JP5179423B2 - Energy system optimization method, energy system optimization apparatus and program - Google Patents

Description

  The present invention relates to an energy system optimizing method, an energy system optimizing device, and a program for calculating an equipment design and operation plan of an optimal energy system with high cost competitiveness while ensuring environmental performance.

  Businesses that operate business facilities such as factories, hospitals, hotels, schools, and office buildings have a need to reduce the cost of energy systems while clearing environmental regulations that are becoming stricter year by year due to social demands. In order to meet such needs, a method for calculating an energy system design and operation plan has been devised (see Patent Document 1 and Patent Document 2).

In Patent Document 1, by using mixed integer linear programming, an optimal function of a combined heat source system including a heat storage tank is set with any one of system operation cost, primary energy amount (electric power, city gas, water), and CO2 emission as an objective function. A method for calculating the driving schedule is shown.
Patent Document 2 discloses a method of calculating an optimum facility design of a composite heat source system including a storage battery by using mixed integer linear programming as an objective function of either cost, CO2 emission, or the like.

JP 2004-317049 A JP 2009-26092 A

  However, Patent Document 1 and Patent Document 2 do not consider a charge menu that can be selected for each business operator for electricity charges and gas charges. In addition, when a plurality of heat source devices are introduced, an actual system is operated with a load apportioned. However, in Patent Document 1 and Patent Document 2, such a thermal load apportionment calculation cannot be performed. As described above, the methods of Patent Document 1 and Patent Document 2 have a large deviation from reality and cannot be said to be a practical level.

  The present invention has been made in view of the above-mentioned problems, and its purpose is to accurately model actual conditions and to ensure cost effectiveness while ensuring environmental performance within a practical calculation time. It is to provide an energy system optimizing method for calculating a facility design and an operation plan of an optimum energy system with high power.

In order to achieve the above-described object, a first invention is an energy system optimization method in which an energy system optimization apparatus calculates an equipment design and an operation plan of an optimal energy system, wherein the energy system optimization apparatus includes: The storage unit purchases demand data including a plurality of energy demands at the calculation target facility, equipment data including the rated efficiency value, initial cost and maintenance cost of the equipment supplying the energy, and purchases the equipment from the outside to operate the equipment. In a linear programming problem in which electricity and gas charge menu data is stored and the energy demand is supplied by the equipment group, the control unit of the energy system optimizing device receives the data stored in the storage unit as input. , cost, the primary energy consumption or CO2 emissions And specific function performs optimization calculation by mixed integer linear programming, and the first step of calculating a combination of combination and the fee menu optimum the device, the storage unit, and the demand data, and the device data A linear program for storing the fee menu data, device detailed data including partial load efficiency and operation constraints of the device, a combination of the devices and a combination of the fee menu, and supplying the energy demand by the device group In the problem, the control unit takes the data stored in the storage unit as input, and uses cost, primary energy consumption or CO2 emission as an objective function, performs optimization calculation by mixed integer linear programming, An energy system optimization comprising: a second step of calculating an operation plan; It is the law.

  According to the first invention, it is possible to accurately model actual conditions and calculate an optimum energy system facility design and operation plan within a practical calculation time.

In the first invention, in the first step and / or the second step, in the case of optimization to minimize the cost, the storage unit further includes an upper limit of primary energy consumption and / or an upper limit of CO2 emission amount. storing, the control unit, it is desirable to optimize calculate the upper limit of the upper and / or CO2 emissions of the primary energy consumption as a constraint condition. Thereby, it is possible to calculate the facility design and operation plan of the optimum energy system with high cost competitiveness while ensuring the environmental performance.

The fee menu data includes a contract condition for each fee menu, and in the first step and / or the second step, in the case of optimization to minimize the cost, the control unit uses the contract condition as a constraint condition. It is desirable to perform optimization calculations. As a result, it is possible to determine the optimum combination of the charge menus that minimizes the cost from the charge menus that satisfy the actual contract conditions, and to reduce the deviation from the reality.

The first invention, wherein in a first step, the storage unit may store a factor proportional to the capacitance of the device as the initial cost of the equipment, the rating of the device as a maintenance cost of the device except for the cogeneration system storing a coefficient proportional to the volume, and stores a coefficient proportional to a unit power generation amount of the cogeneration system as maintenance costs of the cogeneration system, in the second step, the storage unit, the maintenance of the cogeneration system It is desirable to store a coefficient proportional to the unit power generation amount of the cogeneration system as a cost. As a result, it is possible to perform optimization calculation in accordance with reality while preventing excessive calculation time.

The first aspect of the present invention, in the second step, further, the storage unit may store the calculated target flag indicating whether or not the calculated target load apportioning each of the devices, the control unit, the load apportioning It is desirable that the operation of the device that is the calculation target of the optimization calculation is performed under the constraint that the load factor is the same. This makes it possible to model a load apportioning operation performed in an actual system.

In the first invention, for example, in the first step and / or the second step, when the auxiliary machine power of the equipment is a constant flow pump, the storage unit stores the power consumption of the auxiliary machine power of the equipment as the power consumption. A coefficient proportional to the output of the device is stored . As a result, the auxiliary power of the device can be handled as a constant flow pump while preventing an excessive calculation time.

In the first aspect, for example, in the second step, when the auxiliary power of the device is a variable flow pump, the storage unit stores a coefficient for each partial load factor of the device, and the control unit includes: The power consumption of auxiliary power of the equipment is linearly interpolated. As a result, the auxiliary power of the device can be handled as a variable flow pump while preventing excessive calculation time.

In the first invention, for example, in the first step and / or the second step, the storage unit further includes a market price of a CO2 credit procured from the market, a green power certificate, a green heat certificate, and a biofuel certificate. The cost may be stored, and the CO2 credit, the green power certificate, the purchase of the green heat certificate and the use of the biofuel may be one option for the combination of the devices.

A second invention is an energy system optimizing device for calculating an equipment design and an operation plan of an optimal energy system, and includes demand data including a plurality of energy demands at a calculation target facility, and a rating of a device that supplies the energy A first storage means for storing device data including an efficiency value, an initial cost and a maintenance cost, and electricity and gas charge menu data purchased from the outside in order to operate the device, and the first storage means In a linear programming problem in which stored data is input and the energy demand is supplied by the device group, cost, primary energy consumption or CO2 emission is an objective function, and optimization calculation is performed by a mixed integer linear programming method. Calculate the optimal combination of devices and the combination of price menus First calculation means, the demand data, the device data, the fee menu data, device detailed data including partial load efficiency and operation restrictions of the device, a combination of the devices, and a combination of the fee menu second storage means for storing the bets, as input data stored by said second storage means, in the energy demand linear programming problem supplied by the equipment group, costs, primary energy consumption or CO2 emissions An energy system optimizing apparatus comprising: a second calculation unit configured to perform an optimization calculation by a mixed integer linear programming method using a quantity as an objective function and calculate an operation plan of the device.

The third invention is a program for causing a computer to function as the energy system optimizing device of the second invention.

  The present invention makes it possible to accurately model actual conditions and to calculate the facility design and operation plan of an optimal energy system with high cost competitiveness while ensuring environmental performance within a practical calculation time. Can be provided.

Hardware configuration diagram of a computer that realizes the energy system optimization device 1 The figure which shows the data flow in the 1st step of the energy system optimization method The figure which shows the data flow in the 2nd step of the energy system optimization method The figure which shows an example of the demand data 21 The figure which shows the example of an input screen of apparatus data 22 The figure which shows the example of an input screen of the restriction data 23 and the environment data 24 Figure showing an example of an electricity bill entry screen Figure showing an example of the gas charge input screen Figure showing an example of the input screen for the contract conditions for gas charges The figure which shows the example of a display screen of the 1st step calculation result 26 The figure which shows the input screen example (1) of a 2nd step The figure which shows the input screen example (2) of a 2nd step Diagram showing an example of Genelink formulation A figure showing the sum of auxiliary power when using a constant flow pump A figure showing the sum of auxiliary power when using a variable flow pump The figure which shows the example of a calculation result by the 1st step The figure which shows the example of calculation result of the monthly cost by the 1st step The figure which shows the example of a calculation result of the supply balance with respect to the demand of August electric power in the 1st step The figure which shows the example of a calculation result of the supply balance with respect to the demand of August cooling in the 1st step The figure which shows the example of a calculation result of the supply balance with respect to the demand of August steam in the 1st step The figure which shows the example of calculation conditions in the 2nd step The figure which shows the example of calculation result of the monthly cost by 2nd step The figure which shows the example of a calculation result of the supply balance with respect to the demand of August electric power in a 2nd step The figure which shows the example of a calculation result of the supply balance with respect to the demand of August cooling in the 2nd step The figure which shows the example of a calculation result of the supply balance with respect to the demand of August steam in the 2nd step

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a hardware configuration diagram of a computer that realizes the energy system optimizing apparatus 1. Note that the hardware configuration in FIG. 1 is an example, and various configurations can be adopted depending on the application and purpose.

  The energy system optimizing device 1 includes a control unit 3, a storage unit 5, a media input / output unit 7, a communication control unit 9, an input unit 11, a display unit 13, a peripheral device I / F unit 15, etc. via a bus 17. Connected.

  The control unit 3 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like.

The CPU calls and executes a program stored in the storage unit 5, ROM, recording medium, etc. to a work memory area on the RAM, drives and controls each device connected via the bus 17, and an energy system optimization device 1 to realize the later-described processing.
The ROM is a non-volatile memory and permanently holds a computer boot program, a program such as BIOS, data, and the like.
The RAM is a volatile memory, and temporarily stores programs, data, and the like loaded from the storage unit 5, ROM, recording medium, and the like, and includes a work area used by the control unit 3 for performing various processes.

The storage unit 5 is an HDD (hard disk drive), and stores a program executed by the control unit 3, data necessary for program execution, an OS (operating system), and the like. With respect to the program, a control program corresponding to an OS (operating system) and an application program for causing a computer to execute processing described later are stored.
Each of these program codes is read by the control unit 3 as necessary, transferred to the RAM, read by the CPU, and executed as various means.

  The media input / output unit 7 (drive device) inputs / outputs data, for example, a CD drive (-ROM, -R, -RW, etc.), DVD drive (-ROM, -R, -RW, etc.), MO drive, etc. And other media input / output devices.

  The communication control unit 9 includes a communication control device, a communication port, and the like, and is a communication interface that mediates communication between the computer and the network 19, and performs communication control between other computers via the network 19.

The input unit 11 inputs data and includes, for example, a keyboard, a pointing device such as a mouse, and an input device such as a numeric keypad.
An operation instruction, an operation instruction, data input, and the like can be performed on the computer via the input unit 11.

  The display unit 13 includes a display device such as a CRT monitor and a liquid crystal panel, and a logic circuit (such as a video adapter) for realizing a video function of the computer in cooperation with the display device.

  The peripheral device I / F (interface) unit 15 is a port for connecting a peripheral device to the computer, and the computer transmits and receives data to and from the peripheral device via the peripheral device I / F unit 15. The peripheral device I / F unit 15 is configured by USB, IEEE 1394, RS-232C, or the like, and usually has a plurality of peripheral devices I / F. The connection form with the peripheral device may be wired or wireless.

  The bus 17 is a path that mediates transmission / reception of control signals, data signals, and the like between the devices.

In the present embodiment, optimization calculation of the energy system is performed by using mixed integer linear programming with cost as an objective function. Optimization that minimizes costs models the performance and characteristics of equipment and constraints associated with facility operation, and reflects the strict energy charge system to reduce the deviation from reality. However, as the number of variables for modeling the constraints increases, the calculation time becomes excessive. Therefore, in the energy system optimization method according to the present embodiment, the calculation step is divided into two stages and is practical. It can be calculated within the calculation time.
In the first step, an energy system design and its operation plan are calculated based on the rated efficiency value of the equipment. In the second step, the device and the charge menu determined in the first step are input, and a highly accurate operation plan is calculated in consideration of detailed device conditions and actual operation restrictions.

(Input / output data of the first step)
FIG. 2 is a diagram showing a data flow in the first step of the energy system optimization method.
As shown in FIG. 2, in the first step, the demand data 21, the device data 22, the restriction data 23, the environment data 24, and the charge menu data 25 are input, and the first step calculation result 26 is output.
The energy system optimizing apparatus 1 includes an input unit (such as the input unit 11) for inputting demand data 21, device data 22, restriction data 23, environment data 24, and fee menu data 25. Further, the energy system optimizing apparatus 1 includes a calculation unit (control unit 3 or the like) that performs an optimization calculation of an energy system using a linear integer solver with a cost based on a mixed integer linear programming as an objective function. As the linear programming solver, for example, commercially available software can be used. Further, the energy system optimizing apparatus 1 includes output means (such as the display unit 13) that outputs a calculation result (first step calculation result 26) by the calculation means. Further, the energy system optimizing apparatus 1 includes a storage unit (such as the storage unit 5) that stores various data input by the input unit and various data calculated by the calculation unit.

  The demand data 21 is input for each calculation target facility, and is data relating to energy demands such as electric power, cooling, heating, hot water supply, and steam. The demand data 21 is demand data for each month, each day of the week (weekdays, Saturdays, holidays), and each time. The demand data 21 may be past history data, or may be prediction data in consideration of the future usage status of the calculation target facility.

  FIG. 4 is a diagram illustrating an example of the demand data 21. Demand data 21 shown in FIG. 4 is data relating to the energy demand for power in August of the calculation target facility. The horizontal axis shows time, and the vertical axis shows demand. In the first step, the energy system optimizing apparatus 1 calculates an optimal combination of equipment and a charge menu for the demand data 21.

  The equipment included in the equipment data 22 is, for example, a water-cooled chiller, an air-cooled chiller, a turbo refrigerator, a heat pump water heater (hereinafter referred to as an HP water heater), a cogeneration, a hot water boiler, a steam boiler, a gas engine heat pump (hereinafter referred to as a GHP). ), Direct absorption absorption chiller / heater, steam absorption chiller / heater, hot water absorption chiller / heater, heat exchanger, Genelink, etc.

  FIG. 5 is a diagram illustrating an example of an input screen for the device data 22. The device name 51 is the name of each device. The rated efficiency value / auxiliary power coefficient 52 is a rated efficiency value and auxiliary power coefficient of each device. The rated efficiency value indicates the energy consumption efficiency at the rated capacity of each device. For example, cold water COP (Coefficient of Performance) represents the cold water capacity per unit power consumption, and the larger the number, the better the efficiency. For example, in the example shown in FIG. 5, the cold water COP of the water-cooled chiller is 3.78. Auxiliary power is the cold / hot water pump, cooling water pump, fan power, etc. of each device. The auxiliary machine power coefficient shown in FIG. 5 is a coefficient of the total auxiliary starting force with respect to the rated capacity of each device. For example, the auxiliary power coefficient of the water-cooled chiller is 0.08. The pipe loss 53 is a value of the pipe loss of each device. Reference numeral 54 denotes a check box for selecting whether or not each device is a calculation target. This is to exclude devices that cannot be introduced depending on the calculation target facility. The energy system optimizing apparatus 1 inputs the presence / absence of a check box as a calculation target flag.

The restriction data 23 is input for each calculation target facility, and is an upper limit of annual primary energy consumption, an upper limit of annual CO2 emission, and the like.
The environmental data 24 includes primary energy efficiency, CO2 emission coefficient, CO2 credit market price, green power certificate price, green heat certificate price, CO2 reduction coefficient by a cogeneration system (hereinafter, CGS), and the like. A check box for whether or not to include a CO2 credit, a green power certificate, or the like is used for selection.

FIG. 6 is a diagram illustrating an example of an input screen for the restriction data 23 and the environment data 24. In the example shown in FIG. 6, the annual CO2 emission upper limit 61 is 2000 (t). In addition, the annual primary energy consumption upper limit 62 is 100,000 (GJ). The restriction data 23 can be input as a constraint condition at the time of cost minimization calculation. Accordingly, if the calculation target facility is an existing facility, the energy system optimization apparatus 1 can be used to make a plan for reducing the primary energy consumption and the CO2 emission amount. Furthermore, if the energy system optimizing apparatus 1 is used, it becomes possible to calculate the limit value of the reduction amount and the device update timing in the existing equipment, and to consistently carry out the selection of the optimum device at the time of device update.
63 is the power generation efficiency by time (by power reception parameter) by system power reception. 64 is a CO2 emission coefficient by power reception. 65 is a CO2 emission coefficient by gas. In these input items, the primary energy efficiency and CO2 emission coefficient of the environmental data 24 are input. The CO2 credit market price (not shown) is a cost per unit CO2 emission when CO2 credit is procured from the market. For example, when performing an optimization calculation that sets the upper limit of the annual CO2 emission amount and minimizes the cost, it is possible to satisfy the upper limit of the annual CO2 emission amount by purchasing the CO2 credit. The CO2 reduction coefficient (not shown) by CGS is a coefficient of CO2 reduction with respect to the unit power generation amount of CGS.
Biofuels can also be targeted for optimization calculations. In this case, the cost per unit heat quantity of the biofuel, the CO2 emission coefficient per unit heat quantity, the primary energy coefficient, and the upper limit value of the time flow rate are input.
The purchase of CO2 credits, green power certificates, green heat certificates and the use of biofuels are one of the options in determining the optimal equipment combination.

  The charge menu data 25 is a charge menu for power charges and gas charges. In the case of electricity charges, basic charges, pay-as-you-go charges, and in the case of gas charges, the gas flow rate multiplication factor (= annual gas usage / maximum hourly gas usage), load factor (monthly average usage / maximum demand period monthly average usage) Detailed contract conditions such as × 100) are included.

FIG. 7 is a diagram showing an example of an electricity bill input screen.
The business / industrial charge menu 71 is divided into business use and industrial use. Business use includes business use and extra high A. There are high pressure and extra high B for industrial use. These price menus are composed of basic charges and pay-as-you-go charges for summer / other seasons.
Similarly, the commercial / industrial seasonal fee menu 72 is divided into commercial use and industrial use. These price menus are composed of basic charges, and peak / noon / summer / noon other season / night-time pay.
Similarly, the business / industrial holiday high load / high holiday operating charge menu 73 is divided into business use and industrial use. These rate menus are composed of basic rates and pay-as-you-go rates for summer (weekdays) / summer (holidays) / other seasons (weekdays) / other seasons (holidays).
74 is a check box for selecting whether or not each charge menu is subject to calculation. The energy system optimizing apparatus 1 inputs the presence / absence of a check box as a calculation target flag.
Note that the electricity price menu used in the cost minimization calculation is not limited to these.

FIG. 8 is a diagram showing an example of a gas charge input screen. FIG. 9 is a diagram illustrating an example of an input screen for a gas charge contract condition. The contract conditions shown in FIG. 9 correspond to the charge menu shown in FIG.
Reference numeral 81 denotes a business-use seasonal and industrial fee menu. 91 is a contract condition for business season and industry. Business season and industrial use are price menus that are beneficial for customers with high annual gas consumption. As shown in Fig. 91, the business season and industrial rate menus have a lower limit for the maximum contracted hourly flow rate (maximum scheduled usage per hour during the contract period), a lower limit for annual gas usage, and a maximum contracted hourly flow rate. Multiplier (annual usage divided by contract maximum hourly flow or equipment rated flow), contract annual load factor (annual monthly average usage divided by monthly average usage during maximum demand period (December to March)) ) Contract terms. The contract condition becomes a constraint condition in the cost minimization calculation.

  82 is a charge menu for CGS package discount. 92 is a contract condition for CGS package discount. The CGS package discount is a price menu that can be used by customers who use gas in CGS. Therefore, the cost minimization calculation can be selected when the equipment design including CGS is performed. As shown in Fig. 92, the CGS package discount includes the lower limit of the contract maximum hourly flow, the lower limit of annual gas consumption, the contract maximum hourly flow rate, the contract annual load factor, the CGS rating (cogeneration that must be introduced at the conclusion of the contract). (Minimum capacity of the system).

  Reference numeral 83 denotes a high load factor discount fee menu. 93 is a contract condition for discounting the high load factor. The high load factor discount is a price menu that is beneficial to customers with low gas consumption in winter. As shown at 93, the high load factor discount has contract conditions such as a lower limit of the contract maximum hourly flow rate, a lower limit of annual gas usage, a contract maximum hourly flow rate multiplication factor, and a contract annual load factor.

84 is a charge menu for general charges.
Reference numeral 85 denotes a gas air conditioning charge menu. 94 is a contract condition for gas air conditioning. As shown in 94, the contract conditions of gas air conditioning include contract annual load factor and equipment rated flow rate magnification (annual usage divided by equipment rated flow).
Reference numeral 86 denotes a check box for selecting whether or not each charge menu is subject to calculation. The energy system optimizing apparatus 1 inputs the presence / absence of a check box as a calculation target flag.
87 is an air conditioning option (0 or 1). In the case of 0, only one type of air-conditioning gas charge is considered in direct contact with GHP. In the case of 1, it considers including two different types of air-conditioning gas charges directly with GHP.
Note that the gas charge menu used in the cost minimization calculation is not limited to these.

  FIG. 10 is a diagram illustrating a display screen example of the first step calculation result 26. Reference numeral 100a denotes an execution button for minimizing primary energy. Reference numeral 100b denotes an execution button for CO2 minimization. Reference numeral 100c denotes an execution button for cost minimization. When the buttons 100a to 100c are pressed, the energy system optimization device 1 executes optimization calculation for each button. In FIG. 10, an optimal combination of devices and an optimal combination of charge menus are displayed. Other calculation results (primary energy consumption, CO2 emissions, operation plan, etc.) are displayed on a separate display screen.

The device name 101 is the name of each device. 102 to 105 are optimization calculation results. The input 102 is energy input to each device, and the value shown in FIG. 10 is the maximum value throughout the year. The output 103 is energy output from each device, and the value shown in FIG. 10 is the maximum value throughout the year. A device whose output 103 is not 0 means that it has been selected by optimization calculation. The value of the output 103 is the rated capacity value of the device. The auxiliary machine output is the rated power consumption of auxiliary machine power, and is calculated by defining a coefficient as an amount generated in proportion to the output of the equipment.
The user of the energy system optimizing device 1 can determine the optimum combination of devices and the capacity value of the devices based on the value of the output 103.

  The initial cost 104 is an initial introduction cost of each device, and is a value proportional to the capacity of the device. The maintenance cost 105 is a maintenance cost for each device. For devices excluding cogeneration, a coefficient is defined and calculated as a cost generated in proportion to the capacity (kW) of the device. Cogeneration is calculated by defining a coefficient as a cost generated in proportion to the unit power generation amount (kWh). As a result, it is possible to perform optimization calculation in accordance with reality while preventing excessive calculation time.

Reference numerals 106a to 106e denote buttons for switching display of calculation results. In the cost minimization calculation, the energy system optimizing apparatus 1 calculates a combination of the top five fee menus and an optimal combination of devices in order of low cost. For example, when the user presses the cost 1 display 106a, the calculation result with the lowest cost is displayed in the cost minimization calculation.
Reference numeral 107 denotes a charge menu for electricity charges and gas charges.
The user of the energy system optimizing apparatus 1 can determine a low-cost electricity charge and gas charge charge menu from the charge menu 107.

(Input / output data of the second step)
FIG. 3 is a diagram showing a data flow in the second step of the energy system optimization method.
As shown in FIG. 3, in the second step, demand data 21, device data 22, restriction data 23, environmental data 24, fee menu data 25, device detailed data 31, and design data 32 are input, and the second step calculation result 33 is output.
The demand data 21, the device data 22, the restriction data 23, the environment data 24, and the charge menu data 25 are the same as the description of the first step, and thus the description thereof is omitted.
In the second step, unlike the first step, the initial cost of the device data 22 and the maintenance cost excluding CGS are not input.
The energy system optimizing apparatus 1 includes input means (input unit 11 or the like) for inputting device detail data 31 and design data 32. Further, the energy system optimizing apparatus 1 includes a calculation unit (control unit 3 or the like) that performs an optimization calculation of an energy system using a linear integer solver with a cost based on a mixed integer linear programming as an objective function. As the linear programming solver, for example, commercially available software can be used. Further, the energy system optimizing apparatus 1 includes an output unit (display unit 13 or the like) that outputs a calculation result (second step calculation result 33) by the calculation unit. Further, the energy system optimizing apparatus 1 includes a storage unit (such as the storage unit 5) that stores various data input by the input unit and various data calculated by the calculation unit.

The device detailed data 31 includes a partial load rated efficiency value, an auxiliary machine power output coefficient, a CGS minimum operating load factor, a heat source switching load factor, and the like of each device. In the second step, since the electric and thermal load apportioning calculation is performed, the constraint that the operating load factor is the same among the heat source devices is given.
The load apportionment will be described. For example, consider a case where heat source devices having capacities of 600 RT and 1400 RT are introduced into an energy system. In this case, since 600RT: 1400RT = 3: 7, the operation is performed so that each heat source device supplies 300 MJ and 700 MJ to the demand 1000 MJ, and the load is apportioned.
In the first step, the load apportionment calculation is not performed in consideration of the calculation load in order to calculate the optimum combination of devices. On the other hand, in the second step, since a device has already been selected and no calculation load is required to calculate the optimal combination of devices, load apportionment calculation is performed to calculate an actual operation plan.
The design data 32 is a combination of devices and a combination of fee menus. The design data 32 uses the calculation result of the first step when the calculation target facility is considering a new energy system design. On the other hand, when the calculation target facility is an existing facility, an actually installed device can be input. The user can also input a fee menu.

FIG. 11 is a diagram illustrating an input screen example (1) of the second step.
The device name 111 is a name of each device. Reference numeral 112 denotes a partial load efficiency value and auxiliary power output coefficient (power consumption coefficient) of each device. The load factor is 25%, 50%, 75%, and 100%. 113 is the minimum operating load factor of the cogeneration system (CGS). Reference numeral 114 denotes a capacity value (rated output) of the device and auxiliary machine power. Reference numeral 115 denotes a check box for selecting whether or not each device is subject to calculation of apportionment of electricity and heat (cooling and heating) load. The energy system optimizing apparatus 1 inputs the presence / absence of a check box as a calculation target flag.
For example, consider a case where the exhaust heat of CGS is preferentially used and the shortage is compensated by another heat source device. In this case, the user removes the CGS from the heat load apportionment target (uncheck the check box), and makes the other heat source device the heat load apportionment target (check the check box). , Load apportionment calculation is performed for the heat source machine excluding CGS. As a result, the operation plan can be calculated so that the exhaust heat of the CGS is preferentially used and the shortage is compensated for by another heat source device.

FIG. 12 is a diagram illustrating an input screen example (2) of the second step.
In 121, the switching load factor of the heat source is input. For example, consider a case where two heat source machines are load apportioning targets. When the switching load factor input by the first heat source unit is reached due to an increase in demand, the energy system optimizing device 1 calculates the operation plan to operate the second heat source unit and supply the load proportionally. To do. In addition, the operation plan is calculated so that the load exceeds the switching load factor only when all the load apportioning heat source machines are operating.
In 122, the upper limit of primary energy consumption and CO2 emission is input. In the example shown in FIG. 12, since the CO2 emission check box is checked and a value is input, the energy system optimizing device 1 adds the input value to the constraint condition as the upper limit of the CO2 emission amount. Calculate the operation plan.

  123 is the power generation efficiency by time (by power reception parameter) by system power reception. Thereby, the primary energy consumption by system power reception is calculated. Reference numeral 124 denotes a CO2 emission coefficient due to power reception. 125 is a CO2 emission coefficient by gas. In these input items, the primary energy efficiency and CO2 emission coefficient of the environmental data 24 are input. 126 is an electricity price menu. 127 is a gas charge menu. 128 is the maintenance cost of the cogeneration system (CGS). Reference numeral 129a denotes an execution button for primary energy minimization calculation. Reference numeral 129b denotes an execution button for CO2 minimization calculation. Reference numeral 129c denotes an execution button for cost minimization calculation.

  As described above, in the second step, the introduction device and the charge menu, which were the calculation target in the first step, are input, and the device detailed data 31 that is not considered in the first step is input. Calculate the plan.

(Formulation of linear programming problem)
The formulation of the linear programming problem is explained. In common with the first step and the second step, the objective function is cost, primary energy consumption or CO2 emission. The energy system optimizing apparatus 1 performs an optimization calculation that minimizes any of these. Further, for example, in the case of optimization calculation that minimizes the cost, the upper limit of the primary energy consumption amount and the CO2 emission amount can be arbitrarily input to be a constraint condition. Accordingly, it is possible to calculate the cost, primary energy consumption amount, and CO2 emission amount in an integrated manner. Thereby, it is possible to calculate the facility design and operation plan of the optimum energy system with high cost competitiveness while ensuring the environmental performance.

One of the constraint conditions is that the energy demand of the demand data 21 is supplied by a device group included in the device data 22. In addition, arbitrarily entered restriction data 23 is also a constraint condition. Further, the contract conditions included in the fee menu data 25 are also a constraint condition. The gas charge contract conditions shown in FIG. 9 can be formulated as follows, for example.
・ Contract maximum hourly flow rate ≧ Constant ・ Annual gas consumption ≧ Constant / Constant> Contract maximum time flow rate magnification ≧ Constant / Constant> Contract annual load factor ≧ Constant ・ CGS rating ≧ Const.

FIG. 13 is a diagram illustrating an example of Genelink formulation. GENELINK is a gas absorption chiller / heater that uses the exhaust heat generated from the CGS to cool and heat. As shown in the left diagram of FIG. 13, Genelink supplies cold water only with the exhaust heat of CGS at a portion where the load factor is low. On the other hand, when the hot water independent operation load factor is exceeded, gas-fired operation and hot water exhaust heat operation are used in combination. Originally, the hot water exhaust heat operating ratio decreases as the load factor increases. However, in order to prevent the calculation time from becoming excessive, the hot water exhaust heat operating ratio is made constant as shown in the right figure.
Moreover, if the load factor at which hot water can be operated independently is α, the behavior of GENELINK is greatly different before and after α. Therefore, 0-1 variable, which takes 0 when the load factor is less than α and 1 when greater than α, is introduced to formulate the behavior of the gene link.

  FIG. 14 is a diagram showing the total sum of auxiliary power when the constant flow pump is used. As shown in FIG. 14, the power consumption of auxiliary power of the constant flow pump is proportional to the output of the device. Therefore, the power consumption of the constant flow pump is modeled by inputting a coefficient proportional to the output of the device. This makes it possible to perform facility design and operation calculation in consideration of the operation of auxiliary power when using a constant flow pump without excessive calculation time.

  FIG. 15 is a diagram showing the sum of auxiliary machine power when the variable flow pump is used. As shown in FIG. 15, the power consumption of auxiliary power of the variable flow pump increases in a quadratic function with respect to the output of the device. Therefore, the power consumption of the variable flow pump is modeled by inputting a coefficient for each partial load factor (25%, 50%, 75%, 100%) of the device and performing linear interpolation. As a result, an operation plan that also considers the operation of auxiliary power when using a variable flow pump is possible without excessive calculation time.

(Example of calculation results)
An example of calculation results in the present embodiment will be described with reference to FIGS. In the calculation result example described below, optimization calculation that minimizes the cost is performed.
FIG. 16 is a diagram illustrating a calculation result example in the first step. As shown in FIG. 16, the optimal combination of charge menus and the optimal combination of devices are calculated.
FIG. 17 is a diagram illustrating an example of a calculation result of the monthly cost in the first step. As shown in FIG. 17, the initial cost of the heat source device, the initial cost of the CGS, the electricity fee, the gas fee, the maintenance cost of the heat source device, and the maintenance cost of the CGS are calculated. Since no device is determined in the first step, the initial cost of the heat source device, the initial cost of the CGS, the maintenance cost of the heat source device, and the maintenance cost of the CGS are also subject to the optimization calculation.

FIG. 18 is a diagram illustrating an example of a calculation result of supply balance with respect to demand for August power in the first step. FIG. 19 is a diagram illustrating an example of a calculation result of supply balance with respect to demand for August cooling in the first step. FIG. 20 is a diagram illustrating a calculation result example of supply balance with respect to demand for August steam in the first step.
As shown in FIGS. 18 to 20, the supply balance by month and hour is calculated. From these results, the maximum output value of each device throughout the year is calculated to obtain the capacity of the device shown in FIG.

  FIG. 21 is a diagram illustrating an example of calculation conditions in the second step. As shown in FIG. 21, the charge menu may use the result calculated in the first step or may be newly input by the user. As a combination of devices, a device that can be actually introduced is selected based on the result calculated in the first step. As other calculation conditions, the minimum operating load factor of CGS (113 in FIG. 11) is 100%, and the switching load factor of the heat source (121 in FIG. 12) is 100%. In addition, heat source devices are subject to a heat load apportionment calculation for the demand for cooling and heating.

  FIG. 22 is a diagram illustrating an example of a calculation result of the monthly cost in the second step. As shown in FIG. 22, an electricity bill, a gas bill, and a CGS maintenance cost are calculated. Since the device is determined in the second step, the initial cost of the heat source device, the initial cost of the CGS, and the maintenance cost of the heat source device are not targeted for optimization calculation. On the other hand, since the maintenance cost of CGS is proportional to the unit power generation amount, it is an object of optimization calculation.

FIG. 23 is a diagram illustrating a calculation result example of supply balance with respect to demand for August power in the second step. FIG. 24 is a diagram illustrating an example of a calculation result of supply balance with respect to demand for August cooling in the second step. FIG. 25 is a diagram illustrating a calculation result example of supply balance with respect to demand for August steam in the second step.
As shown in FIGS. 23 to 25, the supply balance by month and hour is calculated. Depending on the calculation condition that the minimum operating load factor of CGS is 100%, as shown in FIG. 23, CGS1 and CGS2 operate either 100% or not at all. In addition, as shown in FIG. 24, when the direct heating absorption refrigerator does not exceed 100% load under the calculation condition that the heat source switching load factor is 100%, only the direct absorption absorption refrigerator is operating. . Further, as shown in FIG. 24, the load of the GENELINK and the direct absorption absorption refrigerator is the same due to the calculation condition that the heat source device is subject to the heat load apportionment calculation for the cooling demand.

As described above, in the present embodiment, the detailed calculation of the realistic price menu and the operational restrictions of the equipment such as load apportionment are taken into consideration, so the optimization calculation that accurately reproduces the reality is performed. Can do.
In the present embodiment, the device and the charge menu determined in the first step, the first step, or newly designated by the user, are calculated based on the rated efficiency value of the device and the design and operation plan of the energy system. Since the calculation is performed in two stages of the second step of calculating an operation plan that takes into account detailed equipment conditions and actual operation constraints as input, the calculation can be completed within a practical calculation time.
In the case of optimization calculation that minimizes costs, the upper limit of primary energy consumption and CO2 emissions can be arbitrarily entered and used as a constraint condition. It is possible to calculate the optimal energy system equipment design and operation plan.

  The embodiments of the energy system optimization method and the like according to the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes or modifications can be conceived within the scope of the technical idea disclosed in the present application, and these naturally belong to the technical scope of the present invention. Understood.

DESCRIPTION OF SYMBOLS 1 ......... Energy system optimization apparatus 3 ......... Control part 5 ......... Storage part 7 ......... Media input / output part 9 ......... Communication control part 11 ......... Input part 13 ......... Display part 15 ... ...... Peripheral device I / F section 17 ......... Bus 19 ......... Network 21 ......... Demand data 22 ......... Device data 23 ......... Limited data 24 ......... Environmental data 25 ......... Price menu data 26 ... …… First step calculation result 31 ………… Device detailed data 32 ………… Design data 33 ……… Second step calculation result

Claims (10)

An energy system optimization device is an energy system optimization method for calculating a facility design and an operation plan of an optimal energy system,
The storage unit of the energy system optimizing device includes demand data including a plurality of energy demands in a calculation target facility , device data including a rated efficiency value, initial cost, and maintenance cost of a device that supplies the energy, and the device. storing the electricity and gas charges menu data to be purchased from the outside in order to operate in the energy demand linear programming problem supplied by the equipment group, the control unit of the energy system optimization device, by the storage unit Using stored data as input, using cost, primary energy consumption or CO2 emission as an objective function, and performing optimization calculation by mixed integer linear programming, calculate the optimal combination of the devices and the combination of the charge menu The first step;
The storage unit is stored with the demand data, the and the device data, the a fee menu data, and the device detailed data including the partial load efficiency and operational constraints of the device, and a combination of the combination and the fee menu of the device In the linear programming problem in which the energy demand is supplied by the device group, the control unit receives the data stored in the storage unit, inputs cost, primary energy consumption or CO2 emission as an objective function, and mixes A second step of performing an optimization calculation by integer linear programming and calculating an operation plan of the device;
An energy system optimization method comprising: In the first step and / or the second step, when the optimization to minimize the costs, further, the storage unit may store the upper limit of the upper and / or CO2 emissions of the primary energy consumption, the control 2. The energy system optimization method according to claim 1 , wherein the unit performs optimization calculation using the upper limit of the primary energy consumption and / or the upper limit of CO2 emission as a constraint. The fee menu data includes contract conditions for each fee menu,
2. The control unit according to claim 1, wherein, in the first step and / or the second step, in the case of optimization that minimizes cost, the control unit performs optimization calculation using the contract condition as a constraint condition. Energy system optimization method. In the first step, the storage unit may store a factor proportional to the capacitance of the device as the initial cost of the device, a factor proportional to the rated capacity of the device as a maintenance cost of the device except for the cogeneration system stored, stores factor proportional to the unit power generation amount of the cogeneration system as maintenance costs of the cogeneration system,
In the second step, the storage unit, energy system optimization method according to claim 1, characterized in that storing a coefficient proportional to a unit power generation amount of the cogeneration system as maintenance costs of the cogeneration system . In the second step, further, the storage unit may store the calculated target flag indicating whether or not the calculated target load apportioning each of the devices, the control unit has been subjected to the calculation of the load apportioning The energy system optimization method according to claim 1, wherein the operation of the device performs optimization calculation with a constraint that the load factor is the same as a constraint condition. In the first step and / or the second step, when the auxiliary power of the equipment is a constant flow pump, the storage unit calculates a coefficient proportional to the output of the equipment as power consumption of the auxiliary power of the equipment. The energy system optimization method according to claim 1, wherein the energy system optimization method is stored . In the second step, when the variable flow pump auxiliary power is the device, the storage unit may store the coefficient for each partial load factor of the device, the control unit, the consumption of auxiliary power of the device The energy system optimization method according to claim 1, wherein the power is linearly interpolated. In the first step and / or the second step, further, the storage unit may store the market price of CO2 credits to procure from the market, green power bonds, green heat certificate, and a cost of biofuels, the CO2 The energy system optimization method according to claim 1, wherein credit, purchase of the green power certificate, purchase of the green heat certificate, and use of the biofuel are selected as one option of the combination of the devices. An energy system optimization device that calculates equipment design and operation plan of an optimal energy system,
Demand data including a plurality of energy demands at a calculation target facility, equipment data including rated efficiency value, initial cost and maintenance cost of equipment supplying the energy, and electricity and gas purchased from outside to operate the equipment First storage means for storing the charge menu data of
In the linear programming problem in which the data stored in the first storage means is input and the energy demand is supplied by the device group, the cost, the primary energy consumption or the CO2 emission amount is an objective function, and the mixed integer linear programming method A first calculating means for performing an optimization calculation by calculating the optimal combination of the devices and the combination of the charge menus;
Said demand data, the and the device data, the a fee menu data, and the device detailed data including the partial load efficiency and operational constraints of the device, a second memory for storing the combinations of the combination and the fee menu of the device Means,
In the linear programming problem in which the data stored in the second storage means is input and the energy demand is supplied by the device group, the cost, primary energy consumption or CO2 emission amount is an objective function, and the mixed integer linear programming method A second calculating means for performing an optimization calculation by calculating a device operation plan;
An energy system optimizing device characterized by comprising: The program for functioning a computer as an energy system optimization apparatus of Claim 9.