Facility renewal priority calculation device, facility renewal priority calculation system, facility renewal priority calculation methold, and program

Abstract

To provide a facility renewal priority calculation device capable of reflecting a system influence degree in a non-average status in a priority calculation.SOLUTION: A facility renewal priority calculation device for calculating priority of facility renewal of a power system includes: a model generation unit for generating a probability distribution model of power demand from power demand in each of a plurality of time points, and generating a probability distribution model of photovoltaic power generation output from photovoltaic power generation output in each of a plurality of time points; a false data generating unit for generating false data of the power demand from the probability distribution model of the power demand and generating false data of the photovoltaic power generation output from the probability distribution model of the photovoltaic power generation output; and a priority calculation unit for calculating the priority of the facility renewal of the power system by using the false data of the power demand and the false data of the photovoltaic power generation output.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an equipment renewal priority calculation device, an equipment renewal priority calculation system, an equipment renewal priority calculation method, and a program.

Background Art

[0002] In recent years, general power transmission and distribution operators are required to plan and efficiently implement investments for modernizing the power grid, such as "decarbonization" and "resilience improvement", while reducing the maintenance and preservation costs of equipment due to the introduction of the revenue cap system (entrusted fee system).

[0003] Patent Document 1 discloses a priority calculation device that calculates the priority of renewing equipment in a power grid. This priority calculation device creates post-fault data indicating the grid configuration when the equipment to be evaluated fails, using the grid configuration information of the power grid. Then, the priority calculation device performs a power flow calculation using the post-fault data, and uses the degree of influence on other equipment obtained from the result of the power flow calculation as the priority of renewing the equipment to be evaluated.

[0004] Non-Patent Documents 1 and 2 disclose methods for estimating power demand and photovoltaic power generation output based on the residual demand obtained by subtracting the power generation by photovoltaic power generation (PV, Photovoltaic) from the consumption of consumers. Also, Non-Patent Documents 3 and 4 disclose calculation methods for probabilistic power flow, such as the Monte Carlo method and the arbitrary polynomial chaos expansion method.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Non-Patent Documents

[0006]

Non-Patent Document 1

Non-Patent Document 2

Non-Patent Document 3

Non-Patent Document 4

Summary of the Invention

Problems to be Solved by the Invention

[0007] However, in the priority calculation device described in Patent Document 1, the priority of facility renewal is calculated based on the average power demand and power generation output, and there is a problem that the system impact degree in a severe cross-section (that is, a non-average situation) is not reflected in the priority. When formulating facility renewal and facility formation plans, power companies have been conducting evaluations in severe cross-sections. For example, at the peak of power demand (load) that appears in summer (around August) or winter (around February), the power flow (power flow) value flowing through the power system (transmission line, distribution line) often reaches a peak (peak power flow). Therefore, conventionally, power companies have been conducting evaluations and considerations in these cross-sections. However, in recent years, since a large amount of renewable energy power sources (renewable energy), such as solar power generation and wind power generation (WF, Wind Farm), have been introduced (connected) to the power system, the fluctuations in power flow have become intense and complex. Under such circumstances, it has become difficult to grasp in which cross-section of the year a severe cross-section (peak power flow) appears.

[0008] The present disclosure has been made in view of such circumstances, and provides a facility renewal priority calculation device, a facility renewal priority calculation system, a facility renewal priority calculation method, and a program that can calculate a priority reflecting the system impact degree in a non-average situation.

Means for Solving the Problems

[0009] This invention has been made to solve the above-described problems. One aspect of the present disclosure is a facility renewal priority calculation device that calculates the priority of renewal of facilities in a power system, including a model generation unit that generates a probability distribution model of power demand from power demands at a plurality of time points and generates a probability distribution model of solar power generation output from solar power generation outputs at a plurality of time points, a pseudo-data generation unit that generates pseudo-data of power demand from the probability distribution model of power demand and generates pseudo-data of solar power generation output from the probability distribution model of solar power generation output, and a priority calculation unit that calculates the priority of renewal of facilities in the power system using the pseudo-data of power demand and the pseudo-data of solar power generation output.

[0010] Another aspect of the present disclosure is the facility renewal priority calculation device described above, wherein the model generation unit normalizes the photovoltaic power generation output at each of a plurality of time points using the extraterrestrial theoretical solar irradiance intensity at the corresponding time point, and generates a probability distribution model of the photovoltaic power generation output using the normalized photovoltaic power generation output.

[0011] Another aspect of the present disclosure is the facility renewal priority calculation device described above, wherein the model generation unit generates a probability distribution model of the photovoltaic power generation output using a statistical estimation method.

[0012] Another aspect of the present disclosure is the facility renewal priority calculation device described above, wherein the statistical estimation method is a non-parametric method.

[0013] Another aspect of the present disclosure is the facility renewal priority calculation device described above, wherein the model generation unit displays a screen for allowing a user to select a method for generating the probability distribution model of the photovoltaic power generation output.

[0014] Another aspect of the present disclosure is the facility renewal priority calculation device described above, wherein the pseudo data of the power demand and the pseudo data of the photovoltaic power generation output include a plurality of cases at each of a plurality of predetermined time points.

[0015] Another aspect of the present disclosure is a facility renewal priority calculation system for calculating the priority of renewal of facilities in a power system, comprising: a model generation unit that generates a probability distribution model of power demand from the power demand at each of a plurality of time points and generates a probability distribution model of photovoltaic power generation output from the photovoltaic power generation output at each of a plurality of time points; a pseudo data generation unit that generates pseudo data of power demand from the probability distribution model of power demand and generates pseudo data of photovoltaic power generation output from the probability distribution model of photovoltaic power generation output; and a priority calculation unit that calculates the priority of renewal of facilities in the power system using the pseudo data of power demand and the pseudo data of photovoltaic power generation output.

[0016] Another aspect of the present disclosure is the facility renewal priority calculation system described above, which includes a solar power generation output estimation unit that estimates the power demand and the solar power generation output from the remaining demand.

[0017] Another aspect of the present disclosure is a facility renewal priority calculation method for calculating the priority of renewing facilities in a power system, including generating a probability distribution model of power demand from power demands at a plurality of time points, and generating a probability distribution model of solar power generation output from solar power generation outputs at a plurality of time points; generating pseudo data of power demand from the probability distribution model of power demand, and generating pseudo data of solar power generation output from the probability distribution model of solar power generation output; and calculating the priority of renewing facilities in the power system using the pseudo data of power demand and the pseudo data of solar power generation output.

[0018] Another aspect of the present disclosure is a program for causing a computer to function as a facility renewal priority calculation device for calculating the priority of renewing facilities in a power system. The facility renewal priority calculation device includes a model generation unit that generates a probability distribution model of power demand from power demands at a plurality of time points, and generates a probability distribution model of solar power generation output from solar power generation outputs at a plurality of time points; a pseudo data generation unit that generates pseudo data of power demand from the probability distribution model of power demand, and generates pseudo data of solar power generation output from the probability distribution model of solar power generation output; and a priority calculation unit that calculates the priority of renewing facilities in the power system using the pseudo data of power demand and the pseudo data of solar power generation output.

Advantages of the Invention

[0019] According to this disclosure, the facility renewal priority calculation device, the facility renewal priority calculation system, the facility renewal priority calculation method, and the program can calculate a priority that reflects the system impact degree in a non-average situation.

Brief Description of the Drawings

[0020]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

[0021] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. FIG. 1 is a schematic block diagram showing the configuration of a facility renewal priority calculation system 10 according to an embodiment of the present disclosure. The facility renewal priority calculation system 10 calculates a system impact degree indicating the degree of influence that each facility has on the power system as the renewal priority of each facility in the power system in order to formulate a plan for renewing the facilities in the power system. Note that the system impact degree may be used as the priority for new installation of facilities. Each facility for which the priority is calculated is, for example, a transformer, a bus, a circuit breaker, a disconnector, a transmission line, a transmission line protection relay, an equipment protection relay, a stabilization system, a FACTS (Flexible AC Transmission System) device, a shunt reactor, a power capacitor, etc., but is not limited thereto, and any facility in the power system may be used.

[0022] The equipment renewal priority calculation system 10 includes a smart meter measurement system 100, a PV (Photovoltaic) power generation output estimation system 200, an equipment renewal priority calculation device 300, and an operation terminal device 400. Each of the smart meter measurement system 100, the PV power generation output estimation system 200, the equipment renewal priority calculation device 300, and the operation terminal device 400 is realized by one or more computers reading and executing a program. The smart meter measurement system 100, the PV power generation output estimation system 200, the equipment renewal priority calculation device 300, and the operation terminal device 400 are connected to a communication network 500 typified by an IP (Internet Protocol) network and can communicate with each other. Note that the communication network 500 may be realized by a technology that does not use IP and is not limited to this.

[0023] The smart meter measurement system 100 collects the actual power consumption values measured by smart meters installed at each customer. The power consumption measured by the smart meter is the power supplied from the power grid to the customer. Among the customers equipped with solar power generation (PV) facilities, for customers who have concluded a surplus power purchase contract (hereinafter referred to as surplus purchase customers. Also, the PV that is the subject of the surplus power purchase contract is referred to as surplus purchase PV), the residual demand (also referred to as "apparent demand" or "apparent load") obtained by subtracting the PV power generation output from the power demand (the power consumed, also referred to as "load") of the surplus purchase customer is transmitted to the smart meter measurement system 100. Also, among the customers equipped with solar power generation (PV) facilities, for customers who have concluded a full purchase contract (hereinafter referred to as full purchase customers. Also, the PV that is the subject of the full purchase contract is referred to as full purchase PV), the power demand and the PV power generation output of the full purchase customer are measured separately by the smart meter and transmitted to the smart meter measurement system 100.

[0024] The PV power generation output estimation system 200 estimates (separates) the power demand and the PV power generation output from the actual power consumption values collected by the smart meter measurement system 100. For this estimation, known methods can be used, such as the methods described in Non-Patent Document 1 or Non-Patent Document 2. In this embodiment, only the power demand of the full-purchase customer, the PV power generation output, and the residual demand of the surplus-purchase customer are measured by the smart meter, and an example is described in which the power demand and the PV power generation output of the surplus-purchase customer are estimated from the residual demand, but it is not limited to this form. For example, the power demand and the PV power generation output of the surplus-purchase customer may be measured and transmitted by an EMS (Energy Management System, such as HEMS (Home Energy Management System), BEMS (Building Energy Management System), FEMS (Factory Energy Management System), CEMS (Community Energy Management System), etc.) installed in the customer's home, and those information may be used as the power demand and the PV power generation output. Also, the PV power generation output is not measured by the smart meter or estimated from the smart meter measurement value, but is measured or estimated using meteorological observation data (for example, solar radiation intensity measurement data measured by a pyranometer or sunshine duration measured by AMeDAS (Automated Meteorological Data Acquisition System)) or satellite images (cloud images taken by a meteorological satellite), etc., and then transmitted, and those information may be used as the PV power generation output.

[0025] The equipment renewal priority calculation device 300 calculates the system impact degree indicating the degree of influence that each piece of equipment has on the power grid as the renewal priority of each piece of equipment in the power grid, using the estimated value of power demand estimated by the PV power generation output estimation system 200 and the estimated value of PV power generation output. As described above, the equipment renewal priority calculation device 300 may use the actual value of power demand and the actual value of PV power generation output instead of the estimated value of power demand and the estimated value of PV power generation output of each consumer, or may use the calculated value of power demand and the calculated value of PV power generation output by simulation or the like.

[0026] The operation terminal device 400 is a terminal device for operating the equipment renewal priority calculation device 300, and includes input means such as a keyboard, a mouse, or a touch panel, and output means such as a display or a speaker.

[0027] FIG. 2 is a schematic block diagram showing the configuration of the equipment renewal priority calculation device 300 in the present embodiment. The equipment renewal priority calculation device 300 includes a communication unit 301, a storage unit 302, an input reception unit 303, a probability and statistics model generation unit 304, a system setting unit 305, a pseudo-data generation unit 306, a post-failure system setting unit 307, a power flow calculation unit 308, a system impact degree calculation unit 309, and a renewal priority calculation unit 310.

[0028] The communication unit 301 communicates with each part of the equipment renewal priority calculation device 300 and the smart meter measurement system 100, the PV power generation output estimation system 200, or the operation terminal device 400 via the communication network 500. Further, the communication unit 301 transmits the renewal priority of equipment stored in the storage unit 302 to the operation terminal device 400 for display. The memory unit 302 stores system-related information 321, generator information 322, measurement / estimation data 323, operation limit values 324, and calculated data 325. The system-related information 321 indicates the configuration of the power system, etc. The generator information 322 indicates information about the generators connected to the power system, etc. The measurement / estimation data 323 is the power demand and PV power generation output obtained from the smart meter measurement system 100 or the PV power generation output estimation system 200, etc. The operation limit values 324 are the operation limit values of each facility constituting the power system, etc. The calculated data 325 is the data calculated by the facility renewal priority calculation device 300.

[0029] The input reception unit 303 receives the input of information from outside the facility renewal priority calculation device 300. The input reception unit 303 may acquire information by communication from other devices, may receive input via the keyboard, mouse, etc. of the operation terminal device 400 by an operator, etc., or may receive the input of information by reading information from a storage medium. The input reception unit 303 receives the input of the system-related information 321, the generator information 322, the measurement / estimation data 323, and the operation limit values 324, and stores them in the memory unit 302.

[0030] System-related information 321, generator information 322, and measurement / estimation data 323 are used to generate input data for the power flow calculation described later, and the operation limit value 324 is used to calculate the system impact degree. The system-related information 321 is information indicating the location, connection relationship (topology), failure probability, impedance, target system configuration (such as the open / closed state of circuit breakers, etc.), equipment to be evaluated, and post-fault system of each facility constituting the power system. The facilities constituting the system are, for example, transmission lines, transformers, buses, circuit breakers, disconnectors, transmission lines, transmission line protection relays, equipment protection relays, stabilization systems, FACTS (Flexible AC Transmission System) equipment, shunt reactors, power capacitors, etc., but are not limited thereto. Note that the system-related information 321 may be input in cooperation with databases such as the EMS (power supply and demand control system installed in the central power supply control center, etc.), SCADA (supervisory control system installed in the backbone power supply control center, power supply control station, etc., Supervisory Control And Data Acquisition), DAS (distribution automation system), and digital twin system of the power system via the communication network 500.

[0031] The generator information 322 is information indicating the rated capacity, connection location, operation constraints, power generation amount (active power, reactive power), etc. of the generators connected to the power system. These information may be input in cooperation with databases such as the EMS, SCADA, DAS, and digital twin system of the power system via the communication network 500. The measurement / estimation data 323 is information indicating the power demand generated by each consumer connected to the power system and the PV power generation output (solar power generation output). Note that the power demand and PV power generation output are the sum in unit intervals determined in advance (for example, at the bus of a substation, sectionalizer, pole-mounted transformer, etc.), but may also be the power demand and PV power generation output for each consumer. The same applies to the probability distribution model and pseudo-data of the power demand and PV power generation output described later.

[0032] The operation limit value 324 indicates the operation limit values of each facility constituting the system. The operation limit value 324 is determined based on, for example, the rated capacity, and may be the rated capacity itself or a value less than the rated capacity. Also, the rated capacity may adopt a dynamic rating and may vary depending on the date and time.

[0033] The probability statistical model generation unit 304 (model generation unit) generates a probability distribution model of the power demand from the power demands at a plurality of time points which are the measurement / estimation data 323. Also, the probability statistical model generation unit 304 generates a probability distribution model of the PV power generation output from the PV power generation outputs at a plurality of time points which are the measurement / estimation data 323. These probability distribution models may each be probability density functions generated from the frequency distributions of the power demand and the PV power generation output. For generating the probability density function from the frequency distribution, a statistical estimation method is used. As the statistical estimation method, an example of using a statistical probability density estimation method is shown. Specifically, it may be a parametric method assuming a distribution such as a normal distribution or a binomial distribution, a non-parametric method such as a kernel density estimation method that does not assume a specific distribution, or a machine learning method, but is not limited thereto.

[0034] Also, the probability statistical model generation unit 304 may normalize the PV power generation output at each of the plurality of time points using the extraterrestrial theoretical solar irradiance intensity at the corresponding time point, and generate a probability distribution model of the PV power generation output using the normalized PV power generation output. Also, for this process, the probability statistical model generation unit 304 may perform the process using the solar irradiance intensity instead of the PV power generation output itself, and convert (convert) it into the PV power generation output after considering the PV introduction amount and the PV power generation efficiency. Also, when the probability statistical model generation unit 304 converts (converts) the solar irradiance intensity into the PV power generation output, the methods described in Non-Patent Document 1 or Non-Patent Document 2 may be used.

[0035] The system configuration unit 305 configures the power system using the system-related information 321. More specifically, the system configuration unit 305 generates reference data corresponding to the input data for power flow calculation when no equipment failure in the power system is assumed. The configuration in the system configuration unit 305 may be performed by input via the operation terminal device 400 by an operator or the like, or may be performed by receiving the configuration content from an external device.

[0036] The pseudo-data generation unit 306 generates pseudo-data of power demand from the probability distribution model of power demand generated by the probability and statistics model generation unit 304, and generates pseudo-data of PV power generation output from the probability distribution model of PV power generation output generated by the probability and statistics model generation unit 304. Here, the pseudo-data of power demand is the sum of power demand in unit intervals with a predetermined interval as a unit, and the pseudo-data of PV power generation output is the sum of PV power generation output in unit intervals with a predetermined interval as a unit. Note that the power demand and PV power generation output are the sums in unit intervals with a predetermined interval (for example, substation busbars, sectional switches, pole-mounted transformers, etc.) as a unit, but may also be the power demand and PV power generation output for each consumer.

[0037] When the probability distribution model is the probability density function f(x), the pseudo-data generation unit 306 substitutes a random number between 0 and 1 (a random number following a uniform distribution on [0, 1]) or a predetermined value into u of the inverse function x = F -1 (u), and x = F -1 (u) may be used as pseudo-data. As a result, the pseudo-data becomes a value within the range indicated by the probability distribution model. Further, when the PV power generation output is normalized using the extraterrestrial theoretical solar irradiance, the pseudo-data generation unit 306 may use x = F -1 (u) multiplied by the extraterrestrial theoretical solar irradiance at the date and time of the pseudo-data to be generated as the pseudo-data of PV power generation output. Also, for the generation of pseudo-data, a method such as the MCMC (Markov Chain Monte Carlo) method may be used, and is not limited thereto.

[0038] After a failure, the system configuration unit 307 selects one piece of equipment to be the calculation target of the system impact degree from among the equipment to be evaluated, and generates input data for power flow calculation that reflects the post-failure system configuration when assuming the failure of the selected equipment, that is, the selected equipment, by modifying the reference data generated by the system configuration unit 305.

[0039] The power flow calculation unit 308 performs power flow calculation using the pseudo data generated by the pseudo data generation unit 306 and the input data generated by the post-failure system configuration unit 307. Note that for the power flow calculation by the power flow calculation unit 308, any method can be used, and it may be probabilistic power flow calculation (probabilistic power flow calculation) such as the multiple linear method, convolution method, least squares estimation method, Fourier transform method, FOSM method (First-Order Second-Moment Method), point estimation method, probabilistic sampling interpolation method, arbitrary polynomial chaos expansion method, etc., or probabilistic power flow calculation (probabilistic power flow calculation) using the Monte Carlo method, etc., or ordinary power flow calculation.

[0040] The system impact degree calculation unit 309 calculates the degree of influence on other facilities, which are facilities other than the selected facility (i.e., the facility assuming a fault), when the selected facility (i.e., the facility assuming a fault) fails, using the result of the power flow calculation generated (calculated) by the power flow calculation unit 308. Based on the power flow calculation result by the power flow calculation unit 308 and the operation limit value 324 stored in the storage unit 302, the system impact degree of the facility selected by the post-fault system setting unit 307 is calculated and stored in the storage unit 302 as calculation data 325. Note that the power flow calculation unit 308 may pass the power flow calculation result to the system impact degree calculation unit 309, or the power flow calculation unit 308 may temporarily store the power flow calculation result in the storage unit 302, and the system impact degree calculation unit 309 may read the power flow calculation result from the storage unit 302. The system impact degree is calculated based on, for example, the overload situation (including congestion situation) of facilities other than the facility selected by the post-fault system setting unit 307, the amount of supply interruption to consumers, etc. For the system impact degree, a calculation method similar to that in Patent Document 1 can be used. Also, the system impact degree of facility x may be = max (the amount of power related to facility i when facility x fails / the rated value of facility i). Here, max() is the maximum value within the parentheses, and facility i is any facility in the power system. Note that instead of the maximum value, an average value, a median value, or a mode value may be used.

[0041] The update priority calculation unit 310 calculates the update priority for each facility from the system impact degree calculated by the system impact degree calculation unit 309 for each case of the pseudo data of power demand and PV power generation output. For example, the update priority calculation unit 310 may use, as the update priority of a certain facility, the one with the greatest degree of influence among the system impact degrees calculated for that facility, that is, the maximum value, or instead of the maximum value, use a statistical representative value such as a median value, an average value, or a mode value. Also, the update priority calculation unit 310 may rank each facility according to the system impact degree for each combination of the pseudo data of power demand and PV power generation output, and calculate the update priority based on the ranking. Further, the update priority calculation unit 310 may calculate the update priority based on the soundness of the facility in addition to the system impact degree or the ranking of the system impact degree of the facility.

[0042] Figure 3 is a flowchart for explaining Operation Example 1 of the facility renewal priority calculation device 300 in the present embodiment. First, the communication unit 301 acquires the actual values of the PV power generation output and the power demand from the PV power generation output estimation system 200 (step Sa1). Next, the probability statistical model generation unit 304 generates a probability distribution model of the PV power generation output by performing probability statistical modeling of the PV power generation output using the PV power generation output acquired in step Sa1 (step Sa2). Further, the probability statistical model generation unit 304 generates a probability distribution model of the power demand by performing probability statistical modeling of the power demand using the power demand acquired in step Sa1 (step Sa3).

[0043] Next, the power grid setting unit 305 sets the configuration of the power grid using the power grid-related information 321 stored in the storage unit 302 (step Sa4). Next, the pseudo-data generation unit 306 generates pseudo-data of the PV power generation output (step Sa5), and further generates pseudo-data of the power demand (step Sa6). These pseudo-data may be pseudo-data assuming a predetermined date and time, or may be randomly determined date and time. For example, a plurality of date and time (time points) such as specific date and time (which may be equally spaced) throughout the year, morning, noon, and evening in spring, summer, autumn, and winter are determined in advance, and the pseudo-data generation unit 306 may select one from them for each repetition from step Sa12 described later, thereby determining the above-mentioned predetermined date and time. Further, the pseudo-data generated in steps Sa5 and Sa6 may include a plurality of cases for one date and time. When the above x = F -1 (u) is used for generating pseudo-data, pseudo-data of a plurality of cases are generated by changing the value of u. The value of u in each case may be randomly determined or may be predetermined. Further, as described above, when the PV power generation output is normalized using the extraterrestrial theoretical solar irradiance, the pseudo-data generation unit 306 may use, as the pseudo-data of the PV power generation output, the product of the extraterrestrial theoretical solar irradiance at the date and time of the pseudo-data to be generated and the above x = F-1(u).

[0044] Next, the post-failure system configuration unit 307 selects one piece of equipment to be the target for calculating the system impact degree from among the equipment to be evaluated (step Sa7), and generates input data for power flow calculation that reflects the post-failure system configuration when a failure of the selected equipment is assumed (step Sa8). Next, the power flow calculation unit 308 performs power flow calculation for each case of the pseudo data using the input data generated in step Sa8 (step Sa9).

[0045] Next, the system impact degree calculation unit 309 calculates the system impact degree of the selected equipment based on the power flow calculation results of each case of the pseudo data (step Sa10). The system impact degree indicates the overload status (including congestion status) of equipment other than the selected equipment (i.e., the equipment for which a failure is assumed) and the amount of supply disruption to consumers when the selected equipment (i.e., the equipment for which a failure is assumed) fails. If the system impact degrees of all the equipment to be evaluated have not been calculated for the pseudo data generated in steps Sa5 and Sa6 (step Sa11 - No), the process returns to step Sa7. On the other hand, if the system impact degrees of all the equipment to be evaluated have been calculated (step Sa11 - Yes), and the loop from step Sa5 has not been repeated a predetermined number of times (step Sa12 - No), the process returns to step Sa5. On the other hand, if it has been repeated (step Sa12 - Yes), the update priority calculation unit 310 performs a risk assessment of each piece of equipment based on the system impact degree calculated by the system impact degree calculation unit 309. The update priority calculation unit 310 sets (calculates) the equipment update priority higher in the order of equipment with larger risk assessment values. The communication unit 301 causes the operation terminal device 400 to display the equipment update priority of each piece of equipment set by the update priority calculation unit 310 and presents it to the user (step Sa13). Note that the communication unit 301 may transmit the image to be displayed to the operation terminal device 400, or may transmit text information such as HTML (Hyper Text Markup Language) for generating the image to be displayed to the operation terminal device 400. Also, the operation terminal device 400 may execute general-purpose software such as a web browser and display the equipment update priority according to the information (such as HTML) acquired from the communication unit 301, or may execute dedicated software and display the equipment update priority based on the information acquired from the communication unit 301.

[0046] Here, the risk evaluation value of each facility may be the maximum value, median value, average value, or mode value of the system impact degrees of all cases of that facility. Alternatively, the risk evaluation value of each facility may be the sum of the points corresponding to the ranks for each case of the pseudo-data, where the facilities to be evaluated are ranked in descending order of system impact degree, and the points corresponding to the ranks are totaled for all cases. The points corresponding to the ranks are set to be larger for higher ranks.

[0047] For example, consider a case where there are four facilities to be evaluated, namely Facility A1, A2, A3, and A4, and the points corresponding to the ranks are 10 points for the first rank, 5 points for the second rank, 1 point for the third rank, and no points for ranks below the fourth rank. Further, in the first case, Facility A1 is ranked first, Facility A2 is ranked second, Facility A3 is ranked third, and Facility A4 is ranked fourth, and in the second case, Facility A2 is ranked first, Facility A3 is ranked second, Facility A4 is ranked third, and Facility A1 is ranked fourth. In this case, the risk evaluation value of Facility A1 is 10, which is the sum of 10 points for the first rank and no points for the fourth rank. The risk evaluation value of Facility A2 is 15, which is the sum of 5 points for the second rank and 10 points for the first rank. The risk evaluation value of Facility A3 is 6, which is the sum of 1 point for the third rank and 5 points for the second rank. The risk evaluation value of Facility A4 is 1, which is the sum of no points for the fourth rank and 1 point for the third rank. From these, the priority order for facility renewal is Facility A2, Facility A1, Facility A3, Facility A4.

[0048] FIG. 4 is a flowchart for explaining an operation example 2 of the facility renewal priority calculation device 300 according to the present embodiment. The flowchart of FIG. 4 extracts processes related to the probability distribution model of PV power generation output and the generation of pseudo data. Steps Sb1 to Sb4 in FIG. 4 described below are the details of step Sa2 in FIG. 3, and step Sb5 in FIG. 4 is the detail of step Sa5 in FIG. 3. First, the probability statistical model generation unit 304 converts the PV power generation output acquired from the PV power generation output estimation system 200 into a sunshine index (strictly speaking, a quantity corresponding to the sunshine index) by regarding it as the horizontal plane total solar irradiance (step Sb1). As a result, normalization using the extraterrestrial theoretical solar irradiance is performed on the PV power generation output, and PV power generation outputs that vary depending on the season and time zone can be equivalently handled, so that the PV power generation output over a long period can be used as the population of the probability distribution model.

[0049] The sunshine index Cl is a value indicating how much solar radiation is blocked by clouds or the like, and is represented by Equation (1). In Equation (1), SR is the solar irradiance, and St is the extraterrestrial theoretical solar irradiance. In step Sb1, the PV power generation output is substituted for SR in Equation (1) instead of the solar irradiance. Note that a value obtained by converting the PV power generation output into the solar irradiance, or the solar irradiance itself may be substituted.

[0050]

Equation

[0051] The extraterrestrial theoretical solar irradiance St is a quantity determined by the position (latitude) and date and time, and is defined by the following equation. 00 is the solar constant and is 1365 W / m2. θ is the zenith angle, φ is the latitude, δ is the solar declination, and h is the hour angle from solar noon. Also, M is the month, DAY is the day, HOUR is the hour, n is the solar noon time.

[0052]

Equation

[0053] Next, the probability statistical model generation unit 304 groups the clear sky indices obtained in step Sb1 for each predetermined time period such as seasons (step Sb2). Note that this grouping may not be performed, and the predetermined time period may be the whole year. Also, grouping may be performed for each time zone, and the method is not limited to these. Next, the probability statistical model generation unit 304 generates a histogram of the clear sky indices for each group by step Sb2 (step Sb3). The probability statistical model generation unit 304 estimates a probability density function (probability distribution model) from the histogram generated in step Sb3 (step Sb4). For this estimation, a statistical estimation method is used. As the statistical estimation method, an example of using a statistical probability density estimation method is shown. Specifically, it may be a parametric method assuming a distribution such as a normal distribution or a binomial distribution, a non-parametric method such as a kernel density estimation method that does not assume a specific distribution, or a machine learning method, but is not limited to these.

[0054] The pseudo-data generation unit 306 generates samples according to the probability density function estimated in step Sb4 and uses them as pseudo-data (step Sb5). Here, for the generation of samples, an inverse function method using the inverse function of the cumulative distribution function of the probability density function may be used. Also, for the generation of samples, methods such as the MCMC (Markov Chain Monte Carlo) method may be used, and the method is not limited to these.

[0055] FIG. 5 is a graph for explaining the generation of the probability density function in the present embodiment. The histogram generated in step Sb3 of FIG. 4 often has a complex shape like the histogram Ht1 shown in FIG. 5. Therefore, the probability statistical model generation unit 304 generates a probability density function Pf1 from the histogram Ht1 using a non-parametric method such as kernel density estimation. This is the same for the probability density function of power demand.

[0056] FIG. 6 is a diagram showing an example of a display screen of the operation terminal device 400 in the present embodiment (part 1). The display screen G61 in FIG. 6 is a screen that the probability statistical model generation unit 304 causes to be displayed on the operation terminal device 400, and is a screen for allowing a user to select a method for the probability statistical model generation unit 304 to generate a probability density function from a histogram. The radio button R61 is a radio button for inputting a selection of whether to use a non-parametric method or a parametric method when generating a probability density function. The list L61 is a list for specifying what kind of distribution is assumed when using a parametric method. The graph Ht61 is a graph of a histogram of PV power generation output or sunshine index and a probability density function generated by the method selected by the radio button R61. The cancel button B61 is a button for returning to the previous display screen. The determination button B62 is a button for finalizing the selected method.

[0057] FIG. 7 is a diagram showing an example of a display screen of the operation terminal device 400 in the present embodiment (part 2). The display screen in FIG. 7 is a screen that the communication unit 301 causes to be displayed on the operation terminal device 400, and includes a system diagram of the power system by the update priority calculation unit 310 and information on each facility in the power system. The information on each facility includes soundness, system impact degree, and facility evaluation (update priority) based on soundness and system impact degree. In the notation of the system impact degree in FIG. 7, when it is assumed that a certain facility has failed, the impact on other systems and facilities is ranked as the deviation amount from the rated value. For example, it can be set based on the user's operation such that "load present" is in the range of 50% to 110%, "failure" is in the range of 150% to 200%, etc., and the facility evaluation (update priority) can be performed by converting the impact degree into an impact amount according to the rank.

[0058] FIG. 8 is an explanatory diagram for explaining the hardware configuration of each device according to the present embodiment. The devices include a smart meter measurement system 100, a PV power generation output estimation system 200, a facility renewal priority calculation device 300, and an operation terminal device 400. Each device is configured to include an input / output module I, a memory module M, and a control module P. The input / output module I is realized by including some or all of a communication module H11, a connection module H12, a pointing device H21, a keyboard H22, a display H23, a button H3, a microphone H41, a speaker H42, a camera H51, or a sensor H52. The memory module M is realized by including a drive H7. The memory module M may further be configured to include some or all of a memory H8. The control module P is realized by including a memory H8 and a processor H9. These hardware components are communicably connected to each other via a bus (Bus) and are supplied with power from a power supply H6.

[0059] The connection module H12 is a digital input / output port such as USB (Universal Serial Bus). The pointing device H21, the keyboard H22, and the display H23 may be touch panels. The sensor H52 is an acceleration sensor, a gyro sensor, a GPS reception module, a proximity sensor, etc. The power supply H6 is a power supply unit that supplies the electricity necessary to operate each device. The power supply H6 may be a battery. The drive H7 is an auxiliary storage medium such as a hard disk drive or a solid state drive. The drive H7 may be a non-volatile memory such as an EEPROM or a flash memory, or a magneto-optical disk drive or a flexible disk drive. Also, the drive H7 is not limited to being built into each device, for example, and may be an external storage device connected to the connector of the connection module H12. The memory H8 is a main storage medium such as a random access memory. Note that the memory H8 may be a cache memory. The memory H8 stores these instructions when the instructions are executed by one or more processors H9. The processor H9 is a CPU (Central Processing Unit). The processor H9 may be an MPU (Microprocessing Unit) or a GPU (Graphics Processing Unit). The processor H9 reads a program and various data from the drive H7 via the memory H8 and performs calculations to execute the instructions stored in one or more memories H8.

[0060] The input / output module I is used in the smart meter measurement system 100, the PV power generation output estimation system 200, the facility renewal priority calculation device 300, the operation terminal device 400, etc. The control module P is used for the implementation of each part of the smart meter measurement system 100, the PV power generation output estimation system 200, the facility renewal priority calculation device 300, the operation terminal device 400. Note that in this specification etc., the description of the smart meter measurement system 100, the PV power generation output estimation system 200, the facility renewal priority calculation device 300, the operation terminal device 400 may be replaced with the description of the control module P.

[0061] The present disclosure may be implemented as follows. (1) One embodiment of the present disclosure is a facility renewal priority calculation device that calculates the priority of renewing facilities in a power system. The device includes a model generation unit that generates a probability distribution model of power demand from power demands at a plurality of time points and generates a probability distribution model of solar power generation output from solar power generation outputs at a plurality of time points, a pseudo-data generation unit that generates pseudo-data of power demand from the probability distribution model of power demand and generates pseudo-data of solar power generation output from the probability distribution model of solar power generation output, and a priority calculation unit that calculates the priority of renewing the facilities in the power system using the pseudo-data of power demand and the pseudo-data of solar power generation output.

[0062] (2) Another embodiment of the present disclosure is the facility renewal priority calculation device according to (1), wherein the model generation unit normalizes the solar power generation output at each of the plurality of time points using the extraterrestrial theoretical solar irradiance at the corresponding time point, and generates the probability distribution model of the solar power generation output using the normalized solar power generation output.

[0063] (3) Another embodiment of the present disclosure is the facility renewal priority calculation device according to (1) or (2), wherein the model generation unit generates the probability distribution model of the solar power generation output using a statistical estimation method.

[0064] (4) Another embodiment of the present disclosure is the facility renewal priority calculation device according to (3), wherein the statistical estimation method is a nonparametric method.

[0065] (5) Another embodiment of the present disclosure is the facility renewal priority calculation device according to any one of (1) to (3), wherein the model generation unit displays a screen for allowing a user to select a method for generating the probability distribution model of the solar power generation output.

[0066] (6) Further, another embodiment of the present disclosure is the facility renewal priority calculation device according to any one of (1) to (5), wherein the pseudo data of the power demand and the pseudo data of the photovoltaic power generation output include a plurality of cases at each of a plurality of predetermined time points.

[0067] (7) Further, another embodiment of the present disclosure is a facility renewal priority calculation system for calculating the priority of renewal of facilities in a power system, comprising: a model generation unit that generates a probability distribution model of power demand from the power demand at each of a plurality of time points and generates a probability distribution model of photovoltaic power generation output from the photovoltaic power generation output at each of a plurality of time points; a pseudo data generation unit that generates pseudo data of power demand from the probability distribution model of power demand and generates pseudo data of photovoltaic power generation output from the probability distribution model of photovoltaic power generation output; and a priority calculation unit that calculates the priority of renewal of facilities in the power system using the pseudo data of power demand and the pseudo data of photovoltaic power generation output.

[0068] (8) Further, another embodiment of the present disclosure is the facility renewal priority calculation system according to (7), comprising a photovoltaic power generation output estimation unit that estimates the power demand and the photovoltaic power generation output from the residual demand.

[0069] (9) Further, another embodiment of the present disclosure is a facility renewal priority calculation method for calculating the priority of renewal of facilities in a power system, comprising: generating a probability distribution model of power demand from the power demand at each of a plurality of time points and generating a probability distribution model of photovoltaic power generation output from the photovoltaic power generation output at each of a plurality of time points; generating pseudo data of power demand from the probability distribution model of power demand and generating pseudo data of photovoltaic power generation output from the probability distribution model of photovoltaic power generation output; and calculating the priority of renewal of facilities in the power system using the pseudo data of power demand and the pseudo data of photovoltaic power generation output.

[0070] (10) Further, another embodiment of the present disclosure is a program for causing a computer to function as a facility renewal priority calculation device that calculates the priority of renewal of facilities in a power system. The facility renewal priority calculation device includes a model generation unit that generates a probability distribution model of power demand from power demands at a plurality of time points and generates a probability distribution model of photovoltaic power generation output from photovoltaic power generation outputs at a plurality of time points, a pseudo-data generation unit that generates pseudo-data of power demand from the probability distribution model of power demand and generates pseudo-data of photovoltaic power generation output from the probability distribution model of photovoltaic power generation output, and a priority calculation unit that calculates the priority of renewal of facilities in the power system using the pseudo-data of power demand and the pseudo-data of photovoltaic power generation output.

[0071] Further, a program for realizing the functions of the smart meter measurement system 100, the PV power generation output estimation system 200, the facility renewal priority calculation device 300, and the operation terminal device 400 in FIG. 1 may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be read into a computer system and executed to realize these devices. Here, the "computer system" is assumed to include hardware such as an OS and peripheral devices.

[0072] Further, the "computer system" is assumed to include a homepage providing environment (or display environment) if the WWW system is used. In addition, the "computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, etc., and storage devices such as hard disks built into computer systems. Furthermore, the "computer-readable recording medium" also includes those that dynamically hold a program for a short period of time, such as a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line, and those that hold a program for a certain period of time, such as volatile memory inside a computer system that serves as a server or client in that case. Also, the above program may be for realizing a part of the aforementioned functions, and furthermore, it may be possible to realize the aforementioned functions in combination with a program already recorded in the computer system.

[0073] As described above, the embodiments of this invention have been detailed with reference to the drawings. However, the specific configuration is not limited to this embodiment, and design changes and the like within the scope not departing from the gist of this invention are also included.

Explanation of Reference Numerals

[0074] 10 Equipment Renewal Priority Calculation System 100 Smart Meter Measurement System 200 PV Power Generation Output Estimation System 300 Equipment Renewal Priority Calculation Device 301 Communication Unit 302 Storage Unit 303 Input Reception Unit 304 Probability Statistics Model Generation Unit 305 System Setting Unit 306 Pseudo-Data Generation Unit 307 Post-Failure System Setting Unit 308 Power Flow Calculation Unit 309 System Influence Degree Calculation Unit 310 Renewal Priority Calculation Unit 321 System-Related Information 322 Generator Information 323 Measurement / Estimation Data 324 Operation Limit Value 325 Calculated data 400 Operation terminal device 500 Communication network

Claims

1. An equipment renewal priority calculation device for calculating the priority of renewal of equipment in a power system, A model generation unit that generates a probability distribution model of power demand from power demands at each of a plurality of time points, and generates a probability distribution model of photovoltaic power generation output from photovoltaic power generation outputs at each of the plurality of time points; A pseudo-data generation unit that generates pseudo-data of power demand from the probability distribution model of power demand, and generates pseudo-data of photovoltaic power generation output from the probability distribution model of photovoltaic power generation output; A priority calculation unit that calculates the priority of renewal of the equipment in the power system using the pseudo-data of the power demand and the pseudo-data of the photovoltaic power generation output An equipment renewal priority calculation device comprising the above.

2. The equipment renewal priority calculation device according to claim 1, wherein the model generation unit normalizes the photovoltaic power generation output at each of the plurality of time points using the extraterrestrial theoretical solar irradiance at the corresponding time point, and generates the probability distribution model of the photovoltaic power generation output using the normalized photovoltaic power generation output.

3. The equipment renewal priority calculation device according to claim 2, wherein the model generation unit generates the probability distribution model of the photovoltaic power generation output using a statistical estimation method.

4. The equipment renewal priority calculation device according to claim 3, wherein the statistical estimation method is a non-parametric method.

5. The equipment renewal priority calculation device according to claim 2, wherein the model generation unit displays a screen for allowing a user to select a method for generating the probability distribution model of the photovoltaic power generation output.

6. The equipment renewal priority calculation device according to any one of claims 3 to 5, wherein the pseudo-data of the power demand and the pseudo-data of the photovoltaic power generation output include a plurality of cases at each of a plurality of predetermined time points.

7. An equipment replacement priority calculation system for calculating the priority of equipment replacement in a power system, A model generation unit that generates a probability distribution model of power demand from power demands at a plurality of time points, and generates a probability distribution model of photovoltaic power generation output from photovoltaic power generation outputs at a plurality of time points, A pseudo-data generation unit that generates pseudo-data of power demand from the probability distribution model of power demand, and generates pseudo-data of photovoltaic power generation output from the probability distribution model of photovoltaic power generation output, A priority calculation unit that calculates the priority of equipment replacement in the power system using the pseudo-data of power demand and the pseudo-data of photovoltaic power generation output, An equipment replacement priority calculation system comprising:

8. The equipment replacement priority calculation system according to claim 7, further comprising a photovoltaic power generation output estimation unit that estimates the power demand and the photovoltaic power generation output from the residual demand.

9. An equipment replacement priority calculation method for calculating the priority of equipment replacement in a power system, A step of generating a probability distribution model of power demand from power demands at a plurality of time points, and generating a probability distribution model of photovoltaic power generation output from photovoltaic power generation outputs at a plurality of time points, A step of generating pseudo-data of power demand from the probability distribution model of power demand, and generating pseudo-data of photovoltaic power generation output from the probability distribution model of photovoltaic power generation output, A step of calculating the priority of equipment replacement in the power system using the pseudo-data of power demand and the pseudo-data of photovoltaic power generation output, An equipment replacement priority calculation method having:

10. A program for causing a computer to function as an equipment replacement priority calculation device for calculating the priority of equipment replacement in a power system, wherein the equipment replacement priority calculation device is as described above, A model generation unit that generates a probability distribution model of power demand from the power demand at each of a plurality of time points, and generates a probability distribution model of photovoltaic power generation output from the photovoltaic power generation output at each of the plurality of time points; A pseudo-data generation unit that generates pseudo-data of power demand from the probability distribution model of power demand, and generates pseudo-data of photovoltaic power generation output from the probability distribution model of photovoltaic power generation output; A priority calculation unit that calculates the priority of updating the facilities of the power system using the pseudo-data of power demand and the pseudo-data of photovoltaic power generation output; A program comprising the above.

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