Facility configuration proposal system and facility configuration proposal method

Abstract

To formulate a facility investment scenario that contributes to KPI optimization within a practical time period.SOLUTION: A facility configuration proposal system evaluates a plurality of facility investment scenarios. The facility configuration proposal system comprises: an information-before-statistics database that records time-series information on energy consumption of consumers; a statistical processing section that groups the time-series information recorded in the information-before-statistics database on the basis of predetermined criteria, and statistically processes for each group; an information-after-statistics database that records the time-series information statistically processed by the statistical processing section; a rough optimum calculation section that calculates KPIs on the basis of the time-series information recorded in the information-after-statistics database; a scenario selection section that selects some facility investment scenarios from among a plurality of facility investment scenarios on the basis of KPIs that the rough optimum calculation section has evaluated; and a precise optimum calculation section that calculates KPIs for the facility investment scenarios selected by the scenario selection section on the basis of the time-series information recorded in the information-before-statistics database.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a facility configuration proposal system and a facility configuration proposal method for formulating a facility investment scenario for optimizing KPIs. [Background technology]

[0002] In order to mitigate the progress of global warming, decarbonization is being called for worldwide, 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 updating to such equipment. However, many users do not have sufficient knowledge about decarbonization technologies, and therefore it has been difficult for them to independently formulate capital investment plans that optimize their contribution to decarbonization within limited budgets.

[0003] Here, an equipment planning method described in Patent Document 1 is known as a conventional technique for using a computer to create an equipment investment plan. 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 for solving this problem, claim 1 discloses "an equipment planning method for a distributed energy system in which energy is supplied by a plurality of energy supply devices, the method determining an optimal combination of the plurality of 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 about the energy supply devices." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-125643 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology of Patent Document 1 has a problem in that it requires an excessively long computer processing time to optimize a capital investment plan. This is because, as is clear from the configuration of claim 1 of the document, the capital investment planning method of the document determines the optimal combination of multiple energy supply equipment and the timing of its introduction using the predicted energy demand, predicted fuel price for the energy supply equipment, and equipment information for the energy supply equipment as parameters for a predetermined future period, so if the "predetermined future period" is long, the processing time increases. Furthermore, if the variations in each of the "predicted energy demand," "predicted fuel price for the energy supply equipment," and "equipment information for the energy supply equipment" increase, a combination explosion occurs, and there is a possibility that the optimization process for the capital investment plan cannot be completed within a practical time frame.

[0006] Therefore, an object of the present invention is 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 time frame. [Means for solving the problem]

[0007] 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 of energy consumption by consumers; 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 a KPI 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 KPI evaluated by the rough optimization calculation unit; and a precise 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 database. [Effects of the Invention]

[0008] According to the facility configuration proposal system and facility configuration proposal method of the present invention, it is possible to formulate a facility investment scenario that contributes to KPI optimization within a practical time frame. [Brief explanation of the drawings]

[0009] [Figure 1] Overall configuration of the equipment configuration proposal system. [Figure 2] An example of an input screen image. [Figure 3] An example of the data format of the pre-statistical demand database. [Figure 4] An example of a data format for a facility configuration database. [Figure 5] 1 shows an example of a data format for a coefficient database. [Figure 6] Processing flow of the grouping part. [Figure 7] An example of the data format of the post-statistics demand database. [Figure 8] Processing flow of the rough optimum calculation part. [Figure 9]An example of the data format for a KPI database by facility configuration. [Figure 10] An example of a management format for input scenarios. [Figure 11] 10 is a processing flow of the scenario generation unit. [Figure 12] An example of the data format for the scenario-specific summary KPI database. [Figure 13] 10 is an example of a screen image of the output unit 11. [Figure 14] Processing flow of the scenario selection section. [Figure 15A] An image of the process for checking the number of included plots. [Figure 15B] An image of the process for checking the number of included plots. [Figure 16] Processing flow of the precise optimum calculation part. [Figure 17] An example of the data format for a scenario-specific refined KPI database. DETAILED DESCRIPTION OF THE INVENTION

[0010] An equipment configuration proposal system 100 according to an embodiment of the present invention will be described below with reference to the drawings.

[0011] <Overall configuration of facility configuration proposal system 100> FIG. 1 is a diagram showing the overall configuration of an equipment configuration proposal system 100 of this embodiment, and 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 each function described below, but the following explanation will omit such well-known techniques as appropriate.

[0012] As shown in Figure 1, the facility configuration proposal system 100 of this embodiment includes 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 shown 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. Below, the details of each unit that makes up the facility configuration proposal system 100 will be explained in order.

[0013] <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 the present system sets various setting values, and transmits the values ​​set by the operator to the statistical processing unit 5 and the like.

[0014] 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.

[0015] 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 FIG. 2, three grouping criteria are provided: "seasonal unit," "monthly unit," and "weekday / holiday unit," and an example is shown in which two of them, "monthly unit" and "weekday / holiday unit," have been selected.

[0016] 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 Figure 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.

[0017] The setting area 303 is an area where the operator sets the scenario to be input to the scenario generation unit 9. In this example, "10 years" is selected as the number of years for generating capital investment scenarios, and the following state is shown as scenario No. 1: equipment configuration #1 for the first to third years, equipment configuration #3 for the fourth and fifth years, equipment configuration #4 for the sixth to eighth years, and equipment configuration #2 for the ninth and tenth years. If the "Register" button is pressed in this state, the combination information of the set equipment configurations # is sent to the scenario generation unit 9 as scenario No. 1 and saved in the scenario database (FIG. 10) described later. 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 the capital investment scenario is not limited to a combination of equipment configurations # for multiple years, but may be a combination of equipment configurations # for a single year only.

[0018] 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.

[0019] <Pre-statistics Demand Database 2> The pre-statistics demand database 2 is a database that chronologically records the energy consumed over a certain period in the past by a consumer who uses this system to create a capital investment plan (hereinafter referred to as a "specific consumer").

[0020] 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 "Power Consumption." As an example, this shows a year's worth of power consumption data from April 1, 2022 to March 31, 2023, recorded in 30-minute increments.

[0021] <Facility Configuration Database 3> The facility configuration database 3 is a database that defines a combination of facilities operated by a specific customer.

[0022] Figure 4 is an example of the data format of the facility configuration database 3. In this example, data such as "facility configuration #," "photovoltaic power generation (first unit)," "photovoltaic power generation (second unit)," "storage battery (first unit)," and "storage battery (second unit)" are registered in order from the leftmost column.

[0023] The "equipment configuration #" in this figure 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.

[0024] 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 setting up facility configuration #1: 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 setting up facility configuration #3: 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 battery storage 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 battery storage system will continue to operate. (4) Years 9 and 10 after configuration #2 is set up: In year 9, the existing solar power generation system and battery storage system will continue to operate, and a second battery storage system unit will be added and put into operation. In year 10, the existing solar power generation system and battery storage system will continue to operate.

[0025] <Coefficient Database 4> The coefficient database 4 is a database in which various set values ​​such as the electricity rate unit price and the CO2 emission coefficient are recorded by fiscal year.

[0026] Figure 5 is an example of the data format of the coefficient database 4. In this example, the columns from the left are "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.

[0027] <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.

[0028] FIG. 6 shows the processing flow of the statistical processing unit 5.

[0029] 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 Fig. 2, "monthly" and "weekday / holiday" are selected, so the statistical processing unit 5 recognizes that the data in the pre-statistics demand database 2 should be grouped into groups such as "April and weekday," "April and holiday," "May and weekday," and "May and holiday."

[0030] Step S52 is a loop function for grouping criterion variations. Based on the criterion recognized in step S51, the following steps S53 to S55 are repeatedly executed in the order of, for example, "April and weekday," "April and holiday," "May and weekday," "May and holiday," and so on.

[0031] 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 weekday" is extracted from the database of FIG.

[0032] Step S54 is a time loop function. Since power consumption is registered in 30-minute increments in the pre-statistics demand database 2 of Fig. 3, step S55 (described later) is executed in the order of, for example, 00:00, 00:30, 01:00, 01:30, ...

[0033] 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 and weekday" and "00:00 to 00:29." Note that although average value calculation is given 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.

[0034] In step S56, the statistical processing unit 5 records the above processing results in the post-statistics demand database 6.

[0035] <Post-Statistical 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.

[0036] Fig. 7 shows an example of the data format of the post-statistics demand database 6. In this example, data for "season," "month," "weekday / holiday," "time," and "power consumption" after statistical processing are registered, starting from the leftmost column. Note that "seasonal unit" is not selected in the setting area 301 of Fig. 2, so the "season" column in Fig. 7 is marked "N / A."

[0037] <Rough Optimal 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 based on the KPIs set by the operator for each statistical energy consumption waveform recorded in the post-statistics demand database 6, 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.

[0038] FIG. 8 shows the processing flow of the rough optimum calculation unit 7.

[0039] First, in step S71, the rough optimization calculation unit 7 recognizes the optimization element. Specifically, it checks the setting area 302 in Fig. 2 and confirms that the KPI is minimization of OPEX.

[0040] In step S72, the rough optimum calculation unit 7 recognizes the grouping criteria. Specifically, it checks the setting area 301 in Fig. 2 and confirms that the grouping criteria are "monthly" and "weekday / holiday."

[0041] 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 is performed from the first year to the tenth year in order.

[0042] In step S74, the approximate optimum calculation unit 7 acquires a set of relevant data from the coefficient database 4.

[0043] Step S75 is a loop for the equipment configuration #. Here, the rough optimization calculation unit 7 performs a loop process for all equipment configurations # recorded in the equipment configuration database 3.

[0044] 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.

[0045] Step S77 is a loop for grouping criteria variations. Based on the information recognized in step S72, the following steps S78 to S7c are repeatedly executed in the order of "April and weekday," "April and holiday," "May and weekday," "May and holiday," etc.

[0046] In step S78, the rough optimum calculation unit 7 extracts data corresponding to the setting of the loop in step S77 from the post-statistics demand database 6.

[0047] In step S79, the rough optimization calculation unit 7 optimizes the operation plan for the equipment of the target equipment configuration # based on the KPI acquired in step S71. That is, it optimizes Equation 1 by minimizing OPEX.

[0048]

number

[0049] 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 storage (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 systems (PV) (total of new and additional installations), and M is the number of battery storage systems (BT) (total of new and additional installations). Note that constraints are considered: the remaining energy does not exceed the battery capacity and does not become negative.

[0050] 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 by the optimum calculation in step S79. Also, P(y, g, t) is the amount of received power (kWh) in year y, group g, and time t. Hereafter, descriptions of elements already mentioned will be omitted.

[0051]

number

[0052] In step S7b, the rough optimization calculation unit 7 calculates the CO2 emissions using Equation 3, where UC(y) is the CO2 emission coefficient (t-CO2 / kWh) for year y, and CO2(y, g) is the CO2 emissions (t-CO2 / kWh) for year y and group g.

[0053]

number

[0054] In step S7c, the rough optimization calculation unit 7 calculates OPEX using Equation 4. Here, OPEX(y, g) is the OPEX (yen) for the year y and group g.

[0055]

number

[0056] 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.

[0057]

number

[0058]

number

[0059] In step S7e, the rough optimization calculation unit 7 records the results calculated by the formulas 5 and 6 for each fiscal year in the facility configuration-specific KPI database 8.

[0060] <KPI database by facility configuration 8> Figure 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.

[0061] <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.

[0062] Figure 10 shows an example of a management format for capital investment scenarios entered by an operator. Here, the equipment configuration numbers for the first to tenth years 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.

[0063] FIG. 11 shows a processing flow of the KPI calculation by the scenario generation unit 9.

[0064] In step S91, the scenario generating unit 9 refers to the table in FIG. 10 and performs loop processing by scenario number.

[0065] In step S92, a loop is performed by year, in this case from the first year to the tenth year.

[0066] In step S93, the scenario generating unit 9 recognizes the equipment configuration #. The equipment configuration # is identified by the scenario No. and year with reference to the table in FIG.

[0067] 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.

[0068] In step S95, the scenario generating unit 9 accumulates the values ​​in step S94 for a predetermined number of years (here, 10 years).

[0069] In step S96, the scenario generating unit 9 records the calculation results (cumulative OPEX, cumulative CO 2 emissions) in step S95 in the scenario-specific summary KPI database 10.

[0070] <10 KPI databases by scenario> The scenario-specific summary KPI database 10 is a database in which the output results of the scenario generation unit 9 are stored.

[0071] Fig. 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 values ​​calculated in step S95 of Fig. 11 are recorded in the "Cumulative OPEX" and "Cumulative CO2 Emissions" columns.

[0072] <Output section 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.

[0073] Figure 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.

[0074] <Scenario Selection Section 12> The scenario selection unit 12 is a functional unit that selects, based on the operator's settings, from the capital investment scenarios handled by the above-mentioned general evaluation unit, capital investment scenarios to be used in the detailed evaluation of the detailed optimization calculation unit 13 and the scenario-specific detailed KPI database 14.

[0075] FIG. 14 shows the processing flow of the scenario selection unit 12.

[0076] First, in step S121, the scenario selection unit 12 recognizes the allowable number of plots set in the setting area 304. In Fig. 2, "1" is set as the allowable value.

[0077] In step S122, the scenario selection unit 12 refers to the scenario-specific outline KPI database 10 and checks the number of capital investment scenarios that have been outline-evaluated by the outline evaluation unit ("13" in the example of FIG. 12).

[0078] In step S123, a loop is performed using the scenario number.

[0079] In step S124, the scenario selection unit 12 checks the number of included plots for each capital investment scenario in the outline evaluation graph 305. A specific image of the processing in this step will be explained using Figs. 15A and 15B.

[0080] FIG. 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 number of plots within the rectangle that corresponds to the number of scenarios superior to scenario No. 7 is only 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."

[0081] 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 superior scenarios compared 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 corresponding to the number of scenarios superior to scenario No. 11 is four, namely scenarios Nos. 3, 4, 5, and 7, so the scenario selection unit 12 determines that the number of plots included in scenario No. 11 is "4."

[0082] In step S125, the scenario selection unit 12 makes a branching decision 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; scenario numbers with an included plot number of "2" or more do not proceed to step S126.

[0083] 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 FIG. 15A is recorded in memory because the number of included plots is "1," while scenario No. 11 in FIG. 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 FIG. 13 and recorded in memory.

[0084] <Precise optimal calculation section 13> The precise optimization calculation unit 13 is a functional unit that uses data from the pre-statistics 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.

[0085] FIG. 16 shows the processing flow of the precise optimization calculation unit 13.

[0086] In step S131, the precise optimization calculation unit 13 recognizes the optimization elements. The processing content of this step is the same as that of step S71.

[0087] In step S132, the precise optimization calculation unit 13 performs loop processing in the order of the selected scenario numbers stored in the memory in step S126 of FIG.

[0088] Step S133 loops through the years of the set number of years of the capital investment scenario (from the first year to the tenth year in the example of FIG. 2).

[0089] In step S134, the precise optimization calculation unit 13 refers to the coefficient database 4 and acquires various coefficients corresponding to the year.

[0090] In step S135, the precise optimization calculation unit 13 refers to the table in FIG. 10 and recognizes the facility configuration # corresponding to the scenario No. and year.

[0091] In step S136, the precise optimization calculation unit 13 refers to the equipment configuration database 3 and recognizes an equipment configuration corresponding to the equipment configuration # recognized in step S135.

[0092] Step S137 loops by date. The date used here is the date recorded in the pre-statistics demand database 2. That is, the subsequent processing is performed based on the detailed data recorded in the pre-statistics demand database 2.

[0093] In step S138, the precise optimization calculation unit 13 refers to the pre-statistics demand database 2 and extracts the power demand data for the target date.

[0094] In step S139, the precise optimization calculation unit 13 optimizes the operation pattern of each piece of equipment, that is, optimizes Equation 7 by minimizing OPEX.

[0095]

number

[0096] 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 power generation (kWh) of PV(n) on date d and time t, Pbt(m, d, t) is the charge / discharge power (kWh) of storage battery (m) on date d and time t (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 PV units (new and additional), and M is the number of storage batteries (new and additional). Note that constraints apply: the remaining energy must not exceed the battery capacity and must not become negative.

[0097] 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. Also, P(y, d, t) is the amount of received power (kWh) at time t on date d in year y. Hereafter, descriptions of elements already mentioned will be omitted.

[0098]

number

[0099] In step S13b, the precise optimization calculation unit 13 calculates the CO2 emissions using Equation 9, where UC(y) is the CO2 emission coefficient (t-CO2 / kWh) for the year y, and CO2(y, d) is the CO2 emissions (t-CO2 / kWh) for the year y and date d.

[0100]

number

[0101] In step S13c, the precise optimization calculation unit 13 calculates OPEX using equation 10. Here, OPEX(y, d) is the OPEX (yen) for the fiscal year y and the date d.

[0102]

number

[0103] 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, and are calculated using Equations 11 and 12.

[0104]

number

[0105]

number

[0106] In step S13e, the precise optimization calculation unit 13 uses Equations 13 and 14 to add up the annual OPEX value and the annual CO2 emission value on an annual basis.

[0107]

number

[0108]

number

[0109] In step S13f, the refined optimization calculation unit 13 records the OPEX and CO2 emissions calculated using equations 13 and 14 in the scenario-specific refined KPI database 14. Note that the data recorded in the scenario-specific refined KPI database 14 in this step is calculated based on the refined 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 outline KPI database 10, which is calculated based on the compressed data recorded in the post-statistics demand database 6.

[0110] <14 Scenario-Specific KPI Databases> 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.

[0111] Figure 17 shows an example of the data format of the scenario-specific detailed 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.

[0112] Now, let's return to Figure 13. The detailed evaluation graph 306 on the right side of this figure is drawn based on 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.

[0113] It should be noted that the present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to an embodiment having all of the described configurations. [Explanation of symbols]

[0114] 100 Facility configuration proposal system 1 Input section 2 Pre-statistical demand database 3. Facility configuration database 4 Coefficient database 5 Statistical Processing Unit 6 Post-statistics demand database 7. Approximate Optimal Calculation Section 8 KPI database by facility configuration 9 Scenario Generation Section 10 Scenario-specific KPI database 11 Output section 12 Scenario Selection Department 13 Fine optimization calculation section 14 Detailed KPI database by scenario 200 Instruction input device 300 display device

Claims

1. 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:

2. 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.

3. 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.

4. 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.

5. The facility configuration proposal system according to claim 1, The KPI is OPEX or CO 2 A facility configuration proposal system characterized by the emission amount.

6. 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.

7. A computer-implemented method for evaluating multiple capital investment scenarios for equipment configuration proposals, 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:

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