Prediction method, prediction device, and control method for information terminal

Abstract

The prediction method predicts an index related to an electric power cost of an electric power consumer over a plurality of years after introduction of a distributed electricity supply system including a natural energy electricity generation device and / or a fuel cell device in consideration of a target reduction rate of an annual electric power demand of the electric power consumer.

Description

Prediction method, prediction apparatus, and control method for information terminal

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

[0002] Patent Document 1 below discloses a technique 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 technique described in Patent Document 1, a distributed power system is evaluated from an economic perspective, but there are still insufficient points in the evaluation for the introduction of a distributed power system.

[0005] It is an object of the present disclosure to provide a prediction method and a prediction apparatus that can perform a more appropriate evaluation from an economic perspective than conventional techniques for the introduction of a distributed power system by an electric power consumer, and a control method for an information terminal that displays an indicator for performing the evaluation.

[0006] In order to solve the above problem, a prediction method according to one aspect of the present disclosure predicts an indicator related to the power cost of an electric power consumer over a plurality of years after the introduction of a distributed power system including at least one of a natural energy power generation device and a fuel cell device, in consideration of a target reduction rate of the annual power demand of the electric power consumer.

[0007] Further, a prediction apparatus according to one aspect of the present disclosure includes: a storage that stores information related to a target reduction rate of annual power demand of an electric power consumer; and a controller that predicts an indicator related to the power cost of the electric power consumer over a plurality of years after the introduction of a distributed power system including at least one of a natural energy power generation device and a fuel cell device, in consideration of the information stored in the storage.

[0008] Furthermore, a control method for an information terminal in one aspect of the present disclosure includes the step of displaying information on the display of the information terminal that shows an indicator of the electricity cost of a power consumer over several years since the introduction of a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, wherein the indicator is predicted taking into account the target reduction rate of the annual electricity demand of the power consumer.

[0009] A prediction method and prediction apparatus according to one aspect of this disclosure have the effect of enabling a more appropriate evaluation from an economic standpoint compared to the prior art when introducing a distributed power source system for electricity consumers. Furthermore, a control method for an information terminal according to one aspect of this disclosure has the effect of enabling a more appropriate evaluation from an economic standpoint compared to the prior art when introducing a distributed power source system.

[0010] 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 costs over several years. Figure 8 shows the second implementation costs over several years. Figure 9 shows the running costs over several years. Figure 10 shows the first electricity costs over several years. Figure 11 compares the first and second electricity unit prices over several years.

[0011] 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.

[0012] In this context, consumers who adopt the distributed power sources described above generally consume large amounts of electricity. In Japan, it is necessary to report actual and planned energy usage to government agencies, and there are also targets to strive for improving energy consumption intensity (electricity demand) by a predetermined percentage or more on an annual average over the medium to long term. Therefore, the inventors focused on the need to consider the target reduction rate (energy saving rate) of the consumer's future annual electricity demand when introducing a distributed power source system.

[0013] The first aspect of the prediction method predicts an indicator of electricity costs for an electricity consumer over several years after the introduction of a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, taking into account the target reduction rate of the electricity consumer's annual electricity demand.

[0014] This forecasting method predicts indicators related to electricity costs for electricity consumers, taking into account the target reduction rate of the electricity consumers' annual electricity demand. Therefore, evaluating the introduction of distributed power generation systems by electricity consumers using these electricity cost indicators allows for a more appropriate economic assessment compared to conventional technologies. Furthermore, the above forecasting method predicts indicators related to electricity costs for electricity consumers over multiple years after the introduction of distributed power generation systems. Therefore, it is possible to appropriately understand the trends of the above indicators over multiple years, taking into account the target reduction rate of the electricity consumers' annual electricity demand, when introducing distributed power generation systems by electricity consumers.

[0015] A second aspect of the prediction method of the present disclosure further includes, in the first aspect of the prediction method, a distributed power system which further includes an energy storage device.

[0016] 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.

[0017] 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.

[0018] Therefore, the prediction method of the third aspect of this disclosure, in the prediction method of the first or second aspect, includes, in the indicator relating to electricity costs, the electricity costs themselves, and the electricity costs include the cost of introducing a distributed power source system and the cost of purchasing electricity from the power grid.

[0019] This forecasting method includes not only the cost of introducing a distributed power generation system but also the cost of purchasing electricity from the power 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, this method allows for a more appropriate economic evaluation of the introduction of a distributed power generation system by electricity consumers compared to conventional technologies.

[0020] 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.

[0021] Therefore, the forecasting method of the fourth aspect of this disclosure, in the forecasting method of the third aspect, includes the cost of purchasing CO2 emission rights or CO2 emission reduction rights to achieve a target CO2 emission as a target value for an indicator of CO2 emissions over multiple years for electricity consumers.

[0022] This forecasting method includes the cost of purchasing CO2 emission credits or CO2 emission reduction credits as an indicator of electricity costs to be predicted. Therefore, it allows for a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0023] 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.

[0024] Therefore, the forecasting method of the fifth aspect of this disclosure, in the forecasting method of the third or fourth aspect, includes the cost of purchasing electricity certificates to achieve the target renewable energy rate as a target value for an indicator of CO2 emissions over multiple years for electricity consumers.

[0025] 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.

[0026] The sixth aspect of the present disclosure is a prediction method in any one of the third to fifth aspects, wherein the distributed power system includes a fuel cell device, and the introduction cost of the distributed power system includes the cost of purchasing hydrogen used for power generation by the fuel cell device.

[0027] In this prediction method, the cost of introducing a distributed power system includes the cost of purchasing hydrogen used for power generation by fuel cell devices. Therefore, it allows for a more appropriate economic evaluation of the introduction of distributed power systems, including fuel cell devices, by electricity consumers compared to conventional technologies.

[0028] The seventh aspect of the present disclosure is a forecasting method in any one of the third to sixth aspects, wherein the indicator for electricity costs includes the unit price of electricity used by electricity consumers, and the unit price of electricity is the value obtained by dividing the electricity cost by the amount of electricity demanded by the electricity consumer.

[0029] In this forecasting method, the indicator for electricity costs includes the unit price of electricity, 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 distributed power generation systems. Consequently, it is possible to make a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0030] In recent years, some companies have adopted internal carbon tax systems to promote decarbonization, where they assign a price to their CO2 emissions (internal carbon tax) and express emissions in monetary terms. This internal carbon tax is also known as ICP (Internal Carbon Pricing).

[0031] Therefore, the forecasting method of the eighth aspect of this disclosure, in any one of the forecasting methods of the third to seventh aspects, includes an internal carbon tax (internal carbon pricing) within the electricity consumer.

[0032] This forecasting method takes carbon taxes within electricity consumers into account when predicting electricity costs, allowing for a more accurate comparison of electricity costs with and without distributed power generation systems. This enables a more appropriate economic evaluation of the adoption of distributed power generation systems by electricity consumers compared to conventional technologies.

[0033] 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 significant 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.

[0034] Therefore, the forecasting method of the ninth aspect of this disclosure, in any one of the forecasting methods of the first to eight aspects, formulates a multi-year introduction plan for a distributed power generation system based on target values ​​for indicators related to CO2 emissions over multiple years for electricity consumers and a budget plan for reducing CO2 emissions, and forecasts indicators related to electricity costs over multiple years after the introduction of the distributed power generation system based on the introduction plan, taking into account the target reduction rate of the electricity consumer's annual electricity demand.

[0035] This forecasting method predicts multi-year electricity cost indicators based on a multi-year deployment plan for distributed power systems, taking into account the target reduction rate of electricity consumers' annual electricity demand. Therefore, evaluating the deployment of distributed power systems by electricity consumers using these indicators allows for a more appropriate economic assessment compared to conventional technologies. Furthermore, it allows for a proper understanding of the trends of the above indicators over multiple years, taking into account the target reduction rate of electricity consumers' annual electricity demand.

[0036] A prediction device according to a tenth aspect of the present disclosure includes a memory that stores information regarding a target reduction rate for an electricity consumer's annual electricity demand, and a controller that predicts an index relating to the electricity cost of an electricity consumer over several years after the introduction of a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, taking into account the information stored in the memory.

[0037] This forecasting device predicts indicators related to electricity costs for electricity consumers, taking into account the target reduction rate of the electricity consumer's annual electricity demand. Therefore, evaluating the introduction of distributed power generation systems by electricity consumers using these indicators allows for a more appropriate economic assessment compared to conventional technologies. Furthermore, the forecasting device predicts indicators related to electricity costs for electricity consumers over several years after the introduction of a distributed power generation system. Therefore, it is possible to appropriately understand the trends of the above indicators over several years, taking into account the target reduction rate of the electricity consumer's annual electricity demand, when introducing a distributed power generation system by electricity consumers.

[0038] An information terminal control method according to an eleventh aspect of the present disclosure includes a step of displaying information on the display of the information terminal that shows an indicator of the electricity cost of an electricity consumer over several years since the introduction of a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, wherein the indicator of electricity cost is predicted taking into account the target reduction rate of the electricity consumer's annual electricity demand.

[0039] In this control method, information showing an indicator of electricity costs for electricity consumers, taking into account the target reduction rate of the electricity consumer's annual electricity demand, is displayed on the display device. Therefore, electricity consumers who are users of the information terminal, either those who propose the introduction of a distributed power system or those who are presented with the information displayed on the display device by the proposer, can evaluate the introduction of the distributed power system using this indicator, thereby making a more appropriate economic evaluation compared to conventional technologies.

[0040] Furthermore, the above control method predicts indicators related to electricity costs over several years after the introduction of the distributed power system. Therefore, the proposer or electricity consumer can appropriately understand the trends of the above indicators over several years, taking into account the target reduction rate of the electricity consumer's annual electricity demand, when introducing the distributed power system.

[0041] The control method for an information terminal in the twelfth aspect of this disclosure includes a step in the control method for an information terminal in the eleventh aspect of this disclosure to display on a display the introduction plan for a distributed power system formulated based on the target value of an indicator related to the CO2 emissions of electricity consumers and the budget plan for reducing CO2 emissions.

[0042] This control method displays the distribution power system implementation plan on a display unit. Therefore, the proposer or electricity consumer can easily understand the distribution power system implementation plan.

[0043] 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.

[0044] Among the constituent elements described below, those that are not described in the independent claims representing the top-level concepts of the present disclosure are described as optional constituent elements. In addition, in the drawings, components denoted by the same reference numerals may have their descriptions omitted. For ease of understanding, the drawings schematically show each constituent element, and shapes, dimensional ratios, and the like may not be accurately represented.

[0045] Furthermore, in the operation of the device, if necessary, the order of steps may be changed, or known steps may be added.

[0046] (Example of Installation of Distributed Power Supply System) First, an example of installation of a distributed power supply system 12 by a power consumer 10 will be described. Fig. 1 is a diagram showing an example of installation of the distributed power supply system 12. Fig. 1(a) shows a mode of power supply to the power consumer 10 before the start of installation of the distributed power supply system 12. The power consumer 10 of the present embodiment is a factory that manufactures products, and the power consumer 10 includes power consumption equipment 11 such as production equipment for manufacturing products. In the present embodiment, as shown in Fig. 1(a), before the start of installation of the distributed power supply system 12, power is supplied to the power consumption equipment 11 of the power consumer 10 only from a power generation business operator 20 such as an electric power company. Hereinafter, the power supplied from the power generation business operator 20 to the power consumer 10 is referred to as "grid power".

[0047] Fig. 1(b) shows a mode of power supply to the power consumer 10 in the intermediate stage of installation of the distributed power supply system 12. In the present embodiment, as shown in Fig. 1(b), in the intermediate stage of installation of the distributed power supply system 12, power is supplied not only from the grid but also from the distributed power supply system 12 to the power consumption equipment 11 of the power consumer 10. The distributed power supply system 12 includes distributed power sources such as a renewable energy power generation device 13, a fuel cell device 14, and a power storage device 15. However, the distributed power supply system 12 is not limited to the above configuration. For example, the distributed power supply system 12 may include only one of the above-described distributed power sources.

[0048] The natural energy power generation device 13 is a device that generates power using natural energy, and corresponds to solar power generation devices, wind power generation devices, and the like. Further, the fuel cell device 14 is a device that generates power by causing a chemical reaction between hydrogen, which is a fuel, and an oxide such as oxygen. There are a plurality of types of hydrogen serving 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 power storage device 15 is a device that temporarily stores power generated by other devices and supplies the stored power.

[0049] FIG. 1(c) shows a mode of power supply to a power consumer 10 after the completion of introduction of the distributed power supply system 12. In the present embodiment, as shown in FIG. 1(c), after the introduction of the distributed power supply system 12 is completed, power is supplied to the power consumption equipment 11 of the power consumer 10 only from the distributed power supply system 12. However, even after the introduction of the distributed power supply system 12 is completed, power may be supplied to the power consumption equipment 11 of the power consumer 10 from both the power generation business operator 20 and the distributed power supply system 12 in some cases.

[0050] (Information Terminal) Next, the information terminal 30 used in the prediction method according to the present embodiment will be described. FIG. 2 is a conceptual diagram of the information terminal 30. The information terminal 30 includes a prediction device 35, an input unit 33, and a display unit 34. The prediction device 35 includes a controller 31 and a storage unit 32.

[0051] The controller 31 includes an arithmetic processing unit and a storage unit that stores a control program. The arithmetic processing unit is, for example, a processor, and the storage unit is, for example, a volatile memory. The controller 31 may be a single controller, or may be a controller group in which a plurality of controllers cooperate. The storage unit 32 is, for example, a non-volatile memory, and is a device that stores various programs and information. The input unit 33 is, for example, a keyboard operated by an operator, and is a device for inputting various types of information. The display unit 34 is, for example, a monitor, and is a device that displays various types of information.

[0052] In this embodiment, the memory 32 stores information regarding the target reduction rate of the annual electricity demand of the electricity consumer 10 (hereinafter sometimes referred to as the "energy saving rate"). The controller 31 predicts an indicator of the electricity cost of the electricity consumer 10 over several years after the introduction of the distributed power system 12, taking into account the above information stored in the memory 32.

[0053] Here, the "energy saving rate" can be expressed, for example, as the annual percentage reduction in the amount of electricity consumed by electricity consumer 10 compared to the amount of electricity consumed in the previous year. The "energy saving rate" may be, for example, around 1%, but is not limited to this.

[0054] Furthermore, if energy-saving rates are set by dividing the next several decades into multiple periods of predetermined number of years, appropriate values ​​may be set for each individual period.

[0055] For example, the "energy saving rate" may be set so that energy saving of approximately 0.5% is achieved in stages as each period progresses, such as "0%" in the first period, "0.5%" in the second period, "1%" in the third period, and "1.5%" in the fourth period.

[0056] 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.

[0057] (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 that can evaluate the introduction of the distributed power system 12 from an economic standpoint. More specifically, the prediction method according to this embodiment predicts an indicator of the electricity cost of the electricity consumer 10 over several years after the introduction of the distributed power system 12, taking into account the target reduction rate of the annual electricity demand of the electricity consumer 10. This will be explained in detail below.

[0058] <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.

[0059] However, the above is merely an example and is not limited to this example. The worker may set a target value for an indicator related to the CO2 emissions of the electricity consumer 10 over a single year. In other words, the target value for the indicator may be set on the premise that the distributed power system 12 will be introduced all at once over a single year, rather than being introduced in stages over several years.

[0060] The following example illustrates a scenario where the worker sets target values ​​for CO2 emission indicators for electricity consumer 10 over a multi-year period.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] <Formulation of Implementation Plan> Next, in the flow of the prediction method according to this embodiment, the worker formulates a multi-year implementation plan for the distributed power system 12 (step S20). In this embodiment, the worker formulates a multi-year 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 implementation plan for the distributed power system 12 may be formulated by the controller 31.

[0065] Figure 5 shows a multi-year implementation plan for the distributed power system 12. In Figure 5, the multi-year implementation plan is shown using the rated output of the distributed power sources that make up the distributed power system 12 (the units of rated output are kW, MW, etc.). In the implementation 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.

[0066] 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.

[0067] 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.

[0068] Furthermore, in this embodiment, the introduction plan for the distributed power system 12 is formulated based on both target values ​​for CO2 emission indicators and a budget plan for CO2 emission reduction, but it may also be formulated based on either one of them. In addition, 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.

[0069] Furthermore, while the above describes a multi-year implementation plan for the distributed power system 12, it is not limited to this. A single-year implementation plan for the distributed power system 12 may be formulated based on single-year target values ​​for CO2 emission indicators and a single-year budget plan for CO2 emission reduction. In other words, the above implementation plan may be formulated on the premise that the distributed power system 12 will be implemented all at once in a single year.

[0070] <Display of Implementation Plan> Next, in the flow of the prediction method according to this embodiment, the operator displays the multi-year implementation plan for 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 implementation plan for 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 implementation plan for the distributed power system 12 on the display device 34 based on that implementation data.

[0071] <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.

[0072] 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 gradually decrease, taking into account the target reduction rate of the power consumer 10's annual demand amount. This is for the following reasons.

[0073] Electricity consumers 10 that introduce a distributed power generation system 12 generally consume large amounts of electricity. In this case, in Japan, it is necessary to report actual and planned energy usage to government agencies, etc., and there is a target to strive for improving the energy consumption intensity (amount of electricity demanded) by a predetermined percentage or more on an annual average over the medium to long term. For this reason, when introducing a distributed power generation system 12, it may be desirable to set a target reduction rate (energy saving rate) for the consumer's future annual electricity demand.

[0074] Figure 6 shows a breakdown of the amount of electricity supplied to electricity consumer 10 over several years (units of electricity are kHh, MHh, etc.). Of the two adjacent bar graphs in Figure 6, the left bar graph shows the annual electricity demand of electricity consumer 10.

[0075] In this example, the annual electricity demand of electricity consumer 10 is calculated when the annual rate of reduction (%) of electricity demand compared to the previous year's electricity demand for electricity consumer 10 is "0%" in the first period, "0.5%" in the second period, "1%" in the third period, and "1.5%" in the fourth through sixth periods. As a method for calculating the annual electricity demand of electricity consumer 10 using such an annual rate of reduction (%) of electricity demand, for example, one method is to calculate the total amount of electricity for each year in the above period, and one method is to calculate the amount of electricity for the first year and the last year in each period, and then calculate the amount of electricity for the years in between using linear interpolation from the amount of electricity for the first year and the last year. The latter method of calculating electricity takes less time than the former method, so the latter method is used in this example.

[0076] In Figure 6, the rightmost of the two adjacent bar graphs shows the breakdown of the amount of electricity supplied to electricity consumer 10 in order to meet the annual electricity demand of electricity consumer 10.

[0077] In the example shown in Figure 6, in the first phase, all of the electricity supplied to the electricity consumer 10 is from the grid. From the second to the fourth phase, the proportion of electricity supplied to the electricity consumer 10 from the distributed power system 12 (renewable energy power generation device 13, fuel cell device 14, and energy storage device 15) gradually increases, and from the fourth to the sixth phase, all of the electricity supplied to the electricity consumer 10 is supplied from the distributed power system 12.

[0078] <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 after the introduction of the distributed power system 12, taking into account the target reduction rate of the annual power demand of the power consumer 10 (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 running costs incurred after the introduction of the distributed power system 12.

[0079] <Prediction of the First Introduction Cost> First, in predicting the first electricity cost, the controller 31 predicts the first introduction cost over several years after the introduction of the distributed power supply system 12, taking into account the target reduction rate of the annual electricity demand of the electricity consumer 10 (step S51). The "first introduction cost" here refers to the cost incurred in connection with the introduction of the distributed power supply system 12, and is the cost if the introduction cost of the distributed power supply system 12 were paid in a lump sum in the year of introduction, based on the introduction plan.

[0080] 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 after the introduction of the distributed power system 12, taking into account the target reduction rate of the annual electricity demand of the electricity consumer 10.

[0081] 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.

[0082] <Prediction of the Second Introduction Cost> Next, in predicting the first electricity cost, the controller 31 predicts the second introduction cost over several years after the introduction of the distributed power system 12, taking into account the target reduction rate of the annual electricity demand of the electricity consumer 10 (step S52). The second introduction cost is the cost incurred in conjunction with the introduction of the distributed power system 12, and is the introduction cost of the distributed power system 12 if it is introduced according to the introduction plan, 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.

[0083] 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.

[0084] 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 cost of purchasing hydrogen 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 costs 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 costs but also the running costs incurred after the introduction of the distributed power system 12 (e.g., the cost of purchasing hydrogen).

[0085] <Predicting Running Costs> Next, in predicting the first electricity cost, the controller 31 predicts the running costs over several years since the introduction of the distributed power system 12, taking into account the target reduction rate of the annual electricity demand of the electricity consumer 10 (step S53). The running costs in this embodiment include electricity purchase costs, environmental value purchase costs, etc. In addition, carbon tax may be included in these running costs. The electricity purchase costs, environmental value purchase costs, and carbon tax will be explained in order below.

[0086] [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.

[0087] [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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [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.

[0093] 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.

[0094] 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 from the grid, but does not include the environmental value purchase cost or the carbon tax. Furthermore, the running costs for the second and third periods include the electricity purchase cost from the grid, the environmental value purchase cost, and the carbon tax. Moreover, since the introduction of the distributed power system 12 is completed from the fourth period onward, the running cost includes the carbon tax, but does not include the electricity purchase cost from the grid or the environmental value purchase cost. If the distributed power system 12 includes a fuel cell device 14, the hydrogen purchase cost, which will be incurred as a running cost after the introduction of the distributed power system 12, may be added to the running costs shown in Figure 9. In this case, the hydrogen purchase cost will be added to the running costs from the second period onward.

[0095] <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 that combines the second installation cost over several years shown in Figure 8 and the running cost over several years shown in Figure 9. In addition, in Figure 10, the power cost assuming that all the power supplied to the power consumer 10 is grid power is shown with a thick dashed line, and the second power cost, which will be described later, is shown with a thin dashed line.

[0096] 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.

[0097] <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, taking into account the target reduction rate of the annual electricity demand of the electricity consumer 10 (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.

[0098] 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 considering the target reduction rate of the annual electricity demand of the electricity consumer 10, but this target reduction rate is a small value of about 1%. Therefore, the first electricity unit price over multiple years, which is obtained by dividing the first electricity cost over multiple years by the amount of electricity demanded by the electricity consumer 10, also follows almost the same trend as the first electricity cost over multiple years (see Figures 10 and 11).

[0099] <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.

[0100] The controller 31 predicts, in the same manner as described in step S53, the cost of purchasing grid power, the cost of purchasing environmental value, and carbon taxes over several years, taking into account the target reduction rate of the annual electricity demand of the electricity consumer 10, in the case where the distributed power system 12 is not introduced. By summing these up, the controller predicts a second electricity cost over several years.

[0101] <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, taking into account the target reduction rate of the annual electricity demand of the electricity consumer 10 (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.

[0102] <Comparison Display> Next, in the flow of the prediction method according to this embodiment, the controller 31 displays a comparison of the first electricity unit price and the second electricity unit price over several years (step S90). In other words, the controller 31 displays a comparison of the electricity unit price when the distributed power supply 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 electricity unit price over several years predicted in step S60 and the second electricity unit price over several years predicted in step S80.

[0103] Figure 11 compares the first and second electricity unit prices over several years. The solid line in Figure 11 represents the first electricity unit price, which corresponds to the value obtained by dividing the cost in the bar graph of Figure 10 by the amount of electricity demanded by electricity consumer 10. The thin dashed line in Figure 11 represents the second electricity unit price. In addition, Figure 11 also shows the electricity unit price assuming that all the electricity supplied to electricity consumer 10 is grid power, indicated by a thick dashed line.

[0104] In the example shown in Figure 11, in the first phase before the introduction of the distributed power system 12, the first and second electricity unit prices are the same. In the second phase, the first electricity unit price is higher than the second electricity unit price. However, in the third and fourth phases, the first and second electricity unit prices remain roughly the same. In the fifth and sixth phases, the first electricity unit price is lower than the second electricity unit price. As mentioned above, in this embodiment (Figure 11), the purchase cost of environmental value is taken into consideration for both the first and second electricity unit prices.

[0105] Furthermore, in this embodiment, the controller 31 displays a comparison between the first power unit price and the second power unit price, but it may also display a comparison between the first power cost and the second power cost. Specifically, the controller 31 displays a figure on the display 34 comparing the first power cost over multiple years predicted in step S50 with the second power cost over multiple years predicted in step S70. 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.

[0106] <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.

[0107] 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.

[0108] As described above, the prediction method according to this embodiment predicts an index relating to the electricity costs 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, taking into account the target reduction rate of the electricity consumer's annual electricity demand. Therefore, if the introduction of the distributed power system 12 is evaluated using this index relating to electricity costs, a more appropriate evaluation from an economic standpoint can be made compared to conventional technology. Furthermore, the above prediction method predicts an index relating to the electricity costs of electricity consumers over a multi-year period after the introduction of the distributed power system 12. Therefore, with regard to the introduction of the distributed power system by electricity consumer 10, it is possible to appropriately know the trend of the above index over a multi-year period, taking into account the target reduction rate of the electricity consumer's annual electricity demand.

[0109] 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.

[0110] One aspect of this disclosure can be used in a prediction method and device that can perform a more appropriate evaluation from an economic standpoint compared to conventional technology regarding the introduction of a distributed power source system by electricity consumers, as well as a control method for an information terminal that displays indicators for performing such evaluation.

[0111] 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 35: Prediction devices 40: Output devices

Claims

1. A forecasting method for predicting an indicator of electricity costs for an electricity consumer over several years after the introduction of a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, taking into account the target reduction rate of the electricity consumer's annual electricity demand.

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 indicator includes the cost of electricity itself, and the cost of electricity includes the cost of introducing the distributed power system and the cost of purchasing electricity from the power grid.

4. The forecasting method according to claim 3, wherein the 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 for an indicator of CO2 emissions over several years for the electricity consumer.

5. The forecasting method according to claim 3 or 4, wherein the electricity cost includes the cost of purchasing electricity certificates to achieve a target renewable energy rate as a target value for an indicator of CO2 emissions over several years for the electricity consumer.

6. The prediction method according to any one of claims 3 to 5, wherein the distributed power system includes the fuel cell device, and the cost of introducing the distributed power system includes the cost of purchasing hydrogen used for power generation by the fuel cell device.

7. The forecasting method according to any one of claims 3-6, wherein the indicator includes the unit price of electricity used by the electricity consumer, and the unit price of electricity is the value obtained by dividing the electricity cost by the amount of electricity demanded by the electricity consumer.

8. The method for predicting electricity costs according to any one of claims 3-7, wherein the electricity cost includes an internal carbon tax (internal carbon pricing) within the electricity consumer.

9. The prediction method according to any one of claims 1 to 8, comprising: formulating a multi-year introduction plan for the distributed power system based on the target values ​​of the CO2 emission indicators of the electricity consumer over a multi-year period and a budget plan for reducing CO2 emissions; and predicting the indicators over a multi-year period after the introduction of the distributed power system based on the introduction plan, taking into account the target reduction rate of the electricity consumer's annual electricity demand.

10. A prediction device comprising: a memory that stores information on the target reduction rate of an electricity consumer's annual electricity demand; and a controller that predicts an index of an electricity consumer's electricity cost over several years after the introduction of a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, taking into account the information stored in the memory.

11. A method for controlling an information terminal, comprising the step of displaying information on a display of the information terminal that shows an indicator of the electricity cost of an electricity consumer over several years since the introduction of a distributed power system including at least one of a renewable energy power generation device and a fuel cell device, wherein the indicator is predicted taking into account the target reduction rate of the electricity consumer's annual electricity demand.

12. The control method for an information terminal according to claim 11, further comprising the step of displaying on the display the introduction plan for the distributed power system, which has been formulated based on the target values ​​of the CO2 emission indicators of the electricity consumer and the budget plan for reducing CO2 emissions.

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