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

Abstract

This prediction method predicts, in consideration of deterioration of a distributed power supply system including a natural energy power generation device and / or a fuel cell device, an index relating to an electric power cost of an electric power consumer over a plurality of years when the distributed power supply system is introduced.

Description

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

[0001] The present disclosure relates to a prediction method for an indicator related to CO2 emission, a prediction apparatus, and a control method for an information terminal.

[0002] In Patent Document 1 below, a technique is disclosed that, when introducing distributed power sources such as solar cells, fuel cells, and storage batteries, performs evaluation from an economic perspective and the like, and presents a combination of distributed power sources.

[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 as an evaluation for the introduction of a distributed power system.

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

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

[0008] Furthermore, one embodiment of the present disclosure is a control method for an information terminal, comprising the step of displaying information on a display of the information terminal that indicates an indicator of electricity costs for a power consumer over several years when a distributed power system including an energy storage device and at least one of a renewable energy power generation device and a fuel cell device is introduced, wherein the indicator is predicted taking into account the deterioration of the distributed power system.

[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 it comes to the introduction of a distributed power source system by 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 it comes to the introduction of a distributed power source system by electricity consumers.

[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 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 lump-sum implementation cost over several years. Figure 8 shows the implementation cost paid in installments over several years. Figure 9 shows the running costs over several years. Figure 10 shows the electricity costs 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] Here, the inventors examined the relationship between the degradation of distributed power systems and electricity costs. As a result, they found that when a distributed power system degrades, the efficiency of its output decreases, reducing the amount of electricity supplied from the distributed power system to electricity consumers, while the amount of electricity supplied from the power grid to electricity consumers increases. This increases the running electricity costs. Therefore, while considering the degradation of distributed power systems may lower the economic evaluation of introducing them, it is possible to make a more appropriate economic evaluation than before.

[0013] Therefore, the prediction method of the first aspect of this disclosure predicts an index relating to the electricity costs of electricity consumers over several years when a distributed power system including at least one of a natural energy power generation device and a fuel cell device is introduced, taking into account the deterioration of the distributed power system.

[0014] This prediction method forecasts indicators related to electricity costs for electricity consumers, taking into account the degradation of distributed power generation systems. Therefore, using these indicators, it is possible to evaluate the introduction of distributed power generation systems by electricity consumers from an economic standpoint more appropriately than with conventional technologies.

[0015] A second aspect of the prediction method of the present disclosure is a prediction method of the first aspect, wherein the distributed power system includes the renewable energy power generation device, and the prediction of the indicator over several years after the introduction of the distributed power system takes into account the degradation of the renewable energy power generation device.

[0016] This prediction method takes into account the degradation of renewable energy generation equipment and forecasts indicators of electricity costs for electricity consumers over several years after the introduction of a distributed power system. Therefore, using these indicators, electricity consumers can make a more appropriate economic evaluation of the introduction of a distributed power system, including renewable energy generation equipment, compared to conventional technologies.

[0017] A third aspect of the prediction method of the present disclosure is a prediction method of the first aspect, wherein the distributed power system includes the fuel cell device, and the prediction of the indicator over several years after the introduction of the distributed power system takes into account the degradation of the fuel cell device.

[0018] This prediction method takes into account the degradation of fuel cell equipment and forecasts indicators of electricity costs for electricity consumers over several years after the introduction of a distributed power system. Therefore, using these indicators, electricity consumers can make a more appropriate economic evaluation of the introduction of a distributed power system, including fuel cell equipment, compared to conventional technologies.

[0019] A fourth aspect of the prediction method of the present disclosure is a prediction method of any one aspect of the first aspect, wherein the distributed power system includes the renewable energy power generation device and the fuel cell device, and the prediction of the indicator over several years after the introduction of the distributed power system takes into account the degradation of the renewable energy power generation device and the fuel cell device, respectively.

[0020] This prediction method takes into account the degradation of both renewable energy power generation equipment and fuel cell equipment, and forecasts indicators of electricity costs for electricity consumers over several years after the introduction of a distributed power system. Therefore, using these indicators, electricity consumers can make a more appropriate economic evaluation of the introduction of a distributed power system, including renewable energy power generation equipment and fuel cell equipment, compared to conventional technologies.

[0021] A fifth aspect of the present disclosure is a prediction method in any one of the second to fourth aspects, wherein the distributed power system further includes an energy storage device, and the deterioration of the energy storage device is also taken into consideration, and the indicator is predicted over several years after the introduction of the distributed power system.

[0022] This prediction method takes into account the degradation of energy storage devices and forecasts indicators related to electricity costs for power consumers over several years after the introduction of a distributed power system. Therefore, using these indicators, power consumers can make a more appropriate economic evaluation of the introduction of a distributed power system, including energy storage devices, compared to conventional technologies.

[0023] The sixth aspect of the prediction method of this disclosure is a prediction method of any one of the first to fifth aspects, wherein the indicator includes the electricity cost itself, and the electricity cost includes the costs incurred in connection with the introduction of the distributed power system and the cost of purchasing electricity from the grid.

[0024] This forecasting method includes not only the costs incurred by electricity consumers in introducing distributed power generation systems but also the costs of purchasing electricity from the grid. Therefore, it allows for a more appropriate economic evaluation of the introduction of distributed power generation systems by electricity consumers, including cases where electricity generated by the distributed power generation system and grid power are supplied to the electricity consumer, compared to conventional technologies.

[0025] 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.

[0026] The forecasting method of the seventh aspect of this disclosure, in the forecasting method of the sixth aspect, includes the cost of purchasing CO2 emission rights or CO2 emission reduction rights to achieve a target CO2 emission as a target value of an indicator relating to the CO2 emissions of the electricity consumer over several years.

[0027] In this forecasting method, the indicators related to electricity costs for electricity consumers include the cost of purchasing CO2 emission credits or CO2 emission reduction credits. Therefore, it is possible to evaluate the introduction of distributed power generation systems for electricity consumers from an economic standpoint more appropriately than with conventional technologies.

[0028] Furthermore, electricity consumers can purchase renewable energy certificates (electricity certificates) that prove that the grid electricity supplied by power generators is generated from renewable energy sources (solar, wind, hydro, geothermal, etc.). By purchasing these electricity certificates, electricity consumers can demonstrate that the electricity supplied by power generators is generated from renewable energy sources. Therefore, electricity consumers may purchase electricity certificates if their renewable energy ratio falls below a predetermined target value. The "renewable energy ratio" refers to the percentage of electricity supplied to electricity consumers or consumed by electricity consumers that is generated from renewable energy sources.

[0029] The forecasting method of the eighth aspect of this disclosure, in the forecasting method of the sixth or seventh aspect, includes the cost of purchasing electricity certificates to achieve the target renewable energy rate as a target value for the multi-year CO2 emission indicators of the electricity consumer.

[0030] This forecasting method includes the cost of purchasing electricity certificates as an indicator of electricity costs for electricity consumers. Therefore, it allows for a more appropriate economic evaluation of the introduction of distributed power generation systems for electricity consumers compared to conventional technologies.

[0031] A prediction method according to a ninth aspect of the present disclosure is a prediction method according to any one of the sixth to eighth aspects, wherein the distributed power system includes the fuel cell device, and the power cost includes the cost of purchasing hydrogen used for power generation by the fuel cell device.

[0032] In this forecasting method, the indicators for electricity consumers' electricity costs include the cost of purchasing hydrogen used to generate electricity for fuel cell systems. Therefore, it allows for a more appropriate economic evaluation of electricity consumers' adoption of distributed power generation systems, including fuel cell systems, compared to conventional technologies.

[0033] The forecasting method of the tenth aspect of this disclosure is a forecasting method of any one of the sixth to ninth aspects, wherein the index 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.

[0034] This forecasting method includes an electricity unit price, calculated by dividing the electricity cost by the amount of electricity the electricity consumer demands, as an indicator of electricity consumer electricity costs. Therefore, it makes it easier to understand the electricity costs of distributed power generation systems. Consequently, it allows for a more appropriate evaluation from an economic standpoint regarding the introduction of distributed power generation systems by electricity consumers compared to conventional technologies.

[0035] 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).

[0036] Therefore, the prediction method of the eleventh aspect of the present disclosure, in the prediction method of the tenth aspect, includes a carbon tax within the electricity consumer.

[0037] This forecasting method includes a carbon tax within the electricity consumer as an indicator of 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.

[0038] The prediction method of the twelfth aspect of this disclosure is a prediction method of any one of the first to eleven aspects, in which a multi-year introduction plan for the distributed power system is formulated based on the target values ​​of the multi-year CO2 emission indicators of the electricity consumer and the budget plan for CO2 emission reduction, and the indicators for the multi-year period after the introduction of the distributed power system are predicted based on the introduction plan, taking into account the deterioration of the distributed power system.

[0039] This forecasting method predicts indicators related to electricity costs for electricity consumers over several years after the introduction of a distributed power system, taking into account the degradation of the distributed power system, based on a multi-year deployment plan for the distributed power system. Therefore, using these indicators, electricity consumers can make a more appropriate economic evaluation of the introduction of a distributed power system compared to conventional technologies.

[0040] A prediction apparatus according to a thirteenth aspect of the present disclosure comprises: a storage device that stores information related to degradation of a distributed power supply system including a power storage device and at least one of a natural energy power generation device and a fuel cell device; and a controller that predicts an index related to the power cost of a power consumer over a plurality of years after the distributed power supply system is introduced, in consideration of the information related to degradation of the distributed power supply system stored in the storage device.

[0041] In this prediction apparatus, the controller predicts the index related to the power cost of the power consumer in consideration of degradation of the distributed power supply system. Therefore, by using this index, a more appropriate evaluation of the introduction of the distributed power supply system by the power consumer can be performed from an economic perspective compared to conventional techniques.

[0042] A control method for an information terminal according to a fourteenth aspect of the present disclosure is a control method for an information terminal, comprising the step of causing a display of the information terminal to display information indicating an index related to the power cost of a power consumer over a plurality of years when a distributed power supply system including a power storage device and at least one of a natural energy power generation device and a fuel cell device is introduced, wherein the index is predicted in consideration of degradation of the distributed power supply system.

[0043] In this control method, information indicating the index related to the power cost of the power consumer predicted in consideration of degradation of the distributed power supply system is displayed on the display. Therefore, a proposer of the introduction of the distributed power supply system who is a user of the information terminal, or a power consumer who has been presented with the information displayed on the display by the proposer, can perform a more appropriate evaluation of the introduction of the distributed power supply system from an economic perspective compared to conventional techniques.

[0044] A control method for an information terminal according to a fifteenth aspect of the present disclosure is the control method for an information terminal according to the fourteenth aspect, further comprising the step of causing the display to display an introduction plan for the distributed power supply system formulated based on at least one of a target value of an index related to CO₂ emission of the power consumer and a budget plan for reducing CO₂ emissions.

[0045] In this control method, information indicating an index related to the power cost of a power consumer predicted in consideration of the deterioration of a distributed power supply system is displayed on a display device. Therefore, the aforementioned proposer or the power consumer can perform a more appropriate evaluation on the introduction of the distributed power supply system for the power consumer from an economic perspective compared with conventional techniques.

[0046] Hereinafter, specific examples of the above aspects of the present disclosure will be described with reference to the accompanying drawings. All the specific examples described below are illustrative of the above aspects of the present disclosure. Therefore, the shapes, numerical values, constituent elements, arrangement positions of constituent elements, connection forms, and the like shown below do not limit the scope of the claims unless they are recited in the claims.

[0047] In addition, among the constituent elements described below, constituent elements not recited in the independent claims representing the broadest concept of the present disclosure are described as optional constituent elements. In the drawings, description of components denoted by the same reference numerals may be omitted in some cases. For the purpose of facilitating understanding, the drawings schematically show each constituent element, and there are cases where shapes, dimensional ratios, and the like are not accurately represented.

[0048] Furthermore, in the operation of the apparatus, the order of steps may be changed, or known steps may be added as necessary.

[0049] (Example of Introducing a Distributed Power Supply System) First, an example of introducing a distributed power supply system 12 by a power consumer 10 will be described. FIG. 1 is a diagram showing an example of introducing the distributed power supply system 12. FIG. 1(a) shows a mode of power supply to the power consumer 10 before the start of introduction 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 that manufactures products. In the present embodiment, as shown in FIG. 1(a), before the start of introduction 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".

[0050] 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.

[0051] The above-mentioned natural energy power generation device 13 is a device that generates electricity using natural energy, and includes solar power generation devices and wind power generation devices. 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.

[0052] 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. In particular, if the amount of power supplied from the distributed power system 12 to the power consumer 10 decreases due to deterioration of the distributed power system 12, power will 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.

[0053] (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. In this embodiment, the information terminal 30 functions as a prediction device that predicts a first indicator, which will be described later. The information terminal 30 includes a controller 31, a memory 32, an input 33, and a display 34.

[0054] 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.

[0055] Furthermore, the information terminal 30 is connected to an information server (not shown) 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.

[0056] (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, taking into account the deterioration of the distributed power system 12. Specifically, the prediction method according to this embodiment predicts indicators related to the electricity costs of electricity consumers 10 over several years after the introduction of the distributed power system, taking into account the deterioration of the distributed power system. This will be explained in detail below.

[0057] <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.

[0058] 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 the electricity consumer 10 or the electricity consumed by the electricity consumer 10. Figure 4 shows the multi-year target renewable energy rate for the electricity consumer 10. The thick line in Figure 4 represents the multi-year target renewable energy rate for the electricity consumer 10. The worker sets the multi-year target renewable energy rate for the 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 the 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.

[0059] 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 10 correspond to Periods 1 through 6 shown in Figure 4, respectively.

[0060] 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.

[0061] <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 multi-year target value (target renewable energy rate) of the second indicator set in step S10 and the multi-year budget plan for CO2 emission reduction. The multi-year implementation plan for the distributed power system 12 may also be formulated by the controller 31.

[0062] 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.

[0063] 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.

[0064] Furthermore, the introduction plan for the distributed power system 12 may include an introduction plan for the replacement of at least a portion of the distributed power system 12. In this embodiment, although not shown in Figure 5, the introduction plan includes the timing of replacement of the renewable energy power generation device 13, the fuel cell device 14, and the energy storage device 15. The timing of replacement of the renewable energy power generation device 13, the fuel cell device 14, and the energy storage device 15 may be predicted based on degradation information, which will be described in step S40 later.

[0065] 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.

[0066] <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.

[0067] <Acquisition of Degradation Information> Next, in the flow of the prediction method according to this embodiment, the controller 31 acquires degradation information from the memory 32 (step S40). The degradation information stored in the memory 32 may be input by the operator using the input device 33 each time step S40 is performed, or it may be information that the memory 32 has acquired in advance.

[0068] The degradation information may include, for example, at least one of the following: the degradation rate relative to the usage period of the distributed power source, the degradation rate relative to the number of times the distributed power source is operated, and the degradation rate relative to the operating time of the distributed power source. The usage period of the distributed power source is the elapsed time since the introduction of the distributed power source, and the operating time of the distributed power source is the time during which the distributed power source is in operation within the above usage period. Operation means power generation if the distributed power source is a natural energy power generation device 13 or a fuel cell device 14, and charging or discharging if it is an energy storage device 15. Furthermore, if the degradation information acquired by the controller 31 includes the degradation rate relative to the number of times the distributed power source is operated, the degradation information may also include the frequency of operation of the distributed power source (for example, the number of times it is operated in one year). In addition, if the degradation information acquired by the controller 31 includes the degradation rate relative to the operating time of the distributed power source, the degradation information may also include the operating time of the distributed power source during a specified period (for example, the operating time in one year).

[0069] Furthermore, the above degradation rate may include an output degradation rate, which is the percentage decrease in the power supply (output) of the distributed power source due to degradation, and a capacity degradation rate, which is the percentage decrease in the power capacity due to degradation, in addition to the degradation rate of the energy storage device 15.

[0070] <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 source over several years, taking into account the deterioration of the distributed power system 12 (step S50). The 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.

[0071] Of the above breakdown, the amount of electricity supplied to the electricity consumer 10 from each distributed power source (natural energy power generation device 13, fuel cell device 14, energy storage device 15) can be predicted based on the introduction plan for the distributed power system 12 formulated in step S20 and the degradation information acquired in step S40. Here, the output degradation rate and capacity degradation rate of each distributed power source are mainly used from the degradation information.

[0072] For example, in the breakdown above, for the first year of the second phase, since each distributed power source has not yet deteriorated, the same amount of electricity as the first year of the second phase of the introduction plan formulated in step S20 is reflected. On the other hand, for the second year and beyond of the second phase, the amount of electricity reflected for each distributed power source is the amount of electricity obtained by subtracting the amount of electricity corresponding to the output deterioration rate (and capacity deterioration rate) of each distributed power source from the amount of electricity one year prior. As a result, the breakdown of the amount of electricity supplied from each distributed power source to the electricity consumer 10 decreases from the first to the fifth year of the same period due to the deterioration of each distributed power source.

[0073] Furthermore, of the above breakdown, the amount of grid power supplied to the electricity consumer 10 can be predicted by subtracting the amount of power supplied to the electricity consumer 10 by the distributed power system 12 from the amount of electricity demanded (amount of electricity consumed) by the electricity consumer 10. In this embodiment, since the electricity demand of the electricity consumer 10 is predicted to be the same every year, the breakdown of the amount of grid power supplied to the electricity consumer 10 will increase from the first to the fifth year within the same period due to the deterioration of each distributed power source. In other words, the electricity cost of the electricity consumer will increase due to the deterioration of each distributed power source. Note that the prediction that the electricity demand of the electricity consumer 10 will be the same every year is just an example and is not limited to this.

[0074] Figure 6 shows the breakdown of electricity supplied to electricity consumer 10 over several years (units of electricity are kHh, MHh, etc.). In the example shown in Figure 6, in the first period, all of the electricity supplied to electricity consumer 10 is grid electricity. From the second to the fourth period, the proportion of electricity supplied by the distributed power system 12 (renewable energy power generation device 13, fuel cell device 14, energy storage device 15) to the total electricity supplied to electricity consumer 10 gradually increases. However, from the second to the fourth period, due to the deterioration of the distributed power system 12, the amount of electricity supplied by the distributed power system 12 to electricity consumer 10 decreases little by little from the first to the fifth year of each period.

[0075] <Prediction of Electricity Cost Indicators> Next, in the flow of the prediction method according to this embodiment, the controller 31 predicts indicators related to the electricity costs of electricity consumers 10 over several years when the distributed power supply system 12 is introduced, taking into account the deterioration of the distributed power supply system 12 (step S60). The "indicators related to the electricity costs of electricity consumers 10" in this embodiment include costs incurred with the introduction of the distributed power supply system 12, running costs, electricity costs (total costs), and electricity unit prices. In this embodiment, these indicators are predicted in steps S61 to S64. The prediction methods for each indicator will be described in order below.

[0076] <Prediction of Costs Associated with the Introduction of a Distributed Power System> First, the controller 31 predicts the costs incurred over several years when introducing the distributed power system 12, taking into account the degradation of the distributed power system 12 (step S61). The costs associated with the introduction of the distributed power system 12 include at least the introduction cost of the distributed power system 12, and may also include running costs incurred after the introduction of the distributed power system 12. Here, the introduction cost of the distributed power system 12 paid in a lump sum is predicted, and then the introduction cost of the distributed power system 12 paid in installments is also predicted.

[0077] Here, memory 32 stores predicted data of the unit cost of distributed power capacity over multiple years. The unit cost of distributed power capacity is at least one of the unit cost of the renewable energy power generation equipment and the unit cost of the fuel cell equipment to be introduced based on the introduction plan of the distributed power system. However, if the distributed power system includes an energy storage device, the unit cost of the energy storage device 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 unit cost of distributed power capacity over multiple years obtained from memory 32, the controller 31 predicts the multi-year lump-sum introduction cost when the distributed power system 12 is introduced.

[0078] 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 S50. 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.

[0079] As described above, the hydrogen purchase cost in this embodiment is predicted based on the amount of electricity supplied by the fuel cell device 14 to the electricity consumer 10, which was calculated in step S50. However, as previously stated, the amount of electricity supplied by the fuel cell device 14 to the electricity consumer 10 in this embodiment is calculated based on degradation information. Therefore, it can be said that the hydrogen purchase cost in this embodiment is predicted taking into account the degradation of the distributed power system 12.

[0080] Figure 7 shows the lump-sum installation costs over multiple years when the distributed power system 12 predicted by the controller 31 is introduced. In the example shown in Figure 7, the renewable energy power generation device 13, fuel cell device 14, and energy storage device 15 are introduced in the first year of the second phase, and the lump-sum installation costs for these are shown. Furthermore, the fuel cell device 14 is introduced in the first year of the third and fourth phases, respectively, and the lump-sum installation costs for these are shown. Figure 7 shows the case where the service life of the fuel cell device 14 and energy storage device 15 is 15 years. That is, assuming that the fuel cell device 14 and energy storage device 15 introduced in the first year of the second phase are replaced in the first year of the fifth phase, the lump-sum installation costs for these are shown in the first year of the fifth phase. Furthermore, assuming that the fuel cell device 14 introduced in the first year of the third phase is replaced in the first year of the sixth phase, the lump-sum installation cost for this is shown. Note that the above service life is the guaranteed service life for each distributed power source, but instead, it may be the replacement time predicted based on the degradation information of each distributed power source. The replacement timing will be the same as the guaranteed service life for each distributed power source if the degradation information, such as the operating frequency and operating hours of each distributed power source during the specified period, corresponds to standard operating conditions. However, if it corresponds to operating conditions with a heavier load than standard, it is predicted that the replacement will be brought forward to the guaranteed service life.

[0081] Next, the controller 31 predicts the installment costs for introducing the distributed power supply system 12 over multiple years. The installment costs are the total introduction costs for introducing the distributed power supply system 12 based on the introduction plan, divided into installments for each year from the introduction year to the end of its useful life. In the case of introducing the distributed power supply system 12, distributed power sources with various useful lives may be introduced in various introduction years. In this embodiment, the installment costs for each distributed power source are predicted for each useful life and each introduction year, and these are added together to predict the installment costs for the distributed power supply system 12. As mentioned above, the useful life of each distributed power source is the guaranteed useful life for each distributed power source, but instead, it may be the replacement time predicted based on the degradation information of each distributed power source.

[0082] Figure 8 shows the installment-based installation costs over multiple years. Figure 8 corresponds to the lump-sum installation costs shown in Figure 7. For example, the installment-based installation costs for each year of the second period are calculated by adding the 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 cost of the fuel cell device 14 and energy storage device 15 divided by their respective useful lives of 15 years. Similarly, the installment-based installation costs for each year of the third period are calculated by adding the cost of the fuel cell device 14 purchased in the first year of the third period divided by its useful life of 15 years to the installment-based installation costs for each year of the second period. The installment-based installation costs for each year from the fourth period onward are predicted in the same manner. Note that Figures 7 and 8 may also include not only the lump-sum or installment-based installation costs but also the running costs incurred after the installation of the distributed power system 12 as costs associated with the introduction of the distributed power system 12.

[0083] <Predicting Running Costs> Next, the controller 31 predicts the running costs over several years when the distributed power system 12 is introduced, taking into account the degradation of the distributed power system 12 (step S62). 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.

[0084] [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 data on the unit price of grid electricity over several years. The controller 31 predicts the electricity purchase cost over several years based on the amount of grid electricity to be supplied to electricity consumer 10 predicted in step S50, and the predicted data on the unit price of grid electricity obtained from the memory 32.

[0085] As described above, the electricity purchase cost in this embodiment is predicted based on the amount of grid power supplied to the electricity consumer 10 predicted in step S50. However, as previously stated, the amount of grid power supplied to the electricity consumer 10 in this embodiment is predicted based on degradation information. Therefore, it can be said that the electricity purchase cost in this embodiment is predicted taking into account the degradation of the distributed power system 12.

[0086] [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.

[0087] 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.

[0088] 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 S50, 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.

[0089] 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.

[0090] The controller 31 predicts the renewable energy rate of electricity consumer 10 over multiple years from the breakdown of electricity consumption from each power source over multiple years calculated in step S50, and predicts the amount of electricity certificates to be purchased over multiple years from the difference between the predicted renewable energy rate of electricity consumer 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.

[0091] The controller 31 predicts the renewable energy rate of the electricity consumer 10 by predicting the proportion of electricity generated from renewable energy supplied to the electricity consumer 10 from the distributed power system 12 within the electricity consumer 10's total electricity demand. The proportion of electricity generated from renewable energy supplied to the electricity consumer 10 from the distributed power system 12 within the electricity consumer 10's total electricity demand can be predicted from the breakdown of electricity supplied to the electricity consumer 10 predicted in step S50.

[0092] However, the amount of electricity generated from renewable energy supplied from the distributed power system 12 to the electricity consumer 10 does not include the amount of electricity supplied from the fuel cell device 14 to the electricity consumer 10 when the fuel cell device 14 generates electricity using gray hydrogen, but does include the amount of electricity supplied from the fuel cell device 14 to the electricity consumer 10 when the fuel cell device 14 generates electricity using green hydrogen. Since green hydrogen is hydrogen obtained by water electrolysis using electricity generated using renewable energy, it is included in renewable energy in this disclosure.

[0093] As described above, the purchase cost of environmental value in this embodiment is predicted based on the breakdown of electricity consumption from each power source over multiple years, as predicted in step S50. However, as mentioned earlier, the breakdown of electricity consumption from each power source over multiple years is predicted based on degradation information. Therefore, it can be said that the purchase cost of environmental value in this embodiment is predicted taking into account the degradation of the distributed power system 12.

[0094] [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.

[0095] 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 S50. 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.

[0096] As described above, the carbon tax in this embodiment is predicted based on the breakdown of electricity consumption from each power source over multiple years, which is predicted in step S50. As previously mentioned, the breakdown of electricity consumption from each power source over multiple years is predicted based on degradation information. Therefore, it can be said that the carbon tax in this embodiment is predicted taking into account the degradation of the distributed power system 12.

[0097] 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 several years. Note that Figure 9 shows the predicted results assuming that the electricity unit price of grid power, the unit price of environmental value, and the carbon tax unit price are constant (Figure 10 is similar). 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 purchase cost of environmental value and the carbon tax. Furthermore, the running costs for the second and third periods include the electricity purchase cost of grid power, the purchase cost of environmental value, and the carbon tax. Furthermore, 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 of grid power or the purchase cost of environmental value. Note that if the distributed power system 12 includes a fuel cell device 14, the running cost shown in Figure 9 may be increased by the hydrogen fuel cost, which will be incurred as a running cost after the introduction of the distributed power system 12. In this case, the cost of hydrogen fuel will be added to the running costs from the second phase onward.

[0098] In the example shown in Figure 9, running costs increase from the first to the fifth year, assuming the configuration of the distributed power system 12 remains unchanged for the same period. One reason for this is that the deterioration of the distributed power system 12 increases CO2 emissions from the electricity consumer 10, leading to increased environmental value purchase costs and carbon taxes. Another reason is the increase in electricity purchase costs for the electricity consumer 10. This is because, as the distributed power system 12 deteriorates, the output efficiency of the distributed power system 12 decreases, reducing the amount of electricity supplied from the distributed power system 12 to the electricity consumer 10, while the amount of grid power supplied to the electricity consumer 10 increases. In other words, the deterioration of the distributed power system 12 contributes to increased running costs. Furthermore, in Figure 9, the running costs in the fourth period are lower than in the third period because the energy storage device 15 and fuel cell device 14 are replaced in the fourth period.

[0099] <Predicting Power Costs> Next, the controller 31 predicts the power costs over several years, taking into account the degradation of the distributed power system 12 (step S63). The power costs in this embodiment are total costs. In this embodiment, the controller 31 predicts the power costs over several years by adding up the multi-year installment payment for the introduction cost of the distributed power system 12 predicted in step S61, the multi-year running costs incurred after the introduction of the distributed power system, and the multi-year running costs predicted in step S62. Figure 10 is a diagram showing the multi-year power costs (total costs). Figure 10 is a diagram that combines the multi-year introduction cost shown in Figure 8 and the multi-year running costs shown in Figure 9.

[0100] In this embodiment, the installment payment introduction cost predicted in step S61 is used to predict the 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 installment payment 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.

[0101] <Predicting Electricity Unit Price> Next, in the flow of the prediction method according to this embodiment, the controller 31 predicts the electricity unit price for the electricity usage of the electricity consumer 10 over several years, taking into account the deterioration of the distributed power supply system 12 (step S60). The "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 electricity unit price by dividing the electricity cost (total cost) shown in Figure 10 by the amount of electricity demanded by the electricity consumer 10.

[0102] 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 electricity unit price over multiple years, obtained by dividing the electricity cost over multiple years by the amount of electricity demanded by the electricity consumer 10, will also show the same trend as the 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.

[0103] <Display of Electricity Cost Indicators> Next, the controller 31 displays indicators related to electricity costs (step S70). Specifically, the controller 31 displays information on the display 34 that shows the electricity cost indicators (installation cost, running cost, electricity cost, electricity unit price) predicted in step S60. The display method of the electricity cost indicators is not limited, and the indicators may be displayed as graphs or as numerical values. When step S70 is completed, the flow of the prediction method according to this embodiment is finished. The above is a description of the flow of the prediction method according to this embodiment.

[0104] As explained above, steps S10 to S70 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 S70.

[0105] As described above, the prediction method according to this embodiment predicts a first indicator of CO2 emissions from electricity consumers 10 over several years after the introduction of the distributed power system 12, taking into account the deterioration of the distributed power system 12. Therefore, by evaluating the introduction of the distributed power system 12 using this first indicator, a more appropriate evaluation from an environmental standpoint can be made compared to the conventional technology.

[0106] 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.

[0107] One aspect of this disclosure can be used in a method for predicting indicators for evaluating the introduction of a distributed power supply system from an environmental perspective more appropriately than conventional technologies, a device for predicting said indicators, and a method for controlling an information terminal that displays said indicators.

[0108] 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

Claims

1. A prediction method for predicting an index relating to the electricity costs of electricity consumers over several years when a distributed power system including at least one of a renewable energy power generation device and a fuel cell device is introduced, taking into account the degradation of the said distributed power system.

2. The prediction method according to claim 1, wherein the distributed power system includes the renewable energy power generation device, and the method predicts the indicator over several years after the introduction of the distributed power system, taking into account the deterioration of the renewable energy power generation device.

3. The prediction method according to claim 1, wherein the distributed power system includes the fuel cell device, and the method predicts the indicator over several years after the introduction of the distributed power system, taking into account the deterioration of the fuel cell device.

4. The prediction method according to claim 1, wherein the distributed power system includes the renewable energy power generation device and the fuel cell device, and the method predicts the indicator over several years after the introduction of the distributed power system, taking into account the deterioration of the renewable energy power generation device and the fuel cell device, respectively.

5. The prediction method according to any one of claims 2-4, wherein the distributed power system further includes an energy storage device, and the prediction method for the indicator over several years after the introduction of the distributed power system also takes into account the deterioration of the energy storage device.

6. The prediction method according to any one of claims 1 to 5, wherein the indicator includes the cost of electricity itself, and the cost of electricity includes the costs incurred in connection with the introduction of the distributed power system and the cost of purchasing electricity from the grid.

7. The forecasting method according to claim 6, 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.

8. The forecasting method according to claim 6 or 7, 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.

9. The prediction method according to any one of claims 6-8, 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.

10. The forecasting method according to any one of claims 6-9, 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.

11. The prediction method according to claim 10, wherein the electricity cost includes a carbon tax within the electricity consumer.

12. A prediction method according to any one of claims 1 to 11, comprising: formulating a multi-year introduction plan for the distributed power generation system based on target values ​​for CO2 emission indicators and a budget plan for CO2 emission reduction of the electricity consumer; and predicting the indicators for a multi-year period after the introduction of the distributed power generation system based on the introduction plan, taking into account the deterioration of the distributed power generation system.

13. A prediction device comprising: an energy storage device; a memory for storing information relating to the deterioration of a distributed power system including at least one of a renewable energy power generation device and a fuel cell device; and a controller for predicting an index relating to the electricity costs of electricity consumers over several years after the introduction of the distributed power system, taking into account the information relating to the deterioration of the distributed power system stored in the memory.

14. A method for controlling an information terminal, comprising the step of displaying information on a display of the information terminal that indicates an indicator of electricity costs for electricity consumers over several years when a distributed power system including an energy storage device and at least one of a renewable energy power generation device and a fuel cell device is introduced, wherein the indicator is predicted taking into account the deterioration of the distributed power system.

15. The control method for an information terminal according to claim 14, further comprising the step of displaying on the display a plan for introducing the distributed power system, which is formulated based on at least one of the target value of an indicator related to the CO2 emissions of the electricity consumer and a budget plan for reducing CO2 emissions.

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