Prediction method and method for controlling information terminal

Abstract

This prediction method comprises: formulating a stepwise introduction plan over several years for a distributed electric power supply system that includes at least one of a natural energy electric power generation device and a fuel cell device; and predicting an index related to the electric power cost of an electric power consumer over several years for cases in which the distributed electric power supply system is introduced on the basis of the introduction plan.

Description

Prediction Method and Control Method for Information Terminal

[0001] The present disclosure relates to a method for predicting an indicator related to power cost and a control method for an information terminal.

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

[0003] Japanese Patent No. 7058254

[0004] In the technology described in Patent Document 1, a distributed power system is evaluated from an economic perspective, but there are still insufficient points in the evaluation of the introduction of a distributed power system.

[0005] An object of the present disclosure is to provide a prediction method of an indicator for performing a more appropriate evaluation from an economic perspective compared with conventional techniques for the introduction of a distributed power system by a power consumer, 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 introduction plan over multiple years for a distributed power system including at least one of a natural energy power generation device and a fuel cell device, and predicts an indicator related to the power cost of the power consumer over multiple years when the distributed power system is introduced based on the introduction plan.

[0007] A control method for an information terminal according to one aspect of the present disclosure is a control method for an information terminal, comprising the steps of: causing a display of the information terminal to display a phased introduction plan over multiple years 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 display information indicating an indicator related to the power cost of the power consumer over multiple years, which is predicted when the distributed power system is introduced based on the introduction plan.

[0008] One aspect of the prediction method of this disclosure has the effect of enabling a more appropriate economic evaluation of the introduction of a distributed power system by electricity consumers compared to conventional technologies. Furthermore, one aspect of the information terminal control method of this disclosure has the effect of enabling a more appropriate economic evaluation of the introduction of a distributed power system compared to conventional technologies.

[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 target 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 first implementation cost over several years. Figure 8 shows the second implementation cost over several years. Figure 9 shows the running costs over several years. Figure 10 shows the first electricity cost over several years. Figure 11 compares the first and second electricity costs over several years.

[0010] In recent years, due to growing environmental awareness, there has been an increasing trend for electricity consumers to install distributed power systems themselves, 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 economic standpoint, 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 an indicator of the electricity cost of the electricity consumer over several years if the distributed power system is introduced based on the introduction plan.

[0013] This forecasting method predicts multi-year electricity cost indicators based on a multi-year phased deployment plan for distributed power systems. Therefore, evaluating the deployment of distributed power systems by electricity consumers using these electricity cost indicators allows for a more appropriate economic assessment 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] Because this prediction method includes energy storage devices in the distributed power generation system, it allows for a more appropriate economic evaluation of the introduction of distributed power generation systems including energy storage devices by electricity consumers compared to conventional technologies.

[0016] Here, even after the implementation of a distributed power system is complete, as well as during the implementation phase, grid power generated by power generators may be supplied to electricity consumers.

[0017] Therefore, the prediction method of the third aspect of the present disclosure, in the prediction method of the first or second aspect, includes, in the indicator relating to electricity costs, a first electricity cost which is the electricity cost itself, and the first electricity cost includes the costs incurred in connection with the introduction of the distributed power system based on the introduction plan and the cost of purchasing electricity from the grid.

[0018] This forecasting method includes not only the cost of introducing a distributed power generation system but also the cost of purchasing electricity from the grid as indicators for predicted electricity costs. Therefore, even when electricity generated by a distributed power generation system and grid power are supplied to electricity consumers, it is possible to evaluate the introduction of a distributed power generation system by electricity consumers from an economic standpoint more appropriately than with conventional technology.

[0019] A fourth aspect of the prediction method of the present disclosure is a prediction method of the third aspect, wherein the introduction cost of the distributed power system includes a first introduction cost, which is the introduction cost of introducing the distributed power system based on the introduction plan, paid in a lump sum in the year of introduction.

[0020] This prediction method forecasts the implementation cost when paid in a lump sum, making it easier to intuitively understand the implementation cost. In other words, it allows for a more appropriate evaluation from an economic perspective of the implementation of distributed power generation systems for electricity consumers compared to conventional technologies.

[0021] A fifth aspect of the prediction method of the present disclosure is a prediction method of the third aspect, wherein the introduction cost of the distributed power system includes a second introduction cost, which is calculated by dividing the introduction cost of introducing the distributed power system based on the introduction plan into installments for each year from the introduction year to the useful life.

[0022] This prediction method forecasts the initial investment cost when paid in installments, making it easier to understand the annual investment cost when the initial investment is paid in installments. In other words, it allows for a more appropriate economic evaluation of the introduction of distributed power generation systems for electricity consumers compared to conventional technologies.

[0023] 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 that allows consumers to buy and sell CO2 emission reduction credits (credits) for the amount of CO2 emissions reduced 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.

[0024] Accordingly, the sixth aspect of the prediction method of this disclosure, in any one of the third to fifth aspects of the prediction method, includes the cost of purchasing CO2 emission rights or CO2 emission reduction rights to achieve the target CO2 emissions as a target value of the indicator relating to CO2 emissions.

[0025] This forecasting method includes the cost of purchasing CO2 emission credits or CO2 emission reduction credits as an indicator of predicted electricity costs. Therefore, it allows for a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0026] 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 the 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.

[0027] Therefore, the prediction method of the seventh aspect of the present disclosure, in any one of the prediction methods of the third to sixth aspects, includes the cost of purchasing electricity certificates to achieve the target renewable energy rate as a target value for the CO2 emission indicator.

[0028] This forecasting method includes the cost of purchasing electricity certificates as an indicator of predicted electricity costs. Therefore, it allows for a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0029] The prediction method of the eighth aspect of this disclosure is a prediction method of any one of the third to seventh aspects, wherein the distributed power system includes the fuel cell device, and the costs incurred in connection with the introduction of the distributed power system include the cost of purchasing hydrogen used for power generation by the fuel cell device.

[0030] This forecasting method includes the cost of purchasing hydrogen used for power generation by fuel cell systems as an indicator of electricity costs. Therefore, it can predict indicators that allow electricity consumers to make a more appropriate economic evaluation of the introduction of distributed power generation systems, including fuel cell systems, compared to conventional technologies.

[0031] The forecasting method of the ninth aspect of this disclosure is a forecasting method of any one of the first to eighth aspects, wherein the indicator for electricity costs includes a first electricity unit price for the electricity use of the electricity consumer, and the first electricity unit price is the value obtained by dividing the electricity cost by the amount of electricity demanded by the electricity consumer.

[0032] In this forecasting method, the indicator for electricity costs includes a first electricity unit price, which is calculated by dividing the electricity cost by the amount of electricity demanded by the electricity consumer. Therefore, it is easier to grasp the electricity costs of a distributed power generation system. Thus, it is possible to make a more appropriate economic evaluation of the introduction of a distributed power generation system by electricity consumers compared to conventional technologies.

[0033] A prediction method according to a tenth aspect of the present disclosure predicts a second electricity cost over several years for the electricity usage of the electricity consumer in the case where the distributed power system is not introduced, based on the cost of purchasing electricity from the grid, and displays a comparison of the first electricity cost and the second electricity cost over several years, as described in the prediction method of the third aspect.

[0034] This prediction method allows for a comparative display of electricity costs with and without the introduction of a distributed power system, making it easy to understand the economic benefits of implementing such a system. This enables a more appropriate economic evaluation of the introduction of a distributed power system by electricity consumers compared to conventional technologies.

[0035] The prediction method of the 11th aspect of this disclosure predicts a second electricity unit price over several years for the electricity usage of the electricity consumer in the case where the distributed power system is not introduced, based on the electricity unit price of the grid power, and displays a comparison of the first electricity unit price and the second electricity unit price over several years, in the prediction method of the 9th aspect.

[0036] This prediction method allows for a comparison of electricity unit costs with and without the introduction of a distributed power system, making it easy to understand the economic benefits of implementing such a system. This enables electricity consumers to make a more appropriate economic evaluation of the introduction of a distributed power system compared to conventional technologies.

[0037] The prediction method of the twelfth aspect of this disclosure predicts both the first electricity unit price and the second electricity unit price taking into account the cost of purchasing CO2 emission rights or CO2 emission reduction rights to achieve the target CO2 emission as a target value of the indicator relating to CO2 emissions.

[0038] This prediction method compares and displays electricity costs with and without a distributed power system, taking into account the cost of purchasing CO2 emission credits or CO2 emission reduction credits to achieve target CO2 emissions. Therefore, the economic benefits of implementing a distributed power system can be easily understood. This allows for a more appropriate economic evaluation of the implementation of a distributed power system by electricity consumers compared to conventional technologies.

[0039] The prediction method of the 13th aspect of this disclosure predicts both the first and second electricity unit prices in the 11th prediction method by taking into account the cost of purchasing electricity certificates to achieve the target renewable energy rate, which is the target value of the CO2 emission indicator.

[0040] This prediction method allows for a comparative display of electricity costs with and without a distributed power generation system, taking into account the cost of purchasing electricity certificates to achieve the target renewable energy rate. Therefore, the economic benefits of implementing a distributed power generation system can be easily understood. This enables a more appropriate economic evaluation of the implementation of a distributed power generation system for electricity consumers compared to conventional technologies.

[0041] Here, the unit price of grid electricity supplied to electricity consumers differs depending on the supplier.

[0042] Therefore, the prediction method of the 14th aspect of this disclosure, in the prediction method of the 11th aspect, displays the electricity unit price of the grid power for each grid power supplier in a menu selectable format, and predicts the first electricity unit price and the second electricity unit price by considering the electricity unit price of the grid power supplier selected by the user.

[0043] In this prediction method, the unit electricity price of grid power supplied by the grid power supplier selected by the user is taken into account, so that the unit electricity price with and without the introduction of a distributed power system can be compared and displayed. Therefore, compared with the prior art, a more appropriate evaluation from an economic perspective can be performed for the introduction of a distributed power system by a power consumer.

[0044] Here, in recent years, some companies, in order to promote decarbonization, set a price on their own CO₂ emissions (internal carbon tax) and sometimes adopt an internal carbon tax system that monetizes emissions. Internal carbon tax is also called ICP (Internal Carbon Pricing).

[0045] Therefore, the prediction method according to the fifteenth aspect of the present disclosure is the prediction method according to any one of the eleventh to fourteenth aspects, wherein both the first unit electricity price and the second unit electricity price are predicted in consideration of the carbon tax within the power consumer's facility.

[0046] In this prediction method, since the first unit electricity price and the second unit electricity price are predicted in consideration of the carbon tax within the power consumer's facility, the unit electricity price with and without the introduction of a distributed power system can be compared more accurately. Accordingly, compared with the prior art, a more appropriate evaluation from an economic perspective can be performed for the introduction of a distributed power system by a power consumer.

[0047] The prediction method according to the sixteenth aspect of the present disclosure is the prediction method according to any one of the first to fifteenth aspects, wherein the prediction result of the indicator related to the power cost of the power consumer is output as a report to an electronic medium or a paper medium.

[0048] In this prediction method, since the prediction result of the indicator related to the power cost is output as a report to an electronic medium or a paper medium, compared with the prior art, a more appropriate evaluation from an economic perspective can be performed for the introduction of a distributed power system by a power consumer.

[0049] Here, the distributed power sources included in the distributed power system may require regular replacement, for example, because a service life is set for them.

[0050] Therefore, the prediction method of the 17th aspect of the present disclosure includes, in any one of the prediction methods of the 1st to 16th 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.

[0051] This forecasting method includes a plan for replacing at least a portion of the distributed power system, which forms the basis for forecasting indicators related to electricity costs. Therefore, it enables the accurate formulation of distributed power system deployment plans. This allows for a more appropriate economic evaluation of the deployment of distributed power systems for electricity consumers compared to conventional technologies.

[0052] The prediction method of the 18th aspect of this disclosure is a prediction method in any one of the prediction methods of the 3rd to 8th aspects, in which the introduction cost of the distributed power system is predicted based on prediction data of the unit cost of distributed power capacity over several years.

[0053] The "distributed power capacity unit price" mentioned above refers to the capacity unit price of at least one of the renewable energy power generation equipment and the fuel cell equipment to be introduced based on the introduction plan for the distributed power system. Furthermore, if the distributed power system includes an energy storage device, the capacity unit price of the energy storage device is also included. Therefore, the above prediction method can accurately predict the introduction cost of a distributed power system, and consequently, accurately predict indicators related to electricity costs. This makes it possible to predict indicators for a more appropriate evaluation of the introduction of a distributed power system from an economic standpoint compared to conventional technologies.

[0054] The prediction method of the 19th aspect of this disclosure is a prediction method in any one of the prediction methods of the 3rd to 8th aspects, in which the cost of purchasing electricity from the grid power is predicted based on prediction data of the unit price of grid power over several years.

[0055] This prediction method allows for accurate forecasting of electricity purchase costs, and consequently, accurate forecasting of electricity cost indicators. This enables a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0056] The forecasting method of the 20th aspect of this disclosure, in any one of the forecasting methods of the 6th or 12th aspect, predicts the cost of purchasing CO2 emission rights or CO2 emission reduction rights to achieve the target CO2 emissions based on forecast data of the unit price of CO2 emission rights or CO2 emission reduction rights over multiple years.

[0057] This prediction method uses data predicting the purchase costs of CO2 emission credits or CO2 emission reduction credits, allowing for accurate prediction of electricity cost indicators when considering the purchase costs of CO2 emission credits or CO2 emission reduction credits. This enables a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0058] The forecasting method of the 21st aspect of this disclosure is a forecasting method of either the 7th or 13th aspect in which the purchase cost of the electricity certificates is forecast based on forecast data of electricity certificate unit prices over several years.

[0059] This prediction method uses predicted data on the purchase cost of electricity certificates, allowing for accurate prediction of electricity cost indicators that take the purchase cost of electricity certificates into account. This enables a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0060] The prediction method of the 22nd aspect of this disclosure, in any one of the prediction methods of the 8th aspect, predicts the cost of purchasing hydrogen used for power generation by the fuel cell system based on predicted hydrogen unit price data over several years.

[0061] This prediction method can accurately predict the cost of purchasing hydrogen used for power generation by fuel cell systems, and consequently, accurately predict indicators related to electricity costs. This allows electricity consumers to make more appropriate economic evaluations of the introduction of distributed power generation systems, including fuel cell systems, compared to conventional technologies.

[0062] The forecasting method of the 23rd aspect of this disclosure is one of the forecasting methods of the 15th aspect, in which the carbon tax within the electricity consumer is forecasted based on the carbon tax unit price within the electricity consumer over several years.

[0063] This forecasting method can accurately predict carbon taxes within electricity consumers, and consequently, accurately predict indicators related to electricity costs. This allows for the prediction of indicators that enable a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0064] A control method for an information terminal according to a 24th aspect of the present disclosure is a control method for an information terminal comprising the steps of: causing 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 to be displayed on a display of the information terminal; and causing the display of information on the display indicating an indicator of the electricity cost of the electricity consumer over a multi-year period, which is predicted when the distributed power system is introduced based on the introduction plan.

[0065] This control method displays the distribution power system implementation plan on a display unit. Therefore, users of the information terminal, such as the proposer of the distribution power system implementation or electricity consumers who are 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 indicators of the electricity consumer's electricity costs over several years. Therefore, the proposer or electricity consumer can make a more appropriate evaluation of the electricity consumer's distribution power system implementation compared to conventional technologies.

[0066] 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.

[0067] 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.

[0068] Furthermore, in the operation of the apparatus, the order of the processes may be changed or known processes may be added as needed.

[0069] (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".

[0070] 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.

[0071] 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.

[0072] 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.

[0073] (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.

[0074] 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.

[0075] Furthermore, the information terminal 30 is connected to the output device 40 in a communicative manner. The output device 40 is a device that outputs various information using paper media. The output device 40 is, for example, a printer. The information terminal 30 is also connected to an information server (not shown in the figure) in a communicative manner, and this information server may execute 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 in a communicative manner.

[0076] (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 economic standpoint. More specifically, the prediction method according to this embodiment predicts indicators related to the electricity costs of electricity consumers 10 over several years when the distributed power system 12 is introduced. A detailed explanation follows below.

[0077] <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 S10). Specifically, the operator sets target values ​​for indicators related to CO2 emissions of the electricity consumer 10 over several years. Indicators related to CO2 emissions include, for example, CO2 emissions, CO2 emission reduction, electricity emission coefficient, renewable energy rate, etc.

[0078] In this embodiment, a multi-year target renewable energy rate is set as the target value for the CO2 emission indicator. The renewable energy rate refers to the proportion of electricity generated from renewable energy sources in the electricity supplied to electricity consumers or the electricity consumed by electricity consumers. Figure 4 shows the multi-year target renewable energy rate for electricity consumer 10. The thick line in Figure 4 represents the multi-year target renewable energy rate for electricity consumer 10. The worker sets the multi-year target renewable energy rate for electricity consumer 10, as shown by the thick 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 reference values ​​and standard 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 (for example, 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.

[0079] In the example shown in Figure 4, 30 years are divided into six periods of 5 years each, from Period 1 to Period 6, with target renewable energy rates set for Periods 1 through 4. Periods 5 and 6 are periods to maintain the state of Period 4, and the same target renewable energy rates as Period 4 are set. Period 1 is before the introduction of the distributed power system 12 and corresponds to the state shown in Figure 1(a). Periods 2 and 3 are intermediate stages in the introduction of the distributed power system 12 and correspond to the state shown in Figure 1(b). Periods 4 through 6 are after the completion of the introduction of the distributed power system 12 and correspond to the state shown in Figure 1(c). Furthermore, Periods 1 through 6 shown in Figures 5 through 11 correspond to Periods 1 through 6 shown in Figure 4, respectively.

[0080] Furthermore, targets other than the target renewable energy rate may be set as target values ​​for indicators related to CO2 emissions. In this case, the same method as for the target renewable energy rate can be used to set the target. For example, when setting a target CO2 emission as a target value for indicators related to CO2 emissions, the operator can set multi-year target CO2 emission for electricity consumer 10 based on information such as the standard values ​​and benchmark values ​​for CO2 emissions of the industry, country, or local government to which electricity consumer 10 belongs, as well as electricity consumer 10's decarbonization plan. The above standard values ​​and benchmark values ​​are the target CO2 emission values ​​that electricity consumer 10 wants to achieve in its industry, the target CO2 emission values ​​of other electricity consumers (e.g., other companies) in its industry, and the target CO2 emission values ​​that electricity consumer 10 wants to achieve in its country or local government.

[0081] <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 S20). In this embodiment, the worker formulates a multi-year phased implementation plan for the distributed power system 12 based on the target values ​​of the CO2 indicators (target renewable energy rate) and the budget plan for CO2 emission reduction set in step S10. Note that the multi-year phased implementation plan for the distributed power system 12 may be formulated by the controller 31.

[0082] 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, the number of fuel cell devices 14 is further increased in the fourth phase, and the configuration of the fourth phase is maintained in the fifth and sixth phases.

[0083] 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.

[0084] Furthermore, the introduction plan for the distributed power system 12 may include an introduction plan for replacing at least a portion of the distributed power system 12. In this embodiment, the service life of the renewable energy power generation device 13 is 25 years, and the service life of the fuel cell device 14 and the energy storage device 15 is 15 years. Therefore, although not shown in Figure 5, the introduction plan in this embodiment includes an introduction plan to replace the renewable energy power generation device 13 with a new one after 25 years from the time of introduction, and to replace the fuel cell device 14 and the energy storage device 15 with new ones after 15 years from the time of introduction.

[0085] Furthermore, in this embodiment, the introduction plan for the distributed power system 12 is formulated based on both multi-year target values ​​for CO2 emission indicators and a 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 target values ​​for other CO2 emission indicators, such as target CO2 emissions. In addition, 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 values ​​for the second indicator and the multi-year budget plan for CO2 emission reduction, and may be formulated on any criterion. For example, the multi-year phased 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 values ​​for the second indicator or the multi-year budget plan for CO2 emission reduction.

[0086] <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 S30). 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 S20 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.

[0087] <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 S40). 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.

[0088] Based on the data regarding the introduction plan of the distributed power system 12 entered by the operator in step S30, the controller 31 predicts or acquires the amount of electricity supplied from the renewable energy power generation device 13 to the power consumer 10, the amount of electricity supplied from the fuel cell device 14 to the power consumer 10, and the amount of electricity supplied from the energy storage device 15 to the power consumer 10. The controller 31 also predicts the amount of grid power supplied to the power consumer 10 by subtracting the amount of electricity supplied to the power consumer 10 by the distributed power system 12 (renewable energy power generation device 13, fuel cell device 14, and energy storage device 15) from the power consumer 10's demand amount (amount of electricity consumed). In this embodiment, the power consumer 10's demand amount is predicted to be the same every year, but this is just an example and is not limited to this.

[0089] 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 from the grid. From the second to the fourth period, the proportion of electricity supplied from the distributed power system 12 (natural energy power generation device 13, fuel cell device 14, energy storage device 15) to electricity consumer 10 gradually increases, and from the fourth to the sixth period, all of the electricity supplied to electricity consumer 10 is supplied from the distributed power system 12.

[0090] <Prediction of the First Power Cost> Next, in the flow of the prediction method according to this embodiment, the controller 31 predicts the first power cost of the power consumer 10 over several years when the distributed power system 12 is introduced (step S50). The "first power cost" here refers to the power cost itself. The first power cost in this embodiment is the total cost, which is the sum of the costs incurred in connection with the introduction of the distributed power system 12 based on the introduction plan for the distributed power system 12 and the running costs. However, the first power cost is not limited to the total cost. In this embodiment, the first power cost is predicted by performing steps S51 to S54. These steps will be described in order below. Furthermore, the costs incurred in connection with the introduction of the distributed power system 12 may include at least the introduction cost of the distributed power system 12 and may also include the running costs incurred after the introduction of the distributed power system 12.

[0091] <Prediction of the First Installation Cost> First, in predicting the first power cost, the controller 31 predicts the first installation cost over several years if the distributed power supply system 12 is installed (step S51). The "first installation cost" here refers to the cost incurred in connection with the installation of the distributed power supply system 12, and is the cost if the installation cost of the distributed power supply system 12 is paid in a lump sum in the year of installation, based on the installation plan.

[0092] Here, memory 32 stores predicted data of the distributed power capacity unit price over several years. The distributed power capacity unit price is the capacity unit price of at least one of the renewable energy power generation equipment 13 and the fuel cell equipment 14 to be introduced based on the introduction plan of the distributed power system 12. Furthermore, if the distributed power system 12 includes an energy storage device 15, the capacity unit price of the energy storage device 15 is also included. Based on the multi-year introduction plan of the distributed power system 12 formulated in step S20 and the predicted data of the distributed power capacity unit price over several years obtained from memory 32, the controller 31 predicts the first introduction cost over several years if the distributed power system 12 is introduced.

[0093] Figure 7 shows the initial multi-year implementation costs when the distributed power system 12 predicted by the controller 31 is implemented. In the example shown in Figure 7, the renewable energy power generation device 13, fuel cell device 14, and energy storage device 15 are installed in the first year of the second phase, and the implementation costs for these are shown. In addition, the fuel cell device 14 is installed in the first year of the third and fourth phases, and the implementation costs for these are shown. Furthermore, since the fuel cell device 14 and energy storage device 15 have a service life of 15 years, the fuel cell device 14 and energy storage device 15 installed in the first year of the second phase are replaced in the first year of the fifth phase. Therefore, the implementation costs for these are shown in the first year of the fifth phase. Furthermore, in the first year of the sixth phase, the fuel cell device 14 installed in the first year of the third phase is replaced, and the implementation costs for this are shown.

[0094] <Prediction of the second introduction cost> Next, in predicting the first power cost, the controller 31 predicts the second introduction cost over several years when the distributed power system 12 is introduced (step S52). The second introduction cost is the introduction cost of the distributed power system 12, which is the introduction cost of the distributed power system 12 when the distributed power system 12 is introduced based on the introduction plan, and is paid in installments each year from the introduction year to the useful life. In addition, when introducing the distributed power system 12, distributed power sources with various useful lives may be introduced in various introduction years. In this embodiment, the installment introduction cost of each distributed power source is predicted for each useful life and each introduction year, and these are added together to predict the installment introduction cost of the distributed power system 12.

[0095] Figure 8 shows the second installation cost over multiple years. Figure 8 corresponds to the first installation cost shown in Figure 7. For example, the second installation cost for each year of the second period in Figure 8 is the sum of the installation cost of the renewable energy power generation device 13 installed in the first year of the second period divided by its useful life of 25 years, and the installation costs of the fuel cell device 14 and energy storage device 15 divided by their respective useful lives of 15 years. Similarly, the second installation cost for each year of the third period is the sum of the second installation cost for each year of the second period, plus the installation cost of the fuel cell device 14 purchased in the first year of the third period divided by its useful life of 15 years. The second installation costs for each year from the fourth period onward are predicted in the same manner.

[0096] Furthermore, in this embodiment, the running costs incurred after the introduction of the distributed power system 12 may include the cost of purchasing hydrogen. The hydrogen purchase cost is the cost of purchasing hydrogen used for power generation by the fuel cell device 14. The memory 32 stores predicted hydrogen unit price data for multiple years. The memory 32 may also store predicted hydrogen unit price data for multiple years for each type of hydrogen, such as green hydrogen and gray hydrogen. The controller 31 predicts the amount of hydrogen used by the fuel cell device 14 over multiple years from the amount of electricity that the fuel cell device 14 will supply to the power consumer 10, as predicted in step S40. Then, the controller 31 predicts the hydrogen purchase cost over multiple years based on the predicted amount of hydrogen used by the fuel cell device 14 and the predicted hydrogen unit price data for multiple years obtained from the memory 32. Figures 7 and 8 may also show the costs incurred with the introduction of the distributed power system 12, including not only the first or second introduction cost but also the running costs incurred after the introduction of the distributed power system 12.

[0097] <Predicting Running Costs> Next, in predicting the first electricity cost, the controller 31 predicts the running costs over several years when the distributed power system 12 is introduced (step S53). The running costs in this embodiment include electricity purchase costs, environmental value purchase costs, and carbon tax. These costs will be explained in order below.

[0098] [Electricity Purchase Cost] The electricity purchase cost is the cost for electricity consumer 10 to purchase grid electricity. The electricity purchase cost varies depending on the supplier, such as the power company. The memory 32 stores predicted electricity unit price data for grid electricity over several years for each supplier. In this embodiment, the controller 31 displays the electricity unit price for grid electricity for each supplier on the display 34 in a menu selectable format, and the operator (user) uses the input 33 to select the electricity unit price of the supplier. Based on this, the controller 31 predicts the electricity purchase cost over several years, using the amount of grid electricity supplied to electricity consumer 10 predicted in step S40 and the predicted electricity unit price data for grid electricity selected by the operator obtained from the memory 32.

[0099] [Cost of purchasing environmental value] The cost of purchasing environmental value is the cost of purchasing environmental value such as CO2 emission rights, CO2 emission reduction rights, and electricity certificates. Electricity consumer 10 may purchase all environmental value or only some of it. Therefore, it is possible to predict the cost of purchasing all environmental value or the cost of purchasing some of the environmental value.

[0100] In some cases, the government or local authorities 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 credits from other consumers. Therefore, consumers may purchase CO2 emission credits to achieve their CO2 emission targets. In addition to the above-mentioned CO2 emission credits, there is also a growing movement to allow the buying and selling of CO2 emission reduction credits (credits) resulting from CO2 emission reduction measures such as the introduction of energy-saving equipment and the use of renewable energy. Therefore, consumers may also purchase CO2 emission reduction credits to achieve their CO2 emission targets.

[0101] The controller 31 predicts the CO2 emissions of electricity consumers 10 over multiple years from the breakdown of electricity consumption from each power source over multiple years predicted in step S40, and predicts the amount of CO2 emission credits or CO2 emission reduction credits to be purchased over multiple years from the difference between the predicted CO2 emissions of electricity consumers 10 over multiple years and the predetermined target CO2 emissions over multiple years. The memory 32 also stores predicted data on the unit price of CO2 emission credits or CO2 emission reduction credits over multiple years. The controller 31 then predicts the cost of purchasing CO2 emission credits or CO2 emission reduction credits over multiple years based on the predicted amount of CO2 emission credits or CO2 emission reduction credits to be purchased over multiple years and the predicted data on the unit price of CO2 emission credits or CO2 emission reduction credits over multiple years obtained from the memory 32.

[0102] Furthermore, electricity consumers 10 can purchase renewable energy certificates (electricity certificates) that prove that the grid electricity supplied by power generators 20 is generated from renewable energy sources (solar, wind, hydro, geothermal, etc.). By purchasing these electricity certificates, electricity consumers 10 can demonstrate that the electricity supplied by power generators 20 is generated from renewable energy sources. For this reason, electricity consumers 10 may purchase electricity certificates in order to achieve their target renewable energy rate.

[0103] The controller 31 predicts the renewable energy rate of electricity consumers 10 over multiple years from the breakdown of electricity consumption from each power source over multiple years predicted in step S40, and predicts the amount of electricity certificates to be purchased over multiple years from the difference between the predicted renewable energy rate of electricity consumers 10 over multiple years and a preset target renewable energy rate over multiple years. The memory 32 also stores predicted data on the unit price of electricity certificates over multiple years. The controller 31 then predicts the cost of purchasing electricity certificates over multiple years based on the predicted amount of electricity certificates to be purchased over multiple years and the predicted data on the unit price of electricity certificates over multiple years obtained from the memory 32.

[0104] [Carbon Tax] Furthermore, some companies may adopt an internal carbon tax system to promote decarbonization, where they assign a price to their own CO2 emissions (internal carbon tax) and express emissions in monetary terms. An internal carbon tax is also called ICP (Internal Carbon Pricing). Therefore, electricity consumers 10 may have to pay a carbon tax depending on the agreement of the company they belong to.

[0105] The controller 31 predicts the CO2 emissions of electricity consumers 10 over multiple years based on the breakdown of electricity consumption from each power source over multiple years predicted in step S40. The memory 32 stores the carbon tax rates within electricity consumers 10 over multiple years. The controller 31 then predicts the carbon tax over multiple years based on the predicted CO2 emissions of electricity consumers 10 over multiple years and the carbon tax rates within electricity consumers 10 over multiple years obtained from the memory 32.

[0106] In this embodiment, the running cost is the sum of the above-mentioned electricity purchase cost, environmental value purchase cost, and carbon tax. Figure 9 shows the running costs over multiple years. In the example shown in Figure 9, since the first period is before the introduction of the distributed power system 12, the running cost includes the electricity purchase cost of grid power, but does not include the environmental value purchase cost or carbon tax. Furthermore, the running costs for the second and third periods include the electricity purchase cost of grid power, the environmental value purchase cost, and carbon tax. Moreover, since the introduction of the distributed power system 12 is completed from the fourth period onward, the running cost includes the electricity purchase cost and carbon tax, but does not include the electricity purchase cost of grid power or the environmental value purchase cost.

[0107] <Total Cost Prediction> Next, in predicting the first power cost, the controller 31 predicts the total cost over several years (step S54). As mentioned above, in this embodiment, the total cost corresponds to the first power cost. In this embodiment, the controller 31 predicts the total cost over several years by adding the second installation cost over several years predicted in step S52 and the running cost over several years predicted in step S53. Figure 10 is a diagram showing the first power cost (total cost) over several years. Figure 10 is a diagram showing the second installation cost over several years shown in Figure 8 and the running cost over several years shown in Figure 9.

[0108] In this embodiment, the second introduction cost (installment-based introduction cost) predicted in step S52 is used to predict the first power cost (total cost), but other introduction costs may be used. For example, the lease fee for the distributed power system 12 may be used instead of the second introduction cost (installment-based introduction cost). In other words, the introduction cost for the distributed power system 12 includes not only the cost based on the purchase of the distributed power system 12, but also the lease fee incurred from leasing the distributed power system 12.

[0109] <Prediction of the First Electricity Unit Price> Next, in the flow of the prediction method according to this embodiment, the controller 31 predicts the first electricity unit price for the electricity usage of the electricity consumer 10 over several years (step S60). The "first electricity unit price" here is the value obtained by dividing the electricity cost by the amount of electricity demanded by the electricity consumer 10. Therefore, the controller 31 predicts the first electricity unit price by dividing the first electricity cost (total cost) shown in Figure 10 by the amount of electricity demanded by the electricity consumer 10.

[0110] In this embodiment, as shown in Figure 6, the total amount of electricity supplied to the electricity consumer 10, that is, the amount of electricity demanded by the electricity consumer 10, is predicted to be the same every year. Therefore, the first electricity unit price over multiple years, obtained by dividing the first electricity cost over multiple years by the amount of electricity demanded by the electricity consumer 10, will also show the same trend as the first electricity cost over multiple years (see Figure 10). However, the prediction that the amount of electricity demanded by the electricity consumer 10 will be the same every year is just an example and is not limited to this.

[0111] <Prediction of the Second Power Cost> Next, in the flow of the prediction method according to this embodiment, the controller 31 predicts the second power cost over several years (step S70). The "second power cost" here refers to the power cost of electricity usage by the power consumer 10 when the distributed power system 12 is not introduced. The second power cost in this embodiment includes the cost of purchasing grid power, the cost of purchasing environmental value, and the carbon tax. In other words, the second power cost includes the cost of purchasing CO2 emission rights and CO2 emission reduction rights to achieve the target CO2 emission amount, and the cost of purchasing electricity certificates to achieve the target renewable energy rate.

[0112] The controller 31 predicts the cost of purchasing grid electricity, the cost of purchasing environmental value, and the carbon tax over several years in the same manner as described in step S53, assuming that the distributed power system 12 is not introduced, and then predicts a second electricity cost over several years by summing these up.

[0113] <Prediction of the Second Electricity Unit Price> Next, in the flow of the prediction method according to this embodiment, the controller 31 predicts the second electricity unit price for the electricity usage of the electricity consumer 10 over several years (step S80). The "second electricity unit price" here is the value obtained by dividing the electricity cost when the distributed power system 12 is not introduced by the amount of electricity demanded by the electricity consumer 10. Therefore, the controller 31 predicts the second electricity unit price by dividing the second electricity cost by the amount of electricity demanded by the electricity consumer 10.

[0114] <Comparative Display> Next, in the flow of the prediction method according to this embodiment, the controller 31 displays a comparison of the first power cost and the second power cost over several years (step S90). In other words, the controller 31 displays a comparison of the power costs when the distributed power system 12 is introduced and when it is not introduced. In this embodiment, the controller 31 displays a diagram on the display 34 comparing the first power cost over several years predicted in step S50 and the second power cost over several years predicted in step S70.

[0115] Figure 11 is a diagram comparing the first and second electricity costs over several years. The solid line in Figure 11 represents the first electricity cost and corresponds to the bar graph in Figure 10. The dashed line in Figure 11 represents the second electricity cost. In the example shown in Figure 11, the first and second electricity costs are the same in the first period before the introduction of the distributed power system 12. From the second to the fifth period, the first electricity cost is higher than the second electricity cost, but in the sixth period, the first electricity cost is lower than the second electricity cost. As mentioned above, in this embodiment, both the first and second electricity costs take into account the cost of purchasing environmental value.

[0116] In this embodiment, the controller 31 displays a comparison between the first power cost and the second power cost, but in addition to this, it also displays a comparison between the first power unit price and the second power unit price. Specifically, the controller 31 displays a figure on the display 34 comparing the first power unit price over multiple years predicted in step S60 with the second power unit price over multiple years predicted in step S80. However, the controller 31 may display only one of the comparison between the first power cost and the second power cost over multiple years, or the comparison between the first power unit price and the second power unit price over multiple years.

[0117] <Output of Prediction Results> Next, in the flow of the prediction method according to this embodiment, the controller 31 outputs the results of the prediction of indicators related to the electricity cost of the electricity consumer 10 as a report (step S100). In this embodiment, the controller 31 outputs at least one of the following information as paper media from the output device 40: the predicted first electricity cost over multiple years, the first electricity unit price, the second electricity cost, the second electricity unit price, the comparison between the first and second electricity costs, and the comparison between the first and second electricity unit prices. However, the controller 31 may output the above information as electronic media instead of or together with paper media. Furthermore, the display method of the above information is not limited and may be displayed as a graph or as numerical values. After step S100 is performed, the flow of the prediction method according to this embodiment is completed. The above is a description of the flow of the prediction method according to this embodiment.

[0118] As explained above, steps S10 to S100 are performed by an operator or the controller 31, but the entity performing each step is not limited to those described above. For example, the controller 31 may perform all of steps S10 to S100.

[0119] As described above, the prediction method according to this embodiment predicts an indicator of electricity costs for electricity consumers 10 over a multi-year period, 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 electricity cost indicator, a more appropriate evaluation from an economic standpoint can be made compared to conventional technology.

[0120] 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.

[0121] One aspect of this disclosure can be used for a method of predicting an indicator that allows for a more appropriate evaluation from an economic standpoint compared to conventional technologies regarding the introduction of a distributed power supply system, and for a method of controlling an information terminal that displays said indicator.

[0122] 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: Output devices

Claims

1. A forecasting 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 forecasting indicators relating to the electricity costs of electricity consumers 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 forecasting method according to claim 1 or 2, wherein the indicator relating to electricity costs includes a first electricity cost which is the electricity cost itself, and the first electricity cost includes the costs incurred in connection with the introduction of the distributed power system based on the introduction plan and the cost of purchasing grid electricity.

4. The prediction method according to claim 3, wherein the cost of introducing the distributed power system includes a first introduction cost, which is the cost of introducing the distributed power system based on the introduction plan, paid in a lump sum in the year of introduction.

5. The prediction method according to claim 3, wherein the cost of introducing the distributed power system includes a second introduction cost, which is calculated by dividing the cost of introducing the distributed power system based on the introduction plan into installments for each year from the introduction year to the useful life.

6. The forecasting method according to any one of claims 3-5, wherein the first electricity cost includes the cost of purchasing CO2 emission rights or CO2 emission reduction rights to achieve a target CO2 emission as a target value of the indicator relating to CO2 emissions.

7. The prediction method according to any one of claims 3-6, wherein the first electricity cost includes the cost of purchasing electricity certificates to achieve the target renewable energy rate as a target value for the CO2 emission indicator.

8. The prediction method according to any one of claims 3-7, wherein the distributed power system includes the fuel cell device, and the costs incurred in connection with the introduction of the distributed power system include the cost of purchasing hydrogen used for power generation by the fuel cell device.

9. The forecasting method according to any one of claims 1 to 8, wherein the indicator relating to electricity costs includes a first electricity unit price for the electricity use of the electricity consumer, and the first electricity unit price is the value obtained by dividing the electricity cost by the amount of electricity demanded by the electricity consumer.

10. The prediction method according to claim 3, which predicts a second electricity cost over several years for the electricity usage of the electricity consumer in the case where the distributed power system is not introduced, based on the cost of purchasing electricity from the grid, and displays a comparison of the first electricity cost and the second electricity cost over several years.

11. The prediction method according to claim 9, which predicts a second electricity unit price over several years for the electricity usage of the electricity consumer in the case where the distributed power system is not introduced, based on the electricity unit price of the grid power, and displays a comparison of the first electricity unit price and the second electricity unit price over several years.

12. The prediction method according to claim 11, wherein both the first electricity unit price and the second electricity unit price are predicted taking into account the cost of purchasing CO2 emission rights or CO2 emission reduction rights to achieve the target CO2 emission amount as a target value of the indicator relating to CO2 emissions.

13. The prediction method according to claim 11, wherein both the first electricity unit price and the second electricity unit price are predicted taking into account the cost of purchasing electricity certificates to achieve the target renewable energy rate as a target value for the CO2 emission indicator.

14. The prediction method according to claim 11, which displays the electricity unit price of the grid power for each grid power supplier in a menu selectable format, and predicts the first electricity unit price and the second electricity unit price taking into account the electricity unit price of the grid power supplier selected by the user.

15. The prediction method according to any one of claims 11-14, wherein both the first electricity unit price and the second electricity unit price are predicted taking into account the carbon tax within the electricity consumer.

16. The forecasting method according to any one of claims 1 to 15, wherein the forecasting results of the indicators relating to the electricity costs of the aforementioned electricity consumer are output as a report in electronic or paper format.

17. The prediction method according to any one of claims 1 to 16, 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.

18. The prediction method according to any one of claims 3-8, wherein the cost of introducing the distributed power system is predicted based on prediction data of the unit cost of distributed power capacity over several years.

19. The prediction method according to any one of claims 3-8, wherein the cost of purchasing electricity from the grid is predicted based on predicted electricity unit price data of the grid over several years.

20. The prediction method according to claim 6 or 12, wherein the cost of purchasing CO2 emission rights or CO2 emission reduction rights to achieve the target CO2 emissions is predicted based on predicted data of the unit price of CO2 emission rights or CO2 emission reduction rights over a multi-year period.

21. The prediction method according to claim 7 or 13, wherein the purchase cost of the electricity certificates is predicted based on predicted electricity certificate unit price data over several years.

22. The prediction method according to claim 8, wherein the cost of purchasing hydrogen used for power generation by the fuel cell device is predicted based on predicted hydrogen unit price data over several years.

23. The prediction method according to claim 15, wherein the carbon tax within the electricity consumer is predicted based on the carbon tax unit price within the electricity consumer over several years.

24. 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 an indicator of the electricity cost of the electricity consumer over a multi-year period, which is predicted when the distributed power system is introduced based on the introduction plan.

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