JP2014186503A - Profit analyzer for power generation facility, method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable an accurate analysis of profit in power generation.SOLUTION: A weather sampler generates a weather data sample showing weathers at multiple time points. An aging abnormality sampler generates a stress data sample showing performance deterioration or life reduction due to aging of a power generation facility at the multiple time points. The simulation part generates a coefficient sample showing performances of the power generation facility at the multiple time points and a maintenance cost sample showing maintenance costs at the multiple time points by repeatedly deteriorating the performance or life of the power generation facility by the stress data sample, determining whether it can be detected that the deteriorated performance or life satisfies a prescribed reference by using a random number and updating the performance or life by conducting maintenance activities when it can be detected while advancing the time points. A generated power amount estimation part generates a generated power amount sample showing power amounts generated by the power generation facility at the multiple time points. A power generation value calculation part calculates at least a profit at one time point or an accumulated profit.

Description

  Embodiments described herein relate generally to a profit analysis apparatus for power generation equipment, a method thereof, and a program.

  The spread of power generation facilities using natural energy such as photovoltaic power generation systems (hereinafter referred to as PV systems) is required. In order to promote investment in power generation facilities, accurate power generation revenue analysis technology is required.

  There are various uncertainties regarding profit analysis when given rules for purchasing generated power in renewable energy generation. For this reason, there is a demand for an analysis that considers probability distribution such as VaR (Value at Risk) instead of average profit. For such an analysis, it is necessary to obtain a probability distribution of profits by performing simulations in consideration of uncertain factors multiple times.

  There are two main uncertainties in power generation using natural energy: (A) weather conditions such as sunshine, temperature, and wind conditions, and (B) deterioration and breakdown of equipment that affects power generation performance. In addition, considering the recent progress of equipment abnormality monitoring systems, (C) an uncertain element of maintenance by the abnormality monitoring system is also required.

  As a method for performing profit analysis based on such a probability distribution, a method targeting solar power generation has been proposed. This method uses the performance guarantee information in the equipment / equipment specifications related to PV modules and PCS (Power Conditioning System) for weather data samples sampled using weather statistical information, and the guaranteed minimum performance value continues for the warranty period. The power generation revenue analysis is performed using the performance state value according to the assumption.

Japanese Patent Laid-Open No. 2002-299668

  The equipment of the actual renewable energy power generation system is exposed to the natural world, and there is uncertainty of power generation and failure depending on weather conditions. Moreover, it can be expected that profits can be improved by diagnosing a fault with an abnormality monitoring system and actively performing maintenance even if the performance exceeds the guaranteed performance value. Accurate earnings analysis that takes such factors into account has been a challenge.

  The present embodiment provides a revenue analysis apparatus and method, and a program that enable accurate revenue analysis.

  A profit analysis apparatus as an embodiment of the present invention is a profit analysis apparatus for a natural energy power generation facility, which includes a weather sampler, an aging abnormality sampler, a simulation unit, a power generation amount estimation unit, a power generation value calculation unit, Is provided.

  The weather sampler generates weather data samples representing the weather at a plurality of time points based on statistical information related to the weather.

  The aging abnormality sampler generates a stress data sample representing a performance deterioration or a life reduction due to aging of the power generation facility at the plurality of time points using a random number.

  The simulation unit (A) updates the performance or life of the power generation facility due to the performance degradation or life reduction of the stress data sample at the time point, and (B) the performance or life after the update satisfies a predetermined standard Then, it is determined using a random number whether it can be detected that the predetermined criterion is satisfied, and (C) when it is detected that the predetermined criterion is satisfied, Performing the maintenance action, and updating the performance or lifetime according to the maintenance action, repeating the time point while proceeding, the performance coefficient sample representing the performance of the power generation equipment at the plurality of time points, A maintenance cost sample representing the cost of the maintenance at a plurality of points in time is generated.

  The power generation amount estimation unit generates a power generation amount sample representing the power generation amount of the power generation facility at the plurality of times based on the weather data sample and the performance coefficient sample.

  The power generation value calculation unit calculates revenue or accumulated revenue at at least one point in time based on information on the power generation amount and revenue, the power generation amount sample, and the maintenance cost sample.

1 is a block diagram of a power generation facility profit analysis apparatus according to an embodiment of the present invention. The flowchart of the operation | movement which concerns on embodiment of this invention. The flowchart of the operation | movement which concerns on embodiment of this invention. The figure showing the example of weather statistical information. The figure showing the example of field correction information. The figure showing the example of apparatus reliability information. The figure showing the example of abnormality monitoring information. The figure showing the example of an equipment and apparatus specification. The figure showing the example of generated electric power value information. The figure showing the example of an analysis query. The figure showing an example of a power generation profit output. The figure showing the example of a weather data sample. The figure showing the example of a performance state value. The figure showing the example of an apparatus stress data sample. The figure showing the example of a sudden abnormality sample. The figure showing the example of a performance coefficient sample. The figure showing the example of a maintenance cost sample. The figure showing the example of an electric power generation amount sample. The figure explaining the difference of the sampling of apparatus stress and the sampling of a lifetime. The figure explaining the difference of the sampling of apparatus stress and the sampling of a lifetime. The figure which shows the hardware structural example of this embodiment.

  The embodiment of the present invention is a long-term profit analysis device generated by a power generation system using natural energy, such as a photovoltaic power generation system (PV system), particularly for aged and sudden failures of equipment and their performance degradation. The present invention relates to a power generation facility revenue analysis apparatus capable of calculating a highly accurate revenue in consideration of the effect of an abnormality monitoring system for performing the above.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a block diagram of a power generation facility profit analysis system including a power generation profit analysis apparatus according to an embodiment of the present invention. As shown in FIG. 1, the power generation facility profit analysis system includes a meteorological statistics information storage unit 101, a field correction information storage unit 102, a device reliability information storage unit 103, an abnormality monitoring information storage unit 104, and facility / equipment specification storage. Unit 105, power generation price information storage unit 106, analysis query storage unit 107, power generation revenue analysis apparatus 108, and power generation revenue output unit 109. Revenue analysis is performed with the query from the analysis query storage unit 107 as input, and the power generation revenue obtained by the analysis is output from the power generation revenue output unit 109. In the following description, information stored in the storage units 101 to 17 may also be indicated by the same reference numerals 101 to 107.

  The meteorological statistics information storage unit 101 stores meteorological statistics information of the points to be analyzed or neighboring points. For example, statistical data such as the amount of solar radiation, temperature, and weather at a site considering the construction of a PV system and a nearby weather station are accumulated. The weather statistical information may be long-term raw data or a probability distribution calculated from the raw data.

  FIG. 4 shows an example of weather statistical information. A probability distribution 402 of solar radiation and a probability distribution 403 of temperature are accumulated for each month. Each probability distribution has an average value and upper and lower limits. In addition, information such as monthly weather distribution can be accumulated. Further, statistical data for every day or every hour may be accumulated instead of every month.

  The field correction information storage unit 102 stores field correction information that is information related to the installation site of the PV system. The field correction information corrects the weather statistics information in accordance with the site when there is a difference between the weather observation conditions at the point of the weather statistics information (for example, the meteorological observatory) and the weather conditions at the analysis target point (for example, the PV system construction site) Used for. For example, if the solar system has a lower solar radiation amount than a nearby weather station, the weather statistics information can be corrected according to the difference.

  FIG. 5 shows an example of field correction information. Information such as the direction in which the PV module of the PV system is installed, the elevation angle of the panel, and the presence or absence of nearby sunlight obstructions such as buildings is accumulated.

  In the device reliability information storage unit 103, life information of each device constituting the equipment, possible aging abnormality or sudden abnormality, model information indicating an occurrence condition, occurrence probability, treatment action and treatment cost that can be taken in case of failure Etc. are accumulated.

  FIG. 6 shows an example of device reliability information. Abnormal information related to the PV module and the PCS is accumulated as the abnormal information of the constituent devices of the PV system. Here, reference numeral 601 represents an initial performance value and initial life parameter of the module, and reference numeral 602 represents information on various abnormalities.

  For example, the initial life of the PV module is 3000. The unit of lifetime may be arbitrary. For example, the day. The initial performance value follows a normal distribution with an average of 1.00 and a standard deviation of 0.005.

  “Deterioration”, which is an aged abnormality of the PV module, occurs regardless of specific conditions. The degree of deterioration can be sampled from a normal distribution with an average of 0.005 and a standard deviation of 0.005 per period (for example, one period when one year is divided into four periods) and attached with a negative sign. The performance value decreases by this value. If the “guarantee” action based on the guarantee contract can be applied to the deterioration, the performance state value (power generation capacity) can be returned to the initial value at 30 thousand yen (for example, exchange for a new one at 30000 yen). In addition, if the “exchange” action is performed without applying the guarantee, the performance state value can be returned to the initial value for 300 thousand yen.

  In addition, a “failure” that is an aging abnormality of a module occurs regardless of a specific condition. The lifetime is consumed according to a normal distribution with an average of 200 and a standard deviation of 50 per period. A failure occurs when the lifetime reaches a predetermined standard (for example, 0).

  In addition, the “failure” included in the sudden failure of the module may occur only on a rainy day, and the probability of occurrence follows a Bernoulli distribution of 0.0001 (that is, 0.01%). If it occurs, it will consume 2000 on average.

  Furthermore, the probability that a sudden abnormal “down” related to PCS will occur follows a Bernoulli distribution of 0.001, and the recovery cost is 30 thousand yen.

  In the above description, “guarantee” and “replacement” are shown as a maintenance action when deterioration or failure occurs. However, another maintenance action such as “repair” or “inspection” may be added. It is also possible to add a maintenance action “do nothing”. A plurality of “guarantee” contents may also be prepared.

  By using device reliability information that collects information on the occurrence conditions, occurrence probability and amount, possible treatment actions and treatment costs for various abnormal modes of such devices, abnormality sampling and abnormality as described later A monitoring simulation can be performed.

  The abnormality monitoring information storage unit 104 stores performance information of the abnormality monitoring system. The monitoring performance can accumulate different values for each abnormal mode (such as aging, sudden occurrence). In addition, diagnostic performance can be set according to abnormal events (deterioration, failure, down, etc.).

  FIG. 7 shows an example of abnormality monitoring information. The monitoring performance is represented by a miss rate that determines that the abnormality is normal and a false alarm rate that determines that the abnormality is normal. For example, it can be seen that sudden module abnormalities can be detected with an overlook rate of 0.01 (ie, with 99% accuracy), and false alarms occur at 5%. It can also be seen that the PCS can be completely identified with no oversight or misinformation. By using the abnormality monitoring information in this way, it is possible to accurately simulate the effect of the abnormality monitoring system as will be described later. In the example of FIG. 7, when the performance value of the module is 30% or less of the initial value, the module is treated as an abnormality due to deterioration, and is a target for treatment (exchange, etc.). In addition to deterioration (30%), deterioration (40%), deterioration (50%), etc. may be added. Further, here, it is assumed that the failure abnormality is when the lifetime becomes zero. Further, when the lifetime is other than 0, for example, when the lifetime becomes 30 or 20 or less, it may be defined as “quasi-failure 1” or “quasi-failure 2” indicating that the failure is approaching. These degradation criteria (30%, 40%, 50%, etc.) and lifetime criteria (0, 20, 30, etc.) correspond to predetermined criteria.

  The equipment / equipment specification storage unit 105 includes equipment and equipment specification data for evaluating the performance of the equipment. Includes device type, specifications, location, number, performance characteristics, warranty information, purchase costs, etc.

  FIG. 8 shows an example of facility / equipment specifications. Using a PV system as an example, PV module and PCS information is stored. The guarantee information can be used when the abnormality monitoring simulator 114 selects a treatment. For example, the PCS of P1 has a 10-year warranty, and if a failure occurs within the 10-year warranty period, for example, the cost of “guarantee” indicated in the device reliability information shown in FIG. PCS can be replaced.

  The generated power price information storage unit 106 stores information for converting the amount of power generation into value. For example, a coefficient for converting the amount of electricity purchased every year, the CO2 suppression effect, and the like is stored.

  FIG. 9 shows an example of generated power value information. It shows the transition of purchase price per unit electric energy from the present to 25 years ahead.

  The analysis query storage unit 107 stores analysis conditions set by the analyst. In accordance with the conditions, the power generation revenue analysis apparatus 108 calculates power generation revenue and outputs the calculation result to the power generation revenue output unit 109.

  FIG. 10 shows an example of an analysis query. The revenue calculation year (analysis target period) is 25 years, which means that the revenue up to 25 years from now is evaluated. The number of weather samples and the number of equipment abnormality samples (number of equipment stress data samples 131) that affect the analysis accuracy are 5000 and 200, respectively. It is shown that the evaluation is based on VaR (Value at Risk) at the 95% point of the probability distribution.

  Various types of power generation revenue output unit 109 are conceivable depending on the form of the system, such as a display or a smartphone. FIG. 11 shows an example of power generation revenue output. Reference numeral 1101 represents a probability distribution of accumulated earnings up to the 25th year in time series. Reference numeral 1102 shows only the probability distribution of the accumulated revenue in the 25th year. From the probability distribution of 1102, the average revenue in the 25th year is 110 million yen, the 95% VaR revenue is 99.1 million yen (that is, the probability that the 25th year earnings will be less than 99.1 million yen is 5%), etc. Can be calculated. Instead of cumulative revenue, a probability distribution of revenue for each year may be calculated and displayed.

  In the following, an embodiment of the power generation revenue analysis apparatus 108 for performing such revenue output will be described.

  The power generation revenue analysis apparatus 108 includes a weather sampler 110, a secular abnormality sampler 111, a sudden abnormality sampler 112, a performance state value storage unit (temporary storage unit) 113, an abnormality monitoring simulator 114, a power generation amount estimation unit 115, a power generation value calculation unit 116, A weather data sample storage unit 121, a performance coefficient sample storage unit 122, a maintenance cost sample storage unit 123, and a power generation amount sample storage unit 124 are provided. The aging abnormality sampler 111 includes an equipment stress sampler 130 and an aging abnormality calculation period 132. The abnormality monitoring simulator 114 and the aging abnormality calculation unit 132 form a simulation unit 126.

  The meteorological sampler 111 generates various meteorological data by sampling using random numbers based on the meteorological statistical information and on-site correction information as necessary, and stores it in the meteorological data sample storage unit 121. For example, weather data samples such as the number of samples N and the sample length L are calculated.

  If the weather statistical information is a probability distribution, sampling may be performed using the probability distribution, or if the weather statistical information is a weather database, etc., the meteorological data at the corresponding time in a specific year is selected at random. You may use as it is.

  For example, when analyzing the data for 25 years and calculating the power generation amount every day, it is necessary to generate weather data with a sample length of L = 365 × 25 = 9125. Or you may perform the calculation for every week, and the calculation for every hour.

  FIG. 12 shows an example of a weather data sample. Data such as the amount of sunlight, temperature, and weather are determined on a daily basis. By calculating the power generation amount under such various weather conditions, a long-term probability distribution of the power generation amount can be obtained. In the example of the analysis query 107 shown in FIG. 10, since the revenue calculation year is 25 years and the number of weather samples is 5000, 5000 weather data having the sample length of 9125 are created.

  In addition to weather conditions, factors that affect the capacity of the equipment may include abnormalities such as the progress of equipment deterioration and breakdowns. Since equipment degradation and failure are also stochastic phenomena, sampling is required. In the present embodiment, in order to perform efficient sampling, deterioration and failure that are long-term abnormalities and failure that are short-term sudden abnormalities (sudden failures) are sampled separately.

  Here, the performance state value storage unit 113 stores information necessary for calculating the performance of the facility at a certain point in time. Information in the performance state value storage unit 113 is stored at the time of simulation, and is updated sequentially every time the simulation time is advanced, and is used to calculate the performance of the equipment at the next time.

  FIG. 13 shows an example of the performance state value 113. The state value in the first period (April to June) of the third year since the facility started operation is shown. The performance coefficient (performance value and lifetime) at the beginning of this period (hereinafter sometimes referred to as term) is shown in the third line.

  Here, {} represents a list of values for each object, and {information related to the module M1, information related to the module M2,. FIG. 13 shows that the performance coefficient of the module M1 is 0.47 and the lifetime is 1730.

  Assuming that the performance of the rated power generation amount under the conditions such as the reference temperature and the reference sunshine amount is 1.0, the performance value 0.47 means the power generation performance of 47% of the rated power generation amount under the same conditions.

  Based on the device reliability information 103, the aging abnormality sampler 111 calculates deterioration and failure that are aging abnormalities in the facility in the analysis target period range using random numbers. Let M be the number of samples of an aged abnormal sampler (number of instrument stress data samples). The aging abnormality sampler 111 includes an equipment stress sampler 130, an equipment stress data sample storage unit 131, and an aging abnormality calculation unit 132.

  The device stress sampler 130 calculates a stress value related to deterioration or failure of each device based on the device reliability information 103.

  FIG. 14 shows an example of a device stress data sample. One record is generated for each term. Each record is composed of stress values relating to failure and deterioration of each PV module. For example, the item “M1 abnormality” can be sampled from the model in the second row of the reliability information 602 shown in FIG. 6, and the module life of the module M1 (see 601 in FIG. 6) is consumed by the stress value. means. The item “M1 degradation” means that sampling can be performed from the model in the first row of the reliability information 602, and the performance value of the module M1 is consumed by the stress value.

  The device stress data sample storage unit 131 stores device stress data samples sampled by the device stress sampler 130.

  The secular abnormality calculation unit 132 uses the performance coefficient of the performance state value of the corresponding term in the performance state value storage unit 113 to calculate the performance coefficient after the occurrence of the secular abnormality, and the performance coefficient in the performance state value 113 is calculated. Update. For example, the stress value of the M1 abnormality in the first period in the third year in FIG. 14 is 200, the M1 deterioration is 0.00, the initial life 1730 in the first period in the third year shown in the performance state value 113 in FIG. Subtract 200 and 0.00 respectively from the value 0.47. As a result, the life is 1530 and the performance value is 0.47 as described in the item “Aging abnormality” of the performance state value 113 in FIG.

  In the sudden abnormality sampler 112, an abnormality related to a short-term failure of each device (abnormal event such as down, failure, deterioration, etc., life or performance based on the device reliability information 103 and the weather data sample as necessary. Is calculated).

  FIG. 15 shows an example of a sudden abnormality sample. On July 3rd (July 3rd) of the first year, a sudden anomaly that PCS P1 was down occurred and the performance value was consumed by 0.01 (see the model in the fourth line of 602 in FIG. 6). In addition, a sudden abnormality called a failure of the module M1 occurred on 4/1 of the third year, and the lifetime was consumed by 2000 (see the model in the third row of 602 in FIG. 6). As shown in FIG. 6, the sudden failure of the module needs to be “rain” as a condition. When there is such a weather condition, it is necessary to use a weather data sample.

  The sudden abnormality sampler 112 stores a sudden abnormal case of the corresponding term in the performance state value. Note that sudden abnormalities are not necessarily one case per term. In the item “sudden abnormality” of the performance state value 113 in FIG. 13, {4/1: M1: failure: 200} in the first period of the third year in FIG. 15 is stored.

  The abnormality monitoring simulator 114 determines the maintenance cost in the target term and the performance coefficient in the next term based on the performance state value 113, the abnormality monitoring information 104, the facility / equipment specification 105, and the device reliability information 103. To do. Further, the performance coefficient and the maintenance cost in the performance state value 113 are stored in the performance coefficient sample 122 and the maintenance cost sample 123.

The operation of the abnormality monitoring simulator 114 is shown based on the example of FIG. When the sudden abnormality is reflected in FIG.

{M1: 0.47: 0, M2:…}

It becomes. Here, 1530-2000 = -470, but negative values are converted to 0.

  This state is a failure phenomenon of the module M1. According to 701 in FIG. 7, it can be seen that the oversight rate of the abnormality monitoring system for this event is 1% and the false alarm rate is 5%. Here, when it is determined using a random number whether or not this abnormality can be detected, it can be seen that the detection succeeds with a probability of 99%. In the example of FIG. 13, the detection result regarding the failure of M1 is Y (YES), and the detection is successful.

  It can be seen that there are options of “guarantee” and “exchange” as measures when the detection is successful (see FIG. 6). However, according to the facility / equipment specifications 105 of FIG. 8, it can be seen that there is no warranty contract for the module M1, and only the option of “exchange” can be obtained. Therefore, the maintenance cost of 300,000 yen is included in this term. In the example of FIG. 13, “M1: replacement: 300” is stored in the “treatment” item, and “300” is stored in the “maintenance cost” item.

  The performance factor can be returned to the initial state by the replacement, and the next performance factor is determined in accordance with the condition 601 in FIG. In the example of FIG. 13, the module M1 is replaced with one having a lifetime of 3000 and a performance value of 0.99. ({M1: 0.99: 3000} is stored in the item of “Next Performance Factor”).

  After performing the simulation in this manner, the performance coefficient in the performance state value 113 in FIG. 13 is added to the performance coefficient sample storage unit 122, and the maintenance cost in the performance state value 113 in FIG. Further, the performance coefficient in the performance state value 113 of FIG. 13 is updated to the performance coefficient of the next period, and the simulation time (time point) is advanced by 1term. In the example of FIG. 13, since the current simulation time is 3 years and 1 period, the next simulation time is 3 years and 2 periods.

  FIG. 16 shows an example of the performance coefficient sample, and FIG. 17 shows an example of the maintenance cost sample. Such samples are accumulated for N × M, and power generation revenue analysis is performed.

  The power generation amount estimator 115 generates and accumulates a power generation amount sample by estimating the power generation amount that can be generated by the PV system (facility) based on the weather data sample 121 and the performance coefficient sample 122.

  The amount of power generation can be estimated by a technique called power generation performance modeling (see: D. L. King, W. E. Boyson, and J. A. Kratochvil, Photovoltaic Array Performance Model, Sandia National Laboratories Report, SAND2004-3535, August 2004.).

  By using the power generation performance modeling technology, it is possible to estimate the amount of power generation (power generation capacity) from meteorological data based on the specification data of the facility / equipment specification data. For example, from the specification data of facility / equipment specification data, a model that outputs the amount of power generation using the amount of sunlight, temperature, and performance values as variables can be generated and used.

In the performance modeling of the above reference,
Using the sunshine value G (W / m ² ) and the estimated module temperature Tm (° C):
Ir (Tm, G ') = (c0 * (1-G') + G ') * (1 + α * (Tm-25))
Estimate the current by
Vmp (Tm, G ') = Vstd + β * (Tm-25) + c2 * δ (Tm) * ln (G') + c3 * (δ (Tm) * ln (G ')} ^ 2
Estimate the voltage by
P = Ir (Tm, G ') * G' * Istd * Vpm (Tm, G ')
Predict power by the
Where (Istd, Vstd) = Maximum power in normal state
G '= G / 1000
α, β, c0, c2, c3 are coefficients estimated from the data, and δ (T) is a function called the thermal voltage at temperature T.

  The calculated power generation amount sample is stored in the power generation amount sample storage unit 124. FIG. 18 shows an example of a power generation amount sample.

  The power generation value calculation unit 116 calculates the probability distribution of the value generated by the power generation facility based on the maintenance cost sample 123 and the power generation amount sample 124. At that time, by using the generated power price information 106, the power generation amount is converted into a monetary value, and the maintenance cost is subtracted from the monetary value, thereby calculating the probability distribution of the value as the income considering the income and the expenditure. Then, according to the conditions set in the analysis query, the profit analysis result is output to the power generation profit output unit 109.

For example, in calculating revenue, at each term t

Power generation value (t) = Power generation capacity (t) x Purchase price (t)-Maintenance cost (t)

And a cumulative value is calculated. By performing this for N × M samples, a probability distribution of cumulative revenue can be obtained for each term. FIG. 11 shows an example of the accumulated revenue output of power generation.

  In the aging abnormality sampler 111, the device stress that affects the deterioration or failure of the device is sampled based on the device reliability information 103. However, as another method, a method of sampling the life of the device itself can be considered.

  19 and 20 are diagrams for explaining the difference between sampling of equipment stress and sampling of life. Since equipment failures do not occur deterministically, some uncertainty must be introduced.

  FIG. 19 shows such a mechanism. For example, a plurality of device lifetimes can be sampled so as to be close to the survival curve (survival function) of 1901 (1902). On the other hand, a method of introducing uncertainty that the life of the device is constant or almost constant and the stress applied to the device is different is also conceivable (1903, 1904).

  FIG. 20 shows the difference between these two sampling methods. In the former sampling method, as shown by 2002 and 2003 in FIG. 20, if the device is replaced with a device having a short life after the replacement despite the early replacement, the failure will be detected despite the preventive replacement. Increasing events occur. On the other hand, in the latter sampling method, it is guaranteed that the failure time is surely delayed if the preventive replacement is performed as in 2004 and 2005 in FIG. This latter property is efficient when comparing a plurality of maintenance strategies at what timing maintenance should be performed.

The latter is a method that is suitable when the environment has a major influence on the failure of the device, and may not be appropriate when the quality inherent to the device, such as the presence or absence of an initial failure, has a major influence on the failure. However, even if there is an initial failure, there are cases where such factors may be neglected in the profit analysis when repairs are performed free of charge under product warranty.
Each component of the apparatus in FIG. 1 can be realized as, for example, a program module. In this case, the function can be realized by executing a program including each program module in the computer system shown in FIG. This computer system includes a CPU 2101 for executing program instructions, a main storage device 2104 such as a memory, an external storage device 2103 such as a hard disk, a magnetic disk device or a magneto-optical disk device, an input device 2105 for inputting data by the user, A display device 2106 for displaying data and a bus 2102 for connecting them to each other are provided. The program is stored in the external storage device 2103, and the CPU 2101 expands the program in the main storage device 2104, and sequentially reads and executes the expanded program.

  FIG. 2 shows a flowchart of the operation according to the embodiment of the present invention.

  The power generation revenue analysis apparatus 108 reads the weather statistical information 101, the field correction information 102, the device reliability information 103, the abnormality monitoring information 104, the facility / equipment specifications 105, and the generated power price information 106 (S201).

  Further, the power generation revenue analysis apparatus 108 reads the analysis query 107. The analysis query includes analysis conditions such as the analysis target period, weather sample number N, and abnormal sample number M (S202).

  The weather sampler 110 generates N weather data samples (S203).

  The secular abnormality sampler 111 performs secular abnormality sampling, and the sudden abnormality sampler 112 performs sudden abnormality sampling (S204). By using these sampling results, the abnormality monitoring simulator 114 performs abnormality monitoring simulation while advancing the time, and updates the performance state value 113 for each time, so that the performance coefficient sample 122 and the maintenance cost sample 123 are obtained. Generate (S204). When the sudden abnormality sampler 112 uses a weather data sample, step S204 is performed for each weather data sample (S205). Details of step S204 will be described later.

  The power generation amount estimator 115 generates a power generation amount sample 124 by estimating the power generation amount based on the weather data sample 121 and the performance coefficient sample 112.

  The power generation value calculation unit 116 calculates the probability distribution of power generation revenue at each time based on the generated power price information 106, the power generation amount sample 124, and the maintenance cost sample 123 (S207). Further, a probability distribution of accumulated power generation revenue at each time is generated from the probability distribution at each time (S207). A probability distribution of power generation revenue at at least one time, or a probability distribution of accumulated power generation revenue at one time may be calculated.

  The power generation revenue output unit 109 outputs the information generated by the power generation value calculation unit 116 so that it can be viewed by the user.

  FIG. 3 is a flowchart showing details of step S204 in FIG.

  The equipment stress sampler 130 generates M pieces of equipment stress samples 131 (S301).

  The power generation revenue analysis apparatus 108 initializes the performance state value 113 and sets time (t) = 0 (S302). This process may be performed by the aged abnormality sampler 111, or may be performed by an initialization processing unit provided by this processing unit, or may be performed by another processing unit.

  The aging abnormality calculation unit 132 calculates a performance coefficient after the aging abnormality occurs based on the device stress data sample 131, and updates the performance state value 113 based on the calculated performance coefficient (S303).

  The sudden abnormality sampler 112 generates a sudden abnormality case based on the device reliability information 103 and, if necessary, the weather data sample 121, and writes the generated sudden abnormality case in the performance state value 113 (S304).

  The abnormality monitoring simulator 114 executes an abnormality monitoring simulation based on the performance state value 113, the abnormality monitoring information 104, and the facility / equipment specifications 105, and updates the performance state value 113 (S305).

  The power generation revenue analyzer 108 adds the maintenance cost stored in the performance state value 113 after the abnormality monitoring simulation to the corresponding time field of the maintenance cost sample, and the performance coefficient stored in the performance state value 113 as well. Is added to the corresponding time field of the performance coefficient sample (S306). Then, the time t is incremented.

  If the time after the increment does not exceed the last time of the analysis target period (the last time of the weather data sample), the process returns to step S303 (S307). If so, it is determined whether all M device stress data samples have been processed. If there are samples that have not yet been processed, the process returns to step S302.

  As described above, according to the present embodiment, it is possible to perform an accurate power generation revenue simulation reflecting the three processes of weather conditions, equipment deterioration / failure, abnormality monitoring and maintenance with a small amount of calculation. . As a result, high-speed power generation revenue analysis can be performed.

  In this embodiment, the solar power generation facility has been described. However, any power generation facility that uses natural energy and is affected by the weather can be applied to other power generation facilities such as wind power generation and geothermal power generation. is there.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

Claims (10)

A revenue analysis device for renewable energy power generation equipment,
A weather sampler that generates weather data samples representing weather at multiple points in time based on weather statistics,
A stress data sample representing performance deterioration or life reduction due to aging of the power generation facility at the plurality of time points, an aging abnormality sampler that generates using random numbers,
(A) Update the performance or life of the power generation facility due to the performance degradation or life reduction of the stress data sample at the time point
(B) When the reduced performance or lifetime meets a predetermined criterion, it is determined using a random number whether it can be detected that the predetermined criterion is satisfied,
(C) When it is detected that the predetermined standard is satisfied, maintenance action according to the predetermined standard is performed on the power generation facility, and the performance or life is updated according to the maintenance action. A simulation unit that generates a performance coefficient sample that represents the performance of the power generation facility at the plurality of time points and a maintenance cost sample that represents the cost of the maintenance action at the plurality of time points by repeating while proceeding with the time points. ,
A power generation amount estimation unit that generates a power generation amount sample representing the power generation amount of the power generation facility at the plurality of time points based on the weather data sample and the performance coefficient sample;
A power generation value calculation unit that calculates information on the power generation amount and revenue, the power generation amount sample, and the revenue or accumulated revenue at at least one time point based on the maintenance cost sample;
Profit analysis device with. The weather sampler generates a plurality of the weather data samples,
The aging abnormal sampler generates a plurality of the stress data samples,
The simulation unit generates a plurality of the performance coefficient samples and the maintenance cost samples based on the plurality of stress data samples,
The power generation amount estimation unit generates a plurality of power generation amount samples,
The profit analysis apparatus according to claim 1, wherein the power generation value calculation unit calculates a probability distribution of profit or a probability distribution of cumulative profit at the at least one time point. The random number is used to determine whether or not performance degradation or life reduction occurs due to sudden occurrence of the power generation equipment at the plurality of time points, and whether or not performance degradation or life reduction occurs at the plurality of time points. A sudden anomaly sampler that generates a sudden anomaly sample representing the value of the decrease;
The profit analysis apparatus according to claim 1, wherein the simulation unit performs the processes (A), (B), and (C) using the sudden abnormality sample together with the stress data sample. The profit analysis apparatus according to claim 3, wherein the sudden abnormality sampler determines whether or not the performance deterioration or the life deterioration has occurred using the weather sample. The profit analysis apparatus according to any one of claims 1 to 4, wherein the aging abnormality sampler generates the stress data sample using a probability distribution of values of performance deterioration or life reduction of the power generation equipment. The revenue analysis apparatus according to any one of claims 1 to 5, wherein the simulation unit determines the maintenance cost based on the presence or absence of a warranty contract for the power generation facility and the content of the warranty contract. The statistical information includes statistical information on solar radiation and temperature,
The power generation facility includes a solar power generation module and a power conditioner,
The profit analysis apparatus according to any one of claims 1 to 6, wherein the secular abnormality sampler and the simulation unit handle the solar power generation module and the power conditioner as the power generation equipment. The profit analysis device according to any one of claims 1 to 7, wherein the information on the power generation amount and the profit is information in which the power generation amount and the power price are associated with each other. Revenue analysis method for natural energy power generation equipment,
Generating weather data samples representing weather at multiple time points based on weather statistics;
Generating a stress data sample representing a performance degradation or a life reduction due to aging of the power generation facility at the plurality of time points using a random number;
(A) Decreasing the performance or life of the power generation facility due to performance deterioration or life reduction of the stress data sample at the time point,
(B) When the reduced performance or lifetime meets a predetermined criterion, it is determined using a random number whether it can be detected that the predetermined criterion is satisfied,
(C) When it is detected that the predetermined standard is satisfied, maintenance action according to the predetermined standard is performed on the power generation facility, and the performance or life is updated according to the maintenance action. Generating a performance coefficient sample representing the performance of the power generation facility at the plurality of time points and a maintenance cost sample representing the cost of the maintenance at the plurality of time points by repeating while proceeding with the time points;
A power generation amount estimation step for generating a power generation amount sample representing the power generation amount of the power generation facility at the plurality of times based on the weather data sample and the performance coefficient sample;
Calculating revenue or cumulative revenue distribution at at least one time point based on information on power generation and revenue, the power generation sample, and the maintenance cost sample;
Revenue analysis method with A program for revenue analysis of renewable energy power generation facilities,
Generating weather data samples representing weather at multiple time points based on weather statistics;
Generating a stress data sample representing a performance degradation or a life reduction due to aging of the power generation facility at the plurality of time points using a random number;
(A) Decreasing the performance or life of the power generation facility due to performance deterioration or life reduction of the stress data sample at the time point,
(B) When the reduced performance or lifetime meets a predetermined criterion, it is determined using a random number whether it can be detected that the predetermined criterion is satisfied,
(C) When it is detected that the predetermined standard is satisfied, maintenance action according to the predetermined standard is performed on the power generation facility, and the performance or life is updated according to the maintenance action. Generating a performance coefficient sample representing the performance of the power generation facility at the plurality of time points and a maintenance cost sample representing the cost of the maintenance at the plurality of time points by repeating while proceeding with the time points;
A power generation amount estimation step for generating a power generation amount sample representing the power generation amount of the power generation facility at the plurality of times based on the weather data sample and the performance coefficient sample;
Calculating at least one point in time or cumulative revenue based on information on power generation and revenue, the power generation sample, and the maintenance cost sample;
A program that causes a computer to execute.

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