Business viability evaluation system, business viability evaluation method, and equipment operation control system

Abstract

To formulate decarbonization solutions while shortening the period from ordering decarbonization solutions to operational commencement.SOLUTION: In a business viability evaluation system, an investment simulator transmits to a planning simulator multiple equipment configurations, a rated output of each actual equipment included in each configuration, and a simulation evaluation period. The planning simulator transmits to the planning simulator multiple equipment configurations, a simulation evaluation period, and operation plans for each actual equipment included in the multiple configurations. The control simulator transmits to an investment simulator the system feasibility of the multiple equipment configurations. The planning simulator transmits evaluated KPI values for each equipment configuration, achieved by optimally operating each actual equipment included in the multiple equipment configurations. The planning simulator copies the processing program to the planning system of the equipment operation control system. The control simulator copies the control parameters to the control system of the equipment operation control system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a business feasibility evaluation system and a business feasibility evaluation method that transmit processing programs and control parameters for energy facilities, and an equipment operation control system that controls the operation of energy facilities based on the received processing programs and control parameters. [Background technology]

[0002] There is a need for global decarbonization to slow the progression of global warming. In response to calls for decarbonization from governments and other organizations, many of the entities implementing decarbonization are private companies. For private companies, decarbonization, i.e., devising innovative ways to use energy such as electricity and gas, is not their main business, and it is difficult for them to devise investment plans for appropriate decarbonization solutions, such as which energy facilities to install or upgrade and when, in order to meet the energy demands of their own factories and other facilities under their own management. Furthermore, after having consultants or other professionals devise investment plans, deciding to install decarbonization solutions, and placing orders, it is natural for them to want to start operating the actual facilities as soon as possible.

[0003] An example of a conventional technology used in the operational planning stage of decarbonization solutions is the energy system operational plan creation device described in Patent Document 1. For example, paragraph 0009 of the document states, "The present invention aims to create an operational plan while taking into account a long-term operational plan created with a planning period longer than the planning period of the target operational plan." Furthermore, claim 1 of the document states, "The energy system operational plan creation device creates an operational plan including operational plan values ​​used to control the operation of equipment managed by an energy system, characterized in that, when creating an operation plan for a predetermined planning period, the device includes a creation means for creating the operation plan using operational plan values ​​set for the end of a predetermined planning period in a long-term operational plan created in advance with a planning period that includes the predetermined planning period and is longer than the predetermined planning period as target values ​​for the end of the predetermined planning period." Furthermore, paragraph 0023 of the document states, "The equipment plan creation unit 11 inputs time-series data on energy supply and demand, etc., and creates an optimal equipment configuration that minimizes annual costs and CO2 emissions, i.e., the equipment configuration that the facility should have, as an equipment plan."

[0004] In this way, the energy system operation plan creation device of Patent Document 1 creates an equipment plan that minimizes annual costs and CO2 emissions, and then creates a long-term energy system operation plan. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2022-188870 Summary of the Invention [Problem to be solved by the invention]

[0006] However, Patent Document 1 does not mention updating the equipment plan, and does not create an energy system operation plan taking into account changes in equipment configuration (addition / reduction of equipment or updates) that inevitably occur after the energy system is put into operation.

[0007] Therefore, if there are any issues with the technology in Patent Document 1, they are that it does not mention the development of investment plans for decarbonization solutions, such as which energy facilities should be newly introduced or updated and when, and therefore does not assist business managers and others in making investment decisions; and that adjustment of the control parameters of the actual system is done by tuning at the on-site facility, as in the past, and therefore does not shorten the period from ordering the decarbonization solution to starting operation.

[0008] Therefore, the present invention aims to provide a business feasibility assessment system that can support the investment decisions of business managers and others by presenting the business feasibility of decarbonization solutions, and can shorten the period from ordering a decarbonization solution to the start of operation of the actual system. [Means for solving the problem]

[0009] To solve the above problem, for example, the configuration described in the claims is adopted. The present invention includes a plurality of means for solving the above problem, and one example thereof is a business feasibility evaluation system that transmits information to an equipment operation control system that controls a group of actual equipment in accordance with power demand, the business feasibility evaluation system having an investment simulator that evaluates the outlook for costs and CO2 emissions of the group of actual equipment, a planning simulator that generates an equipment operation plan for the group of actual equipment, and a control simulator that pre-determines the system feasibility of the group of actual equipment, the investment simulator transmitting a plurality of equipment configurations, the rated output of each actual equipment included in each equipment configuration, and an evaluation period for the simulation to the planning simulator, and the planning simulator transmitting the plurality of equipment configurations, the evaluation period for the simulation, and the evaluation period for the multiple equipment configurations to the control simulator. the control simulator transmits to the investment simulator an operation plan for each actual equipment included in a plurality of equipment configurations, the control simulator transmits to the investment simulator the system feasibility of the plurality of equipment configurations, the planning simulator transmits to the investment simulator an evaluation value of a KPI for each equipment configuration resulting from optimal operation of each actual equipment included in the plurality of equipment configurations, the planning simulator copies a processing program for the equipment configuration evaluated as having a viable system to the planning system of the equipment operation control system as a processing program for a group of actual equipment, and the control simulator copies control parameters for the equipment configuration evaluated as having a viable system to the control system of the equipment operation control system as control parameters for the group of actual equipment. [Effects of the Invention]

[0010] According to the business feasibility evaluation system of the present invention, by presenting the business feasibility of decarbonization solutions, it is possible to support the investment decisions of business managers and others, and to shorten the period from ordering a decarbonization solution to the start of operation of the actual system. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a functional block diagram of a business feasibility evaluation system and a facility operation control system according to an embodiment. [Figure 2]Screen image of input function 11 of Investment Simulator 10. [Figure 3] 10 shows the processing flow of the processing function 12 of the investment simulator 10. [Figure 4] Screen image of output function 13 of Investment Simulator 10. [Figure 5A] A management table T11 of facility configuration generated by the investment simulator 10. [Figure 5B] Management table T12 of equipment specifications generated by investment simulator 10. [Figure 5C] 1. A management table T13 for evaluation periods generated by the investment simulator 10. [Figure 6] Screen image of input function 21 of planning simulator 20. [Figure 7] 10 shows a processing flow of the processing function 22 of the planning simulator 20. [Figure 8] Screen image of output function 23 of Planning Simulator 20. [Figure 9A] KPI table T21 for each facility configuration generated by the planning simulator 20. [Figure 9B] An equipment operation plan table T22 for each equipment configuration generated by the plan simulator 20. [Figure 9C] A planning value table T23 of time series data of power demand and PV power generation generated by the planning simulator 20. [Figure 10] 10 is a screen image of the input function 31 of the control simulator 30. [Figure 11] 3 shows a processing flow of the processing function 32 of the control simulator 30. [Figure 12] Screen image of output function 33 of control simulator 30. [Figure 13A] A system feasibility table T31 for each facility configuration generated by the control simulator 30. [Figure 13B] A control parameter table T32 generated by the control simulator 30. [Figure 14] 10 is a screen image of the input function 41 of the planning system 40. [Figure 15]10 is a screen image of the input function 41 of the planning system 40. [Figure 16] 1 is a process flow of the forecasting function 42 of the planning system 40. [Figure 17] Processing flow of processing function 43 of planning system 40. [Figure 18] 1 is a screen image of an output function 44 of the planning system 40. [Figure 19A] A management table T41 of equipment configuration generated by the planning system 40. [Figure 19B] An equipment operation plan table T42 for each equipment configuration generated by the planning system 40. [Figure 19C] A KPI table T43 for each facility configuration generated by the planning system 40. [Figure 20] 10 is a screen image of an input function 51 of a control system 50. [Figure 21] 10 shows a processing flow of the processing function 52 of the control system 50. [Figure 22] A screen image of an output function 53 of a control system 50. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, an embodiment of a business feasibility evaluation system and a facility operation control system according to the present invention will be described with reference to the accompanying drawings.

[0013] FIG. 1 is a functional block diagram of a business feasibility evaluation system 1 and a facility operation control system 2 according to one embodiment. As shown in this diagram, the business feasibility evaluation system 1 is composed of an investment simulator 10, a planning simulator 20, and a control simulator 30, while the facility operation control system 2 is composed of a planning system 40 and a control system 50. To meet the demand for electricity under self-management at a low cost while also responding to government and other demands for decarbonization, a group of actual facilities, consisting of a combination of various energy facilities 60 (storage batteries 61, private power generation facilities 62, and photovoltaic (PV) power generation facilities 63), is set up under self-management as a power source other than the power grid 80. Sensor 71 in the diagram measures the electricity demand under self-management and transmits the measured value to control system 51, and sensor 72 measures the PV power generation amount of the photovoltaic power generation facilities 63 and transmits the measured value to control system 51.

[0014] Here, each of the investment simulator 10, the planning simulator 20, the control simulator 30, the planning system 40, and the control system 50 is specifically a computer equipped with hardware such as a calculation device such as a CPU, a storage device such as a semiconductor memory, and a communication device. The calculation device executes a predetermined program to realize each function described below, but the following explanation will omit such well-known techniques as appropriate. Note that the following explanation will be given assuming that a dedicated computer is provided for each function, but multiple functions may also be realized by a single computer.

[0015] To outline the processing of the present invention chronologically, first, in the feasibility assessment stage, a feasibility assessment system 1 is used to generate candidate decarbonization solutions that meet the energy demand under self-management. Then, based on the feasibility assessment results of each candidate, the business manager or the like orders an actual system equivalent to one of the decarbonization solutions. In the subsequent actual system construction stage, by utilizing an equipment operation control system 2 that reflects the various data obtained during the feasibility assessment, it is possible to appropriately implement equipment group control that achieves decarbonization and cost reductions without requiring on-site work such as tuning, which was previously required. The details of the feasibility assessment system 1 and the equipment operation control system 2 that operate in this way are explained below in order.

[0016] <Business feasibility evaluation system 1> The business feasibility assessment system 1 is a system used at the stage of assessing the business feasibility of decarbonization solutions, and is implemented on the cloud or elsewhere. Based on the estimated costs and CO2 emissions for each facility configuration obtained here, business managers and others can decide to invest in decarbonization solutions. Each simulator within the business feasibility assessment system 1 will be explained in turn below.

[0017] <<Investment Simulator 10>> First, we will explain the investment simulator 10, which is the first simulator of the business feasibility evaluation system 1. The investment simulator 10 is a simulator that outputs forecasts of costs and CO2 emissions according to the equipment configuration of a decarbonization solution, and as shown in Figure 1, has an input function 11, a processing function 12, and an output function 13.

[0018] <<<Input function 11>>> Figure 2 is a screen image 11a of the input function 11. In this screen image 11a, the system designer or the like defines the proposed equipment configuration that is a decarbonization solution, and defines the evaluation period for estimating the costs (new equipment installation costs, electricity costs, fuel costs, etc.) and CO2 emissions for this equipment configuration.

[0019] First, we will explain "1. Definition of equipment configuration." Here, we will show three areas: "Module menu," "Building equipment configuration," and "Equipment specifications, price, and service life."

[0020] The "Module Menu" is pre-populated with modules such as receiving points, power demand, PV (photovoltaic power generation), storage batteries, private power generation equipment, and wiring. System designers can drag and drop the necessary modules from these modules into the "Build equipment configuration" area and connect the modules with wiring. In this example, we have shown an equipment configuration in which the power received from the receiving point is connected to PV, private power generation equipment, and storage batteries with wiring No. 1 to supply power to the power demand.

[0021] In the "Equipment specifications, price, and useful life" area, the system designer, etc., enters the rated output (kW), capacity (kWh), equipment cost (10,000 yen), and useful life (years) for each module included in the equipment configuration. After completing these settings, the system designer, etc., enters and registers the "equipment configuration number." In this example, the above equipment configuration is registered as number 001. By repeating the above process, it is possible to define multiple equipment configuration proposals, such as number 002, 003, etc.

[0022] The evaluation period registered in "2. Evaluation Period" is the period for which the costs and CO2 emissions incurred when operating an equipment configuration that realizes a decarbonization solution are estimated. In this example, a system designer or other person has entered one year, from 00:00 on April 1, 2022 to 23:00 on March 31, 2023, as the evaluation period. Note that the simulation time interval for calculating costs and CO2 emissions is assumed to be one hour, and the intended evaluation period is from 00:00 on April 1, 2022 to 23:00 on March 31, 2023.

[0023] <<<Processing Function 12>>> FIG. 3 is a flowchart of the processing performed by the processing function 12.

[0024] First, in S12a, the processing function 12 sequentially adds the equipment configuration defined in the "Equipment Configuration Construction" area of ​​Fig. 2 to the "Equipment Configuration Management Table T11" each time an equipment configuration number is registered in "1. Equipment Configuration Definition" of Fig. 2. Similarly, it sequentially adds the equipment specifications and useful life defined in the "Equipment Specification, Price, and Useful Life" area of ​​Fig. 2 to the "Equipment Specification Management Table T12", and sequentially adds the evaluation start date and evaluation end date defined in "2. Evaluation Period" of Fig. 2 to the "Evaluation Period Management Table T13".

[0025] FIG. 5A shows the format of the equipment configuration management table T11. This equipment configuration management table T11 defines the connection configuration between modules. The horizontal axis defines the equipment configuration number, module name, type of energy input to the module, the number of the wiring from which the input energy flows, the type of energy output from the module, and the number of the wiring to which the output energy flows. The vertical axis represents the equipment configuration number, which is added sequentially downward. For example, for a storage battery module, the energy flowing in is electric power (= charging power) and the energy flowing out is also electric power (= discharging power), and both the wiring number of the charging source and the wiring number of the discharging source are number 1.

[0026] FIG. 5B shows the format of the equipment specification management table T12. This equipment specification management table T12 defines the specifications of each module. The horizontal axis is defined by the equipment configuration number, module name, rated output (kW) of the module, capacity (kWh) of the module, price (10,000 yen), and useful life (years). The vertical axis is the equipment number, which is added sequentially downward. For example, a storage battery module is recorded as having a rated output of 100 kW, capacity of 500 kWh, price of 100 million yen, and useful life of 10 years.

[0027] FIG. 5C shows the format of the evaluation period management table T13. This evaluation period management table T13 defines the start date and time and the end date and time of the evaluation period for a simulation that estimates costs and CO2 emissions. Both the evaluation start point and the evaluation end point are defined using a date (YYMMDD) and a time (hhmm). Here, the evaluation period is defined as starting from 00:00 on April 1, 2022 and ending at 23:00 on March 31, 2023. As mentioned above, the time interval for the simulation that estimates costs and CO2 emissions is assumed to be one hour, and the evaluation period is intended to be from 00:00 on April 1, 2022 to 23:00 on March 31, 2023.

[0028] Next, in S12b, the processing function 12 transmits the above-mentioned equipment configuration management table T11, equipment specification management table T12, and evaluation period management table T13 to the plan simulator 20. Thereafter, the plan simulator 20 and the control simulator 30 perform processing using the management tables T11, T12, and T13 as materials. After a while, the plan simulator 20 and the control simulator 30 transmit the processing results (tables T21 and T31).

[0029] In S12c, the processing function 12 receives the "KPI table T21 for each equipment configuration" sent by the plan simulator 20. Here, the KPI table T21 for each equipment configuration will be described with reference to FIG. 9A. As shown in FIG. 9A, the KPI table T21 for each equipment configuration records the cumulative cost (in million yen) and cumulative CO2 emissions (t-CO2) for each equipment configuration calculated by the plan simulator 20. For example, in the case of equipment configuration 001, the cumulative cost is 15,000 (in million yen) and the cumulative CO2 emissions is 5,000 (t-CO2).

[0030] In S12d, the processing function 12 receives the "system feasibility table T31 for each equipment configuration" transmitted by the control simulator 30. Here, the system feasibility table T31 for each equipment configuration will be described with reference to FIG. 13A. As shown in FIG. 13A, the system feasibility table T31 for each equipment configuration records the system feasibility for each equipment configuration determined by the control simulator 30. System feasibility is an index indicating whether a supply-demand balance can always be maintained under the conditions of the contracted power and the equipment specifications (whether the required energy can always be supplied to the demand side) even in a situation where power demand and PV power generation amount fluctuate frequently. Note that, as shown in FIG. 13A, a system feasibility of "1" indicates system feasibility (the required energy can always be supplied), and a system feasibility of "0" indicates system infeasibility (there are time periods when the required energy cannot be supplied). In the example of FIG. 13A, equipment configurations 001 and 003 are determined to be system feasible, and equipment configuration 002 is determined to be system infeasible.

[0031] In S12e, the processing function 12 draws, as a Pareto solution, the equipment configuration that has been determined to be a viable system by the control simulator 30. Drawing of the Pareto solution will be described with reference to FIG.

[0032] <<<Output Function 13>>> Figure 4 is a screen image 13a of the output function 13. On this screen, the positioning of each equipment configuration is shown as a Pareto solution on a coordinate system where the horizontal axis is cumulative cost and the vertical axis is cumulative CO2 emissions. As materials, KPI table T21 for each equipment configuration and system feasibility table T31 for each equipment configuration are used. The plots drawn as Pareto solutions are limited to equipment configurations that result in a viable system.

[0033] That is, the data in the KPI table T21 for each equipment configuration is limited to those determined to be system viable in the system feasibility table T31 for each equipment configuration. In this example, equipment configurations 001 and 003 are determined to be system viable (value 1) in table T31, so in Figure 4, equipment configurations 001 and 003 recorded in the KPI table T21 for each equipment configuration are displayed as Pareto solutions.

[0034] <<Planning Simulator 20>> Next, we will explain the plan simulator 20, which is the second simulator of the business feasibility evaluation system 1. The plan simulator 20 is a simulator that generates an optimal operation plan for each piece of equipment in the equipment configuration of a decarbonization solution so as to minimize costs and CO2 emissions, and as shown in Figure 1, has an input function 21, a processing function 22, and an output function 23.

[0035] <<<Input function 21>>> Figure 6 is a screen image 21a of the input function 21. Here, the system designer or the like defines three areas: "1. Time-series data planned value," "2. Fee unit price and CO2 emission coefficient," and "3. Optimal processing KPI."

[0036] In "1. Time-series data planning values," system designers set the power demand (kW) and PV power generation (kW) for each date and time. These power demand and PV power generation values ​​are used in common to simulate costs and CO2 emissions for each facility configuration. The data entered here is, for example, the average of past performance data for each date and time.

[0037] In "2. Unit Price and CO2 Emission Coefficient," system designers and others input the unit price (electricity, gas) and CO2 emission coefficient (electricity, gas) for each date and time. The data input here is, for example, the average of past performance data for each date and time.

[0038] In "3. Optimal Processing KPI," system designers select KPIs (Key Performance Indicators), which are the objective functions when generating optimal operation plans for each piece of equipment to minimize costs and CO2 emissions. Here, we show a method for selecting either cost minimization or CO2 emissions minimization as the KPI.

[0039] <<<Processing Function 22>>> 7 is a flowchart of the processing performed by the processing function 22. This processing is executed, for example, at one-hour intervals.

[0040] First, in S22a, the processing function 22 reads the equipment configuration management table T11, the equipment specification management table T12, and the evaluation period management table T13 sent from the investment simulator 10, and recognizes the recorded contents.

[0041] S22b is a loop based on the equipment configuration number. Therefore, the processes of S22c, S22d, and S22e, which will be described later, are looped in the order of the equipment configurations.

[0042] In S22c, the processing function 22 reads the time-series data plan value of the power demand, the time-series data plan value of the PV power generation, the unit price, the CO2 emission coefficient, and the KPI settings input by the input function 21, and recognizes the recorded contents.

[0043] In S22d, the processing function 22 generates an optimal operation plan for each piece of equipment so as to maximize the KPI, i.e., minimize costs or CO2 emissions, and calculates the resulting cumulative cost and cumulative CO2 emissions. The cumulative cost is calculated using Equation 1, and the cumulative CO2 emissions are calculated using Equation 2.

[0044]

number

[0045]

number

[0046] however, TotalCost is the cumulative cost (in yen) TotalCO2 is cumulative CO2 emissions (t-CO2) n is the facility type Cx(n) is the equipment cost of equipment n (in millions of yen) L(n) is the useful life (years) of equipment n Y is the evaluation period (years) t is the simulation cross section number ty is the number of the last simulation section of the evaluation period (if the evaluation period is one year, ty is 8760), and P(t) is the received power (kW) at simulation section t. Ut is the simulation time interval (h) Upe(t) is the electricity price (yen / kWh) at simulation cross section t Cce(t) is the CO2 emission coefficient for electricity at simulation time t (t-CO2 / kWh) G(t) is the gas consumption (Nm 3 ) Upg(t) is the gas price (yen / Nm 3 ) Ccg(t) is the CO2 emission coefficient of gas at simulation time point t (t-CO2 / Nm 3 ) is.

[0047] In S22e, the processing function 22 records the cumulative cost and cumulative CO2 emissions calculated in S22d in a KPI table T21 (FIG. 9A) for each equipment configuration, and also records them in an equipment operation plan table T22 for each equipment configuration, and transmits them to the control simulator 30. In addition, the KPI table T21 for each equipment configuration is transmitted to the investment simulator 10. Here, the equipment operation plan table T22 for each equipment configuration will be described.

[0048] Figure 9B shows the format of the equipment operation plan table T22 for each equipment configuration. The equipment configuration number, date, time, and load factor for each equipment type are arranged horizontally. Here, the equipment types shown are private power generation equipment and storage batteries, but other equipment elements may also be included. Generally, the load factor is expressed as the ratio of actual output (kW, etc.) to rated output (kW, etc.). Here, the load factor of a storage battery is defined as the charge / discharge output (kW) relative to the rated output (kW), and one proposal is to describe charging as negative and discharging as positive, for example.

[0049] Furthermore, in S22e, the processing function 22 records the planned values ​​of the time-series data of power demand and PV power generation set by the input function 21 in a "power demand and PV power generation time-series data planned value table T23" and transmits it to the control simulator 30. Here, the format of the power demand and PV power generation time-series data planned value table T23 will be described with reference to FIG. 9C. As shown here, the power demand and PV power generation time-series data planned value table T23 records the power demand and PV power generation time-series data for each date and time. Each value is the same as the value set by the input function 21.

[0050] <<<Output Function 23>>> Figure 8 is a screen image 23a of the output function 23. In this example, two display areas are provided: "1. Accumulative cost and cumulative CO2 emissions" and "2. Operation plan." The "1. Accumulative cost and cumulative CO2 emissions" area displays the contents of the KPI table T21 (Figure 9A) for each equipment configuration on a coordinate system where the horizontal axis is cumulative cost and the vertical axis is cumulative CO2 emissions. The "2. Operation plan" area displays the recorded contents of the equipment operation plan table T22 (Figure 9B) for each equipment configuration. Note that Figure 8 shows an example of displaying the load factor for each time section of each piece of equipment.

[0051] <<Control Simulator 30>> Next, we will explain the control simulator 30, which is the third simulator of the business feasibility evaluation system 1. The control simulator 30 is a simulator that assumes a situation in which power demand and PV-generated power fluctuate frequently for the equipment configuration of a decarbonization solution, establishes control parameters while ensuring a supply-demand balance, and determines in advance whether constraints such as received power not exceeding contracted power are met (system feasibility), and as shown in Figure 1, has an input function 31, a processing function 32, and an output function 33.

[0052] <<<Input Function 31>>> 10 is a screen image 31a of the input function 31. Here, the system designer or the like defines two areas: "1. Definition of detailed values ​​of time-series data" and "2. System establishment conditions."

[0053] In "1. Definition of detailed time-series data values," the system designer or the like inputs the power demand and PV power generation for each date and time. In this case, the time interval is set to be shorter than the time interval set in the input function 21 of the planning simulator 20. For example, whereas one year's worth of data is set at one-hour intervals in the input function 21 of the planning simulator 20, data is input here at one-second intervals for 24 hours. This allows for a more realistic representation of fluctuations. Note that in order to perform a simulation at one-second intervals, an example is shown in which 24 hours' worth is used instead of one year's worth in order to shorten the processing time.

[0054] In "2. System establishment conditions," the system designer etc. selects either the received power (kW) does not exceed the upper limit value, or the rate of change of the received power (kW) does not exceed the upper limit value as a method for determining whether the system is established. Note that other criteria than those shown as examples may also be registered as "2. System establishment conditions."

[0055] <<<Processing Function 32>>> 11 is a flowchart of the process performed by the processing function 32. This process is executed at intervals of, for example, one second.

[0056] In S32a, the processing function 32 receives the facility configuration management table T11, the facility specification management table T12, and the time-series data plan value table T23 of power demand and PV power generation from the plan simulator 20 and recognizes the recorded contents.

[0057] In S32b, the processing function 32 reads and recognizes the detailed time-series data values ​​of the power demand, the detailed time-series data values ​​of the PV power generation, and the system feasibility input to the input function 31.

[0058] S32c is a loop based on the equipment configuration number. Therefore, the processes of S32d, S32e, S32f, and S32g, which will be described later, are looped in the order of the equipment configurations.

[0059] In S32d, the processing function 32 receives the equipment operation plan table T22 (FIG. 9B) for each equipment configuration from the plan simulator 20 as the planned value of the equipment load factor of the equipment configuration currently being processed, and recognizes the recorded content.

[0060] In S32e, the processing function 32 sets control parameters for each piece of equipment to maintain a balance between supply and demand. The control parameters set here are, for example, gain values ​​in PID control.

[0061] In S32f, the processing function 32 determines whether the system is viable, records the result in the “system viability table T31 for each facility configuration,” and transmits the table to the investment simulator 10.

[0062] In S32g, the processing function 32 records the control parameters established in S32e in the "control parameter table T32." An example of the format of the control parameter table T32 is shown in Fig. 13B. As shown here, the control parameter table T32 records the control parameters of each piece of equipment for each equipment configuration number.

[0063] <<<Output Function 33>>> 12 is a screen image 33a of the output function 33. In this example, two display areas are provided: "1. System feasibility" and "2. Control parameters." The "1. System feasibility" area displays the recorded contents of the system feasibility table T31 for each equipment configuration, and the "2. Control parameters" area displays the recorded contents of the control parameter table T32.

[0064] <Facility operation control system 2> The facility operation control system 2 is an actual system that will be constructed after receiving an order for the decarbonization solution. Each subsystem within the facility operation control system 2 will be explained below in order.

[0065] <<Planning System 40>> First, we will explain the planning system 40, which is the first subsystem of the equipment operation control system 2. The planning system 40 is a system that generates an optimal equipment operation plan for an actual system, and as shown in Fig. 1, it has an input function 41, a prediction function 42, a processing function 43, and an output function 44. A feature of this planning system 40 is that it transcribes and utilizes a processing algorithm (or processing program) that generates an optimal operation plan from the plan simulator 20.

[0066] <<<Input function 41>>> FIG. 14 shows a first screen image 41a of the input function 41, and FIG. 15 shows a second screen image 41b displayed as a continuation of the first screen image 41a.

[0067] First, in the first screen image 41a in Fig. 14, the system designer or the like defines the equipment configuration of the actual system and the target period for generating an optimal operation plan using the same operations as for the screen image 11a in Fig. 2. That is, the area "1. Equipment configuration definition" has three areas, similar to the input function 11: "Module menu," "Equipment configuration construction," and "Equipment specifications, price, and useful life."

[0068] The "Module Menu" is pre-populated with modules such as power receiving points, power demand, PV, storage batteries, private power generation equipment, and wiring. System designers can drag and drop the necessary modules from these modules into the "Build Equipment Configuration" area and connect the modules with wiring. In this example, we have shown an equipment configuration in which power received from the power receiving point is connected to PV, private power generation equipment, and storage batteries with wiring No. 1 to supply power to the power demand.

[0069] In the "Equipment Specifications, Price, and Service Life" area, system designers, etc., enter the rated output (kW), capacity (kWh), equipment cost (10,000 yen), and service life (years) for each module included in the equipment configuration. After completing these settings, they enter and register the "Equipment Configuration Number." In this example, the equipment configuration is registered as number 001. The equipment configuration registered here is assumed to be one of the multiple decarbonization solution proposals considered in the investment simulator 10, planning simulator 20, and control simulator 30, which will be constructed as an actual equipment group. Since only one actual equipment group will be constructed, only one equipment configuration (equipment configuration number 001) is registered in the first screen image 41a.

[0070] The evaluation period registered in "2. Evaluation Period" is the future period for which the optimal operation plan for the equipment will be generated, and the end time is determined using the current time as the starting point. In this example, the current time is assumed to be 00:00 on July 1, 2023, and the evaluation period is set to the 24 hours from that time until 23:00 on July 1, 2023. Note that the optimal operation plan is assumed to be generated at one-hour intervals, and in this case, the intended target period is from 0:00 on July 1, 2023 to 23:00 on July 1, 2023.

[0071] Next, in the second screen image 41b of FIG. 15, the system designer or the like defines three items: "3. Time series data planned value," "4. Fee unit price and CO2 emission coefficient," and "5. Optimal processing KPI."

[0072] In "3. Time-series data planned value," the system designer etc. sets the predicted value for the time period corresponding to "2. Evaluation period," which is the power demand (kW) and PV power generation (kW) for each date and time.

[0073] In "4. Unit Price and CO2 Emission Coefficient," the system designer etc. inputs the unit price (electricity, gas) and CO2 emission coefficient (electricity, gas) for each date and time.

[0074] In "5. Optimal Processing KPI," system designers select KPIs to generate optimal operation plans for each piece of equipment to minimize costs and CO2 emissions. Here, we show a method for selecting either cost minimization or CO2 emissions minimization.

[0075] <<<Prediction Function 42>>> FIG. 16 is a flowchart of the process performed by the prediction function 42.

[0076] First, in S42a, the prediction function 42 recognizes the evaluation period registered in the first screen image 41a of Fig. 14. In this example, as shown in Fig. 14, the evaluation period is set to be from midnight on July 1, 2023 to 11pm on July 1, 2023.

[0077] Next, in S42b, the prediction function 42 reads the actual power demand values ​​for the same days of the week as the target period for a certain period in the past and calculates the average value for each time. The actual power demand values ​​are data measured by the sensor 71 and stored in a memory unit (not shown).

[0078] Finally, in S42c, the prediction function 42 reads the PV power generation result value for a certain period in the past and calculates the average value at each time. The PV power generation result value is data measured by the sensor 72 and stored in a storage unit (not shown).

[0079] <<<Processing Function 43>>> Fig. 17 is a flowchart of the processing performed by the processing function 43 after the prediction processing by the prediction function 42. A feature of this processing function 43 is that it copies and uses the processing algorithm prepared by the processing function 22 of the planning simulator 20. Therefore, when constructing the processing function 43 of the planning system 40, there is no need to go through the trouble of newly reviewing, coding, and implementing a program, which can shorten the time until the actual system starts operating. The processing of the processing function 43 is executed, for example, at one-hour intervals.

[0080] First, in S43a, the processing function 43 reads the equipment configuration, equipment price, useful life, and evaluation period input via the first screen image 41a of the input function 41 (FIG. 14).

[0081] S43b is a loop using the equipment configuration number, but since there is only one pattern for the configuration of the actual equipment group, the subsequent processing is executed only once.

[0082] In S43c, the processing function 43 reads the time series data planned value of power demand, the time series data planned value of PV power generation, the unit price, the CO2 emission coefficient, and the KPI settings input via the second screen image 41b (Figure 15) of the input function 41, and recognizes the recorded contents.

[0083] In S43d, the processing function 43 generates an optimal operation plan for each piece of equipment to maximize the KPI selected via the second screen image 41b (FIG. 15) of the input function 41, i.e., to minimize costs or CO2 emissions, and calculates the resulting cumulative cost and cumulative CO2 emissions. Note that the initial value of the remaining battery capacity is the value at the current time. The cumulative cost and cumulative CO2 emissions are calculated using Equations 3 and 4.

[0084]

number

[0085]

number

[0086] however, Y is the evaluation period (years) (the evaluation period is 24 hours, so 24 hours / 8760 hours = 0.003 years) ty is the number of the last simulation cross section in the evaluation period (since the evaluation period is 24, ty is 24), is.

[0087] In S43e, the processing function 43 records the configuration of the actual equipment group in the "equipment configuration management table T41" and also in the "equipment operation plan table T42 for each equipment configuration," and records the cumulative cost and cumulative CO2 emissions calculated in S43d in the KPI table T43 for each equipment configuration and transmits them to the control system 50. Note that FIG. 19A shows the format of the equipment configuration management table T41, FIG. 19B shows the format of the equipment operation plan table T42 for each equipment configuration, and FIG. 19C shows the format of the KPI table T43 for each equipment configuration. As is obvious from a comparison of the formats of each table, the formats of tables T41, T42, and T43 are the same as the management table T11 in FIG. 5A, table T22 in FIG. 9B, and table T21 in FIG. 9A, respectively.

[0088] <<<Output Function 44>>> 18 is a screen image 44a of the output function 44. Here, two display areas are prepared: "1. Accumulated cost and accumulated CO2 emissions" and "2. Operation plan."

[0089] In the "1. Accumulated Cost" area, the horizontal axis represents accumulated cost and the vertical axis represents accumulated CO2 emissions, and the position according to the KPI recorded in the KPI table T43 (FIG. 19C) for each facility configuration is displayed on the coordinate system.

[0090] In the "2. Operation Plan" area, the recorded contents of the equipment operation plan table T42 (FIG. 19B) for each equipment configuration are displayed, and an example is shown in which the load factor for each time section of each piece of equipment is displayed.

[0091] <<Control System 50>> Next, we will explain the control system 50, which is the second subsystem of the equipment operation control system 2. The control system 50 is a system that generates and outputs control commands for each piece of equipment based on real-time measurement values ​​of power demand and PV power generation, and as shown in Fig. 1, has an input function 51, a processing function 52, and an output function 53. A feature of this control system 50 is that when generating control commands, it transcribes and utilizes the values ​​of control parameters that have been established in advance by the control simulator 30.

[0092] <<<Input function 51>>> FIG. 20 is a screen image 51a of the input function 51. Here, an input area for "1. Detailed time-series data values" is provided. For this "1. Detailed time-series data values", the current power demand and PV power generation measured by sensors 71 and 72 are input at 1-second intervals. In this example, the state at 12:00:00 on July 1, 2023 is entered.

[0093] <<<Processing Function 52>>> 21 is a flowchart of the processing performed by the processing function 52. A feature of this processing function 52 is that it copies and uses the control parameters prepared by the processing function 32 of the control simulator 30. Therefore, when constructing the processing function 52 of the control system 50, there is no need to perform new tuning, which can shorten the time until the actual equipment group starts operating. This processing is executed at intervals of, for example, one second.

[0094] First, in S52a, the processing function 52 reads the management table T41 (FIG. 19A) of the equipment configuration output by the planning system 40, and recognizes the equipment configuration defined in the table.

[0095] Next, in S52b, the processing function 52 reads and recognizes the real-time power demand and real-time PV power generation defined in the input function 51.

[0096] In S52c, the processing function 52 reads the equipment operation plan table T42 (FIG. 19B) for each equipment configuration output by the planning system 40, and recognizes the operation plan for each equipment defined in the table.

[0097] In S52d, the processing function 52 reads and recognizes the control parameter table T32 (FIG. 13B) output by the control simulator 30. At this time, the control parameter having the same number as the registration number of the actual equipment group is recognized.

[0098] Finally, in S52e, the processing function 52 controls various facilities to maintain a balance between supply and demand by using the control parameters recognized in S52d.

[0099] <<<Output Function 53>>> 22 is a screen image 53a of the output function 53. Here, an example is shown in which the control command value is displayed as a load factor for each facility type.

[0100] <Effects of this Example> According to the business feasibility evaluation system and equipment operation control system of this embodiment described above, by presenting the business feasibility of decarbonization solutions, it is possible to support the investment decisions of business managers and others, and to shorten the period from ordering a decarbonization solution to the start of operation of the actual equipment.

[0101] It should be noted that the present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to an embodiment having all of the described configurations. [Explanation of symbols]

[0102] 1. Business feasibility evaluation system 2. Equipment operation control system 10 Investment Simulator 11 Input Function 12 Processing Functions 13 Output Function 20 Planning Simulator 21 Input Function 22 Processing Functions 23 Output Function 30 Control Simulator 31 Input Function 32 Processing Functions 33 Output Function 40 Planning System 41 Input Function 42 Prediction Function 43 Processing Functions 44 Output Function 50 Control System 51 Input Function 52 Processing Functions 53 Output Function 60 Energy Facilities 61 Storage battery 62 Private power generation facilities 63 Photovoltaic power generation facilities (PV) 71, 72 Sensors 80 Power system

Claims

1. A business feasibility evaluation system that transmits information to an equipment operation control system that controls a group of actual equipment in accordance with power demand, Costs and CO 2 an investment simulator to assess emissions projections; a plan simulator that generates an equipment operation plan for the actual equipment group; a control simulator that determines in advance whether the actual equipment group is a system viable; The investment simulator transmits to the planning simulator a plurality of facility configurations, a rated output of each actual facility included in each facility configuration, and an evaluation period for the simulation; the planning simulator transmits to the control simulator the plurality of equipment configurations, an evaluation period of the simulation, and an operation plan of each actual equipment included in the plurality of equipment configurations; the control simulator transmits the system feasibility of the plurality of facility configurations to the investment simulator; the planning simulator transmits to the investment simulator an evaluation value of a KPI for each facility configuration obtained by optimally operating each actual facility included in the plurality of facility configurations; the planning simulator copies the processing program for the equipment configuration evaluated as a system feasible into the planning system of the equipment operation control system as a processing program for the actual equipment group; The control simulator copies control parameters for an equipment configuration that has been evaluated as a viable system to the control system of the equipment operation control system as control parameters for an actual equipment group.

2. The business feasibility evaluation system according to claim 1, The actual facility is a storage battery, a private power generation facility, or a solar power generation facility connected to a wiring that connects a receiving point of a power grid with an electric power demand, The KPI is the cost minimization or CO 2 A business feasibility assessment system characterized by minimizing emissions.

3. 3. The business feasibility evaluation system according to claim 2, The business feasibility evaluation system is characterized in that the plan simulator calculates the evaluation value using an operation plan that maximizes the KPI.

4. 3. The business feasibility evaluation system according to claim 2, A business feasibility evaluation system characterized in that the control simulator determines that a system is viable for an equipment configuration in which the power received from the power grid does not exceed a predetermined upper limit value, or an equipment configuration in which the rate of change of the power received from the power grid does not exceed a predetermined upper limit value.

5. A business feasibility evaluation method for transmitting information to an equipment operation control system that controls a group of actual equipment in accordance with power demand, a step of transmitting, from the investment simulator to the planning simulator, a plurality of facility configurations, a rated output of each actual facility included in each facility configuration, and an evaluation period of the simulation; transmitting, from the planning simulator to a control simulator, the plurality of equipment configurations, an evaluation period of the simulation, and an operation plan for each actual equipment included in the plurality of equipment configurations; a step of transmitting, from the planning simulator to the investment simulator, an evaluation value of a KPI for each facility configuration obtained by optimally operating each actual facility included in the plurality of facility configurations; transmitting system feasibility of the plurality of facility configurations from the control simulator to the investment simulator; copying a processing program for an equipment configuration evaluated as a viable system from the planning simulator to a planning system of the equipment operation control system as a processing program for an actual equipment group; copying control parameters for an equipment configuration evaluated as a viable system from the control simulator to a control system of the equipment operation control system as control parameters for an actual equipment group; A business feasibility evaluation method comprising:

6. 5. A facility operation control system that receives the processing program and the control parameters from the business feasibility evaluation system according to claim 1, the planning system generates an operation plan for the actual facility group using the processing program received from the plan simulator; The control system outputs a control signal for the group of actual facilities by utilizing the control parameters received from the control simulator.

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