Investment decision support system and investment decision support method

Abstract

To provide an investment determination assistance system and investment determination assistance method for evaluating a risk of investment to an electric power system.SOLUTION: Provided is an investment determination assistance system for presenting an effect of capital investment to a local energy system, including: a user input part 110 that receives selection of risk factors and setting of an assumed fluctuation range; a forecasting part 120 that generates future forecast data using historical and current data, the risk factors, and the setting of the assumed fluctuation range; and a calculation evaluation part 130 that includes an environmental simulation part 131 for simulating the movement of the local energy system, a short-term operational evaluation part 132 for calculating how to operate grids to optimize daily power operation, and a long-term investment evaluation part 133 for outputting a result of long-term investment evaluation that includes investment effectiveness and risk. The long-term investment evaluation part 133 calculates the investment risk on the basis of the risk factor and the setting of the assumed fluctuation range, and generates data for displaying the fluctuation range of the investment effect on the basis of the calculated investment risk.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an investment decision support system. [Background technology]

[0002] To curb global warming, sustainable development in line with greenhouse gas reduction targets is desired. To this end, it is necessary to increase the proportion of renewable energy sources such as solar and wind power. However, renewable energy sources have large fluctuations in power output due to weather, and investment decisions must take into account risks such as investment returns, power outages, and disasters.

[0003] The following prior art exists as background art in this technical field. Patent Document 1 (U.S. Patent Application Publication No. 2002 / 0194113) describes a system, method, and computer program product for minimizing risks associated with weather-dependent activities. Such activities may include the operation of renewable power sources and the delivery of power output from those renewable power sources for sale in a market. The system and method identify risks for market participants in the event of potential shortages and provide indicators and mitigation processes to address the risks before power supply failures result in contract breaches or imbalances in grid operation. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] US Patent Application Publication No. 2002 / 0194113 Summary of the Invention [Problem to be solved by the invention]

[0005] When investing in renewable energy facilities, the expected internal rate of return (IRR) is taken into consideration, but it is also necessary to evaluate the fluctuation range of various risks and the frequency of power outages during normal times and disasters, but the system in the background technology mentioned above does not have the function to evaluate and display these risks. In particular, when operating a grid that utilizes renewable energy without connecting it to the grid, solar power generation equipment will make a profit by selling electricity on sunny days, but power will run out in stormy weather, and risk assessment will affect investment decisions.

[0006] Therefore, an object of the present invention is to provide an investment decision support system that evaluates and displays the risks as well as the expected profits from investment in a power system. [Means for solving the problem]

[0007] A representative example of the invention disclosed in the present application is as follows: That is, an investment decision support system for presenting the effect of capital investment in a regional energy system, comprising a computer having an arithmetic unit that executes predetermined arithmetic processing and a storage device connected to the arithmetic unit, the regional energy system having one or more grids including at least one of a power generation device and a power storage device, and a vehicle equipped with a storage battery, the investment decision support system comprising an input unit in which the arithmetic unit presents a user with options for risk factors to be considered in a forecast and receives from the user the selection of risk factors and the setting of an expected fluctuation range for each of the risk factors, and the arithmetic unit inputs past and present data including data around the grid and data on the costs required for introducing, maintaining, and operating the regional energy system, as well as the risk factors selected by the user and the setting of an expected fluctuation range for each of the risk factors, and an environmental simulation unit in which the computing device simulates the operation of the local energy system using a model representing the behavior of the environment including the grid, the past and present data, and the future prediction data; a short-term operation evaluation unit in which the computing device receives as input an output from the environmental simulation unit and uses a short-term operation algorithm to calculate an operation method for the grid to optimize daily power operation; and a long-term investment evaluation unit in which the computing device uses the output from the prediction unit and the output from the environmental simulation unit to output a long-term investment evaluation result including the effects and risks of an investment in installing equipment on the grid, wherein the computing device calculates the risks of the investment based on risk factors selected by a user and an expected fluctuation range for at least one risk factor, and generates data to display the fluctuation range of the investment effect based on the calculated risk. [Effects of the Invention]

[0008] According to one aspect of the present invention, it is possible to accurately evaluate the risk of investing in a power system. Objects, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram showing an overview of a regional energy system to which investment decisions are made by the investment decision support system of the present invention. [Figure 2] 1 is a block diagram showing the logical configuration of an investment decision support system. [Figure 3] FIG. 2 is a block diagram showing the logical configuration of an operation evaluation unit. [Figure 4] FIG. 1 is a diagram illustrating a physical configuration of an investment decision support system. [Figure 5] 10 is a flowchart of an investment decision support process executed by the investment decision support system. [Figure 6] 10 is a flowchart of a calculation evaluation process executed by a calculation evaluation unit. [Figure 7] 10 is a flowchart of a process executed by a risk assessment unit. [Figure 8] FIG. 10 is a diagram illustrating an example of an input screen. [Figure 9] FIG. 10 is a diagram illustrating an example of an input screen. [Figure 10] FIG. 10 is a diagram illustrating an example of an output screen. [Figure 11] FIG. 10 is a diagram illustrating an example of an output screen. [Figure 12] FIG. 10 is a diagram illustrating an example of an output screen. [Figure 13] FIG. 10 is a diagram illustrating an example of an output screen. DETAILED DESCRIPTION OF THE INVENTION

[0010] FIG. 1 is a diagram showing an outline of a local energy system to which investment decisions are made by an investment decision support system 100 of the present invention.

[0011] The local energy system includes multiple grids. Each grid is communicatively connected to a power management system (not shown). To exchange power between the grids without relying on a grid system, electric vehicles may be operated between the grids.

[0012] For example, each grid has a power source (such as a solar power generation device or an engine generator) that generates electricity, a load device that consumes electricity, and a power control device (not shown) that controls the amount of electricity generated and consumed. The grid may also have a storage battery that stores electricity to fill the gap between electricity supply and demand. The power source is mainly a device that generates electricity using natural energy such as solar power generation, wind power generation, or geothermal power generation, and an engine (internal combustion engine) generator may also be installed on the grid. The load device is mechanical equipment that consumes electricity within the grid, public institutions such as hospitals, agricultural machinery, etc. Electric vehicles that travel between grids also consume electricity.

[0013] The power management system is communicatively connected to multiple grids and load devices that do not belong to the grid. The power control devices of the grid and load devices send their own power supply and demand status and requests for transporting cargo or people to the control system. The control system sends instructions to secure surplus power for charging electric vehicles and sends transport completion reports obtained from the electric vehicles to the grid. The control system also outputs calculation results to an administrator and receives input from the administrator, such as the selection of an EV operation plan.

[0014] In an area including a grid, electric vehicles, which are vehicles that run on electricity, travel between grids using charged electricity. Electric vehicles include delivery trucks, on-demand buses, taxis, etc. that transport cargo and people within the area. Electric vehicles are equipped with a rechargeable traction battery and a power transport battery. Electric vehicles charge the power transport battery in grids where there is a surplus in power supply and demand, and discharge the power transport battery in grids where there is a tight power supply and demand, thereby adjusting the power supply and demand between the grids. The power transport battery and the traction battery may be the same battery or may be separate batteries. It is preferable that electric vehicles be equipped with a battery that can be quickly charged, but they may also be equipped with a battery that can only be normally charged, or a battery that can be easily replaced in a short time.

[0015] The electric vehicle may be an electric vehicle that runs on charged electricity, but may also be a vehicle that runs on energy from an internal combustion engine or a fuel cell. In this case, the grid does not provide the electric vehicle with power for running, but rather provides power to be transported between grids. The electric vehicle may also not be equipped with a battery for power transport. In this case, the grid does not provide the electric vehicle with power to be transported between grids, but rather adjusts power supply and demand depending on the location where power for running is provided.

[0016] FIG. 2 is a block diagram showing the logical configuration of the investment decision support system 100 of this embodiment.

[0017] The investment decision support system 100 includes a user input unit 110, a prediction unit 120, a calculation and evaluation unit 130, a result output unit 140, and a database 150.

[0018] The user input unit 110 accepts data input from the user. As shown in FIGS. 8 and 9, the data input for the simulation includes the selection of the region to be simulated, selectable risks, constraint values ​​and target values ​​for the simulation, and optimization weighting coefficients. The constraint values ​​and target values ​​for the simulation are constraint values ​​for investment costs and target values ​​for profits. The optimization weighting coefficient is an index indicating the items to be emphasized in short-term management evaluation and long-term investment evaluation.

[0019] The prediction unit 120 acquires past and current data from the database 150 based on the conditions input by the user, and generates future prediction data. When generating future prediction data, it may read already created future prediction data from the database 150, or acquire prediction data from an external source (for example, a weather forecast site). The prediction unit 120 then outputs the data input by the user, the past and current data acquired from the database 150, and the generated future prediction data to the calculation and evaluation unit 130.

[0020] The calculation and evaluation unit 130 evaluates the investment effect and investment risk using the data output from the prediction unit 120, and outputs the evaluation result. The detailed configuration of the calculation and evaluation unit 130 will be described with reference to FIG.

[0021] The result output unit 140 presents to the user the investment cost, investment effect (profit), and investment risk, which are the evaluation results by the calculation evaluation unit 130. The user refers to the information output from the investment decision support system 100 to make an investment decision or re-input the simulation conditions to further simulate the investment effect.

[0022] The database 150 stores past and current data and future prediction data. The past and current data is data around the grid, including, for example, geographic data around the grid, weather data around the grid, electricity demand around the grid, electricity rates on the grid and in the market (e.g., inter-grid trading), fuel costs for power generation on the grid, freight charges for electric vehicles traveling between grids, maintenance costs for various grid facilities, and data on deterioration over time of various grid facilities. The future prediction data is prediction data around the grid calculated by the prediction unit 120 from past and current data and user-input data, and data on predicted costs required for the introduction, maintenance, and operation of the local energy system, including, for example, weather data around the grid, electricity demand around the grid, electricity rates on the grid and in the market (e.g., inter-grid trading), fuel costs for power generation on the grid, freight charges for electric vehicles traveling between grids, maintenance costs for various grid facilities, and data on deterioration over time of various grid facilities.

[0023] FIG. 3 is a block diagram showing the logical configuration of the calculation evaluation unit 130 of this embodiment.

[0024] The calculation evaluation unit 130 includes an environment simulation unit 131 , a short-term operation evaluation unit 132 , and a long-term investment evaluation unit 133 .

[0025] The environmental simulation unit 131 uses a model representing the behavior of the environment including the grid to simulate the daily behavior of power generation, power consumption, charging and discharging, transportation requests, electric vehicles, etc. The short-term operation evaluation unit 132 optimizes daily power operation based on data received from the environmental simulation unit 131. The long-term investment evaluation unit 133 evaluates the grid's long-term (e.g., one year) power operation and derives optimization conditions. The long-term investment evaluation unit 133 includes an investment effect evaluation unit 134, an optimization unit 135, and a risk evaluation unit 136. The investment effect evaluation unit 134 calculates the investment amount (CAPEX) and internal rate of return (IRR), which are indicators for evaluating investment effects. The optimization unit 135 uses the CAPEX and IRR calculated by the investment effect evaluation unit 134 to create an investment plan to propose to the user. The risk evaluation unit 136 evaluates the risk level of the CAPEX and IRR calculated by the investment effect evaluation unit 134.

[0026] Next, the operation of each part of the calculation evaluation unit 130 will be described in detail.

[0027] The environmental simulation unit 131 receives user input data such as geographic data, meteorological data, electricity demand, electricity rates, freight rates, fuel costs, aging, and region and optimization weighting from the prediction unit 120 (A). The environmental simulation unit 131 performs a simulation using the data A input from the prediction unit 120 to calculate parameters related to electricity at each time (power generation amount, power demand amount, power storage amount, external purchase amount, surplus disposal amount, charge / discharge amount), transportation request information (timing, number of times, etc.), and electric vehicle operation information (location, cargo, etc.), and outputs these to the short-term operation evaluation unit 132 (D). The environmental simulation unit 131 also performs a simulation using the data A input from the prediction unit 120 and the output (E) from the short-term operation evaluation unit 132 to calculate the power balance, fuel consumption, number of power outages, power outage duration, transportation revenue, carbon dioxide emissions, and number of untransported vehicles, and outputs these to the long-term investment evaluation unit 133 (C). In this way, the environmental simulation unit 131 simulates the behavior of the local energy system including the grid and electric vehicles using a model that represents the behavior of the environment including the grid, past and current data, and future prediction data.

[0028] The short-term operation evaluation unit 132 uses a short-term operation algorithm to evaluate the daily power operation of the grid due to short-term environmental fluctuation factors based on the information (D) input from the environmental simulation unit 131 and short-term environmental fluctuation factors (such as fluctuations in weather and power demand), and outputs fuel engine power generation instructions, charge / discharge instructions, transportation instructions, and power generation suppression instructions, which are conditions for optimizing daily power operation (E).

[0029] The long-term investment evaluation unit 133 receives inputs from the prediction unit 120 of data (A) plus maintenance costs and user input data 1 (e.g., constraints such as budgets and conditions such as profit targets), resulting in data (A'), and user input data 2 (e.g., risk factors to be taken into account and an investment plan to be subject to risk evaluation (location, introduction amount, etc.)) (A''). The long-term investment evaluation unit 133 uses the information (A') input from the prediction unit 120 to output the installation location of the equipment and the introduction amount of the equipment to the environment simulation unit 131 (B). The long-term investment evaluation unit 133 may also use the information (A' and A'') input from the prediction unit 120 to output a random number seed and arguments (worst case conditions, best case conditions, etc.) used by the environment simulation unit 131 for simulation (B). The long-term investment evaluation unit 133 uses the information (A' and A'') input from the prediction unit 120 and the information (C) input from the environment simulation unit 131 to output the long-term investment evaluation results (J, K).

[0030] Next, the processing of each unit of the long-term investment evaluation unit 133 will be described. First, the investment effect evaluation unit 134 receives input of the installation location of the equipment and the amount of equipment introduced from the optimization unit 135 (F) and sends the received data to the environmental simulation unit 131 (B). The environmental simulation unit 131 sends simulation results based on data B (e.g., power balance, fuel consumption used for power generation, transportation revenue, carbon dioxide emissions, number and duration of power outages, number of untransported vehicles, etc.) to the investment effect evaluation unit 134 (C). The investment effect evaluation unit 134 evaluates the investment effect using the received data C and outputs simulation results summarized by CAPEX, IRR, and evaluation period to the optimization unit 135 (G). Based on the received data G, the optimization unit 135 determines whether to finalize the installation location of the equipment and the amount of equipment introduced, or to update or select. If updating, it sends the updated installation location of the equipment and the amount of equipment introduced to the environmental simulation unit 131 (B). If a final decision is made, the processing ends and the results are output (K). The above process is described in detail in the flowchart shown in FIG. 6. After the above process is completed, if the user selects risk assessment, the investment effect assessment unit 134 receives the equipment installation location, the equipment installation amount, the random number seed, and arguments (worst-case conditions, best-case conditions, etc.) from the risk assessment unit 136 (H). The investment effect assessment unit 134 sends the received data to the environment simulation unit 131 (B). As in the previous process, the environment simulation unit 131 sends simulation results based on data B (e.g., power balance, fuel consumption used for power generation, transportation revenue, carbon dioxide emissions, number and duration of power outages, number of items not yet transported, etc.) to the investment effect assessment unit 134 (C). The investment effect assessment unit 134 evaluates the investment effect using the received data C and outputs the simulation results summarized for CAPEX, IRR, and evaluation period to the risk assessment unit 136 (I). The risk assessment unit 136 determines whether any random number seeds or arguments remain to be calculated, and if so, sends them again to the environment simulation unit 131 (B). If no more remain, the process ends and the results are output (J). The above process is described in detail in the flowchart shown in FIG.

[0031] CAPEX is calculated as follows: (CAPEX)=(PV COST)+(GEN COST)+(BAT COST) +(EV COST)+(SYS COST)-(SUB)

[0032] Here, each term on the right side is expressed by the following equation.

[0033]

number

[0034] The IRR is calculated, for example, using the following formula:

number

[0035] Here, [INI INV COST] represents the initial investment amount, and [CASH INFLOW at t] is the sum of transportation revenue and electricity sales revenue in period t minus the sum of fuel costs, maintenance costs, electricity purchase costs, taxes, etc., and corresponds to the final revenue in period t.

[0036] The optimization unit 135 uses the data G input from the investment effect evaluation unit 134 to determine whether to make a final decision on the installation location of the equipment and the amount of equipment introduced, or to update or select. If updating, the optimization unit 135 sends the updated installation location of the equipment and the amount of equipment introduced to the environment simulation unit 131 (B). If a final decision is made, the optimization unit 135 ends the processing and outputs the resulting optimized investment plan proposal (such as the installation location of the equipment and the amount of equipment introduced) to the result output unit 140 (K). In addition, the optimization unit 135 outputs the installation location of the equipment and the amount of equipment introduced in the optimized simulation results to the investment effect evaluation unit 134 (F).

[0037] The risk assessment unit 136 receives user input of risk factors to be considered and investment plans to be risk assessed (location, introduction amount, etc.) from the prediction unit 120 (A''), evaluates the risks of the simulation results by the environmental simulation unit 131, and outputs the IRR distribution by risk, power outage frequency, power outage duration, and number of undelivered items for each investment plan to the result output unit 140 (J). In addition, the risk assessment unit 136 outputs the evaluated investment plan proposal (location of equipment, introduction amount of equipment, etc.) to the investment effect assessment unit 134 (H). Furthermore, the risk assessment unit 136 may output a random number seed and arguments (worst case conditions, best case conditions, etc.) used by the environmental simulation unit 131 for the simulation.

[0038] FIG. 4 is a diagram showing the physical configuration of the investment decision support system 100 of this embodiment.

[0039] The investment decision support system 100 of this embodiment is configured by a computer having a processor (CPU) 101 , a memory 102 , an auxiliary storage device 103 and a communication interface 104 .

[0040] The processor 101 executes a program stored in the memory 102. Note that part of the processing performed by the processor 101 when executing the program may be executed by another arithmetic device (for example, a hardware arithmetic device such as an FPGA or an ASIC).

[0041] The memory 102 includes a ROM, which is a non-volatile storage element, and a RAM, which is a volatile storage element. The ROM stores unchanging programs (e.g., BIOS), etc. The RAM is a high-speed, volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by the processor 101 and data used when the programs are executed.

[0042] The auxiliary storage device 103 is a large-capacity non-volatile storage device such as a magnetic storage device (HDD) or a flash memory (SSD), and stores programs to be executed by the processor 101 and data to be used when the programs are executed. That is, the programs are read from the auxiliary storage device 103, loaded into the memory 102, and executed by the processor 101.

[0043] The communication interface 104 is a network interface device that controls communication with other devices (such as power control devices in a grid) according to a predetermined protocol.

[0044] The investment decision support system 100 may have an input interface 105 and an output interface 108. The input interface 105 is an interface to which input devices such as a keyboard 106 and a mouse 107 are connected and which receives input from an operator. The output interface 108 is an interface to which output devices such as a display device 109 and a printer are connected and which outputs the results of program execution in a format that can be viewed by the operator. Note that the input interface 105 and the output interface 108 may be provided by a terminal connected to the investment decision support system 100 via a network.

[0045] The program executed by the processor 101 is provided to the investment decision support system 100 via removable media (CD-ROM, flash memory, etc.) or a network, and is stored in a non-volatile auxiliary storage device 103, which is a non-transitory storage medium. For this reason, the investment decision support system 100 preferably has an interface for reading data from removable media.

[0046] The investment decision support system 100 is a computer system configured on one physical computer or on multiple logically or physically configured computers, and may operate on one or multiple threads on the same computer, or on a virtual computer constructed on multiple physical computer resources. Each part of the investment decision support system 100 may operate on a different computer.

[0047] FIG. 5 is a flowchart of the power system investment decision support process executed by the investment decision support system 100 of this embodiment.

[0048] The user input unit 110 transmits evaluation conditions input by the user (user input data) to the prediction unit 120 (S501). The prediction unit 120 acquires past and current data from the database 150 (S502), generates future prediction data using the acquired data and the user input data, and transmits the data to the calculation and evaluation unit 130 (S503). The prediction unit 120 may acquire future prediction data calculated in the past from the database 150 and transmit it to the calculation and evaluation unit 130, or may acquire data from an external source (such as a weather forecast).

[0049] The computation evaluation unit 130 evaluates the investment effect using the data transmitted from the prediction unit 120, and outputs the evaluation result to the result output unit 140 (S504). Details of the processing executed by the computation evaluation unit 130 will be described with reference to FIG. 6.

[0050] In the simulations performed by the environmental simulation unit 131, multiple evaluation indices have a trade-off relationship. For example, an improvement in transportation service value reduces the electricity supply value, and vice versa. This is because, for example, increasing the number of electric buses or electric taxis in operation to reduce waiting times or increasing transportation speeds to improve transportation service value increases electricity consumption. The short-term operation evaluation unit 132 and the long-term investment evaluation unit 133 optimize daily operations and long-term investment plans, respectively, based on evaluation values ​​obtained by weighting these indices input by the user.

[0051] The result output unit 140 presents the investment cost and investment effect (profit), which are the evaluation results by the calculation evaluation unit 130, to the user (S505).

[0052] The user then decides whether to make a capital investment by referring to the investment effect and investment risk (S506). If the user wishes to continue the evaluation for capital investment judgment, he / she changes the simulation conditions (S507), and the process returns to step S502 and repeats the simulation. On the other hand, if the user wishes to evaluate the investment risk without changing the simulation conditions (Y in S508), the user inputs the risk factors to be taken into consideration and the investment plan to be the subject of risk evaluation (placement location, introduction amount, etc.) (S509).

[0053] Then, the prediction unit 120 acquires past and present data from the database 150 (S510), generates future prediction data using the acquired data and user input data, and transmits the data to the calculation and evaluation unit 130 (S511). The prediction unit 120 may acquire future prediction data calculated in the past from the database 150 and transmit it to the calculation and evaluation unit 130, or may acquire data from an external source (such as a weather forecast).

[0054] The computation evaluation unit 130 evaluates the investment effect and investment risk using the data transmitted from the prediction unit 120, and outputs the evaluation result to the result output unit 140 (S512). Details of the risk evaluation process executed by the computation evaluation unit 130 will be described with reference to Fig. 7. Thereafter, the process returns to step S505, and the result output unit 140 presents the investment cost, investment effect (profit), and investment risk, which are the evaluation results by the computation evaluation unit 130, to the user (S505).

[0055] FIG. 6 is a flowchart of the computation evaluation process executed by the computation evaluation unit 130.

[0056] First, the calculation and evaluation unit 130 receives user input data, past and current data, and future prediction data from the prediction unit 120 (S511). Then, it initializes the investment plan (placement location, introduction amount, etc.) (S512). Alternatively, if it is the second or subsequent execution (processing after returning from N in S518), it updates the investment plan (placement location, introduction amount, etc.) based on the CAPEX, IRR, and simulation results for each evaluation period obtained in step S517, and extracts and selects an investment plan that satisfies the target values ​​and constraint values ​​specified by the user (S512). Then, it sends the initialized or updated investment plan (placement location, introduction amount, etc.) to the environment simulation unit 131 (S513). The environment simulation unit 131 uses a model representing the behavior of the environment to simulate daily behaviors such as power generation, power consumption, charging and discharging, transportation requests, and electric vehicles based on the data received from the prediction unit 120.

[0057] Then, the short-term operation evaluation unit 132 optimizes the daily power operation and transportation plan of the grid using the simulation results by the environment simulation unit 131 (S514), and outputs the optimized power operation and transportation plan to the long-term investment evaluation unit 133 (S515).

[0058] The investment effect evaluation unit 134 evaluates the investment effect (profit) obtained by the investment, outputs the CAPEX, IRR, and simulation results for each evaluation period (S516), and sends them to the optimization unit 135 (S517).

[0059] Then, the calculation evaluation unit 130 determines whether to end the optimization evaluation process (S518). The optimization evaluation process can be ended based on whether the calculation time range specified by the user has been exceeded, whether a target value specified by the user has been achieved, or whether the number of solutions (placement candidates) that satisfy a reference value calculated based on the target value has exceeded a number specified by the user.

[0060] FIG. 7 is a flowchart of the process executed by the risk assessment unit 136.

[0061] When the risk assessment unit 136 receives user input of risk factors to be considered and investment plans (placement location, introduction amount, etc.) to be risk assessed from the prediction unit 120 (S521), it determines whether there are arguments to be used in the next assessment (e.g., among the assessment conditions entered by the user (e.g., worst-case and best-case conditions for fuel costs and electricity charges) that have not yet been subjected to risk assessment) (S522). If there are next arguments, the next arguments are sent to the environment simulation unit 131 (S527).

[0062] On the other hand, if there is no next argument, it is determined whether the random number seed is sufficient (S523). For example, it is possible to use methods such as determining whether the confidence interval of the population variance of the IRR falls within a threshold, allowing the user to directly specify the number of samplings, or sampling so that it falls within the range of calculation time specified by the user.

[0063] If the random number seed is sufficient, the risk assessment result is output (S528). For example, the CAPEX received from the investment effect assessment unit 134, the IRR for each argument and seed, and the number and duration of power outages are tallied, and the upper and lower limits, distribution, average values, etc. for each risk factor are presented to the user as output data in S505 of FIG. 5.

[0064] On the other hand, if the random number seed is insufficient, a new random number seed is created (S524), the created random number seed is sent to the environment simulation unit 131 (S525), and the simulation result is obtained from the environment simulation unit 131 (S526).

[0065] 8 and 9 are diagrams showing examples of input screens: Fig. 8 shows an example of inputting simulation conditions at the start of a simulation, and Fig. 9 shows an example of inputting detailed simulation conditions.

[0066] As shown in Figure 8, the input screen at the start of a simulation has a region selection section, a constraint and target value input section, and an optimization weighting input section. The region selection section accepts input of the region (administrative unit of prefecture or city) to be simulated. The constraint and target value input section accepts input of the upper limit of the calculation time, the lower limit of the profit, the simulation period, and the budget plan. Since the investment must be recovered within the simulation period, the simulation period is the period for recovering the investment. For the budget plan, it is recommended that the input be a file describing the investment amount for each fiscal year. The optimization weighting input section is an optional input item and accepts input of important indicators such as transportation (providing transportation at low fares), power operation (stable power supply), carbon dioxide emissions, agriculture, and disaster response.

[0067] As shown in FIG. 9, the input screen for detailed simulation conditions has a risk calculation target result selection section, an event risk selection section, and a fluctuation risk selection section.

[0068] The risk calculation target result selection unit accepts the selection of investment candidates for which risk is to be calculated. Selection can be made by selecting a range on a graph where simulation results are plotted with CAPEX and IRR as axes, or by selecting a grid on a map. It is also advisable to aggregate and display the power generation volume and profit margin for the selected range by power generation type.

[0069] The event risk selection section and the variation risk selection section accept the selection of risk factors. The risk factors that can be selected by the event risk selection section are power outages during normal times, power outages during disasters, whether or not the system is connected to the grid, and the occurrence of untransported goods. The risk factors that can be selected by the variation risk selection section are weather, power demand, electricity charges, fuel costs, maintenance costs, and deterioration over time. As some items in the variation risk selection section (electricity charges, fuel costs, maintenance costs, and deterioration over time) are difficult to predict, the operator can specify the fluctuation range that they expect and allow.

[0070] The user can run a simulation once by entering data into at least the region selection section and the constraint and target value input section on the input screen (Figure 8) at the start of the simulation, and then look at the short-term and long-term evaluation results of the simulation and enter other conditions (the constraint and target value input section and optimization weighting input section in Figure 8, and each item in Figure 9) to obtain an investment plan proposal based on the simulation results that the user desires.

[0071] 10 to 13 are diagrams showing examples of output screens. Fig. 10 shows an example of an output displaying a risk range, Fig. 11 shows an example of an output displaying a power outage risk, Fig. 12 shows an example of an output displaying a transportation risk, and Fig. 13 shows an example of an output displaying an intermediate profit / loss risk.

[0072] As shown in Figure 10, an example of an output displaying the risk range displays the evaluation results on a graph with IRR and CAPEX as axes. Each point on the graph corresponds to an evaluation result under different conditions, such as equipment location and installation volume. Selecting a point on the graph displays the probability that the IRR of the evaluation result falls within the range. For example, with regard to weather fluctuations, the probability that the IRR falls within the range can be evaluated using methods such as Monte Carlo sampling based on the fluctuation distribution of past data and future forecast data. As a specific example, the simulation results (received from the environmental simulation unit 131) obtained for each random number seed in steps S524 to S526 of Figure 7 are used as input, and the investment effect evaluation unit 134 calculates CAPEX and the IRR for each random number seed, and the probability is evaluated by the aggregation process in step 528. In addition to the probability that the IRR falls within the range, the simulation results received from the environmental simulation unit may also be used to calculate and display the frequency of power outages during the simulation period (e.g., 0.1% power outage during normal times (once every three years) and 14% power outage during disasters (once a week)).

[0073] Furthermore, the worst-case and best-case IRR values ​​for fluctuations in other indicators may be displayed on a graph with IRR and CAPEX as axes. For example, when an evaluation result is selected in the graph, the range between the worst-case and best-case IRR values ​​for fluctuations in other indicators (such as power demand and maintenance costs) is displayed for that evaluation result. For example, for indicators that are difficult to predict, such as electricity rates and fuel costs, the risk range can be displayed by using forecast values ​​or trends from research organizations or by the user specifying an acceptable fluctuation range (see FIG. 9). For example, the lower and upper limits of the fluctuation range of each indicator specified by the user in steps S522, S527, and S526 of FIG. 7 are used as arguments, and the obtained simulation results (received from the environmental simulation unit 131) are input to the investment effect evaluation unit 134, whereby the risk range can be displayed.

[0074] As shown in Figure 11, an output example showing the risk of power outages displays the evaluation results on a graph with IRR and CAPEX as axes under specified conditions, and when an evaluation result in the graph is selected, the facility layout is displayed on a map. Furthermore, when a nanogrid or evacuation shelter is selected on the map, the risk of power outages due to power shortages at each grid or shelter is displayed as a frequency distribution or the like. As with the power outage frequency in Figure 10, the number and duration of power outages included in the simulation results sent from the environmental simulation unit 131 to the long-term investment evaluation unit 133 are sent to the result output unit and displayed.

[0075] As shown in Figure 12, an output example showing transportation risk displays the evaluation results on a graph with IRR and CAPEX as axes under specified conditions, and when an evaluation result in the graph is selected, the predicted results for the risk of transportation not being achieved are displayed. For example, the number of days that an event may occur for each transportation not being achieved rate is displayed as a frequency distribution. Specifically, this can be displayed based on the number of untransported items included in the simulation results sent from the environmental simulation unit 131 to the long-term investment evaluation unit 133.

[0076] As shown in Figure 13, an example of output displaying interim profit and loss risk displays the evaluation results on a graph with IRR and CAPEX as axes under specified conditions. Selecting an evaluation result in the graph displays the capital investment plan and profit trends for each year. For example, in the case of a budget plan that gradually adds equipment each year, the change in cumulative profit over time and the range of fluctuation can be seen, allowing the long-term break-even point to be determined. Specifically, similar to the CASH INFLOW at t used in calculating the IRR, the investment effect evaluation unit 134 calculates profits at each time t based on the electricity balance, fuel consumption, and transportation revenue sent from the environmental simulation unit 131 to the long-term investment evaluation unit 133, and the electricity rate, fuel cost, and maintenance cost sent from the forecasting unit 120 to the long-term investment evaluation unit 133. The calculation results are then sent to the result output unit 140, allowing the expected value (thick solid line in the center) of the graph in Figure 13 to be displayed. The trends in the power demand and maintenance costs in the graph of Fig. 13 can be displayed by using the upper and lower limits of the fluctuation range of each index specified by the user as arguments, inputting the obtained simulation results (received from the environmental simulation unit 131), and calculating the CAPEX at each time and the IRR of each argument in the investment effect evaluation unit 134, in the same way as in the display method of the lower diagram of Fig. 10. In addition, the calculation evaluation unit 130 can also optimize the facility location and installation amount for each year using processing similar to that of the flowchart shown in Fig. 6.

[0077] The investment decision support system 100 of this embodiment makes it possible to know the magnitude of profit and risk for each facility location and installation amount, thereby providing information for appropriate investment decisions. For example, if a capital investment plan (Plan A) that will bring about large profits has high risk, the system investigates the risks of evaluation results near Plan A and searches for a capital investment plan (Plan B) that has low risk but not much different investment effectiveness. In this way, it becomes possible to select a capital investment plan that strikes a good balance between capital investment effectiveness and risk. Furthermore, because evaluation results are obtained by setting expected risk factors and the fluctuation range of risk factors, it is possible to clarify the risks expected in capital investment and obtain a capital investment plan that takes various risks into consideration.

[0078] The present invention is not limited to the above-described embodiments, but includes various modifications and equivalent configurations within the spirit and scope of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to configurations including all of the described configurations. Furthermore, part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Furthermore, the configuration of another embodiment may be added to the configuration of one embodiment. Furthermore, part of the configuration of each embodiment may be added, deleted, or replaced with other configurations.

[0079] Furthermore, the aforementioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole in hardware, for example by designing them as integrated circuits, or may be realized in software by a processor interpreting and executing a program that realizes each function.

[0080] Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), or in a recording medium such as an IC card, an SD card, or a DVD.

[0081] In addition, the control lines and information lines shown are those that are considered necessary for explanation, and do not necessarily represent all the control lines and information lines that are necessary for implementation. In reality, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0082] 100 Investment decision support system 101 processors 102 memory 103 Auxiliary storage device 104 Communication Interface 105 Input Interface 106 keyboard 107 Mouse 108 Output Interface 110 User input section 120 Prediction Department 130 Calculation and Evaluation Unit 131 Environmental Simulation Department 132 Short-Term Operational Evaluation Department 133 Long-Term Investment Evaluation Department 134 Investment Evaluation Department 135 Optimization Department 136 Risk Assessment Department 140 Result output section 150 databases

Claims

1. An investment decision support system that presents the effects of capital investment in a regional energy system, The computer is configured by an arithmetic unit that executes predetermined arithmetic processing and a storage device connected to the arithmetic unit, The local energy system includes one or more grids including at least one of a power generation device and a power storage device, and a vehicle equipped with a storage battery, The investment decision support system comprises: an input unit in the computing device that presents options of risk factors to be considered in the prediction to a user and receives selection of risk factors from the user and setting of an expected fluctuation range for each of some of the risk factors; a prediction unit in the computing device that receives as input past and current data including data on the periphery of the grid and data on costs required for introducing, maintaining, and operating the local energy system, as well as risk factors and expected fluctuation range settings selected by the user, and generates future prediction data including prediction data on the periphery of the grid and data on estimated costs required for introducing, maintaining, and operating the local energy system; an environmental simulation unit configured to simulate the behavior of the local energy system using a model representing the behavior of the environment including the grid, the past and present data, and the future prediction data; a short-term operation evaluation unit configured to receive an output from the environmental simulation unit and use a short-term operation algorithm to calculate an operation method for the grid to optimize daily power operation; the computing device has a long-term investment evaluation unit that uses an output from the prediction unit and an output from the environmental simulation unit to output a long-term investment evaluation result including effects and risks of an investment in installing equipment in the grid, The long-term investment evaluation unit is configured by the calculation device: Calculating the risk of the investment based on the risk factors selected by the user and the expected fluctuation range for at least one of the risk factors; An investment decision support system that generates data for displaying the fluctuation range of investment effects based on the calculated risk.

2. An investment decision support system that presents the effects of capital investment in a regional energy system, The computer is configured by an arithmetic unit that executes predetermined arithmetic processing and a storage device connected to the arithmetic unit, The district energy system includes one or more grids including at least one of a power generation device and a power storage device; The investment decision support system comprises: an input unit in the computing device that presents options of risk factors to be considered in the prediction to a user and receives selection of risk factors from the user and setting of an expected fluctuation range for each of some of the risk factors; a prediction unit in the computing device that receives as input past and current data including data on the periphery of the grid and data on costs required for introducing, maintaining, and operating the local energy system, as well as risk factors and expected fluctuation range settings selected by the user, and generates future prediction data including prediction data on the periphery of the grid and data on estimated costs required for introducing, maintaining, and operating the local energy system; an environmental simulation unit configured to simulate the behavior of the local energy system using a model representing the behavior of the environment including the grid, the past and present data, and the future prediction data; a short-term operation evaluation unit configured to receive an output from the environmental simulation unit and use a short-term operation algorithm to calculate an operation method for the grid to optimize daily power operation; the computing device has a long-term investment evaluation unit that uses an output from the prediction unit and an output from the environmental simulation unit to output a long-term investment evaluation result including effects and risks of an investment in installing equipment in the grid, The long-term investment evaluation unit is configured by the calculation device: Calculating the risk of the investment based on the risk factors selected by the user and the expected fluctuation range for at least one of the risk factors; An investment decision support system that generates data for displaying the fluctuation range of investment effects based on the calculated risk.

3. 3. The investment decision support system according to claim 1 or 2, The investment decision support system is characterized in that the input unit receives an investment amount and a profit range for calculating the risk.

4. 3. The investment decision support system according to claim 1 or 2, The investment decision support system is characterized in that the long-term investment evaluation unit generates data for displaying a fluctuation range of profits based on the calculated risk.

5. 3. The investment decision support system according to claim 1 or 2, The investment decision support system is characterized in that the long-term investment evaluation unit generates data for displaying the frequency and duration of power shortages based on the calculated risk.

6. 3. The investment decision support system according to claim 1 or 2, The investment decision support system is characterized in that the input unit receives an upper limit of the time required for risk calculation.

7. 3. The investment decision support system according to claim 1 or 2, The investment decision support system is characterized in that the input unit receives a budget plan including changes in the amount of the investment over time and a target profit value.

8. 3. The investment decision support system according to claim 1 or 2, The investment decision support system is characterized in that the risk factors include weather fluctuations.

9. An investment decision support method in which an investment decision support system presents the effects of capital investment in a regional energy system, the investment decision support system is configured by a computer having an arithmetic unit that executes predetermined arithmetic processing and a storage device connected to the arithmetic unit, The local energy system includes one or more grids including at least one of a power generation device and a power storage device, and a vehicle equipped with a storage battery, The investment decision support method includes: an input step in which the computing device presents to a user options for risk factors to be considered in the prediction and receives from the user a selection of risk factors and a setting of an expected fluctuation range for each of some of the risk factors; a prediction step in which the computing device receives as input past and current data including data on the periphery of the grid and data on costs required for introducing, maintaining, and operating the local energy system, as well as risk factors and expected fluctuation range settings for each risk factor selected by the user, and generates future prediction data including prediction data on the periphery of the grid and data on estimated costs required for introducing, maintaining, and operating the local energy system; an environmental simulation procedure in which the computing device simulates the behavior of the local energy system using a model representing the behavior of the environment including the grid, the past and current data, and the future prediction data; a short-term operation evaluation procedure in which the computing device receives an output from the environmental simulation procedure and uses a short-term operation algorithm to calculate an operation method for the grid to optimize daily power operation; a long-term investment evaluation procedure in which the computing device uses an output of the prediction procedure and an output of the environmental simulation procedure to output a long-term investment evaluation result including effects and risks of an investment in installing equipment in the grid; The investment decision support method is characterized in that, in the long-term investment evaluation procedure, the computing device calculates the risk of the investment based on the risk factors selected by the user and the setting of an expected fluctuation range for at least one risk factor, and generates data for displaying the fluctuation range of the investment effect based on the calculated risk.

10. An investment decision support method in which an investment decision support system presents the effects of capital investment in a regional energy system, the investment decision support system is configured by a computer having an arithmetic unit that executes predetermined arithmetic processing and a storage device connected to the arithmetic unit, The district energy system includes one or more grids including at least one of a power generation device and a power storage device; The investment decision support method includes: an input step in which the computing device presents to a user options for risk factors to be considered in the prediction and receives from the user a selection of risk factors and a setting of an expected fluctuation range for each of some of the risk factors; a prediction step in which the computing device receives as input past and current data including data on the periphery of the grid and data on costs required for introducing, maintaining, and operating the local energy system, as well as risk factors and expected fluctuation range settings for each risk factor selected by the user, and generates future prediction data including prediction data on the periphery of the grid and data on estimated costs required for introducing, maintaining, and operating the local energy system; an environmental simulation procedure in which the computing device simulates the behavior of the local energy system using a model representing the behavior of the environment including the grid, the past and current data, and the future prediction data; a short-term operation evaluation procedure in which the computing device receives an output from the environmental simulation procedure and uses a short-term operation algorithm to calculate an operation method for the grid to optimize daily power operation; a long-term investment evaluation procedure in which the computing device uses an output of the prediction procedure and an output of the environmental simulation procedure to output a long-term investment evaluation result including effects and risks of an investment in installing equipment in the grid; The investment decision support method is characterized in that, in the long-term investment evaluation procedure, the computing device calculates the risk of the investment based on the risk factors selected by the user and the setting of an expected fluctuation range for at least one risk factor, and generates data for displaying the fluctuation range of the investment effect based on the calculated risk.

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