Optimization device, optimization method and program

Abstract

To provide an optimization device capable of deriving an optimized equipment configuration and operation method.SOLUTION: An optimization device comprises: an acquisition unit for acquiring demand data indicating demand for thermal energy per unit time and performance data of energy equipment; and a calculation unit for calculating, based on the performance data and by considering performance according to loads of the energy equipment, an operation method of the energy equipment that meets thermal demand per unit time indicated by the demand data and minimizes an amount of energy consumed by the energy equipment in a prescribed period of time.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an optimization device, an optimization method, and a program. [Background technology]

[0002] Patent Document 1 discloses an optimization device that performs optimization calculations using the CO2 or cost emitted by an energy system as an objective function based on energy demand and supply data and characteristic data of the equipment that constitutes the energy system, and calculates the capacity of the equipment that minimizes the CO2 or cost. Patent Document 2 discloses a heat source control system for an air conditioning facility equipped with multiple heat sources, and discloses a system that optimizes the operation of the heat sources so that the energy consumption of the entire heat source system is minimized according to the heat quantity of the air conditioning load and the outside air wet bulb temperature. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6669148 [Patent Document 2] JP 2017-101862 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional methods, when considering the optimal equipment configuration and equipment operation method for an energy system, the capacity and configuration of the equipment are determined so as to satisfy the maximum load, and often the load fluctuations throughout the year and the characteristics of the equipment in response to the load fluctuations (for example, the coefficient of performance (COP) of the equipment) are not taken into consideration. As a result, it may not be possible to calculate a truly optimized equipment configuration and equipment operation method.

[0005] The present invention provides an optimization device, an optimization method, and a program that can solve the above problems. [Means for solving the problem]

[0006] The optimization device disclosed herein includes an acquisition unit that acquires demand data indicating the demand for thermal energy per unit time and performance data of energy equipment that supplies the thermal energy, and a calculation unit that calculates, based on the performance data and taking into account performance according to the load of the energy equipment, an operation method of the energy equipment that satisfies the thermal demand per unit time indicated by the demand data and minimizes the amount of energy consumed by the energy equipment in a specified period of time.

[0007] The optimization method disclosed herein includes the steps of acquiring demand data indicating the demand for thermal energy per unit time and performance data of energy equipment that supplies the thermal energy, and calculating, based on the performance data and taking into account performance according to the load of the energy equipment, an operation method for the energy equipment that satisfies the thermal demand per unit time indicated by the demand data and minimizes the amount of energy consumed by the energy equipment in a specified period of time.

[0008] The program disclosed herein causes a computer to execute the steps of acquiring demand data indicating the demand for thermal energy per unit time and performance data of energy equipment that supplies the thermal energy, and calculating, based on the performance data and taking into account performance according to the load of the energy equipment, an operation method for the energy equipment that satisfies the thermal demand per unit time indicated by the demand data and minimizes the amount of energy consumed by the energy equipment in a specified period of time. Effect of the Invention

[0009] According to the above-described optimization device, optimization method, and program, it is possible to derive an optimized equipment configuration and operation method. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 illustrates an example of an optimization device according to an embodiment. [Diagram 2] FIG. 1 is a diagram illustrating an example of a system to be optimized in an embodiment. [Diagram 3] FIG. 1 is a diagram illustrating an overview of an optimization process according to an embodiment. [Figure 4] FIG. 11 is a first diagram showing an example of output data in the embodiment. [Diagram 5] FIG. 2 is a second diagram showing an example of output data in the embodiment. [Figure 6] 11 is a flowchart illustrating an example of an optimization process according to an embodiment. [Figure 7] FIG. 11 is a first diagram showing another example of a system to be optimized in the embodiment. [Figure 8] FIG. 2 is a second diagram showing another example of a system to be optimized in the embodiment. [Figure 9] FIG. 2 is a diagram illustrating an example of a hardware configuration of an optimization device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] <Embodiment> Hereinafter, a process for calculating an optimal configuration and an optimal operation of energy equipment according to an embodiment of the present disclosure will be described with reference to FIGS.

[0012] (composition) FIG. 1 is a diagram illustrating an example of an optimization device according to an embodiment. The optimization device 10 includes a data acquisition unit 11, a calculation unit 12, an output unit 13, and a storage unit 14. The data acquisition unit 11 accepts input of various information required for calculating the optimal configuration and optimal operation of energy equipment. Energy equipment refers to heat source equipment such as chillers and water heaters, and cogeneration equipment such as gas engines. In the following explanation, an example will be given in which the energy equipment is a chiller 3 (FIG. 2). The optimal equipment configuration refers to the configuration of energy equipment that can minimize power consumption, CO2 emissions, costs, etc., that is, an appropriate combination pattern of energy equipment, and the optimal operation refers to the load amount and load rate per unit time (e.g., 1 hour) that can minimize the amount of energy consumed by the energy equipment. For example, the data acquisition unit 11 acquires, as calculation conditions for the optimal configuration and optimal operation, demand data indicating the hourly demand for energy equipment (heat demand in the case of the chiller 3), hourly charges (fuel costs required to operate the energy equipment, electricity bills, etc.), hourly weather data (e.g., temperature, humidity, etc.), equipment performance data under various conditions (for example, in the case of the chiller 3, data indicating the chilled water outlet temperature, the chilled water inlet / outlet temperature difference, the load factor, and the COP value of the chiller 3 relative to the cooling water temperature), etc.

[0013] The calculation unit 12 calculates the optimal operation for each hour for various equipment configurations while considering the partial load characteristics of the energy equipment, using hourly (for example, each month and time, December x 24 hours = 288 ways) demand data, charges, weather data, and equipment performance data as inputs for calculation. The partial load characteristics are, for example, the COP of a chiller when operating under a load lower than the rated load. Then, the calculation unit 12 calculates the annual energy consumption amount based on the result of the optimal operation for each hour, and calculates candidates for the optimal equipment configuration in terms of cost and CO2 emissions. Here, for example, if an optimization calculation is performed using linear programming with the amount of energy consumption and cost as objective functions, a stable solution can be obtained in a realistic calculation time, but if the performance of the energy equipment is expressed in a linear form, the calculation accuracy of the amount of energy consumption decreases. Therefore, in this embodiment, the performance characteristics of the energy equipment are expressed in a nonlinear formula, and an optimization calculation is performed using the amount of energy consumption and the like as an objective function by nonlinear programming, aiming to improve the calculation accuracy. However, in nonlinear programming, when the number of equations and variables for modeling constraints increases, the calculation time becomes excessive and the convergence of the calculation deteriorates. Therefore, in this embodiment, the following measures are taken to simplify the nonlinear equations and reduce the variables, so that a solution can be obtained in a realistic calculation time while ensuring the calculation accuracy of the amount of consumed energy.

[0014] The output unit 13 outputs the calculation results of the optimal equipment configuration and optimal operation calculated by the calculation unit 12 to a display device, an electronic file, or the like. The memory unit 14 stores data acquired by the data acquisition unit 11, data during calculation or calculation results by the calculation unit 12, data regarding the configuration of the heat supply system including the energy equipment to be optimized (e.g., piping length, piping pressure loss coefficient, heat loss coefficient, etc.), and the like.

[0015] FIG. 2 shows an example of a system to be optimized. The heat supply system 1 includes a cooling tower 2, a chiller 3, an air conditioner 4, a cooling water pipe 5a through which the cooling water generated by the cooling tower 2 passes when it is supplied to the chiller 3, a cooling water pipe 5b through which the cooling water used by the chiller 3 passes when it returns to the cooling tower 2, a pump 6 provided on the cooling water pipe 5b, a chilled water pipe 7a through which the chilled water generated by the chiller 3 passes when it is supplied to the air conditioner 4, a chilled water pipe 7b through which the chilled water used by the air conditioner 4 passes when it returns to the chiller 3, and a pump 8 provided on the chilled water pipe 7b. In the heat supply system 1, the chiller 3 supplies chilled water to the air conditioner 4, and the air conditioner 4 performs air conditioning using the supplied chilled water. The air conditioner 4 returns the chilled water, whose temperature has increased after use, to the chiller 3. The chiller 3 cools the chilled water using the cooling water supplied from the cooling tower 2, and supplies the cooled chilled water to the air conditioner 4 again. The chiller 3 is, for example, a turbo chiller or an absorption chiller. The cooling water used to cool the cold water is returned to the cooling tower 2 and is cooled again in the cooling tower 2. The cooled cooling water is supplied again to the chiller 3. As an example, a case will be described below in which an optimal configuration and optimal operation of the chiller 3 that minimizes the amount of energy consumption in the heat supply system 1 is calculated.

[0016] FIG. 3 shows an overview of the optimization process in the embodiment. The optimization device 10 acquires the heat demand for chilled water for each hour required by the air conditioner 4, climate data such as temperature and humidity for each hour, electricity charges, and performance data of the chiller 3. The optimization device 10 performs optimization calculations using nonlinear programming to calculate an operation method that minimizes the amount of energy consumed for each combination pattern of equipment configuration that satisfies the heat demand of the air conditioner 4, such as the load, load factor, and power consumption of the chiller 3 for each hour. Furthermore, the optimization device 10 counts up the power consumption of the chiller 3 for each hour, calculates the power consumption, cost, CO2 emission, and the like for a predetermined period, and outputs the calculated values. An example of output data output by the optimization device 10 is shown in FIG. 4 to FIG. 5.

[0017] The vertical axis of FIG. 4 indicates the annual CO2 consumption, and the horizontal axis indicates the amount of energy consumed in a predetermined period. In the example shown in FIG. 4, the combinations of the chillers 3 that can satisfy the heat demand from the air conditioner 4 are the combinations 41 to 45 shown in the figure. For example, the combination pattern 41 is a combination of one chiller 3 of 400 USRt (US refrigeration ton), five chillers 3 of 600 USRt, and one chiller 3 of 700 USRt. The combination pattern 42 is a configuration of six chillers of 400 USRt. The combination patterns 43 to 45 are also as shown in the figure. The calculation unit 12 calculates the hourly load factor, etc. for each of the combination patterns 41 to 45 by optimization calculation, which minimizes the total amount of power consumption of each chiller 3 constituting the combination pattern while satisfying the heat demand for each hour. The calculation unit 12 calculates the hourly load factor, etc. for one year.

[0018] FIG. 5 shows an example of the calculation results of the load factor and the like in combination pattern 41 for January and August, among calculations for one year. In this example, calculations are performed assuming that the heat demand is constant for each hour during January and August. Graphs 511 and 581 in FIG. 5 show the change in heat demand (kW) for each hour in January and August, respectively. The vertical axis of graphs 511 and 581 is heat demand, and the horizontal axis is time. Graphs 512 and 582 show the load (kW) allocated to each chiller 3 for each hour in January and August, respectively. The vertical axis of graphs 512 and 582 is load, and the horizontal axis is time. Referring to graph 512, during times of low heat demand, one 400 USRt chiller 3 and one or two 600 USRt chillers 3 are operated, and the number of 600 USRt chillers 3 in operation is increased as the heat demand increases, but the 700 USRt chillers 3 are not operated. In contrast, looking at graph 582 for August, when heat demand is high, all seven units (one 400, five 600, and one 700) are operated throughout the 24 hours. These are load allocations calculated by calculation unit 12 as an operation method that minimizes power consumption while satisfying heat demand. The same is true for graphs 513 and 583. Graphs 513 and 583 respectively show the power consumption (kWh) per hour when each chiller 3 is operated by allocating the load as in graphs 512 and 582, and the vertical axis of each graph shows the power consumption and the horizontal axis shows the time.

[0019] Graph 584 shows the change in the load factor assigned to each chiller 3 over a one-day (24-hour) period in August, and graph 585 shows the COP corresponding to the partial load of each chiller 3. The vertical axis of graph 584 shows the load factor, and the horizontal axis shows the time. The vertical axis of graph 585 shows the COP, and the horizontal axis shows the load factor. Graph 585 shows performance data for each chiller 3 of 400 USRt, 600 USRt, and 700 USRt obtained by simplifying the performance formula described later. Referring to graph 585, each chiller 3 has a relatively high COP at a load factor of 40 to 85%. As a result of optimization calculation by calculation unit 12, during the time period when a load is required, each chiller 3 is assigned a load factor that results in these relatively high COPs. In addition, when the COPs of the chillers 3 of 400 USRt and 600 USRt are compared with the COP of the chiller 3 of 700 USRt in graph 585, the COP of the 700 USRt reaches its peak at a lower load factor. Reflecting these characteristics, the calculation results show that during the time periods when the load of graph 58 is required, a larger load factor is assigned to the 400 USRt and 600 USRt chillers 3 than to the 700 USRt chiller 3.

[0020] The calculation unit 12 calculates the load amount and power consumption to be allocated to each chiller 3 for each hour by optimization calculation as described with reference to FIG. 5. The calculation unit 12, for example, calculates the annual power consumption when operating in the configuration of the combination pattern 41 by accumulating the calculated daily power consumption for each month. Furthermore, the calculation unit 12 converts the calculated annual power consumption into CO2 emission using a predetermined conversion formula. The calculation unit 12 also calculates the total cost for 10 years by multiplying the calculated annual power consumption by, for example, 10 times and further multiplying it by the electricity charge. The calculation unit 12 similarly performs optimization calculation for the other combination patterns 42 to 45 to calculate the optimal operation, and calculates the annual CO2 emission and the cost for the predetermined period by tallying up the results for a predetermined period. FIG. 4 shows a graph calculated in this way. In this way, the optimization device 10 has a function of optimizing the operation method for each month and each time and presenting the optimized operation method.

[0021] (operation) Next, the operation of the optimization device 10 according to this embodiment will be described with reference to FIG. FIG. 6 is a flowchart showing an example of the optimization process according to the embodiment. First, the data acquisition unit 11 acquires demand data, climate data, electricity charges, and performance data (step S10). For the demand data, hourly heat demand analyzed according to the needs and past performance of the installation environment of the air conditioner 4 can be used. For the climate data, data such as temperature provided by the Meteorological Agency can be used. For the electricity charges, a tariff provided by an electric power company can be used. For the performance data, performance data provided by the manufacturer of the chiller 3 can be used. The data acquisition unit 11 records these data in the storage unit 14. Next, the calculation unit 12 performs optimization calculation, but in this embodiment, before that, the processing of steps S11 to S16 (calculation of heat and pressure loss in piping, simplification of performance formula, etc.) is performed, and the heat demand including heat loss due to piping is used as an input for the optimization calculation. This makes it possible to reduce or simplify the constraint formula of the optimization calculation, shortening the calculation time and ensuring convergence.

[0022] First, calculation unit 12 calculates the amount of piping heat loss from the hourly heat demand and piping information (step S11). Using a known method, calculation unit 12 calculates the amount of piping heat loss that occurs when chilled water is sent from chiller 3 to air conditioner 4. Values ​​such as the piping length (m) of chilled water pipes 7a-7b and the heat loss coefficient (W / m·K) required to calculate the amount of piping heat loss are registered in advance in storage unit 14, and calculation unit 12 calculates the amount of piping heat loss using these values ​​and a predetermined formula.

[0023] Next, the calculation unit 12 calculates the heat demand required for the chiller 3 from the heat demand per hour and the piping heat loss calculated in step S11 (step S12). The calculation unit 12 corrects the heat demand by adding the piping heat loss to the input heat demand from the air conditioner 4, and sets the corrected heat demand (°C, Kwth) as the true heat demand required for the chiller 3. By steps S11 to S12, the calculation of the piping heat loss can be excluded from the optimization calculation process.

[0024] Next, the calculation unit 12 calculates the required capacity of the chillers 3 from the heat demand, and calculates a combination pattern of the chillers 3 that satisfies the required capacity (step S13). The calculation unit 12 calculates the required capacity of the chillers 3 from the heat demand calculated in step S12, and calculates a combination pattern of the chillers 3 that satisfies the required capacity. For example, the calculation unit 12 calculates combination patterns 41 to 45 exemplified in FIG. 4. Furthermore, since the output of the chillers 3 is affected by temperature and humidity, in calculating the combination pattern, the weather data acquired in step S10 may be referenced, and the output of the chillers 3 according to the weather conditions may be acquired from the performance data, and a combination pattern of the chillers 3 that satisfies the required capacity may be calculated.

[0025] Next, the calculation unit 12 calculates the head of the entire system for each combination pattern (step S14). The calculation unit 12 calculates the head of the entire heat supply system 1 for each combination pattern by a known method. For example, a piping configuration type in which all the chillers 3 related to the combination pattern calculated in step S13 are connected in parallel, the cooling water generated by the cooling tower 2 is supplied to each chiller 3, and the cold water supplied by each chiller 3 is joined and supplied to the air conditioner 4 is prepared in advance for each number of chillers 3 to be connected, and the calculation unit 12 may calculate the head based on the type according to the number of chillers 3 constituting the combination pattern. In addition, the piping length and pressure loss coefficient required for calculating the head are registered in the storage unit 14 in advance, and the calculation unit 12 calculates the head by referring to these values. The calculation of the head and pressure loss is performed to determine the capacity of the pumps 6 and 8. The capacity of the pumps 6 and 8 can be determined as long as the maximum water flow rate and head of the entire system are determined. By calculating the pressure loss in advance, it is no longer necessary to include the calculation of the pressure loss in the optimization calculation process, and the optimization calculation can be simplified.

[0026] Next, the calculation unit 12 calculates the capacity of the auxiliary equipment from the head of each combination pattern (step S15). The calculation unit 12 determines the combination of the auxiliary equipment (cooling tower 2, air conditioner 4, pump 6, pump 8) based on the head calculated in step S14. The processing in step S15 determines the capacity X1 (kW) of the cooling tower 2, the capacity X2 (kW) of the air conditioner 4, the capacity X3 (kW) of the pump 6, and the capacity X4 (kW) of the pump 8. Climate data may be used to select the cooling tower 2 and the air conditioner 4. For example, a table or the like in which the output capacity of the cooling tower 2 and the air conditioner 4 for each temperature and humidity is previously registered in the storage unit 14, and the calculation unit 12 may determine the capacity of the cooling tower 2, etc. by referring to this table.

[0027] Next, the calculation unit 12 calculates a simplified performance formula (step S16). For example, the calculation unit 12 linearly complements a table that lists COP values ​​for the chilled water outlet temperature, the chilled water inlet / outlet temperature difference, the load factor, and the cooling water temperature included in the performance data acquired in step S10, and calculates a nonlinear formula expressing the COP of the chiller 3 as a function of four variables, namely, the chilled water inlet / outlet temperature difference ΔT, the chilled water outlet temperature Tout, the cooling water inlet temperature Tincl, and the chiller load factor X, for each chiller 3, by regression analysis or the like from the result. Next, the calculation unit 12 substitutes known ΔT, Tout, and Tincl into the created nonlinear formula, inputs several points of X into the formula after substitution, and calculates a nonlinear formula (COP=f(X)) expressing the relationship between X and COP for each chiller 3 included in the combination pattern calculated in step S13 by regression analysis or the like of the relationship between X and COP at that time. In general, the performance characteristics of many heat source machines such as the chiller 3 are multivariable nonlinear, and if a multivariable nonlinear equation is used as a constraint equation for the optimization calculation, there is a high possibility that the calculation will not converge. Therefore, by simplifying the multivariable nonlinear equation (four-variable nonlinear equation) to a one-variable nonlinear equation that expresses COP only with the load factor X, and setting the one-variable nonlinear equation as the constraint equation for the optimization calculation, it becomes possible to stably obtain a solution within a realistic calculation time.

[0028] Next, the calculation unit 12 performs an optimization calculation (operation optimization calculation) using the simplified performance formula (COP=f(X)) calculated in step S16 by nonlinear programming with the objective function of minimizing the amount of energy consumed by all the chillers 3 constituting the combination pattern (step S17). A commonly available solver can be used for the optimization calculation. The optimization calculation in step S17 calculates an optimized operation method of the chillers 3 (such as the load amount per hour exemplified in FIG. 5) for each combination pattern. For example, the calculation unit 12 calculates the load amount, load factor, and power consumption of each chiller 3 for each hour of each month for one year for each combination pattern.

[0029] Next, the calculation unit 12 calculates the amount of CO2 emissions and the cost (step S18). For example, the calculation unit 12 calculates the annual CO2 emissions and the total cost (electricity charge) for each combination pattern by tallying up the power consumption of each chiller 3 calculated in step S18 for each combination of chillers 3 calculated in step S13 and converting it into the CO2 emissions and the cost. Next, the output unit 13 generates a graph as shown in FIG. 4 from the calculation result of step S18 and outputs it to a display device or the like (step S19). By referring to the graph in FIG. 4, the user can consider introducing a combination pattern 41 in which both the annual CO2 emissions and the total cost in the predetermined period are minimized. Also, for example, when the user selects the combination pattern 41 in FIG. 4, the output unit 13 may output the group of graphs in FIG. 5 showing the optimal operation in the combination pattern 41 to a display device or the like based on the calculation result of step S17. The user can realize an operation in which the amount of power consumption is minimized by operating each chiller 3 at the load factor shown by the group of graphs in FIG. 5.

[0030] (effect) As described above, according to this embodiment, by deriving the annual energy consumption amount while optimizing the operation for each hour, it is possible to calculate a truly optimized equipment configuration and equipment operation method that cannot be obtained by the conventional method that does not consider the load fluctuation throughout the year and the characteristics of the equipment according to the load fluctuation. Furthermore, compared to the optimization calculation by nonlinear programming, which has difficulty in ensuring the calculation time and calculation stability, it is possible to ensure the calculation time and calculation stability by simplifying the performance formula. Furthermore, as shown in the example of FIG. 5, it is possible to output the optimized operation method for each month and each time.

[0031] In the above embodiment, the configuration of FIG. 2 has been described as an example, but the present invention is not limited thereto. Other system examples are shown in FIG. 7 and FIG. 8. The heat supply system 1A of FIG. 7 includes a heat pump chiller 21, an indoor unit 22 of an air conditioner, a cold water pipe 24a through which cold water generated by the heat pump chiller 21 passes when it is supplied to the indoor unit 22, a cold water pipe 24b through which cold water used in the indoor unit 22 passes when it returns to the heat pump chiller 21, and a pump 23 provided on the cold water pipe 24b. In the heat supply system 1A, the heat pump chiller 21 is an air-cooled type, so there is no cooling water circuit. In the heat supply system 1A, a process of calculating the optimal configuration and optimal operation of the heat pump chiller 21 that minimizes the amount of energy consumed will be described with reference to FIG. 6. The same process as in the configuration of FIG. 2 will be briefly described. First, the data acquisition unit 11 acquires demand data, weather data, electricity charges, and performance data (step S10). Next, the calculation unit 12 calculates the amount of piping heat loss from the hourly heat demand and the piping information (step S11). Next, the calculation unit 12 calculates the heat demand by adding the amount of piping heat loss calculated in step S11 to the hourly heat demand (step S12). Next, the calculation unit 12 calculates the required capacity of the heat pump chiller 21 from the heat demand, and calculates a combination pattern of the heat pump chiller 21 that satisfies the required capacity (step S13). Next, the calculation unit 12 calculates the head of the entire system for each combination pattern (step S14). Next, the calculation unit 12 calculates the capacity of the auxiliary equipment from the head of each combination pattern (step S15). Next, the calculation unit 12 calculates a simplified performance formula (step S16). For example, the calculation unit 12 linearly interpolates a table that lists the maximum capacity (kW) of the heat pump chiller 21 relative to the chilled water outlet temperature and the outdoor air temperature, which is included in the performance data acquired in step S10, and from the result, using regression analysis or the like, calculates a nonlinear equation for each heat pump chiller 21 that expresses the maximum capacity of the heat pump chiller 21 as a function of two variables, the chilled water outlet temperature Tout and the outdoor air temperature Ta.Also, for example, the calculation unit 12 linearly complements a table that lists COP values ​​for the chilled water outlet temperature, chilled water inlet / outlet temperature difference, load factor, and outdoor air temperature included in the performance data acquired in step S10, and calculates a nonlinear equation for each heat pump chiller 21 that expresses the COP of the heat pump chiller 21 as a function of four variables, namely, the chilled water inlet / outlet temperature difference ΔT, the chilled water outlet temperature Tout, the outdoor air temperature Ta, and the load factor X of the heat pump chiller 21, by regression analysis or the like from the result. Next, the calculation unit 12 substitutes known ΔT, Tout, and Ta into the two nonlinear equations that have been created. Then, for the COP equation, several points of X are input into the equation after substitution, and the relationship between X and COP at that time is calculated by regression analysis or the like to calculate a nonlinear equation (COP=f(X)) that expresses the relationship between X and COP for each heat pump chiller 21 included in the combination pattern calculated in step S13. In addition, the nonlinear equation expressing the maximum capacity of the heat pump chiller 21 as a function of two variables, the chilled water outlet temperature Tout and the outdoor air temperature Ta, becomes a constant by substituting the known Tout and Ta. Next, the calculation unit 12 performs an optimization calculation by nonlinear programming using the simplified performance equation (COP=f(X)) calculated in step S16, with the objective function being to minimize the total energy consumption of the heat pump chiller 21 constituting the combination pattern (step S17). Next, the calculation unit 12 calculates the CO2 emission amount and the cost (step S18). Next, the output unit 13 generates a graph or the like from the calculation result of step S18 and outputs it to a display device or the like (step S19). In this way, the configuration and operation method of the optimized heat pump chiller 21 can be derived for the heat supply system 1A illustrated in FIG. 7 as well. Note that, although the explanation here has been given using the example where the energy equipment 21 is a heat pump chiller, the configuration and operation method of the heat pump water heater 21 can be optimized using a similar concept and a similar calculation method in a system in which the energy equipment 21 is a heat pump water heater and the heat pump water heater 21 supplies hot water to the indoor unit 22 to provide heating.

[0032] The heat supply system 1B in FIG. 8 includes a steam heat pump 31, an indoor unit 32, a steam pipe 34 through which the steam generated by the steam heat pump chiller 31 passes when it is supplied to the indoor unit 32, a water supply pipe 33 that supplies water to the steam heat pump 31, and a pipe 35 that exhausts the steam from the indoor unit 32. In the heat supply system 1B, the process of calculating the optimal configuration and optimal operation of the steam heat pump 31 that minimizes the amount of energy consumed is approximately the same as that of the heat supply system 1A in FIG. 7. However, in step S16, for example, the calculation unit 12 linearly complements a table that lists the maximum capacity (kW) of the steam heat pump 31 for the steam pressure, water supply temperature, and outdoor air temperature included in the performance data acquired in step S10, and calculates a nonlinear equation that expresses the maximum capacity of the steam heat pump 31 as a function of three variables, namely, the steam pressure Pout, the water supply temperature Tin, and the outdoor air temperature Ta, for each steam heat pump 31, by regression analysis or the like from the result. Also, for example, the calculation unit 12 linearly complements a table that lists COP values ​​for steam pressure, water supply temperature, load factor, and outdoor air temperature included in the performance data acquired in step S10, and calculates a nonlinear equation that expresses the COP of the steam heat pump 31 as a function of four variables, namely, the steam pressure Pout, the water supply temperature Tin, the outdoor air temperature Ta, and the load factor X of the steam heat pump 31, for each steam heat pump 31, by regression analysis or the like from the result. Next, the calculation unit 12 substitutes the known Pout, Tin, and Ta into the two nonlinear equations that have been created, inputs several points of X into the COP equation after substitution, analyzes the relationship between X and COP at that time by regression analysis or the like, and calculates a nonlinear equation (COP=f(X)) that expresses the relationship between X and COP for each steam heat pump 31 included in the combination pattern calculated in step S13. In addition, the nonlinear equation expressing the maximum capacity of the steam heat pump 31 as a function of three variables, steam pressure Pout, water supply temperature Tin, and outside air temperature Ta, becomes a constant by substituting the known values ​​Pout, Tin, and Ta.Similarly, the calculation unit 12 performs optimization calculations using the simplified performance formula (COP=f(X)) calculated in step S16 by nonlinear programming with the objective function of minimizing the total energy consumption of the vapor heat pumps 31 constituting the combination pattern (step S17), calculates the CO2 emissions and costs (step S18), and the output unit 13 outputs graphs reflecting the calculation results of step S18 (step S19). In this way, the configuration and operation method of the optimized vapor heat pump 31 can be derived for the heat supply system 1B illustrated in FIG. 8. Note that, although the energy equipment 21 is a vapor heat pump, for example, the energy equipment 21 can be a boiler, and the boiler 31 can be used to optimize the configuration and operation method of the boiler 31 in a system in which the energy equipment 21 is a boiler that supplies steam to the indoor unit 32 for heating.

[0033] FIG. 9 is a diagram illustrating an example of a hardware configuration of an optimization device according to an embodiment. The computer 900 includes a CPU 901, a main memory device 902, an auxiliary memory device 903, an input / output interface 904, and a communication interface 905. The optimization device 10 described above is implemented in the computer 900. The above-mentioned functions are stored in the auxiliary memory device 903 in the form of a program. The CPU 901 reads the program from the auxiliary memory device 903, loads it in the main memory device 902, and executes the above-mentioned processing according to the program. The CPU 901 also secures a storage area in the main memory device 902 according to the program. The CPU 901 also secures a storage area in the auxiliary memory device 903 for storing data being processed according to the program.

[0034] A program for realizing all or part of the functions of the optimization device 10 may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer system and executed to perform processing by each functional unit. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. In addition, if a WWW system is used, the term "computer system" also includes a homepage providing environment (or display environment). In addition, the term "computer-readable recording medium" refers to a portable medium such as a CD, DVD, or USB, or a storage device such as a hard disk built into a computer system. In addition, when the program is distributed to the computer 900 via a communication line, the computer 900 that receives the program may expand the program in the main storage device 902 and execute the above processing. In addition, the above program may be for realizing part of the above-mentioned functions, and may further be capable of realizing the above-mentioned functions in combination with a program already recorded in the computer system.

[0035] As described above, several embodiments according to the present disclosure have been described, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and gist of the invention.

[0036] <Additional Notes> The optimization device, optimization method, and program described in each embodiment can be understood, for example, as follows.

[0037] (1) An optimization device according to a first aspect includes an acquisition unit that acquires demand data indicating a demand for thermal energy per unit time and performance data of an energy equipment that supplies the thermal energy, and a calculation unit that calculates, based on the performance data and taking into consideration performance according to the load of the energy equipment, an operation method of the energy equipment that satisfies the thermal demand per unit time indicated by the demand data and minimizes the amount of energy consumed by the energy equipment in a specified period of time. This makes it possible to derive an optimized operation method for the energy equipment. In addition, by comparing the optimized operation methods for the energy equipment and selecting a configuration that minimizes, for example, the amount of energy consumption, CO2 emissions, costs, etc., it is possible to derive an optimized equipment configuration.

[0038] (2) An optimization device according to a second aspect is the optimization device of (1), wherein the performance data is data defining a relationship between a load rate of the energy equipment, other parameters that affect the performance of the energy equipment, and the performance of the energy equipment, and the calculation unit calculates a simplified formula for calculating the performance of the energy equipment from the load rate of the energy equipment from the performance data, and calculates an operation method of the energy equipment using the simplified formula. As a result, by simplifying the performance formula, it is possible to ensure the calculation time and calculation stability, unlike optimization calculations using nonlinear programming, which have been difficult to ensure the calculation time and calculation stability.

[0039] (3) An optimization device according to a third aspect is the optimization device of (1) to (2), wherein the calculation unit calculates heat loss that occurs when the thermal energy supplied by the energy equipment reaches a destination of the thermal energy, adds the heat loss to the thermal demand indicated by the demand data, and calculates an operation method of the energy equipment that satisfies the thermal demand after the addition. This eliminates the need to calculate heat loss during the optimization calculation process, making it possible to shorten the calculation time and improve stability (convergence) of the optimization calculation.

[0040] (4) An optimization device according to a fourth aspect is the optimization device of (1) to (3), wherein the calculation unit calculates a combination of the energy equipment that can satisfy the heat demand indicated by the demand data, and calculates an operation method for each of the energy equipment included in the calculated combination that minimizes the total energy consumption of the energy equipment. This makes it possible to select an optimal equipment configuration from among various combination patterns of energy equipment.

[0041] (5) An optimization device according to a fifth aspect is an optimization device according to any one of (1) to (4), wherein the operation method of the energy equipment calculated by the calculation unit includes a load rate or a load amount of the energy equipment per unit time, and further includes an output unit that outputs the load rate or the load amount per unit time. This allows the user to check the optimal operation method for the energy appliances.

[0042] (6) An optimization device according to a sixth aspect is an optimization device according to any one of (1) to (5), wherein the operation method of the energy equipment calculated by the calculation unit includes an amount of energy consumed by the energy equipment per unit time, and further includes an output unit that outputs a value obtained by converting the amount of energy consumed per unit time, the amount being calculated over a predetermined period of time, into a total amount of energy consumed, and / or a value obtained by converting the total amount of energy consumed into a cost. This allows users to determine the optimal configuration of energy equipment, taking into account CO2 emissions and total costs.

[0043] (7) An optimization device according to a seventh aspect is the optimization device of (2), wherein the energy equipment is a chiller that receives a supply of cooling water from a cooling tower and generates chilled water to be supplied to a load device, and the performance data defines a COP of the chiller with respect to a temperature Tout of the chilled water supplied by the chiller, a temperature difference ΔT between the chilled water supplied by the chiller and the chilled water returning from the load device, a temperature Tincl of the chiller supplied from the cooling tower, and a load factor X during operation of the chiller, and the calculation unit calculates a first nonlinear equation from the performance data for calculating the COP using the temperature Tout, the temperature difference ΔT, the temperature Tincl, and the load factor X as variables, and calculates the simplified equation for calculating the COP using the load factor X as a variable, based on a relationship between the load factor X and the COP based on the first nonlinear equation when predetermined known values ​​are set for the temperature Tout, the temperature difference ΔT, and the temperature Tincl in the first nonlinear equation. As a result, by simplifying the performance formula, it is possible to ensure the calculation time and stability, unlike optimization calculations using nonlinear programming, which have been difficult to ensure in terms of calculation time and stability.

[0044] (8) An optimization device according to an eighth aspect is the optimization device of (2), wherein the energy equipment is a heat pump that generates water to be supplied to a load device, and the performance data defines a COP of the heat pump with respect to a temperature Tout of the water supplied by the heat pump, a temperature difference ΔT between the water supplied by the heat pump and the water returned from the load device, an outside air temperature Ta, and a load factor X during operation of the heat pump, and the calculation unit calculates a first nonlinear equation from the performance data for calculating the COP using the temperature Tout, the temperature difference ΔT, the outside air temperature Ta, and the load factor X as variables, and calculates the simplified equation for calculating the COP using the load factor X as a variable based on a relationship between the load factor X and the COP based on the first nonlinear equation when predetermined known values ​​are set for the temperature Tout, the temperature difference ΔT, and the outside air temperature Ta in the first nonlinear equation. As a result, by simplifying the performance formula, it is possible to ensure the calculation time and calculation stability, unlike optimization calculations using nonlinear programming, which have been difficult to ensure the calculation time and calculation stability.

[0045] (9) An optimization device according to a ninth aspect is the optimization device of (2), wherein the energy equipment is a steam heat pump that generates steam to be supplied to a load device, and the performance data defines a COP of the steam heat pump with respect to a pressure Pout of steam supplied by the steam heat pump, a temperature Tin of feed water supplied to the steam heat pump, an outdoor air temperature Ta, and a load factor X during operation of the steam heat pump, and the calculation unit calculates a first nonlinear equation from the performance data for calculating the COP using the pressure Pout, the temperature Tin, the outdoor air temperature Ta, and the load factor X as variables, and calculates the simplified equation for calculating the COP using the load factor X as a variable based on a relationship between the load factor X and the COP based on the first nonlinear equation when predetermined known values ​​are set for the pressure Pout, the temperature Tin, and the outdoor air temperature Ta in the first nonlinear equation. As a result, by simplifying the performance formula, it is possible to ensure the calculation time and calculation stability, unlike optimization calculations using nonlinear programming, which have been difficult to ensure the calculation time and calculation stability.

[0046] (10) An optimization method according to a tenth aspect includes the steps of acquiring demand data indicating the demand for thermal energy per unit time and performance data of an energy equipment that supplies the thermal energy, and calculating, based on the performance data and taking into account performance according to the load of the energy equipment, an operation method of the energy equipment that satisfies the thermal demand per unit time indicated by the demand data and minimizes the amount of energy consumed by the energy equipment in a specified period of time.

[0047] (11) A program relating to an eleventh aspect causes a computer to execute the steps of acquiring demand data indicating the demand for thermal energy per unit time and performance data of an energy equipment that supplies the thermal energy, and calculating, based on the performance data and taking into account performance according to the load of the energy equipment, an operation method of the energy equipment such that the thermal demand per unit time indicated by the demand data is satisfied and the amount of energy consumed by the energy equipment in a specified period is minimized. [Explanation of symbols]

[0048] 1. Heat supply system 2...cooling tower 3. Refrigeration unit 4...Air conditioner 5a, 5b...Cooling water piping 6. Pump 7a, 7b...Cold water piping 8. Pump 10. Optimization device 11. Data acquisition section 12...Calculation section 13 Output section 14...Storage section 900...Computer 901···CPU 902...Main memory 903...Auxiliary storage device 904 Input / Output Interface 905 Communication Interface

Claims

1. An acquisition unit that acquires demand data indicating a demand for thermal energy per unit time and performance data of an energy device that supplies the thermal energy; a calculation unit that calculates an operation method of the energy equipment based on the performance data, taking into consideration performance corresponding to a load of the energy equipment, so as to satisfy the heat demand per unit time indicated by the demand data and minimize an amount of energy consumed by the energy equipment in a predetermined period of time; An optimization device comprising:

2. The performance data is data that defines a relationship between a load factor of the energy equipment, other parameters that affect the performance of the energy equipment, and the performance of the energy equipment, and the calculation unit calculates a simplified formula for calculating the performance of the energy equipment from the load factor of the energy equipment from the performance data, and calculates an operation method of the energy equipment using the simplified formula. The optimization device according to claim 1 .

3. the calculation unit calculates a heat loss that occurs while the thermal energy supplied by the energy equipment reaches a destination of the thermal energy, adds the heat loss to the thermal demand indicated by the demand data, and calculates an operation method of the energy equipment that can satisfy the thermal demand after the addition.

3. The optimization device according to claim 1 or 2.

4. the calculation unit calculates a combination of the energy equipment that can satisfy the heat demand indicated by the demand data, and calculates an operation method for each of the energy equipment included in the calculated combination that minimizes the total energy consumption of the energy equipment.

3. The optimization device according to claim 1 or 2.

5. an output unit that includes a load factor or a load amount of the energy equipment per unit time, the load factor or the load amount per unit time being calculated by the calculation unit; The optimization device according to claim 1 or 2, further comprising:

6. an output unit which outputs a value obtained by converting the total amount of energy consumption calculated by the calculation unit per unit time into a CO2 emission amount and / or a value obtained by converting the total amount of energy consumption calculated by the calculation unit per unit time into a cost; The optimization device according to claim 1 or 2, further comprising:

7. The energy equipment is a chiller that receives cooling water from a cooling tower and generates chilled water to be supplied to a load device, The performance data defines a COP of the chiller with respect to a temperature Tout of the chilled water supplied by the chiller, a temperature difference ΔT between the chilled water supplied by the chiller and the chilled water returning from the load device, a temperature Tincl of the cooling water supplied from the cooling tower, and a load factor X during operation of the chiller, The calculation unit calculates a first nonlinear equation for calculating the COP using the temperature Tout, the temperature difference ΔT, the temperature Tincl, and the load factor X as variables from the performance data, and calculates the simplified equation for calculating the COP using the load factor X as a variable based on a relationship between the load factor X and the COP based on the first nonlinear equation when the temperature Tout, the temperature difference ΔT, and the temperature Tincl are set to predetermined known values ​​in the first nonlinear equation. The optimization device according to claim 2 .

8. The energy equipment is a heat pump that generates water to be supplied to a load device, The performance data defines a temperature Tout of the water supplied by the heat pump, a temperature difference ΔT between the water supplied by the heat pump and the water returning from the load device, an outside air temperature Ta, and a COP of the heat pump relative to a load factor X during operation of the heat pump, The calculation unit calculates a first nonlinear equation for calculating the COP using the temperature Tout, the temperature difference ΔT, the outside air temperature Ta, and the load factor X as variables from the performance data, and calculates the simplified equation for calculating the COP using the load factor X as a variable based on a relationship between the load factor X and the COP based on the first nonlinear equation when the temperature Tout, the temperature difference ΔT, and the outside air temperature Ta are set to predetermined known values ​​in the first nonlinear equation. The optimization device according to claim 2 .

9. The energy equipment is a steam heat pump that generates steam to be supplied to a load device, The performance data defines a COP of the steam heat pump with respect to a pressure Pout of steam supplied by the steam heat pump, a temperature Tin of water supplied to the steam heat pump, an outside air temperature Ta, and a load factor X during operation of the steam heat pump, the calculation unit calculates a first nonlinear equation for calculating the COP using the pressure Pout, the temperature Tin, the outside air temperature Ta, and the load factor X as variables from the performance data, and calculates the simplified equation for calculating the COP using the load factor X as a variable based on a relationship between the load factor X and the COP based on the first nonlinear equation when the pressure Pout, the temperature Tin, and the outside air temperature Ta are set to predetermined known values ​​in the first nonlinear equation. The optimization device according to claim 2 .

10. Acquiring demand data indicating a demand for thermal energy per unit time and performance data of an energy device that supplies the thermal energy; calculating an operation method of the energy equipment based on the performance data, taking into consideration the performance according to the load of the energy equipment, so as to satisfy the heat demand per unit time indicated by the demand data and minimize the amount of energy consumed by the energy equipment in a predetermined period of time; The optimization method has the following structure:

11. On the computer, Acquiring demand data indicating a demand for thermal energy per unit time and performance data of an energy device that supplies the thermal energy; calculating an operation method of the energy equipment based on the performance data, taking into consideration the performance according to the load of the energy equipment, so as to satisfy the heat demand per unit time indicated by the demand data and minimize the amount of energy consumed by the energy equipment in a predetermined period of time; A program that executes the following.

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