System evaluation device, system evaluation system, system evaluation method, and program

Abstract

To properly evaluate appropriateness of a system configuration in consideration of a severe cross section that is a non-average condition and can actually occur, and make a system facility forming plan with accuracy.SOLUTION: A system evaluation device includes a model generation unit that generates, on the basis of a power generation output of a power generator connected to a target system which is a power system as an evaluation target, a probability distribution model of a population of the power generation outputs, and generates, on the basis of a power demand in the target system, a probability distribution model of a population of the power demands, a pseudo data generation unit that generates, on the basis of the probability distribution model of a population of the power generation outputs and the probability distribution model of the population of the power demands generated by the model generation unit, pseudo data concerning thereof, and a system evaluation unit that calculates a system influence degree, which is the degree of influence at an abnormal time when a failure or an accident occurs in a facility of the target system, using the pseudo data generated by the pseudo data generation unit, and evaluates the target system on the basis of the system influence degree.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a system evaluation device, a system evaluation system, a system evaluation method, and a program. [Background technology]

[0002] In recent years, with the introduction of the revenue cap system (wheeling charge system), general electricity transmission and distribution companies are being required to reduce equipment maintenance and conservation costs, as well as to make planned and efficient investments to next-generation power systems, such as "low carbonization" and "improving resilience."

[0003] Patent Document 1 discloses a priority calculation device that calculates the priority of updating equipment in a power system. This priority calculation device uses system configuration information of the power system to create post-failure data that indicates the system configuration when the equipment being evaluated fails. The priority calculation device then performs a power flow calculation using the post-failure data, and determines the degree of impact on other equipment obtained from the result of the power flow calculation as the priority of updating the equipment being evaluated.

[0004] Furthermore, 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 power generation by photovoltaic (PV) power generation from consumption at a consumer. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Publication No. 2021-191112 [Non-patent literature]

[0006] [Non-Patent Document 1] Kazuhiro Yasunami: "Photovoltaic power generation output estimation method using smart meters", Journal of Electrical Engineering C, Vol.141, No.12, pp.1484-1491 (2021-12) [Non-patent document 2] Kazuhiro Yasunami: "A Method for Estimating the Power Generation Output of Overloaded PV Systems Using Integrated Energy and Solar Radiation Intensity," Joint Research Meeting on Electric Power Technology, Power System Technology, and Semiconductor Power Conversion, PE-23-065, PSE-23-071, SPC-23-121, Vol. 3, pp. 101-107 (2023-3) Summary of the Invention [Problem to be solved by the invention]

[0007] However, in conventional technologies such as those described in Patent Document 1, the priority of equipment upgrades is calculated based on average power demand or power generation output. Therefore, the priorities do not reflect the degree of system impact in a severe scenario, which is an atypical situation, and there is a possibility that the actual severe scenarios that may occur cannot be evaluated. Electric power companies have traditionally evaluated equipment upgrades or facility construction plans based on the severe scenarios. For example, during peak power demand (load) times in summer (around August) or winter (around February), the power flow (power flow) values ​​in power systems (transmission lines, distribution lines) often peak (peak power flow). Therefore, electric power companies have traditionally evaluated and considered these scenarios. However, in recent years, renewable energy sources (renewable energy), such as solar power generation and wind power generation (WF; wind farms), have been introduced (connected) in large quantities to power systems, resulting in drastic and complex fluctuations in power flow. Under these circumstances, it is becoming increasingly difficult to determine at what point in the year a severe scenario (peak current) will occur. Furthermore, with conventional technology, it has been difficult to evaluate the adequacy of the current system configuration in the future scenario, or to accurately formulate an "equipment formation plan" that covers appropriate new installation, expansion, and removal of equipment.

[0008] The present disclosure has been made to solve the above problems, and its purpose is to provide a system evaluation device, system evaluation system, system evaluation method, and program that can appropriately evaluate the adequacy of system configuration based on severe cross-sections, which are not average conditions, and that can accurately formulate system equipment formation plans. [Means for solving the problem]

[0009] In order to solve the above problems, one aspect of the present disclosure is a system evaluation device that includes a model generation unit that generates a probability distribution model of a population of power generation output based on the power generation output of generators connected to a target system, which is an electric power system to be evaluated, and generates a probability distribution model of a population of power demand based on the power demand of the target system; a pseudo data generation unit that generates pseudo data based on the probability distribution model of the population of power generation output and the probability distribution model of the population of power demand generated by the model generation unit; and a system evaluation unit that calculates a system impact, which is the impact in the event of an abnormality such as a failure or accident of equipment in the target system, using the pseudo data generated by the pseudo data generation unit, and evaluates the target system based on the system impact.

[0010] Another aspect of the present disclosure is a system evaluation system that includes a model generation unit that generates a probability distribution model of a population of power generation output based on the power generation output of a generator connected to a target system, which is an electric power system to be evaluated, and generates a probability distribution model of a population of power demand based on the power demand of the target system; a pseudo data generation unit that generates pseudo data based on the probability distribution model of the population of power generation output and the probability distribution model of the population of power demand generated by the model generation unit; and a system evaluation unit that calculates a system impact, which is the impact in the event of an abnormality such as a failure or accident of equipment in the target system, using the pseudo data generated by the pseudo data generation unit, and evaluates the target system based on the system impact.

[0011] Another aspect of the present disclosure is a system evaluation method including the steps of: a model generation unit generating a probability distribution model of a population of power generation output based on the power generation output of a generator connected to a target system, which is an electric power system to be evaluated, and generating a probability distribution model of a population of power demand based on the power demand of the target system; a pseudo data generation unit generating pseudo data based on the probability distribution model of the population of power generation output and the probability distribution model of the population of power demand generated by the model generation unit, respectively; and a system evaluation unit calculating a system impact, which is the impact in the event of an abnormality such as a failure or accident of equipment in the target system, using the pseudo data generated by the pseudo data generation unit, and evaluating the target system based on the system impact.

[0012] Another aspect of the present disclosure is a program for causing a computer to execute the following steps: a model generation step of generating a probability distribution model of a population of power generation output based on the power generation output of a generator connected to a target system, which is an electric power system to be evaluated, and generating a probability distribution model of a population of power demand based on the power demand of the target system; a pseudo data generation step of generating pseudo data based on the probability distribution model of the population of power generation output and the probability distribution model of the population of power demand generated by the model generation step; and a system evaluation step of calculating a system impact, which is the impact in the event of an abnormality such as a failure or accident of equipment in the target system, using the pseudo data generated by the pseudo data generation step, and evaluating the target system based on the system impact. [Effects of the Invention]

[0013] According to the present disclosure, it is possible to appropriately evaluate the merits and demerits of a system configuration by taking into consideration not-average conditions, such as severe conditions that may actually occur, and to formulate an accurate system equipment formation plan. [Brief explanation of the drawings]

[0014] [Figure 1]1 is a schematic block diagram illustrating an example of a system evaluation system according to a first embodiment. [Figure 2] 1 is a functional block diagram illustrating an example of a system evaluation device according to a first embodiment. [Figure 3] FIG. 2 is a diagram illustrating a processing image of the system evaluation system according to the first embodiment. [Figure 4] FIG. 2 is a diagram illustrating an image of probability statistical modeling in the first embodiment. [Figure 5] FIG. 2 is a diagram illustrating an example of the operation of the system evaluation system according to the first embodiment. [Figure 6] 4 is a flowchart showing an example of the operation of the system evaluation device according to the first embodiment. [Figure 7] 5 is a flowchart showing an example of a process for generating a probability distribution model and pseudo data of a PV power generation output of the system evaluation device according to the first embodiment. [Figure 8] FIG. 3 is a diagram illustrating generation of a probability density function in the first embodiment. [Figure 9] FIG. 4 is a diagram illustrating an example of an inverse function method for generating a probability density function in the first embodiment. [Figure 10] FIG. 10 is a functional block diagram illustrating an example of a system evaluation system and a system evaluation device according to a second embodiment. [Figure 11] 10 is a flowchart showing an example of the operation of the system evaluation device according to the second embodiment. [Figure 12] FIG. 2 is an explanatory diagram illustrating the hardware configuration of each device according to each embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, a system evaluation device, a system evaluation system, and a system evaluation method according to an embodiment of the present disclosure will be described with reference to the drawings.

[0016] (First embodiment) FIG. 1 is a schematic block diagram showing an example of a system evaluation system 10 according to the first embodiment. In order to formulate facility formation plans for the new construction, expansion, removal, etc. of facilities in a power system, the power system evaluation system 10 according to this embodiment takes into consideration not only the average conditions, but also the severe scenarios that may actually occur, and also the amount of overload and supply disruption that may occur in the event of an abnormality in the power system (such as a failure or accident in power system facilities), and evaluates the power system. The power system evaluation system 10 calculates, as an evaluation index for the power system, the degree of system impact (for example, the amount of overload and supply disruption) that indicates the degree of impact that each facility has on the power system.

[0017] The various pieces of equipment in the power system include, for example, transformers, busbars, circuit breakers, disconnecting switches, transmission lines, transmission line protective relays, equipment protective relays, stabilization systems, FACTS (Flexible AC Transmission System) devices, shunt reactors (shunts), and power capacitors (power capacitors), but are not limited to these and may be any equipment in the power system.

[0018] 1, the grid evaluation system 10 includes a smart meter measurement system 100, a PV (Photovoltaic) power generation output estimation system 200, a grid evaluation 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 grid evaluation device 300, and the operation terminal device 400 is realized by one or more computers reading and executing programs.

[0019] The smart meter measurement system 100, the PV power generation output estimation system 200, the system evaluation device 300, and the operation terminal device 400 are connected to a communication network 500, such as an IP (Internet Protocol) network, and are capable of communicating 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.

[0020] The smart meter measurement system 100 collects actual values ​​of power consumption at each consumer measured by a smart meter installed at the consumer. The power consumption measured by the smart meter is power supplied to the consumer from the power grid, and for consumers equipped with photovoltaic (PV) facilities (an example of power generation facilities) who have concluded a surplus power purchase contract (hereinafter referred to as a surplus purchase consumer; the PV subject to the surplus power purchase contract is referred to as a surplus purchase PV), the power consumption is the residual demand (also referred to as "apparent demand" or "apparent load") obtained by subtracting the PV power generation output from the power demand (consumed power, also referred to as "load") at the surplus purchase consumer, and is transmitted to the smart meter measurement system 100. In this embodiment, the PV power generation output (photovoltaic power generation output) is an example of power generation output.

[0021] In addition, among consumers equipped with photovoltaic (PV) facilities, those who have concluded a full-amount purchase contract (hereinafter referred to as full-amount purchase consumers; furthermore, PVs that are the subject of a full-amount purchase contract are referred to as full-amount purchase PVs) have their electricity demand and PV power generation output measured individually by smart meters and transmitted to the smart meter measurement system 100.

[0022] The PV power generation output estimation system 200 (an example of a power generation output estimation unit) estimates an actual value of power demand and an actual value of power generation output from the residual demand. The PV power generation output estimation system 200 estimates (separates) the power demand (actual value of power demand) and the PV power generation output (actual value of PV power generation output) from the actual value of power consumption collected by the smart meter measurement system 100. For this estimation, a known method such as the method described in Non-Patent Document 1 or Non-Patent Document 2 can be used. Note that in this embodiment, an example is described in which only the power demand of a full-purchase consumer, the PV power generation output, and the residual demand of a surplus-purchase consumer are measured by a smart meter, and the power demand of the surplus-purchase consumer and the PV power generation output are estimated from the residual demand, but the present invention is not limited to this form.

[0023] For example, the electricity demand and PV power generation output of surplus purchasing consumers are measured and transmitted by an EMS (Energy Management System, such as a Home Energy Management System (HEMS), Building Energy Management System (BEMS), Factory Energy Management System (FEMS), or Community Energy Management System (CEMS)) installed within the consumer, and this information may be used as the electricity demand and PV power generation output.

[0024] Furthermore, the PV power generation output (actual value of the PV power generation output) may not be measured by a smart meter or estimated from the smart meter measurement value, but may be measured or estimated using meteorological observation data (for example, solar radiation intensity measurement data measured by a pyranometer or sunshine hours measured by an AMeDAS (Automated Meteorological Data Acquisition System)) or satellite images (cloud images taken by a meteorological satellite), and the PV power generation output may then be further estimated from the transmitted solar radiation intensity and used.

[0025] The system evaluation device 300 calculates a system influence indicating the degree of influence in the event of an abnormality in a target system, which is the power system to be evaluated, using the actual value of power demand (or the estimated value of the actual power demand) and the actual value of PV power generation output (or the estimated value of the actual PV power generation output) estimated by the PV power generation output estimation system 200, and evaluates the target system based on the system influence. As described above, the system evaluation device 300 may use estimated values ​​of power demand (estimated values ​​of actual power demand) and estimated values ​​of PV power generation output (estimated values ​​of actual power demand) instead of actual values ​​of power demand and actual values ​​of PV power generation output of each consumer, or may use calculated values ​​of power demand and calculated values ​​of PV power generation output by simulation or the like. Details of the system evaluation device 300 will be described later.

[0026] The operation terminal 400 is a terminal device for operating the system evaluation device 300, and includes input means such as a keyboard, a mouse, or a touch panel, and output means such as a display (display unit) or a speaker.

[0027] Next, the detailed configuration of the system evaluation device 300 will be described with reference to FIG. FIG. 2 is a functional block diagram showing an example of a system evaluation device 300 according to this embodiment. As shown in FIG. 2, the system evaluation device 300 includes a communication unit 301, a memory unit 302, an input reception unit 303, a probability statistical model generation unit 304, a system setting unit 305, a pseudo data generation unit 306, a system abnormality simulation unit 307, a power flow calculation unit 308, a system influence calculation unit 309, and a system reliability evaluation unit 310.

[0028] The communication unit 301 communicates with each unit of the system evaluation 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. The communication unit 301 acquires actual values ​​of PV power generation output and power demand, or estimated values ​​thereof, from the PV power generation output estimation system 200 or the like, and stores them in the memory unit 302 as measurement estimation data. The communication unit 301 also transmits the evaluation result of the target system stored in the memory unit 302 to the operation terminal device 400 to display (output). Here, the evaluation result of the target system is, for example, a determination result obtained by determining whether the target system is suitable or unsuitable, taking system reliability into consideration.

[0029] The storage unit 302 stores various information used by the power system evaluation device 300. The storage unit 302 stores, for example, power system-related information, generator information, measurement and estimation data, operational limit values, and calculation data. The storage unit 302 also includes a power system-related information storage unit 321, a generator information storage unit 322, a measurement and estimation data storage unit 323, an operational limit value storage unit 324, and a calculation data storage unit 325.

[0030] The system-related information storage unit 321 stores system-related information. The system-related information is information that indicates the configuration of the power system, etc. The system-related information storage unit 321 stores, as the system-related information, for example, the system topology, impedance, the target system configuration (open / closed state of circuit breakers, etc.), the failure rate of distribution equipment, the probability of a system accident, etc.

[0031] The generator information storage unit 322 stores generator information. The generator information is information that indicates information related to generators connected to the power grid. The generator information storage unit 322 stores generator information such as the generator output of each generator.

[0032] The measurement estimation data storage unit 323 stores the measurement estimation data. The measurement estimation data includes the power demand and the PV power generation output acquired from the smart meter measurement system 100 or the PV power generation output estimation system 200, etc.

[0033] The operational limit value storage unit 324 stores operational limit values. The operational limit values ​​include the operational limit values ​​of each piece of equipment that constitutes the power system. The operational limit value storage unit 324 stores, as operational limit values, for example, power flow constraints (such as the rated capacity of transmission lines, distribution lines, and transformers), allowable overloads, and allowable supply disruptions. The allowable overloads and allowable supply disruptions are used to calculate the system impact.

[0034] The calculated data storage unit 325 stores the calculated data calculated by the system evaluation device 300. The calculated data storage unit 325 stores, as the calculated data, for example, statistical models (probability distribution models such as probability density functions and cumulative distribution functions), pseudo data, power flow calculation results, system influence levels, and judgment results (evaluation results).

[0035] The input receiving unit 303 receives input of information from outside the system evaluation device 300. The input receiving unit 303 may acquire information from another device via communication, may receive input from an operator or the like via a keyboard, mouse, or the like of the operation terminal device 400, or may receive input of information by reading information from a storage medium. The input receiving unit 303 receives input of system-related information, generator information, measured and estimated data, and operation limit values, and stores these in the storage unit 302.

[0036] The grid-related information, generator information, and measurement and estimation data received by the input receiving unit 303 are used to generate input data for the power flow calculation described below, and the operational limit values ​​are used to calculate the grid influence. The grid-related information is information indicating the location, connection relationship (topology), failure probability, impedance, target grid configuration (open / closed state of circuit breakers, etc.), evaluation target equipment, and the grid after a failure, etc. Examples of the equipment that constitutes the grid include, but are not limited to, transmission lines, transformers, busbars, circuit breakers, disconnecting switches, transmission lines, transmission line protective relays, component protective relays, stabilization systems, FACTS (Flexible AC Transmission System) devices, shunt reactors (shunts), and power capacitors (power capacitors).

[0037] The grid-related information may be input in cooperation with a database of the power grid's EMS (a supply and demand control system installed in a central load dispatching center or the like), SCADA (a supervisory control and data acquisition system installed in a main grid load dispatching center or a load control center or the like), DAS (a distribution automation system), digital twin system, etc. via the communication network 500. The input receiving unit 303 receives input of the grid-related information and stores it in the grid-related information storage unit 321.

[0038] The generator information is information indicating the rated capacity, connection position, operation constraints, power generation amount (active power, reactive power), etc. of a generator connected to the power grid. This information may be input via the communication network 500 in cooperation with databases of the power grid's EMS, SCADA, DAS, digital twin system, etc. The input receiving unit 303 receives input of the generator information and stores it in the generator information storage unit 322.

[0039] The measurement estimation data is information indicating the power demand and PV power generation output (photovoltaic power generation output) of each consumer connected to the power grid. The power demand and PV power generation output are sums for each section (for example, a substation bus, a section switch, a pole transformer, etc.), but may also be the power demand and PV power generation output for each consumer. The input receiving unit 303 receives input of the measurement estimation data and stores it in the measurement estimation data storage unit 323.

[0040] The operational limit value indicates the operational limit value of each piece of equipment that constitutes the grid. The operational limit value is determined based on, for example, the rated capacity, and may be the rated capacity itself or a value less than the rated capacity. The rated capacity may also employ a dynamic rating, and may be variable depending on the date and time. The input receiving unit 303 receives input of the operational limit value and stores it in the operational limit value storage unit 324.

[0041] The probability statistical model generation unit 304 (an example of a model generation unit) generates (estimates) a probability distribution model (statistical model) of a population of power generation output based on the power generation output of generators connected to a target system, which is a power system to be evaluated, and generates (estimates) a probability distribution model (statistical model) of a population of power demand based on the power demand of the target system. Note that the probability distribution model of power demand and power generation output may be generated (estimated) for each section, which is a predetermined section (e.g., a substation bus, a section switch, a pole transformer, etc.), or may be generated (estimated) for each consumer. The probability statistical model generation unit 304 generates (estimates) a probability distribution model of power demand, for example, from power demand at each of multiple time points (each of multiple times), which are measurement estimation data stored in the measurement estimation data storage unit 323. Furthermore, the probability statistical model generating unit 304 generates (estimates) a probability distribution model of the PV power generation output from the PV power generation output at each of a plurality of time points (each of a plurality of times), which is the measurement estimation data stored in the measurement estimation data storage unit 323.

[0042] These probability distribution models may be probability density functions or cumulative distribution functions generated from the frequency distribution of power demand and PV power output, respectively. A statistical estimation method is used to generate (estimate) the probability density function from the frequency distribution. An example of using a statistical probability density estimation method is shown as the statistical estimation method. Specifically, the statistical estimation method may be a parametric method that assumes 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. Furthermore, the cumulative distribution function may be generated (estimated) from the generated probability density function, or may be generated (estimated) directly from the frequency distribution.

[0043] Furthermore, the probability and statistical model generation unit 304 may normalize the PV power generation output at each of a plurality of time points (each of a plurality of times) using the theoretical solar radiation intensity outside the atmosphere at the corresponding time point (time), and generate (estimate) a probability distribution model of the PV power generation output using the normalized PV power generation output. Furthermore, in this process, the probability and statistical model generation unit 304 may perform processing using the solar radiation intensity instead of the PV power generation output itself, and convert (convert) it to the PV power generation output after taking into consideration the PV introduction amount, PV power generation efficiency, and the like. Furthermore, when converting (converting) the solar radiation intensity to the PV power generation output, the probability and statistical model generation unit 304 may use the method described in Non-Patent Document 1 or Non-Patent Document 2. Furthermore, here, instead of the probability distribution model of the PV power generation output, a probability distribution model of the solar radiation intensity may be generated (estimated), and then converted (converted) to the PV power generation output when generating pseudo data, which will be described later. Furthermore, the probability statistical model generation unit 304 stores the generated probability distribution model of the power demand and the probability distribution model of the PV power generation output (or solar radiation intensity) in the calculated data storage unit 325.

[0044] The system setting unit 305 sets the configuration of the power system using the system-related information. More specifically, the system setting unit 305 uses the system-related information stored in the system-related information storage unit 321 to generate reference data equivalent to input data for power flow calculation when no failure of equipment in the power system is assumed. The setting in the system setting unit 305 may be performed by an operator or the like inputting the information via the operation terminal device 400, or may be performed by receiving the setting contents from an external device. Furthermore, the system setting unit 305 stores the generated reference data corresponding to the input data for the power flow calculation in the calculated data storage unit 325.

[0045] The pseudo data generation unit 306 generates pseudo data of power demand from the probability distribution model of power demand (probability distribution model of the population of power demand) generated by the probability statistical model generation unit 304, and generates pseudo data of PV power generation output from the probability distribution model of PV power generation output (probability distribution model of the population of power generation output) generated by the probability statistical model generation unit 304. Here, the pseudo data of power demand is the sum of power demand per interval, with a predetermined interval as the unit, and the pseudo data of PV power generation output is the sum of PV power generation output per interval, with a predetermined interval as the unit.

[0046] The pseudo data of the power demand and PV power generation output is a sum of values ​​for each predetermined section (for example, a substation busbar, a section switch, a pole transformer, etc.), but may be the power demand and PV power generation output for each consumer. Details of the generation of pseudo data by the pseudo data generator 306 will be described later. Furthermore, the pseudo data generating unit 306 stores the generated pseudo data in the calculated data storage unit 325 .

[0047] The system abnormality simulation unit 307 generates input data for power flow calculation that reflects the system configuration at the time of a system abnormality that causes a distribution equipment failure or a system accident to occur probabilistically in the system configuration to be evaluated (the configuration of the target system) taking into account the failure rate of the distribution equipment and the probability of a system accident. The system abnormality simulation unit 307 generates input data for power flow calculation that reflects the system configuration at the time of an abnormality that causes a distribution equipment failure or a system accident to occur in the configuration of the target system, based on at least one of the failure rate of the distribution equipment and the occurrence rate of the system accident in the target system. Note that the system abnormality simulation unit 307 may not cause a distribution equipment failure or a system accident to occur in the configuration of the target system (no failure or system accident occurs) based on at least one of the failure rate of the distribution equipment and the occurrence rate of the system accident in the target system. In that case, the reference data generated by the system setting unit 305 is used as input data for power flow calculation as is.

[0048] The system abnormality simulation unit 307 generates input data for power flow calculation in the event of a system abnormality, based on, for example, the reference data generated by the system setting unit 305 and the system-related information (such as the failure rate of distribution equipment and the probability of system accidents) stored in the system-related information storage unit 321. The system abnormality simulation unit 307 generates a system abnormality based on the failure rate of distribution equipment and the probability of system accidents, and corrects the reference data generated by the system setting unit 305 in accordance with the system abnormality, thereby generating input data for power flow calculation that reflects the system configuration in the event of a system abnormality. Furthermore, the system abnormality simulator 307 stores the generated input data for power flow calculation in the calculated data storage unit 325.

[0049] Power flow calculation unit 308 performs power flow calculation based on the input data for power flow calculation generated by system abnormality simulator 307 and the respective pseudo data generated by pseudo data generator 306. That is, power flow calculation unit 308 performs power flow calculation using the pseudo data stored in calculated data storage unit 325 and the input data for power flow calculation. Note that the power flow calculation by power flow calculation unit 308 can use any method, and may be a stochastic power flow calculation (probabilistic power flow calculation) such as a multilinear method, a convolution method, a least-squares estimation method, a Fourier transform method, a first-order second-moment method (FOSM) method, a point estimation method, a stochastic collocation interpolation method, or an arbitrary polynomial chaos expansion method, or a stochastic power flow calculation (probabilistic power flow calculation) using a Monte Carlo method or the like, or may be a normal power flow calculation. Furthermore, the power flow calculation unit 308 stores the power flow calculation results in the calculated data storage unit 325.

[0050] The system influence calculation unit 309 calculates the amount of supply disruption and the amount of overload as the system influence based on the power flow calculation result obtained by the power flow calculation unit 308 and the operational limit values ​​stored in the operational limit value storage unit 324 of the storage unit 302. The system influence calculation unit 309 calculates the amount of supply disruption and the amount of overload as the system influence by using the power flow calculation result (the power flow calculation result stored in the calculation data storage unit 325) for the set time of the system abnormality and the power flow constraints (such as the rated capacity of the transmission line, distribution line, transformer, etc.) stored in the operational limit value storage unit 324 of the storage unit 302. For example, when a piece of equipment assumed to have failed fails, the system influence calculation unit 309 calculates the degree of influence on equipment other than the equipment assumed to have failed. Furthermore, the system influence calculation unit 309 stores the calculated system influence (the supply disruption amount and the overload amount) in the calculation data storage unit 325.

[0051] The power flow calculation unit 308 may pass the power flow calculation results to the system influence calculation unit 309, or the power flow calculation unit 308 may temporarily store the power flow calculation results in the storage unit 302, and the system influence calculation unit 309 may read the power flow calculation results from the storage unit 302. The same calculation method as in Patent Document 1 may be used for the system influence. Alternatively, the system influence of facility x may be expressed as max (power amount related to facility i when facility x fails / rated value of facility i). Here, max() is the maximum value in parentheses, and facility i is any facility in the power system. The average value, median value, or mode may be used instead of the maximum value.

[0052] The system reliability evaluation unit 310 (an example of a system evaluation unit) calculates a system influence, which is the influence of a target system, using the pseudo data generated by the pseudo data generation unit 306, and evaluates the target system based on the system influence. The system reliability evaluation unit 310 calculates the system influence (amount of supply disruption and amount of overload) using, for example, the system influence calculation unit 309, and evaluates the target system based on at least one of the amount of supply disruption and the amount of overload calculated by the system influence calculation unit 309. In other words, the system reliability evaluation unit 310 evaluates the target system based on the system influence calculated using the pseudo data generated by the pseudo data generation unit 306.

[0053] The system reliability evaluation unit 310 evaluates the system reliability and determines whether the target system is viable by taking into consideration, for example, the system influence (amount of overload or amount of supply disruption) calculated by the system influence calculation unit 309 and the tolerable amount of overload or amount of supply disruption stored in the operational limit value storage unit 324. That is, the system reliability evaluation unit 310 evaluates the target system based on the system influence and the tolerable threshold value of the system influence.

[0054] Furthermore, the system reliability evaluation unit 310 stores the evaluation result of the target system in the calculation data storage unit 325. Note that, for example, in response to a request from the operation terminal device 400, the system reliability evaluation unit 310 may transmit the evaluation result of the target system to the operation terminal device 400 via the communication unit 301 and the communication network 500, and cause the operation terminal device 400 to display (output) it.

[0055] Next, the operation of the system evaluation system 10 and the system evaluation device 300 according to this embodiment will be described with reference to the drawings. FIG. 3 is a diagram illustrating a processing image of the system evaluation system 10 according to this embodiment.

[0056] 3, the grid evaluation system 10 first separates the SM (smart meter) metering value (residual demand) into the PV power generation output and the power demand (step S11). For example, the PV power generation output estimation system 200 generates the PV power generation output and the power demand based on the SM metering value (residual demand).

[0057] Next, the system evaluation system 10 estimates a probability distribution (probability density function, cumulative distribution function, etc.) of the population using the PV power generation output (step S12). The system evaluation device 300 of the system evaluation system 10 generates a probability distribution model (probability density function, cumulative distribution function, etc.) of the PV power generation output based on the PV power generation output.

[0058] The power system evaluation system 10 also estimates a population probability distribution (probability density function, cumulative distribution function, etc.) using the output demand (step S13). The power system evaluation device 300 of the power system evaluation system 10 generates a power demand probability distribution model (probability density function, cumulative distribution function, etc.) based on the power demand.

[0059] Next, the system evaluation system 10 generates pseudo data for PV power generation output from the probability density function or cumulative distribution function of PV power generation output, and further generates pseudo data for power demand from the confirmation density function or cumulative distribution function of power demand, and performs power flow calculations based on these to check supply disruptions and overload conditions in the event of equipment failure (step S14).The system evaluation device 300 of the system evaluation system 10 calculates power flow prediction values ​​(power flow diagrams) for multiple cross sections based on a probability distribution model of PV power generation output (probability density function, cumulative distribution function, etc.) and a probability distribution model of power demand (probability density function, cumulative distribution function, etc.), and uses statistical processing to check supply disruptions and overload (congestion) conditions in the target system, thereby evaluating the target system.

[0060] Next, the stochastic modeling in this embodiment will be described with reference to FIG. FIG. 4 is a diagram for explaining an image of the stochastic modeling in this embodiment.

[0061] As shown in Figure 4, the probabilistic modeling of the power system evaluation system 10 estimates the probability distribution of an unknown population from observed values ​​(a subset of the population). The power system evaluation system 10 generates pseudo data based on the estimated probability distribution of the population (probability density function, cumulative distribution function, etc.). This pseudo data has the same statistical properties as the observed values. The power system evaluation system 10 uses this pseudo data to perform power flow calculations, making it possible to evaluate the target power system for which the observed values ​​were obtained.

[0062] Next, the operation of the system evaluation system 10 according to this embodiment will be described with reference to FIG. FIG. 5 is a diagram showing an example of the operation of the system evaluation system 10 according to this embodiment.

[0063] 5, in the grid evaluation system 10, first, the smart meter measurement system 100 transmits SM metered values ​​to the PV power generation output estimation system 200 (step S101). The smart meter measurement system 100 collects the power consumption (SM metered values) of each consumer measured by a smart meter installed in the consumer, and transmits the SM metered values ​​to the PV power generation output estimation system 200 via the communication network 500.

[0064] Next, the PV power generation output estimation system 200 separates the PV power generation output and the power demand (step S102). The PV power generation output estimation system 200 separates the PV power generation output and the power demand from the SM metered value received from the smart meter measurement system 100.

[0065] Next, the PV power generation output estimation system 200 transmits the PV power generation output and the power demand to the system evaluation device 300 via the communication network 500 (step S103).

[0066] Next, the system evaluation device 300 executes a system evaluation process based on the received PV power generation output and power demand (step S104). Details of the system evaluation process performed by the system evaluation device 300 will be described later with reference to FIG.

[0067] Next, the system evaluation device 300 transmits the evaluation result of the system evaluation process, ie, the evaluation result of the target system, to the operation terminal 400 via the communication network 500 (step S105).

[0068] Next, the operation terminal device 400 displays (outputs) the received evaluation results (step S106). The operation terminal device 400 may display (output) the received evaluation results on a display unit, or may output them to a printing device such as a printer.

[0069] Next, the operation of the system evaluation device 300 according to this embodiment will be described with reference to FIG. FIG. 6 is a flowchart showing an example of the operation of the system evaluation device 300 in this embodiment.

[0070] 6, the communication unit 301 of the system evaluation device 300 first acquires the PV power generation output and the power demand from the PV power generation output estimation system 200 (step S201). The communication unit 301 stores the acquired PV power generation output and the power demand in the measurement estimation data storage unit 323.

[0071] Next, the probability statistical model generation unit 304 of the system evaluation device 300 generates a probability distribution model of the PV power generation output by performing probabilistic statistical modeling of the PV power generation output using the PV power generation output acquired in the processing of step S201 (step S202).

[0072] Furthermore, the probability and statistical model generation unit 304 generates a probability distribution model of the power demand by performing probabilistic and statistical modeling of the power demand using the power demand acquired in the process of step S201 (step S203).

[0073] Next, the system setting unit 305 of the system evaluation device 300 sets the system configuration and generates regular system configuration data (step S204). The system setting unit 305 sets the system configuration (future cross section, new installation, expansion, removal, consolidation, etc.) using the system-related information stored in the system-related information storage unit 321, and generates reference data equivalent to input data for power flow calculation.

[0074] Next, the pseudo data generator 306 of the system evaluation device 300 generates pseudo data of the PV power generation output (step S205). The pseudo data generator 306 generates the pseudo data of the PV power generation output using a probability distribution model of the PV power generation output.

[0075] Furthermore, the pseudo data generating unit 306 generates pseudo data of the power demand (step S206). The pseudo data generating unit 306 generates the pseudo data of the power demand using a probability distribution model of the power demand.

[0076] These pseudo data may be pseudo data assuming a predetermined date and time, or may be randomly determined dates and times. For example, multiple dates and times (time points) may be determined in advance, such as specific dates and times throughout the year (which may be at equal intervals), or morning, afternoon, and evening in the spring, summer, fall, and winter, and the pseudo data generation unit 306 may determine the above-mentioned predetermined dates and times by selecting one of them for each repetition of the process from step S212 described below. Furthermore, the pseudo data generated in the processes of steps S205 and S206 may include multiple cases for one date and time.

[0077] Next, the system abnormality simulation unit 307 of the system evaluation device 300 sets the occurrence of a failure (step S207). The system abnormality simulation unit 307 sets the location of the failure in the target system. The location of the failure is set based on the failure rate of the distribution equipment based on past performance, the probability of a system accident, etc. The system abnormality simulation unit 307 also sets the location of the failure in the target system, even if no failure occurs. The system abnormality simulation unit 307 sets the system configuration of the target system at the time of an abnormality that causes a failure of the distribution equipment or a system accident, based on the system-related information stored in the system-related information storage unit 321 (the failure rate of the distribution equipment, the probability of a system accident, etc.).

[0078] Next, the system abnormality simulator 307 generates system configuration data after the failure (step S208). The system abnormality simulator 307 generates input data for power flow calculation that reflects the system configuration in the event of a system abnormality by modifying reference data, which corresponds to the input data for power flow calculation when no failure is assumed, in accordance with the system configuration in the event of an abnormality. If no failure occurs, the system abnormality simulator 307 generates input data for power flow calculation using the normal system configuration as is (reference data, which corresponds to the input data for power flow calculation when no failure is assumed).

[0079] Next, power flow calculation unit 308 of system evaluation device 300 performs power flow calculation (step S209). Power flow calculation unit 308 performs power flow calculation using the input data for power flow calculation generated in the process of step S208 and the pseudo data generated in the processes of steps S205 and S206. Power flow calculation unit 308 stores the power flow calculation results in calculated data storage unit 325.

[0080] Next, the system influence calculation unit 309 of the system evaluation device 300 calculates the system influence (step S210). The system influence calculation unit 309 calculates the system influence (such as the amount of supply disruption and the amount of overload) based on the power flow calculation results stored in the calculation data storage unit 325. The system influence calculation unit 309 stores the calculated system influence (such as the amount of supply disruption and the amount of overload) in the calculation data storage unit 325.

[0081] Next, the system reliability evaluation unit 310 of the system evaluation device 300 determines whether the processes from step S207 to step S210 described above have been repeated a preset number of times (first set number of times) (step S211). If the processes from step S207 to step S210 described above have been repeated a preset number of times (first set number of times) (step S211: YES), the system reliability evaluation unit 310 proceeds to step S212. If the processes from step S207 to step S210 described above have not been repeated a preset number of times (first set number of times) (step S211: NO), the system reliability evaluation unit 310 returns the process to step S207 and repeats the processes from step S207 to step S210 until the preset number of times (first set number of times) is reached.

[0082] In step S212, the system reliability evaluation unit 310 determines whether the processes from step S205 to step S211 described above have been repeated a preset number of times (second set number of times). If the processes from step S205 to step S211 described above have been repeated a preset number of times (second set number of times) (step S212: YES), the system reliability evaluation unit 310 proceeds to step S213. If the processes from step S205 to step S211 described above have not been repeated a preset number of times (second set number of times) (step S212: NO), the system reliability evaluation unit 310 returns the process to step S205 and repeats the processes from step S205 to step S211 until the preset number of times (second set number of times) is reached.

[0083] In step S213, the system reliability evaluation unit 310 determines whether the system influence is equal to or less than a threshold. For example, the system reliability evaluation unit 310 determines whether the system influence is equal to or less than a threshold, using the tolerable overload amount or the tolerable supply disruption amount stored in the operational limit value storage unit 324 as the threshold. If the system influence is equal to or less than the threshold (step S213: YES), the system reliability evaluation unit 310 proceeds to step S216. If the system influence exceeds the threshold (step S213: NO), the system reliability evaluation unit 310 proceeds to step S214.

[0084] In step S214, the system reliability evaluation unit 310 determines that the set system configuration has a problem. Next, the system reliability evaluation unit 310 determines whether or not the system configuration needs to be reviewed and recalculated (step S215). The system reliability evaluation unit 310 determines whether or not the system configuration needs to be reviewed and recalculated, for example, based on the received information received by the communication unit 301 from the operation terminal device 400 or the input information received by the input reception unit 303. If the system reliability evaluation unit 310 determines that the system configuration needs to be reviewed and recalculated (step S215: YES), the system reliability evaluation unit 310 returns the process to step S204. If the system configuration needs to be reviewed and recalculated (step S215: NO), the system reliability evaluation unit 310 ends the process.

[0085] Furthermore, in step S216, the system reliability evaluation unit 310 determines that there is no problem with the set system configuration. After the process of step S216, the system reliability evaluation unit 310 ends the process.

[0086] Next, a process for generating a probability distribution model of PV power generation output and pseudo data will be described with reference to Fig. 7. Here, an example of generating a probability distribution model of PV power generation output using theoretical solar radiation intensity outside the atmosphere will be described.

[0087] FIG. 7 is a flowchart showing an example of a process for generating a probability distribution model and pseudo data of PV power generation output by the system evaluation device 300 in this embodiment. The processes of steps S301 to S304 in FIG. 7 described below are details of the process of step S202 in FIG. 6, and the process of step S305 in FIG. 7 is details of step S205 in FIG.

[0088] 7, 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 clear weather index (strictly speaking, an amount equivalent to the clear weather index) by regarding it as a horizontal global solar irradiance (step S301). As a result, the PV power generation output is normalized using the theoretical solar irradiance outside the atmosphere, and PV power generation outputs that differ depending on the season or time of day can be treated equivalently. Therefore, the PV power generation output over a long period of time can be used as a sample (subset) obtained from the population of the probability distribution model.

[0089] The clear weather index Cl is a value that indicates how much solar radiation is blocked by clouds and the like, and is expressed by the following formula (1). In formula (1), SR is solar radiation intensity, and St is theoretical solar radiation intensity outside the atmosphere. In step S301, PV power generation output is substituted for SR in formula (1) instead of solar radiation intensity. Note that a value obtained by converting PV power generation output into solar radiation intensity, or the solar radiation intensity itself may be substituted.

[0090]

number

[0091] The theoretical solar radiation intensity outside the atmosphere, St, is a quantity determined by the location (latitude) and the date and time, and is defined by the following formula: 00 is the solar constant, 1365 W / m 2 θ is the zenith angle, φ is latitude, δ is the solar declination, and h is the hour angle from the solar noon. Also, M is the month, DAY is the day, HOUR is the hour, and Hn is the noon time.

[0092]

number

[0093] Next, the probability statistical model generation unit 304 groups the clearness index obtained in the processing of step S301 by predetermined time periods such as seasons or months (step S302). Note that this grouping may not be performed, and the predetermined time periods may be the whole year. Alternatively, grouping may be performed by time period, and the method is not limited to these.

[0094] Next, the probability and statistics model generation unit 304 generates a histogram of the clearness index for each group obtained by the process of step S302 (step S303). The probability and statistics model generation unit 304 generates a histogram such as histogram Ht1 in Fig. 8, which will be described later.

[0095] Next, the probability statistical model generation unit 304 estimates a probability density function (probability distribution model) from the histogram generated in the process of step S303 (step S304). A statistical estimation method is used for this estimation. An example of using a statistical probability density estimation method is shown as the statistical estimation method. Specifically, the statistical estimation method may be a parametric method that assumes 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. The process of estimating a probability density function (probability distribution model) from a histogram will be described later with reference to FIG. 8. Alternatively, a cumulative distribution function may be used as the probability distribution model, or the cumulative distribution function may be estimated directly from the histogram.

[0096] Next, the pseudo data generation unit 306 generates samples according to the probability density function estimated in the process of step S304, and uses the samples as pseudo data (step S305). Here, the samples may be generated using an inverse function method that uses the inverse function of the cumulative distribution function of the probability density function. Alternatively, the samples may be generated using a method such as, but not limited to, the MCMC (Markov Chain Monte Carlo) method. The inverse function method will be described later with reference to FIG. 9.

[0097] FIG. 8 is a diagram for explaining generation of a probability density function in this embodiment. The histogram generated in step S303 of Fig. 7 often has a complex shape, such as the histogram Ht1 shown in Fig. 8. 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. The same applies to the probability density function of power demand.

[0098] Next, the inverse function method for generating pseudo data will be described with reference to FIG. FIG. 9 is a diagram showing an example of the inverse function method for generating pseudo data in this embodiment.

[0099] 9, waveform W1 represents the waveform of cumulative distribution function F(x) obtained by integrating probability density function f(x). Regions A1 and A3 of x represent regions where the slope of waveform W1 is gentle and where it is unlikely to occur, while region B represents a region where the slope of waveform W1 is steep and where it is likely to occur.

[0100] When the probability distribution model is a probability density function f(x), the inverse function x=F of the cumulative distribution function F(x) as shown in waveform W1 -1 x=F when u in (u) is substituted with a random number between 0 and 1 (random number uniformly distributed on [0,1]), or a predetermined value. -1 (u) may be used as pseudo data. As a result, the pseudo data will have a value within the range indicated by the probability distribution model. Furthermore, when the PV power generation output is normalized using the theoretical solar radiation intensity outside the atmosphere, the pseudo data generating unit 306 may calculate the above x=F -1 The pseudo data of the PV power generation output may be obtained by multiplying (u) by the theoretical solar radiation intensity outside the atmosphere at the date and time of the pseudo data to be generated. In addition, the pseudo data may be generated using a method such as, but not limited to, the MCMC (Markov Chain Monte Carlo) method.

[0101] Thus, the above x=F -1When (u) is used to generate pseudo data, the pseudo data generation unit 306 generates pseudo data for a plurality of cases by changing the value of u. The value of u in each case may be determined randomly (a random number in the range R1 from "0" to "1") or may be determined in advance. Furthermore, as described above, when the PV power generation output is normalized using the theoretical solar radiation intensity outside the atmosphere, the pseudo data generation unit 306 generates pseudo data for a plurality of cases by changing the value of x=F. -1 The pseudo data of the PV power generation output may be obtained by multiplying (u) by the theoretical solar radiation intensity outside the atmosphere at the date and time of the pseudo data to be generated.

[0102] As described above, the system evaluation device 300 according to this embodiment includes a probability and statistics model generation unit 304 (model generation unit), a pseudo data generation unit 306, and a system reliability evaluation unit 310 (system evaluation unit). The probability and statistics model generation unit 304 generates a probability distribution model of a population of power generation output based on the power generation output of generators connected to a target system, which is a power system to be evaluated, and generates a probability distribution model of a population of power demand based on the power demand of the target system. The pseudo data generation unit 306 generates pseudo data based on the probability distribution model of the population of power generation output and the probability distribution model of the population of power demand generated by the probability and statistics model generation unit 304. The system reliability evaluation unit 310 calculates a system influence, which is the influence in the event of an abnormality such as a failure or accident of equipment in the target system, using the pseudo data generated by the pseudo data generation unit 306, and evaluates the target system based on the system influence.

[0103] As a result, the system evaluation device 300 according to this embodiment uses the pseudo data generated based on the probability distribution model, thereby enabling accurate analysis of the target system while taking into account even the most severe scenarios that could actually occur, which are non-average situations. Furthermore, the system evaluation device 300 according to this embodiment also considers the probability of occurrence of abnormalities (equipment failures and system accidents) that could actually occur in the target system, and uses the system influence, which is the degree of influence in the event of an abnormality in the target system, to appropriately evaluate the suitability of the system configuration. Therefore, by using such evaluation results, the system evaluation device 300 according to this embodiment can accurately formulate a system facility formation plan.

[0104] In this embodiment, the system influence degree includes the amount of supply disruption and the amount of overload in the target system when an abnormality occurs. The system reliability evaluation unit 310 evaluates the target system based on at least one of the amount of supply disruption and the amount of overload.

[0105] As a result, the system evaluation device 300 according to this embodiment can accurately and appropriately evaluate the suitability of the system configuration using a simple method by using the supply disruption amount and the overload amount as the system influence degree.

[0106] The system evaluation device 300 according to this embodiment also includes a system abnormality simulator 307, a power flow calculator 308, and a system influence calculator 309. The system abnormality simulator 307 generates input data for power flow calculation that reflects the system configuration of the target system at the time of an abnormality that causes a distribution facility failure or a system fault, based on at least one of the failure rate of the distribution facility and the occurrence rate of the system fault in the target system. The power flow calculator 308 performs power flow calculation based on the input data for power flow calculation generated by the system abnormality simulator 307 and pseudo-data such as power demand and power generation output. The system influence calculator 309 calculates the amount of supply disruption and the amount of overload as the system influence based on the results of the power flow calculation performed by the power flow calculator 308. The system reliability evaluater 310 evaluates the target system based on at least one of the amount of supply disruption and the amount of overload calculated by the system influence calculator 309.

[0107] As a result, the system evaluation device 300 according to this embodiment can more accurately evaluate the suitability of the system configuration in abnormal situations that may actually occur, by using power flow calculations in abnormal situations that may actually occur, such as when a distribution facility failure or a system accident occurs.

[0108] In this embodiment, the system reliability evaluation unit 310 evaluates the target system based on the system influence and the allowable threshold of the system influence (for example, the allowable amount of overload, the allowable amount of supply disruption).

[0109] As a result, the system evaluation device 300 according to this embodiment can quantitatively evaluate the target system using the allowable threshold value of the system influence degree, and can formulate a system facility formation plan with higher accuracy.

[0110] In this embodiment, the power generation output includes photovoltaic power generation output (PV power generation output). The probability statistical model generation unit 304 normalizes the photovoltaic power generation output at each of a plurality of times (time points) using the theoretical solar irradiance outside the atmosphere at the corresponding time (time point), and generates a probability distribution model of the photovoltaic power generation output (PV power generation output) using the normalized photovoltaic power generation output.

[0111] As a result, the system evaluation device 300 according to this embodiment can handle time-of-day data equivalently, and can remove time-of-day trend components that cause problems in statistical modeling. Therefore, the system evaluation device 300 according to this embodiment can appropriately generate a probability distribution model of photovoltaic power generation output (PV power generation output) using a simple method.

[0112] In this embodiment, the probability and statistical model generation unit 304 generates a probability distribution model of the power generation output using a statistical estimation method, which is a non-parametric method.

[0113] As a result, the system evaluation device 300 according to this embodiment can deal with a statistically unknown population by using a non-parametric method, and can appropriately evaluate the target system.

[0114] In this embodiment, the pseudo data of the power demand and the pseudo data of the power generation output include a plurality of cases at each of a plurality of predetermined times. As a result, the system evaluation device 300 according to this embodiment can evaluate the target system more accurately by performing evaluation at multiple times.

[0115] The power system evaluation system 10 according to this embodiment also includes a probability and statistical model generation unit 304 (model generation unit), a pseudo data generation unit 306, and a power system reliability evaluation unit 310 (power system evaluation unit). The probability and statistical model generation unit 304 generates a probability distribution model of a population of power generation output based on the power generation output of generators connected to a target power system, which is the power system to be evaluated, and generates a probability distribution model of a population of power demand based on the power demand of the target power system. The pseudo data generation unit 306 generates pseudo data based on the probability distribution model of the population of power generation output and the probability distribution model of the population of power demand generated by the probability and statistical model generation unit 304. The power system reliability evaluation unit 310 calculates a system influence, which is the influence in the event of an abnormality such as a failure or accident of equipment in the target power system, using the pseudo data generated by the pseudo data generation unit 306, and evaluates the target power system based on the system influence.

[0116] As a result, the system evaluation system 10 according to this embodiment has the same effect as the system evaluation device 300 described above, and can appropriately evaluate the suitability of the system configuration based on the severe cross-section, thereby enabling the formulation of an accurate system equipment formation plan.

[0117] The system evaluation system 10 according to this embodiment also includes a PV power generation output estimation system 200 (power generation output estimation unit) that estimates the power demand and power generation output from the residual demand. As a result, the system evaluation system 10 according to this embodiment can easily obtain samples of the power demand and power generation output of the target system by using the residual demand.

[0118] The system evaluation method according to this embodiment also includes a model generation step, a pseudo-data generation step, and a system evaluation step. In the model generation step, the probability and statistical model generation unit 304 generates a probability distribution model of a population of power generation output based on the power generation output of generators connected to a target system, which is a power system to be evaluated, and generates a probability distribution model of a population of power demand based on the power demand of the target system. In the pseudo-data generation step, the pseudo-data generation unit 306 generates pseudo data based on the probability distribution model of the population of power generation output and the probability distribution model of the population of power demand generated by the probability and statistical model generation unit 304. In the system evaluation step, the system reliability evaluation unit 310 calculates a system influence, which is the influence in an abnormal event such as a failure or accident of equipment in the target system, using the pseudo-data generated by the pseudo-data generation unit 306, and evaluates the target system based on the system influence.

[0119] As a result, the system evaluation method according to this embodiment has the same effects as the system evaluation device 300 described above, and can appropriately evaluate the suitability of the system configuration based on the severe cross-section, thereby enabling the formulation of an accurate system equipment formation plan.

[0120] (Second embodiment) Next, a system evaluation system 10a and a system evaluation device 300a according to a second embodiment will be described with reference to the drawings. In the second embodiment, a modified example will be described in which an evaluation regarding effective utilization of renewable energy is performed in addition to an evaluation during an abnormality. In this embodiment, the system evaluation device 300a performs an optimal power flow calculation instead of a power flow calculation.

[0121] FIG. 10 is a functional block diagram showing an example of a system evaluation system 10a and a system evaluation device 300a according to the second embodiment. 10, the power system evaluation system 10a includes a smart meter measurement system 100, a PV power generation output estimation system 200, a power system evaluation device 300a, and an operation terminal device 400. The power system evaluation device 300a also includes a communication unit 301, a storage unit 302a, an input reception unit 303, a probability and statistical model generation unit 304, a power system setting unit 305, a pseudo data generation unit 306, a power system abnormality simulation unit 307, an optimal power flow calculation unit 308a, a power system influence calculation unit 309a, and a power system reliability evaluation unit 310a.

[0122] In FIG. 10, the same components as those in the first embodiment are given the same reference numerals, and the description thereof will be omitted.

[0123] The storage unit 302a stores various types of information used by the power system evaluation device 300a. The storage unit 302a includes a power system-related information storage unit 321, a generator information storage unit 322a, a measurement and estimation data storage unit 323, an operational limit value storage unit 324, and a calculation data storage unit 325a.

[0124] The generator information storage unit 322a stores generator information. The generator information is information that indicates information related to generators connected to a power grid, and in this embodiment, the generators connected to the target grid include generators that use renewable energy, such as photovoltaic (PV) facilities, as well as generators that use fossil fuels (generators that do not use renewable energy). The generator information storage unit 322a stores generator information such as the generator output of each generator, including power generation using renewable energy and power generation using fossil fuels.

[0125] The communication unit 301, memory unit 302a, input receiving unit 303, probability statistical model generation unit 304, system setting unit 305, pseudo data generation unit 306, and system abnormality simulation unit 307 are the same as those in the first embodiment described above, and therefore their description will be omitted here.

[0126] The system setting unit 305 generates input data for the optimal power flow calculation that reflects the system configuration to be evaluated. The pseudo data of the PV power generation output generated by the pseudo data generating unit 306 is used as the maximum value of the PV power generation output during the optimal power flow calculation.

[0127] In addition, the system abnormality simulation unit 307 generates input data for optimal power flow calculations that reflects the system configuration under abnormal conditions in which a distribution facility failure or a system accident occurs probabilistically in the system configuration to be evaluated.

[0128] Instead of the power flow calculation of the first embodiment described above, the optimal power flow calculation unit 308a (an example of a power flow calculation unit) performs optimal power flow calculation based on input data for optimal power flow calculation and pseudo data such as power demand and power generation output, so as to minimize the amount of renewable energy output suppression. The optimal power flow calculation unit 308a sets the pseudo data for PV power generation output to the maximum value of PV power generation output, and then performs optimal power flow calculation while minimizing the amount of renewable energy suppression (renewable energy (PV) suppression amount) using input data for optimal power flow calculation that reflects the system configuration during a grid abnormality. The optimal power flow calculation unit 308a stores the results of the optimal power flow calculation (PV power generation output and transmission / distribution line power flow) in the calculated data storage unit 325a.

[0129] The system influence calculation unit 309a calculates the amount of overload and supply disruption, and also calculates the amount of renewable energy (PV) suppression, based on the simulated optimal power flow calculation result during an abnormality and the power flow constraints stored in the operational limit value storage unit 324 of the storage unit 302. The amount of renewable energy (PV) suppression is obtained by subtracting the PV power generation output, which is the optimal power flow calculation result, from pseudo data (sample data) of the PV power generation output. The system influence calculation unit 309a calculates the amount of overload and supply disruption, as well as the renewable energy (PV) suppression amount, as the degree of system influence. The power system influence calculation unit 309a stores the calculated power system influence in the calculation data storage unit 325a.

[0130] The system reliability evaluation unit 310a (an example of a system evaluation unit) evaluates the target system based on the system influence (amount of overload, amount of supply disruption, and amount of renewable energy (PV) suppression) calculated by the system influence calculation unit 309. The system reliability evaluation unit 310a evaluates the system reliability by considering the system influence (amount of overload, amount of supply disruption, and amount of renewable energy (PV) suppression) calculated by the system influence calculation unit and the tolerable amount of overload, tolerable amount of supply disruption, tolerable amount of renewable energy (PV) suppression, etc. stored in the operation limit value storage unit 324, and determines whether the target system is acceptable.

[0131] Next, the operation of the system evaluation device 300a in this embodiment will be described with reference to FIG. FIG. 11 is a flowchart showing an example of the operation of the system evaluation device 300a in this embodiment.

[0132] In FIG. 11, the processes from step S401 to step S408 are the same as the processes from step S201 to step S208 shown in FIG. 6 described above, and therefore a description thereof will be omitted here. In the processes of steps S404 and S408, the system setting unit 305 and the system abnormality simulator 307 generate input data for the optimal power flow calculation.

[0133] In step S409, the optimal power flow calculation unit 308a performs an optimal power flow calculation. Based on input data for the optimal power flow calculation and pseudo data such as power demand and power generation output, the optimal power flow calculation unit 308a performs the optimal power flow calculation so that the amount of renewable energy output suppression is minimized. That is, the optimal power flow calculation unit 308a sets the pseudo data for PV power generation output to the maximum value of PV power generation output, and then performs the optimal power flow calculation while minimizing the amount of renewable energy suppression (renewable energy (PV) suppression amount) using input data for the optimal power flow calculation that reflects the system configuration during a system abnormality.

[0134] Next, the system influence calculation unit 309a calculates the system influence (step S410). The system influence calculation unit 309a calculates the overload amount, the supply disruption amount, and further the renewable energy (PV) suppression amount as the system influence.

[0135] The subsequent processing from step S411 to step S416 is similar to the processing from step S211 to step S216 shown in FIG. 6 described above, and therefore description thereof will be omitted here. In step S413, the system reliability evaluation unit 310a evaluates the target system using the amount of overload, the amount of supply disruption, and the amount of renewable energy (PV) suppression as the degree of system influence.

[0136] As described above, in the system evaluation device 300a and the system evaluation system 10a according to this embodiment, the power generation output includes both the power generation output of renewable energy and the power generation output of non-renewable energy (power generation output using fossil fuels). The system abnormality simulation unit 307 generates input data for an optimal power flow calculation that minimizes the amount of output suppression of renewable energy. The optimal power flow calculation unit 308a (an example of a power flow calculation unit) performs an optimal power flow calculation based on the input data for the optimal power flow calculation and pseudo data such as power demand and power generation output, so as to minimize the amount of output suppression of renewable energy. Here, the system influence includes the amount of output suppression of renewable energy in the optimal power flow calculation result obtained by the optimal power flow calculation performed by the optimal power flow calculation unit 308a. The system reliability evaluation unit 310a evaluates the target system based on the system influence (amount of overload, amount of supply disruption, and amount of renewable energy (PV) suppression) calculated by the system influence calculation unit 309.

[0137] As a result, the system evaluation device 300a and the system evaluation system 10a according to this embodiment can achieve the same effects as those of the first embodiment described above, and can also evaluate the target system while taking into account the effective use of power generation output from renewable energy.

[0138] FIG. 12 is an explanatory diagram illustrating the hardware configuration of each device in each embodiment of the present disclosure. The devices are the smart meter measurement system 100, the PV power generation output estimation system 200, the system evaluation device 300 (300a), and the operation terminal device 400. Each device includes an input / output module I, a memory module M, and a control module P. The input / output module I is implemented by including some or all of the communication module H11, the connection module H12, the pointing device H21, the keyboard H22, the display H23, the button H3, the microphone H41, the speaker H42, the camera H51, and the sensor H52. The memory module M is implemented by including a drive H7. The memory module M may further be configured by including some or all of the memory H8. The control module P is implemented by including the memory H8 and a processor H9. These hardware components are connected to each other so as to be able to communicate with each other via a bus, and are supplied with power from a power supply H6.

[0139] The connection module H12 is a digital input / output port such as a USB (Universal Serial Bus). The pointing device H21, keyboard H22, and display H23 may be touch panels. The sensor H52 may be an acceleration sensor, a gyro sensor, a GPS receiver module, a proximity sensor, or the like. 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. The drive H7 is not limited to being built into each device, but may also 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. The memory H8 may be a cache memory. The memory H8 stores instructions when 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 programs and various data from the drive H7 via the memory H8 and performs calculations to execute instructions stored in one or more memories H8.

[0140] The input / output module I is used in the smart meter measurement system 100, the PV power generation output estimation system 200, the grid evaluation device 300 (300a), the operation terminal device 400, etc. The control module P is used to implement each part of the smart meter measurement system 100, the PV power generation output estimation system 200, the grid evaluation device 300 (300a), and the operation terminal device 400. Note that in this specification and the like, the terms smart meter measurement system 100, the PV power generation output estimation system 200, the grid evaluation device 300 (300a), and the operation terminal device 400 may be replaced with the term control module P.

[0141] The present disclosure is not limited to the above-described embodiments, and can be modified within the scope of the present disclosure. For example, in each of the above embodiments, the system evaluation device 300 (300a) has been described as a single device, but this is not limited thereto and may be realized by a plurality of devices (for example, a plurality of server devices, etc.).

[0142] Furthermore, in each of the above embodiments, the system evaluation device 300 (300a) has been described as a device different from the smart meter measurement system 100 and the PV power generation output estimation system 200, but the system evaluation device 300 (300a) may be configured to include part or all of the smart meter measurement system 100 and the PV power generation output estimation system 200. For example, the system evaluation device 300 (300a) may be configured to include the PV power generation output estimation system 200 (power generation output estimation unit).

[0143] Furthermore, in each of the above embodiments, an example has been described in which the renewable energy generator connected to the target grid is a photovoltaic (PV) facility, but this is not limited to this and may be, for example, a generator using other renewable energy sources such as a wind power generator.

[0144] In the second embodiment, the system evaluation device 300a performs an optimal power flow calculation instead of a power flow calculation. However, the present invention is not limited to this, and the system evaluation device 300a may perform both a power flow calculation and an optimal power flow calculation. The system evaluation device 300a may select and perform either a power flow calculation or an optimal power flow calculation in response to communication by the communication unit 301 or reception of input by the input reception unit 303.

[0145] In addition, in each of the above embodiments, an example has been described in which the system reliability evaluation unit 310 (310a) reviews the system configuration and determines whether recalculation is necessary, but the present invention is not limited to this. For example, a person (operator) may manually review the system configuration and determine whether recalculation is necessary based on the received information received by the communication unit 301 from the operation terminal device 400 or the input information received by the input receiving unit 303.

[0146] Each component of the above-described system evaluation system 10 (10a) has an internal computer system. A program for implementing the functions of each component of the above-described system evaluation system 10 (10a) may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer system and executed to perform processing in each component of the above-described system evaluation system 10 (10a). Here, "reading a program recorded on a recording medium into a computer system and executing it" includes installing the program into a computer system. The term "computer system" here includes an OS and hardware such as peripheral devices.

[0147] Furthermore, a "computer system" may include multiple computer devices connected via a network, including communication lines such as the Internet, WAN, LAN, and dedicated lines. Furthermore, a "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into a computer system. Thus, the recording medium storing the program may be a non-transitory recording medium such as a CD-ROM.

[0148] The recording medium also includes internal or external recording media accessible from a distribution server for distributing the program. The program may be divided into multiple parts, downloaded at different times, and then combined into each component of the system evaluation system 10 (10a), or each divided program may be distributed by a different distribution server. Furthermore, the term "computer-readable recording medium" also includes a medium that retains a program for a certain period of time, such as volatile memory (RAM) within a computer system that acts as a server or client when a program is transmitted over a network. The program may also be a medium that realizes part of the above-described functions. Furthermore, the program may be a so-called differential file (differential program) that can realize the above-described functions in combination with a program already stored in the computer system.

[0149] Various aspects of the present disclosure are summarized below as appendices. (Appendix 1) a model generation unit that generates a probability distribution model of a population of power generation outputs based on the power generation outputs of generators connected to a target system that is an electric power system to be evaluated, and generates a probability distribution model of a population of power demands based on the power demands of the target system; a pseudo data generation unit that generates pseudo data based on the probability distribution model of the power generation output population and the probability distribution model of the power demand population generated by the model generation unit; a system evaluation unit that calculates a system influence level, which is an influence level in an abnormal event in which a failure or an accident occurs in equipment of the target system, using the pseudo data generated by the pseudo data generation unit, and evaluates the target system based on the system influence level; A system evaluation device comprising: (Appendix 2) The system impact degree includes a supply disruption amount and an overload amount when an abnormality occurs in the target system, The system evaluation unit evaluates the target system based on at least one of the supply disruption amount and the overload amount. 2. The system evaluation device of claim 1. (Appendix 3) a system abnormality simulation unit that generates input data for power flow calculation in which a system configuration at the time of the abnormality that caused the failure of the distribution equipment or the system accident is reflected in a configuration of the target system based on at least one of a failure rate of the distribution equipment and an occurrence rate of the system accident; and a power flow calculation unit that executes power flow calculation based on the input data for power flow calculation generated by the system abnormality simulation unit and each of the pseudo data; a power system influence calculation unit that calculates the supply disruption amount and the overload amount as the power system influence amount based on a power flow calculation result obtained by the power flow calculation unit executing the power flow calculation; Equipped with The system evaluation unit evaluates the target system based on at least one of the supply disruption amount and the overload amount calculated by the system influence degree calculation unit. 2. A system evaluation device as described in appendix 2. (Appendix 4) The power generation output includes a power generation output of renewable energy, the system abnormality simulator generates input data for an optimal power flow calculation that minimizes an output suppression amount of the renewable energy; the power flow calculation unit performs an optimal power flow calculation based on the input data for the optimal power flow calculation and the respective pseudo data so as to minimize an output suppression amount of renewable energy; the power system influence degree includes an output suppression amount of the renewable energy in an optimal power flow calculation result obtained by the power flow calculation unit executing the optimal power flow calculation, The system evaluation unit evaluates the target system based on the output suppression amount of the renewable energy in the optimal power flow calculation result. 3. A system evaluation device as described in appendix 3. (Appendix 5) The system evaluation unit evaluates the target system based on the system influence degree and an allowable threshold value of the system influence degree. 5. The system evaluation device according to claim 3 or 4. (Appendix 6) The power generation output includes a solar power generation output, The model generation unit normalizes the photovoltaic power generation output at each of a plurality of times using a theoretical solar radiation intensity outside the atmosphere at the corresponding time, and generates a probability distribution model of the photovoltaic power generation output using the normalized photovoltaic power generation output. 6. A system evaluation device according to any one of claims 1 to 5. (Appendix 7) The model generation unit generates a probability distribution model of the power generation output using a statistical estimation method. 7. The system evaluation device according to any one of claims 1 to 6. (Appendix 8) The statistical estimation method is a non-parametric method. 8. The system evaluation device of claim 7. (Appendix 9) The pseudo data of the power demand and the pseudo data of the power generation output include a plurality of cases at each of a plurality of predetermined times. 9. The system evaluation device according to any one of Supplementary Note 1 to Supplementary Note 8. (Appendix 10) a model generation unit that generates a probability distribution model of a population of power generation outputs based on the power generation outputs of generators connected to a target system that is an electric power system to be evaluated, and generates a probability distribution model of a population of power demands based on the power demands of the target system; a pseudo data generation unit that generates pseudo data based on the probability distribution model of the power generation output population and the probability distribution model of the power demand population generated by the model generation unit; a system evaluation unit that calculates a system influence level, which is an influence level in an abnormal event in which a failure or an accident occurs in equipment of the target system, using the pseudo data generated by the pseudo data generation unit, and evaluates the target system based on the system influence level; A systematic evaluation system comprising: (Appendix 11) A power generation output estimation unit is provided to estimate the power demand and the power generation output from the residual demand. The phylogenetic evaluation system described in Appendix 10. (Appendix 12) a model generation unit generating a probability distribution model of a population of power generation output based on actual values ​​of power generation output of a generator connected to a target system that is an electric power system to be evaluated, and generating a probability distribution model of a population of power demand based on actual values ​​of power demand of the target system; a pseudo data generation unit generating pseudo data based on the probability distribution model of the power generation output population and the probability distribution model of the power demand population generated by the model generation unit; a system evaluation unit calculating a system influence level, which is an influence level in an abnormal event in which a failure or an accident occurs in equipment of the target system, using the pseudo data generated by the pseudo data generation unit, and evaluating the target system based on the system influence level; Systematic evaluation methods including: (Appendix 13) On the computer, a model generation step of generating a probability distribution model of a population of power generation outputs based on the power generation outputs of generators connected to a target system, which is a power system to be evaluated, and generating a probability distribution model of a population of power demands based on the power demands of the target system; a pseudo data generation step of generating pseudo data based on the probability distribution model of the power generation output population and the probability distribution model of the power demand population generated by the model generation step; a system evaluation step of calculating a system influence level, which is the degree of influence in an abnormal event in which a failure or accident occurs in equipment of the target system, using the pseudo data generated in the pseudo data generation step, and evaluating the target system based on the system influence level; A program to execute. [Explanation of symbols]

[0150] 10, 10a... system evaluation system, 100... smart meter measurement system, 200... PV power generation output estimation system, 300, 300a... system evaluation device, 301... communication unit, 302... memory unit, 303... input reception unit, 304... probability statistical model generation unit, 305... system setting unit, 306... pseudo data generation unit, 307... system abnormality simulation unit, 308... power flow calculation unit, 308a... optimal power flow calculation unit, 309, 309a... system influence calculation unit, 310, 310a... system reliability evaluation unit, 321... system related information storage unit, 322... generator information storage unit, 323... measurement estimation data storage unit, 324... operation limit value storage unit, 325... calculation data storage unit, 400... operation terminal device, 500... communication network

Claims

1. a model generation unit that generates a probability distribution model of a population of power generation outputs based on the power generation outputs of generators connected to a target system that is an electric power system to be evaluated, and generates a probability distribution model of a population of power demands based on the power demands of the target system; a pseudo data generation unit that generates pseudo data based on the probability distribution model of the power generation output population and the probability distribution model of the power demand population generated by the model generation unit; a system evaluation unit that calculates a system influence level, which is an influence level in an abnormal event in which a failure or an accident occurs in equipment of the target system, using the pseudo data generated by the pseudo data generation unit, and evaluates the target system based on the system influence level; A system evaluation device comprising:

2. The system impact degree includes a supply disruption amount and an overload amount when an abnormality occurs in the target system, The system evaluation unit evaluates the target system based on at least one of the supply disruption amount and the overload amount. The system evaluation device according to claim 1 .

3. a system abnormality simulation unit that generates input data for power flow calculations in which a system configuration at the time of the abnormality that caused the failure of the distribution equipment or the system accident is reflected in a configuration of the target system based on at least one of a failure rate of the distribution equipment and an occurrence rate of the system accident; and a power flow calculation unit that executes power flow calculation based on the input data for power flow calculation generated by the system abnormality simulation unit and each of the pseudo data; a power system influence calculation unit that calculates the supply disruption amount and the overload amount as the power system influence amount based on a power flow calculation result obtained by the power flow calculation unit executing the power flow calculation; Equipped with The system evaluation unit evaluates the target system based on at least one of the supply disruption amount and the overload amount calculated by the system influence degree calculation unit. The system evaluation device according to claim 2 .

4. The power generation output includes a power generation output of renewable energy, the system abnormality simulator generates input data for an optimal power flow calculation that minimizes an output suppression amount of the renewable energy; the power flow calculation unit performs an optimal power flow calculation based on the input data for the optimal power flow calculation and the respective pseudo data so as to minimize an output suppression amount of renewable energy; the power system influence degree includes an output suppression amount of the renewable energy in an optimal power flow calculation result obtained by the power flow calculation unit executing the optimal power flow calculation, The system evaluation unit evaluates the target system based on the output suppression amount of the renewable energy in the optimal power flow calculation result. The system evaluation device according to claim 3 .

5. The system evaluation unit evaluates the target system based on the system influence degree and an allowable threshold value of the system influence degree. The system evaluation device according to claim 3 or 4.

6. The power generation output includes a solar power generation output, The model generation unit normalizes the photovoltaic power generation output at each of a plurality of times using a theoretical solar radiation intensity outside the atmosphere at the corresponding time, and generates a probability distribution model of the photovoltaic power generation output using the normalized photovoltaic power generation output. The system evaluation device according to any one of claims 1 to 4.

7. The model generation unit generates a probability distribution model of the power generation output using a statistical estimation method. The system evaluation device according to any one of claims 1 to 4.

8. The statistical estimation method is a non-parametric method. The system evaluation device according to claim 7 .

9. The pseudo data of the power demand and the pseudo data of the power generation output include a plurality of cases at each of a plurality of predetermined times. The system evaluation device according to any one of claims 1 to 4.

10. a model generation unit that generates a probability distribution model of a population of power generation outputs based on the power generation outputs of generators connected to a target system that is an electric power system to be evaluated, and generates a probability distribution model of a population of power demands based on the power demands of the target system; a pseudo data generation unit that generates pseudo data based on the probability distribution model of the power generation output population and the probability distribution model of the power demand population generated by the model generation unit; a system evaluation unit that calculates a system influence level, which is an influence level in an abnormal event in which a failure or an accident occurs in equipment of the target system, using the pseudo data generated by the pseudo data generation unit, and evaluates the target system based on the system influence level; A systematic evaluation system comprising:

11. A power generation output estimation unit is provided to estimate the power demand and the power generation output from the residual demand. The system for evaluating a lineage according to claim 10.

12. a model generation unit generating a probability distribution model of a population of power generation outputs based on the power generation outputs of generators connected to a target system, which is a power system to be evaluated, and generating a probability distribution model of a population of power demands based on the power demands of the target system; a pseudo data generation unit generating pseudo data based on the probability distribution model of the power generation output population and the probability distribution model of the power demand population generated by the model generation unit; a system evaluation unit calculating a system influence level, which is an influence level in an abnormal event in which a failure or an accident occurs in equipment of the target system, using the pseudo data generated by the pseudo data generation unit, and evaluating the target system based on the system influence level; Systematic evaluation methods including:

13. On the computer, a model generation step of generating a probability distribution model of a population of power generation outputs based on the power generation outputs of generators connected to a target system, which is a power system to be evaluated, and generating a probability distribution model of a population of power demands based on the power demands of the target system; a pseudo data generation step of generating pseudo data based on the probability distribution model of the power generation output population and the probability distribution model of the power demand population generated by the model generation step; a system evaluation step of calculating a system influence level, which is the degree of influence in an abnormal event in which a failure or accident occurs in equipment of the target system, using the pseudo data generated in the pseudo data generation step, and evaluating the target system based on the system influence level; A program to execute.

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