Facility combination type optimization device, facility combination type optimization method, and facility combination type optimization program

Abstract

A facility combination type optimization device (10) is provided with: a conversion type facility condition generation unit (11) that sets, as determination variables, input and output resource amounts for each conversion type facility of a system in which conversion type facilities or storage type facilities are combined, and generates a relationship between these determination variables as a constraint condition; a storage type facility condition generation unit (12) that sets input and output resource amounts for each storage type facility as determination variables, and generates, as a constraint condition, the condition that a total storage resource amount, which is the sum of the difference between the determination variables and a storage resource amount, be at least equal to a given lower limit value and at most equal to a given upper limit value; a network condition generation unit (13) that generates a network constraint condition; an objective function generation unit (14) that generates an objective function regarding a key performance indicator for calculating input and output resource amounts; and an optimal plan creation unit (15) that calculates input and output resource amounts for each time.

Description

Equipment combination optimization device, equipment combination optimization method, and equipment combination optimization program

[0001] The present invention relates to an equipment combination optimization device, an equipment combination optimization method, and an equipment combination optimization program.

[0002] It is desirable to provide customers with energy equipment such as generators, storage batteries, electric vehicles, chargers, etc. in optimal configurations. In providing such systems, it is necessary to successfully achieve the key performance indicators (KPIs) that customers value.

[0003] Patent Document 1 describes an invention for a distributed energy system control device that can create an optimal operation plan even if the characteristics of the device change due to factors such as air temperature, water temperature, or device deterioration.

[0004] Japanese Patent Application Laid-Open No. 2006-325336

[0005] It has been difficult to find an optimal equipment configuration that achieves a given key performance indicator and to quickly create an optimal operation plan. Therefore, an object of the present invention is to find an optimal equipment configuration in a short time and to create an optimal equipment operation plan for the optimal equipment configuration.

[0006] In order to solve the above-mentioned problems, the equipment combination optimization device of the present invention is a system that combines transformation-type equipment that receives one or more types of resources as input and outputs one or more types of resources, or storage-type equipment that receives one or more types of resources as input and outputs the same types of resources and stores the difference therebetween, the system including: a transformation-type equipment condition generation unit that, for each of the transformation-type equipment, uses at least an input resource amount and an output resource amount for each time as decision variables and generates a relationship between the input resource amount and the output resource amount as a constraint condition; a storage-type equipment condition generation unit that, for each of the storage-type equipment, uses at least an input resource amount and an output resource amount for each time as decision variables and generates a constraint condition that a stored resource amount, which is the sum of the difference between the input resource amount and the output resource amount and the stored resource amount at the time, is equal to or greater than a given lower limit value and equal to or less than an upper limit value; and and 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 amounts or the output resource amounts. The optimal planning unit is characterized by comprising: a network condition generation unit that generates a network 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 greater than the input resource amount; 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 amounts or the output resource amounts; and an optimal planning unit that calculates the input resource amount and the output resource amount for each hour of the conversion-type equipment and the storage-type equipment using the zero or more decision variables and constraint conditions of the conversion-type equipment generated by the conversion-type equipment condition generation unit, the zero or more decision variables and constraint conditions of the storage-type equipment generated by the storage-type equipment condition generation unit, the one or more network constraint conditions generated by the network condition generation unit, and the objective function generated by the objective function generation unit.

[0007] The equipment combination optimization method of the present invention is for a system that combines conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, or storage-type equipment that receives one or more types of resources as input and outputs the same types of resources and stores the difference, and includes the steps of: a conversion-type equipment condition generation unit, for each of the conversion-type equipment, using input resource amounts and output resource amounts at least for each time as decision variables, and generating constraint conditions that represent the relationship between the input resource amounts and the output resource amounts; a storage-type equipment condition generation unit, for each of the storage-type equipment, using input resource amounts and output resource amounts at least for each time as decision variables, and generating constraint conditions that represent the relationship between the input resource amounts and the output resource amounts, which is the sum of the difference between the input resource amounts and the output resource amounts and the stored resource amount at the time, that is, the difference between the input resource amounts and the output resource amounts and the stored resource amount at the time, that is, the sum of the input resource amounts and the output resource amounts and the stored resource amount at the time, that is, the difference between the input resource amounts and the output resource amounts, is equal to or greater than a given lower limit value and equal to or less than an upper limit value; and a network condition generation unit, for the conversion-type equipment or the storage-type equipment, generating constraint conditions that represent the relationship between the input resource amounts and the output resource amounts, and a step of generating, as a network constraint condition, a condition that the sum of the amount of output resources of the same type from a conversion-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 or the amount of input resources is greater; a step of an objective function generation unit generating an objective function related to a key performance indicator using one or more values ​​of the input resource amount or the output resource amount; and a step of an optimal planning unit calculating the amount of input resources and the amount of output resources for each hour of the conversion-type equipment and the storage-type equipment using the zero or more decision variables and constraint conditions of the conversion-type equipment generated by the conversion-type equipment condition generation unit, the zero or more decision variables and constraint conditions of the storage-type equipment generated by the storage-type equipment condition generation unit, one or more constraint conditions of the network generated by the network condition generation unit, and the objective function generated by the objective function generation unit.

[0008] The equipment combination optimization program of the present invention is a system that combines conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, or storage-type equipment that receives one or more types of resources as input and outputs the same types of resources and stores the difference between them, and the program includes the steps of generating, for each of the conversion-type equipment, an input resource amount and an output resource amount for at least each time as decision variables, and a constraint condition being the relationship between the input resource amount and the output resource amount; and for each of the storage-type equipment, the decision variables are at least the input resource amount and the output resource amount for at least each time as decision variables, and a constraint condition being that the storage resource amount, which is the sum of the difference between the input resource amount and the output resource amount and the storage resource amount at the time, is greater than or equal to a given lower limit value and less than or equal to an upper limit value. the step of generating a network constraint 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 or the amount of input resources is greater, the step of generating an objective function related to a key performance indicator using one or more values ​​of the amount of input resources or the amount of output resources, and the step of calculating the amount of input resources and the amount of output resources for each hour of the conversion-type equipment and the storage-type equipment using the generated decision variables and constraints for the zero or more conversion-type equipment, the generated decision variables and constraints for the zero or more storage-type equipment, the generated one or more network constraints, and the generated objective function. Other means will be described in the description of the embodiment of the invention.

[0009] According to the present invention, it is possible to search for an optimal facility configuration in a short time, and further to formulate an optimal facility operation plan for the optimal facility configuration.

[0010] Fig. 1 is a logical configuration diagram of an equipment combination optimization device according to an embodiment of the present invention. Fig. 2 is a hardware configuration diagram of an equipment combination optimization device. Fig. 3 is a diagram illustrating the operation of an equipment combination optimization device. Fig. 4 is a diagram illustrating an example of a plant in which equipment is combined. Fig. 5 is an example of a piecewise linear graph. Fig. 6 is an example of a piecewise linear graph. Fig. 7 is a scatter plot showing the relationship between cost and CO2 emissions. Fig. 8 is a diagram illustrating the results of an optimized operation plan.

[0011] Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. FIG. 1 is a logical configuration diagram of an equipment combination optimization device 10 according to this embodiment. The equipment combination optimization device 10 includes, as functional units, a conversion-type equipment condition generation unit 11, a storage-type equipment condition generation unit 12, a network condition generation unit 13, an objective function generation unit 14, an optimal plan development unit 15, an optimal equipment configuration selection support unit 16, an optimal equipment configuration selection unit 19, an equipment configuration generation unit 17, and an electricity rate conversion unit 18. The equipment combination optimization device 10 further stores an equipment catalog group 21, a system template group 22, and an equipment combination optimization program 23. The equipment combination optimization device 10 virtually generates a system in which multiple conversion-type equipment and storage-type equipment are connected, and creates a plan to optimize key performance indicators of the generated system. Here, conversion-type equipment refers to equipment that converts energy, such as solar power generation panels, thermal power generators, gas turbine generators, gas cogeneration systems, and wind power generators. Here, the storage facility is a facility that selectively stores and releases energy, such as a storage battery or a gas tank. Key performance indicators of the system are, for example, the cost of purchasing electricity, the cost of procuring gas, and CO ２ Here, resources are 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 the like. When input to a facility, it is an input resource, and when output from the facility, it is an output resource.

[0012] The equipment catalog group 21 stores the specifications of a plurality of conversion-type equipment and the specifications of a plurality of storage-type equipment.

[0013] The system template group 22 stores a plurality of system templates in which interconnection relationships of equipment are defined. Here, a system template is a template for configuring a system by applying either conversion-type equipment or storage-type equipment whose specifications are stored in the equipment catalog group 21.

[0014] The equipment configuration generation unit 17 references the system template group 22 and the equipment catalog group 21 to virtually generate a system configured by connecting conversion-type equipment or storage-type equipment through a network. The conversion-type equipment condition generation unit 11 generates decision variables and constraint conditions that indicate the behavior of the conversion-type equipment. Here, the decision variables are controllable variables. The constraint conditions are conditions that specify the possible ranges of the decision variables.

[0015] The storage-type facility condition generating unit 12 generates decision variables and constraint conditions that indicate the behavior of the storage-type facility. The network condition generating unit 13 generates network constraint conditions, and generates network conditions using the constraint condition that the sum of the inputs and outputs of the conversion-type facility and the storage-type facility are equal.

[0016] The objective function generation unit 14 generates an objective function related to key performance indicators using one or more values ​​of the input / output resource amounts of the conversion-type equipment and the storage-type equipment. The optimal plan creation unit 15 calculates the configuration of one or more pieces of equipment and the input / output of the network from the decision variables and constraints of the conversion-type equipment or the decision variables and constraints of the storage-type equipment, the network constraints, and the objective function, and creates an optimal plan. The optimal plan creation unit 15 calculates an optimized plan from the decision variables, constraints, and objective function through mathematical optimization. The optimized plan here refers to equipment operation parameters such as the input / output resource amounts for each time period, or the load factor for each time period that results in the input / output resource amounts, as shown in FIG. 8.

[0017] The optimal equipment configuration selection support unit 16 displays, in a comparable form, the multiple key performance indicators calculated by the optimal plan formulation unit 15 for each equipment configuration of the multiple systems generated by the equipment configuration generation unit 17. This supports the selection of the optimal equipment configuration. Note that the optimal equipment configuration selection support unit 16 is not essential and may be omitted.

[0018] The optimal equipment configuration selection unit 19 compares the key performance indicators calculated by the optimal plan formulation unit 15 for each equipment configuration of the multiple systems generated by the equipment configuration generation unit 17, and selects the equipment configuration that calculates the best key performance indicator. Note that in FIG. 1 , the optimal equipment configuration selection unit 19 is provided subsequent to the optimal equipment configuration selection support unit 16, but the configuration may be reversed. The power rate conversion unit 18 calculates the relationship between power and charges, where the power rate varies depending on the time of day. If either the input resource and output resource of the conversion-type equipment or the input resource and output resource of the storage-type equipment is power, the power rate conversion unit 18 calculates the relationship between power and charges. By implementing the power rate conversion unit 18 as an add-in, various rate plans that indicate the relationship between power and charges can be easily considered.

[0019] FIG. 2 is a hardware configuration diagram of an equipment combination optimization device 10. The equipment combination optimization device 10 optimizes a system configured by combining equipment. The equipment combination optimization device 10 is a computer including a CPU (Central Processing Unit) 101, a ROM (Read Only Memory) 102, and a RAM (Random Access Memory) 103. The equipment combination optimization device 10 formulates an optimal plan for equipment combination and searches for an optimal equipment configuration in a short period of time. Here, the optimal equipment configuration refers to, for example, an equipment configuration that can achieve performance indicators specified by a customer. Note that the term "optimization" is not necessarily limited to an optimal equipment configuration, but may also include an equipment configuration that is optimized or optimized so that key performance indicators satisfy specified conditions.

[0020] The CPU 101 is a central processing unit that embodies each functional unit by executing a program and accessing data stored in the ROM 102 or the RAM 103. 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.

[0021] The combinational optimization device 10 further includes an input unit 104, a communication unit 105, a display unit 106, and a storage unit 107. The input unit 104 is, for example, a keyboard, a mouse, or a touch panel, and is a component that allows a user to operate the combinational optimization device 10 to input data.

[0022] The communication unit 105 is, for example, a network interface card, and is a component for communicating with other devices and on-demand ordering platforms via a network. The display unit 106 is, for example, a liquid crystal display, and is used to display characters, figures, images, etc.

[0023] The storage unit 107 is, for example, a hard disk drive or a solid state drive (SSD) that stores a relatively large amount of data and programs. The storage unit 107 stores an equipment catalog group 21, a system template group 22, and an equipment combination optimization program 23. The CPU 101 executes the equipment combination optimization program 23 to realize the various functional units shown in FIG. 1 .

[0024] 3 is a diagram showing the operation of the equipment combination optimization device 10. The equipment catalog group 21 provides specification information for multiple transformation-type equipment and multiple storage-type equipment to the equipment configuration generation unit 17. The system template group 22 provides any of the system templates in which the interconnection relationships of equipment are defined to the equipment configuration generation unit 17. The system template is for fitting any transformation-type equipment or any storage-type equipment. By having the designer prepare the system template in advance and fitting the equipment into it, a system with an optimal equipment configuration can be generated in a short time.

[0025] The equipment configuration generation unit 17 virtually generates an equipment configuration of a system configured by connecting conversion-type equipment or storage-type equipment to a network by fitting the conversion-type equipment or storage-type equipment in the equipment catalog group 21 to the system template. The equipment configuration generation unit 17 outputs parameters of the conversion-type equipment to the conversion-type equipment condition generation unit 11, outputs parameters of the storage-type equipment to the storage-type equipment condition generation unit 12, outputs the system configuration to the network condition generation unit 13, and outputs types of decision variables to the objective function generation unit 14.

[0026] The conversion-type equipment condition generation unit 11 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 the 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.

[0027] 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 receives one or more types of energy resources as input and outputs one or more types of resources, such as a gas cogeneration (generator) that converts gas as an input resource into an electric power output resource, a power conditioner that converts DC power as an input resource into an AC power output resource, or a solar power generation panel that converts sunlight as an input resource into a DC power output resource.

[0028] 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 formulation unit 15. In other words, 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 decision variables that include at least the input resource amount and the output resource amount for each time, and 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 for 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 is, 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.

[0029] The network condition generation unit 13 generates 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 conversion-type equipment. The objective function generation unit 14 generates an objective function that calculates the amount of input and output resources of conversion-type equipment or 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 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 greater than the input resource amount.

[0030] The optimal plan creation unit 15 creates an optimal plan for operating each facility based on the decision variables and constraints of the conversion-type facility or the decision variables and constraints of the storage-type facility, 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.

[0031] The equipment configuration evaluation unit 16 calculates the input / output of each piece of equipment and the input / output of the network for each time period in the equipment configuration generated by the equipment configuration generation unit 17, and evaluates them using key performance indicators. If the key performance indicators satisfy predetermined conditions, the equipment configuration evaluation unit 16 proposes that equipment configuration to the user, and if the key performance indicators do not satisfy the predetermined conditions, the equipment configuration evaluation unit 16 causes the equipment configuration generation unit 17 to generate a system with a different equipment configuration. This makes it possible to quickly search for a system with an optimal equipment configuration whose key performance indicators satisfy the predetermined conditions.

[0032] FIG. 4 is a diagram showing an example of a system 3 that combines facilities. The system 3 includes a power sales facility 30, a photovoltaic power generation panel 32, a photovoltaic power generation panel power conditioner 33, a storage battery 36, and storage battery power conditioners 34 and 35 as a power supply network, and supplies power to a power demand 31. In the drawing, the power conditioner is abbreviated as "PCS," and the photovoltaic 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 predefines or inputs the amount of power demand for each time period as the power demand 31.

[0033] Of these facilities, the power selling 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 receive one or more types of resources as input and output one or more types of resources. The storage battery 36 is a storage-type facility that receives one or more types of resources as input and outputs the same types of resources, storing the difference between the inputs and the output.

[0034] The power node 301 bundles together the purchased power from the power sales equipment 30, the generated power output by the solar power generation 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.

[0035] The power selling facility 30 is supplied with purchased power, and the purchased power is output to the power node 301, and the power selling cost and CO ２ When solar light is input to the photovoltaic power generation panel 32, the photovoltaic power generation panel 32 supplies DC power to the photovoltaic power generation panel power conditioner 33 via the power node 302. The photovoltaic power generation panel power conditioner 33 converts the DC power to AC power and supplies the AC power to the power node 301.

[0036] 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.

[0037] 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.

[0038] Gas procured by a gas purchasing facility 40 is input to a gas node 401, which supplies the gas to a gas cogeneration system 41 and a gas boiler 42. The gas cogeneration system 41 receives the supplied gas and auxiliary power as inputs, supplies generated power to the power node 301, and supplies steam heat to a steam node 402, thereby supplying steam to a steam demand 43, and furthermore, hot water heat and CO ２ is discharged.

[0039] The gas boiler 42 receives the supplied gas and auxiliary power as inputs, supplies steam heat to the steam node 402, and further ２ The steam heat supplied to the steam node 402 is finally supplied to the steam demand 43.

[0040] The system consisting of the power supply network, gas supply network, and steam supply network is stored in a system template. By applying conversion-type equipment or storage-type equipment to these system templates, the equipment configuration of the system can be generated.

[0041] FIG. 5 is an example of a piecewise linear graph. The horizontal axis of the graph represents power. The vertical axis of the graph represents the cost of power. ０ When the power cost is a linear function of the power, ０ When the power cost exceeds P, the power cost becomes a linear function of the power, but the rate of change is ０ is greater than when the power is P ０ When , the electricity cost is C １When the power is 0, the power cost is C ０ is.

[0042] Such piecewise linear functions require less computational effort than higher-order polynomials and can suitably approximate higher-order polynomials.

[0043] FIG. 6 is an example of a piecewise linear graph. The horizontal axis of the graph represents the amount of gas. The vertical axis of the graph represents the amount of power. ０ Above and L １ When the gas volume is L, the power is a linear function of the gas volume. １ Above and L ２ In the following cases, the power is a linear function of the gas amount, but the rate of change is ０ Above and L １ It is smaller than when the gas volume is L ２ In the above cases, the power is a linear function of the gas volume, but the rate of change is proportional to the gas volume. ０ Above and L １ When the gas amount is L １ Above and L ２ is smaller than:

[0044] Figure 7 shows the relationship between cost and CO ２ This is a scatter plot showing the relationship with 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 the ongoing costs required for business operations. The sum of CAPEX and OPEX shows the overall costs. The vertical axis of the scatter plot is CO ２ The scatter plot in Figure 7 shows the amount of CO2 emissions. ２ This shows the relationship between CO emissions and CO2 emissions in a comparable form. ２ To reduce emissions, CAPEX + OPEX will increase, but reducing CAPEX + OPEX will reduce CO ２Emissions will increase, so you can compare and choose which value to adopt based on your key performance indicators (KPIs).

[0045] 8 is a graph showing the results of the optimized operation plan. The horizontal axis of the graph represents time. The vertical axis of the upper graph represents power. The vertical axis of the lower graph represents heat.

[0046] In the graph, the initial period when the electricity purchase price is low at 10 yen / KWh is, for example, from midnight to 8:00 a.m., and all electricity is covered by purchased electricity. The purchased electricity also covers the charging power for the storage battery PCS.

[0047] 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.

[0048] 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. During this time, the demand for steam gradually increases, so it is met by both the steam from the gas cogeneration and the steam from the gas boiler.

[0049] 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.

[0050] The period when the electricity purchase price drops to 10 yen / KWh is, for example, from 10 PM to 12 PM, during which all electricity is supplied by purchased electricity. This purchased electricity also supplies the power to charge the storage battery PCS. At this time, steam demand occurs, and this is supplied by steam from the gas boiler.

[0051] According to the present invention, it is possible to quickly propose an optimal configuration of energy equipment such as generators, storage batteries, electric vehicles, and chargers for KPIs that are important to customers, and to quickly deploy a planning system for operations.

[0052] [1] A system (3) that combines conversion-type equipment (photovoltaic power generation panels 32, photovoltaic power generation panel power conditioners 33, gas cogeneration 41, storage battery power conditioners 34, 35, gas boiler 42) that receives one or more types of resources as input and outputs one or more types of resources, or storage-type equipment (storage battery 36) that receives one or more types of resources as input and outputs the same types of resources and stores the difference therebetween, the system (3) includes: a conversion-type equipment condition generation unit (11) that, for each of the conversion-type equipment, generates an input resource amount and an output resource amount for each time as decision variables, and generates a relationship between the input resource amount and the output resource amount as a constraint condition; and a storage-type equipment condition generation unit (12) that, for each of the storage-type equipment, generates an accumulated resource amount that is the sum of the difference between the input resource amount and the output resource amount and the accumulated resource amount at the time as a decision variable, and generates an accumulated resource amount that is equal to or greater than a given lower limit value and equal to or less than an upper limit value. an objective function generation unit (14) that generates an objective function related to a key performance indicator using one or more values ​​of the input resource amounts or the output resource amounts; and an optimal plan creation unit (15) that calculates the input resource amounts and output resource amounts for each hour of the conversion-type equipment and the storage-type equipment using the zero or more decision variables and constraint conditions of the conversion-type equipment generated by the conversion-type equipment condition generation unit (11), the zero or more decision variables and constraint conditions of the storage-type equipment generated by the storage-type equipment condition generation unit (12), one or more constraint conditions of the network generated by the network condition generation unit (13), and the objective function generated by the objective function generation unit (14).

[0053] According to [1], it is possible to quickly search for the optimal equipment configuration in a system that combines conversion-type equipment and storage-type equipment.

[0054] [2] The equipment combination optimization device according to [1], characterized in that the conversion-type equipment condition generation unit (11) receives input of a piecewise linear conversion map that converts input resource amounts (DC power, AC power, gas, solar power) of the conversion-type equipment into output resource amounts (DC power, AC power, steam).

[0055] According to [2], the input / output characteristics of a transformation-type facility are represented by a piecewise linear transformation map, so that the amount of calculation required for simulating the input / output characteristics can be reduced and the calculation can be performed quickly.

[0056] [3] The equipment combination optimization device according to [1], characterized in that the conversion-type equipment receives one or more types of energy resources as input (DC power, AC power, gas, solar power) and outputs one or more types of resources (DC power, AC power, steam).

[0057] According to [3], it is possible to easily calculate the optimal equipment configuration of a system (3) including a generator and a power conditioner that convert various resources into each other.

[0058] [4] The equipment combination optimization device according to [1], characterized in that the storage-type equipment receives one or more types of energy resources as input and outputs the same types of resources, and stores the difference between them.

[0059] According to [4], it is possible to easily calculate the optimal equipment configuration of a system (3) including storage-type equipment such as a storage battery (36).

[0060] [5] The equipment combination optimization device according to [1], characterized in that the network condition generation unit (13) has a unit conversion function between the input resource amount and the output resource amount of the conversion-type equipment.

[0061] According to [5], even if the conversion type equipment has different units of input and output, it is possible to simulate the equipment input and output of the system (3).

[0062] [6] The equipment combination optimization device according to [1], characterized in that either the input resources and output resources of the conversion-type equipment and the input resources and output resources of the storage-type equipment are electricity, and further comprising an electricity rate conversion unit (18) that calculates the relationship between electricity and the rate, where the electricity rate varies depending on the time of day.

[0063] According to [6], even if an electric power company changes its electricity rate plan for each time period, the change can be localized.

[0064] [7] The equipment combination optimization device according to [1], further comprising an equipment configuration generation unit (17) that generates an equipment configuration of a system that combines conversion-type equipment or storage-type equipment.

[0065] According to [7], it is possible to easily calculate the optimal equipment configuration of the system (3) that combines conversion-type equipment or storage-type equipment.

[0066] [8] The equipment combination optimization device according to [7], characterized by comprising: a system template group (22) for fitting any conversion type equipment or any accumulation type equipment; and an equipment catalog group (21) relating to conversion type equipment or accumulation type equipment for fitting into any of the system templates (22).

[0067] According to [8], a group of system templates (22) and a group of equipment catalogs (21) that fit into any of these templates are provided, making it possible to easily and quickly generate variations of the equipment configuration of the system (3).

[0068] [9] The equipment combination optimization device according to [8], characterized in that the equipment configuration generation unit (17) generates an equipment configuration of the system by applying any of the conversion-type equipment or any of the accumulation-type equipment in the equipment catalog group (21) to any of the system template group (22).

[0069] According to [9], the equipment configuration generation unit (17) can quickly generate variations of the equipment configuration of the system (3) by matching the equipment in the equipment catalog group (21) to one of the system template group (22).

[0070]

[10] The equipment combination optimization device described in [7], characterized in that the equipment configuration generation unit (17) has an equipment catalog group related to conversion-type equipment or accumulation-type equipment, and generates an equipment configuration of the system by selecting and combining one or more pieces of equipment from the equipment catalog group.

[0071] According to

[10] , a combination of equipment configurations of a system can be generated from equipment catalogs.

[0072]

[11] The equipment combination optimization device according to [7], further comprising: an optimal equipment configuration selection unit (19) that compares the key performance indicators calculated by the optimal plan formulation unit (15) for each equipment configuration of a plurality of systems generated by the equipment configuration generation unit (17), and selects the equipment configuration that calculates the best key performance indicator.

[0073] According to

[11] , the best equipment configuration can be selected based on key performance indicators related to the equipment configuration of the system.

[0074]

[12] The equipment combination optimization device according to [7], further comprising: an optimal equipment configuration selection support unit (16) that displays, in a comparable form, a plurality of key performance indicators calculated by the optimal plan formulation unit (15) for each equipment configuration of a plurality of systems generated by the equipment configuration generation unit (17).

[0075] According to

[12] , it is possible to support the selection of the equipment configuration of a system based on key performance indicators.

[0076]

[13] For a system (3) that combines conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, or storage-type equipment that receives one or more types of resources as input and outputs the same type of resource and stores the difference, a conversion-type equipment condition generation unit (11) generates, for each of the conversion-type equipment, a relationship between the input resource amount and the output resource amount at least for each time as a decision variable, as a constraint condition; a storage-type equipment condition generation unit (12) generates, for each of the storage-type equipment, a constraint condition 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 the time, is greater than or equal to a given lower limit value and less than or equal to an upper limit value; and a network condition generation unit (13) generates, as a network constraint condition, that the sum of the output resource amount of the same type from the conversion-type equipment or the storage-type equipment and the sum of the input resource amount of the same type to other conversion-type equipment or other storage-type equipment are equal to or the input resource amount is greater than an objective function generating unit (14) generating an objective function related to a key performance indicator using one or more values ​​of the input resource amount or the output resource amount; and an optimal planning unit (15) calculating the input resource amount and the output resource amount for each hour of the conversion-type equipment and the storage-type equipment using the zero or more decision variables and constraint conditions of the conversion-type equipment generated by the conversion-type equipment condition generating unit (11), the zero or more decision variables and constraint conditions of the storage-type equipment generated by the storage-type equipment condition generating unit (12), one or more constraint conditions of the network generated by the network condition generating unit (13), and the objective function generated by the objective function generating unit (14).

[0077] According to

[13] , it is possible to quickly search for the optimal equipment configuration in a system that combines conversion-type equipment and storage-type equipment.

[0078]

[14] A computer is provided with the following procedures for a system that combines conversion-type equipment that receives one or more types of resources as input and outputs one or more types of resources, or storage-type equipment that receives one or more types of resources as input and outputs the same type of resource and stores the difference: for each of the conversion-type equipment, the input resource amount and output resource amount for at least each time are decision variables, and a relationship between the input resource amount and the output resource amount is generated as a constraint; for each of the storage-type equipment, the input resource amount and output resource amount for at least each time are decision variables, and a constraint is generated 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 the time, is greater than or equal to a given lower limit and less than or equal to an upper limit; a network constraint is generated that the sum of the output resource amounts of the same type from the conversion-type equipment or the storage-type equipment and the sum of the input resource amounts of the same type to other conversion-type equipment or other storage-type equipment are equal to or greater than the input resource amount; a procedure for generating an objective function related to a key performance indicator using one or more values ​​of the input resource amount or the output resource amount; a procedure for calculating the input resource amount and the output resource amount for each hour of the conversion type equipment and the storage type equipment using the generated decision variables and constraint conditions of the zero or more conversion type equipment, the generated decision variables and constraint conditions of the zero or more storage type equipment, the generated constraint conditions of the one or more networks, and the generated objective function.

[0079] According to

[14] , it is possible to quickly search for the optimal equipment configuration in a system that combines conversion-type equipment and storage-type equipment.

[0080] (Modifications) 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.

[0081] The above-described configurations, functions, processing units, processing means, etc. may be implemented in part or in whole by hardware such as an integrated circuit. The above-described configurations, functions, etc. may also be implemented by software, with a processor interpreting and executing a program that implements each function. Information such as the program, table, and file that implements each function can be stored in a recording device such as a memory, a hard disk, or an SSD (Solid State Drive), or on a recording medium such as a flash memory card or a DVD (Digital Versatile Disk).

[0082] 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.

[0083] REFERENCE SIGNS LIST 10 Equipment combination optimization device 11 Conversion type equipment condition generation unit 12 Storage type equipment condition generation unit 13 Network condition generation unit 14 Objective function generation unit 15 Optimal plan planning unit 16 Optimal equipment configuration selection support unit 17 Equipment configuration generation unit 18 Electricity rate conversion unit 19 Optimal equipment configuration selection unit 21 Equipment catalog group 22 System template group 23 Equipment combination optimization program 101 CPU 102 ROM 103 RAM 104 Input unit 105 Communication unit 106 Display unit 107 Storage unit 3 System 30 Power selling equipment 31 Power demand 32 Photovoltaic power generation panel 33 Photovoltaic power generation panel power conditioner 35, 34 Storage battery power conditioner 36 Storage battery 301, 302, 304, 305 Power node 40 Gas purchasing equipment 41 Gas cogeneration 42 Gas boiler 43 Steam demand 401 Gas node 402 Steam node

Claims

1. For a system that combines a conversion-type facility that takes one or more types of resources as input and outputs one or more types of resources, or an accumulation-type facility that takes one or more types of resources as input, outputs the same type of resources, and accumulates the difference, for each of the conversion-type facilities, with at least the input resource amount and output resource amount per time as decision variables, a conversion-type facility condition generation unit that generates the relationship between the input resource amount and the output resource amount as a constraint condition; for each of the accumulation-type facilities, with at least the input resource amount and output resource amount per time as decision variables, a constraint condition 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 that time, is greater than or equal to a given lower limit value and less than or equal to an upper limit value, and an accumulation-type facility condition generation unit that generates this as a constraint condition; a network condition generation unit that generates, as a network constraint condition, that the sum of the output resource amounts of the same type from the conversion-type facility or the accumulation-type facility and the sum of the input resource amounts of the same type to another conversion-type facility or another accumulation-type facility are equal, or that the input resource amount is larger; an objective function generation unit that generates an objective function related to key performance evaluation indicators using one or more values of the input resource amount or the output resource amount; and an optimal planning unit that calculates the input resource amount and output resource amount per time of the conversion-type facility and the accumulation-type facility using the zero or more decision variables and constraint conditions of the conversion-type facility generated by the conversion-type facility condition generation unit, the zero or more decision variables and constraint conditions of the accumulation-type facility generated by the accumulation-type facility condition generation unit, the one or more network constraint conditions generated by the network condition generation unit, and the objective function generated by the objective function generation unit. An equipment combination type optimization device characterized by comprising these components.

2. The conversion-type facility condition generation unit receives the input of a piecewise-linear conversion map that converts the input resource amount of the conversion-type facility into an output resource amount. The equipment combination type optimization device according to claim 1, characterized in that.

3. The conversion-type facility takes one or more types of energy resources as input and outputs one or more types of resources. The equipment combination type optimization device according to claim 1, characterized in that.

4. The storage type equipment inputs resources of one or more types of energy, outputs the same type of resources, and accumulates the difference. The equipment combination type optimization device according to claim 1, characterized in that.

5. The network condition generation unit has a unit conversion function between the input resource amount and the output resource amount of the conversion type equipment. The equipment combination type optimization device according to claim 1, characterized in that.

6. Any one of the input resources and output resources of the conversion type equipment, and the input resources and output resources of the storage type equipment is electric power. The equipment combination type optimization device according to claim 1, further comprising an electricity rate conversion unit that calculates the relationship between electricity and electricity rates with different electricity rates by time zone.

7. An equipment configuration generation unit that generates an equipment configuration of a system combining a conversion type equipment or a storage type equipment. The equipment combination type optimization device according to claim 1, characterized in that.

8. A system template group for fitting any conversion type equipment or any storage type equipment, and an equipment catalog group related to the conversion type equipment or the storage type equipment for fitting into any of the system template groups. The equipment combination type optimization device according to claim 7, further comprising.

9. The equipment configuration generation unit fits any one of the conversion type equipment or the storage type equipment of the equipment catalog group into any one of the system template groups to generate the equipment configuration of the system. The equipment combination type optimization device according to claim 8, characterized in that.

10. The equipment configuration generation unit includes an equipment catalog group related to the conversion type equipment or the storage type equipment, and generates the equipment configuration of the system by selecting and combining one or more equipment from the equipment catalog group. The equipment combination type optimization device according to claim 7, characterized in that.

11. An optimal equipment configuration selection unit that compares the important performance evaluation indexes calculated by the optimal planning department for each equipment configuration of a plurality of systems generated by the equipment configuration generation unit, and selects the equipment configuration for which the most excellent important performance evaluation index is calculated. The equipment combination type optimization device according to claim 7, further comprising.

12. An optimal equipment configuration selection support unit that displays, in a comparable manner, a plurality of important performance evaluation indicators calculated by the optimal planning department for each equipment configuration of a plurality of systems generated by the equipment configuration generation unit. The equipment combination type optimization device according to claim 7, further comprising the optimal equipment configuration selection support unit.

13. For a system that combines a conversion type equipment that takes one or more types of resources as input and outputs one or more types of resources, or an accumulation type equipment that takes one or more types of resources as input, outputs the same type of resources, and accumulates the difference, the conversion type equipment condition generation unit, for each of the conversion type equipment, uses at least the input resource amount and the output resource amount per time as decision variables, and generates the relationship between the input resource amount and the output resource amount as a constraint condition; the accumulation type equipment condition generation unit, for each of the accumulation type equipment, uses at least the input resource amount and the output resource amount per time as decision variables, and generates, as a constraint condition, that the accumulation 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 that time, is not less than a given lower limit value and not more than a given upper limit value; the network condition generation unit generates, as a network constraint condition, that the sum of the same type of output resource amounts from the conversion type equipment or the accumulation type equipment, and the sum of the same type of input resource amounts to other conversion type equipment or other accumulation type equipment are equal, or the input resource amount is larger; the objective function generation unit generates an objective function regarding the important performance evaluation indicator using one or more values among the input resource amount or the output resource amount; the optimal planning department calculates the input resource amount and the output resource amount per time of the conversion type equipment and the accumulation type equipment using the decision variables and constraint conditions of 0 or more of the conversion type equipment generated by the conversion type equipment condition generation unit, the decision variables and constraint conditions of 0 or more of the accumulation type equipment generated by the accumulation type equipment condition generation unit, one or more of the network constraint conditions generated by the network condition generation unit, and the objective function generated by the objective function generation unit. The equipment combination type optimization method is characterized by comprising the above steps.

14. For a system that combines a conversion-type facility that takes one or more types of resources as input and outputs one or more types of resources, or an accumulation-type facility that takes one or more types of resources as input, outputs the same type of resources, and accumulates the difference, for each of the conversion-type facilities, a procedure for generating, with at least the input resource amount and the output resource amount at each time as decision variables, the relationship between the input resource amount and the output resource amount as a constraint condition; for each of the accumulation-type facilities, a procedure for generating, with at least the input resource amount and the output resource amount at each time as decision variables, a constraint condition 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 that time, is equal to or greater than a lower limit value and equal to or less than an upper limit value; a procedure for generating, as a network constraint condition, that the sum of the output resource amounts of the same type from the conversion-type facility or the accumulation-type facility and the sum of the input resource amounts of the same type to another conversion-type facility or another accumulation-type facility are equal, or that the input resource amount is larger; a procedure for generating an objective function related to an important performance evaluation index using one or more values of the input resource amount or the output resource amount; a procedure for calculating the input resource amount and the output resource amount at each time of the conversion-type facility and the accumulation-type facility using the generated zero or more decision variables and constraint conditions of the conversion-type facilities, the generated zero or more decision variables and constraint conditions of the accumulation-type facilities, the generated one or more network constraint conditions, and the generated objective function; an equipment combination type optimization program for executing the above.

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