JP2006011769A - Information processing method and apparatus for evaluating effect of investment in power generation equipment - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To evaluate an appropriate effect of an investment in power generation equipment. <P>SOLUTION: An information processing method for evaluating an effect of an investment in power generation equipment includes a step of computing an effect of an investment in power generation equipment, according to a predicted value factoring in the investment and an estimated value factoring out the investment both of an index representing an effect of investment in the power generation equipment and varying with the age, as a time shift of the index, and storing the shift time data in an investment effect data storage part, and a step of calculating a difference for an estimate year between a predicted value factoring in the investment and a predicted value factoring out the investment both of maintenance costs, from regression coefficients of a logarithmic regression curve representing time changes in maintenance costs of model equipment stored in a maintenance cost model curve data storage part, a ratio of maintenance costs of the power generation equipment at a specific time point to the value of the logarithmic regression curve at the specific time point, the time data stored in the investment effect data storage part, and time data for the estimate year, and storing the difference in a maintenance cost investment effect data storage part. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an information processing technique for evaluating the effect of investment in power generation equipment.

  For example, Japanese Patent Laid-Open No. 2002-328989 discloses a technique for determining a repair / repair budget allocation plan that reflects a policy regarding owner facility maintenance. That is, an importance table for storing the importance for each evaluation item set according to the policy of the facility owner, and a priority determination table for storing the degree of deterioration of each repair / repair target location of each facility are provided. For each location, the priority of repair / repair is calculated based on the importance stored in the importance table and the deterioration stored in the priority determination table. In addition, a flag indicating the repair / repair cost and budget tentative allocation of each target location is recorded in the priority determination table, and after execution of the current status and tentative allocation budget based on the reacquired current price and budget tentative allocation setting for each facility The FCI index values are calculated and compared.

Japanese Patent Application Laid-Open No. 2003-284260 discloses a power generation facility renovation and renewal support technology that can reduce the burden caused by capital investment at the time of renovation or renewal of power generation facilities. That is, a power generation state monitoring device provided in the power generation facility for monitoring data related to the power generation state of the power generation facility, and generated power generated by renovation or update of the power generation facility based on the data regarding the power generation state received via the communication line And a generated power increase amount calculating means for calculating the increase amount. As a result, it is possible to calculate the amount of revenue increased by renovating or renewing the power generation facility based on the calculated increase in generated power and the selling price, and this increased revenue can be used to pay for the renovation or renewal of the power generation facility. Therefore, it is said that the burden due to initial capital investment at the time of renovation or renewal of power generation facilities can be reduced.
JP 2002-328989 A JP 2003-284260 A

  In the patent documents mentioned above, the cost of renovation or renewal and the amount of revenue increase due to an increase in generated power are considered, but the effect of capital investment cannot be evaluated appropriately only by increasing the amount of investment or generated power. .

  Accordingly, an object of the present invention is to provide an information processing technique for appropriately evaluating the effect of investment in power generation facilities.

  The information processing method for evaluating the effect of investment in power generation equipment according to the first aspect of the present invention is the implementation of the investment that represents the effect of investment in power generation equipment and that changes over time (for example, thermal efficiency in the embodiment). Based on the later planned value and the estimated value when the investment is not made, the investment effect on the power generation facility is specified as the rewinding time (for example, d in the embodiment) for the index, and the rewinding amount The time data in the investment effect data storage unit, the regression coefficient of the logarithmic regression curve representing the change over time of the maintenance cost of the model equipment stored in the maintenance cost model curve data storage unit, and the specific point in time of the power generation equipment (For example, the ratio between the maintenance cost in the current year (when the process is executed) in the embodiment and the value of the logarithmic regression curve at a specific time) and the investment effect data storage Using the estimated time data and estimated time (e.g., evaluation year in the embodiment), the estimated maintenance cost after the investment and the expected maintenance cost when the investment is not made. Calculating a difference in the estimated year, and storing the difference in the maintenance cost investment effect data storage unit.

  In this way, according to the viewpoint that the inventor of the present invention has contrived a non-trivial idea that the maintenance cost increases logarithmically from the start of operation of the facility under consideration, it is further specified as one of the effects of investment in power generation facilities. Reflecting the amount of time that is rewinded, it becomes possible to grasp the maintenance cost that changes due to the investment in the power generation equipment. That is, it becomes possible to evaluate the increase or decrease in maintenance costs due to capital investment.

  Moreover, in the first aspect of the present invention, the value of the initial output power of the power generation facility stored in the input data storage unit, and the estimated value of the output power of the power generation facility in the estimated year after the investment in the power generation facility is performed. Based on the difference, the influence of the investment on the power generation facility on the power system may be calculated as a monetary amount, and a first statistical influence monetary amount calculating step may be further included. For example, even when a capital investment is made for one power generation facility, a power generation company having a plurality of power generation facilities comprehensively considers the effect of the capital investment unless the impact on the power system is taken into consideration. There is a risk that the investment will be partially optimized rather than evaluated. Therefore, based on the value of the initial output power of the power generation facility, the increase and decrease of the cost and the increase or decrease of the income according to the increase and decrease of the output power are considered.

  Furthermore, in the first aspect of the present invention, the estimated value of the output power of the power generation facility in the estimated year after the investment in the power generation facility is estimated and the estimation of the output power of the power generation facility in the estimated year when no investment is made in the power generation facility. The value is used to calculate the difference in power sales revenue as the investment effect on the power generation facility, and is stored using the step stored in the power sales revenue investment effect data storage unit and the data stored in the investment effect data storage unit. The estimated value of the thermal efficiency of the power generation facility in the estimated year after the investment in the power generation facility, the estimated value of the thermal efficiency of the power generation facility in the estimated year when the investment in the power generation facility is not performed, and the The estimated value of the output voltage of the power generation facility in the estimated year and the estimated value of the output power of the power generation facility in the estimated year when no investment was made in the power generation facility And calculating the difference in fuel cost as the investment effect on the power generation facility and storing it in the fuel cost investment effect data storage unit, the investment amount and discount rate for the power generation facility stored in the input data storage unit, and the maintenance cost investment Data stored in the effect data storage unit, data stored in the system impact amount data storage unit, data stored in the power sale revenue investment effect data storage unit, and data stored in the fuel cost investment effect data storage unit The method may further include calculating a cash flow related to the investment effect on the power generation facility in the estimated year using the data, and storing the cash flow in a cash flow data storage unit.

  As described above, by calculating the cash flow in consideration of the increase / decrease in the power sales revenue and the increase / decrease in the fuel cost, it becomes possible to appropriately evaluate the effect of the investment in the power generation facility.

  Further, in the first aspect of the present invention, the regression coefficient of the logarithmic regression curve representing the change over time of the maintenance cost of the model equipment stored in the maintenance cost model curve data storage unit and the new power generation equipment as an investment in the power generation equipment Based on the initial output power value at the time of replacement, the ratio of the estimated maintenance cost considering inflation and the value of the logarithmic regression curve at the time of replacement to the new power generation facility was used to replace the new power generation facility. A step of calculating a second maintenance cost in a later estimated year and storing it in the maintenance cost investment effect data storage unit may be further included.

  The power generation facility is connected to a power transmission facility having a longer life than the power generation facility, and when the old power generation facility reaches the end of its life, the old power generation facility is often replaced. In the present invention, the replacement of the power generation equipment is also considered, and the maintenance cost in the case of the replacement is conceived by the inventor of the present invention in a non-obvious manner, and the maintenance cost increases logarithmically from the start of operation. It becomes possible to calculate.

  In the first aspect of the present invention, the difference between the initial output power value of the power generation facility stored in the input data storage unit and the estimated value of the output power of the new power generation facility when the new power generation facility is replaced. And calculating the influence of the replacement investment to the new power generation facility on the power system in terms of amount, and further including a second system influence amount calculation step of storing in the system influence amount data storage unit as second system influence amount data. Also good. Even in the case of replacement of a power generation facility, it is possible to evaluate a more appropriate investment effect by considering the influence on the power system.

  Furthermore, in the first aspect of the present invention, the estimated value of the output power of the new power generation facility in the estimated year after the investment for replacement of the new power generation facility is used to obtain the second sales as an effect of the investment for replacement of the new power generation facility. Steps to calculate the electricity revenue and store it in the electricity sales income investment effect data storage unit, the estimated value of thermal efficiency of the new power generation facility in the estimated year after the replacement investment to the new power generation facility, and the replacement investment to the new power generation facility Calculating the second fuel cost as an effect of the replacement investment in the new power generation facility using the estimated value of the output power of the new power generation facility in the later estimated year, and storing the second fuel cost in the fuel cost investment effect data storage unit; , The replacement investment amount and discount rate for the new power generation equipment stored in the input data storage unit, the second maintenance cost data stored in the maintenance cost investment effect data storage unit, and the system impact amount data The second grid influence amount data stored in the data storage unit, the second power sale income data stored in the power sale income investment effect data storage unit, and the second data stored in the fuel cost investment effect data storage unit And calculating the cash flow related to the investment effect on the power generation facility in the estimated year using the fuel cost data of 2 according to a predetermined rule, and storing the cash flow data in the cash flow data storage unit.

  As described above, even in the case of replacement, by calculating the cash flow in consideration of the increase / decrease in the power sales revenue and the increase / decrease in the fuel cost, it becomes possible to appropriately evaluate the effect of the investment in the power generation facility.

  Furthermore, in the first aspect of the present invention, a cash flow rebate process for a specific year stored in the cash flow data storage unit is performed at a discount rate stored in the input data storage unit, and the processing result is stored as present value data. You may make it further contain the step stored in a part. If the present value is obtained by rebating the cash flow at a predetermined discount rate, the processing results can be appropriately compared.

  Further, in the first aspect of the present invention, when the scheduled operation end time of the new power generation facility reaches the end of the evaluation period of the investment effect on the power generation facility, the new power generation facility after the replacement investment to the new power generation facility is implemented. The cash flow after the rebate in the scheduled operation period is read from the present value data storage unit, and the equivalent annual economic value (for example, EAV in the embodiment) is calculated using the read data, and evaluated from the scheduled end time of the new power generation facility You may make it further contain the step stored in the present value data storage part as the cash flow after rebating until the period end. For example, if the repair and replacement times can be specified arbitrarily, the power generation equipment after replacement may reach the end of its life early during the evaluation period, or the power generation equipment after replacement may end at the end of the evaluation period. Can occur. When comparing these cases, the latter cash flow often increases. Therefore, as described above, if the equivalent economic value is calculated and treated as the cash flow after the rebate from the scheduled end of operation of the new power generation facility to the end of the evaluation period, the comparison between cases can be appropriately performed. Will be able to do.

  Furthermore, in the first aspect of the present invention, the data indicating the timing of renovation to the existing power generation facility, which is an investment in the power generation facility, the effect of the renovation (for example, the planned value of output power and thermal efficiency), and the investment in the power generation facility The above input case further includes a step of prompting the input of case data including data indicating the time of replacement to a new power generation facility and the effect of replacement to the new power generation facility (for example, output power and estimated value of thermal efficiency). Each of the above steps is executed according to the data, and further includes the step of displaying the cash flow data after rebate corresponding to the case data on the display device using the data stored in the present value data storage unit. You may make it. If processing is performed for a plurality of cases in this way, it becomes possible to evaluate the superiority or inferiority of the capital investment pattern depending on the time of repair or the time of replacement.

  In the first aspect of the present invention, in the first or second system influence amount calculation step, the power generation facility or the new power generation facility is divided into a case where it is a base plant and a case where it is an ancillary plant. You may make it calculate the influence which it has on a system | strain with an amount of money. This is because the base plant and the ancillary plant have different roles, costs, and revenues.

  The information processing method according to the second aspect of the present invention includes an initial output power value and inflation rate stored in an input data storage unit and replaced with a new power generation facility, and a maintenance cost model curve data storage unit. Based on the value of the initial output power when replaced with a new power generation facility using the regression coefficient of the logarithmic regression curve representing the time-dependent change in the maintenance cost of the model facility stored in Calculate the ratio between the maintenance cost and the value of the logarithmic regression curve at the time of replacement with the new power generation facility, store it in the storage device, and store the ratio data stored in the storage device and the maintenance cost model curve data storage unit Using the regression coefficient of the logarithmic regression curve, the maintenance cost for the estimated year after replacement with the new power generation facility is calculated and stored in the maintenance cost investment effect data storage unit And a step.

  For example, in some cases, the replacement is performed without repairing the power generation equipment. In this case, the inventor of the present invention conceived non-obviously, replacing the maintenance cost in a logarithmic function from the start of operation. The effect of can be evaluated.

  Further, in the second aspect of the present invention, the difference between the initial output power value of the power generation facility stored in the input data storage unit and the estimated value of the output power of the new power generation facility when the new power generation facility is replaced. On the basis of the above, it may be possible to further include a system influence amount calculation step of calculating the influence of the replacement investment to the new power generation facility on the electric power system by the amount and storing it in the system influence amount data storage unit. Considering the impact on the power system, it is possible to avoid partial optimization of capital investment.

  It is also possible to create a program for causing a computer to execute the method according to the present invention, and the program is a storage medium such as a flexible disk, a CD-ROM, a magneto-optical disk, a semiconductor memory, and a hard disk. Alternatively, it is stored in a storage device. The program or the like may be distributed as a digital signal via a network. Further, the data being processed is temporarily stored in a storage device such as a main memory of the computer.

  According to the present invention, it is possible to appropriately evaluate the effect of investment in power generation equipment.

  The outline of the embodiment of the present invention will be described with reference to FIGS. 1 (a) and 1 (b). In the graphs shown in FIGS. 1A and 1B, the horizontal axis represents time [year], and the vertical axis represents cash flow. For example, as shown in FIG. 1 (a), the investment in the power generation facility includes a pattern in which the power generation facility is temporarily repaired and then replaced with a new power generation facility, and the new power generation facility is used up to its economical facility life. As shown in (b), there is a pattern in which an existing power generation facility is used up to its economic equipment life without renovation and then replaced with a new power generation equipment, and the new power generation equipment is used up to its economic equipment life. is there.

  More specifically, as shown in FIG. 1 (a), the cash flow curve a1 of the existing power generation facility is lowered, and the cash flow curve a1 is temporarily repaired at the timing T1 before reaching the economic facility life timing T2. The flow is changed to the curve b1, and the economical facility life of the existing power generation facility that has been repaired is extended to timing T3. The existing power generation facility is replaced with a new power generation facility at timing T3. The cash flow after replacement follows the curve c1. And about new power generation equipment, operation is stopped at timing T4 which is the economical equipment life of new power generation equipment. Further, as shown in FIG. 1B, in this pattern, the existing power generation facility is used until its economical facility life (timing T2) is reached. At that time, the cash flow decreases according to the curve a1. At time T2, the new power generation facility is replaced. The cash flow after replacement follows the curve c2. And about new power generation equipment, the operation is stopped at timing T6 which is the economical equipment life of new power generation equipment. The evaluation period is from timing T0 to T5.

  Since the repair timing and replacement timing differ depending on the setting, the economic facility life of the new power generation facility also varies depending on the setting. For example, the timing T4 which is the economical equipment life of the new power generation facility in FIG. 1A is different from the timing T6 which is the economic equipment life of the new power generation equipment in FIG. Generally, since the cash flow increases when the economic facility life of the new power generation facility is close to or exceeds the evaluation end timing T5, the net present value (NPV: Comparing each setting with Net Present Value), the slower the economic equipment life of the new power generation equipment, the more advantageous. In the present embodiment, in order to avoid such a phenomenon, when the economical equipment life of the new power generation facility is before the evaluation end timing T5, the evaluation end timing T5 is calculated from the economical equipment life of the new power generation equipment. Equivalent Annual Value calculated from the cash flow generated by the new power generation facilities will be allocated in the period up to

  Therefore, in order to evaluate the effect of investment on power generation facilities, the pattern as shown in FIG. 1 (a) depends on the present value of cash flow (area A) increased by renovation at timing T1 and the new power generation facilities. The sum of the present value of the resulting cash flow (area B) and the equivalent annual economic value (area C) assigned from the timing T4 to the timing T5 of the end of evaluation is calculated as NPV. Note that the cash flow in the area surrounded by the curve a1, the vertical axis, and the horizontal axis occurs without making capital investment, and is ignored in this embodiment as a sunk cost. For the pattern as shown in FIG. 1B, the present value of the cash flow (area D) provided by the new power generation facility from timing T2 to timing T6, and the equivalent allocated from timing T6 to timing T5 of the end of evaluation. The sum of annual economic value (area E) is calculated as NPV. Then, by comparing the NPV in the case of FIG. 1 (a) with the NPV in the case of FIG. 1 (b), the presence / absence of repair, timing and scale, the scale of the new power generation facility, and the like are evaluated.

  In some cases, a pattern in which equipment is discarded without renovation and replacement can be considered. Furthermore, since it is difficult to determine an economical equipment life, it may be set in advance.

  FIG. 2 shows a functional block diagram of a facility investment evaluation apparatus according to an embodiment of the present invention for performing the evaluation as described above. The equipment investment evaluation apparatus 10 performs processing described in detail below using the input unit 1, the input data storage unit 3 that stores data input by the input unit 1, and the data stored in the input data storage unit 3. It has the processing part 5 to implement, and the output part 7 which outputs the process result of the processing part 5 to output devices, such as a display apparatus. Although the equipment investment evaluation device 10 is assumed to be a stand-alone device below, the equipment investment evaluation device 10 is implemented as a web server, for example, and performs the equipment investment evaluation processing described below according to instructions from a plurality of terminals. You may make it implement.

  The data input by the input unit 1 and stored in the input data storage unit 3 includes the repair cost, the extended equipment life year, the output after the repair, the thermal efficiency after the repair, the planned repair year, the power generation stoppage time due to the repair work, and the evaluation years , Scenario, current year, plant type, operation start year, planned output before renovation, current output, planned thermal efficiency before renovation, current thermal efficiency, capacity-based maintenance costs, average utilization rate, unit price of electricity sales, output compensation cost for ancillary plant Unit price, unit cost of alternative supply cost, fuel cost, inflation rate, discount rate, new equipment price, new unit plan output, new unit plan thermal efficiency, replacement days, new unit fixed cost, residual value rate, ancillary plant These are the average fuel unit price, the grid average power selling unit price, and the number of years of new unit operation.

  Next, functional block diagrams of the processing unit 5 and the output unit 7 will be described with reference to FIGS. FIG. 3 shows a part of the preprocessing unit in the processing unit 5. As shown in FIG. 3, the time parameter setting unit 501 starts operation with the planned repair year data storage unit 301, the extended equipment life year data storage unit 302, and the current year data storage unit 303 included in the input data storage unit 3. The following process is performed with reference to the year data storage unit 304, the evaluation year data storage unit 305, the scenario data storage unit 334, and the new unit operation year data storage unit 333, and the time parameter as the processing result is set as the time parameter data. The data is stored in the storage unit 502.

  FIG. 4 shows a part of the preprocessing unit in the processing unit 5. As shown in FIG. 4, the thermal efficiency function determination processing unit 503 refers to the current thermal efficiency data storage unit 306, the pre-repair planned thermal efficiency data storage unit 307, and the time parameter data storage unit 502, and performs regression in a logarithmic regression curve of thermal efficiency. The coefficient is calculated and stored in the thermal efficiency function parameter data storage unit 504. The modification effect calculation unit 505 refers to the thermal efficiency function parameter data storage unit 504, the modified thermal efficiency data storage unit 308, and the time parameter data storage unit 502, generates data related to the modification effect, and the modification effect data storage unit 506. It is supposed to be stored in. The first thermal efficiency calculation unit 507 refers to the post-repair thermal efficiency data storage unit 308, the thermal efficiency function parameter data storage unit 504, the time parameter data storage unit 502, and the retrofit effect data storage unit 506, and first and second thermal efficiencies. The first thermal efficiency is stored in the first thermal efficiency data storage unit 509, and the second thermal efficiency is stored in the second thermal efficiency data storage unit 510. The second thermal efficiency calculation unit 508 refers to the thermal efficiency function parameter data storage unit 504, the time parameter data storage unit 502, and the new unit plan thermal efficiency data storage unit 309, calculates the third thermal efficiency, and stores the third thermal efficiency data storage The data is stored in the part 511.

  FIG. 5 shows a part of the preprocessing unit in the processing unit 5. As shown in FIG. 5, the output function determination processing unit 512 refers to the current output data storage unit 310, the pre-repair plan output data storage unit 311, and the time parameter data storage unit 502, and performs regression in the logarithmic regression curve of the output. A coefficient is calculated and stored in the output function parameter data storage unit 513. The first output calculation unit 514 refers to the output function parameter data storage unit 513, the time parameter data storage unit 502, the post-repair output data storage unit 312 and the renovation effect data storage unit 506, and outputs the first and second outputs. , And the first output is stored in the first output data storage unit 516 and the second output is stored in the second output data storage unit 517. The second output calculation unit 515 refers to the time parameter data storage unit 502, the new unit plan output data storage unit 313, and the output function parameter data storage unit 513, calculates the third output, and stores the third output data. This is stored in the part 518.

  FIG. 6 shows a part of the preprocessing unit in the processing unit 5. As shown in FIG. 6, the output decrease calculation unit 519 calculates the output decrease with reference to the pre-repair plan output data storage unit 311, the first output data storage unit 516, and the third output data storage unit 518. The output decrease data storage unit 520 stores the data.

  FIG. 7 shows a part of the preprocessing unit in the processing unit 5. As shown in FIG. 7, the power generation time calculation unit 521 calculates the power generation time with reference to the power generation stop time data storage unit 314, the average utilization rate data storage unit 315, and the replacement day data storage unit 316 due to the renovation work. The power generation time data storage unit 522 stores the power generation time data.

  FIG. 8 shows a part of main processing units in the processing unit 5. As shown in FIG. 8, the difference calculation unit 523 refers to the first output data storage unit 516 and the second output data storage unit 517 to calculate the difference between the first output and the second output, The data is stored in the output difference data storage unit 556. The power sale revenue calculation unit 524 refers to the output difference data storage unit 556, the power generation time data storage unit 522, the power sale unit price data storage unit 317, the time parameter data storage unit 502, and the third output data storage unit 518, The power sales revenue related to the capital investment is calculated and stored in the power sales revenue data storage unit 525.

  FIG. 9 shows a part of main processing units in the processing unit 5. As shown in FIG. 9, the first maintenance cost calculation unit 527 includes a maintenance cost function parameter data storage unit 526, a capacity-based maintenance cost data storage unit 318, a repair effect data storage unit 506, a current output data storage unit 310, and a time. The first and second maintenance costs are calculated by referring to the parameter data storage unit 502, the first maintenance cost is stored in the first maintenance cost data storage unit 528, and the second maintenance cost is stored in the second maintenance cost data. The data is stored in the part 529. The difference calculation unit 530 refers to the first maintenance cost data storage unit 528 and the second maintenance cost data storage unit 529, calculates the difference between the first maintenance cost and the second maintenance cost, and performs the third maintenance cost It is stored in the expense data storage unit 531. The second maintenance cost calculation unit 532 includes a time parameter data storage unit 502, a maintenance cost function parameter data storage unit 526, a capacity-based maintenance cost data storage unit 318, a new unit plan output data storage unit 313, and an inflation rate data storage. The third maintenance cost is calculated with reference to the unit 320 and stored in the third maintenance cost data storage unit 531.

  FIG. 10 shows a part of main processing units in the processing unit 5. As shown in FIG. 10, the fuel cost calculation unit 533 includes a first output data storage unit 516, a second output data storage unit 517, a third output data storage unit 518, a first thermal efficiency data storage unit 509, and a second thermal efficiency data. By referring to the storage unit 510, the third thermal efficiency data storage unit 511, the power generation time data storage unit 522, and the fuel unit price data storage unit 321, the fuel cost related to the capital investment is calculated and stored in the fuel cost data storage unit 534. It is like that.

  FIG. 11 shows a part of main processing units in the processing unit 5. As shown in FIG. 11, the base plant output compensation cost calculation unit 535 refers to the power generation time data storage unit 522, the output decrease data storage unit 520, and the ancillary plant output compensation cost unit price data storage unit 322. The output compensation cost for the base plant is calculated and stored in the output compensation cost data storage unit 536 for the base plant.

  FIG. 12 shows a part of main processing units in the processing unit 5. As shown in FIG. 12, an output compensation cost calculation unit 537 for an ancillary plant (ancillary plant) includes a power generation time data storage unit 522, an output decrease data storage unit 520, an alternative supply cost unit price data storage unit 323 in the plant, , The output compensation cost for the ancillary plant is calculated and stored in the output compensation cost data storage unit 538 for the ancillary plant.

  FIG. 13 shows a part of main processing units in the processing unit 5. As shown in FIG. 13, the output compensation income calculation unit 539 refers to the power generation time data storage unit 522, the output decrease data storage unit 520, and the system average power selling unit price data storage unit 324 to calculate the output compensation income. The output compensation income data storage unit 540 stores the output compensation income data.

  FIG. 14 shows a part of main processing units in the processing unit 5. As shown in FIG. 14, the fuel cost reduction effect calculation unit 541 refers to the power generation time data storage unit 522, the output decrease data storage unit 520, and the average fuel unit price data storage unit 325 of the ancillary plant, thereby reducing the fuel cost. The effect is calculated and stored in the fuel cost reduction effect data storage unit 542.

  FIG. 15 shows a part of main processing units in the processing unit 5. As shown in FIG. 15, the capital investment amount determination processing unit 543 determines the capital investment amount with reference to the repair cost data storage unit 326, the new equipment price data storage unit 327, and the time parameter data storage unit 502, It is stored in the capital investment amount data storage unit 545. In addition, the depreciation cost determination processing unit 544 refers to the repair cost data storage unit 326, the new equipment price data storage unit 327, the time parameter data storage unit 502, and the residual value rate data storage unit 328, so that the depreciation cost and The equipment residual value is calculated and stored in the depreciation expense data storage unit 546 and the equipment residual value data storage unit 547.

  FIG. 16 shows a part of main processing units in the processing unit 5. As shown in FIG. 16, the cash flow calculation unit 548 includes a capital investment amount data storage unit 545, a power sale revenue data storage unit 525, a fuel cost data storage unit 534, an output compensation revenue data storage unit 540, and fuel cost reduction effect data. Storage unit 542, ancillary plant output compensation cost data storage unit 538, base plant output compensation cost data storage unit 536, third maintenance cost data storage unit 531, fixed cost data storage unit 329, and depreciation cost data storage unit 546 With reference to the equipment residual value data storage section 547 and the corporate tax rate data storage section 331, the cash flow of each year related to the capital investment is calculated and stored in the cash flow data storage section 549. Also, the NPV calculation unit 550 refers to the cash flow data storage unit 549, the discount rate data storage unit 330, and the time parameter data storage unit 502 to calculate the net current price NPV by discounting the cash flow by the discount rate. , And is stored in the NPV data storage unit 551.

  FIG. 17 shows a part of main processing units in the processing unit 5. As shown in FIG. 17, an EAV (Equivalent Annual Value) calculation unit 552 refers to a time parameter data storage unit 502, an NPV data storage unit 551, and a discount rate data storage unit 330, calculates EAV, and obtains EAV data. The data is stored in the storage unit 553 and the NPV data storage unit 551.

  FIG. 18 shows a part of main processing units in the processing unit 5. As shown in FIG. 18, the total NPV calculation unit 554 refers to the NPV data storage unit 551 and the time parameter data storage unit 502, calculates the total NPV by adding the NPVs during the evaluation period, and stores the total NPV data This is stored in the section 555.

  Next, processing contents of the equipment investment evaluation apparatus 10 shown in FIGS. 2 to 18 will be described with reference to FIGS. 19 to 43. First, the input unit 1 displays a screen as shown in FIG. 20 to FIG. 22 on the display device, for example, prompts the user to input data, accepts data input from the user, and enters the input data storage unit 3. Store (FIG. 1: step S1). In the example of the input screen of FIG. 20, (1) repair cost, (2) life time after extension, (3) output after repair, (4) thermal efficiency after repair, (5) planned repair year, (6) repair work The following fields are provided: (7) Evaluation years, (8) Scenario for capital investment. For (1) to (6), an entry field is provided for each of the three cases, and for (7) Evaluation years and (8) Scenarios, a common data entry field is provided for the three cases. . For (7) years of evaluation, either 20 years or 40 years is selected. (8) Scenarios are selected from (1) Scenario 1 in which the repair is performed, (2) Scenario 2 in which the repair is not performed, (3) Scenario 3 in which the repair is performed and the equipment is discarded. It should be noted that more scenarios may be selected, or another type of scenario set may be presented.

  The input screen example of FIG. 21 is presented to the user on the same screen as FIG. 20 or a different screen. In the example of the input screen of FIG. 21, (9) current year, (10) plant type, (11) operation start year, (12) planned output before repair, (13) current output, (14) planned thermal efficiency before repair, ( 15) Input columns for current thermal efficiency, (16) capacity-based maintenance cost, (17) 5-year average utilization rate, (18) unit price of electricity sales, and (19) unit cost of repair cost for ancillary plant are provided. These pieces of information are basic information common to all cases, and (9) to (16) are data that does not change within the evaluation period. (17) An input field is provided so that the 5-year average utilization rate is set every 5 years. Since it is very difficult to estimate the usage rate, it is set every five years. For (18) and (19), an input field is provided so as to be set every 10 years.

  The input screen example of FIG. 22 is presented to the user on the same screen as FIG. 20 or a different screen. In the input screen example of FIG. 22, (20) plant alternative supply unit price, (21) fuel cost, (22) inflation rate, (23) discount rate, (24) new equipment price, (25) new unit planned output , (26) New unit planned thermal efficiency, (27) Days for replacement, (28) Fixed cost of new unit, (29) Residual value rate of new unit, (30) Average fuel unit price of ancillary plant, (31) System An entry field for an average selling price and (32) the number of years of new unit operation is provided. This information is basic information common to all cases, and (20) to (23), (28), (30), and (31) are provided with an input field so that they are set every 10 years. It has been. (24) to (27), (29), and (32) are data that do not change during the evaluation period.

  The user inputs data for each case to be evaluated and data common to all cases on the input screens of FIGS. 20 to 22, and the input unit 1 accepts the input and stores the input data in the input data storage unit 3. To do.

  Then, the time parameter setting unit 501 (FIG. 3) includes a scenario data storage unit 334, a planned repair year data storage unit 301, an extended equipment life year data storage unit 302, a current year data storage unit 303, and an operation start year data storage. Referring to the unit 304, the evaluation year data storage unit 305, and the new unit operation year data storage unit 333, the time parameter used in the following processing is set for each case, and the set time parameter is stored in the time parameter data. The data is stored in the unit 502 (step S3).

  In step S1, year data is input, for example, in the Christian era, but output, thermal efficiency, and maintenance costs are functions of the number of years from the operation start year, so conversion is necessary. Further, for example, in case 1 shown in FIGS. 20 to 22, it is assumed that capital investment is made in accordance with a scenario for renovation and replacement, and the renovation scheduled year is “2004”, and the extended equipment The lifetime year is “2010”, the current year is “2002”, the operation start year is “1980”, and the evaluation years are “40” and the new unit operation years are “25”, which is common to all cases. Considering this case, the starting point of the curve such as the output is “1980” before the replacement and “2010” after the replacement. Furthermore, the operation end year of the new unit after replacement is “2034”, and the evaluation end year is “2041”.

  Accordingly, time parameters s = 1979 (= 1980-1) and r1 = 2009 (= 2010-1) for calculating the number of years from the starting point of the curve such as output are set. In addition, the current year c = 2002, the planned repair year k = 2004, the planned replacement year r2 = 2010, the number of years from the start of operation of the existing power generation facilities to the current year t1 = 23 (= 2002-1979), the new unit after replacement Operation end year t4 = 2034 (= 2009 + 25), number of years from the start of operation of the existing power generation facility to replacement year t2 = 31 (= 2010-1979), number of years from the start of operation of the existing power generation facility to the planned repair year t5 = Time parameters such as 25 (= 2004-1979), evaluation end year t6 = 2041, new unit operation years p = 25, and the like are calculated or specified and stored in the time parameter data storage unit 502. The time parameters for all cases are stored in the time parameter data storage unit 502 for each case.

  Note that the time parameter calculated in this step may be calculated as necessary in each process.

  Next, the output function determination processing unit 512 (FIG. 5) performs output function determination processing (step S5). This process will be described with reference to FIG. The output function determination processing unit 512 acquires data of the current output from the current output data storage unit 310 and the plan output before modification from the plan output data storage unit 311 before modification (step S21). In addition, referring to the time parameter data storage unit 502, data of the year t1 from the operation start year to the current year is acquired (step S23). Then, logarithmic regression calculation is performed with the operation start year as 1, the current output as the pre-repair plan output, the current year as t1, and the current output as the current output, and the regression coefficient of the output function OP (t) is calculated and output. The function parameter data is stored in the function parameter data storage unit 513 (step S25).

As shown in FIG. 24, the output of the power generation facility decreases logarithmically year by year. Although it is possible to obtain a standard output function OP (t) curve separately, it has been found that there is a large variation depending on the driving conditions. Therefore, in this embodiment, as shown in step S25, logarithmic regression calculation is performed using two points of data, and the coefficient a of the output function OP (t) expressed as a _op ln (t) + b _{op op} and b _op are calculated and used in the following processing.

  Returning to the description of FIG. 19, the thermal efficiency function determination processing unit 503 (FIG. 4) performs the thermal efficiency function determination process (step S <b> 7). This process will be described with reference to FIG. The thermal efficiency function determination processing unit 503 acquires the current thermal efficiency from the current thermal efficiency data storage unit 306 and the planned thermal efficiency data before modification from the planned thermal efficiency data storage unit 307 before modification (step S31). In addition, referring to the time parameter data storage unit 502, data of the year t1 from the operation start year to the current year is acquired (step S33). Then, logarithmic regression calculation is performed with the operation start year as 1, the thermal efficiency at that time as the planned thermal efficiency before renovation, the current year as t1, and the thermal efficiency at that time as the current thermal efficiency, and the regression coefficient of the thermal efficiency function HR (t) is calculated. Stored in the function parameter data storage unit 504 (step S35).

As shown in FIG. 26, the thermal efficiency of the power generation facility increases logarithmically year by year. A standard curve of the thermal efficiency function HR (t) can be obtained separately, but there is a large variation depending on the operating conditions. Therefore, in this embodiment, as shown in step S35, logarithmic regression calculation is performed using two points of data, and the coefficient a of the thermal efficiency function HR (t) expressed as a _hr ln (t) + b _{hr. hr} and _bhr are calculated and used in the following processing.

  Next, the processing unit 5 selects one unprocessed case (step S9). And the repair effect calculation part 505 (FIG. 4) implements the process which calculates a repair effect, when performing repair (step S11). This process will be described with reference to FIGS. First, the outline of the repair effect will be described with reference to FIG. Thermal efficiency is usually improved when renovation is performed. That is, the thermal efficiency decreases. As shown in FIG. 27, if the renovation is performed at t5 (= k−s) and the thermal efficiency is lowered by C, the present renovation is about the thermal efficiency with the thermal efficiency function HR (t) to the right by time d. There was an effect to shift. That is, the thermal efficiency function after the repair can be expressed as HR (t−d), and it can be said that there was an effect of rewinding by the time d. Therefore, d is calculated as a modification effect according to the processing flow of FIG.

First, the modification effect calculation unit 505 (FIG. 4) acquires the modified thermal efficiency from the modified thermal efficiency data storage unit 308 (step S41). Then, according to the thermal efficiency function HR (t) specified by the coefficient stored in the thermal efficiency function parameter data storage unit 504, a period t3 during which the post-repair thermal efficiency is obtained is calculated (step S43). If a _hr ln (t3) + b _hr = thermal efficiency after repair is developed, t3 = exp {(thermal efficiency after repair−b _hr ) / a _hr }, and t3 can be obtained. Finally, the improvement parameter d = t5−t3 is calculated with reference to the time parameter data storage unit 502, and the calculation result is stored in the modification effect data storage unit 506 (step S45).

  Returning to the explanation of FIG. 19, the processing unit 5 next sets the evaluation year e to the current year c (step S13). Then, it is determined whether e <k (step S15). That is, it is determined whether or not it is before renovation. Before the renovation, there is no impact of capital investment, so only the evaluation year e is updated. In the case where no modification is performed, it is determined whether e <r2. If it is determined in step S15 that it is before refurbishment, e is incremented by 1 (step S17), and the process returns to step S15. On the other hand, when e = k, the processing shifts to the processing in FIG.

  Next, the processing after the terminal A will be described with reference to FIGS. In FIG. 29, first, the processing unit 5 performs an NPV calculation process (step S51). This process will be described with reference to FIG.

  First, the capital investment amount determination processing unit 543 (FIG. 15) refers to the repair cost data storage unit 326 and the new facility price data storage unit 327, specifies the capital investment amount of the evaluation year e, and sets the capital investment amount data. The data is stored in the storage unit 545 (step S81). More specifically, if the evaluation year e is the planned repair year k, the repair cost stored in the repair cost data storage unit 326 is specified as the amount of capital investment. If the evaluation year e is the planned replacement year r2, the new equipment price stored in the new equipment price data storage unit 327 is specified as the equipment investment amount.

  Next, the power sales revenue calculation unit 524 and the like perform power sales revenue calculation processing (step S83). This process will be described with reference to FIG.

  First, when the evaluation year e <the planned replacement year r2 (step S101: Yes route), the first output calculation unit 514 (FIG. 5) displays the first estimated output value after the repair in the evaluation year e. The output is calculated (step S103). In the output function OP (t), t = evaluation year es−repair effect d is calculated. The output calculation process will be described with reference to FIG.

Also in FIG. 32, when the evaluation year e <the planned replacement year r2 (step S121: Yes route), the first output calculation unit 514 (FIG. 5) outputs the output stored in the output function parameter data storage unit 513. The output is calculated using the coefficient of the function OP (t) and stored in the first output data storage unit 516 or the second output data storage unit 517 (step S123). That is, output = a _op ln (t) + b _op is calculated. When the estimated output value after the modification is calculated, it is stored in the first output data storage unit 516, and when the estimated output value before the modification is calculated, it is stored in the second output data storage unit 517.

  When the process of FIG. 32 is called in step S103, the output is calculated as t = evaluation year es−repair effect d. As described above, the effect of the renovation is reflected by the rewind time, but since the refurbishment effect d is the rewind time calculated for the thermal efficiency, the output may not have exactly the same refurbishment effect d. . Depending on the case, the improvement effect may be calculated for the output and used as in the case of thermal efficiency. When the evaluation year e = the planned repair year k, the post-repair output is stored in the post-repair output data storage unit 312, and the value is used.

On the other hand, if the evaluation year e ≧ replacement scheduled year r2 (step S121: No route), the second output calculation unit 515 (FIG. 5) acquires the new unit plan output from the new unit plan output data storage unit 313 ( Step S125). Then, the output (third output) is calculated using the coefficient of the output function OP (t) changed by using the new unit plan output, and stored in the third output data storage unit 518 (step S127). The output function OP (t) of the new unit introduced by the replacement has to be obtained, but since it is introduced by the replacement, the usage environment is considered to be almost the same as that of the existing equipment. Therefore, the coefficients a _op and b _op of the output function OP (t) of the existing equipment are calculated by the ratio of the new unit plan output to the output (= a _op ln (s + 1−s) + b _op ) in the operation start year s + 1 of the existing equipment. Change to calculate a third output. That is, the new unit planned output / (a _op ln (s + 1−s) + b _op ) × (a _op ln (t) + b _op ) is used for calculation.

  Returning to the description of FIG. 31, the first output calculation unit 514 calculates the second output using the coefficient of the output function OP (t) stored in the output function parameter data storage unit 513, and outputs the second output data. The data is stored in the storage unit 517 (step S105). Here, as t = evaluation year es, an output estimated value of the evaluation year e when no repair is performed is calculated.

  Then, the difference calculation unit 523 calculates {(first output stored in the first output data storage unit 516) − (second output stored in the second output data storage unit 517)}. The output difference is stored in the output difference data storage unit 556 (step S106).

  Moreover, the power generation time calculation unit 521 (FIG. 7) performs a power generation time calculation process (step S107). This process will be described with reference to FIG. The power generation time calculation unit 521 (FIG. 7) first determines whether the evaluation year e = the planned repair year k (step S131). If e = k, the power generation stoppage time due to the renovation work is acquired from the power generation stoppage time data storage unit 314 by the renovation work, and the five-year average usage rate corresponding to the evaluation year e is acquired from the average usage rate data storage unit 315 (step S133). Then, the power generation time is calculated by (8760—power generation stoppage time due to renovation) × 5 year average usage rate and stored in the power generation time data storage unit 522 (step S135).

  On the other hand, if e = k is not satisfied, it is determined whether the evaluation year e = replacement scheduled year r2 (step S137). If e = r2, the replacement-use days data storage unit 316 acquires the replacement-use days, and the average usage rate data storage unit 315 acquires the 5-year average usage rate corresponding to the evaluation year e (step S139). Then, the power generation time is calculated by (8760-replacement days × 24) × 5-year average utilization rate and stored in the power generation time data storage unit 522 (step S141).

  Further, if e = r2 is not true, the five-year average usage rate corresponding to the evaluation year e is acquired from the average usage rate data storage unit 315 (step S143). Then, the power generation time is calculated as an average utilization rate of 8760 × 5 years (step S145).

  Returning to the processing of FIG. 31, the power sale revenue calculation unit 524 acquires the power sale unit price from the power sale unit price data storage unit 317 (step S109). Then, the power sale revenue generated by the repair (= output difference × power generation time) based on the output difference stored in the output difference data storage unit 556, the power generation time stored in the power generation time data storage unit 522, and the power selling unit price. X power selling unit price) is calculated and stored in the power selling revenue data storage unit 525 (step S111).

  On the other hand, if e ≧ r2 (step S101: No route), the second output calculation unit 515 (FIG. 5) calculates the third output as t = evaluation year er1, and stores the third output data. The data is stored in the unit 518 (step S113). Here, steps S125 and S127 of FIG. 32 are executed. Then, the power generation time calculation unit 521 calculates the power generation time (step S115). Here, the process of FIG. 33 is executed. Next, the power selling unit price is acquired from the power selling unit price data storage unit 317 (step S117). Then, the power sales revenue calculation unit 524 generates the power sales revenue by using the third output stored in the third output data storage unit 518, the power generation time stored in the power generation time data storage unit 522, and the power sales unit price. = 3rd output × power generation time × power selling unit price) is calculated and stored in the power sales revenue data storage unit 525 (step S119).

Returning to the description of FIG. 30, the fuel cost calculation unit 533 and the like (FIG. 10) perform a fuel cost calculation process (step S85). This process will be described with reference to FIG. When the evaluation year e <the planned replacement year r2 (step S150: Yes route), the first thermal efficiency calculation unit 507 calculates the coefficient of the thermal efficiency function HR (t) stored in the thermal efficiency function parameter data storage unit 504. The first thermal efficiency, which is the estimated thermal efficiency value after the repair in the evaluation year e, is calculated and stored in the first thermal efficiency data storage unit 509 (step S151). That is, in the thermal efficiency function HR (t), t = evaluation year es−renovation effect d. This process will be described with reference to FIG. Also in FIG. 35, when the evaluation year e <the planned replacement year r2 (step S171: Yes route), the first thermal efficiency calculation unit 507 (FIG. 4) is stored in the thermal efficiency function parameter data storage unit 504. The thermal efficiency (= a _hr ln (t) + b _hr ) is calculated using the coefficient of the thermal efficiency function HR (t), and is stored in the first thermal efficiency data storage unit 509 or the second thermal efficiency data storage unit 510 (step S173). . In addition, when the modification effect d is used, that is, when calculating as t = evaluation year es−renovation effect d, the first thermal efficiency is calculated, so it is stored in the first thermal efficiency data storage unit 509 and the modification effect is obtained. When d is not used, that is, when calculating as t = evaluation year es, the second thermal efficiency is calculated and stored in the second thermal efficiency data storage unit.

On the other hand, when the evaluation year e ≧ the planned replacement year r2 (step S171: No route), the second thermal efficiency calculation unit 508 acquires the new unit planned thermal efficiency from the new unit planned thermal efficiency data storage unit 309 (step S175). ). Then, the output (third thermal efficiency) is calculated using the coefficient of the thermal efficiency function HR (t) changed using the new unit planned thermal efficiency, and is stored in the third thermal efficiency data storage unit 511 (step S177). Although the thermal efficiency function HR of the new unit introduced by the replacement has to be obtained, since it is introduced by the replacement, the usage environment is considered to be almost the same as that of the existing equipment. Therefore, the coefficient a _hr and b _hr of the thermal efficiency function HR (t) of the existing equipment is calculated by the ratio of the new unit planned thermal efficiency to the thermal efficiency (= a _hr ln (s + 1−s) + b _hr ) in the operation start year s + 1 of the existing equipment. Change to calculate the third thermal efficiency. That is, the new unit planned thermal efficiency / (a _hr ln (s + 1−s) + b _hr ) × (a _hr ln (t) + b _hr ) is calculated.

  Returning to the description of FIG. 34, the first thermal efficiency calculation unit 507 next uses the coefficient of the thermal efficiency function HR (t) stored in the thermal efficiency function parameter data storage unit 504 to evaluate when no modification is performed. The second thermal efficiency, which is the estimated thermal efficiency value for year e, is calculated and stored in the second thermal efficiency data storage unit 510 (step S153). That is, t = evaluation year es. Here, the process of FIG. 35 is executed.

  In addition, the first output calculation unit 514 uses the coefficient of the output function OP (t) stored in the output function parameter data storage unit 513 to output the first output that is the estimated output value after the repair in the evaluation year e. , T = evaluation year es−repair effect d, and is stored in the first output data storage unit 516 (step S155). Here, the first output calculated in the middle of step S83 can be used as it is if the process of FIG. 32 is executed or the process is executed in the order of the steps shown in FIG. Data stored in the first output data storage unit 516 is acquired. Furthermore, the first output calculation unit 514 uses the coefficient of the output function OP (t) stored in the output function parameter data storage unit 513 as the output estimated value of the evaluation year e when no modification is performed. 2 is calculated as t = evaluation year es and stored in the second output data storage unit 517 (step S157). Here, the second output calculated in the middle of step S83 can be used as it is if the process of FIG. 32 is executed or the process is executed in the order of the steps shown in FIG. Data stored in the second output data storage unit 517 is acquired.

  In addition, the power generation time calculation unit 521 performs power generation time calculation processing and stores it in the power generation time data storage unit 522 (step S159). Here, if the processing of FIG. 33 is executed or the processing is executed in the order of the steps shown in FIG. 30, the power generation time calculated in the middle of step S83 can be used as it is. Data stored in the data storage unit 522 is acquired.

  The fuel cost calculation unit 533 acquires the fuel unit price stored in the fuel unit price data storage unit 321 (step S161). The fuel cost (= ((first output × first thermal efficiency) − (second output) generated by the renovation based on the first and second outputs, the first and second thermal efficiencies, the fuel unit price and the power generation time) Output × second thermal efficiency)) × power generation time × fuel unit price) is calculated and stored in the fuel cost data storage unit 534 (step S163).

  On the other hand, when the evaluation year e ≧ the planned replacement year r2 (step S150: No route), the second output calculation unit 515 calculates the coefficient of the output function OP (t) stored in the output function parameter data storage unit 513. Is used to calculate the third output, which is the estimated output value after replacement, and stores it in the third output data storage unit 518 (step S165). Here, if the processing of FIG. 32 is executed, or if the processing is executed in the order of the steps shown in FIG. 30, the third output calculated in the middle of step S83 can be used as it is. The data stored in the third data storage unit 518 is acquired.

  Further, the second thermal efficiency calculation unit 508 calculates the third thermal efficiency, which is the estimated thermal efficiency value after replacement, using the coefficient of the thermal efficiency function HR (t) stored in the thermal efficiency function parameter data storage unit 504, and 3 is stored in the thermal efficiency data storage unit 511 (step S166). Here, the process of FIG. 35 is executed.

  In addition, the power generation time calculation unit 521 performs a power generation time calculation process and stores it in the power generation time data storage unit 522 (step S167). Here, if the processing of FIG. 33 is executed or the processing is executed in the order of the steps shown in FIG. 30, the power generation time calculated in the middle of step S83 can be used as it is. Data stored in the data storage unit 522 is acquired.

  The fuel cost calculation unit 533 acquires the fuel unit price stored in the fuel unit price data storage unit 321 (step S168). Then, the fuel cost generated by the replacement (= third output × third thermal efficiency × power generation time × fuel unit price) is calculated from the third output, the third thermal efficiency, the fuel unit price and the power generation time, and fuel cost data The data is stored in the storage unit 534 (step S169).

  Returning to the description of the processing in FIG. 30, next, the maintenance cost calculation processing is performed (step S87). This process will be described with reference to FIGS.

The inventor of the present invention found that the maintenance cost of the power generation facility is a maintenance cost excluding personnel costs, and can be expressed by a logarithmic function as shown in FIG. Therefore, a logarithmic regression calculation is performed to obtain an aging function for calculating the maintenance cost for the model case, and coefficients a _m and b _m of the aging function are calculated. The coefficients a _m and b _m of the aging deterioration function are stored in the maintenance cost function parameter data storage unit 526. The aging deterioration function for such a model case is used after being converted into the aging deterioration function for the power generation equipment to be examined. That is, the similarity conversion is performed according to the ratio of the maintenance cost (= capacity-based maintenance cost × output) at a specific time to the value at a specific time of the model case aging function.

Under this assumption, the maintenance cost calculation process will be described with reference to FIG. If evaluation year e <replacement planned year r2 (step S181: Yes route), the first maintenance cost calculation unit 527 (FIG. 9) stores the capacity base maintenance cost stored in the capacity base maintenance cost data storage unit 318. And the current output stored in the current output data storage unit 310 (step S183). Further, the coefficient of the aging deterioration function stored in the maintenance cost function parameter data storage unit 526 is acquired (step S185). Then, using the capacity-based maintenance cost and the coefficient of the aging deterioration function changed using the current output, a first maintenance cost that is a maintenance cost estimate value of the evaluation year e after the repair is calculated, and the first maintenance cost is calculated. Stored in the cost data storage unit 528 (step S187). At this time, the amount of time rewinding due to the repair is reflected as t = evaluation year es−repair effect d. More specifically,
{(Capacity base maintenance cost × current output) / (a _m ln (t1) + b _m )} × (a _m ln (es−d−d) + b _m )
To calculate the first maintenance cost. For capacity-based maintenance costs, if the value does not include inflation, the value multiplied by the cumulative inflation rate from the year when the existing power generation facility started operation is used.

Furthermore, the first maintenance cost calculation unit 527 uses the capacity-based maintenance cost and the coefficient of the aging deterioration function changed by using the current output, and is an estimated value of the maintenance cost of the evaluation year e when the repair is not performed. The second maintenance cost is calculated and stored in the second maintenance cost data storage unit 529 (step S189). At this time, t = evaluation year es is calculated. More specifically,
{(Capacity base maintenance cost × current output) / (a _m ln (t1) + b _m )} × (a _m ln (es) + b _m )
To calculate the second maintenance cost.

  Then, the difference calculation unit 530 calculates a difference between the first maintenance cost stored in the first maintenance cost data storage unit 528 and the second maintenance cost stored in the second maintenance cost data storage unit 529, Stored in the third maintenance cost data storage 531 (step S191). Thereby, it is possible to calculate a maintenance cost change caused by the renovation.

On the other hand, if the evaluation year e ≧ replacement scheduled year r2 (step S181: No route), the second maintenance cost calculation unit 532 includes the capacity base maintenance cost stored in the capacity base maintenance cost data storage unit 318, The new unit plan output stored in the new unit plan output data storage unit 313, the inflation rate stored in the inflation rate data storage unit 320, and t2 stored in the time parameter data storage unit 502 are acquired (step S193). . Further, the coefficient of the aging deterioration function stored in the maintenance cost function parameter data storage unit 526 is acquired (step S195). Then, the second maintenance cost calculation unit 532 uses the capacity-based maintenance cost, the new unit plan output, the inflation rate, and the coefficient of the aged deterioration function changed by using t2, and the maintenance cost in the evaluation year e after the replacement. Is calculated and stored in the third maintenance cost data storage 531 (step S197). At this time, t = evaluation year er1 is calculated. More specifically,
{(Capacity base maintenance cost × cumulative inflation rate × new unit planned output)) / (a _m ln (t2) + b _m )} × (a _m ln (e−r1) + b _m )
To calculate the third maintenance cost. The cumulative inflation rate is the cumulative inflation rate from the current year to the planned replacement year if the capacity-based maintenance cost is the current capacity-based maintenance cost. If inflation is not considered at all, Cumulative inflation rate from the start of operation of power generation facilities to the planned replacement year.

  Returning to the description of FIG. 30, next, output compensation cost calculation processing is performed (step S89). This process will be described with reference to FIG.

  First, an output decrease calculation process is performed (step S201). This process will be described with reference to FIG. When the evaluation year e <the planned replacement year r2 (step S230: Yes route), the output decrease calculation unit 519 (FIG. 6) acquires the pre-repair plan output from the pre-repair plan output data storage unit 311 (step S231). ). Then, the first output calculation unit 514 uses the coefficient of the output function OP (t) stored in the output function parameter data storage unit 513 to output the first output that is the output estimated value after the modification in the evaluation year e. , T = evaluation year es−repair effect d, and is stored in the first output data storage unit 516 (step S233). Here, the first output calculated in the middle of step S83 can be used as it is if the process of FIG. 32 is executed or the process is executed in the order of the steps shown in FIG. Data stored in the first output data storage unit 516 is acquired. Then, the output decrease calculation unit 519 calculates (planned output before modification−first output) and stores it in the output decrease data storage unit 520 (step S235).

  On the other hand, if the evaluation year e ≧ the replacement year r2 (step S230: No route), the output decrease calculation unit 519 acquires the pre-repair plan output from the pre-repair plan output data storage unit 311 (step S237). Then, the second output calculation unit 515 calculates a third output, which is the estimated output value after replacement, using the coefficient of the output function OP (t) stored in the output function parameter data storage unit 513, and The data is stored in the 3-output data storage unit 518 (step S238). Here, if the processing of FIG. 32 is executed, or if the processing is executed in the order of the steps shown in FIG. 30, the third output calculated in the middle of step S83 can be used as it is. The data stored in the third data storage unit 518 is acquired. Then, the output decrease calculation unit 519 calculates (planned output before modification−third output) and stores it in the output decrease data storage unit 520 (step S239).

  When the output decreases, the sign of the output decrease is positive, and when the output increases, the sign of the output decrease becomes negative.

  Returning to the description of FIG. 38, next, the power generation time calculation unit 521 performs a power generation time calculation process and stores it in the power generation time data storage unit 522 (step S203). Here, if the processing of FIG. 33 is executed or the processing is executed in the order of the steps shown in FIG. 30, the power generation time calculated in the middle of step S83 can be used as it is. Data stored in the data storage unit 522 is acquired.

  If the plant type is not an ancillary plant but a base plant (step S205: No route), the base plant output compensation cost calculation unit 535 (FIG. 11) stores it in the ancillary plant output compensation unit price data storage unit 322. The output ancillary plant output compensation cost unit price is acquired (step S207). After that, the base plant output compensation cost (= power generation time x output reduction x ancillary plant output compensation cost unit price) is calculated using the power generation time, output reduction amount, and ancillary plant output compensation cost unit price. This is stored in the output compensation cost data storage unit 536 (step S209).

  Considering the entire power system, if the output of the base plant decreases, it becomes necessary to supply the reduced output by another ancillary plant. If such calculation is performed, the output compensation cost for the base plant can be calculated. When the sign of the output decrease is negative, that is, when the output is increasing, it is income instead of cost.

  Further, the fuel cost reduction effect calculation unit 541 (FIG. 14) determines whether or not the output has increased according to the code for the output decrease stored in the output decrease data storage unit 520 (step S213). When it is determined that the fuel cost has been increased, the fuel cost reduction effect calculation unit 541 acquires the average fuel unit price of the ancillary plant stored in the average fuel unit price data storage unit 325 of the ancillary plant (step S215). . Then, the fuel cost reduction effect (= power generation time x output increase x average fuel unit price of the ancillary plant) is calculated using the power generation time, output increase (-output decrease part) and the average fuel unit price of the ancillary plant, It stores in the fuel cost reduction effect data storage unit 542 (step S217).

  Since the base plant is usually more efficient than the ancillary plant, if the output increases, the fuel cost can be reduced by reducing the power generation output of the ancillary plant. This reduction is calculated as the fuel cost reduction effect. The fuel cost reduction effect may be calculated only in the case of a new unit after replacement, that is, only in the case of evaluation year e ≧ replacement planned year r2.

  When it is determined in step S213 that the output is decreasing, and after step S217, the process proceeds to step S223.

  On the other hand, when the plant type is not the base plant but an ancillary plant (step S205: Yes route), the ancillary plant output compensation cost calculation unit 537 (FIG. 12) stores alternative supply cost unit price data in the plant. The alternative supply cost unit price in the plant stored in the unit 323 is acquired (step S219). Then, using the power generation time, the output decrease, and the alternative supply cost unit price in the plant, the output compensation cost for the ancillary plant (= power generation time x output decrease x alternative supply cost unit price in the plant) is calculated, and the ancillary It stores in the plant output compensation cost data storage unit 538 (step S221).

  As described above, since the ancillary plant originally has the output adjustment range as the whole plant, the output reduction is calculated as the alternative supply cost unit price in the plant assuming that the alternative supply is performed by the adjacent unit in the same plant. Use to calculate. The alternative supply cost unit price in the plant is determined from the fuel cost and the repair cost excluding the fixed cost for maintaining the plant, unlike the supply from another plant. Further, when the sign of the output decrease is negative, that is, when the output is increasing, it is income instead of cost.

  Next, the output compensation revenue calculation unit 539 (FIG. 13) acquires the system average power selling unit price stored in the system average power selling unit data storage unit 324 (step S223). Then, output compensation income (= power generation time × output decrease × system average power selling unit price) is calculated using the power generation time, output reduction amount, and system average power selling unit price, and stored in the output compensation revenue data storage unit 540. (Step S225).

  When the output compensation is performed in this way, it is necessary to consider the power sales revenue from the compensation. Although it is originally the revenue of another plant, here it is added as revenue assuming that it is operated by the same power generation company. When the sign of the output decrease is negative, that is, when the output is increased, the cost is not the income.

  Returning to the description of the processing in FIG. 30, the processing unit 5 performs a fixed cost specifying process (step S91). This process will be described with reference to FIG. For fixed costs, it is not necessary to consider the evaluation year e ≧ replacement scheduled year r2, that is, the pre-replacement as a thunk cost. Therefore, when the evaluation year e ≧ the planned replacement year r2 (step S227: No route), the processing unit 5 uses the fixed cost data of the new unit stored in the input data storage unit 3 as the fixed cost of the evaluation year e. It identifies and stores in the fixed cost data storage part 329 (step S229).

  Returning to the description of the processing in FIG. 30, the depreciation and equipment residual value calculation processing is performed (step S93). This process will be described with reference to FIG. First, the depreciation expense determination processing unit 544 (FIG. 15) determines whether or not the evaluation year e <the planned replacement year r2 (step S241). If evaluation year e <replacement planned year r2, it is determined whether evaluation year e = repair planned year k (step S243). If evaluation year e = renovation scheduled year k, no depreciation cost or equipment residual value is generated, so the process returns to the original processing. On the other hand, if e = k is not satisfied, it is determined whether the evaluation year e = the previous year r1 of the planned replacement year (step S245). If e = r1 is not satisfied, the depreciation cost determination processing unit 544 decrements a predetermined value (for example, 10) by subtracting the residual value (= renovation cost × residual value rate) from the repair cost stored in the repair cost data storage unit 326. %) Is calculated as the depreciation expense and stored in the depreciation expense data storage unit 546 (step S247).

  On the other hand, when e = r1, the depreciation expense determination processing unit 544 subtracts the accumulated depreciation expense (total of the depreciation expense calculated in step S247) from the repair cost x (1-residual value rate). The depreciation cost is calculated and stored in the depreciation cost data storage unit 546 (step S249). Then, the repair cost × residual value rate is calculated as the equipment residual value and stored in the equipment residual value data storage unit 547 (step S251).

  When the evaluation year e ≧ the replacement year r2 (step S241: No route), the depreciation expense determination processing unit 544 determines whether the evaluation year e = the replacement year r2 (step S253). If e = r2, no depreciation cost or equipment residual value is generated, so the process returns to the original processing. On the other hand, if e = r2 is not true, it is determined whether the evaluation year e = t4 (step S255). When e = t4 is not satisfied, a predetermined percentage (for example, 10%) of the amount obtained by subtracting the residual value (= new equipment price × residual value rate) from the new equipment price stored in the new equipment price data storage unit 327 is depreciated. The cost is calculated and stored in the depreciation cost data storage unit 546 (step S257). On the other hand, if e = t4, the new equipment price × residual value rate stored in the new equipment price data storage unit 327 is calculated as the equipment residual value and stored in the equipment residual value data storage unit 547 (step S259). In the present embodiment, it is assumed that the entire amount obtained by subtracting the residual value (= new equipment price × residual value rate) from the new equipment price is depreciated during the operation year of the new unit. If there is an undepreciated portion at the time of e = t4, the undepreciated portion is recorded as it is as a depreciation expense.

Returning to the description of the processing in FIG. 30, the cash flow calculation unit 548 (FIG. 16) includes a fixed cost data storage unit 329, a third maintenance cost data storage unit 531, and a base plant output compensation cost data storage unit 536. Ancillary plant output compensation cost data storage unit 538, fuel cost reduction effect data storage unit 542, output compensation income data storage unit 540, fuel cost data storage unit 534, power sale revenue data storage unit 525, With reference to the capital investment amount data storage unit 545, the depreciation expense data storage unit 546, the equipment residual value data storage unit 547, and the corporate tax rate data storage unit 331, the cash flow of the evaluation year e is calculated, and the cash The data is stored in the flow data storage unit 549 (step S95). In particular,
(-Capital investment + {Power sales revenue-(Fuel cost + Maintenance cost + (Output compensation cost for base plant or ancillary plant output)-Output compensation income-Fuel cost reduction effect))-Fixed cost-Depreciation Depreciation expenses + equipment residual value) x (1-corporate tax rate) + depreciation expenses

Next, the NPV calculation unit 550 discounts the cash flow stored in the cash flow data storage unit 549 to the present value based on the discount rate stored in the discount rate data storage unit 330, and stores it as an NPV in the NPV data storage unit 551. (Step S97). Specifically, it is calculated by cash flow / (1 + discount rate) ^{e-c + 1} .

  Thus, the NPV of the evaluation year e is calculated.

  Returning to the description of the processing in FIG. 29, the EAV calculating unit 552 (FIG. 17) determines whether the new unit operation end year t4 ≧ the evaluation end year t6 (step S53). When the evaluation end year t6 comes after the new unit operation end year, it is necessary to calculate EAV. If t4 ≧ t6, it is not necessary to calculate EAV. Accordingly, it is determined whether the evaluation year e ≦ the evaluation end year t6 (step S55). If e ≦ t6, the evaluation year e is incremented by 1 (step S57), and the process returns to step S51. On the other hand, if the evaluation year e> the evaluation end year t6, the process proceeds to step S67.

When t4 <t6, that is, when the evaluation end year t6 comes after the new unit operation end year (step S53: No route), the EAV is calculated. Therefore, the EAV calculation unit 552 determines whether the evaluation year e> the new unit operation end year t4 (step S61). If e ≦ t4, the evaluation year e is incremented by 1 (step S59), and the process returns to step S51. On the other hand, if e> t4, the EAV calculation unit 552 performs an EAV calculation process (step S63). This process will be described with reference to FIG. The EAV calculation unit 552 refers to the NPV data storage unit 551, sums up the NPVs from r2 to t4, and stores them in a storage device such as a main memory (step S261). Then, EAV is calculated using the discount rate r stored in the discount rate data storage unit 330 and stored in the EAV data storage unit 553 (step S263). In particular,
Total NPV / {1 / r-1 / r / (1 + r) ^ p}

  Returning to the description of the processing in FIG. 29, an EAV is assigned to each year from (t4 + 1) to t6 and stored in the NPV data storage 551 (step S65). Then, the process proceeds to step S67, where the total NPV calculation unit 554 (FIG. 18) totals the NPVs stored in the NPV data storage unit 551 (some of them may be EAVs) and stores them in the total NPV data storage unit 555. To do.

  The processing unit 5 determines whether all cases have been processed (step S69). If there is an unprocessed case, the process returns to step S9 in FIG. On the other hand, when it is determined that the processing has been completed for all cases, the output unit 7 performs output processing (step S71). The output unit 7 outputs not only the total NPV but also data stored in various data storage units as required or fixed. If necessary, statistical processing and graphing processing are performed.

  For example, a graph as shown in FIG. 43 may be output. That is, the horizontal axis indicates the year, and the vertical axis indicates the amount of money. The line graph shows income and the bar graph shows expenditure. Renovation was carried out in 2004, and the expenditure was high. In 2010, replacement was carried out, so the expenditure was high. The difference between income and expenditure is the cash flow.

  Even if such a graph is not used, changes in the values of various data may be indicated by numerical values themselves for each year during the evaluation period. In addition to the display device, there may be a function of outputting a file in a format such as CSV.

  As described above, according to the present embodiment, it is possible to comprehensively and precisely evaluate the effects of capital investment such as renovation and replacement. In addition, since the influence on the power system is also taken into consideration, the device is designed so as not to fall into partial optimization. In addition, since EAV is introduced, the influence on the total NPV due to variations in the operation end years of new units is reduced, so that each case can be evaluated more objectively.

  Although one embodiment of the present invention has been described above, the present invention is not limited to this. The functional block diagrams shown in FIGS. 2 to 18 are examples and do not necessarily correspond to actual program modules. In the above explanation, the end year of operation of the new unit after replacement is entered, but it is an economical way to indicate whether the cost of measures required to continue safe operation can be covered by the subsequent cash flow. The equipment life may be automatically calculated, and the operation end year may be set on that.

  As for the maintenance cost, an example in which the model case aging deterioration function is similarly converted is used. However, the aging deterioration function may be specified from the actual maintenance cost history. Further, when a detailed repair plan is available, the data may be used, and the shortage may be obeyed by an aging deterioration function obtained by regression calculation.

  In addition, in the case where only the replacement is performed without renovation, if the data related to the renovation is not input on the input screen, the unnecessary steps are skipped in the processing described above.

  Further, the processing flow includes steps that can be executed in parallel and steps that can be switched in order. That is, it is possible to make changes such as changing the order of steps in the processing flow as long as the content of the processing does not change.

  The capital investment evaluation apparatus 10 shown in FIG. 2 is a computer, and the computer has a configuration as shown in FIG. That is, the memory 2501, the CPU 2503, the hard disk drive (HDD) 2505, the display control unit 2507 connected to the display device 2509, the drive device 2513 for the removable disk 2511, the input device 2515, and the communication control unit for connecting to the network. 2517 is connected to the bus 2519. An operating system (OS) and an application program for performing the above-described processing are stored in the HDD 2505, and are read from the HDD 2505 to the memory 2501 when executed by the CPU 2503. If necessary, the CPU 2503 controls the display control unit 2507, the communication control unit 2517, and the drive device 2513 to perform necessary operations. Further, data in the middle of processing is stored in the memory 2501 and stored in the HDD 2505 if necessary. Such a computer realizes various functions as described above by organically cooperating hardware such as the CPU 2503 and the memory 2501 described above with the OS and necessary application programs.

(A) And (b) is a figure for demonstrating the outline | summary which concerns on embodiment of this invention. It is a functional block diagram of the equipment investment evaluation device concerning one embodiment of the present invention. It is a functional block diagram which shows a part of pre-processing part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of pre-processing part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of pre-processing part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of pre-processing part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of pre-processing part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a functional block diagram which shows a part of main process part in the process part of a capital investment evaluation apparatus. It is a figure which shows a part of main processing flow. It is a figure which shows a part of input screen. It is a figure which shows a part of input screen. It is a figure which shows a part of input screen. It is a figure which shows the processing flow of an output function determination process. It is a figure for demonstrating an output function. It is a figure which shows the processing flow of a thermal efficiency function determination process. It is a figure for demonstrating a thermal efficiency function. It is a figure for demonstrating the repair effect. It is a figure which shows the processing flow of an improvement effect calculation process. It is a figure which shows a part of main processing flow. It is a figure which shows the processing flow of NPV calculation processing. It is a figure which shows the processing flow of a power sale revenue calculation process. It is a figure which shows the processing flow of an output calculation process. It is a figure which shows the processing flow of an electric power generation time calculation process. It is a figure which shows the processing flow of a fuel cost calculation process. It is a figure which shows the processing flow of a thermal efficiency calculation process. It is a figure for demonstrating the secular deterioration function of a maintenance cost. It is a figure which shows the processing flow of a maintenance cost calculation process. It is a figure which shows the processing flow of an output compensation cost calculation process. It is a figure which shows the processing flow of an output reduction part calculation process. It is a figure which shows the processing flow of fixed cost specific processing. It is a figure which shows the processing flow of a depreciation expense and an equipment residual value calculation process. It is a figure which shows the processing flow of an EAV calculation process. It is a figure which shows an example of output data. It is a figure which shows the functional block diagram of a computer.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Capital investment evaluation apparatus 1 Input part 3 Input data storage part 5 Processing part 7 Output part 301 Planned repair year data storage part 302 Equipment life year data storage part 303 Current year data storage part 304 Operation start year data storage part 305 Evaluation year data Storage unit 306 Current thermal efficiency data storage unit 307 Planned thermal efficiency data storage unit 308 before modification Thermal efficiency data storage unit 309 after modification New unit planned thermal efficiency data storage unit 310 Current output data storage unit 311 Planned output data storage unit 312 before modification Output data Storage unit 313 New unit planned output data storage unit 314 Power generation stoppage time data storage unit 315 Average utilization rate data storage unit 316 Replacement place days data storage unit 317 Power selling unit price data storage unit 318 Capacity base maintenance cost data storage unit 320 Inflation rate Data storage unit 321 Fuel unit price data storage unit 322 Ancillary plant output compensation cost unit price data storage unit 323 Alternative supply cost unit price data storage unit 324 System average power unit price data storage unit 325 Average fuel unit price data storage unit 326 Upgrade cost data storage unit 327 New equipment Price data storage unit 328 Residual value rate data storage unit 329 Fixed cost data storage unit 330 Discount rate data storage unit 331 Corporate tax rate data storage unit 333 New unit operation years data storage unit 334 Scenario data storage unit 501 Time parameter setting unit 502 Time parameter Data storage unit 503 Thermal efficiency function determination processing unit 504 Thermal efficiency function parameter data storage unit 505 Modification effect calculation unit 506 Modification effect data storage unit 507 First thermal efficiency calculation unit 508 Second thermal efficiency calculation unit 509 First thermal efficiency data storage unit 510 Thermal efficiency data storage unit 511 Third thermal efficiency data storage unit 512 Output function determination processing unit 513 Output function parameter data storage unit 514 First output calculation unit 515 Second output calculation unit 516 First output data storage unit 517 Second output data storage unit 518 Third output data storage unit 519 Output decrease calculation unit 520 Output decrease data storage unit 521 Power generation time calculation unit 522 Power generation time data storage unit 523 Difference calculation unit 524 Power sale revenue calculation unit 525 Power sale revenue data storage unit 526 Maintenance Cost function parameter data storage unit 527 First maintenance cost calculation unit 528 First maintenance cost data storage unit 529 Second maintenance cost data storage unit 530 Difference calculation unit 531 Third maintenance cost data storage unit 532 Second maintenance cost calculation unit 533 Fuel Cost calculation unit 534 Fuel cost data storage unit 535 Base plant output compensation cost calculation unit 53 Output compensation cost data storage unit 537 for base plant Output compensation cost calculation unit 538 for ancillary plant Output compensation cost data storage unit 539 for ancillary plant Output compensation income calculation unit 540 Output compensation income data storage unit 541 Fuel cost reduction effect calculation unit 542 Fuel cost reduction effect data storage unit 543 Capital investment amount determination processing unit 544 Depreciation cost determination processing unit 545 Capital investment amount data storage unit 546 Depreciation cost data storage unit 547 Equipment residual value data storage unit 548 Cash flow calculation unit 549 Cash Flow data storage unit 550 NPV calculation unit 551 NPV data storage unit 552 EAV calculation unit 553 EAV data storage unit 554 Total NPV calculation unit 555 Total NPV data storage unit 556 Output difference data storage unit

Claims (15)

An information processing method for evaluating the effect of investment in power generation equipment,
Based on the estimated value after the investment implementation of the index that represents the investment effect on the power generation facility and changes over time and the estimated value when the investment is not performed, the investment effect on the power generation facility is calculated as the time winding of the index. A step of identifying as a return amount and storing the time data for the rewind in the investment effect data storage unit;
A regression coefficient of a logarithmic regression curve representing a change over time of the maintenance cost of the model equipment stored in the maintenance cost model curve data storage unit, a maintenance cost at a specific time of the power generation equipment, and a value of the logarithmic regression curve at the specific time , The time data stored in the investment effect data storage unit, and the time data of the estimated year, the estimated maintenance cost after the investment and the maintenance cost when the investment is not made Calculating a difference in the estimated year from the estimated value of and storing in the maintenance cost investment effect data storage unit;
An information processing method executed by a computer. Based on the difference between the initial output power value of the power generation facility stored in the input data storage unit and the estimated output power value of the power generation facility in the estimated year after investment in the power generation facility, the power generation facility The information processing method according to claim 1, further comprising: a first statistical influence amount calculation step of calculating an influence of the investment on the power system by an amount and storing the amount in a system influence amount data storage unit. Using the estimated value of the output power of the power generation facility in the estimated year after the investment implementation for the power generation facility and the estimated value of the output power of the power generation facility in the estimated year when the investment in the power generation facility is not performed, Calculating the difference in power sales revenue as the investment effect on the power generation facility, and storing it in the power sales revenue investment effect data storage unit;
Calculated using the data stored in the investment effect data storage unit, the estimated value of the thermal efficiency of the power generation facility in the estimated year after the implementation of the investment in the power generation facility, and when the investment in the power generation facility is not performed The estimated value of the thermal efficiency of the power generation facility in the estimated year, the estimated value of the output voltage of the power generation facility in the estimated year after the implementation of the investment in the power generation facility, and the estimated year in the case of not investing in the power generation facility Calculating the difference in fuel cost as the investment effect on the power generation facility using the estimated value of the output power of the power generation facility, and storing it in the fuel cost investment effect data storage unit;
The investment amount and discount rate for the power generation equipment stored in the input data storage unit, the data stored in the maintenance cost investment effect data storage unit, the data stored in the system impact amount data storage unit, and the sales Using the data stored in the electricity revenue investment effect data storage unit and the data stored in the fuel cost investment effect data storage unit, the cash flow related to the investment effect on the power generation facility in the estimated year is a predetermined rule. Calculating according to the above and storing in the cash flow data storage unit;
The information processing method according to claim 2, further comprising: The regression coefficient of the logarithmic regression curve representing the change over time of the maintenance cost of the model facility stored in the maintenance cost model curve data storage unit, and the initial output power when the new generation facility is replaced as an investment in the power generation facility The second in the estimated year after the replacement with the new power generation equipment using the ratio of the estimated maintenance cost based on the value and the inflation-considered estimated maintenance cost and the value of the logarithmic regression curve at the time of replacement with the new power generation equipment Calculating the maintenance cost of and storing it in the maintenance cost investment effect data storage unit,
The information processing method according to claim 3, further comprising: Based on the difference between the value of the initial output power of the power generation facility stored in the input data storage unit and the estimated value of the output power of the new power generation facility when replaced with the new power generation facility, to the new power generation facility 5. The information processing method according to claim 4, further comprising: a second system influence amount calculation step of calculating the influence of the replacement investment on the electric power system in an amount, and storing it in the system influence amount data storage unit as second system influence amount data. . Using the estimated value of the output power of the new power generation facility in the estimated year after the replacement investment to the new power generation facility, the second power sales revenue is calculated as the effect of the replacement investment to the new power generation facility, Storing in the income investment effect data storage unit;
An estimated value of thermal efficiency of the new power generation facility in the estimated year after implementation of the replacement investment to the new power generation facility, and an estimated value of output power of the new power generation facility in the estimated year after the replacement investment to the new power generation facility And calculating a second fuel cost as an effect of replacement investment to the new power generation facility and storing it in a fuel cost investment effect data storage unit;
The amount and discount rate of replacement investment to the new power generation facility stored in the input data storage unit, the second maintenance cost data stored in the maintenance cost investment effect data storage unit, and the system impact amount data storage Second power system impact amount data stored in the power storage unit, second power sale revenue data stored in the power sale revenue investment effect data storage unit, and second data stored in the fuel cost investment effect data storage unit Calculating the cash flow related to the investment effect on the power generation facility in the estimated year using the fuel cost data of 2 according to a predetermined rule, and storing the cash flow in a cash flow data storage unit;
The information processing method according to claim 5, further comprising: A step of performing a rebate process of the cash flow in a specific year stored in the cash flow data storage unit according to the discount rate stored in the input data storage unit, and storing the processing result in the present value data storage unit. Item 7. The information processing method according to Item 6. When the scheduled end time of the new power generation facility reaches the end of the evaluation period of the investment effect on the power generation facility, the cash after the rebate in the planned operation period of the new power generation facility after the replacement investment to the new power generation facility is implemented The flow is read from the present value data storage unit, an equivalent annual economic value is calculated, and stored in the present value data storage unit as a cash flow after rebate from the scheduled operation end time of the new power generation facility to the end of the evaluation period The information processing method according to claim 7, further comprising: The data indicating the time of renovation to the existing power generation equipment, which is an investment in the power generation equipment, and the effect of the renovation, and the time of replacement to the new power generation equipment, which is the investment in the power generation equipment, and the effect of replacement to the new power generation equipment Prompting the user to enter case data containing the data to represent,
Further including
Each of the steps is executed according to the input case data,
further,
Using the data stored in the present value data storage unit, displaying cash flow data after rebate corresponding to the case data on a display device;
The information processing method according to claim 7 or 8, comprising:   In the first or second system impact amount calculation step, the impact on the power system is calculated in terms of amount when the power generation facility or the new power generation facility is a base plant and an ancillary plant. 6. The information processing method according to claim 2, wherein the information processing method is performed. An information processing method for evaluating the effect of investment in power generation equipment,
The initial output power value and inflation rate stored in the input data storage unit when replaced with a new power generation facility, and the change over time of the maintenance cost of the model facility stored in the maintenance cost model curve data storage unit Using the regression coefficient of the logarithmic regression curve to represent, based on the value of the initial output power when replaced with the new power generation equipment and the estimated maintenance cost in consideration of inflation and the time of replacement to the new power generation equipment Calculating a ratio with the value of the logarithmic regression curve and storing it in a storage device;
Using the ratio data stored in the storage device and the regression coefficient of the logarithmic regression curve stored in the maintenance cost model curve data storage unit, the maintenance cost in the estimated year after replacement with the new power generation facility is calculated. Calculating and storing in the maintenance cost investment effect data storage unit;
An information processing method executed on a computer. Based on the difference between the value of the initial output power of the power generation facility stored in the input data storage unit and the estimated value of the output power of the new power generation facility when replaced with the new power generation facility, the new power generation facility The information processing method according to claim 11, further comprising: a system influence amount calculation step of calculating an influence of replacement investment on the power system by an amount, and storing it in a system influence amount data storage unit.   A program for causing a computer to execute the information processing method according to any one of claims 1 to 12. An information processing apparatus for evaluating the effect of investment in power generation equipment,
Based on the estimated value after the investment implementation of the index that represents the investment effect on the power generation facility and changes over time and the estimated value when the investment is not performed, the investment effect on the power generation facility is calculated as the time winding of the index. A means for identifying the return amount and storing the time data for the rewind in the investment effect data storage unit;
A regression coefficient of a logarithmic regression curve representing a change over time of the maintenance cost of the model equipment stored in the maintenance cost model curve data storage unit, a maintenance cost at a specific time of the power generation equipment, and a value of the logarithmic regression curve at the specific time , The time data stored in the investment effect data storage unit, and the time data of the estimated year, the estimated maintenance cost after the investment and the maintenance cost when the investment is not made A difference between the estimated value and the estimated value in the estimated year, and stored in the maintenance cost investment effect data storage unit;
An information processing apparatus. An information processing apparatus for evaluating the effect of investment in power generation equipment,
The initial output power value and inflation rate stored in the input data storage unit when replaced with a new power generation facility, and the change over time of the maintenance cost of the model facility stored in the maintenance cost model curve data storage unit Using the regression coefficient of the logarithmic regression curve to represent, based on the value of the initial output power when replaced with the new power generation equipment and the estimated maintenance cost in consideration of inflation and the time of replacement to the new power generation equipment Means for calculating a ratio with the value of the logarithmic regression curve and storing it in a storage device;
Using the ratio data stored in the storage device and the regression coefficient of the logarithmic regression curve stored in the maintenance cost model curve data storage unit, the maintenance cost in the estimated year after replacement with the new power generation facility is calculated. Means for calculating and storing in the maintenance cost investment effect data storage unit;
An information processing apparatus.

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