Renewable energy procurement means selection support system

Abstract

To provide a technique capable of supporting the selection of a renewable energy procurement means.SOLUTION: A renewable energy procurement means selection support system 1 sets the renewable energy procurement means that are available for selection at a target demand facility based on demand facility information DB1 and power generation facility information DB2. The system sets a probabilistic model of cost factor information DB4 for evaluating renewable energy procurement means based on the renewable energy procurement means information, uncertainty information DB3, and cost factor information DB4. The system evaluates the renewable energy procurement means based on a set of probabilistic models. The system extracts a renewable energy procurement means that meets the conditions from the set renewable energy procurement means based on the evaluation results and pre-defined conditions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a renewable energy procurement means selection support system. [Background technology]

[0002] In recent years, achieving carbon neutrality has become important in business promotion, and as a result, there is a growing need among consumers to procure the electricity they use from renewable energy sources.

[0003] Consumers can procure renewable energy in several ways: purchasing 100% renewable electricity from a retail electricity supplier; purchasing the environmental value created by renewable electricity from the market; building renewable energy facilities on their own nearby premises and consuming the electricity themselves; or building renewable energy facilities in a remote location of their own company and supplying the electricity through the transmission and distribution network.

[0004] In recent years, corporate PPAs, which are bilateral contracts between renewable energy power plants and consumers, have emerged, further increasing the options for procuring renewable energy.

[0005] A technology that makes it easier to realize consumer demands is disclosed in Patent Document 1. Patent Document 1 discloses an environmental value management device that uses environmental value demand information indicating consumer demands regarding the environmental value of supplied electricity and a forecasted value of the consumer's electricity demand to predict a procurement requirement, which is the amount of environmental value required to be procured, and creates a procurement plan for environmental value certificates so that environmental value certificates corresponding to the procurement requirement for a set planning period are procured within the planning period. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2023-98446 Summary of the Invention [Problem to be solved by the invention]

[0007] The above-mentioned corporate PPAs generally have long-term contracts of 15 or 20 years, so consumers need to consider future uncertainties when selecting the renewable energy procurement method to be contracted.

[0008] In addition, corporate PPAs are subdivided into on-site PPAs, physical PPAs, and virtual PPAs, making it difficult for consumers to select renewable energy procurement methods while taking into account future uncertainties.

[0009] Patent Document 1 describes a technology related to a management method for appropriately allocating the amount of environmental value demanded by consumers. However, Patent Document 1 does not mention selecting a renewable energy procurement method while taking into consideration future uncertainties.

[0010] Therefore, an object of the present invention is to provide a technology that can support the selection of renewable energy procurement means. [Means for solving the problem]

[0011] In order to solve the above problems, one representative renewable energy procurement means selection support system of the present invention is a renewable energy procurement means selection support system that supports the selection of renewable energy procurement means, and includes: a renewable energy procurement means setting unit that sets renewable energy procurement means that can be selected for a target demand facility based on demand facility information and power generation facility information; a probabilistic model setting unit that sets a probabilistic model of cost factor information for evaluating renewable energy procurement means based on the renewable energy procurement means information, uncertainty information, and cost factor information set by the renewable energy procurement means setting unit; a renewable energy procurement means evaluation unit that evaluates the renewable energy procurement means set by the renewable energy procurement means setting unit based on the probabilistic model set by the probabilistic model setting unit; and a candidate extraction unit that extracts renewable energy procurement means that satisfy conditions from the renewable energy procurement means set by the renewable energy procurement means setting unit based on the evaluation results of the renewable energy procurement means evaluation unit and preset conditions. [Effects of the Invention]

[0012] According to the present invention, it is possible to support the selection of renewable energy procurement means.

[0013] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a block diagram showing an example of the configuration of a renewable energy procurement means selection support system according to a first embodiment. [Figure 2] FIG. 1 is a block diagram showing an example of the logical configuration of a renewable energy procurement means selection support system. [Figure 3] 10 is a flowchart illustrating an example of processing executed by a renewable energy procurement means setting unit. [Figure 4] FIG. 10 is a diagram showing an example of renewable energy procurement means setting data. [Figure 5] 10 is a flowchart illustrating an example of processing executed by a probabilistic model setting unit. [Figure 6] 10 is a flowchart illustrating an example of processing executed by a renewable energy procurement means evaluation unit. [Figure 7] FIG. 10 is a diagram showing an example of an evaluation result of renewable energy procurement means. [Figure 8] FIG. 10 is a diagram showing an example of a histogram for evaluation items of renewable energy procurement means. [Figure 9] 10 is a flowchart illustrating an example of processing executed by a candidate extraction unit. [Figure 10] FIG. 10 is a diagram showing an example of a screen displayed by a result display unit. [Figure 11] FIG. 10 is a block diagram showing an example of the configuration of a renewable energy procurement means selection support system according to a second embodiment. [Figure 12] FIG. 1 is a block diagram showing an example of the logical configuration of a renewable energy procurement means selection support system. [Figure 13] FIG. 10 is a diagram showing an example of renewable energy procurement means combination data. DETAILED DESCRIPTION OF THE INVENTION

[0015] The following description will be given with reference to the drawings.

[0016] In this embodiment, the renewable energy procurement means is a means by which a consumer procures renewable energy (renewable energy). Examples of the renewable energy procurement means include self-consumption, self-consignment, and corporate PPA.

[0017] FIG. 1 is a block diagram illustrating an example of the configuration of a renewable energy procurement means selection support system according to the first embodiment.

[0018] 1, the renewable energy procurement means selection support system 1 includes a calculation device 2, a memory 3, a storage device 4, an input device 5, and a display device 6. The renewable energy procurement means selection support system 1 is configured, for example, from a general-purpose computer device.

[0019] The arithmetic device 2 is a processor that controls the overall operation of the renewable energy procurement means selection support system 1. The arithmetic device 2 is configured by, for example, a CPU (Central Processing Unit) or a GPU (Graphic Processing Unit).

[0020] The arithmetic unit 2 may be configured with a CPU and a GPU. In this case, the CPU and the GPU may be switched appropriately depending on the characteristics of each program. For example, a program that can be calculated in parallel is executed by the GPU, and a program that requires sequential calculation is executed by the CPU.

[0021] The memory 3 is composed of a semiconductor memory such as a RAM (Random Access Memory). The memory 3 is used as a working memory for the calculation device 2. A renewable energy procurement means setting program 10, a probabilistic model setting program 11, a renewable energy procurement means evaluation program 12, a candidate extraction program 13, and a result display program 14, which will be described later, are read out from the storage device 4 when necessary and stored and held in the memory 3. The calculation device 2 executes each program read out to the memory 3, thereby executing various processes of the renewable energy procurement means selection support system 1 as a whole, as will be described later.

[0022] The storage device 4 is configured by a large-capacity nonvolatile storage device such as a hard disk device or an SSD (Solid State Drive). Various programs and data that need to be stored for a long period of time are stored in the storage device 4. A demand facility information DB1, a power generation facility information DB2, an uncertainty information DB3, and a cost factor information DB4, which will be described later, are also stored and maintained in this storage device 4.

[0023] The input device 5 is configured by, for example, a mouse, a keyboard, and / or a USB (Universal Serial Bus) connector, etc. The input device 5 is used by a user to input various information and commands to the renewable energy procurement means selection support system 1.

[0024] The display device 6 is configured by a liquid crystal display, an organic EL (Electro-Luminescence) display, smart glasses, smart goggles, etc. The display device 6 is used to display various information and screens.

[0025] The renewable energy procurement means selection support system 1 may use a touch panel that integrates the input device 5 and the display device 6, rather than providing them separately.

[0026] FIG. 2 is a block diagram showing an example of the logical configuration of the renewable energy procurement means selection support system 1. As shown in FIG.

[0027] In Figure 2, the renewable energy procurement means setting unit 20, the probabilistic model setting unit 21, the renewable energy procurement means evaluation unit 22, the candidate extraction unit 23, and the result display unit 24 are functional units that are realized by the calculation device 2 in Figure 1 executing the renewable energy procurement means setting program 10, the probabilistic model setting program 11, the renewable energy procurement means evaluation program 12, the candidate extraction program 13, and the result display program 14, respectively, which are read into the memory 3.

[0028] 2, the demand facility information DB1 is a database that stores information about the demand facilities of consumers who wish to procure renewable energy. The demand facility information DB1 includes information such as an ID or name that identifies the demand facility, voltage class, location, power consumption, unused land, target renewable energy rate, contracted or planned retail electricity rate menu, wheeling charge menu, allowable risk, allowable cost, etc.

[0029] 2, power generation facility information DB2 is a database that stores information about power generation facilities that are candidates for renewable energy procurement. The power generation facility information DB2 includes information such as an ID or name that identifies the power generation facility, capacity, total number of units, type of power generation, location (latitude, longitude), desired contract price for consumers, weather data, etc.

[0030] 2, the uncertainty information DB3 is a database that stores scenario information related to future uncertainties. The uncertainty information DB3 includes, for example, information such as future electricity spot market prices, electricity imbalance prices, carbon surcharges, internal carbon prices, renewable energy surcharges, fuel costs, CO2 emission coefficients, solar power generation penetration rates, FIP (Feed in Premium) balancing costs, and correlation coefficients between spot market prices and power generation output at power generation facilities.

[0031] 2, the cost factor information DB4 is a database that stores information on factors necessary for calculating the costs of renewable energy procurement means. The cost factor information DB4 includes, for example, information such as the actual power consumption of demand facilities, actual weather conditions, actual spot market prices for electricity, actual imbalance prices for electricity, actual fuel cost adjustment unit prices, actual market price adjustment unit prices, past planned data and actual data for renewable energy power generation output, and actual environmental certificate prices.

[0032] The above information may be stored in a database in advance, or may be stored in a database via a user interface. Information for one item may be stored across multiple databases. Multiple items may be combined to create a new item.

[0033] The renewable energy procurement means setting unit 20 has a function of setting renewable energy procurement means that can be selected by the target demand facility for the power generation facility based on the demand facility information DB1 and the power generation facility information DB2. The renewable energy procurement means setting unit 20 outputs renewable energy procurement means setting data in which the renewable energy procurement means is set for the power generation facility to the probabilistic model setting unit 21.

[0034] The probabilistic model setting unit 21 has a function of setting a probabilistic model of factors necessary for evaluating renewable energy procurement means, based on the demand facility information DB1, the renewable energy procurement means setting data provided from the renewable energy procurement means setting unit 20, the uncertainty information DB3, and the cost factor information DB4. The probabilistic model setting unit 21 outputs the setting result of the probabilistic model to the renewable energy procurement means evaluation unit 22.

[0035] Furthermore, the probabilistic model setting unit 21 can change or switch the probabilistic model in response to changes in the system (for example, negative market prices, carbon pricing, imbalance settlement methods, etc.).

[0036] The renewable energy procurement means evaluation unit 22 has a function of evaluating renewable energy procurement means by using the probability model setting result provided from the probability model setting unit 21. The renewable energy procurement means evaluation unit 22 outputs the evaluation result of the renewable energy procurement means to the candidate extraction unit 23.

[0037] The candidate extraction unit 23 has a function of extracting candidates for renewable energy procurement means based on the evaluation results of the renewable energy procurement means provided by the renewable energy procurement means evaluation unit 22 and the demand facility information DB1. The candidate extraction unit 23 outputs the extraction results of the renewable energy procurement means to the result display unit 24.

[0038] The result display unit 24 has a function of displaying the renewable energy procurement means candidates provided by the candidate extraction unit 23 on the display device 6.

[0039] Below, each function of the renewable energy procurement means selection support system 1 will be described in detail.

[0040] FIG. 3 is a flowchart showing an example of processing executed by the renewable energy procurement means setting unit 20.

[0041] First, the renewable energy procurement means setting unit 20 reads out various data related to the target demand facility, which will be required in the steps described below, from the demand facility information DB1 (step S200). The various data related to the demand facility include, for example, the voltage class and location of the demand facility.

[0042] Next, the renewable energy procurement means setting unit 20 reads out various data related to the target power generation facility, which will be required in the steps described later, from the power generation facility information DB2 (step S201). The various data related to the power generation facility include, for example, the capacity, number of units, type of power generation, location, price, etc. of the power generation facility.

[0043] Based on the data acquired in steps S200 and S201, the renewable energy procurement means setting unit 20 sets renewable energy procurement means that can be used by the target demand facility for the power generation facility, and creates renewable energy procurement means setting data as shown in Figure 4 (step S202).

[0044] The process of step S202 in FIG. 3 will be specifically described with reference to FIG.

[0045] FIG. 4 is a diagram illustrating an example of renewable energy procurement means setting data.

[0046] In FIG. 4, an example will be described in which the demand facility is in the extra-high voltage category, is located in the Tokyo area, and has a capacity of 500 kW on site.

[0047] For a power generation facility with a power generation facility ID of P1, the location of the power generation facility and the location of the demand facility are both within the jurisdiction of a general electricity transmission and distribution company in the Tokyo area. Therefore, for a power generation facility with a power generation facility ID of P1, both virtual PPA and physical PPA renewable energy procurement methods are set.

[0048] On the other hand, for power generation facilities with a power generation facility ID of P2, the location of the power generation facility and the location of the demand facility are under the jurisdiction of different general electricity transmission and distribution companies. Therefore, only the renewable energy procurement method of the virtual PPA is set for power generation facilities with a power generation facility ID of P2.

[0049] Note that Figure 4 explains how to set up renewable energy procurement methods when the means is a virtual PPA or a physical PPA, but it is also possible to set up other renewable energy procurement methods, taking into account the constraints specific to each renewable energy procurement method.

[0050] Returning to FIG. 3, it is determined whether the renewable energy procurement means has been set for all power generation facilities (S203).

[0051] In step S203, if the setting of renewable energy procurement means has not been completed for all power generation facilities, the process returns to step S201, and the processes from step S201 to step S202 are executed for another power generation facility.

[0052] On the other hand, if it is determined in step S203 that the renewable energy procurement means has been set for all power generation facilities, this process ends.

[0053] According to the process of FIG. 4, it is possible to appropriately set renewable energy procurement means selectable by the target demand facility for the power generation facility.

[0054] FIG. 5 is a flowchart showing an example of processing executed by the probabilistic model setting unit 21.

[0055] First, the probabilistic model setting unit 21 reads out various data required in the steps described below from the uncertainty information DB3, the cost factor information DB4, and the renewable energy procurement means setting data provided by the renewable energy procurement means setting unit 20 (step S210).

[0056] Next, a weather probability model required for calculating the output of the target power generation facility is set based on the power generation type obtained from the renewable energy procurement means setting data and the weather record obtained from the cost factor information DB4 (step S211).

[0057] The weather required for each type of power generation is wind speed for wind turbines and solar radiation for solar power generation. Probability models can use parametric methods that assume a certain number of parameters for the data distribution, or non-parametric methods that estimate the distribution from the data. Examples of parametric methods include normal distribution and Rayleigh distribution. Examples of non-parametric methods include kernel density estimation.

[0058] Note that one stochastic model may be set for the year, or in the case of time-dependent output such as in solar power generation, a day may be divided into 24 hours and a stochastic model may be set for each hour.The parameters of the parametric method can be determined based on the weather records at the location of the power generation facility or nearby locations.

[0059] On the other hand, for example, in the case of wind speed, there are cases where the annual average wind speed varies from year to year, making it impossible to uniquely determine the parameter from actual results. In such cases, a probability model such as a normal distribution is set for the amount of variation in the parameter (hereinafter referred to as the "parameter error"), and the value generated from this probability model is added to or subtracted from the parameter. This makes it possible to set a probability model that takes the parameter error into account.

[0060] To convert meteorological data into power output, in the case of wind turbines, a power curve that shows the relationship between wind speed and power output is used to convert from wind speed to power output. Similarly, in the case of solar power generation, a power curve that shows the relationship between solar radiation and power output is used to convert from solar radiation to power output. It is also possible to calculate power output taking into account output suppression of renewable energy facilities.

[0061] Next, a probabilistic model of the power consumption of the target demand facility is set based on the actual power consumption or future facility plan information acquired from the cost factor information DB4 (step S212).

[0062] In the case of power consumption, parametric and non-parametric methods can also be used as probability models. Because power consumption often has a time dependency, a probability model can be set by dividing it into seasons, days of the week, weekdays, holidays, time, etc.

[0063] In the case of a demand facility whose power consumption in the same time period does not change significantly from year to year, it is possible to use past performance data as is.

[0064] Note that the parameter error of power consumption may be taken into consideration using the method described in step S211.

[0065] Next, a probability model for the fuel cost adjustment unit price and the market price adjustment unit price is set based on the actual results of the fuel cost adjustment unit price and the market price adjustment unit price acquired from the cost factor information DB4 (step S213).

[0066] The fuel cost adjustment unit price is an adjustment unit price that takes into account fluctuations in fuel costs. The market price adjustment unit price is an adjustment unit price that takes into account fluctuations in market prices. The fuel cost adjustment unit price and market price adjustment unit price are necessary to calculate the electricity rates purchased from retail electricity suppliers. Therefore, it is desirable to set up a probability model for each retail electricity supplier with which you have already signed a contract or plan to sign a contract.

[0067] Because fuel cost adjustment unit prices and market price adjustment unit prices change depending on future fuel costs and market prices, it is necessary to be able to change the parameters so that future uncertainty can be anticipated. Therefore, it is desirable to use parametric methods for the probability model. Parameters can be determined using maximum likelihood estimation, the method of moments, or the maximum, minimum, average, and standard deviation within a given period based on the actual fuel cost adjustment unit prices and market price adjustment unit prices.

[0068] In order to be able to estimate future uncertainty in the fuel cost adjustment unit price and market price adjustment unit price, it is necessary to generalize the parameters calculated above.The parameters can be generalized by calculating a regression model based on the calculated parameters and variables such as fuel cost and market price that are correlated with the fuel cost adjustment unit price and market price adjustment unit price.

[0069] It should be noted that the parameter errors of the fuel cost adjustment unit price and the market price adjustment unit price may be taken into consideration in the method described in step S211.

[0070] The above explains a method of directly setting a probability model for the fuel cost adjustment unit price and the market price adjustment unit price, but it is also possible to set a probability model for fuel cost and market price, subtract the base price from the average price for a specified period, and multiply the result by a coefficient to calculate the fuel cost adjustment unit price and the market price adjustment unit price.

[0071] The calculation method for fuel cost adjustment unit prices and market price adjustment unit prices varies depending on the retail electricity supplier, so it is desirable to calculate them in accordance with that method.

[0072] Next, it is determined whether or not there is a virtual PPA in the renewable energy procurement means setting data (step S214).

[0073] In step S214, if the renewable energy procurement means setting data includes a virtual PPA, the process proceeds to step S215.

[0074] On the other hand, if it is determined in step S214 that the renewable energy procurement means setting data does not include a virtual PPA, the process proceeds to step S216.

[0075] In step S215, a probability model of the spot market price is set () based on the actual spot market price data obtained from the cost factor information DB4.

[0076] The spot market price is necessary to calculate the difference settlement fee for environmental certificates in a virtual PPA. Note that since the spot market price varies depending on the area, such as the Tokyo area or Kyushu area, it is desirable to set up a probability model for each area.

[0077] As with other stochastic models, parametric and non-parametric methods can be used to set up a stochastic model for spot market prices. Since it is necessary to be able to change parameters so that future uncertainty in spot market prices can be assumed, it is preferable to use a parametric method.

[0078] The parameters can be estimated using maximum likelihood estimation or the method of moments based on historical spot market prices. Because the mean and variance of the probability model affect the accuracy of reproducing the virtual PPA's net settlement fee, it is preferable to estimate the parameters using the method of moments.

[0079] In order to be able to estimate future uncertainty in spot market prices, it is necessary to generalize the parameters calculated using the moment method described above. Below, we will explain using the log-normal distribution as an example of a probability model. The parameters of the log-normal distribution are μ and σ, and the expected value E can be calculated using Equation 1.

[0080]

number

[0081] Here, assuming that there is a correlation between μ and E, it is possible to calculate a regression model of μ using E as an input variable. Common methods such as linear regression and nonlinear regression can be used for the regression model. Therefore, μ and σ can be calculated using the spot market price set in the uncertainty information DB3, making it possible to set up a probabilistic model that takes future uncertainty into account.

[0082] Note that a similar method can be used for probability distributions other than the log-normal distribution.

[0083] It should be noted that the parameter error of the spot market price may be taken into consideration in the method described in step S211.

[0084] Additionally, the minimum spot market price in Japan is 0.01\ / kWh, and as the penetration rate of solar power generation increases, the frequency of 0.01\ / kWh increases. To account for this effect, a mixed distribution can be used. Alternatively, it is also possible to reproduce the frequency of 0.01\ / kWh by using a Bernoulli distribution in combination. In this case, the parameters of the Bernoulli distribution can be calculated by using a regression model to calculate the relationship between the penetration rate of solar power generation and the frequency of 0.01\ / kWh.

[0085] Additionally, the virtual PPA net settlement unit price can be calculated by multiplying the difference between the PPA price and the spot market price by the power generation output, and then dividing the sum by the total power generation output. Since the power generation output of solar power generation, which only generates power during the day, is time-dependent, the annual average spot market price and the daily average spot market price will diverge. Therefore, by setting the stochastic model of the spot market price separately for times when power is generated by solar power generation and other times, it is possible to take the above effects into account and improve the accuracy of reproducing the net settlement unit price.

[0086] In step S216, it is determined whether the renewable energy procurement means setting data includes self-consignment.

[0087] In step S216, if the renewable energy procurement means setting data includes self-consignment, the process proceeds to step S217.

[0088] On the other hand, if self-consignment is not included in the renewable energy procurement means setting data in step S216, this process is terminated.

[0089] In step S217, a probability model of the imbalance price is set based on the actual imbalance price obtained from the procurement means cost factor information DB4.

[0090] The imbalance price is a price for eliminating the discrepancy between the plan and the actual results based on the system of simultaneous balancing of planned values. The probabilistic model for the imbalance price can be set using the same method as in step S215, so a detailed explanation will be omitted.

[0091] Next, a probability model of renewable energy planning error is set based on the past planned data and actual data of renewable energy power generation output acquired from the procurement means cost factor information DB4 (step S218).

[0092] The probabilistic modeling method, parameter calculation method, and consideration of parameter errors are similar to those of the other probabilistic models described above, and therefore detailed explanations will be omitted.

[0093] On the other hand, a phenomenon specific to renewable energy planning errors is expected to be that the renewable energy planning errors become smaller when the renewable energy power generation output is near 0 or near maximum output. To take this effect into account, it is desirable to divide the renewable energy power generation output into arbitrary bins and set a probabilistic model for each bin.

[0094] According to the process of FIG. 5, it is possible to appropriately set a probabilistic model for evaluating renewable energy procurement means.

[0095] FIG. 6 is a flowchart showing an example of processing executed by the renewable energy procurement means evaluation unit 22.

[0096] The renewable energy procurement means evaluation unit 22 uses Monte Carlo simulation as a method for evaluating renewable energy procurement means.

[0097] First, the renewable energy procurement means evaluation unit 22 reads out various data required in steps described below from the demand facility information DB1 and the uncertainty information DB3 (step S220).

[0098] Next, the renewable energy procurement means evaluation unit 22 initializes the number of simulations num to num=1 (step S221). The number of simulations num is a variable for counting the number of times calculations have been performed in order to perform calculations for evaluating renewable energy procurement means for a preset number of simulations.

[0099] Next, the simulation year number Year is initialized to Year = 1 (step S222). The simulation year number is a variable for counting the number of years for which calculations have been performed in order to perform calculations for evaluating renewable energy procurement means for the number of years of the contract.

[0100] Next, sampling is performed from the probabilistic model based on the various scenario information acquired from the uncertainty information DB3 and the probabilistic model set by the probabilistic model setting unit 21 (step S223). The number of sampling points may be, for example, one point per year, 12 points per month per year, 8760 points per hour per year, or any other number of points.

[0101] In addition, when there is a correlation between probability models, such as between spot market prices and solar power generation output, it is desirable to use Cholesky decomposition to generate correlated random numbers.

[0102] Next, the renewable energy procurement means is evaluated based on the various scenario information acquired from the uncertainty information DB3 and the sampling results of step S223 (step S224). The cost evaluation method for renewable energy procurement means will be described below.

[0103] The procurement cost cost_p of the physical PPA borne by the consumer is calculated using equation 2 based on the sampling result of power generation output g_net(t), the sampling result of power consumption d(t), the desired contract price p_cont, the renewable energy surcharge p_rene, the basic transmission charge p_(cons_base) and the usage charge p_(cons_use), and the number of samples T.

[0104]

number

[0105] The variables to be used are not limited to those in equation 2, and it is also possible to add fees etc. depending on the type of contract.

[0106] In the case of an on-site PPA, there are no renewable energy surcharges or wheeling charges in equation 2, and other calculation methods can be used in the same way as for a physical PPA, so a detailed explanation will be omitted.

[0107] The self-transport procurement cost cost_s borne by the consumer does not include the renewable energy surcharge in Equation 2, but includes the imbalance charge. The imbalance charge p_imb is calculated using Equation 3 using the imbalance price sampling result p_(imb_p)(t) and the renewable energy planning error sampling result g_err(t).

[0108]

number

[0109] The procurement cost cost_v of the virtual PPA borne by the consumer is calculated using equation 4 using the sampling result of power generation output g_net (t), the contract desired price p_cont, the sampling result of the spot market price p_spot (t), and the procurement cost cost_r to be purchased from the retail electricity supplier described below.

[0110]

number

[0111] When combining FIP, the method is not limited to equation 4, and it is also possible to trade at a fixed price for environmental value alone or to use a method of settling the difference taking into account the premium obtained through FIP.

[0112] The procurement unit price cost_r to be purchased from a retail electricity supplier is calculated using equation 5 based on the sampled power consumption result d(t), the target retail electricity supplier's metered charge p_use, the sampled fuel cost adjustment unit price p_fa, the sampled market price adjustment unit price p_ma, the renewable energy surcharge p_rene, and the target retail electricity supplier's basic charge p_base.

[0113]

number

[0114] Furthermore, if there is both electricity procured through renewable energy procurement means and electricity purchased from a retail electricity supplier, the costs may be calculated by combining the methods described above.

[0115] The renewable energy rate for each renewable energy procurement means can be calculated by dividing the total amount of environmental value obtained through each renewable energy procurement means by the sum of the sampling results of power consumption.

[0116] In addition, when considering the impact of carbon pricing, calculations can be made based on the power consumption sampling results, CO2 emission coefficient, environmental value, carbon levy, and internal carbon price. The method for calculating carbon prices is publicly available, so a detailed explanation will be omitted.

[0117] Next, the simulation year number Year is incremented by 1 (step S225).

[0118] Next, it is determined whether the calculation of the renewable energy procurement means evaluation for the number of years of the contract has been completed based on the number of years of simulation Year (step S226).

[0119] In step S226, if the calculation of the renewable energy procurement means evaluation for the number of years of the contract has not been completed, the process returns to step S223 and the process is executed.

[0120] On the other hand, if it is determined in step S226 that the calculation of the renewable energy procurement means evaluation for the number of years of the contract has been completed, the process proceeds to step S227.

[0121] In step S227, the number of simulations num is incremented by 1 (step S227).

[0122] Next, it is determined whether the calculation of the renewable energy procurement means evaluation for the preset number of simulations has been completed based on the number of simulations num (step S228).

[0123] In step S228, if the calculation of the renewable energy procurement means evaluation for the preset number of simulations has not been completed, the process returns to step S222 and executes the process.

[0124] On the other hand, if the calculation of the renewable energy procurement means evaluation for the preset number of simulations has been completed in step S228, the process proceeds to step S229.

[0125] In step S229, the evaluation items are calculated based on the evaluation results of the renewable energy procurement means, and this process ends.

[0126] The calculation method for the evaluation items will be explained below.

[0127] FIG. 7 is a diagram showing an example of the evaluation results of renewable energy procurement means.

[0128] In Figure 7, the evaluation results of renewable energy procurement means store values ​​for evaluation items such as procurement unit price, procurement fee, procurement fee (including carbon price), and renewable energy rate for each ID of the renewable energy procurement means setting data described in Figure 4, corresponding to the number of simulations.

[0129] FIG. 8 is a diagram showing an example of a histogram for the evaluation items of renewable energy procurement means.

[0130] In Fig. 8, the total number of frequencies for each evaluation item matches the number of simulations. Because the evaluation of renewable energy procurement means is performed multiple times, as shown in Fig. 8, the evaluation result value for each evaluation item of renewable energy procurement means is not uniquely determined.

[0131] Therefore, in the calculation of the evaluation items in step S229 of FIG. 7, the expected value, variance, standard deviation, VaR (Value at Risk), etc. are calculated from the value of the evaluation result for each evaluation item.

[0132] According to the processing of FIG. 6, renewable energy procurement means are evaluated based on a probabilistic model set based on the uncertainty information DB3 and the cost factor information DB4, so that renewable energy procurement means can be appropriately evaluated.

[0133] FIG. 9 is a flowchart showing an example of processing executed by the candidate extracting unit 23.

[0134] First, the candidate extractor 23 reads various data required in the steps described below from the demand facility information DB1 (step S220). The various data include, for example, the allowable risk, the allowable cost, and the target renewable energy rate. These data are set in advance according to the needs of the customer and stored in the demand facility information DB1.

[0135] Next, based on the evaluation results of the renewable energy procurement means by the renewable energy procurement means evaluation unit 22 and the allowable risk obtained from the demand facility information DB1, candidates for renewable energy procurement means that have a risk below the allowable risk are extracted from the renewable energy procurement means set by the renewable energy procurement means setting unit 20 (step S231).

[0136] As shown in Fig. 8, the value of the evaluation result of a renewable energy procurement means is not uniquely determined, so there is a risk that the actual cost will be higher than the expected value calculated from the evaluation result. Therefore, a preset allowable risk is stored in the demand facility information DB1, and candidates for renewable energy procurement means that fall below the allowable risk are extracted.

[0137] Next, based on the evaluation results of the renewable energy procurement means by the renewable energy procurement means evaluation unit 22 and the allowable cost obtained from the demand facility information DB1, candidates for renewable energy procurement means with costs below the allowable risk are extracted from the candidates with risks below the allowable risk obtained in step S231 (step S232).

[0138] Next, based on the evaluation results of the renewable energy procurement means by the renewable energy procurement means evaluation unit 22 and the target renewable energy rate obtained from the demand facility information DB1, candidates for renewable energy procurement means with a target renewable energy rate or higher are extracted from the candidates with a risk equal to or lower than the allowable risk obtained in step S232 (step S233), and this process is terminated.

[0139] Although an example in which candidates are extracted in each of steps S231 to S233 has been described in FIG. 9, it is also possible to select whether or not to extract candidates in each step.

[0140] The candidate extraction unit 23 can also extract candidates by optimization calculation based on the allowable risk, allowable cost, and target renewable energy rate.

[0141] According to the process of FIG. 9, it is possible to appropriately extract candidates for renewable energy procurement means that meet the needs of consumers.

[0142] FIG. 10 is a diagram showing an example of a screen displayed by the result display unit 24. As shown in FIG.

[0143] The result display screen 30 in Figure 10 consists of a comparison display section 31 that displays a comparison of the renewable energy procurement means extracted by the candidate extraction section 23, and a prerequisites display section 32 that displays the prerequisites of the simulation that led to the evaluation results of the renewable energy procurement means.

[0144] As an example, the comparison display section 31 displays a comparison of renewable energy procurement means in a graph with the horizontal axis representing risk and the vertical axis representing procurement cost.

[0145] The renewable energy procurement means A1 in the comparison display unit 31 is the renewable energy procurement means finally extracted by the candidate extraction unit 23 because it satisfies all the conditions of the allowable risk, the allowable cost, and the target renewable energy rate.

[0146] The renewable energy procurement means A2, A3, and An in the comparison display unit 31 are renewable energy procurement means that were not ultimately extracted by the candidate extraction unit 23 because they do not meet any of the conditions of allowable risk, allowable cost, or target renewable energy rate.

[0147] A pull-down menu 33 may be provided in the comparison display section 31 to select and display the evaluation results of renewable energy procurement means under other preconditions.

[0148] As an example, the precondition display section 32 displays the preconditions for future carbon levies stored in the uncertainty information DB3.

[0149] A pull-down menu 34 may be provided in the precondition display section 32 to allow other preconditions to be selected and displayed.

[0150] As described above, the renewable energy procurement means selection support system 1 of this embodiment evaluates renewable energy procurement means taking into account future uncertainties, and presents candidates for renewable energy procurement means that meet the needs of consumers to the user. This makes it possible to support the selection of renewable energy procurement means. Second embodiment

[0151] FIG. 11 is a block diagram illustrating an example of the configuration of a renewable energy procurement means selection support system according to the second embodiment.

[0152] In FIG. 11, the same components as those in FIG. 1 are denoted by the same reference numerals, and the description thereof will be omitted.

[0153] In Figure 11, the renewable energy procurement means selection support system 40 differs from the renewable energy procurement means selection support system 1 according to the first embodiment in that a combination setting program 41 has been added to the memory 3, and that a renewable energy procurement means evaluation program 42 that evaluates renewable energy procurement means based on the combination has been updated.

[0154] FIG. 12 is a block diagram showing an example of the logical configuration of the renewable energy procurement means selection support system 40. As shown in FIG.

[0155] In FIG. 12, the same components as those in FIG. 2 are denoted by the same reference numerals, and the description thereof will be omitted.

[0156] In Figure 12, the combination setting unit 51 and the renewable energy procurement means evaluation unit 52 are functional units that are realized by the calculation device 2 of Figure 11 executing the combination setting program 41 and the renewable energy procurement means evaluation program 42 that are read into the memory 3, respectively.

[0157] The combination setting unit 51 has a function of creating data combining renewable energy procurement means as shown in FIG. 13 based on the renewable energy procurement means setting data from the renewable energy procurement means setting unit 20, and outputting the data to the probabilistic model setting unit 21.

[0158] The renewable energy procurement means evaluation unit 52 has a function of evaluating the combination of renewable energy procurement means set by the combination setting unit 51.

[0159] FIG. 13 is a diagram illustrating an example of renewable energy procurement means combination data.

[0160] Based on the ID of the renewable energy procurement method setting data in Figure 4, all combinations of different power generation facilities are created.

[0161] If the power generation facilities are the same but the renewable energy procurement means are different, they cannot be combined. For example, in Figure 4, renewable energy procurement means A1 and renewable energy procurement means A2 are the same power generation facilities, so A1 and A2 cannot be combined.

[0162] The process executed by the renewable energy procurement means evaluation unit 52 will be described, focusing only on the differences from the first embodiment.

[0163] First, a concrete example will be used to explain how to evaluate each renewable energy procurement means in the combination.

[0164] Consider a case where the combination includes a renewable energy procurement method that involves actual power supply, such as a physical PPA. In this case, in the evaluation of the physical PPA, the procurement cost is calculated using Equation 2 based on the sampling result d(t) of power consumption.

[0165] On the other hand, in the next evaluation of renewable energy procurement methods, the procurement cost is calculated by subtracting the consumable electricity under the physical PPA from the sampled power consumption result d(t) and using the remaining demand as the new d(t).

[0166] As described above, if there is a renewable energy procurement means in the combination that involves actual power supply, it is necessary to sequentially calculate the remaining demand when evaluating the renewable energy procurement means that involves actual power supply, and evaluate the next renewable energy procurement means based on that remaining demand.

[0167] This makes it possible to simulate the saturation of the amount of environmental value obtained through renewable energy procurement methods that accompanies actual power supply.

[0168] Next, a method for calculating the procurement cost when combining renewable energy procurement methods will be explained using a specific example.

[0169] Consider the case where the procurement cost_A1 and environmental value rec_A1 of renewable energy procurement means A1, and the procurement cost_A2 and environmental value rec_A2 of renewable energy procurement means A2 are used. In this case, the procurement cost_A1A2 for the combination [A1, A2] can be calculated using equation 6.

[0170]

number

[0171] As described above, the renewable energy procurement means selection support system 40 of this embodiment not only has the effect of the renewable energy procurement means selection support system 1 of the first embodiment, but also makes it possible to evaluate and compare combinations of renewable energy procurement means. This makes it possible to support the selection of a combination of renewable energy procurement means.

[0172] The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0173] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD. [Explanation of symbols]

[0174] 1,40...Renewable energy procurement means selection support system, 2...Calculation device, 3...Memory, 4...Storage device, 5...Input device, 6...Display device, 10...Renewable energy procurement means setting program, 11...Probability model setting program, 12, 42...Renewable energy procurement means evaluation program, 13...Candidate extraction program, 14...Result display program, 20...Renewable energy procurement means setting unit, 21...Probability model setting unit, 22, 52...Renewable energy procurement means evaluation unit, 23...Candidate extraction unit, 24...Result display unit, 30...Result display screen, 31...Comparison display unit, 32...Prerequisite display unit, 41...Combination setting program, 51...Combination setting unit, DB1...Demand facility information, DB2...Power generation facility information, DB3...Uncertainty information, DB4...Cost factor information

Claims

1. A renewable energy procurement means selection support system that supports the selection of renewable energy procurement means, a renewable energy procurement means setting unit that sets renewable energy procurement means that can be selected for a target demand facility based on demand facility information and power generation facility information; a probabilistic model setting unit that sets a probabilistic model of cost factor information for evaluating the renewable energy procurement means based on the information on the renewable energy procurement means, uncertainty information, and cost factor information set by the renewable energy procurement means setting unit; a renewable energy procurement means evaluation unit that evaluates the renewable energy procurement means set by the renewable energy procurement means setting unit based on the probabilistic model set by the probabilistic model setting unit; and a candidate extraction unit that extracts renewable energy procurement means that satisfy predetermined conditions from the renewable energy procurement means set by the renewable energy procurement means setting unit based on the evaluation results of the renewable energy procurement means evaluation unit and the predetermined conditions.

2. In the renewable energy procurement means selection support system according to claim 1, The renewable energy procurement means evaluation unit samples from the probability model based on the uncertainty information and the probability model set by the probability model setting unit, and calculates evaluation items for renewable energy procurement means based on the results of the sampling.

3. In the renewable energy procurement means selection support system according to claim 2, The renewable energy procurement means selection support system further includes a result display unit that displays the extraction results by the candidate extraction unit.

4. In the renewable energy procurement means selection support system according to claim 2, The conditions include at least one of an allowable risk, an allowable cost, and a target renewable energy rate.

5. In the renewable energy procurement means selection support system according to claim 2, The renewable energy procurement means includes at least one of self-consumption, self-transportation, and corporate PPA.

6. In the renewable energy procurement means selection support system according to claim 2, The probabilistic model setting unit sets the probabilistic model using a parametric method and calculates a regression model based on parameters of the probabilistic model and variables correlated with the parameters of the probabilistic model.

7. In the renewable energy procurement means selection support system according to claim 2, The probabilistic model setting unit sets a probabilistic model of parameter error and adds or subtracts a value generated from the probabilistic model of parameter error to or from a parameter.

8. In the renewable energy procurement means selection support system according to claim 2, The probabilistic model setting unit sets the probabilistic model of spot market prices using a parametric method, and estimates parameters using the moment method based on actual data of the spot market prices.

9. In the renewable energy procurement means selection support system according to claim 2, The probabilistic model setting unit is a renewable energy procurement means selection support system that sets a probabilistic model of spot market prices for each of the time periods when solar power generation occurs and other time periods.

10. In the renewable energy procurement means selection support system according to claim 2, The renewable energy procurement means evaluation unit is a renewable energy procurement means selection support system that generates random numbers taking into account correlations between arbitrary probability models.

11. In the renewable energy procurement means selection support system according to claim 2, The evaluation items include at least one of the procurement unit price, procurement fee, procurement fee including carbon price, and renewable energy rate.

12. In the renewable energy procurement means selection support system according to claim 2, The renewable energy procurement means evaluation unit calculates at least one of expected value, variance, standard deviation, and VaR for each evaluation item of the renewable energy procurement means.

13. In the renewable energy procurement means selection support system according to claim 2, Further provided is a combination setting unit that sets a combination of renewable energy procurement means based on the information of the renewable energy procurement means set by the renewable energy procurement means setting unit, the renewable energy procurement means evaluation unit evaluates the combination set by the combination setting unit based on the probabilistic model set by the probabilistic model setting unit; The candidate extraction unit extracts a combination that satisfies the conditions from the combinations set by the combination setting unit based on the evaluation results of the renewable energy procurement means evaluation unit and predetermined conditions.

14. The renewable energy procurement means selection support system according to claim 13, The renewable energy procurement means evaluation unit calculates the procurement unit cost of the combination by taking a weighted average based on the environmental value of the procurement unit cost of each renewable energy procurement means included in the combination.

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