Energy facility operation plan creating device, energy facility operation plan creating method, and energy facility operation plan creating program

Abstract

To provide an energy facility operation plan creating device that repeatedly creates a plan by changing some of facility configurations, facility characteristics, and operating conditions, and creates a plan under different conditions for each date and time by specifying conditions on a calendar.SOLUTION: An energy facility operation plan creating device includes: an information recording unit that records energy system information chronologically; an input parameter generation unit that generates an input parameter from the recorded energy system information; a conversion-type facility condition generation unit that uses the input parameter to set at least an input resource amount and an output resource amount for each time as a decision variable and generates an output parameter that has a relationship between the input resource amount and the output resource amount as a constraint condition; and an optimal plan creation unit that calculates, using the output parameter obtained by the conversion-type facility condition generation unit, the input resource amount and the output resource amount for each time period of the conversion-type facility. The conversion-type facility condition generation unit generates, using information on a relationship between division linear variables, a division linear constraint condition between variables.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an energy equipment operation planning device, an energy equipment operation planning method, and an energy equipment operation planning program. [Background technology]

[0002] When creating an operation plan for energy equipment, even if the equipment configuration is the same, there may be cases where you want to change some of the operating conditions on a daily basis, such as maintenance dates and times, minimum load rates, continuous operation stop conditions, and operating conditions depending on the operating status of other equipment and the external environment.

[0003] Furthermore, there may be cases where it is necessary to switch the characteristics of some equipment in accordance with changes in characteristics due to deterioration of the equipment over time or changes in temperature such as the outside air temperature when formulating a plan.

[0004] Furthermore, because of the capital investment effect of assets, there may be cases where it is necessary to make plans by partially changing the equipment configuration.

[0005] In this regard, Patent Document 1 discloses that, for the purpose of providing a distributed energy system control device that can create an optimal operation plan even if there are changes in device characteristics due to air temperature, water temperature, or device deterioration, etc., it describes a distributed energy system control device that has one or more energy generation devices, one or more energy storage devices, and one or more energy loads, and creates operation plans for the energy generation devices and the energy storage devices of each consumer connected to a power grid, comprising: a modeling operation plan creating means for creating a modeling operation plan for an energy generation device and an energy storage device that are set in advance as a target for creating a modeling operation plan among the energy generation devices and the energy storage devices; and an output characteristics collecting means for collecting output characteristics of devices operated based on the modeling operation plan. an output characteristic storage means for storing the output characteristics; an equipment characteristic modeling means for modeling equipment characteristics based on the stored output characteristics; a demand prediction means for predicting a demand for the energy load; and an optimal operation plan creation means for creating an optimal operation plan using a method for solving a combinatorial optimization problem from the modeled equipment characteristics and the predicted demand, so as to minimize a running cost, which is the sum of a fuel cost incurred when the energy generating device generates energy and a system power cost, which is an integrated value of the amount of money for buying and selling electricity from the generated energy at the electricity price of the power system, while the amount of energy supply from at least one of the energy generating device and the energy storage device and the power system satisfies the energy demand for the energy load in all time periods. A distributed energy system control device characterized by having the following configuration is proposed. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-325336 Summary of the Invention [Problem to be solved by the invention]

[0007] According to Patent Document 1, a method is provided for defining an optimization problem and formulating a plan when the equipment configuration, equipment characteristics, and operating conditions are given, but the entire optimization problem must be recreated every time the conditions change.

[0008] That is, when one wants to create an annual operation plan based on a calendar in which the operation conditions vary depending on the date and time, when one wants to create an operation plan under multiple degradation conditions to estimate the impact of aging degradation, or when one wants to create an operation plan under multiple equipment configurations to estimate the effect of capital investment, the optimization problem will be recreated, but Patent Document 1 and others do not provide a means for switching conditions in this regard and efficiently creating repeated operation plans.

[0009] To facilitate the re-creation of optimization problems, it is necessary to prepare the appropriate inputs required for planning in advance. Then, it is desirable to model the relationships between equipment variables and the complex operating conditions with high accuracy, and reflect this in the planning of operational status in a short time. However, to do this, it is necessary to build a model that can solve the following three issues, and then it is desirable to create a plan in a short time.

[0010] The first challenge here is that there are many complex relationships between variables in power generation equipment. For example, in a gas turbine, the amount of combustion in the first stage determines the upper limit of the amount of combustion that can be performed in the second stage, or the relationship between the input fuel and the amount of power generated is not linear but rather saturation occurs.

[0011] The second issue is that there are many cases where the processing content changes when the operating conditions of the power generation equipment change, which requires if-then type condition switching. For example, the power generation efficiency differs when the cooling water temperature is below 15°C and above.

[0012] The third challenge is that in addition to the above, the following conditions must also be observed. These conditions include minimum output conditions, such as not allowing trickle operation and, if operating, requiring operation at a specified output value or higher; continuous operation / stop conditions, such as not being able to or not wanting to stop operation for a while once started; and constraints on the rate of change of variables per hour, such as not being able to suddenly change the amount of power generated. For this reason, it is important to create a model that accurately reflects these complex equipment characteristics and operation-related conditions.

[0013] Although problem 3 itself is a known technology from a paper, the present invention realizes all problems, including problems 1 and 2, on the same model, and then creates a plan in a short time.

[0014] In view of the above, an object of the present invention is to provide an energy equipment operation planning device, an energy equipment operation planning method, and an energy equipment operation planning program that are capable of repeatedly creating plans by changing parts of the equipment configuration, equipment characteristics, and operating conditions, and designating conditions on a calendar to create plans under different conditions for each date and time. [Means for solving the problem]

[0015] In view of the above, the present invention provides an energy equipment operation plan planning device that plans an operation plan for equipment in an energy system that combines a plurality of equipment including conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, the energy equipment operation plan planning device comprising: an information recording unit that chronologically obtains and records information about the energy system; an input parameter generation unit that generates input parameters from the energy system information recorded in the information recording unit; a conversion-type equipment condition generation unit that, for each conversion-type equipment, uses the input parameters to determine at least the input resource amount and output resource amount for each time period, and generates output parameters with the relationship between the input resource amount and the output resource amount as constraint conditions; and an optimal plan planning unit that calculates the input resource amount and output resource amount for each time period of the conversion-type equipment using at least the output parameters determined by the conversion-type equipment condition generation unit, wherein the conversion-type equipment condition generation unit has a component that, when generating the output parameters, generates piecewise linear constraint conditions between variables using information about the relationship between piecewise linear variables included in the input parameters.

[0016] Furthermore, the present invention is described as "an energy equipment operation planning method for using a computer to plan an operation plan for equipment in an energy system that combines multiple pieces of equipment, including conversion-type equipment that takes one or more types of resources as input and outputs one or more types of resources, the method comprising: obtaining and recording energy system information in chronological order; generating input parameters from the recorded energy system information; using the input parameters to set at least the input resource amount and output resource amount for each time period as decision variables for each piece of conversion-type equipment; generating output parameters with the relationship between the input resource amount and the output resource amount as constraints; calculating the input resource amount and output resource amount for each time period for the conversion-type equipment using at least the output parameters; and generating the output parameters by using information on the piecewise linear variable relationship included in the input parameters to generate piecewise linear constraints between the variables."

[0017] Furthermore, the present invention provides an energy equipment operation plan creation program that uses a computer to create an operation plan for equipment in an energy system that combines multiple pieces of equipment, including conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, the energy equipment operation plan creation program comprising: an information recording program that chronologically obtains and records energy system information; an input parameter generation program that generates input parameters from the energy system information; a conversion-type equipment condition generation program that uses the input parameters to generate output parameters for each conversion-type equipment, with at least the input resource amount and output resource amount for each time period as decision variables and the relationship between the input resource amount and the output resource amount as constraint conditions; and an optimal plan creation program that uses at least the output parameters to calculate the input resource amount and output resource amount for each time period of the conversion-type equipment, wherein the conversion-type equipment condition generation program generates piecewise linear constraint conditions between the variables using information on the relationship between the piecewise linear variables included in the input parameters when generating the output parameters. [Effects of the Invention]

[0018] According to the energy equipment operation planning device of the present invention, plans can be repeatedly made by changing parts of the equipment configuration, equipment characteristics, and operating conditions, and plans can be made under different conditions for each date and time by specifying conditions on a calendar. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a diagram showing an example of the configuration of a processing function of an energy equipment operation plan planning device 10 according to a first embodiment of the present invention. [Figure 2] 1 is a diagram showing an example of a hardware configuration of an energy equipment operation plan planning device 10 according to an embodiment of the present invention. [Figure 3] FIG. 2 is a sequence diagram showing the processing contents of the energy equipment operation plan planning device 10 according to the embodiment of the present invention. [Figure 4] FIG. 2 is a diagram showing an example of a power generation and transmission system 3 including facilities that are the subject of planning. [Figure 5] FIG. 2 is a diagram showing a processing program in the actual processing stage of the energy facility operation plan formulation program 22. [Figure 6] FIG. 10 is a diagram showing a processing program for the advance preparation stage of the energy facility operation plan formulation program 22. [Figure 7A] FIG. 2 is a diagram showing an example of association between variable ranges and inter-variable relational expressions for equipment IDs stored in the equipment characteristic database DB1. [Figure 7B] FIG. 10 is a diagram showing an example in which the minimum and maximum values ​​of variables are described in association with the variable ranges stored in the equipment characteristic database DB1. [Figure 7C] FIG. 2 is a diagram showing an example of the data configuration of segment point parameters representing input / output relationships stored in the facility characteristic database DB1. [Figure 8] FIG. 2 is a diagram showing an example of the configuration of an operation condition database DB2 having operation condition parameters. [Figure 9] FIG. 10 is a diagram showing an example of the configuration of a facility configuration database DB3 having facility configuration parameters. [Figure 10A] FIG. 10 is a diagram showing an example of a correspondence table of equipment configuration patterns for date and time information stored in the calendar database DB4. [Figure 10B] FIG. 2 is a diagram showing an example of a correspondence table of driving patterns with respect to date and time information stored in the calendar database DB4. [Figure 11] A diagram showing an example of an optimized plan. [Figure 12] FIG. 10 is a diagram showing an example of a piecewise linear graph. [Figure 13] FIG. 10 is a diagram showing an example of a piecewise linear graph. [Figure 14] Scatter plot showing the relationship between cost and CO2 emissions. [Figure 15] A diagram showing a one-dimensional piecewise linear case. [Figure 16] A diagram showing a multidimensional piecewise linear case. [Figure 17] A diagram showing an example of the variable relationship between dependent variables and stipulated variables. [Figure 18A] A diagram showing the domain constraint from the output to the input. [Figure 18B]A diagram showing the correlation constraint from input 1 to input 2. [Figure 19A] A diagram showing an example of operating / non-operating variables. [Figure 19B] A diagram showing an example of the time series of operating and non-operating variables. [Figure 20] A diagram showing an example of imposing constraints on the operating / non-operating variables of transformation-type equipment in modeling. [Figure 21] FIG. [Figure 22] A diagram showing the conditions for continuous operation and stoppage. [Figure 23] FIG. [Figure 24] A diagram showing the conditions for the rate of change of operation and shutdown. [Figure 25] A diagram showing a configuration that allows flow in only one direction. DETAILED DESCRIPTION OF THE INVENTION

[0020] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0021] In the following, in Example 1, a configuration example of an equipment combination optimization device suitable for applying the energy equipment operation plan planning device of the present invention will be described using Figures 1 to 14, and in Example 2 and subsequent Examples, measures to address the above-mentioned Issues 1, 2, and 3 for modeling the nonlinear characteristics and nonlinear operation conditions of energy equipment with high accuracy and formulating an operation plan in a realistic time will be described. In Example 2, measures to address Issue 1 will be described using Figures 15 to 18B, in Example 3, measures to address Issue 2 will be described using Figures 19A to 20, and in Example 4, measures to address Issue 3 will be described using Figures 21 to 25. [Example]

[0022] In the first embodiment, a basic configuration example of an energy facility operation planning device according to the first embodiment of the present invention will be described.

[0023] FIG. 1 is a diagram illustrating an example of a configuration of processing functions when an energy facility operation plan planning device 10 according to a first embodiment of the present invention is configured by a computer.

[0024] The energy equipment operation plan development device 10, which is configured using a computer, has the following functional parts: a conversion type equipment condition generation part 11, a storage type equipment condition generation part 12, a network condition generation part 13, an objective function generation part 14, an optimal plan development part 15, and an input parameter generation part 16, when its processing contents are expressed as functional parts in the calculation part of the computer.

[0025] The system also includes functional units for inputting various settings and information, such as an equipment catalog unit 17, an operation condition pattern unit 18, an equipment configuration pattern unit 19, and a calendar setting unit 20. Furthermore, the system includes an energy equipment operation plan development program 22 as a processing function for managing and operating these individual processing functions in an integrated manner. The specific processing contents of these functional units will be described individually below.

[0026] FIG. 2 is a diagram illustrating an example of a hardware configuration of the energy equipment operation plan planning device 10 according to an embodiment of the present invention.

[0027] The energy equipment operation plan planning device 10 optimizes the planning of operation plans for energy equipment. The energy equipment operation plan planning device 10 is a computer equipped with a CPU (Central Processing Unit) 101, a ROM (Read Only Memory) 102, and a RAM (Random Access Memory) 103. The energy equipment operation plan planning device 10 plans an optimal plan based on equipment combination and searches for the optimal plan in a short period of time. Here, the optimal equipment configuration refers to, for example, an equipment configuration that can achieve performance evaluation indicators specified by a customer.

[0028] The term "optimization" does not necessarily refer to an optimal plan or equipment configuration, but may also include an equipment configuration that is optimized and optimized so that key performance indicators satisfy specified conditions.

[0029] The CPU 101 is a central processing unit that executes a program and accesses data stored in the ROM 102 or RAM 103, thereby realizing each functional unit.

[0030] The ROM 102 is a non-volatile read-only memory that stores various data, a BIOS (Basic I / O System), etc. The RAM 103 is a volatile readable and writable memory that temporarily stores various data and programs.

[0031] The energy facility operation plan planning device 10 further includes an input unit 104 , a communication unit 105 , a display unit 106 , and a storage unit 107 .

[0032] The input unit 104 is, for example, a keyboard, a mouse, a touch panel, or the like, and is a unit that allows a user to input data by operating the energy equipment operation plan planning device 10. The communication unit 105 is, for example, a network interface card, and is a unit that allows communication with other devices and on-demand ordering platforms via a network.

[0033] Various settings and information acquisition by the functional units for input processing shown in Figure 1 (equipment catalog unit 17, operation condition pattern unit 18, equipment configuration pattern unit 19, calendar setting unit 20) are performed via input unit 104 or communication unit 105.

[0034] The display unit 106 is, for example, a liquid crystal display, which displays characters, figures, images, etc. Here, various inputs to the computer, intermediate products of calculations, final products, etc. are displayed in an appropriate format so that they can be visually recognized.

[0035] The storage unit 107 is, for example, a hard disk drive or SSD (Solid State Drive) that stores relatively large volumes of data and programs. The storage unit 107 includes an equipment characteristic database DB1, an operation condition database DB2, an equipment configuration database DB3, and a calendar database DB4 as databases DB for storing various settings and information generated by input processing functions (equipment catalog unit 17, operation condition pattern unit 18, equipment configuration pattern unit 19, calendar setting unit 20). It also includes an energy equipment operation plan development program 22. Although not described, in addition to databases related to various inputs to the computer, databases for intermediate products of calculations, final products, etc., are also appropriately formed, although not described.

[0036] Thus, the CPU 101 executes the energy facility operation plan formulation program 22 to implement the processing of the various functional units shown in FIG.

[0037] 3 is a sequence diagram showing the processing contents of the energy equipment operation plan planning device 10 according to the embodiment of the present invention. This sequence is executed in a preparatory stage and an actual processing stage.

[0038] In the preparatory stage, various settings and information are obtained using the input processing functions (equipment catalog section 17, operation condition pattern section 18, equipment configuration pattern section 19, calendar setting section 20), and an equipment characteristics database DB1, operation condition database DB2, equipment configuration database DB3, and calendar database DB4 are configured.

[0039] Next, in the actual processing stage, various parameters are generated by the input parameter generating unit 16 and stored in the input files 11a, 12a, 13a, and 14a.

[0040] In Figure 3, the input files for various parameters, etc., are, for example, an input file 11a of generator parameters for conversion-type equipment A, an input file 12a of battery parameters for storage-type equipment A, an input file 13a of information on the power network as the system configuration, and an input file 14a of information on the objective function, for example, minimizing CO2 emissions.

[0041] Furthermore, in the processing of the actual processing stage, the conversion type equipment condition generation unit 11 uses an input file 11a of generator parameters related to the conversion type equipment A to calculate decision variables and constraint conditions 11b of the conversion type equipment A and stores them in an output file 11b. The storage type equipment condition generation unit 12 uses an input file 12a of storage battery parameters related to the storage type equipment A to calculate decision variables and constraint conditions of the storage type equipment A and stores them in an output file 12b. The network condition generation unit 13 uses an input file 13a of information on the power network, which is the system configuration, to calculate network constraint conditions and stores them in an output file 13b. The objective function generation unit 14 uses an input file of an objective function that minimizes CO2 emissions to calculate an objective function and stores it in an output file 14b.

[0042] In the actual processing stage, the optimum plan formulation unit 15 determines the equipment input / output values ​​for each time period based on the above-mentioned decision variables, constraint conditions, and objective function, and stores these in the final output file 15a.

[0043] It should be noted that whether the processing of the input parameter generating unit 16 should be defined as a preparatory stage or an actual processing stage is left to discretion, but in this embodiment it is positioned as an actual processing stage.

[0044] The processing sequence of the energy equipment operation plan planning device 10 according to the first embodiment of the present invention is as shown in FIG. 3. Before describing the detailed operation, an example of equipment for which the energy equipment operation plan planning device 10 is to plan will be clarified.

[0045] FIG. 4 shows an example of a power generation and transmission system 3 (hereinafter referred to as an energy system) including facilities that are the subject of planning.

[0046] The energy system 3 in FIG. 4 includes a power supply network including a power purchasing facility 30, a solar power generation panel 32, a solar power generation panel power conditioner 33, a storage battery 36, and storage battery power conditioners 34 and 35, and supplies power to a power demand 31. In the drawing, the power conditioner is abbreviated as "PCS," and the solar power generation panel is abbreviated as "PV." The power supply network of the system 3 includes a power node 301, a power node 302, a power node 304, and a power node 305. The user defines or inputs the amount of power demand for each time period in advance as the power demand 31.

[0047] Of these facilities, the power purchasing facility 30, the solar power generation panel 32, the solar power generation panel power conditioner 33, and the storage battery power conditioners 34 and 35 are conversion-type facilities that input one or more types of resources and output one or more types of resources. The storage battery 36 is a storage-type facility that inputs one or more types of resources, outputs the same types of resources, and stores the difference.

[0048] The power node 301 bundles together the purchased power purchased from the power purchasing facility 30, the generated power output by the solar panel power conditioner 33, and the discharged power output by the storage battery power conditioner 35, supplies power to the power demand 31, and outputs power to the storage battery power conditioner 34.

[0049] Purchased power is supplied to power purchasing facility 30, which outputs the purchased power to power node 301, along with the power purchase cost and CO2 emissions. When sunlight is input to photovoltaic power generation panel 32, it supplies DC power to photovoltaic power generation panel power conditioner 33 via power node 302. Photovoltaic power generation panel power conditioner 33 converts the DC power to AC power and supplies this AC power to power node 301.

[0050] Battery power conditioner 34 converts AC power into DC power and supplies it to battery 36 via power node 304. The DC power output from battery 36 is then supplied to battery power conditioner 35 via power node 305. Battery power conditioner 35 converts DC power into AC power and supplies it to power node 301.

[0051] System 3 further includes a gas purchasing facility 40, a gas cogeneration system 41, and a gas boiler 42 as a gas supply network, and supplies steam to steam demand 43 via a steam supply network. The gas supply network of system 3 includes a gas node 401. The steam supply network of system 3 includes a steam node 402. The gas purchasing facility 40, the gas cogeneration system 41, and the gas boiler 42 are conversion-type facilities that receive one or more types of resources as input and output one or more types of resources. The user predefines or inputs the demand amount for each time period as steam demand 43.

[0052] Gas procured by a gas purchasing facility 40 is input to the gas node 401, and the gas is supplied to a gas cogeneration system 41 and a gas boiler .

[0053] The gas cogeneration system 41 receives the supplied gas and auxiliary power as input, supplies generated power to the power node 301, and supplies steam heat to the steam node 402, thereby supplying steam to the steam demand 43 and emitting hot water heat and CO2.

[0054] The gas boiler 42 receives the supplied gas and auxiliary power as inputs, supplies steam heat to the steam node 402, and then emits CO2. The steam heat supplied to the steam node 402 is finally supplied to the steam demand 43.

[0055] A system consisting of these electricity supply network, gas supply network, and steam supply network is stored in a system template. By fitting conversion-type equipment or storage-type equipment to these system templates, the equipment configuration of the system can be generated. Returning to Figure 3, various information required to plan the operation of the above energy system shown in Figure 4 is accumulated in various databases (DB) via the input processing function. To realize this processing in the advance preparation stage, the energy equipment operation plan formulation program 22 in Figure 1 executes the advance preparation stage processing program shown in Figure 6.

[0056] Furthermore, this information is processed appropriately in the actual processing stage and finally provided to the optimal plan formulation. To realize this actual processing stage, the energy facility operation plan formulation program 22 in Fig. 1 executes the actual processing stage processing program shown in Fig. 5.

[0057] First, the actual processing stage processing program shown in Fig. 5 will be described. In Fig. 5, in processing step S1 as the actual processing stage of the energy equipment operation plan formulation program 22, the input parameter generation unit 16 generates input parameters for each optimization execution unit by referencing the calendar database DB4, the equipment configuration database DB3, the operating condition database DB2, and the equipment characteristic database DB1 based on information acquired from the energy system and processed and stored as necessary, and stores the generated input parameters in the input files 11a, 12a, 13a, and 14a.

[0058] In addition, in processing step S2 of the energy equipment operation plan development program 22, as a processing step in the actual processing stage, the conversion type equipment condition generation unit 11 generates constraint conditions for each conversion type equipment from the input parameters stored in the input file 11a, adds them to the constraint condition list, and stores them in the output list 11b.

[0059] In addition, in processing step S3 of the energy equipment operation plan formulation program 22, as processing in the actual processing stage, the storage type equipment condition generation unit 12, the network condition generation unit 13, and the objective function generation unit 14 generate constraint conditions, decision variables, objective functions, etc. from the input parameters stored in the respective input files 12a, 13a, and 14a, add them to the constraint condition lists, and store them in the output lists 12b, 13b, and 14b. Then, the energy equipment operation plan formulation program 22 executes processing of the optimal plan formulation unit 15, and stores the generated optimal plan in the output list 15b.

[0060] 6 is a diagram showing a processing program for the preparation stage of the energy equipment operation plan formulation program 22. In processing step S11, an equipment characteristic database DB1 is created, in processing step S12 an operating condition database DB2 is created, and in processing step S13 an equipment characteristic database DB3 is created. After these databases are created, in processing step S14 a calendar database DB4 is created.

[0061] The creation of an equipment characteristic database DB1 containing equipment characteristic parameters will be described with reference to Figures 7A, 7B, and 7c. In this example, the equipment characteristic parameters require minimum and maximum parameters as equipment characteristics and segment point parameters that represent input-output relationships. The equipment catalog unit 17 in Figure 3 prepares data in advance that associates sets of segment point parameters that represent input-output relationships and minimum and maximum variable values ​​with equipment IDs, and stores this data in the equipment characteristic DB.

[0062] Figures 7A and 7B are diagrams showing examples of the data configuration of minimum and maximum parameters as equipment characteristics stored in the equipment characteristic database DB1. Figure 7A is a correspondence table that associates variable range D1b and inter-variable relational expression D1c for equipment ID (D1a), and Figure 7B is a table that describes the minimum value D1e and maximum value D1f of variable D1d linked to variable range D1b. The equipment represented by conv001 has two inputs and one output, and the minimum and maximum values ​​for each are values ​​shown in the table.

[0063] Figure 7c is a diagram showing an example of the data structure of segment point parameters representing input / output relationships stored in the equipment characteristic database DB1. Figure 7c shows an example of the conv001_struct of the inter-variable relationship D1c, with the equipment characteristic parameters (segment point parameters) associated with it described and stored. Two pairs of segment points related to input are described in D1g and D1h, and the segment point related to output is described in D1i.

[0064] In the example of Figure 7c, for example, the second line represents one point in the input-output relationship, where output1 = 100 when (input1, input2) = (100, 0). Furthermore, when these points are given, the output point for an input point not in the table (for example, (input1, input2) = (100, 25)) is given by the average of the points shown in the table (piecewise linear relationship). In this case, the input point is the midpoint between the points in the second and fourth lines, so the output is also determined to be 125, which is the midpoint. Piecewise linear mathematical models are generally described using ordered variables where at most two adjacent points are non-zero. This relationship is a general-purpose technique, so we will not explain it here.

[0065] Next, the creation of an operating condition database DB2 containing operating condition parameters will be explained with reference to Fig. 8. In this example, the operating condition parameters required include minimum and maximum operating condition parameters, operation / stop condition parameters, and condition parameters that depend on other equipment and the external environment. In the operating condition pattern unit 18 in Fig. 3, a pattern table of operating conditions is prepared in advance.

[0066] Figure 8 shows an example of an operation condition pattern table for equipment conv001. Linked to the equipment ID (D1a), the status for each operation ID is stored as an operation condition pattern ID (D2a), the minimum operational value D2b of input 1, the continuous operation time D2c, and the operation condition D2d.

[0067] In this example, for example, when the equipment ID (D1a) is conv001, there are two operation patterns, weekday and weekend. The operation pattern with the operation condition pattern ID (D2a) on the first line conv001_weekday implies that input1 has the minimum and maximum equipment characteristics plus the operation condition "input1 >= 20 if operating" (it will not operate with an input value below that). The pattern with the operation condition pattern ID (D2a) on the first line weekend means that no such additional operation condition exists for the minimum value of input1. Although not shown, it is also possible to impose a continuous operation condition in the weekday pattern such as "once started, it will continue to operate for one hour without stopping," and further impose a condition such as "conv001 can operate only when equipment ID conv002 is on (operating)."

[0068] Next, the creation of the equipment configuration database DB3 containing equipment configuration parameters will be explained with reference to Fig. 9. In this example, equipment configuration pattern parameters are required as equipment configuration parameters. In the equipment configuration pattern unit 19 in Fig. 3, a pattern table of equipment configuration is prepared in advance.

[0069] 9 is a diagram showing an example of the equipment configuration pattern list 001. The states of conv001 (D3b), conv002 (D3c), and conv003 (D3d) are stored in association with the equipment configuration pattern ID (D3a).

[0070] In this example, for example, the first line means that convlist001 is an equipment configuration pattern consisting of equipment IDs, conv001 and conv002, and the second line means that convlist002 is an equipment configuration pattern consisting of equipment IDs, conv001, conv002 and conv003.

[0071] Next, the creation of a calendar database DB4 that specifies all parameter combinations will be described with reference to Figures 10A and 10B. In the example of Figure 10A, the calendar database DB4 specifies combinations of equipment characteristic parameters, operating condition parameters, and equipment configuration parameters for date and time information. The calendar setting unit 20 of Figure 3 prepares an equipment configuration pattern correspondence table for date and time information.

[0072] 10A is a diagram showing an example of an equipment configuration pattern correspondence table for date and time information, in which an equipment configuration pattern D4b is stored for each date D4a.

[0073] In this example, for example, the equipment configuration consisted of conv001 and conv002 until 2023 / 04 / 02, but on 2023 / 04 / 03, conv003 was newly introduced, resulting in an equipment configuration consisting of conv001, conv002, and conv003.

[0074] The equipment characteristic parameters are read from the equipment characteristic DB as parameters for the equipment IDs that appear in the equipment configuration pattern. For the date and time when convlist002 is specified, the equipment characteristic parameters for conv001, conv002, and conv003 are read.

[0075] 10B is a diagram showing an example of a table of correspondence between date and time information and operation patterns. For each date D4a, an operation condition pattern D4c for conv001 and an operation condition pattern D4d for conv002 are stored.

[0076] In this example, for example, both facility IDs conv001 and conv002 are shown to operate in the weekend pattern on 2023 / 04 / 01 and 2023 / 04 / 02, and in the weekday pattern on 2023 / 04 / 03 and 2023 / 04 / 04.

[0077] Returning to the operational flow of Figure 5, in processing step S1 in the actual processing stage, the input parameter generation unit 16 next generates input parameters for each optimization execution unit by referring to the calendar database DB4, the equipment configuration database DB3, the operational condition database DB2, and the equipment characteristic database DB1.

[0078] Here, the input parameters consist of a combination of equipment characteristic parameters, operating condition parameters, and equipment configuration parameters for date and time information. A single input file is generated for each optimization run by referencing the calendar database DB4. For example, if optimization is performed on a daily basis for 365 days (365 optimizations), an input file for 2023 / 04 / 01 is generated by reading the equipment configuration pattern convlist001 for 2023 / 04 / 01 from the equipment configuration database DB3. Next, the equipment characteristic parameters for equipment IDs conv001 and conv002 that appear in convlist001 are read from the equipment characteristic database DB1. Furthermore, the operating condition patterns for equipment IDs conv001 and conv002 for 2023 / 04 / 01 are read from the operating condition database DB2. All of the information on the equipment configuration parameters, equipment characteristic parameters, and operating condition parameters that have been read is generated as input parameters. An input file for 2023 / 04 / 02 is generated in the same way, and this process is repeated to generate input files for 365 days.

[0079] These input parameters generated by the input parameter generation unit 16 are saved together as an input file 11a relating to the conversion type equipment A, an input file 12a relating to the storage type equipment A, an input file 13a relating to the system configuration, and an input file 14a relating to the objective function.

[0080] In this case, conversion-type equipment refers to equipment that converts energy, for example, in the energy system configuration of Figure 4, such as solar power generation panels 32, thermal power generators, gas turbine generators, gas cogeneration, and wind power generators. Storage-type equipment refers to equipment that selectively stores and releases energy, such as storage batteries 34, 35 and gas tanks. Key performance indicators for the system include, for example, the cost of purchasing electricity, the cost of procuring gas, and CO2 emissions. Resources here refer to electricity [kWh], gas [m 3 ], electricity purchase costs, gas procurement costs, etc. [yen], CO2 emissions [m 3 ] and other quantities related to power, energy, and other things. When input to a facility, it is an input resource, and when output from a facility, it is an output resource.

[0081] Returning to Fig. 5, in processing step S2 of the operation flow in Fig. 5, the conversion type equipment condition generation unit 11 in Fig. 3 next generates constraint conditions for each piece of equipment from the input parameters, adds them to a constraint condition list, and stores them in the output file 15a. At this time, the output file 15a contains outputs such as constraint conditions generated for each input parameter prepared in the input files 11a, 12a, 13a, and 14a.

[0082] These include, for example, generating constraints from equipment characteristic parameters, generating maximum and minimum value constraints and piecewise linear relational constraints for the input and output variables of the equipment, and adding them to the list of optimization constraints. This technology itself is a general-purpose technology, so a detailed explanation will be omitted.

[0083] Constraint conditions are also generated from operational condition parameters, and additional constraint conditions are added to the corresponding input / output variables in relation to the modeling of minimum and maximum operational conditions. Because these are additional constraints, they are applied in conjunction with the input / output constraints on the equipment characteristics. This technology itself is general-purpose, so a detailed explanation will be omitted. In this case, a variable representing the operation / non-operation of each piece of equipment (a 0 / 1 variable that takes the value 1 when operating and 0 when not operating) is introduced for continuous operation / stop conditions and condition parameters that depend on other equipment or the external environment. These are added as constraint conditions for this operation / non-operation variable. This technology itself is general-purpose, so a detailed explanation will be omitted.

[0084] Constraint conditions are also generated from equipment configuration parameters, and for equipment that is 0 (non-existent) in the equipment configuration pattern, the constraint "always non-operating" is applied to the operating / non-operating variables. This technology itself is general-purpose, so a detailed explanation will be omitted.

[0085] Returning to Fig. 5, in processing step S3 of the operation flow in Fig. 5, the parts after the conversion type equipment condition generation unit 11 in Fig. 3 (processing of the storage type network objective function generation units 12, 13, 14, and processing of the optimal plan planning unit 15) are executed in sequence. The outline of these processing contents is as follows.

[0086] The following description will be made with reference to Figure 3. First, regarding the transformation type equipment condition generation unit 11, this function generates decision variables and constraint conditions that indicate the behavior of the transformation type equipment. Here, the decision variables are controllable variables. The constraint conditions are conditions that give the range that the decision variables can take.

[0087] Regarding further details of the conversion-type equipment condition generation unit 11, this function generates decision variables and constraint conditions that indicate the behavior of the conversion-type equipment based on the parameters of the conversion-type equipment, and outputs them to the optimal plan formulation unit 15. In other words, for each piece of conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, the conversion-type equipment condition generation unit 11 uses at least the input resource amount and output resource amount for each time as decision variables, and generates constraint conditions that represent the relationship between the input resource amount and the output resource amount.

[0088] The conversion-type equipment condition generation unit 11 accepts input of a piecewise linear conversion map that converts the input resource amount of the conversion-type equipment into the output resource amount. This makes it possible to more quickly calculate the output resource of the conversion-type equipment. The conversion-type equipment takes one or more types of energy resources as input and outputs one or more types of resources, such as a gas cogeneration (generator) that uses gas as an input resource to convert it into an electric power output resource, a power conditioner that uses DC power as an input resource to convert it into an AC power output resource, or a solar power generation panel that uses sunlight as an input resource to convert it into a DC power output resource.

[0089] Next, the storage-type equipment condition generation unit 12 in FIG. 3 generates decision variables and constraint conditions that indicate the behavior of the storage-type equipment. The storage-type equipment condition generation unit 12 generates decision variables and constraint conditions that indicate the behavior of the storage-type equipment based on the parameters of the storage-type equipment, and outputs them to the optimal plan creation unit 15. That is, for each storage-type equipment that receives one or more types of resources as input and outputs the same types of resources and accumulates the difference between them, the storage-type equipment condition generation unit 12 generates constraint conditions that the accumulated resource amount, which is the sum of the difference between the input resource amount and the output resource amount and the accumulated resource amount at each time, is greater than or equal to a given lower limit and less than or equal to an upper limit. Here, the storage-type equipment may be, for example, a storage battery when electricity is the input / output resource, or a high-pressure steam tank when steam is the input / output resource.

[0090] The network condition generation unit 13 generates network constraint conditions, and generates the network conditions with the constraint that the sum of inputs and outputs of the conversion-type equipment and the storage-type equipment must be equal. The network condition generation unit 13 generates the network constraint conditions based on the system configuration and outputs them to the optimal plan creation unit 15. The network condition generation unit 13 has a unit conversion function between the amount of input resources and the amount of output resources of the conversion-type equipment.

[0091] The objective function generation unit 14 generates an objective function related to a key performance indicator using one or more values ​​of the input / output resource amounts of the conversion-type equipment and the storage-type equipment. The objective function generation unit 14 generates an objective function that calculates the input / output resource amount of the conversion-type equipment or the storage-type equipment from the decision variables. In other words, for a network combining conversion-type equipment or storage-type equipment, the network condition generation unit 13 generates a constraint condition that the sum of the amount of output resources of the same type from the conversion-type equipment or the storage-type equipment and the sum of the amount of input resources of the same type to other conversion-type equipment or other storage-type equipment are equal to or the input resource amount is larger than the sum.

[0092] The optimal plan creation unit 15 calculates the configuration of one or more facilities and the input / output of the network from the decision variables and constraint conditions of the conversion-type facilities or the decision variables and constraint conditions of the storage-type facilities, as well as the network constraint conditions and the objective function, and creates an optimal plan. The optimal plan creation unit 15 calculates an optimized plan from the decision variables, constraint conditions and objective function by mathematical optimization.

[0093] The optimal plan creation unit 15 creates an optimal plan for operating each facility from the decision variables and constraints of the conversion-type facility or the decision variables and constraints of the storage-type facility, as well as the network constraints and the objective function, and calculates the configuration of one or more facilities and the input / output of the network. In other words, the optimal plan creation unit 15 uses the decision variables and constraints of zero or more conversion-type facilities generated by the conversion-type facility condition generation unit 11, the decision variables and constraints of zero or more storage-type facilities generated by the storage-type facility condition generation unit 12, the decision variables and constraints of one or more networks generated by the network condition generation unit 13, and the objective function generated by the objective function generation unit 14 to calculate, through mathematical optimization, the input resource amounts and output resource amounts for each time period of the conversion-type facility and the storage-type facility that maximize or minimize the objective function.

[0094] The optimized plan here refers to the amount of input / output resources for each time period, or the equipment operation parameters such as the load factor for each time period that results in the amount of input / output resources, as shown in FIG.

[0095] 12, 13, and 14 below, several examples of the relationships between various parameters that should be considered when obtaining the optimization plan of FIG. 11 will be described to aid in understanding the present invention.

[0096] First, Figure 12 is an example of a piecewise linear graph. The horizontal axis of the graph represents power. The vertical axis of the graph represents power cost. When power is equal to or less than P0, the power cost is a linear function of power. When power exceeds P0, the power cost is a linear function of power, but the rate of change is greater than when power is equal to or less than P0. When power is P0, the power cost is C1. When power is 0, the power cost is C0. Such piecewise linear functions require less computational effort than higher-order polynomials and can suitably approximate higher-order polynomials.

[0097] Figure 13 is an example of a piecewise linear graph. The horizontal axis of the graph represents gas volume. The vertical axis of the graph represents power. When the gas volume is greater than or equal to L0 and less than or equal to L1, power is a linear function of the gas volume. When the gas volume is greater than or equal to L1 and less than or equal to L2, power is a linear function of the gas volume, but the rate of change is smaller than when the gas volume is greater than or equal to L0 and less than or equal to L1. When the gas volume is greater than or equal to L2, power is a linear function of the gas volume, but the rate of change is smaller than when the gas volume is greater than or equal to L0 and less than or equal to L1, or when the gas volume is greater than or equal to L1 and less than or equal to L2.

[0098] Figure 14 is a scatter plot showing the relationship between costs and CO2 emissions. Of the CAPEX + OPEX on the horizontal axis of the scatter plot, CAPEX is Capital Expenditure, which refers to equipment investment and initial costs. OPEX is an abbreviation for Operating Expense or Operating Expenditure, and is a general term for ongoing costs required for business operations. The sum of CAPEX and OPEX shows overall costs. The vertical axis of the scatter plot shows CO2 emissions.

[0099] The scatter plot in Figure 14 displays multiple key performance indicators (KPIs) (here, the relationship between CAPEX + OPEX and CO2 emissions) in a comparative format. To reduce CO2 emissions, CAPEX + OPEX must be increased, but reducing CAPEX + OPEX will increase CO2 emissions. Therefore, it is possible to compare and select which value to adopt based on the key performance indicators (KPIs).

[0100] In the optimized operation plan shown in Fig. 11, which was finally obtained by taking into consideration the relationships between the parameters shown in Fig. 12 to Fig. 14, the horizontal axis of the graph in Fig. 11 represents time, the vertical axis of the upper graph represents power, and the vertical axis of the lower graph represents heat.

[0101] In the graph, the initial period when the electricity purchase price is low at 10 yen / KWh is from midnight to 8 am, for example, and all electricity is covered by purchased electricity. The purchased electricity also covers the charging power for the storage battery PCS.

[0102] The period when the electricity purchase price is high at 20 yen / KWh is, for example, from 8:00 AM to 10:00 PM. Initially, the output of the PV-PCS (photovoltaic power conditioner) is insufficient, so the shortfall in electricity is made up by discharging the storage battery PCS, generating electricity from the gas cogeneration system, and purchasing electricity. At this time, steam demand occurs, so this is met by steam from the gas boiler.

[0103] As the output of the PV-PCS (photovoltaic power conditioner) increases over time, the amount of power that is compensated for by the shortfall gradually decreases. At this time, there is a period when the battery PCS is charged with purchased power and gas cogeneration power generation. At this time, steam demand gradually increases, so this is met by both steam from the gas cogeneration and steam from the gas boiler.

[0104] As time passes and the output of the PV-PCS (photovoltaic power conditioner) decreases, the shortfall in power is again compensated for by discharging the battery PCS, generating electricity from the gas cogeneration system, and purchasing power. Here, as shown by the solid line in the upper graph, the battery PCS discharges until its charge reaches zero. The demand for steam at this time is met by steam from the gas cogeneration system.

[0105] For example, the period when the electricity purchase price drops to 10 yen / KWh is from 10:00 PM to 12:00 PM, and all electricity is supplied by purchased electricity. This purchased electricity is also used to charge the battery PCS. At this time, steam demand occurs, and this is supplied by steam from the gas boiler.

[0106] According to the first embodiment of the present invention described above, when an annual operation plan is to be created based on a calendar in which the operation conditions vary depending on the date and time, when an operation plan is to be created under a plurality of deterioration conditions in order to estimate the impact of aging deterioration, or when an operation plan is to be created under a plurality of equipment configurations in order to estimate the effect of capital investment, it is possible to switch conditions and create operation plans efficiently and repeatedly.

[0107] Furthermore, if you want to create an annual operation plan based on a calendar in which the operation conditions vary by date and time, you can register the appropriate operation condition patterns in the operation condition database DB2 and specify the operation condition patterns corresponding to each date and time in the calendar database DB4. After specifying the calendar, you can efficiently perform optimization for multiple days included in the calendar, either continuously or separately.

[0108] When creating an operation plan under multiple degradation conditions to estimate the impact of aging degradation, multiple degradation versions of equipment characteristics are registered in the equipment characteristics database DB1, and an equipment configuration ID corresponding to date and time information such as year is specified in the calendar database DB4. After specifying the calendar, optimization for multiple years included in the calendar can be efficiently performed either continuously or separately.

[0109] When creating an operation plan with multiple equipment configurations to estimate the effects of capital investment, multiple equipment configuration patterns are registered in the equipment configuration database DB3, and the equipment configuration ID corresponding to date and time information such as year is specified in the calendar database DB4. After specifying the calendar, optimization for multiple years included in the calendar can be efficiently performed either continuously or separately. [Example]

[0110] Next, the processing executed in the conversion-type equipment condition generation unit 11 in Fig. 3 will be described in Example 2 and subsequent examples. First, in Example 2, specific means for realizing Problem 1 will be described regarding modeling the nonlinear characteristics and nonlinear operating conditions of energy equipment with high accuracy and formulating an operation plan within a realistic time. Here, Problem 1 mentioned above is as follows.

[0111] The first issue is that there are many complex relationships between variables in power generation facilities. For example, in a gas turbine, the amount of combustion in the first stage determines the upper limit of the amount of combustion that can be performed in the second stage, or the relationship between the input fuel and the amount of power generated is not linear but rather saturates.

[0112] To address Problem 1, because there are complex relationships between variables in a power generation facility, the present invention involves setting relationships between variables in a multidimensional piecewise linear manner. This will be explained using Figs. 15 to 18B.

[0113] In the present invention, when the relationship between variables is one-dimensional piecewise linear, if the input variable or output variable o (dependent variable) of the transformation-type equipment g has a relationship determined according to the value of one input variable or output variable i (basis variable) of the transformation-type equipment g, the relationship between the variables is set as piecewise linear.

[0114] FIG. 15 is a diagram showing a one-dimensional piecewise linear case, where the vertical axis shows the value CP of the basis variable i and the horizontal axis shows the value P of the dependent variable o. Here, each point (CP 1 g0 , C.P. 2 g0 , C.P. 3 g0 , C.P. 4 g0 ) and the corresponding points on the horizontal axis (P 1 g0 , P 2 g0 , P 3 g0 , P 4 g0 ) are linearly represented. Each of these points is a division point.

[0115] Also, the value of variable i is CP 1 g0 When , the value of variable o is P 1 g0 The points in between are linearly interpolated between the division points. 1 g0 , P 1 g0 ) is given as a set value, the SOS2 variable λ 1 g0 Using (t), the following equations (1) and (2) are introduced as constraints.

[0116]

number

[0117]

number

[0118] FIG. 16 shows a multidimensional piecewise linear example, where the first axis represents the value CP of the variable i. i g0 , the value of variable j on the second axis is CP j g0 , the third axis is the value P of the variable o ij g0 Here, when the relationship between variables is multidimensional piecewise linear, and the input variable or output variable o (dependent variable) of the transformation-type equipment g has a relationship that is determined according to the values ​​of multiple input variables or output variables i, j (basis variables) of the transformation-type equipment g, the relationship between the variables is set as piecewise linear.

[0119] In Figure 16, the values ​​of variables i and j are (CP i g0 , C.P. j g0 ) the value of variable o is P ij g0 The points between them are expressed as the average of the four points and are not uniquely determined. Linear interpolation is performed between the division points. 1 g0 , P 1 g0 ) is given as a set value, the SOS2 variable λ lj1、lj21 g0 Using (t), the following equations (3), (4), and (5) are introduced as constraints. Note that the variable λ lj1、lj21 g0 The variable λ mapped onto each axis is the SOS2 variable.

[0120]

number

[0121]

number

[0122]

number

[0123] The variable relationship between the dependent variable and the specified variable can be organized as shown in Figure 17. In this example, the output current is assumed as the dependent variable, and the amount of hydrogen and city gas as the specified variables. To use this, first specify one or more base variables for one dependent variable. In this example, the power output (dependent variable) of the hydrogen mixed combustion cogeneration is determined according to the input values ​​of hydrogen and city gas (basic variables). Next, a division point is given. If there are i, j, ... division points for the base variables on each axis, the division point is i * j * ...Then, the relationship between the boundary surface determined by the division points and the dependent variable is specified using an equality or inequality sign. Note that the relationship equations between variables can be set in layers, and the number of constraint equations increases each time.

[0124] The benefits of organizing the complex inter-variable relationships of power generation facilities as multidimensional piecewise linear relationships are as follows. First, the inter-variable relationship equations can be given as piecewise linear (as broken lines if there is one basis variable). The more piecewise points are added, the more accurately the nonlinear inter-variable relationship equations can be expressed. Second, because the input / output characteristics of actual energy facilities are often expressed by piecewise points (representative points) rather than by nonlinear functions, values ​​can be set directly by referring to a catalog. Third, linear inter-variable relationship equations can be expressed as two-point piecewise linear. Fourth, by combining multiple inter-variable relationship equations, the characteristics of complex power generation facilities can also be expressed.

[0125] Specific examples of the effect are explained using Figures 18A and 18B. Figure 18A is a diagram showing the domain constraints from output to input. In this case, the gas turbine output, which is the power generation amount, and the reheating power generation amount have a relationship expressed as a function of the gas turbine inputs, which are gas, reheating gas, and electricity, and the reheating amount of the gas turbine has an upper limit determined by the power generation amount in the first stage. In addition, in this case, the output (power generation amount) has a relationship that constrains the domain of the input (reheating gas).

[0126] According to the present invention, even in the case of such a relationship (domain transformation from the output side to the input side), it is possible to set a polygonal domain indicating the upper limit determined from the power generation amount in the first stage on a two-dimensional plane with the power generation amount as the output variable on the horizontal axis and the amount of reheating gas as the input variable on the vertical axis.

[0127] Figure 18B is a diagram showing the correlation constraints from input 1 to input 2. In this case, steam, which is the gas boiler output, is expressed as a function of hydrogen and city gas, which are the gas boiler inputs; for example, the fuel ratio of hydrogen in a gas boiler needs to be 30%. In this example, there is a correlation (30%) between the inputs, hydrogen and city gas, but by displaying a three-dimensional area with hydrogen and city gas as input variables on the two horizontal axes and the amount of steam, which is the output variable, on the vertical axis, it becomes possible to model the output using dividing lines in a three-dimensional space where there is a correlation between the inputs. [Example]

[0128] In Example 3, specific means for realizing Problem 2 will be described, which involves modeling the nonlinear characteristics and nonlinear operating conditions of energy facilities with high accuracy and formulating an operation plan in a realistic time frame. Problem 2 mentioned above refers to the fact that there are many cases where the processing content changes when the operating conditions of the power generation facility change. For example, a case in which the power generation efficiency differs when the cooling water temperature is 15°C or below and when it is above 15°C.

[0129] In this invention, problem 2 is addressed by introducing an operating / non-operating variable and assigning conditions to the operating / non-operating variable by using an If-then type switching.

[0130] First, regarding the introduction of the operating / non-operating variable, the present invention introduces the operating / non-operating variable shown in FIG. 19A. First, Ug(t) is the period t between the division points set for solving the problem 1 (for example, the CP in FIG. 15). 1 g0 and CP 2 g0and is a 0-1 variable that is 1 when the transformation-type equipment is operating in period t (hereinafter simply referred to as period t). vg(t) is a 0-1 variable that is 1 when the transformation-type equipment starts up in period t. wg(t) is a 0-1 variable that is 1 when the transformation-type equipment is stopped in period t. This makes it possible to define three states: the transformation-type equipment is operating in period t, the transformation-type equipment has started up in period t, and the transformation-type equipment has stopped in period t.

[0131] Figure 19B is a time series example with period t on the horizontal axis and the operating / non-operating variables Ug(t), vg(t), and wg(t) on the vertical axis, expressed as 0 and 1. This shows that the conversion-type equipment was started up and in operation in period t, continued operating until period t+2, and finally stopped in period t+3.

[0132] The relationship constraints of the variables U, v, and w based on the operating and non-operating variables Ug(t), vg(t), and wg(t) are expressed by equation (6).

[0133]

number

[0134] In response to issue 2, when "setting conditions for operation / non-operation variables by if-then type switching," the present invention clarifies the following items A, B, C, and D. A: Conditions under which this function can be set B: Input parameters C: Modeling D: Constraint expression First, we will explain "A: Conditions that can be set using this function." In response to issue 2, conditions are assigned to the operating / non-operating variables using If-then switching, but the conditions that can be set using If-then switching are as follows:

[0135] First, when setting the operation / non-operation of conversion-type equipment on a calendar, for example, non-operation from 22:00 to 5:00 and non-operation all day on maintenance days, set wg(t) = 1 for the period t that includes 22:00, set vg(t) = 1 for the period t that includes 5:00, and set Ug(t) = 0 for the other periods t in between. In if-then type switching, it is determined that this period t is the relevant time or hour.

[0136] The second is when setting the conditions for simultaneous operation of multiple conversion-type equipment, for example, when one of three units is always out of operation due to maintenance.

[0137] Third, there is the case where conversion-type equipment is switched on or off depending on the operating / non-operating state or the value of an input / output variable of any conversion-type equipment. In this example, examples include not wheeling when purchasing electricity, not discharging when charging, operating only when the output value of a certain equipment exceeds a threshold, or operating only when equipment 1 is operating and the output of equipment 2 is above a threshold.

[0138] In the third case, equipment operation priority can be set depending on the operating / non-operating status of any conversion-type equipment or the values ​​of input / output variables. In this example, of the three gas turbines, gas turbine 1 always operates with priority, and when gas turbine 1 alone does not have enough output, gas turbine 2 operates, and if that is still not enough, gas turbine 3 operates.

[0139] In the third case, the characteristics of a transformation-type equipment are switched between variables depending on the operating / non-operating state of the equipment or the values ​​of input / output variables. In this example, when the output value of a certain equipment is Category 1, the minimum / maximum output is a / A, when it is Category 2, the minimum / maximum output is b / B, when it is Category 3, the minimum / maximum output is c / C, etc. Alternatively, when the output value of a certain equipment is Category 1, output = f1 (input), when it is Category 2, output = f2 (input), when it is Category 3, output = f3 (input), etc.

[0140] Next, we will explain "B: Input parameters." To address issue 2, it is recommended to use the following input parameters in if-then type switching.

[0141] In input parameter usage 1, when setting the operating / non-operating calendar for conversion-type equipment, the conversion-type equipment is set to ".json", and "must_run" is used to set 1 to force operation at each time, -1 to force non-operation, and 0 if optional, using the time series array "must_run"=[0, 0, 0, 1, -1, -1, ..., 0, 0, 1, 0].

[0142] Input parameter usage 2 sets the simultaneous operation conditions for multiple conversion-type equipment. Here, the group of conversion-type equipment is defined as {C1, C2, ..., CN}, and the number of units that can operate simultaneously is m.

[0143] In input parameter usage 3, any transformation-type equipment is switched on or off depending on its operating or non-operating state or the value of an input / output variable. For example, when "condition format 1, 2, or 3" is used, the equipment is in operation / non-operating, condition format 1 is "equipment name is in operation / non-operating", condition format 2 is "linear condition consisting of one or more input / output variables Axc1 + Bxc2 + Cxc3 ≧ δ", and condition format 3 is "(condition format 1 or 2) and / or (condition format 1 or 2)".

[0144] Furthermore, according to this example, it is possible to stack three or more conditional expressions by introducing virtual conversion-type equipment and using condition format 3 in combination. If you want to express the condition that conversion-type equipment X can operate when "(the output of equipment A is a or greater and equipment B is operating) or the output of equipment C is c or less", then you can set up a new virtual conversion-type equipment Y that operates under the condition "(the output of equipment A is a or greater and equipment B is operating)", and set the condition that X can operate when "Y is operating or the output of equipment C is c or less".

[0145] Next, we will explain "C: Modeling." In addressing issue 2, it is advisable to consider the following points when modeling if-then switching.

[0146] First, in modeling, it is advisable to use a calendar to set the operation and non-operation of conversion-type equipment. Second, it is advisable to set the simultaneous operation conditions of multiple conversion-type equipment.

[0147] Third, it is preferable to switch the operation of any transformation-type equipment on or off depending on the operation status or input / output variable values ​​of the transformation-type equipment, and to set priority operation settings for the equipment. Figure 20 shows an example of assigning constraints to the operation / non-operation variables of transformation-type equipment in modeling. The upper part of Figure 20 shows an example of setting priority operation settings for equipment. For example, if there are three gas turbines and priority is set for their operation order, gas turbine 1 has no condition constraints, gas turbine 2 is started when the output of gas turbine 1 is equal to or greater than δ, and gas turbine 3 is started when the output of gas turbine 2 is equal to or greater than δ2. This setting results in the priority operation setting being gas turbine 1, gas turbine 2, and gas turbine 3 being started in this order.

[0148] Third, when switching between operation and non-operation of any transformation-type equipment depending on the operation / non-operation state of the equipment or the value of the input / output variables, it is advisable to switch the input / output characteristics of the transformation-type equipment. As an example of switching the input / output characteristics of transformation-type equipment, the bottom of Figure 20 shows an example of equipment in which equipment 1 with characteristic 1, equipment 2 with characteristic 2, and equipment 3 with characteristic 3 are connected in parallel and operated. It is also possible to set the simultaneous operation conditions so that only one of them is in operation, and only the operation conditions for equipment with characteristic i can be set.

[0149] Next, we will explain "D: Constraints." In addressing issue 2, the following points are considered as constraints for if-then switching.

[0150] First, the constraint equation for the forced operation / non-operation conditions is given by equation (7).

[0151]

number

[0152] Second, regarding the simultaneous operation condition between groups, only m units of the group {C1, C2, ..., CN} of conversion-type equipment can be started simultaneously, and this is the constraint equation (8).

[0153]

number

[0154] Third, the following can be used as conditions based on the operating / non-operating state of the transformation-type equipment or the values ​​of the input / output variables:

[0155] It can operate only when device C1 is operating (ug≦ug cn ) and that it can only operate when "Device C1 is operating and Device C2 is not operating", (ug≦0.5*ug cn +0.5), and the input and output variables x of equipment C1, C2, and C3 c1 , x c2 , x c3 Ax c1 +Bx c2 +Cx c3 ≥ δ (this determination may be made by using equations (9) and (10) with a sufficiently large number M), and the input / output variables x c1 , x c2 , x c3 Ax c1 +Bx c2 +Cx c3 This corresponds to the fact that when ≧δ is satisfied, equation (11) holds for non-operation, that when "(condition 1) and (condition 2)" it is operable (ug≦0.5*(condition 1)+0.5*condition 2)), and that when "(condition 1) or (condition 2)" it is operable (ug≦(condition Bxc2+1)+condition 2)).

[0156]

number

[0157]

number

[0158]

number

[0159] According to the third embodiment, it is possible to limit the number of devices that can be operated simultaneously among multiple devices. For example, this applies to the case where it is desired to limit the number of devices that can be operated simultaneously among the power purchasing, charging, and discharging equipment in the system of Fig. 4 to two.

[0160] According to the third embodiment, when there are multiple devices of the same type, they can be operated in order of priority. For example, in the system of Fig. 4, it is desired to set priorities for boiler 1, boiler 2, and boiler 3 in that order and operate them in this order. [Example]

[0161] In Example 4, a specific means for realizing Problem 3 will be described, which involves modeling the nonlinear characteristics and nonlinear operating conditions of energy facilities with high accuracy and formulating an operation plan within a realistic time frame. Problem 3 mentioned above means that in addition to Problems 1 and 2 above, the following conditions must also be observed.

[0162] These conditions include minimum output conditions, such as not allowing trickle operation and requiring that if the plant is operated, the output must be above a specified value, or continuous operation / stop conditions, such as not being able to stop the plant for a while once it is started, or not wanting to stop it, or even constraints on the rate of change of variables per hour, such as not being able to suddenly change the amount of power generated, etc. For this reason, it is important to create a model that accurately reflects these complex equipment characteristics and operation-related conditions.

[0163] In the following description of Example 4, conditions that are preferable to add to the operating / non-operating variables from the viewpoint of minimum output and the like will be described.

[0164] First, in period t, the operating g When the output P go is the minimum output P operation go The minimum output condition that defines that the output is equal to or greater than the g and output P go and minimum output P operation go Using, , is defined as shown in Figure 21.

[0165] The conditions that define continuous operation and stoppage are as shown in Figure 22. The upper row shows the constraint equation when the constraint condition is the minimum operation time constraint, which indicates that if the system is not operating in period t, it cannot start from t-UTg to t. The lower row shows the constraint equation when the constraint condition is the minimum stoppage time constraint, which indicates that if the system is operating in period t, it cannot be stopped from t-DTg to t.

[0166] The conditions that define the rate of change are as shown in Figure 23. The upper row shows the constraint equation when the constraint condition is {output energy of conversion module g·output o in period t} - {output energy of conversion module g·output o in period t-1} is less than or equal to the ramp-up rate, and the lower row shows the constraint equation when the constraint condition is {output energy of conversion module g·output o in period t-1} - {output energy of conversion module g·output o in period t} is less than or equal to the ramp-down rate.

[0167] The change rate conditions during operation and shutdown are as shown in Figure 24, where the symbol P aoThe upper and lower limits are indicated by an overline and an underline above and below the constraint. The first row of Figure 24 shows the constraint equation when the constraint is that the output energy of the conversion module g and output o is less than or equal to the maximum output - minimum output. The constraint in this example can also be applied when the power generation amount is 0 when the variable u is 0, or when the constraint is that the power generation amount when applied to period t is less than or equal to the difference between the lower limit of SUg and Pg.

[0168] The second row in Figure 24 shows the constraint equations that are applied when the constraint condition is not set (the upper limit of the ramp rate at the start, ramp_startup_limit). For example, it can be applied when the output energy of the conversion module g·output o is equal to or less than the maximum output minus the minimum output, or when the variable u is 0, the power generation amount is also 0.

[0169] The third row in Figure 24 shows constraint equations that can be applied when the constraint conditions are that the output energy of the conversion type module g·output o is equal to or less than the maximum output minus the minimum output, when the variable u is 0, the amount of power generated is also 0, when the conversion type module g is stopped in period t+1, the amount of power generated in period t is equal to or less than the difference between SDg and the lower limit of Pg, etc.

[0170] The fourth row in Figure 24 shows the constraint equation that applies when the constraint condition is the upper limit of the rate of change during shutdown. For example, this can be applied when the output energy of the conversion module g·output o is equal to or less than the maximum output minus the minimum output, or when the variable u is 0, the amount of power generation is also 0.

[0171] In response to the above-mentioned issue 3, the same system can be used to create plans for a variety of cases, from problems with only simple linear characteristics to problems with equipment that has complex piecewise linear characteristics and / or if-then switching conditions, and / or problems with conditions such as minimum output.In addition, it can be used universally for different equipment targets, different operating entities, and different operating methods.

[0172] Figure 25 shows the setting when the conversion equipment is a diode that allows only one-way flow, and can be expressed as a two-point division by setting i(t) = o(t) for input i and output o. The minimum output condition can be expressed as O(t) ≥ 0.

[0173] According to the present invention, it is possible to quickly propose an optimal configuration for energy equipment such as generators, storage batteries, electric vehicles, and chargers in response to KPIs that are important to customers. It is also possible to quickly deploy a planning system for operations.

[0174] The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the described configurations. It is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is also possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0175] The above-described configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware such as an integrated circuit. The above-described configurations, functions, etc. may be realized by software by a processor interpreting and executing a program that realizes each function. Information such as the 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 on a storage medium such as a flash memory card or a DVD (Digital Versatile Disk).

[0176] In each embodiment, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are interconnected. [Explanation of symbols]

[0177] 10: Energy equipment operation planning device 11: Transformation type equipment condition generation unit 12: Storage type equipment condition generation part 13: Network condition generation unit 14: Objective function generation unit 15: Optimal Planning Department 16: Input parameter generation unit 17: Equipment Catalog Department 18: Operational condition pattern section 19: Equipment configuration pattern section 20: Calendar setting section 20 22: Energy-related facility planning program 11a, 12a, 13a, 14a: Input files 11b, 12b, 13b, 14b: Output files 15b: Output file DB1: Equipment characteristics database DB2: Operational Conditions Database DB3: Facility configuration database DB4: Calendar Database

Claims

1. An energy equipment operation plan creation device that creates an operation plan for equipment in an energy system that combines a plurality of equipment including conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, an information recording unit that acquires and records information about the energy system in chronological order; an input parameter generation unit that generates input parameters from the information of the energy system recorded in the information recording unit; a conversion-type equipment condition generation unit that generates, for each of the conversion-type equipment, output parameters using the input parameters as decision variables at least for input resource amounts and output resource amounts for each time, and that sets the relationship between the input resource amounts and the output resource amounts as constraint conditions; an optimal planning unit that calculates an input resource amount and an output resource amount for each time period of the conversion-type equipment by using at least the output parameters determined by the conversion-type equipment condition generating unit, The energy equipment operation plan planning device is characterized in that the transformation type equipment condition generation unit has a component that generates piecewise linear constraint conditions between variables using information on the piecewise linear variable relationships included in the input parameters when generating the output parameters.

2. The energy equipment operation planning device according to claim 1, The conversion type equipment condition generation unit has a component that applies if-then type switching conditions to the operation / non-operation of the equipment using information on if-then type switching conditions included in the input parameters when generating the output parameters. An energy equipment operation planning device.

3. The energy equipment operation planning device according to claim 1, The energy equipment operation plan planning device is characterized in that the conversion-type equipment condition generation unit has a component that has a component that generates constraint conditions using information on minimum output conditions, continuous operation / stop conditions, and hourly change rate conditions of variables included in the input parameters when generating the output parameters.

4. The energy equipment operation planning device according to claim 1, the information recording unit records at least information on equipment configuration patterns in the energy system, information on equipment characteristics, information on equipment operation condition patterns, and information on equipment configuration and operation condition patterns corresponding to date and time information, The energy equipment operation plan planning device is characterized in that the input parameter generation unit generates the input parameters from information about the energy system recorded in the information recording unit.

5. The energy equipment operation plan planning device according to any one of claims 1 to 4, further comprising, as equipment in the energy system, an accumulation type equipment that receives one or more types of resources as input and outputs the same types of resources and accumulates the difference therebetween, a storage-type equipment condition generation unit that generates, for each of the storage-type equipment, output parameters using the input parameters as decision variables at least for the input resource amount and the output resource amount for each time, and that includes, as a constraint, a storage resource amount that is the sum of the difference between the input resource amount and the output resource amount and the storage resource amount at the time, being equal to or greater than a given lower limit value and equal to or less than an upper limit value; an energy equipment operation plan planning device comprising: an optimal plan planning unit that calculates an input resource amount and an output resource amount for each hour of the equipment using at least the output parameters determined by the conversion type equipment condition generation unit and the accumulation type equipment condition generation unit.

6. The energy equipment operation planning device according to claim 5, a network condition generating unit that generates, as an output parameter, a network constraint condition that a sum of the amount of output resources of the same type from the conversion type equipment or the storage type equipment and a sum of the amount of input resources of the same type to other conversion type equipment or other storage type equipment are equal to or greater than the amount of input resources; an objective function generation unit that generates an objective function related to a key performance indicator using one or more values ​​of the input resource amount or the output resource amount; the optimal plan formulation unit calculates input resource amounts and output resource amounts for each hour of the conversion-type equipment and the storage-type equipment using decision variables and constraint conditions for the zero or more conversion-type equipment generated by the conversion-type equipment condition generation unit, decision variables and constraint conditions for the zero or more storage-type equipment generated by the storage-type equipment condition generation unit, one or more constraint conditions for the network generated by the network condition generation unit, and the objective function generated by the objective function generation unit.

7. An energy equipment operation plan creation method that uses a computer to create an operation plan for equipment in an energy system that combines multiple equipment including conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, the method comprising: Obtaining and recording information on the energy system in chronological order; generating input parameters from recorded information of the energy system; For each of the transformation-type equipment, using the input parameters, at least an input resource amount and an output resource amount for each time are set as decision variables, and output parameters are generated with the relationship between the input resource amount and the output resource amount as a constraint condition; Calculating an input resource amount and an output resource amount for each time period of the conversion-type equipment using at least the output parameters; A method for formulating an energy facility operation plan, characterized in that, when generating the output parameters, piecewise linear constraint conditions are generated between variables using information on piecewise linear variable relationships included in the input parameters.

8. An energy equipment operation plan creation program that uses a computer to create an operation plan for equipment in an energy system that combines multiple equipment including conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, an information recording program that chronologically obtains and records information about the energy system; an input parameter generation program that generates input parameters from the information on the energy system; a conversion-type equipment condition generation program for generating, for each of the conversion-type equipment, output parameters using the input parameters as decision variables at least for input resource amounts and output resource amounts for each time, and using the input parameters as constraint conditions for the relationship between the input resource amounts and the output resource amounts; an optimal planning program that calculates input resource amounts and output resource amounts for each time period of the conversion-type equipment using at least the output parameters; The energy equipment operation plan development program is characterized in that, when generating the output parameters, the transformation-type equipment condition generation program generates piecewise linear constraint conditions between variables using information on the piecewise linear variable relationships contained in the input parameters.

Images