1701> In the flowchart of the first embodiment shown in FIG. 10, a search is performed in step S1005 after “objective function setting” in step S1004. However, in the fourth embodiment shown in FIG. 17, an index value that cannot be directly calculated is used in the search. Therefore, in the flowchart of the fourth embodiment shown in Figure 17, in the "search point setting / update" step S1701 after step S1004, the severe forecast current cross section generation unit 203C (Figure 16) calculates the current cross section (provisional) D11 (Figure 16) as a search point. Then, proceed to step S1702.

[0117] <Step S1702> Next, in step S1702 "perform reliability evaluation", the reliability evaluation unit 1601 (FIG. 16) evaluates reliability based on the tidal current cross section (provisional) D11 (FIG. 16). The result is then returned to the severe forecast current profile generating unit 203C (FIG. 16) as the index value (provisional) D12 (FIG. 16). Then, proceed to step S1703.

[0118] <Step S1703> In step S1703 "convergence determination", it is determined whether the change in the index value (provisional) D12 (FIG. 16) falls below the threshold, that is, whether convergence has occurred. If "convergence" has occurred, proceed to step S1006. If convergence has not occurred, that is, if "non-convergence" has occurred, the process returns to step S1701.

[0119] <<Additional information on steps S1701 to S1703>> A supplementary explanation will be given regarding the loop process of steps S1701 to S1703. In step S1701, search points are set and updated in various ways, their reliability is evaluated in step S1702, and the result is returned to the severe forecast tidal flow cross section generator 203C (Fig. 16) as index value (provisional) D12 (Fig. 16). The processes and judgments of steps S1701, S1702, and S1703, including the "convergence judgment" of step S1703, are repeatedly executed, and when the change in index value (provisional) D12 (Fig. 16) falls below a threshold value, it is deemed that convergence has occurred, and that the optimal search point has been obtained. If it is determined that convergence has occurred, the process proceeds to step S1006.

[0120] <Step S1006> In "data output" in step S1006, the converged calculation result is output as data in response to the "convergence" determination in step S1703.

[0121] <<Supplementary information on the configuration in FIG. 16 and the flowchart in FIG. 17>> In this way, by combining and operating the severe forecast current profile generator 203C and the reliability evaluator 1601 in Figure 16, it becomes possible to set indicators relating to dynamic characteristics, which are difficult to calculate directly, as reliability indicators. In addition, the method of using the configuration shown in FIG. 16 to obtain the processing flow (flowchart) shown in FIG. 17 can also be interpreted as a configuration and method for finding a severe forecast current cross section as an optimization problem with the reliability index value as an objective function.

[0122] <Effects of the Fourth Embodiment> It is also possible to set indices relating to dynamic characteristics, which are difficult to calculate directly, as reliability indices, thereby providing a power system monitoring and control device that can predict (predict) the conditions of the power system more reliably and severely.

[0123] Other embodiments The present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to the embodiment having all of the configurations described. It is also possible to replace a part of the configuration of one embodiment with a part of the configuration of another embodiment, and it is also possible to add, delete, or replace a part or all of the configuration of another embodiment to the configuration of one embodiment. Other embodiments and modifications will be further described below.

[0124] <<Generation of state estimation cross section D4>> Although it has been described that the state estimation cross section D4 is generated by the calculation of the state estimation unit 202 in FIG. 2, the generation method is not limited to this. For example, the process from acquiring the measurement value D1 from the input unit 201 to generating the state estimation cross section D4 may be performed outside the power system monitoring device 1, in which case the state estimation cross section D4 may be acquired directly from the input unit 201.

[0125] 《Generation of severe predictive current profiles by combining expected faults, uncertainties, and reliability assessment》 In the second embodiment, the process of generating a severe forecast power flow profile including a contingency fault has been described with reference to Figs. In the third embodiment, a method for generating a severe forecast power flow cross section taking into account both uncertainties and contingency faults has been described. In the fourth embodiment, a processing method using reliability evaluation in the severe forecast current profile generating unit has been described.

[0126] However, the processing method for generating these severe forecast current profiles is not limited to the above-mentioned methods. The processing methods for generating severe foreseeable power flow cross sections of the second, third and fourth embodiments may be combined. For example, when the flowchart shown in Fig. 15 is combined with the flowchart shown in Fig. 17, the configuration of the flowchart becomes complicated, so description thereof is omitted, but it is possible to generate severe foreseeable power flow cross sections by combining contingency faults, uncertainty and reliability evaluation. By using this severe forecast power flow profile generation unit that combines expected faults, uncertainty, and reliability evaluation, it is possible to provide a power system monitoring device and method that can forecast (predict) future power flow conditions that will be severe in terms of the reliability of the power system.

[0127] <<Storage location of various databases>> 1 and 2 showing the configuration of the first embodiment, an example has been described in which various databases are provided in the auxiliary storage device 16 in the power system monitoring device 1. However, the present invention is not limited to this example. For example, for various databases, the power system monitoring and control device may not own the storage device (auxiliary storage device) that stores these databases, but the databases may be virtually owned by the power system monitoring and control device, such as existing on the cloud. [Explanation of symbols]

[0128] 1,1B,1C Power system monitoring and control device 11 Central control unit 12 Input Devices 13 Output Devices 14 Display device 15 Main memory 16 Auxiliary storage 17 Databases (various databases) 18 Programs (Various Programs) 19 Bus 101 Communication lines 102 Measuring Instruments 103 Power system 201 Input section 202 State Estimation Unit 203, 203B, 203C Severe forecast current profile generator 204 Display section 205 Output section 311,312,313 Transformers 345,349,356,367,378,389 Power lines 1601 Reliability Evaluation Department DB1 Measurement database DB2 system parameter database DB3 State Estimation Cross-Section Database DB4 Predictive Information Database DB5 Variable / Constant Type Database DB6 Severe Prediction Current Profile Database DB7 Contingency Case Database D1 Measurement Value D2 Measurement section D3 System parameters D4 State Estimation Section D5 Reliability Index D6 Prediction Information D7 Variable / constant type D8 Severe forecast current cross section D9 Reliability Index Value D10 Expected failure cases D11 Tidal flow cross section (tentative) D12 Index value (provisional) G1, G2 generators G3 generator (renewable energy generator, renewable energy generator, renewable energy power generation) L1,L2,L3 load Nodes #1 to #9

Claims

1. A power system monitoring device that estimates a future power flow state of a power system, An input unit for inputting a reliability index which is an index representing reliability when assuming a state of a power system; a system parameter database for storing system parameters of the power system; a state estimation section database for storing state estimation sections generated by a state estimation calculation of a power system; a forecast information database for storing forecast information on power supply and demand in a power grid; a variable / constant type database for storing a variable / constant type that sets whether a factor of the power system varies or is constant; a severe forecast current flow profile generating unit that generates a severe forecast current flow profile and a reliability index value that is an index value for the reliability index using the reliability index, the system parameters, the state estimation profile, the forecast information, and the variable / constant type; The severe forecast power flow cross section generating unit sets a provisional power flow cross section and includes a reliability evaluation unit that calculates a provisional index value using the provisional power flow cross section, The reliability evaluation unit evaluates the provisional index value, and sets the provisional index value that satisfies a condition for judgment in the evaluation as the reliability index value to be displayed on a display unit or output to the outside. A power system monitoring device comprising:

2. In claim 1, The input unit inputs a measurement value of the power system measured by a measuring instrument, a measurement value database that stores the measurement values ​​as measurement cross sections; a state estimation unit that calculates a state estimation cross section by using the measurement cross section and the system parameters; Equipped with storing a state estimation section generated by the state estimation calculation in the state estimation unit in the state estimation section database; A power system monitoring device comprising:

3. In claim 1 or 2, A contingency case database for storing contingency cases is provided, the severe forecast power flow cross section generating unit generates the severe forecast power flow cross section and the reliability index value by referring to the contingency fault cases; A power system monitoring device comprising:

4. In claim 1, The forecast information comprises a forecast value and an uncertainty range. A power system monitoring device comprising:

5. In claim 1, The variable / constant type is set by dividing the electrical characteristics into variables and constants in the severe forecast current section. A power system monitoring device comprising:

6. In claim 1, A severe forecast current cross section is obtained as an optimization problem with the reliability index value as an objective function. A power system monitoring device comprising:

7. A power system monitoring method for a power system monitoring device that estimates a future power flow state of a power system, comprising: The control device of the power system monitoring device includes: Using a reliability index, system parameters, a state estimation section, forecast information, and variable / constant types in the power system, a severe forecast current section and a reliability index value which is an index value for the reliability index are generated; A provisional power flow section is set, and a provisional index value is calculated using the provisional power flow section; The determined provisional index value is evaluated, and the provisional index value that satisfies the judgment condition in the evaluation is set as the reliability index value to be displayed on a display unit or output to the outside. A power system monitoring method comprising:

8. In claim 7, The control device generates the severe forecast power flow cross section and the reliability index value using contingency cases. A power system monitoring method comprising:

9. In claim 7, The forecast information comprises a forecast value and an uncertainty range. A power system monitoring method comprising:

10. In claim 7, The control device sets the variable / constant type by type, in the severe forecast current cross section, to treat electrical characteristics as variables and treat electrical characteristics as constants. A power system monitoring method comprising:

11. In claim 8, The control device calculates a severe forecast current cross section as an optimization problem having the reliability index value as an objective function. A power system monitoring method comprising:

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