Prediction method and information terminal control method

Abstract

This prediction method comprises: formulating, over a plurality of years, a phased implementation plan for a distributed power supply system including at least one of natural energy power generation equipment and fuel cell equipment; and predicting a first indicator regarding the CO2 emissions of a power consumer over a plurality of years when the distributed power supply system is implemented on the basis of the implementation plan.

Description

Prediction Method and Control Method for Information Terminal

[0001] The present disclosure relates to a method for predicting indicators related to CO₂ emissions and a control method for an information terminal.

[0002] Patent Document 1 below discloses a technology for evaluating distributed power sources such as solar cells, fuel cells, and storage batteries from an environmental perspective and presenting a combination of distributed power sources when introducing them.

[0003] Japanese Patent No. 7058254

[0004] The technology described in Patent Document 1 evaluates distributed power supply systems from an environmental perspective, but there are still insufficient points in the evaluation of the introduction of distributed power supply systems.

[0005] An object of the present disclosure is to provide an indicator prediction method for performing a more appropriate evaluation from an environmental perspective compared to the prior art regarding the introduction of a distributed power supply system, and a control method for an information terminal that displays the indicator.

[0006] In order to solve the above problem, a prediction method according to one aspect of the present disclosure formulates a phased multi-year introduction plan for a distributed power supply system including at least one of a natural energy power generation device and a fuel cell device, and predicts a first indicator related to CO₂ emissions of a power consumer over multiple years when the distributed power supply system is introduced based on the introduction plan.

[0007] In addition, a control method for an information terminal according to one aspect of the present disclosure comprises the steps of: causing a display of the information terminal to display a phased multi-year introduction plan for a distributed power supply system including at least one of a natural energy power generation device and a fuel cell device; and causing the display to display information indicating a first indicator related to CO₂ emissions of the power consumer over multiple years predicted when the distributed power supply system is introduced based on the introduction plan.

[0008] A prediction method according to one aspect of this disclosure has the effect of being able to predict indicators for a more appropriate evaluation from an environmental perspective compared to conventional technology when introducing a distributed power system. Furthermore, a control method for an information terminal according to one aspect of this disclosure has the effect of being able to display indicators for a more appropriate evaluation from an environmental perspective compared to conventional technology when introducing a distributed power system.

[0009] Figure 1 shows an example of a distributed power system implementation. Figure 2 is a conceptual diagram of an information terminal. Figure 3 is a flowchart of the prediction method. Figure 4 shows the renewable energy rate of electricity consumers over several years. Figure 5 shows a multi-year implementation plan for a distributed power system. Figure 6 shows a breakdown of the amount of electricity supplied to electricity consumers over several years. Figure 7 shows the predicted CO2 emissions of electricity consumers over several years. Figure 8 shows the predicted CO2 emission reductions of electricity consumers over several years. Figure 9 shows the predicted electricity emission coefficients of electricity consumers over several years. Figure 10 shows the predicted CFP of products produced by electricity consumers over several years.

[0010] In recent years, due to growing environmental awareness, there has been an increasing trend for electricity consumers to introduce distributed power systems, including distributed power sources such as renewable energy generators, fuel cell systems, and energy storage systems. In these distributed power systems, various combinations of distributed power sources can be freely combined, so various combinations of distributed power sources were evaluated from an environmental perspective, and the configuration of the distributed power system was then considered.

[0011] Traditionally, indicators for evaluating distributed power systems have been calculated based on the assumption that the system would be implemented all at once. However, the inventors focused on the possibility that distributed power systems may be implemented in stages over several years. For example, considering factors such as the gradual implementation of CO2 emission regulations and the high costs involved in implementing distributed power systems, it may be preferable to implement a distributed power system in stages over several years rather than all at once.

[0012] Therefore, the prediction method of the first aspect of this disclosure formulates a multi-year phased introduction plan for a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, and predicts a first indicator of the CO2 emissions of the electricity consumer over a multi-year period when the distributed power system is introduced based on the introduction plan.

[0013] This forecasting method predicts a "multi-year" first indicator based on a multi-year phased deployment plan for distributed power systems. Therefore, evaluating the deployment of distributed power systems by electricity consumers using this first indicator allows for a more appropriate assessment from an environmental perspective compared to conventional technologies.

[0014] A second aspect of the prediction method of the present disclosure is the prediction method of the first aspect, wherein the distributed power system further includes an energy storage device.

[0015] This prediction method allows for a more appropriate evaluation from an environmental perspective compared to conventional technologies regarding the introduction of distributed power generation systems, including energy storage devices, by electricity consumers.

[0016] Here, fuel cell devices generate electricity by chemically reacting a fuel such as hydrogen-containing gas with an oxidizer such as oxygen. There are several types of hydrogen that can be used as fuel for fuel cell devices, including gray hydrogen extracted from fossil resources and green hydrogen produced using renewable energy. Therefore, even when introducing fuel cell devices in the same way, the evaluation of the distributed power system from an environmental perspective changes depending on the type of hydrogen used in the fuel cell device.

[0017] Therefore, the prediction method of the third aspect of the present disclosure, in the prediction method of the first or second aspect, wherein the distributed power system includes the fuel cell device, a switching plan for the hydrogen species used for power generation by the fuel cell device is performed, and the first indicator is predicted taking the switching plan into consideration.

[0018] This prediction method also takes into account the plan for switching the type of hydrogen used in power generation by fuel cell systems when predicting the first indicator. Therefore, it allows for a more appropriate evaluation from an environmental perspective of the introduction of distributed power generation systems, including fuel cells, by electricity consumers compared to conventional technologies.

[0019] Here, the power emission factor for grid electricity may fluctuate from year to year. For example, the power emission factor for grid electricity can change due to the upgrading of power generation facilities by power generators.

[0020] Therefore, the prediction method of the fourth aspect of this disclosure predicts the first index by also considering the prediction of the power emission coefficient of grid power over multiple years in any one of the prediction methods of the first to third aspects.

[0021] This forecasting method predicts the first indicator by also considering the forecast of grid power emission factors over multiple years. Therefore, it allows for a more appropriate evaluation from an environmental perspective of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies. The "electricity emission factor" refers to the value obtained by dividing the amount of CO2 emitted in the process of supplying electricity by the amount of electricity supplied.

[0022] A fifth aspect of the prediction method of the present disclosure is a prediction method in any one of the first to fourth aspects, wherein the distributed power system includes the fuel cell device, and the first index is predicted taking into account the power emission coefficient of the electricity from the fuel cell over a period of several years, the power emission coefficient of the electricity from the fuel cell being derived based on the type of hydrogen used for power generation.

[0023] This prediction method forecasts the first indicator by also considering the power emission factors of fuel cell electricity over multiple years. Therefore, it allows for a more appropriate environmental assessment of the introduction of distributed power generation systems, including fuel cell equipment, by electricity consumers compared to conventional technologies. Furthermore, since this prediction method derives the power emission factors of fuel cell electricity based on the type of hydrogen used for power generation, it can accurately predict the power emission factors of fuel cell electricity.

[0024] In recent years, governments and local authorities have sometimes set CO2 emission limits for electricity consumers. In this case, if an electricity consumer's CO2 emissions exceed their limit, they must purchase the excess amount as CO2 emission credits from other consumers. There is also a system in place to buy and sell CO2 emission reduction credits (CO2 emission reduction rights) for CO2 emission reductions achieved through measures such as CO2 emission reduction. Therefore, electricity consumers may purchase CO2 emission credits or CO2 emission reduction credits if their CO2 emissions exceed predetermined target values.

[0025] Therefore, the sixth aspect of the prediction method of this disclosure, in any one of the first to fifth aspects of the prediction method, predicts the amount of CO2 emission rights or CO2 emission reduction rights to be purchased based on the target CO2 emission as a target value of the second indicator over multiple years and the first CO2 emission, wherein the first indicator includes the first CO2 emission.

[0026] This prediction method allows for a more appropriate environmental assessment of electricity consumers' adoption of distributed power generation systems compared to conventional technologies, in order to predict the amount of CO2 emission credits or CO2 emission reduction credits to be purchased.

[0027] The seventh aspect of the prediction method of this disclosure predicts a second CO2 emission by modifying the first CO2 emission based on the predicted purchase amount of the CO2 emission rights or CO2 emission reduction rights in the prediction method of the sixth aspect.

[0028] This prediction method allows for a more appropriate environmental assessment of the introduction of distributed power generation systems by electricity consumers, compared to conventional technologies, because it predicts a second CO2 emission that has been corrected from the first CO2 emission.

[0029] In recent years, it has become common practice to convert the total greenhouse gas emissions from raw material procurement to disposal and recycling throughout the entire lifecycle of a product or service into CO2 equivalents and display them as CFP (Carbon Footprint of Products). The adjusted CO2 emissions (the second CO2 emissions mentioned above) are used to calculate this CFP.

[0030] Therefore, the prediction method of the eighth aspect of this disclosure predicts CFP based on the predicted second CO2 emissions in the prediction method of the seventh aspect.

[0031] This prediction method allows for a more appropriate environmental assessment of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies, in order to predict CO2 emissions (CFP). Furthermore, because it uses a second CO2 emission factor in the CFP prediction, it can predict CFP with greater accuracy.

[0032] A forecasting method according to the ninth aspect of this disclosure is a forecasting method according to any one of the first to fifth aspects, wherein the first indicator includes a first CO2 emission that does not take into account the amount of CO2 emission rights or CO2 emission reduction rights purchased by electricity consumers, and the CFP is forecasted based on the first CO2 emission.

[0033] This prediction method allows for a more appropriate environmental assessment of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies, in order to predict CFP. Furthermore, since the first CO2 emission is used to predict CFP, CFP can be easily predicted.

[0034] A prediction method according to a tenth aspect of the present disclosure is a prediction method according to a sixth aspect, wherein the first indicator further includes a first CO2 emission reduction, and a second CO2 emission reduction is predicted, which is obtained by modifying the first CO2 emission reduction based on the predicted amount of CO2 emission rights or CO2 emission reduction rights purchased.

[0035] This prediction method allows for a more appropriate environmental assessment of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies, because it predicts a second CO2 emission amount that has been corrected for the first CO2 emission reduction.

[0036] An eleventh aspect of the present disclosure is a prediction method in the sixth aspect, wherein the first indicator further includes a first power emission coefficient, and a second power emission coefficient is predicted, which is obtained by modifying the first power emission coefficient based on the predicted purchase amount of CO2 emission rights or CO2 emission reduction rights.

[0037] This prediction method allows for a more appropriate environmental assessment of electricity consumers' introduction of distributed power generation systems compared to conventional techniques, by predicting a second power emission factor that is a modified version of the first power emission factor.

[0038] Here, electricity consumers can purchase renewable energy certificates (electricity certificates) that prove that the grid electricity supplied by power generators is generated from renewable energy sources (solar, wind, hydro, geothermal, etc.). By purchasing these electricity certificates, electricity consumers can demonstrate that the electricity supplied by power generators is generated from renewable energy sources. Therefore, electricity consumers may purchase electricity certificates if their renewable energy rate falls below a predetermined target value. The "renewable energy rate" refers to the percentage of electricity supplied to electricity consumers or consumed by electricity consumers that is generated from renewable energy sources.

[0039] Therefore, the prediction method of the twelfth aspect of the present disclosure, in any one of the prediction methods of the first to eleven aspects, predicts the amount of electricity certificates to be purchased based on the target renewable energy rate as a target value of the second indicator over a multi-year period and the first renewable energy rate, wherein the first indicator includes a first renewable energy rate as the first renewable energy rate as the first renewable energy rate as the first renewable energy rate as the first renewable energy rate as the first renewable energy rate as the first renewable energy rate as the first renewable energy rate as the first renewable energy rate.

[0040] This prediction method allows for a more appropriate environmental assessment of electricity consumers' adoption of distributed power generation systems compared to conventional technologies, in order to predict the amount of electricity certificates to be purchased.

[0041] A prediction method according to a thirteenth aspect of this disclosure predicts a second renewable energy rate obtained by modifying the first renewable energy rate based on the predicted amount of electricity certificates purchased, in the prediction method according to a twelfth aspect.

[0042] This prediction method allows for a more appropriate environmental assessment of electricity consumers' adoption of distributed power generation systems compared to conventional methods, by predicting a second renewable energy rate that is a modified version of the first renewable energy rate.

[0043] The prediction method according to the 14th aspect of the present disclosure is the prediction method according to the 12th aspect, wherein the first indicator includes a first CO₂ emission amount, and predicts a second CO₂ emission amount obtained by correcting the first CO₂ emission amount based on the predicted purchase amount of the power certificates.

[0044] The aforementioned power certificates can complement each other with CO₂ emission reduction credits. Therefore, in this prediction method, by predicting the second CO₂ emission amount obtained by correcting the first CO₂ emission amount based on the predicted purchase amount of the power certificates, the second CO₂ emission amount can be predicted with high accuracy. Therefore, the introduction of distributed power supply systems by power consumers can be evaluated more appropriately from an environmental perspective compared with conventional techniques.

[0045] Here, there are institutions that evaluate the environmental contribution of power consumers such as companies and local governments. By inputting indicators such as the CO₂ emission amount of power consumers into the evaluation system operated by such institutions, the evaluation system can be caused to evaluate the environmental contribution of the power consumers.

[0046] Therefore, the prediction method according to the 15th aspect of the present disclosure is the prediction method according to the first aspect, wherein the predicted first indicator is input to an evaluation system that evaluates the environmental contribution of power consumers, and the evaluation system is caused to evaluate the environmental contribution of the power consumers over a plurality of years.

[0047] In this prediction method, an evaluation of the environmental contribution of power consumers over a plurality of years can be obtained. Therefore, the introduction of distributed power supply systems by power consumers can be evaluated more appropriately from an environmental perspective compared with conventional techniques.

[0048] The prediction method according to the 16th aspect of the present disclosure is the prediction method according to the 15th aspect, further comprising inputting the second indicator to the evaluation system, and causing the evaluation system to evaluate the environmental contribution of the power consumers over a plurality of years.

[0049] This prediction method allows for a more accurate assessment of a power consumer's environmental contribution by inputting not only the first indicator but also the second indicator into the evaluation system. Therefore, it enables a more appropriate evaluation from an environmental perspective of a power consumer's introduction of a distributed power generation system compared to conventional technologies.

[0050] A forecasting method according to a 17th aspect of the present disclosure, in which the forecasting method according to a 16th aspect, the first indicator includes at least one of CO2 emissions, electricity emission coefficient, renewable energy rate, and the amount of CO2 emission rights or CO2 emission reduction rights purchased, the amount of electricity certificates purchased, and CFP.

[0051] This prediction method allows for the prediction of an appropriate indicator as the first indicator. Therefore, it enables a more appropriate evaluation from an environmental perspective of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0052] The prediction method of the 18th aspect of this disclosure modifies the multi-year deployment plan of the distributed power system in any one of the prediction methods of the 15th to 17th aspects so that the evaluated environmental contribution becomes the environmental contribution targeted by the power consumer.

[0053] This prediction method allows electricity consumers to create appropriate deployment plans for distributed power systems by revising multi-year deployment plans for distributed power systems based on their assessed environmental contribution.

[0054] In this context, the distributed power sources included in a distributed power system may require periodic replacement for reasons such as having a set service life.

[0055] Therefore, the prediction method of the 19th aspect of the present disclosure includes, in any one of the prediction methods of the 1st to 18th aspects, an introduction plan for the distributed power system that includes an introduction plan for the replacement of at least a portion of the distributed power system.

[0056] This prediction method allows for the accurate formulation of distributed power system deployment plans because the deployment plan for the distributed power system, which forms the basis for predicting the first indicator, includes a deployment plan for replacing at least a portion of the distributed power system. Furthermore, this enables a more appropriate evaluation from an environmental perspective of the deployment of distributed power systems by electricity consumers compared to conventional technologies.

[0057] A control method for an information terminal in a 20th aspect of the present disclosure is a control method for an information terminal comprising the steps of: displaying on a display of the information terminal a multi-year phased introduction plan for a distributed power system including at least one of a natural energy power generation device and a fuel cell device; and displaying on the display information indicating a first indicator of the multi-year CO2 emissions of the electricity consumer that is predicted when the distributed power system is introduced based on the introduction plan.

[0058] This control method displays the distribution power system implementation plan on a display unit. Therefore, the proposer of the distribution power system, or the electricity consumer who is presented with the information displayed on the display unit by the proposer, can easily understand the distribution power system implementation plan. Furthermore, this control method displays information on the display unit that shows a primary indicator of the electricity consumer's CO2 emissions over several years. Therefore, the proposer or electricity consumer can evaluate the electricity consumer's distribution power system implementation more appropriately from an environmental perspective compared to conventional technology.

[0059] The following describes specific examples of the above embodiments of this disclosure with reference to the attached drawings. The specific examples described below are all examples of the above embodiments of this disclosure. Therefore, the shapes, numerical values, components, arrangement positions of components, and connection configurations shown below do not limit the scope of the claims unless they are described in the claims.

[0060] Furthermore, among the components described below, those not described in the independent claim representing the highest-level concept of this disclosure will be described as optional components. Also, in the drawings, components with the same reference numeral may not be described. The drawings are schematic representations of each component for ease of understanding, and the shape and dimensional ratios may not be accurately represented.

[0061] Furthermore, in the operation of the apparatus, the order of the processes may be changed or known processes may be added as needed.

[0062] (Example of Introduction of a Distributed Power System) First, an example of the introduction of a distributed power system 12 by a power consumer 10 will be described. Figure 1 is a diagram showing an example of the introduction of a distributed power system 12. Figure 1(a) shows the manner of power supply to the power consumer 10 before the introduction of the distributed power system 12. In this embodiment, the power consumer 10 is a factory that produces products, and the power consumer 10 is equipped with power consumption equipment 11 such as production equipment for producing products. In this embodiment, as shown in Figure 1(a), before the introduction of the distributed power system 12, power is supplied to the power consumption equipment 11 of the power consumer 10 only from a power generator 20 such as a power company. Hereinafter, the power supplied from the power generator 20 to the power consumer 10 will be referred to as "grid power".

[0063] Figure 1(b) shows the configuration of power supply to the power consumer 10 during an intermediate stage of the introduction of the distributed power system 12. In this embodiment, as shown in Figure 1(b), during an intermediate stage of the introduction of the distributed power system 12, power is supplied not only from the grid but also from the distributed power system 12 to the power consumption equipment 11 of the power consumer 10. The distributed power system 12 includes distributed power sources such as a renewable energy power generation device 13, a fuel cell device 14, and an energy storage device 15. However, the distributed power system 12 is not limited to the above configuration. For example, the distributed power system 12 may include only one of the above-mentioned distributed power sources.

[0064] The renewable energy power generation device 13 is a device that generates electricity using natural energy, such as a solar power generation device or a wind power generation device. The fuel cell device 14 is a device that generates electricity by chemically reacting hydrogen, which is the fuel, with oxides such as oxygen. There are several types of hydrogen that can be used as fuel for the fuel cell device 14, such as gray hydrogen extracted from fossil resources and green hydrogen produced using renewable energy. Furthermore, the energy storage device 15 is a device that temporarily stores and supplies electricity generated by other devices.

[0065] Figure 1(c) shows the configuration of power supply to the power consumer 10 after the completion of the installation of the distributed power system 12. In this embodiment, as shown in Figure 1(c), after the completion of the installation of the distributed power system 12, power is supplied to the power consumption equipment 11 of the power consumer 10 only from the distributed power system 12. However, even after the completion of the installation of the distributed power system 12, power may be supplied to the power consumption equipment 11 of the power consumer 10 from both the power generator 20 and the distributed power system 12.

[0066] (Information Terminal) Next, the information terminal 30 used in the prediction method according to this embodiment will be described. Figure 2 is a conceptual diagram of the information terminal 30. The information terminal 30 comprises a controller 31, a memory 32, an input 33, and a display 34.

[0067] The controller 31 comprises an arithmetic processing unit and a memory unit for storing control programs. The arithmetic processing unit is, for example, a processor, and the memory unit is, for example, volatile memory. The controller 31 may be a single controller or a group of controllers working together. The memory unit 32 is, for example, non-volatile memory and is a device for storing various programs and information. The input device 33 is, for example, a keyboard operated by an operator and is a device for inputting various information. The display device 34 is, for example, a monitor and is a device for displaying various information.

[0068] The information terminal 30 is connected to the evaluation system 40 via a network such as the Internet. The evaluation system 40 is a system operated by an external organization and can calculate the environmental contribution, which will be described later. The information terminal 30 may also be connected to an information server (not shown in the figure) which may perform the processing of the controller 31, which will be described later. In other words, the controller 31 may consist of multiple devices, including a server that is connected via communication.

[0069] (Flowchart of the prediction method) Next, the prediction method according to this embodiment will be described. The prediction method according to this embodiment is a method for predicting indicators for evaluating the introduction of the distributed power system 12 from an environmental perspective. Specifically, the prediction method according to this embodiment predicts a first indicator related to the CO2 emissions of electricity consumers 10 over several years when the distributed power system 12 is introduced. This will be explained in detail below.

[0070] <Setting Target Values> Figure 3 is a flowchart of the prediction method according to this embodiment. As shown in Figure 3, in the flowchart of the prediction method according to this embodiment, the operator first sets target values ​​(step S1). Specifically, the operator sets target values ​​for a second indicator related to the CO2 emissions of the electricity consumer 10 over several years. The second indicator is, for example, CO2 emissions, CO2 emission reduction, electricity emission coefficient, renewable energy rate, etc.

[0071] In this embodiment, a target renewable energy rate over multiple years is set as the target value for the second indicator. The renewable energy rate refers to the proportion of electricity generated from renewable energy sources in the total electricity supplied or consumed. Figure 4 shows the renewable energy rate of electricity consumer 10 over multiple years. The dashed line in Figure 4 represents the target renewable energy rate of electricity consumer 10 over multiple years. The worker sets the target renewable energy rate for electricity consumer 10 over multiple years, as shown by the dashed line in Figure 4, based on information such as the standard values ​​and benchmark values ​​for renewable energy rates of the industry, country, and local government to which electricity consumer 10 belongs, as well as the electricity consumer 10's decarbonization plan. The above standard values ​​and benchmark values ​​are the target renewable energy rate to be achieved in the industry to which electricity consumer 10 belongs, the target renewable energy rate of other electricity consumers (e.g., other companies) in the industry to which electricity consumer 10 belongs, and the target renewable energy rate to be achieved in the country or local government to which electricity consumer 10 belongs.

[0072] In the example shown in Figure 4, the 20-year period is divided into four 5-year periods, from the first to the fourth, with target renewable energy rates set for each period. The first period is before the introduction of the distributed power system 12 and corresponds to the state shown in Figure 1(a). In the first period, no target renewable energy rate is set. The second and third periods are intermediate stages in the introduction of the distributed power system 12 and correspond to the state shown in Figure 1(b). Target renewable energy rates are set in the second and third periods, with the target renewable energy rate for the third period being higher than that for the second period. The fourth period is after the completion of the introduction of the distributed power system 12 and corresponds to the state shown in Figure 1(c). In the fourth period, the target renewable energy rate is set at 100%. Note that the first to fourth periods shown in Figures 5 to 10 correspond to the first to fourth periods shown in Figure 4, respectively.

[0073] Furthermore, a target value other than the target renewable energy rate may be set as the target value for the second indicator. In this case, the target value can be set in the same way as the target renewable energy rate. For example, a target CO2 emission may be set as the target value for the second indicator. In this case, the worker can set multi-year target CO2 emission for electricity consumer 10 based on information such as the baseline values ​​and standard values ​​of CO2 emissions in the industry, country, or local government to which electricity consumer 10 belongs, as well as electricity consumer 10's decarbonization plan. The baseline values ​​and standard values ​​mentioned above are the target CO2 emission values ​​that electricity consumer 10 wants to achieve in the industry to which electricity consumer 10 belongs, the target CO2 emission values ​​of other electricity consumers (e.g., other companies) in the industry to which electricity consumer 10 belongs, and the target CO2 emission values ​​that electricity consumer 10 wants to achieve in the country or local government to which electricity consumer 10 belongs.

[0074] <Formulation of Implementation Plan> Next, in the flow of the prediction method according to this embodiment, the worker formulates a multi-year phased implementation plan for the distributed power system 12 (step S2). In this embodiment, the worker formulates a multi-year implementation plan for the distributed power system 12 based on the multi-year target values ​​(target renewable energy rate) of the second indicator set in step S1 and the multi-year budget plan for CO2 emission reduction. Note that the multi-year phased implementation plan for the distributed power system 12 may be formulated by the controller 31.

[0075] Figure 5 shows a multi-year phased introduction plan for the distributed power system 12. In Figure 5, the rated output of the distributed power sources constituting the distributed power system 12 is used to represent the multi-year phased introduction plan (the units of rated output are kW, MW, etc.). In the introduction plan shown in Figure 5, no distributed power sources are introduced in the first phase, a renewable energy power generation device 13, a fuel cell device 14, and an energy storage device 15 are introduced in the second phase, the number of fuel cell devices 14 is increased in the third phase, and the number of fuel cell devices 14 is further increased in the fourth phase.

[0076] The introduction plan for the distributed power system 12 is not limited to that shown in Figure 5. For example, an introduction plan may be formulated to introduce at least one of the renewable energy power generation device 13 and the fuel cell device 14, or an introduction plan may be formulated to introduce at least one of the renewable energy power generation device 13 and the fuel cell device 14 and the energy storage device 15.

[0077] Furthermore, the introduction plan for the distributed power system 12 may include an introduction plan for the replacement of at least a portion of the distributed power system 12. For example, if at least one of the devices among the renewable energy power generation device 13, fuel cell device 14, and energy storage device 15 has a predetermined service life and requires replacement every few years, a multi-year introduction plan for the distributed power system 12 that includes an introduction plan for the replacement of that device may be formulated.

[0078] Furthermore, in this embodiment, the multi-year phased introduction plan for the distributed power system 12 is formulated based on both the multi-year target value (target renewable energy rate) of the second indicator and the multi-year budget plan for CO2 emission reduction, but it may also be formulated based on either one of them. Moreover, in this embodiment, the introduction plan for the distributed power system 12 is formulated based on the target renewable energy rate, but instead of this target renewable energy rate, or in addition to the target renewable energy rate, the introduction plan for the distributed power system 12 may also be formulated based on the target value of other CO2 emission indicators, such as target CO2 emissions. Furthermore, the multi-year phased introduction plan for the distributed power system 12 does not have to be formulated based on at least one of the multi-year target value of the second indicator and the multi-year budget plan for CO2 emission reduction, and may be formulated based on any arbitrary criteria. For example, the introduction plan for the distributed power system 12 may be formulated based on the target value of the electricity self-sufficiency rate of the electricity consumer 10, without considering either the multi-year target value of the second indicator for the distributed power system 12 or the multi-year budget plan for CO2 emission reduction.

[0079] <Display of Implementation Plan> Next, in the flow of the prediction method according to this embodiment, the operator displays the multi-year phased implementation plan of the distributed power system 12 using the information terminal 30 (step S3). In this embodiment, the operator inputs the data related to the multi-year phased implementation plan of the distributed power system 12 formulated in step S2 into the input device 33, and the controller 31 of the information terminal 30 displays the multi-year phased implementation plan of the distributed power system 12 on the display device 34 based on the implementation data.

[0080] <Prediction of the breakdown of electricity consumption> Next, in the flow of the prediction method according to this embodiment, the controller 31 predicts the breakdown of the amount of electricity supplied to the electricity consumer 10 from each power supply source over several years (step S4). This breakdown of electricity consumption includes the amount of electricity supplied to the electricity consumer 10 from the renewable energy power generation device 13, the amount of electricity supplied to the electricity consumer 10 from the fuel cell device 14, the amount of electricity supplied to the electricity consumer 10 from the energy storage device 15, and the amount of grid power supplied to the electricity consumer 10.

[0081] In predicting the breakdown of electricity supplied to the electricity consumer 10, the controller 31 first predicts or obtains the amount of electricity supplied to the electricity consumer 10 from the renewable energy power generation device 13, the amount of electricity supplied to the electricity consumer 10 from the fuel cell device 14, and the amount of electricity supplied to the electricity consumer 10 from the energy storage device 15, based on the data regarding the introduction plan of the distributed power system 12 entered by the operator in step S3. Furthermore, the controller 31 predicts the amount of grid power supplied to the electricity consumer 10 by subtracting the amount of electricity supplied to the electricity consumer 10 by the distributed power system 12 (renewable energy power generation device 13, fuel cell device 14, energy storage device 15) from the electricity demand (amount of electricity consumed) of the electricity consumer 10. In this embodiment, the electricity demand of the electricity consumer 10 is predicted to be the same every year. However, predicting that the electricity demand of the electricity consumer 10 is the same every year is just an example and is not limited to this.

[0082] Figure 6 shows the breakdown of electricity supplied to electricity consumer 10 over several years (units of electricity are kHh, MHh, etc.). In the example shown in Figure 6, in the first period, all of the electricity supplied to electricity consumer 10 is grid electricity. From the second to the fourth period, the proportion of electricity supplied to electricity consumer 10 from the distributed power system 12 (renewable energy power generation device 13, fuel cell device 14, energy storage device 15) gradually increases, and in the fourth period, all of the electricity supplied to electricity consumer 10 is supplied from the distributed power system 12.

[0083] <First Prediction> Next, in the flow of the prediction method according to this embodiment, the controller 31 performs a first prediction (step S5). In this embodiment, a first indicator for evaluating the introduction of the distributed power system 12 is predicted. In the first prediction, predictions are made for the first CO2 emissions, the first CO2 emission reduction, the first power emission coefficient, and the first renewable energy rate among the first indicators. The prediction methods for these indicators will be described in order below.

[0084] [First CO2 Emissions] In this embodiment, the first CO2 emissions are also called unadjusted CO2 emissions and represent the amount of CO2 emitted during the process of supplying electricity. In this embodiment, the controller 31 predicts the first CO2 emissions of the electricity consumer 10 over several years when the distributed power supply system 12 is introduced. These first CO2 emissions of the electricity consumer 10 can be predicted based on the breakdown of the amount of electricity supplied to the electricity consumer 10 predicted in step S4 (i.e., the amount of grid power supplied to the electricity consumer 10 and the amount of electricity supplied to the electricity consumer 10 by each distributed power source), the power emission coefficient of the grid power, and the power emission coefficient of each distributed power source.

[0085] In this context, "electricity emission factor" refers to the value obtained by dividing the amount of CO2 emitted during the process of supplying electricity by the amount of electricity supplied. The electricity emission factors for grid power and each distributed power source are stored in the memory 32 of the information terminal 30, and the controller 31 predicts the first CO2 emission amount based on the electricity emission factors for grid power and each distributed power source obtained from the memory 32. In this embodiment, the memory 32 stores the electricity emission factors for grid power and each distributed power source "over multiple years". This is because the electricity emission factors for grid power and each distributed power source may fluctuate from year to year. For example, the electricity emission factor for grid power may fluctuate due to the updating of power generation equipment by power generators.

[0086] Furthermore, as mentioned above, there are multiple types of hydrogen, which are the fuel for the fuel cell device 14. Therefore, the power emission coefficient of the fuel cell device 14 stored in the memory 32 will differ depending on the type of hydrogen used for power generation. In addition, it is possible to change the type of hydrogen after the introduction of the fuel cell device 14. In this case, a plan for switching the type of hydrogen used for power generation by the fuel cell device 14 may be formulated, and the memory 32 may store the power emission coefficient of the fuel cell device 14 based on the switching plan. This allows the controller 31 to predict the first CO2 emissions corresponding to the change in the type of hydrogen. Note that the plan for switching the type of hydrogen used for power generation by the fuel cell device 14 may be formulated, for example, based on the budget plan of the distributed power system 12, but is not limited to this. Also, the plan for switching the type of hydrogen used for power generation by the fuel cell device 14 may be formulated by an operator or by the controller 31.

[0087] Figure 7 shows the predicted CO2 emissions of electricity consumer 10 over several years (the unit of CO2 emissions is kg-CO2 or t-CO2, etc.). Of the two adjacent bar graphs in Figure 7, the left bar graph shows the predicted value of the first CO2 emissions. In the example shown in Figure 7, the predicted value of the first CO2 emissions gradually decreases from the first to the fourth period, and in the fourth period, the predicted value of the first CO2 emissions is zero. In this embodiment, the first CO2 emissions are predicted taking into account changes in the power emission coefficient of grid power. Therefore, even in the first period, when all of the electricity supplied to electricity consumer 10 is grid power, the predicted value of the first CO2 emissions changes.

[0088] [First CO2 Emission Reduction Amount] In this embodiment, the first CO2 emission reduction amount is also called the unadjusted CO2 emission reduction amount, and is the amount of CO2 emissions reduced by implementing CO2 emission reduction measures. In this embodiment, the first CO2 emission reduction amount for electricity consumers 10 over several years when a distributed power system 12 is introduced is predicted. The first CO2 emission reduction amount can be predicted by subtracting the above-mentioned first CO2 emissions from the CO2 emissions assuming that all the electricity supplied to electricity consumers 10 is grid power.

[0089] Figure 8 shows the projected CO2 emission reductions for electricity consumers 10 over several years (the units for CO2 emission reductions are kg-CO2 or t-CO2, etc.). Of the two adjacent bar graphs in Figure 8, the left bar graph shows the projected value of the first CO2 emission reduction. In the example shown in Figure 8, the projected value of the first CO2 emission reduction is zero in the first period, increases in the second period compared to the first period, increases in the third period compared to the second period, and is about the same as the third period in the fourth period. The reason why the projected value of the first CO2 emission reduction is about the same in the third and fourth periods is that the CO2 emission reduction of grid power, which is used as the basis for projecting the first CO2 emission reduction, decreases year by year.

[0090] [First Power Emission Factor] In this embodiment, the first power emission factor is also called the unadjusted power emission factor and is the power emission factor for the entire power consumer 10. In this embodiment, the controller 31 predicts the first power emission factor of the power consumer 10 over several years when the distributed power supply system 12 is introduced. The first power emission factor can be predicted by dividing the first CO2 emissions of the power consumer 10 described above by the amount of electricity demanded by the power consumer 10. The amount of electricity demanded by the power consumer 10 is also the total amount of electricity supplied to the power consumer 10.

[0091] Figure 9 shows the predicted values ​​of the electricity emission coefficient for electricity consumer 10 over several years (the unit of the electricity emission coefficient is kg-CO2 / kWh or t-CO2 / kWh, etc.). Of the two adjacent bar graphs in Figure 9, the left bar graph shows the predicted value of the first electricity emission coefficient. The trend of the predicted value of the first electricity emission coefficient shown in Figure 9 is similar to the trend of the predicted value of the first CO2 emissions shown in Figure 7. That is, the predicted value of the first electricity emission coefficient gradually decreases from the first to the fourth period, and in the fourth period, the predicted value of the first electricity emission coefficient is zero. The reason why the trend of the predicted value of the first electricity emission coefficient for electricity consumer 10 is the same as the trend of the predicted value of the first CO2 emissions for electricity consumer 10 is that, in this embodiment, the amount of electricity demanded by electricity consumer 10 used to predict the first electricity emission coefficient for electricity consumer 10 is predicted to be the same every year (see Figure 6).

[0092] [First Renewable Energy Rate] In this embodiment, the first renewable energy rate is also called the internal renewable energy rate or the unadjusted renewable energy rate, and refers to the proportion of electricity generated from renewable energy among the amount of electricity supplied to the electricity consumer 10. In this embodiment, the controller 31 predicts the first renewable energy rate of the electricity consumer 10 over several years when the distributed power system 12 is introduced. The first renewable energy rate of the electricity consumer 10 can be predicted by calculating the proportion of electricity generated from renewable energy supplied from the distributed power system 12 among the amount of electricity supplied to the electricity consumer 10. However, the amount of electricity generated from renewable energy mentioned above does not include the amount of electricity generated by the fuel cell device 14 if the fuel cell device 14 uses gray hydrogen, but it does include the amount of electricity generated by the fuel cell device 14 if the fuel cell device 14 uses green hydrogen. Note that green hydrogen is hydrogen obtained by water electrolysis using electricity generated using renewable energy, and therefore in this disclosure, it is included in renewable energy.

[0093] In the bar graph shown in Figure 4, the white areas represent the predicted values ​​for the first renewable energy rate. In the example shown in Figure 4, the predicted value for the first renewable energy rate is zero in the first period, and it increases from the second to the fourth period. In the example shown in Figure 4, the first renewable energy rate does not reach the target renewable energy rate in the second and third periods.

[0094] <Secondary Forecast> Next, in the forecasting method flow according to this embodiment, the controller 31 performs a secondary forecast (step S6). In the secondary forecast, the purchase quantities of CO2 emission rights, CO2 emission reduction rights, and electricity certificates are predicted from the first indicators. Hereinafter, CO2 emission rights, CO2 emission reduction rights, and electricity certificates will be referred to as "environmental value". In the secondary forecast, the purchase quantities of all environmental values ​​may be predicted, or the purchase quantities of some environmental values ​​may be predicted. The method for predicting the purchase quantities of each environmental value will be explained below.

[0095] [CO2 Emission Rights] For example, a government or local authority may set CO2 emission limits for electricity consumers 10. If an electricity consumer 10's CO2 emissions exceed these limits, the consumer 10 must purchase the excess amount as CO2 emission rights from others. Therefore, electricity consumers 10 may also purchase target CO2 emission rights.

[0096] Therefore, in this embodiment, the controller 31 predicts the amount of CO2 emission allowances to be purchased over multiple years when the distributed power system 12 is introduced. The controller 31 predicts the amount of CO2 emission allowances to be purchased by subtracting the first predicted value of CO2 emissions predicted in the initial forecast from the target CO2 emissions of the electricity consumer 10. The target CO2 emissions may be the upper limit of the CO2 emission allowance, or it may be any other value. The memory 32 stores the target CO2 emissions, and the controller 31 retrieves the target CO2 emissions from the memory 32 and predicts the amount of CO2 emission allowances to be purchased.

[0097] [CO2 Emission Reduction Rights] In recent years, in addition to the CO2 emission rights mentioned above, there has been a growing movement to allow the buying and selling of CO2 emission reductions achieved by implementing CO2 emission reduction measures such as the introduction of energy-saving equipment and the use of renewable energy, as CO2 emission reduction rights (credits). Therefore, electricity consumers 10 may also purchase target CO2 emission reduction rights.

[0098] Therefore, in this embodiment, the controller 31 predicts the amount of CO2 emission reduction credits to be purchased over multiple years when the distributed power system 12 is introduced. The controller 31 predicts the amount of CO2 emission reduction credits to be purchased by subtracting the predicted value of the first CO2 emission reduction amount predicted in the first prediction from the predetermined target CO2 emission reduction amount. The memory 32 stores the target CO2 emission reduction amount, and the controller 31 retrieves the target CO2 emission reduction amount from the memory 32 and predicts the amount of CO2 emission reduction credits to be purchased.

[0099] Alternatively, the controller 31 may predict the amount of CO2 emission allowances to be purchased by subtracting the predicted value of the first CO2 emission predicted in the initial forecast from the target CO2 emission of the electricity consumer 10, and convert the predicted amount of CO2 emission allowances to be purchased into the amount of CO2 emission reduction allowances to be purchased. In other words, the controller 31 may predict the amount of CO2 emission reduction allowances to be purchased based on the target CO2 emission of the electricity consumer 10 and the predicted value of the first CO2 emission.

[0100] [Electricity Certificates] Here, electricity consumer 10 can purchase renewable energy electricity certificates (electricity certificates) that prove that the grid electricity supplied by power generator 20 is electricity generated from renewable energy sources (solar, wind, hydro, geothermal, etc.). By purchasing these electricity certificates, electricity consumer 10 can demonstrate that the electricity supplied by power generator 20 is generated from renewable energy sources. For this reason, electricity consumer 10 may purchase electricity certificates.

[0101] Therefore, in this embodiment, the controller 31 predicts the amount of electricity certificates to be purchased over several years when the distributed power system 12 is introduced. The controller 31 predicts the amount of electricity certificates to be purchased by subtracting the predicted value of the first renewable energy rate predicted in the primary forecast from the target renewable energy rate set in step S1. In other words, the external renewable energy rate, which is the difference between the target renewable energy rate and the predicted value of the first renewable energy rate shown in Figure 4, corresponds to the amount of electricity certificates to be purchased.

[0102] In this embodiment, electricity consumer 10 purchases environmental value in the second and third phases. However, since the introduction of the distributed power system 12 has not yet begun in the first phase, electricity consumer 10 does not purchase environmental value in the first phase. Furthermore, in the fourth phase, the first CO2 emissions, the first CO2 emission reduction, and the first renewable energy rate have all reached their target values; therefore, electricity consumer 10 does not purchase environmental value in the fourth phase.

[0103] <Third-order prediction> Next, in the flow of the prediction method according to this embodiment, the controller 31 performs a third-order prediction (step S7). In the third-order prediction, the second CO2 emissions, the second CO2 emission reduction, the second electricity emission coefficient, and the second renewable energy rate are predicted from the first indicators. The prediction methods for these indicators will be explained in order below.

[0104] [Second CO2 Emissions] In this embodiment, the second CO2 emissions are also called adjusted CO2 emissions and are a modified version of the first CO2 emissions. In this embodiment, the controller 31 predicts the second CO2 emissions of electricity consumers 10 over several years when a distributed power system 12 is introduced. The second CO2 emissions can be predicted based on the purchase amount of environmental values ​​(CO2 emission rights, CO2 emission reduction rights, and electricity certificates) predicted in the second prediction. More specifically, the second CO2 emissions can be predicted by subtracting the CO2 emissions corresponding to the purchase amount of each environmental value predicted in the second prediction from the first CO2 emissions predicted in the first prediction.

[0105] In Figure 7, the rightmost of the two adjacent bar graphs shows the predicted value of the second CO2 emission. As shown in Figure 7, in this embodiment, environmental value is purchased in the second and third phases, so the predicted value of the second CO2 emission (adjusted CO2 emission) in the second and third phases is less than the predicted value of the first CO2 emission (unadjusted CO2 emission).

[0106] [Second CO2 Emission Reduction] In this embodiment, the second CO2 emission reduction is also called the adjusted CO2 emission reduction, and is a modified version of the first CO2 emission reduction. In this embodiment, the controller 31 predicts the second CO2 emission reduction for electricity consumers 10 over several years when the distributed power supply system 12 is introduced. The second CO2 emission reduction can be predicted based on the amount of environmental value purchased predicted in the second prediction. More specifically, the second CO2 emission reduction can be predicted by adding the amount of CO2 emission reduction corresponding to the amount of each environmental value purchased predicted in the second prediction to the predicted value of the first CO2 emission reduction predicted in the first prediction.

[0107] In Figure 8, the rightmost of the two adjacent bar graphs shows the predicted value of the second CO2 emission reduction. As shown in Figure 8, in this embodiment, environmental value is purchased in the second and third phases, so the predicted value of the second CO2 emission reduction (adjusted CO2 emission reduction) in the second and third phases is greater than the predicted value of the first CO2 emission reduction (unadjusted CO2 emission reduction).

[0108] [Second Power Emission Factor] The second power emission factor in this embodiment is also called the adjusted power emission factor and is a modified version of the first CO2 emission reduction. In this embodiment, the controller 31 predicts the second power emission factor of the power consumer 10 over several years when the distributed power supply system 12 is introduced. The second power emission factor can be predicted based on the amount of environmental value purchased predicted in the second prediction. More specifically, the second power emission factor can be predicted by dividing the second CO2 emission amount predicted based on the amount of environmental value purchased by the amount of electricity demanded by the power consumer 10.

[0109] In Figure 9, the rightmost of the two adjacent bar graphs shows the predicted value of the second power emission factor. As shown in Figure 9, in this embodiment, environmental value is purchased in the second and third phases, so the predicted value of the second power emission factor (adjusted power emission factor) in the second and third phases is smaller than the predicted value of the first power emission factor (unadjusted power emission factor).

[0110] [Second Renewable Energy Rate] The second renewable energy rate in this embodiment is also called the adjusted renewable energy rate, and is a modified version of the first renewable energy rate. In this embodiment, the controller 31 predicts the second renewable energy rate of the electricity consumer 10 over several years when the distributed power system 12 is introduced. The second renewable energy rate can be predicted based on the amount of environmental value purchased predicted in the second forecast. More specifically, the second renewable energy rate can be predicted by adding the renewable energy rate (external renewable energy rate) corresponding to the amount of environmental value purchased predicted in the second forecast to the first renewable energy rate (internal renewable energy rate) predicted in the first forecast. Note that electricity certificates can be mutually complementary with CO2 emission reduction rights.

[0111] In the bar graph in Figure 4, the shaded portion represents the external renewable energy rate. The value obtained by adding the external renewable energy rate to the predicted value of the first renewable energy rate (the white portion) is the predicted value of the second renewable energy rate. As shown in Figure 4, in this embodiment, in the second and third phases, the predicted value of the first renewable energy rate (unadjusted renewable energy rate) does not reach the target renewable energy rate. However, in the second and third phases, by purchasing environmental value, the predicted value of the second renewable energy rate (adjusted renewable energy rate) reaches the target renewable energy rate.

[0112] <Fourth-order prediction> Next, in the flow of the prediction method according to this embodiment, the controller 31 performs a fourth-order prediction (step S8). In the fourth-order prediction, the CFP (Carbon Footprint of Products) of the first indicator is predicted.

[0113] In recent years, a system has been gaining traction in which greenhouse gas emissions from products and services throughout their entire lifecycle, from raw material procurement to disposal and recycling, are converted to CO2 equivalents and displayed as CFP (Coefficient of Foods). For example, when considering products produced at the production facilities (electricity consumption facilities 11) of electricity consumers 10, the CFP is the sum of the CO2 emissions generated from material procurement, production, distribution, use, and disposal / recycling for each unit of that product.

[0114] In this embodiment, the controller 31 predicts the multi-year CO2 emissions for products produced by the power consumer 10 when a distributed power system 12 is introduced. The controller 31 predicts CO2 emissions per product in the lifecycle other than production (material procurement, distribution, use, and waste recycling) by adding the predicted CO2 emissions per product in production to the predicted CO2 emissions per product in the lifecycle.

[0115] The CO2 emissions per product in the above-mentioned production can be predicted by dividing the second CO2 emissions (adjusted CO2 emissions) predicted in the third forecast by the number of products produced by the electricity consumer 10. However, the CO2 emissions per product in production may also be predicted by dividing the first CO2 emissions (unadjusted CO2 emissions) predicted in the first forecast by the number of products produced by the electricity consumer 10. Furthermore, the predicted values ​​of CO2 emissions per product for items other than production are stored in the memory 32 in advance, and the controller 31 can obtain this information from the memory 32. Note that the predicted values ​​of CO2 emissions per product for items other than production may remain constant over several years, or they may change from year to year.

[0116] Figure 10 shows the projected CFP values ​​for products produced by electricity consumer 10 over several years (CFP units include kg-CO2 / 1 product and t-CO2 / 1 product). As shown in Figure 10, if the CO2 emissions per product in items other than production remain constant, the projected CFP gradually decreases from the first to the fourth period. This is because the projected CO2 emissions per product in production decrease as the second CO2 emission gradually decreases (see Figure 7).

[0117] <Display of the First Indicator> Next, in the flow of the prediction method according to this embodiment, the controller 31 displays the first indicator (step S9). Specifically, the controller 31 displays information on the display 34 indicating the first indicator (CO2 emissions, power emission coefficient, renewable energy rate, amount of CO2 emission rights purchased, amount of CO2 emission reduction rights purchased, amount of electricity certificates purchased, CFP) predicted in the first to fourth predictions. Note that the display method of the first indicator is not limited, and the first indicator may be displayed as a graph or as a numerical value.

[0118] <Evaluation of Environmental Contribution> Next, in the prediction method according to this embodiment, the operator uses the information terminal 30 (see Figure 2) to have the evaluation system 40 evaluate the environmental contribution of the electricity consumer 10 (step S10). The evaluation system 40 is operated by an organization such as CDP (Carbon Disclosure Project), and when predetermined indicators are input, it evaluates the environmental contribution of the electricity consumer 10 with a score such as A to D.

[0119] In this embodiment, the worker accesses the evaluation system 40 using the information terminal 30, and inputs the second indicator and the first indicator of the electricity consumer 10 over several years into the evaluation system 40 via the input device 33. The evaluation system 40 then evaluates the environmental contribution of the electricity consumer 10 over several years. As explained in step S2, the second indicator is an indicator used when formulating the introduction plan for the distributed power system 12 (in this embodiment, the renewable energy rate), and as explained in steps S5 to S8, the first indicator is an indicator predicted in the first to fourth forecasts. The evaluation system 40 only needs to input the first indicator; it does not need to input the second indicator.

[0120] Furthermore, in this embodiment, the second indicator input to the evaluation system 40 is the renewable energy rate, but it may also be other indicators related to CO2 emissions, such as CO2 emissions, CO2 emission reductions, and electricity emission factors. Moreover, in this embodiment, the first indicator input to the evaluation system 40 is CO2 emissions, electricity emission factors, renewable energy rate, amount of CO2 emission rights purchased, amount of CO2 emission reduction rights purchased, amount of electricity certificates purchased, and CFP, but the first indicator is not limited to these indicators.

[0121] <Determination of Environmental Contribution Evaluation> Next, in the flow of the prediction method according to this embodiment, the operator determines whether the environmental evaluation score of the power consumer 10 evaluated by the evaluation system 40 is equal to or greater than the target environmental evaluation score (step S11). For example, if the target environmental evaluation score is "A", and the environmental evaluation score of the power consumer 10 evaluated in step S10 is "B", the operator determines that the environmental evaluation score of the power consumer 10 evaluated by the evaluation system 40 is not equal to or greater than the target environmental evaluation score.

[0122] <Revision of the Implementation Plan> If, in step S11, the operator determines that the environmental evaluation level of the power consumer 10, as evaluated by the evaluation system 40, is not equal to or greater than the target environmental evaluation level (NO in step S11), the operator revises the implementation plan for the distributed power system 12 (step S12). For example, the operator may revise the plan by increasing the amount of the distributed power system 12 to be implemented or by accelerating the implementation period compared to the original implementation plan for the distributed power system 12.

[0123] After the worker modifies the implementation plan for the distributed power system 12 in step S12, they return to step S3 and repeat steps S3 onward. In other words, the worker modifies the multi-year implementation plan for the distributed power system 12 so that the environmental contribution evaluated by the evaluation system 40 matches the environmental contribution targeted by the electricity consumer 10.

[0124] On the other hand, if the operator determines in step S11 that the environmental evaluation level of the power consumer 10 evaluated by the evaluation system 40 is equal to or greater than the target environmental evaluation level (YES in step S11), the flow of the prediction method according to this embodiment ends. The above is a description of the flow of the prediction method according to this embodiment.

[0125] As explained above, steps S1 to S12 are performed by an operator or the controller 31, respectively, but the entity performing each step is not limited to those described above. For example, the controller 31 may perform all of steps S1 to S12.

[0126] As described above, the prediction method according to this embodiment predicts a first indicator regarding the CO2 emissions of electricity consumers 10 over a multi-year period when the distributed power system 12 is introduced, based on a multi-year introduction plan for the distributed power system 12. Therefore, if the introduction of the distributed power system 12 is evaluated using this first indicator, a more appropriate evaluation from an environmental standpoint can be made compared to the conventional technology.

[0127] From the above description, many improvements and other embodiments of the disclosure will be apparent to those skilled in the art. Therefore, the above description should be construed as illustrative only and is provided for the purpose of teaching those skilled in the art the best mode of carrying out the disclosure. The details of its structure and / or function can be substantially modified without departing from the spirit of the disclosure.

[0128] One aspect of this disclosure can be used for a method of predicting indicators for evaluating the introduction of a distributed power supply system from an environmental perspective more appropriately than conventional technologies, and for a method of controlling an information terminal that displays said indicators.

[0129] 10: Electricity consumers 11: Electricity consumption equipment 12: Distributed power generation systems 13: Renewable energy power generation equipment 14: Fuel cell equipment 15: Energy storage equipment 20: Power generation operators 30: Information terminals 31: Controllers 32: Memory devices 33: Input devices 34: Displays 40: Evaluation systems

Claims

1. A prediction method for formulating a multi-year phased introduction plan for a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, and for predicting a first indicator of CO2 emissions for a power consumer over a multi-year period when the distributed power system is introduced based on the introduction plan.

2. The prediction method according to claim 1, wherein the distributed power system further includes an energy storage device.

3. The prediction method according to claim 1 or 2, wherein the distributed power system includes the fuel cell device, a switching plan is made for the hydrogen species used in power generation by the fuel cell device, and the first indicator is predicted taking the switching plan into consideration.

4. A prediction method according to any one of claims 1 to 3, which also takes into account predictions of grid power emission factors over multiple years to predict the first index.

5. The prediction method according to any one of claims 1 to 4, wherein the distributed power system includes the fuel cell device, and the first index is predicted taking into consideration the power emission factor of the electricity from the fuel cell over several years, and the power emission factor of the electricity from the fuel cell is derived based on the type of hydrogen used for power generation.

6. The prediction method according to any one of claims 1 to 5, wherein the first indicator includes a first CO2 emission, and the amount of CO2 emission rights or CO2 emission reduction rights to be purchased is predicted based on the first CO2 emission and a target CO2 emission as a target value of a second indicator relating to the CO2 emissions of electricity consumers over several years.

7. The prediction method according to claim 6, which predicts a second CO2 emission amount obtained by modifying the first CO2 emission amount based on the predicted purchase amount of the CO2 emission rights or CO2 emission reduction rights.

8. The prediction method according to claim 7, wherein CFP is predicted based on the predicted second CO2 emissions.

9. The forecasting method according to any one of claims 1 to 5, wherein the first indicator includes a first CO2 emission that does not take into account the amount of CO2 emission rights or CO2 emission reduction rights purchased by electricity consumers, and CFP is predicted based on the first CO2 emission.

10. The prediction method according to claim 6, wherein the first indicator further includes a first CO2 emission reduction, and a second CO2 emission reduction is predicted, which is obtained by modifying the first CO2 emission reduction based on the predicted amount of CO2 emission rights or CO2 emission reduction rights purchased.

11. The prediction method according to claim 6, wherein the first indicator further includes a first power emission coefficient, and a second power emission coefficient is predicted, which is obtained by modifying the first power emission coefficient based on the predicted purchase amount of CO2 emission rights or CO2 emission reduction rights.

12. The prediction method according to any one of claims 1 to 11, wherein the first indicator includes a first renewable energy rate, and the amount of electricity certificates to be purchased is predicted based on the first renewable energy rate and a target renewable energy rate as a target value of a second indicator relating to the CO2 emissions of electricity consumers over a multi-year period.

13. The prediction method according to claim 12, which predicts a second renewable energy rate obtained by modifying the first renewable energy rate based on the predicted amount of electricity certificates to be purchased.

14. The prediction method according to claim 12, wherein the first indicator further includes a first CO2 emission, and a second CO2 emission is predicted, which is obtained by modifying the first CO2 emission based on the predicted amount of electricity certificates purchased.

15. The prediction method according to claim 1, comprising inputting the predicted first indicator into an evaluation system for evaluating the environmental contribution of electricity consumers, and causing the evaluation system to evaluate the environmental contribution of electricity consumers over a period of several years.

16. The prediction method according to claim 15, further comprising inputting a second indicator relating to the CO2 emissions of electricity consumers over several years into the evaluation system, and having the evaluation system evaluate the environmental contribution of electricity consumers over several years.

17. The forecasting method according to claim 16, wherein the first indicator includes CO2 emissions, electricity emission coefficient, renewable energy rate, and at least one of the amount of CO2 emission rights or CO2 emission reduction rights purchased, the amount of electricity certificates purchased, and CFP.

18. The prediction method according to any one of claims 15-17, wherein the evaluated environmental contribution is modified to match the environmental contribution targeted by the electricity consumer, thereby modifying the multi-year deployment plan for the distributed power system.

19. The prediction method according to any one of claims 1 to 18, wherein the introduction plan for the distributed power system includes an introduction plan for the replacement of at least a portion of the distributed power system.

20. A method for controlling an information terminal, comprising the steps of: causing the information terminal's display to show a multi-year phased introduction plan for a distributed power system including at least one of a natural energy power generation device and a fuel cell device; and causing the display to show information on the display indicating a first indicator of the multi-year CO2 emissions of the electricity consumer, which is predicted when the distributed power system is introduced based on the introduction plan.

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