JP7813259B2 - Facility configuration proposal system and facility configuration proposal method - Google Patents

Description

The present invention relates to an equipment configuration proposal system and equipment configuration proposal method that develops equipment investment scenarios to optimize KPIs.

There is a global demand for decarbonization to mitigate the progress of global warming, and large-scale users of energy such as private companies that consume large amounts of electricity and gas are being asked to promote decarbonization by introducing highly energy-efficient equipment or upgrading to such equipment. However, many users do not have sufficient knowledge of decarbonization technologies, making it difficult for them to independently develop capital investment plans that optimize their contribution to decarbonization within limited budgets.

Here, the equipment planning method of Patent Document 1 is known as a prior art technique for using a computer to create equipment investment plans. For example, the abstract of this document states that the problem is to "optimize investment effectiveness while responding to future increases in energy demand," and as one means of solving this problem, claim 1 discloses, "an equipment planning method for a distributed energy system in which energy is supplied by multiple energy supply devices, the method determining an optimal combination of multiple energy supply devices and the timing of their introduction based on an evaluation function including, as parameters, a predicted energy demand for the distributed energy system over a predetermined future period, a predicted fuel price for the energy supply devices over the predetermined period, and equipment information for the energy supply devices."

Japanese Patent Application Laid-Open No. 2015-125643

However, the technology in Patent Document 1 had the problem of requiring excessive computer processing time to optimize capital investment plans. This was because, as is clear from the structure of claim 1 of the document, the capital planning method in this document determines the optimal combination of multiple energy supply equipment and the timing of its introduction using the predicted energy demand, predicted fuel prices for energy supply equipment, and equipment information for the energy supply equipment as parameters for a predetermined future period. Therefore, if the "predetermined future period" is long, the processing time increases. Furthermore, if the variations in each of the "predicted energy demand," "predicted fuel prices for energy supply equipment," and "equipment information for energy supply equipment" increase, a combination explosion occurs, and it is possible that the capital investment plan optimization process will not be completed within a practical amount of time.

The present invention therefore aims to provide an equipment configuration proposal system and an equipment configuration proposal method that can create equipment investment scenarios that contribute to KPI optimization within a practical timeframe.

The present invention includes multiple means for solving the above-mentioned problems. One example is an equipment configuration proposal system that evaluates multiple equipment investment scenarios, comprising: a pre-statistics information database that records time-series information on consumer energy consumption; a statistical processing unit that groups the time-series information recorded in the pre-statistics information database based on predetermined criteria and performs statistical processing on each group; a post-statistics information database that records the time-series information statistically processed by the statistical processing unit; a rough optimization calculation unit that calculates KPIs based on the time-series information recorded in the post-statistics information database; a scenario selection unit that selects some equipment investment scenarios from the multiple equipment investment scenarios based on the KPIs evaluated by the rough optimization calculation unit; and a precise optimization calculation unit that calculates the KPIs for the equipment investment scenarios selected by the scenario selection unit based on the time-series information recorded in the pre-statistics database.

The equipment configuration proposal system and equipment configuration proposal method of the present invention make it possible to develop equipment investment scenarios that contribute to KPI optimization within a practical amount of time.

Overall configuration of the equipment configuration proposal system. An example of an input screen image. An example of the data format of the pre-statistical demand database. An example of a data format for a facility configuration database. 1 shows an example of a data format for a coefficient database. Processing flow of the grouping part. An example of the data format of the post-statistics demand database. Processing flow of the rough optimum calculation part. An example of the data format for a KPI database by facility configuration. An example of a management format for input scenarios. 10 is a processing flow of the scenario generation unit. An example of the data format for the scenario-specific summary KPI database. 10 is an example of a screen image of the output unit 11. Processing flow of the scenario selection section. An image of the process for checking the number of included plots. An image of the process for checking the number of included plots. Processing flow of the precise optimum calculation part. An example of the data format for a scenario-specific refined KPI database.

The following describes an equipment configuration proposal system 100 according to one embodiment of the present invention, using the accompanying drawings.

<Overall configuration of facility configuration proposal system 100>
FIG. 1 shows the overall configuration of an equipment configuration proposal system 100 according to this embodiment, as well as an instruction input device 200 (keyboard, mouse, touch panel, etc.) and a display device 300 (liquid crystal display, etc.) connected to the system. Specifically, the equipment configuration proposal system 100 is a computer equipped with hardware such as a calculation device such as a CPU, a main storage device such as a semiconductor memory, an auxiliary storage device such as a hard disk, and a communication device. The calculation device executes a program while referencing a predetermined database to realize the various functions described below. The following description will omit such well-known techniques as appropriate.

As shown in Figure 1, the facility configuration proposal system 100 of this embodiment comprises an input unit 1, a pre-statistics demand database 2, a facility configuration database 3, a coefficient database 4, a statistical processing unit 5, a post-statistics demand database 6, a rough optimization calculation unit 7, a facility configuration-specific KPI database 8, a scenario creation unit 9, a scenario-specific rough KPI database 10, an output unit 11, a scenario selection unit 12, a precise optimization calculation unit 13, and a scenario-specific precise KPI database 14. As indicated by the dashed frame in the figure, the statistical processing unit 5 to the scenario-specific rough KPI database 10 form a rough evaluation unit, and the precise optimization calculation unit 13 and the scenario-specific precise KPI database 14 form a precise evaluation unit. The details of each unit that makes up the facility configuration proposal system 100 will be explained in order below.

<Input section 1>
The input unit 1 is a functional unit that communicates with the instruction input device 200 and the display device 300 when an operator of this system sets various setting values, and transmits the values set by the operator to the statistical processing unit 5 and the like.

Figure 2 shows an example of a screen image displayed on the display device 300 when various setting values are being set. In this example, there are setting areas for four items.

The setting area 301 is an area for defining the grouping criteria used by the statistical processing unit 5. In this area, multiple grouping criteria can be selected by checking any checkbox. In Figure 2, three grouping criteria are available: "Seasonal," "Monthly," and "Weekday/Holiday." The example shows two of these criteria, "Monthly" and "Weekday/Holiday," selected.

The setting area 302 is an area for alternatively selecting a KPI (Key Performance Indicator) to be used in the rough optimization calculation unit 7 and the precise optimization calculation unit 13. In Fig. 2, two KPIs are prepared: business operating expenses "OPEX" and carbon dioxide emissions " _CO2 emissions", and an example is shown in which minimization of "OPEX" has been selected.

The setting area 303 is an area where the operator sets the scenario to be input into the scenario generation unit 9. Here, "10 years" is selected as the number of years for generating capital investment scenarios, and the example shows that Scenario No. 1 is set to equipment configuration #1 for years 1 to 3, equipment configuration #3 for years 4 and 5, equipment configuration #4 for years 6 to 8, and equipment configuration #2 for years 9 and 10. Pressing the "Register" button in this state sends the combination of the set equipment configurations to the scenario generation unit 9 as Scenario No. 1 and saves it in the scenario database (Figure 10) described below. In the following, it is assumed that the operator repeatedly generates scenarios, resulting in the generation of 13 scenarios, from Scenario No. 1 to Scenario No. 13. Note that capital investment scenarios are not limited to those that set combinations of equipment configurations for multiple years, and may set only equipment configurations for a single year.

The setting area 304 is an area for defining the rules used by the scenario selection unit 12. These rules are used to select scenarios for detailed evaluation from all scenarios for general evaluation, and in Figure 2, "1" is registered as the tolerance value that defines the rule. The significance of this tolerance value will be explained in detail in Figures 15A and 15B, and will not be explained here.

<Pre-statistics demand database 2>
The pre-statistics demand database 2 is a database that chronologically records the energy consumed over a certain period of time in the past by consumers who use this system to create capital investment plans (hereinafter referred to as "specific consumers").

Figure 3 shows an example of the data format of the pre-statistics demand database 2. In this example, the following data are registered, starting from the leftmost column: "Date and Time," "Season," "Month," "Weekday/Holiday," and "Electricity Consumption." As an example, this shows one year's worth of electricity consumption data from April 1, 2022 to March 31, 2023, recorded in 30-minute increments.

<Facility configuration database 3>
The facility configuration database 3 is a database that defines a combination of facilities operated by a specific customer.

Figure 4 shows an example of the data format of the equipment configuration database 3. In this example, data such as "Equipment configuration #," "Photovoltaic power generation (first unit)," "Photovoltaic power generation (second unit)," "Storage battery (first unit)," and "Storage battery (second unit)" are registered, starting from the leftmost column.

The "Equipment Configuration #" in this diagram corresponds to the "Equipment Configuration #" in the setting area 303 in Figure 2. Therefore, by referring to this database, it is possible to know the transition of the equipment configuration for each scenario set by the operator. Furthermore, "1" data in the database means that it is applicable, and "0" data means that it is not applicable. In the following, the letters PV will be used to identify elements related to photovoltaic power generation, and the letters BT will be used to identify elements related to storage batteries.

By referring to this database, Scenario No. 1 shown in Figure 2 can be interpreted as the following capital investment scenario.
(1) Years 1 to 3 after facility configuration #1 is established: In year 1, the first unit of the solar power generation system is newly installed and begins operation. In years 2 and 3, the existing solar power generation system continues to operate.
(2) Years 4 and 5 after facility configuration #3 is established: In year 4, the existing solar power generation system will continue to operate, and the first unit of the battery storage system will be newly installed and put into operation. In year 5, the existing solar power generation system and battery storage system will continue to operate.
(3) Years 6 to 8 after setting up facility configuration #4: In year 6, the existing solar power generation system and storage battery system will continue to operate, and a second solar power generation system unit will be added and put into operation. In years 7 and 8, the existing solar power generation system and storage battery system will continue to operate.
(4) Years 9 and 10 after setting up facility configuration #2: In year 9, the existing solar power generation system and storage battery system will continue to operate, and a second storage battery system unit will be added and put into operation. In year 10, the existing solar power generation system and storage battery system will continue to operate.

<Coefficient database 4>
The coefficient database 4 is a database in which various set values such as the electricity rate unit price and _CO2 emission coefficient are recorded by fiscal year.

5 is an example of the data format of the coefficient database 4. In this example, the leftmost column lists "item" and fiscal year (year), and data by fiscal year is recorded for each item such as electricity unit price, _CO2 emission coefficient, and carbon tax.

<Statistical processing unit 5>
The statistical processing unit 5 is a functional unit that groups the energy consumption recorded in the pre-statistics demand database 2 based on settings made by an operator, and outputs a statistical energy consumption waveform for each group.

Figure 6 shows the processing flow of the statistical processing unit 5.

First, in step S51, the statistical processing unit 5 checks the grouping criteria selected by the operator in the setting area 301. In the example of Figure 2, "monthly" and "weekday/holiday" have been selected, so the statistical processing unit 5 recognizes that the data in the pre-statistics demand database 2 will be grouped into groups such as "April and weekday," "April and holiday," "May and weekday," and "May and holiday."

Step S52 is a loop function for grouping criteria variations. Based on the criteria recognized in step S51, the following steps S53 to S55 are repeatedly executed in the following order: "April and weekday," "April and holiday," "May and weekday," "May and holiday," etc.

In step S53, the statistical processing unit 5 extracts data corresponding to the group being processed from the pre-statistics demand database 2. For example, in the first processing loop, a set of data corresponding to "April and weekdays" is extracted from the database in Figure 3.

Step S54 is a time loop function. Since the pre-statistics demand database 2 in Figure 3 stores power consumption in 30-minute increments, step S55 (described below) is executed in the following order, for example, 00:00, 00:30, 01:00, 01:30, etc.

In step S55, the statistical processing unit 5 calculates the average value of the acquired data. For example, the average value is calculated for power consumption data that corresponds to "April, weekdays" and "00:00 to 00:29." Note that while average value calculation is used as an example of statistical processing here, other statistical processing methods such as MBR (Memory Based Reasoning), multiple regression analysis, and exponential smoothing may also be used for the statistical processing in this step.

In step S56, the statistical processing unit 5 records the results of the above processing in the post-statistical demand database 6.

<Post-statistics demand database 6>
The post-statistics demand database 6 is a database in which the statistical energy consumption waveforms for each group generated by the statistical processing unit 5 are recorded.

Figure 7 shows an example of the data format of the post-statistics demand database 6. In this example, starting from the leftmost column, data on "season," "month," "weekday/holiday," "time," and "power consumption" after statistical processing are registered. Note that since "seasonal unit" was not selected in the setting area 301 of Figure 2, the "season" column in Figure 7 is marked "N/A."

<Rough Optimization Calculation Unit 7>
The rough optimization calculation unit 7 is a functional unit that performs optimization calculations for each year using the coefficient database 4 to optimally operate the equipment in the equipment configuration database 3 for each statistical energy consumption waveform recorded in the post-statistics demand database 6, based on the KPIs set by the operator, and records the results in the equipment configuration-specific KPI database 8. Note that the post-statistics demand database 6 generated through statistical processing in the statistical processing unit 5 has a significantly compressed amount of data compared to the pre-statistics demand database 2, so calculation processing based on the post-statistics demand database 6 can be completed in an extremely short time compared to the same type of calculation processing based on the pre-statistics demand database 2.

Figure 8 shows the processing flow of the approximate optimization calculation unit 7.

First, in step S71, the rough optimization calculation unit 7 recognizes the optimization factors. Specifically, it checks the setting area 302 in Figure 2 and confirms that the KPI is minimizing OPEX.

In step S72, the approximate optimization calculation unit 7 recognizes the grouping criteria. Specifically, it checks the setting area 301 in Figure 2 and confirms that the grouping criteria are "monthly" and "weekday/holiday."

Step S73 is an annual loop. To determine the number of times this loop is executed, the rough optimization calculation unit 7 checks the number of years for generating capital investment scenarios in the setting area 303 in Figure 2. In Figure 2, "10" is registered as the number of years for generating capital investment scenarios, so the loop will run from year 1 to year 10 in order.

In step S74, the approximate optimization calculation unit 7 obtains the relevant set of data from the coefficient database 4.

Step S75 is a loop for the equipment configuration #. Here, the rough optimization calculation unit 7 performs loop processing for all equipment configurations # recorded in the equipment configuration database 3.

In step S76, the rough optimization calculation unit 7 recognizes the equipment configuration. Specifically, it recognizes the equipment configuration # that is the target of the loop in step S75 from the equipment configuration database 3.

Step S77 is a loop for varying grouping criteria. Based on the information recognized in step S72, the following steps S78 to S7c are repeatedly executed in the following order: "April and weekday," "April and holiday," "May and weekday," "May and holiday."

In step S78, the approximate optimization calculation unit 7 extracts data corresponding to the loop settings in step S77 from the post-statistics demand database 6.

In step S79, the rough optimization calculation unit 7 optimizes the operation plan for the equipment of the target equipment configuration # based on the KPIs obtained in step S71. In other words, it optimizes Equation 1 by minimizing OPEX.

Here, OPEX(g) is the OPEX (¥) for group g, D(g,t) is the power consumption (kWh) for group g at time t, Ppv(n,g,t) is the power generation (kWh) of photovoltaic power generation (PV)(n) for group g at time t, Pbt(m,g,t) is the charge/discharge power (kWh) of battery BT(m) for group g at time t (where charging is positive and discharging is negative), UP(y) is the electricity rate (¥/kWh) for fiscal year y, and Pbt(m,g,t) is the optimization design variable. T is the final value of the time loop, N is the number of photovoltaic power generation devices (PV) (total of new and additional installations), and M is the number of battery BT devices (total of new and additional installations). Note that constraints apply: the remaining energy does not exceed the battery capacity and does not become negative.

In step S7a, the approximate optimum calculation unit 7 calculates the amount of received power as a result of executing step S79 using equation 2. Here, Pbt(m, g, t) uses the value obtained as a result of the optimum calculation in step S79. Furthermore, P(y, g, t) is the amount of received power (kWh) for year y, group g, and time t. Hereafter, descriptions of elements already mentioned will be omitted.

In step S7b, the approximate optimization calculation unit 7 calculates the _CO2 emissions using Equation 3, where UC(y) is the _CO2 emission coefficient for year y (t- _CO2 /kWh), and _CO2 (y, g) is the _CO2 emissions for year y and group g (t- _CO2 /kWh).

In step S7c, the rough optimization calculation unit 7 calculates OPEX using Equation 4, where OPEX(y, g) is the OPEX (¥) for year y and group g.

In step S7d, the rough optimization calculation unit 7 calculates the annual values of OPEX and _CO2 emissions. To calculate the annual values, both OPEX and _CO2 emissions can be integrated by group g, and are calculated using Equations 5 and 6.

In step S7e, the rough optimization calculation unit 7 records the results calculated by fiscal year using equations 5 and 6 in the equipment configuration-specific KPI database 8.

<KPI database by facility configuration 8>
9 shows an example of the data format of the equipment configuration-specific KPI database 8. In this example, "equipment configuration #,""year,""OPEX," and " _CO2 emissions" are registered in order from the leftmost column. As is clear from this figure, the rough optimization calculation unit 7 records the calculation result of Equation 5 in the "OPEX" column for each combination of "equipment configuration #" and "year," and records the calculation result of Equation 6 in the " _CO2 emissions" column.

<Scenario Generation Unit 9>
The scenario creation unit 9 is a functional unit that defines the equipment configuration (capital investment scenario) for each fiscal year based on the settings of the operator, and calculates the KPI for each capital investment scenario.

Figure 10 shows an example of a management format for capital investment scenarios entered by an operator. Here, the equipment configuration numbers for years 1 through 10 are recorded for each scenario number. The correspondence between scenario numbers and equipment configuration numbers is the result of settings made by the operator in the setting area 303 in Figure 2.

Figure 11 shows the processing flow for KPI calculation by the scenario generation unit 9.

In step S91, the scenario generation unit 9 references the table in Figure 10 and performs loop processing by scenario number.

In step S92, the program loops by year. In this case, the loop runs from year 1 to year 10.

In step S93, the scenario generation unit 9 recognizes the equipment configuration #. The equipment configuration # is identified by the scenario number and year by referring to the table in Figure 10.

In step S94, the scenario generation unit 9 refers to the equipment configuration-specific KPI database 8 to recognize the OPEX and _CO2 emissions. For example, by referring to the table in Fig. 9, the corresponding OPEX and _CO2 emissions are identified from the equipment configuration number and year recognized in step S93.

In step S95, the scenario generation unit 9 accumulates the values from step S94 for a predetermined number of years (here, 10 years).

In step S96, the scenario generation unit 9 records the calculation results (cumulative OPEX, cumulative CO ₂ emissions) in step S95 in the scenario-specific summary KPI database 10.

<Scenario-specific KPI database 10>
The scenario-specific summary KPI database 10 is a database in which the output results of the scenario generation unit 9 are stored.

12 shows an example of the data format of the scenario-specific summary KPI database 10. In this example, "Scenario No.", "Cumulative OPEX," and "Cumulative _CO2 Emissions" are registered in order from the leftmost column. The "Cumulative OPEX" and "Cumulative _CO2 Emissions" columns record the values calculated in step S95 of FIG. 11.

<Output unit 11>
The output unit 11 is a functional unit for displaying the KPIs of each scenario on the display device 300 based on the records of the scenario-specific outline KPI database 10 and the records of the scenario-specific detailed KPI database 14 described below.

13 is an example of a screen image displayed on the display device 300 connected to the output unit 11. The outline evaluation graph 305 on the left shows the results of the outline evaluation, and the detailed evaluation graph 306 on the right shows the results of the detailed evaluation. In both graphs, the horizontal axis represents cumulative OPEX and the vertical axis represents cumulative _CO2 emissions. The outline evaluation graph 305 is displayed with reference to a table in the scenario-specific outline KPI database 10, while the detailed evaluation graph is displayed with reference to a table in the scenario-specific detailed KPI database 14, which will be described later. The outline evaluation graph 305 on the left shows, as an example, KPIs for scenarios No. 1 to No. 13, while the detailed evaluation graph 306 on the right shows KPIs for selected scenarios No. 1 to No. 8.

<Scenario Selection Section 12>
The scenario selection unit 12 is a functional unit that selects, based on the operator's settings, from the equipment investment scenarios handled in the above-mentioned general evaluation unit, equipment investment scenarios to be used in the detailed evaluation of the detailed optimization calculation unit 13 and the scenario-specific detailed KPI database 14.

Figure 14 shows the processing flow of the scenario selection unit 12.

First, in step S121, the scenario selection unit 12 recognizes the allowable number of plots set in the setting area 304. In Figure 2, the allowable value is set to "1."

In step S122, the scenario selection unit 12 refers to the scenario-specific summary KPI database 10 and confirms the number of capital investment scenarios that have been roughly evaluated by the summary evaluation unit ("13" in the example of Figure 12).

In step S123, the scenario number is looped.

In step S124, the scenario selection unit 12 checks the number of included plots for each capital investment scenario in the overview evaluation graph 305. An image of the specific processing of this step is explained using Figures 15A and 15B.

Figure 15A is an image showing the procedure for checking the number of plots included in scenario No. 7. If a rectangle is drawn with the plot of scenario No. 7 and the origin as its diagonal, the plots within this rectangle are superior scenarios compared to scenario No. 7 in terms of both the cumulative OPEX and cumulative CO2 emissions KPIs. In this example, the only plot within the rectangle that corresponds to a scenario superior to scenario No. 7 is one plot, that of scenario No. 3, so the scenario selection unit 12 determines that the number of plots included in scenario No. 7 is "1."

Similarly, Figure 15B is an image showing the procedure for checking the number of plots included in scenario No. 11. If a rectangle is drawn with the plot of scenario No. 11 and the origin as its diagonal, the plots within this rectangle are scenarios that are superior to scenario No. 11 in terms of both the cumulative OPEX and cumulative CO2 emissions KPIs. In this example, the number of plots within the rectangle that correspond to the number of scenarios superior to scenario No. 11 is four, namely scenarios Nos. 3, 4, 5, and 7, and therefore the scenario selection unit 12 determines that the number of plots included in scenario No. 11 is "4."

In step S125, the scenario selection unit 12 makes a branching judgment as to whether the number of included plots is less than or equal to the allowable number of plots. If the requirement is met, the process proceeds to step S126; if the requirement is not met, step S126 is skipped. As shown in Figure 2, if the allowable value set in the setting area 304 is "1", only scenario numbers with an included plot number of "0" or "1" proceed to step S126, and scenario numbers with an included plot number of "2" or more do not proceed to step S126.

In step S126, the scenario numbers that satisfy the requirements of step S125 are recorded in memory (not shown). In the example described above, scenario No. 7 in Figure 15A is recorded in memory because the number of included plots is "1," while scenario No. 11 in Figure 15B is not recorded in memory because the number of included plots is "4." Therefore, in this step, scenarios No. 1 to No. 8 are selected from the capital investment scenarios in the overview evaluation graph 305 in Figure 13 and recorded in memory.

<Precise optimal calculation unit 13>
The precise optimization calculation unit 13 is a functional unit that uses data from the pre-statistical demand database 2, the equipment configuration database 3, and the coefficient database 4 for the equipment investment scenario selected by the scenario selection unit 12, and also creates an optimal operation plan for the equipment based on settings made by the operator.

Figure 16 shows the processing flow of the precise optimization calculation unit 13.

In step S131, the precise optimization calculation unit 13 recognizes the optimization elements. The processing content of this step is the same as step S71.

In step S132, the precise optimization calculation unit 13 performs a loop process in the order of the selected scenario numbers stored in memory in step S126 of Figure 14.

Step S133 loops through the years set for the capital investment scenario (in the example of Figure 2, from year 1 to year 10).

In step S134, the precise optimization calculation unit 13 references the coefficient database 4 and obtains various coefficients corresponding to the year.

In step S135, the precise optimization calculation unit 13 refers to the table in Figure 10 and identifies the equipment configuration # corresponding to the scenario number and year.

In step S136, the precise optimization calculation unit 13 refers to the equipment configuration database 3 and recognizes the equipment configuration corresponding to the equipment configuration # recognized in step S135.

Step S137 loops by date. The date used here is the date recorded in the pre-statistics demand database 2. In other words, subsequent processing is performed based on the precise data recorded in the pre-statistics demand database 2.

In step S138, the precise optimization calculation unit 13 references the pre-statistics demand database 2 and extracts electricity demand data for the target date.

In step S139, the precise optimization calculation unit 13 optimizes the operation pattern of each piece of equipment. In other words, it optimizes Equation 7 by minimizing OPEX.

Here, OPEX(d) is the OPEX (¥) on date d, D(d, t) is the power consumption (kWh) on date d and time t, Ppv(n, d, t) is the amount of power generated by PV(n) on date d and time t (kWh), Pbt(m, d, t) is the amount of power charged and discharged by battery (m) on date d and time t (kWh) (where charging is positive and discharging is negative), UP(y) is the electricity rate (¥/kWh) for fiscal year y, and Pbt(m, d, t) is the optimization design variable. T is the final value of the time loop, N is the number of PVs (total of new and additional installations), and M is the number of storage batteries (total of new and additional installations). Note that constraints apply: the remaining power does not exceed the battery capacity and does not become negative.

In step S13a, the precise optimization calculation unit 13 calculates the amount of received power as a result of executing step S139. The amount of received power is calculated using equation 8. Here, Pbt(m, d, t) uses the value obtained by the optimization calculation in step S139. Furthermore, P(y, d, t) is the amount of received power (kWh) at time t on date d in year y. Below, descriptions of elements already mentioned will be omitted.

In step S13b, the precise optimization calculation unit 13 calculates the _CO2 emissions using Equation 9, where UC(y) is the _CO2 emission coefficient for fiscal year y (t- _CO2 /kWh), and _CO2 (y, d) is the _CO2 emissions for fiscal year y and date d (t- _CO2 /kWh).

In step S13c, the precise optimization calculation unit 13 calculates OPEX using equation 10, where OPEX(y, d) is the OPEX (¥) for fiscal year y and date d.

In step S13d, the precise optimization calculation unit 13 calculates the annual values of OPEX and _CO2 emissions. To calculate the annual values, both OPEX and _CO2 emissions can be integrated on date d using Equations 11 and 12.

In step S13e, the precise optimization calculation unit 13 uses Equations 13 and 14 to add up the annual OPEX value and the annual CO ₂ emissions value on an annual basis.

In step S13f, the precise optimization calculation unit 13 records the OPEX and _CO2 emissions calculated using equations 13 and 14 in the scenario-specific precise KPI database 14. Note that the data recorded in the scenario-specific precise KPI database 14 in this step is calculated based on the precise data recorded in the pre-statistics demand database 2, and is therefore closer to the true values than the data recorded in the scenario-specific summary KPI database 10, which was calculated based on the compressed data recorded in the post-statistics demand database 6.

<Detailed KPI Database by Scenario 14>
The scenario-specific refined KPI database 14 is a database in which the output results of the refined optimization calculation unit 13 are recorded. These results are displayed on the display device 300 via the output unit 11 described above.

Figure 17 shows an example of the data format of the scenario-specific refined KPI database 14. In this example, the following data are registered, starting from the leftmost column: "Scenario No.", "Cumulative OPEX," and "Cumulative _CO2 emissions." The values calculated in step S13f of Figure 16 are recorded in the "Cumulative OPEX" and "Cumulative _CO2 emissions" columns.

Now, let's return to Figure 13. The detailed evaluation graph 306 on the right side of this figure is drawn from the records in the scenario-specific detailed KPI database 14 in Figure 17. This detailed evaluation graph 307 is based on data before statistical processing, and therefore shows a more detailed evaluation that is closer to the true value than the general evaluation graph 306 on the left, which is based on data (such as average values) from which some information has been lost due to statistical processing. Therefore, by referring to the detailed evaluation graph 306, the operator can easily select an appropriate capital investment scenario that suits their needs.

Note that the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those having all of the described configurations.

100 Facility configuration proposal system 1 Input unit 2 Pre-statistics demand database 3 Facility configuration database 4 Coefficient database 5 Statistical processing unit 6 Post-statistics demand database 7 Rough optimization calculation unit 8 Facility configuration-specific KPI database 9 Scenario generation unit 10 Scenario-specific rough KPI database 11 Output unit 12 Scenario selection unit 13 Fine optimization calculation unit 14 Scenario-specific fine KPI database 200 Instruction input device 300 Display device

Claims (7)

A facility configuration proposal system for evaluating a plurality of facility investment scenarios,
a pre-statistics information database that records time-series information on energy consumption of consumers;
a statistical processing unit that groups the time-series information recorded in the pre-statistics information database based on a predetermined criterion and performs statistical processing for each group;
a post-statistics information database that records the time-series information statistically processed by the statistical processing unit;
an approximate optimum calculation unit that calculates a KPI based on the time-series information recorded in the post-statistics information database;
a scenario selection unit that selects some of the capital investment scenarios from the plurality of capital investment scenarios based on the KPIs evaluated by the rough optimization calculation unit;
a refined optimization calculation unit that calculates the KPI for the equipment investment scenario selected by the scenario selection unit based on the time-series information recorded in the pre-statistics information database;
An equipment configuration proposal system comprising: The facility configuration proposal system according to claim 1,
The facility configuration proposing system is characterized in that the statistical processing performed by the statistical processing unit for each group is one of average value calculation, MBR, multiple regression analysis, and exponential smoothing. The facility configuration proposal system according to claim 1,
The equipment configuration proposal system is characterized in that the scenario selection unit selects the part of the equipment investment scenarios based on the magnitude relationship of the KPI values of each equipment investment scenario calculated by the rough optimization calculation unit. The facility configuration proposal system according to claim 1,
The facility configuration proposal system is characterized in that the scenario selection unit selects a certain facility investment scenario when the number of facility investment scenarios with KPIs superior to the KPI of the certain facility investment scenario is equal to or less than a predetermined value. The facility configuration proposal system according to claim 1,
The facility configuration proposal system is characterized in that the KPI is OPEX or _CO2 emissions. The facility configuration proposal system according to claim 1,
The capital investment scenario is information defining a capital investment configuration for a single period, or
An equipment configuration proposal system characterized by information defining transitions of equipment configurations over multiple periods. 1. A computer-implemented method for proposing equipment configurations for evaluating multiple equipment investment scenarios, comprising:
recording time-series information of energy consumption of the consumer in a pre-statistics information database;
a step of grouping the time-series information recorded in the pre-statistics information database based on a predetermined criterion and performing statistical processing for each group;
a step of recording the statistically processed time series information in a post-statistical information database;
calculating KPIs based on the time-series information recorded in the post-statistics information database;
selecting a portion of the capital investment scenarios from the plurality of capital investment scenarios based on the KPI;
calculating the KPIs for the selected capital investment scenarios based on the time-series information recorded in the pre-statistics information database;
An equipment configuration proposing method comprising: