Management devices and management methods

Abstract

To provide an energy plant management device that can perform optimization calculation in a shorter time and with higher accuracy.SOLUTION: A management device includes a processing device and a storage device. The storage device stores information on heat demand in an energy plant and equipment characteristic information indicating a correlation between a load on a heat source machine and energy consumption for each of a plurality of heat source machines included in the energy plant. The processing device creates, on the basis of the equipment characteristic information, the plurality of heat source machine groups, each of which includes one or more heat source machines included in the plurality of heat source machines. The processing device identifies, on the basis of the information of the heat demand and the information on the plurality of heat source machine groups, one or more heat source machine groups to be adopted as a control target corresponding to the heat demand, among the plurality of heat source machine groups, The processing device creates a heat source control plan, which is a plan for supplying heat to meet the heat demand using the heat source machines included in the identified heat source machine group.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present invention relates to a management device and a management method.

Background Art

[0002] As the background art of the present invention, for example, Japanese Patent Application Laid-Open No. 2011-002112 discloses a technique for simulating an optimal combination of a plurality of heat source machines and an optimal operation schedule for each heat source machine.

Prior Art Document

Patent Document

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an energy plant control system including various heat source facilities, it is necessary to perform an optimization calculation considering various parameters in order to formulate a heat source control plan. On the other hand, in the optimization calculation for formulating a heat source control plan, as the number of types of heat source facility configurations and the scale of the energy plant control system increase, the number and combinations of parameters to be considered also increase. And when there are an enormous number of parameters and combinations, it may require a very long calculation time, or it may become impossible to consider all parameters in the optimization calculation. Here, when it becomes impossible to consider all parameters in the optimization calculation, the plan created based on the result of the optimization calculation may not necessarily be superior to other possible plan candidates under the same conditions, so there is a possibility that the accuracy of the optimization calculation may be reduced.

[0005] The inventors have identified a problem where performing optimization calculations without considering the equipment characteristics of individual heat source facilities results in including heat source facilities that are less relevant to the heat source control plan, ultimately affecting the time and accuracy of the optimization calculations. Therefore, there is a need for an energy plant management device that can perform optimization calculations in a shorter time and with higher accuracy, taking into account the equipment characteristics of individual heat source facilities. Note that the technology described in Patent Document 1 does not take the above-mentioned problem into consideration. [Means for solving the problem]

[0006] According to a first aspect of the present invention, the following management device is provided. This management device comprises a processing unit and a storage device. The storage device stores information on the heat demand in an energy plant and equipment characteristic information showing the correlation between the load on each of the multiple heat source units included in the energy plant and the energy consumption of each heat source unit. The aforementioned equipment characteristic information includes information on the heat source unit width, which is the range of values ​​that can be taken for energy consumption efficiency when a load of a predetermined range is applied to the heat source. The apparatus , heat source unit width Based on this, multiple heat source groupings are created, each containing one or more heat source units from the multiple heat source units. The processing unit identifies one or more heat source groupings from among the multiple heat source groupings to be adopted as the control target corresponding to the heat demand, based on the heat demand information stored in the memory device and the information of the multiple heat source groupings. The processing unit creates a heat source control plan, which is a plan to supply heat to meet the heat demand using the heat source units included in the identified heat source groupings.

[0007] According to a second aspect of the present invention, the following management method is provided. This management method is performed using a processing device and a memory device. In this method, the memory device stores information on the heat demand in the energy plant and equipment characteristic information showing the correlation between the load on each of the multiple heat source devices included in the energy plant and the energy consumption of each heat source device. The aforementioned equipment characteristic information includes information on the heat source unit width, which is information on the range of values ​​that can be taken for energy consumption efficiency when a load of a predetermined range is applied to the heat source. This method allows the processing unit , in the heat source unit widthThe process includes the steps of: creating a plurality of heat source groupings, each of which contains one or more heat source units from the plurality of heat source units; the processing device identifying one or more heat source groupings from the plurality of heat source groupings to be adopted as control targets corresponding to the heat demand, based on the heat demand information stored in the storage device and the information of the plurality of heat source groupings; and the processing device creating a heat source control plan, which is a plan to supply heat to meet the heat demand using the heat source units included in the identified heat source groupings. [Effects of the Invention]

[0008] According to the present invention, good optimization calculations can be achieved even in energy plants composed of various heat source equipment. Other problems, configurations, and effects not mentioned above will be clarified by the following description of embodiments for carrying out the invention. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example of the relationship between an energy plant management device and an integrated equipment system. [Figure 2] This is a block diagram showing an example of the specific configuration of the operation planning processing unit. [Figure 3] This is an example of a performance characteristic diagram for a heat source unit. [Figure 4] This flowchart shows an example of the overall flow from pre-processing to plan creation. [Figure 5] This flowchart shows an example of the processing procedure performed by the heat source unit priority determination unit. [Figure 6A] This is a conceptual diagram illustrating an example of the procedure for creating a heat source unit group. [Figure 6B] This table shows an example of management information for a heat source unit. [Figure 7] This is a conceptual diagram showing the processing procedure by the heat source unit priority determination unit. [Figure 8] This is an example of an operation plan created by the planning department of an energy plant management system. [Figure 9]This is a block diagram showing an example of the specific configuration of the operation planning processing unit. [Figure 10] This is a flowchart illustrating an example of the processing procedure in the CGS Waste Heat Recovery Planning Department. [Figure 11] This flowchart shows an example of the processing procedure performed by the heat source unit priority determination unit. [Figure 12] This is a conceptual diagram showing the processing procedure by the heat source unit priority determination unit. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. The embodiments are illustrative examples for explaining the present invention, and have been omitted and simplified as appropriate for clarity of explanation. The present invention can also be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. The positions, sizes, shapes, and ranges of the components shown in the drawings may not represent their actual positions, sizes, shapes, and ranges in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the positions, sizes, shapes, and ranges disclosed in the drawings. Examples of various types of information may be described using terms such as "table," "list," and "queue," but these types of information may also be represented by other data structures. For example, various types of information such as "XX table," "XX list," and "XX queue" may be referred to as "XX information." When describing identification information, terms such as "identification information," "identifier," "name," "ID," and "number" are used, and these terms are interchangeable. When there are multiple components with the same or similar function, they may be described using the same symbol but with different subscripts. Furthermore, when it is not necessary to distinguish between these multiple components, the subscripts may be omitted in the description. In the embodiments, the processes performed by executing a program may be described. Here, a computer executes a program by a processor (e.g., a CPU or a GPU), and performs the processes defined by the program while using a storage resource (e.g., a memory) and an interface device (e.g., a communication port), etc. Therefore, the subject of the processes performed by executing the program may be the processor. Similarly, the subject of the processes performed by executing the program may be a controller, a device, a system, a computer, or a node having a processor. The subject of the processes performed by executing the program may be an arithmetic unit and may include a dedicated circuit for performing specific processes. Here, the dedicated circuit is, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), a CPLD (Complex Programmable Logic Device), or the like. The program may be installed in a computer from a program source. The program source may be, for example, a program distribution server or a storage medium readable by a computer. When the program source is a program distribution server, the program distribution server includes a processor and a storage resource for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. Also, in the embodiments, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0011] In the embodiments, a technique that can realize an optimization calculation with high accuracy in a short time even in an energy plant including various heat source facilities is described. According to this technique, it is possible to contribute from the environmental perspective of energy saving and from the economic perspective of cost reduction.

[0012] <First Embodiment> <Configuration around the Energy Plant Management Device> First, referring to FIGS. 1 - 3, an energy plant management device and a configuration example around it will be described. FIG. 1 is a diagram showing an example of the configuration of an energy plant management system including an energy plant management device 10.

[0013] The energy plant management system may include an energy plant management device 10 (management device), a control output device 20, a power generation facility management device 30, a heat source facility management device 40, a weather provider 50, a unit price information site 60, the Internet 70, a user terminal 80, a generator 1, and a heat source machine 2. Note that the form of the above energy plant management system is an example, and the functions described in one configuration may be divided into different configurations, or the configurations described as different configurations may be aggregated into one configuration.

[0014] The energy plant management device 10 performs data processing using various data. The control output device 20 outputs control information for the power generation facility and the heat source facility to the power generation facility management device 30 and the heat source facility management device 40 based on the information acquired from the energy plant management device 10. The power generation facility management device 30 controls the power generation facility including the generator 1. The heat source facility management device 40 controls the heat source facility including the heat source machine 2. The weather provider 50 provides information related to the weather. The user terminal 80 may be, for example, a PC terminal of an administrator at a remote management base, a PC terminal used by a system administrator, a mobile terminal held by an operator, etc.

[0015] The energy plant management device 10 includes a prediction unit 100, a planning unit 200, an input unit 300, a display unit 400, a storage unit 500, and a communication interface 600. These can be configured using appropriate hardware.

[0016] The prediction unit 100 and the planning unit 200 can be configured using appropriate processing devices for data processing. The processing device only needs to be able to perform data processing; for example, a processor can be used. The energy plant management device 10 may also be equipped with RAM (Random Access Memory) from which the processing device reads data.

[0017] The input unit 300 is configured to input information for setting and changing the information recorded in the storage unit 500. The display unit 400 is configured to output the information recorded in the storage unit 500. The input unit 300 may be implemented, for example, by including an operating device for a terminal used by the user. The display unit 400 may be implemented, for example, by including a display screen for a terminal viewed by the user.

[0018] The memory unit 500 is configured using an appropriate storage device, for example, an HDD (Hard Disk Drive). The communication interface 600 is configured for communication with other components of the energy plant management system.

[0019] The memory unit 500 stores equipment characteristics information 510, actual power and heat demand information 520, power and heat demand forecast information 530, weather information 540, unit price and emission factor information 550, constraint information 560, power generation and heat source control plan information 570, KPI information 580, and calendar information 590.

[0020] The equipment characteristics information 510 stores multidimensional approximate surface graph information showing the relationship between the load factor, which is the ratio of output to the rated capacity of the generator 1 and heat source 2 that constitute the energy plant, and energy consumption. Furthermore, since the relationship between the load factor and energy consumption changes depending on the environmental conditions that control the generator 1 and heat source 2, the equipment characteristics information 510 includes information on the relationship between the load factor and energy consumption for each different environmental condition. A specific example of the equipment characteristics information 510 will be described later in relation to Figure 3.

[0021] The actual power and heat demand information 520 is information on actual power and heat demand in the energy supply system. The power and heat demand forecast information 530 is information on forecast values ​​for power and heat demand in the energy supply system. The power and heat demand forecast information 530 relates to information on demand forecast results calculated by the demand forecast calculation unit 110, which will be described later. The weather information 540 is information on actual weather information and weather forecast information obtained by the energy plant management device 10 through communication with the weather provider 50.

[0022] The unit price and emission factor information 550 is information on market electricity trading unit prices and non-fossil fuel certificate unit prices obtained by the energy plant management device 10 through communication with the unit price information site 60. The information recorded in the unit price and emission factor information 550 can be viewed and modified by the user. The constraint information 560 is information such as non-operating time information for power generation equipment and heat source equipment, the number of operations per day, continuous operation duration, continuous shutdown duration, and upper and lower limits of grid-received power, and can be viewed and modified by the user.

[0023] The KPI information 580 stores information on target items to be prioritized for minimization in optimization calculations, such as cost, CO2, and primary energy, as well as the annual CO2 emission target. The calendar information unit 590 stores the type of day, such as weekdays and holidays, and allows users to view and modify it.

[0024] The power generation / heat source control plan information 570 is information on the control plan for power generation equipment and heat source equipment calculated by the planning unit 200. The information stored in the storage unit 500 can be viewed via the display unit 400 or the user terminal 80, and can be modified via the input unit 300.

[0025] The forecasting unit 100 consists of a demand forecasting calculation unit 110 and a CO2 emission target calculation unit 120. The demand forecasting calculation unit 110 predicts future energy demand using, for example, the correlation between actual electricity and heat demand information 520, weather information 540, and calendar information 590. The CO2 emission target calculation unit 120 calculates daily CO2 emission targets using, for example, the annual CO2 emission target value set in KPI information 580 and calendar information 590.

[0026] The planning unit 200 consists of a pre-operation planning processing unit 210 that performs pre-processing to shorten the optimization calculation time, an optimization calculation unit 220, and a post-operation planning processing unit 230 that corrects the deviation range from the constraint conditions after the optimization calculation.

[0027] Figure 2 is a block diagram showing an example of the functional configuration of the operation planning preprocessing unit 210. The operation planning preprocessing unit 210 includes a heat source machine priority determination unit 210a, which consists of an operational machine determination unit 211a, a heat intensity calculation unit 212a, a group creation unit 213a, and a priority / load assignment unit 214a. The functions of each component will be described later.

[0028] Here, the above-mentioned equipment characteristic information will be explained in more detail with reference to Figure 3. Figure 3 shows an example of equipment characteristic information for heat source unit 2. Equipment characteristic information 510 stores information on heat demand and information showing the correlation between the load on each of the multiple heat source units included in the energy plant and energy consumption.

[0029] In Figure 3, the horizontal axis represents the load factor and the vertical axis represents energy consumption, illustrating the relationship between the load factor and energy consumption. Here, the load factor is the ratio of the actual heat produced to the rated heat produced, which is a value unique to each heat source unit. Therefore, by multiplying the load factor on the horizontal axis of Figure 3 by the rated heat produced of the corresponding heat source unit, the heat produced by that heat source unit can be calculated. Here, the case where the horizontal axis of Figure 3 represents the load factor is shown, but it could also represent the value of the heat produced by each heat source unit. Since the relationship between the load factor and energy consumption also changes depending on the environmental condition, the cooling water inlet temperature, the equipment characteristic information 510 for the heat source unit 2 stores multidimensional approximate surface graph information that takes into account the relationship with environmental conditions.

[0030] Figure 3 shows a graph with cooling water inlet temperature as an example of environmental conditions, but the equipment characteristic information 510 may store graphs based on other environmental conditions. The equipment characteristic information 510 may also express performance characteristics using a multi-surface approximation function in which the energy consumed by the heat source unit (electricity, gas, steam, waste hot water) fluctuates according to the cooling water temperature, chilled water temperature, and capacity load factor.

[0031] Next, we will explain the data processing performed by the energy plant management device 10, referring to Figures 4-8. First, we will explain an example of the optimization calculation procedure, referring to Figure 4. Figure 4 is a flowchart showing an example of the optimization calculation procedure. In detail, the processing unit of the energy plant management device 10 is the main component of the data processing.

[0032] In S101, the prediction unit 100 calculates predicted values ​​for electricity demand and heat demand from the present time into the future.

[0033] In S102, the pre-operation planning processing unit 210 limits the scope of the optimization calculation unit 220's search for the optimal solution by determining the startup priority and load allocation of heat source machines, excluding operating machines to the extent that they meet predetermined conditions, calculating the operating time of generators and the operating load ratio, based on equipment characteristic information 510, power and heat demand forecast information 530, weather information 540, unit price and emission factor information 550, constraint information 560, and KPI information 570.

[0034] In S103, the optimization calculation unit 220 applies optimization calculation logic such as brute-force calculation, mathematical programming, and particle swarm optimization to calculate the optimal solution according to the target items to be minimized preferentially from cost, CO2, and primary energy set in the KPI information 570, and the CO2 emission target for the planning period. If the CO2 emissions during the planning period exceed the CO2 emission target, non-fossil fuel certificates are purchased so that the CO2 emissions become less than or equal to the CO2 emission target.

[0035] In S104, the operation planning post-processing unit 230 compares the results calculated by the optimization calculation unit 220 with the non-operating time information, number of operations per day, continuous operation duration, continuous shutdown duration, and upper and lower limits of grid power reception for the power generation equipment and heat source equipment set in the constraint condition information 560. If the constraint conditions are deviated, the unit makes corrections to ensure that the constraint conditions are not deviated.

[0036] In S105, the corrected result from the operation plan post-processing unit 230 is stored in the power generation / heat source control plan information 570.

[0037] Next, the details of the processing performed by the operation planning preprocessing unit 210 will be explained with reference to Figures 5-7. The processing performed by the operation planning preprocessing unit 210 described here corresponds to S102 in Figure 4. Figure 5 is a flowchart showing an example of the processing procedure in the heat source equipment priority determination unit 210a included in the operation planning preprocessing unit 210. More specifically, the processing unit of the energy plant management device 10 is the main data processing unit.

[0038] In S201, the operational unit determination unit 211a receives the operating status of the heat source unit 2 managed by the heat source equipment management device 40. If the unit is malfunctioning or undergoing inspection, it is deemed operational and excluded from subsequent processing. Similarly, if the unit is set to a non-operating time zone in the planning target time of the constraint condition information 560, it is deemed operational and excluded from subsequent processing.

[0039] In S202, the heat intensity unit is calculated, which represents the amount of a first indicator required for each heat source to produce a predetermined amount of heat. Here, the first indicator may be cost, CO2 emissions, or primary energy consumption. Next, an example of calculating the heat intensity unit is shown.

[0040] The heat energy unit calculation unit 212a refers to the heat demand forecast information for the time period targeted for planning from the power and heat demand forecast information 530 and calculates the minimum and maximum load rates that the heat source machine can take from the equipment characteristics information 510. The heat energy unit calculation unit 212a also calculates the heat source unit width. The heat source unit width indicates the range that the value related to energy consumption efficiency (here, the heat energy unit) can take when a load within a predetermined range (for example, a load within the range of the maximum load rate and the minimum load rate) is applied to the heat source machine. Here, the value related to energy consumption efficiency is a value with units of [¥ / GJ], as shown in Figure 6A later. Note that the value related to energy consumption efficiency may be an index other than the heat energy unit.

[0041] The heat intensity calculation unit 212a determines that if the heat demand forecast information for the planned time period is less than the minimum load of the heat source unit in question, the unit in question is not operational during that time period and is excluded from subsequent processing. If the heat demand forecast information for the planned time period is greater than or equal to the minimum load and less than or equal to the maximum load of the heat source unit in question, the range of loads that the heat source unit can handle is determined to be from the minimum load of the heat source unit to the heat demand forecast information. If the heat demand forecast information for the planned time period is greater than or equal to the maximum load of the heat source unit in question, the range of loads that the heat source unit can handle is determined to be from the minimum load to the maximum load of the heat source unit. The heat intensity calculation unit 212a performs the above determination process for the range of loads that the heat source unit can handle for all heat source units that make up the energy plant.

[0042] In addition, in S202, the heat source unit calculation unit 212a may also use weather forecast information for the time period targeted for planning from the weather information 540 and calculate the minimum and maximum load rates that the heat source machine can take from the equipment characteristics information 510. That is, the heat source unit calculation unit 212a may use the weather information 540 to refer to the graph in the equipment characteristics information 510 corresponding to the environmental conditions and calculate the load rates that the heat source machine can take.

[0043] The heat source unit calculation unit 212a then calculates the energy consumption for each heat source unit for each specified step width defined by an internal setting value, within the range of loads that each heat source unit can handle, as obtained as a result of the determination process. The energy consumption for each heat source unit obtained in this way is multiplied by the unit price / emission coefficient information 550 according to the target set in the KPI information (i.e., converted to a metered charge unit price, CO2 emission coefficient, etc. according to the KPI), and divided by the amount of heat produced at the corresponding capacity load rate point to calculate the heat intensity unit (intensity unit) as a value that indicates the cost, CO2 emissions, or primary energy consumption required per 1 GJ / h of heat produced. Here, the higher the heat intensity unit, the more energy the heat source unit needs to generate a predetermined amount of heat.

[0044] In S203, the group creation unit 213a compares the magnitude of the heat energy values ​​of each heat source machine, which are calculated in the heat energy unit calculation unit 212a for each specified step width defined by the internal setting value.

[0045] Figure 6A shows an example of processing related to the heat intensity calculation unit 212a and the group creation unit 213a. The group creation unit 213a groups heat sources together if the minimum or maximum value of another heat source is included between the minimum and maximum values ​​of the heat intensity calculated by the heat intensity calculation unit 212a. On the other hand, if neither the minimum nor the maximum value of another heat source is included between the minimum and maximum values ​​of the heat intensity, the heat sources are grouped into separate groups. As a result, multiple heat source groupings are created so that heat sources with overlapping heat source unit widths are included in the same group. Furthermore, multiple heat source groupings are created so that the heat source unit widths of different heat source groups do not overlap.

[0046] Here, the same group includes heat sources with similar performance, that is, heat sources with similar heat intensity in heat production. On the other hand, heat sources with different performance, that is, heat sources with different heat intensity in heat production, are included in other groups.

[0047] Furthermore, when heat source A and heat source B are grouped into separate groups, a priority order is assigned, which will be explained in detail later. In the example in Figure 6A, the priority order for heat source A is higher than the priority order for heat source B, resulting in a priority order of 1st for heat source A and 2nd for heat source B.

[0048] Figure 6B is a table showing an example of heat source unit management information that can be referenced by the group creation unit 213a. This heat source unit management information includes information on the minimum and maximum values ​​of the heat intensity of each heat source unit calculated by the heat intensity calculation unit 212a. In other words, the heat source unit management information includes the heat source unit width, which is information on the range of values ​​that can take for energy consumption efficiency. Furthermore, as shown in Figure 6B, it may also include information indicating whether or not there is a waste heat recovery function. The waste heat recovery function relates to a function that supplies heat using waste heat from the facility's power generation system. When the group creation unit 213a creates a group of heat source units in S203, it may refer to heat source unit management information such as the example shown in Figure 6B. This heat source unit management information may also be stored in the equipment characteristics information 510.

[0049] Next, the processing related to the priority and load allocation step in S204 will be explained. Figure 7 is a diagram showing a conceptual example of the processing procedure of the priority and load allocation unit 214a. First, the priority and load allocation unit 214a prioritizes the groups created by applying the logic of Figure 5 (S201~S203) to all heat source machines. That is, in S214a-1, the priority and load allocation unit 214a prioritizes the groups that include heat source machines with low heat energy consumption per unit of measurement. In the example in Figure 7, three groups are created in S201~S203, and the energy consumption efficiency value (heat energy consumption per unit of measurement) of each heat source machine included in the multiple heat source machine groups is evaluated. Group A, which includes the heat source machine evaluated to have the lowest heat energy consumption per unit of measurement, is given the highest priority, Group B, which includes the heat source machine evaluated to have the next lowest heat energy consumption per unit of measurement, is given the second highest priority, and Group C, which includes the heat source machine evaluated to have the next lowest heat energy consumption per unit of measurement, is given the third highest priority.

[0050] Subsequently, the priority / load allocation unit 214a assigns the maximum load of the heat source machines to the groups in order of priority, according to S214a-3. The priority / load allocation unit 214a continues the above process until the total value of the allocated loads exceeds the heat demand, according to S214a-2. If the total value of the allocated loads exceeds the heat demand, the load of the last allocated group must be operated at a partial load rather than the maximum load, and is therefore designated as an optimal solution search element. In this example, group B is the optimal solution search element. That is, it identifies one or more first heat source machine groups, like group A, where all heat source machines included in the group are operated at the maximum load, and a second heat source machine group, like group B, where a portion of the heat demand is allocated among the heat source machines included in the group. As a result, the first heat source machine group is not designated as an optimal solution search element, and only the second heat source machine group needs to be designated as an optimal solution search element, thus contributing to a reduction in the number of parameters that need to be considered.

[0051] According to S214a-4, groups belonging to subsequent priority levels have a higher heat intensity than the group selected by the above process, and are therefore excluded from the operational planning process. In this example, group C is excluded.

[0052] The range of loads that the last assigned heat source machine can handle is "heat demand" - "heat production amount of heat source machines that have already been assigned rated load". Then, in S214a-5, for example, there are cases where the load is shared among all machines in the group, or where it is shared among the minimum number of machines possible. In the latter case, where the load is shared among the minimum number of machines possible, the minimum and maximum values ​​of the load to be assigned are determined, and these minimum and maximum values ​​are used as the search range for the optimization calculation unit 220.

[0053] In the energy plant management device described in this embodiment, the preprocessing for optimization calculations involves grouping heat source equipment based on the equipment characteristics of each individual heat source facility, and making it possible to determine whether or not to include each group as an optimal solution search element in the optimization calculation. As a result, it is not necessary to include all heat source equipment included in the energy plant control system as an optimal solution search element in the optimization calculation, thus reducing the number of parameters to be considered and enabling optimization calculations to be performed in a shorter time and with higher accuracy.

[0054] Next, an example of an operation plan will be explained with reference to Figure 8. Figure 8 is an example of an operation plan created by the planning unit 200. That is, the planning unit 200 uses pre-processed data to create a heat source control plan, which is a plan for supplying heat to meet heat demand. In the example of the operation plan shown in Figure 8, the amount of heat to be generated by each heat source machine used in the operation plan is determined for periods divided into 30-minute intervals. The horizontal axis represents the start time of each 30-minute period, and the vertical axis represents the amount of heat. In Figure 8, the amount of heat generated by each heat source machine for each period is represented as a stacked bar graph. Note that it is sufficient to create an appropriate operation plan, and data in a manner different from that shown in Figure 8 may be created.

[0055] <Second Embodiment> Next, a second embodiment will be described. Note that some explanations similar to those for the first embodiment may be omitted. In the second embodiment, the energy plant configuration includes a cogeneration system (hereinafter referred to as CGS). In the CGS, waste heat generated from generators, etc., can be recovered and utilized by a waste heat recovery mechanism of the heat source unit.

[0056] Figure 9 is a block diagram showing an example of the functional configuration of the operation planning preprocessing unit 210 when the energy plant configuration includes a CGS. The operation planning preprocessing unit 210 is configured by adding a CGS waste heat recovery planning unit 210b, which consists of a waste heat recovery benefit calculation unit 211b, a peak cut presence / absence determination unit 212b, and a CGS operation planning unit 213b, to the heat source unit priority determination unit 210a described in the first embodiment. The functions of each component will be described later.

[0057] Figure 10 is a flowchart showing an example of the processing procedure in the CGS waste heat recovery planning unit 210b. In detail, the processing unit of the energy plant management system is the main data processing unit.

[0058] In S301a, the heat source unit priority determination unit 210a determines the priority of the heat source units when CGS is not used, using the same method as in the first embodiment, and determines the waste heat recovery unit to be planned. The determination of the waste heat recovery unit can be done, for example, using management information as shown in Figure 6B.

[0059] In S302a, the heat recovery benefit calculation unit 211b calculates the effect (heat recovery benefit) of operating the CGS. As an example, based on the information of the heat recovery unit determined in the S301a process, the unit calculates the difference between the energy consumption of the heat source unit when the heat recovery function is used, converted to the target value defined in the KPI information, and the energy consumption of the heat source unit when the heat recovery function is not used (for example, in the first embodiment), converted to the target value defined in the KPI information. The reduction amount of the target defined in the KPI information is then calculated as the heat recovery benefit.

[0060] In S302b, the waste heat recovery benefit calculation unit 211b calculates the power generation intensity of the CGS. The power generation intensity is calculated by multiplying the gas consumption of the CGS by the unit price / emission coefficient information 550 according to the target set in the KPI information, subtracting the waste heat recovery benefit from the result, and then dividing these by the value obtained by subtracting the auxiliary equipment losses from the power generated by the CGS.

[0061] Furthermore, if the KPI is cost, the maintenance unit price defined in equipment characteristic information 510 is added to the above calculation. In other words, the higher the power generation intensity, the more target values ​​set in the KPI that the generator needs to generate a predetermined amount of power, so a lower power generation intensity indicates a higher priority.

[0062] In S303a, the peak cut determination unit 212b calculates the difference between the power demand forecast information 530 and the maximum power received set in the constraint information 560 during the time period targeted for planning.

[0063] In S303b, it is determined whether the value calculated in S303a is greater than 0. If it is, the process proceeds to S303c. If it is not greater than 0, the process proceeds to S304.

[0064] In S303c, the peak cut determination unit 212b plans to operate the generators in the energy plant facility in order of their lowest power generation intensity. The power generation intensity of the CGS is the value calculated in S302c. Here, for generators other than the CGS that make up the energy plant facility, the peak cut determination unit 212b calculates the power generation intensity based on the information defined in the equipment characteristics information 510. For example, if a battery is present as a generator other than the CGS, the power generation intensity of the battery is calculated by multiplying the charge amount for each time period by the charge efficiency defined in the equipment characteristics information 510 and the unit price / emission coefficient information 550 according to the target set in the KPI information, and then dividing the sum of these values ​​for all charging time periods by the charge amount of the battery, and then multiplying the result by the discharge efficiency.

[0065] In S304, the CGS operation planning unit 213b determines the operating time periods for each generator, as determined in S303b. In addition, it plans to operate the CGS during the time periods in S302a and S302b when the effect of CGS operation is greater than zero.

[0066] Then, in S305, the priority of the heat source equipment is determined for the time period during which the CGS is operated. An example of the method for determining the priority of the heat source equipment when using the CGS in S305 will be explained in Figure 11 and later.

[0067] Figure 11 shows an example of the processing flow of the heat source unit priority determination unit 210a when CGS waste heat is present. In detail, the processing unit of the energy plant management device is the main data processing unit. When CGS waste heat recovery is considered, the heat intensity of the waste heat recovery unit becomes lower, i.e., its priority becomes higher. Therefore, in S201, heat source units with waste heat recovery functions are extracted from the operational units extracted. The extraction of waste heat recovery units can be performed using management information such as that shown in Figure 6B.

[0068] In S401, the heat source priority determination unit 210a calculates the maximum exhaust heat output of the CGS. Power demand forecast information is obtained from the power and heat demand forecast information 530 in the storage unit 500, and the maximum value that the CGS can generate is determined by subtracting the lower limit of the received power set in the constraint condition information 560 from it. From the maximum value that the CGS can generate, the maximum exhaust heat output that the CGS can output is calculated using the CGS equipment characteristic information stored in the equipment characteristic information 510.

[0069] In S402, the heat source unit calculation unit 212a calculates the heat unit quantity considering the amount of heat exhausted by the CGS. The heat unit quantity is calculated using the equipment characteristic information of each heat source unit stored in the equipment characteristic information 510, based on the value when the heat recovery unit consumes the maximum amount of heat exhausted by the CGS calculated in S401. Here, as an example, if a heat recovery unit capable of producing a second amount of heat when consuming the maximum amount of heat exhausted by the CGS produces a first amount of heat that includes the second amount of heat produced by the heat recovery function, the energy consumption of the heat recovery unit only needs to be the energy consumption consumed to produce the amount of heat obtained by subtracting the second amount of heat from the first amount of heat. In this case, the energy consumption required to produce the amount of heat obtained by subtracting the second amount of heat from the first amount is multiplied by the unit price / emission factor information of 550 according to the target set in the KPI information, and then divided by the first amount of heat including the second amount of heat to calculate the heat intensity, which represents the cost, CO2 emissions, or primary energy consumption required per 1 GJ / h of heat produced. After that, the group creation process of S203 is performed.

[0070] In S403, the heat source unit priority determination unit 210a applies the logic from S201 to S203 in Figure 11, prioritizes the created groups considering waste heat recovery, and assigns CGS waste heat and load to the groups with the highest priority.

[0071] In S404, the heat source unit priority determination unit 210a determines whether the total amount of exhaust heat recovered by the CGS exhaust heat assigned units exceeds the maximum CGS exhaust heat amount. If it does not, in S405, the amount of exhaust heat recovered in that group is maximized, and the load factor at the maximum amount of exhaust heat recovered is set. After setting, the process returns to S403, and CGS exhaust heat is assigned to the next priority.

[0072] If, in S404, the total amount of heat recovered from the CGS heat-dissipating units exceeds the maximum CGS heat-dissipating amount, the process proceeds to S406.

[0073] In S406, the heat source unit priority determination unit 210a determines that the next priority unit cannot recover CGS waste heat, and therefore sets the waste heat recovery amount to 0, and revises the heat intensity unit.

[0074] In S407, the heat source unit priority determination unit 210a calculates the load factor when all of the remaining CGS waste heat is used up. If there are multiple waste heat recovery units in the same group, the allocation of the remaining CGS waste heat cannot be determined, so the unit determines the minimum load factor among the combinations that will use up all of the CGS waste heat.

[0075] Thereafter, the heat source priority determination unit 210a assigns loads in the same manner as S214a-1 to S214a-5 in the first embodiment.

[0076] Figure 12 is a diagram illustrating a conceptual example of the processing procedure of the priority / load allocation unit 214a in the second embodiment. First, the priority / load allocation unit 214a assigns priorities to the groups of heat source units in S203. Processing S403 allocates CGS exhaust heat in order of priority, and since group A in this figure can recover the maximum amount of CGS exhaust heat, the load factor is set to 100% in S405.

[0077] Next, in the same manner as above, when waste heat is allocated to Group B, if Group B recovers the maximum amount of CGS waste heat, there will be a shortage of CGS waste heat, so it is necessary to adjust the amount of waste heat recovered in Group B. Since there are multiple heat source units belonging to Group B, the minimum and maximum recoverable values ​​for each unit are calculated from the combinations of allocation of the remaining CGS waste heat. The load factor when the amount of waste heat recovered is the minimum is calculated as the minimum load factor. RA5, a waste heat recovery unit belonging to Group C, has no remaining recoverable CGS waste heat, so according to S406, the amount of waste heat recovered is set to 0, the minimum and maximum unit values ​​are reviewed, and the grouping and priority are reviewed again.

[0078] The priority / load allocation unit 214a sets the load factor search range for group B from the minimum load factor calculated in process S407 up to 100%. In other words, since the combination of exhaust heat recovery amount and load factor in group B cannot be uniquely determined, it is calculated by the subsequent optimization logic and not determined in the preprocessing. However, the minimum load factor when the total exhaust heat recovery amount is 1.0 is defined, and the load factor search range is determined accordingly.

[0079] The priority / load allocation unit 214a sets the load factor to 100% in group C if Σ(heat output of the load-allocated unit) ≤ heat demand, even when the heat source unit belonging to group B is at its maximum load factor.

[0080] In Group D, the range of load factors for Group D is determined by calculating the range of load factors for Group D heat sources, from the heat demand at the maximum load factor of Group B heat sources - Σ (heat produced by the assigned load source) to the heat demand at the minimum load factor of Group B heat sources - Σ (heat produced by the assigned load source). Since the heat produced by Group D results in Σ (heat produced by the assigned load source) = heat demand, heat sources belonging to groups later than Group D (groups different from Groups A to D) are excluded from the planning.

[0081] Subsequently, similar to the above, the planning unit 200 uses the pre-processed data to create a heat source control plan, which is a plan for supplying heat to meet the heat demand.

[0082] In energy plant control systems equipped with CGS, the types and number of parameters to be considered, such as the performance and amount of heat recovered, increase further. In the energy plant management device described in this embodiment, in the preprocessing of the optimization calculation, heat source equipment is grouped based on the equipment characteristics, including the heat recovery function of each heat source equipment, and it is possible to determine whether or not to use each group as an optimal solution search element in the optimization calculation. This makes it possible to reduce the number of parameters to be considered when planning an energy plant control system equipped with CGS.

[0083] According to the above embodiment, as an example, an energy plant management device is provided, comprising: a storage unit that stores information on heat demand in a heat source control system, equipment characteristic information which is information showing the correlation between the load on each of the multiple heat source machines constituting the heat source control system and energy consumption; a processing unit that creates a plurality of heat source machine groups, each containing one or more heat source machines included in the plurality of heat source machines, based on the equipment characteristic information; a data preprocessing unit that identifies one or more heat source machine groups from the plurality of heat source machine groups to be adopted as control targets in creating a heat source control plan related to the heat demand, based on the heat demand information and the information of the plurality of heat source machine groups; and a planning unit that creates a heat source control plan which provides heat supply to respond to the heat demand using the heat source machines included in the heat source machine groups identified by the data preprocessing unit.

[0084] Although embodiments have been described above, the present invention is not limited to the embodiments described above, and includes various modifications and equivalent configurations within the spirit of the attached claims. For example, the embodiments described above are described in detail for the purpose of explaining the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to having all the configurations described. Also, for example, some of the configurations of the embodiments may be added, deleted, or replaced with other configurations.

[0085] In an energy plant control system, even if a CGS mechanism is provided, depending on the type of KPI to be emphasized, it may be better for the system as a whole not to use the CGS. Therefore, a system that can assist the user in deciding whether or not to use the CGS is desirable. Accordingly, the energy plant management device may, as an example, separately create a first heat source control plan for a heat source control plan that does not utilize the waste heat recovery function and the waste heat amount information, and a second heat source control plan for a heat source control plan that utilizes the waste heat recovery function and the waste heat amount information, and output each heat source control plan along with values ​​related to the indicators for each heat source control plan (e.g., operating costs, CO2 emissions, etc.). Here, each heat source control plan may be output in a comparable manner on a display screen of a terminal viewed by the user. Furthermore, the method for creating the first heat source control plan and the second heat source control plan may be the method described in the first or second embodiment above.

[0086] Furthermore, the energy plant management system may output control information to respond to heat demand based on the heat source control plan selected by the user. Alternatively, the energy plant management system may automatically select the heat source control plan with the best indicator values ​​from each heat source control plan and output control information to respond to heat demand. [Explanation of symbols]

[0087] 10 Energy plant management system 100 Prediction Section 200 Planning Department 500 Memory Department

Claims

1. A device comprising a processing unit and a memory device, The aforementioned storage device is Information on heat demand in an energy plant, and equipment characteristic information showing the correlation between the load on each of the multiple heat source units included in the energy plant and energy consumption, Remember this, The aforementioned equipment characteristic information includes information on the heat source unit width, which is information on the range of values ​​that can be taken for energy consumption efficiency when a predetermined range of load is applied to the heat source. The aforementioned processing apparatus is Based on the heat source unit width, multiple heat source unit groups are created, each containing one or more heat source units from the multiple heat source units. Based on the heat demand information stored in the memory device and the information of the plurality of heat source machine groups, one or more heat source machine groups are selected from the plurality of heat source machine groups to be adopted as the control target corresponding to the heat demand. A management device characterized by creating a heat source control plan, which is a plan to supply heat to meet the heat demand using heat source machines included in the identified heat source machine group.

2. A control device according to claim 1, The aforementioned processing apparatus is Create the multiple heat source unit groups such that heat source units with overlapping heat source unit widths are included in the same group. A management device characterized by the following features.

3. A control device according to claim 1, The aforementioned processing apparatus is Create multiple heat source unit groups so that the heat source unit widths of the heat source units included in different heat source unit groups do not overlap. A management device characterized by the following features.

4. A control device according to claim 1, The aforementioned processing apparatus is The energy consumption efficiency values ​​of each heat source included in the aforementioned group of heat source units are evaluated, and based on this evaluation, the group of heat source units is assigned a priority order for identifying which heat source unit group to be adopted as the control target in the creation of the heat source control plan. A management device characterized by the following features.

5. A control device according to claim 1, The aforementioned equipment characteristic information includes: The graph includes the correlation relationship linked to environmental conditions. The aforementioned processing apparatus is Using the graph corresponding to the operating environment of the heat source, the unit width of the heat source is calculated. The process of creating the multiple heat source unit widths is performed based on the calculated heat source unit widths. A management device characterized by the following features.

6. A control device according to claim 1, The aforementioned processing apparatus is Identify one or more first heat source group that operates all heat source machines included in the group at maximum load, and a second heat source group that allocates a portion of the heat demand among the heat source machines included in the group. A management device characterized by the following features.

7. A control device according to claim 1, The aforementioned storage device is The facility stores information about its waste heat recovery function, which is a function that provides heat supply using waste heat from the facility's power generation system. The aforementioned processing apparatus is Based on the information regarding the heat recovery function, it is possible to identify one or more heat recovery heat sources, which are heat source machines to be adopted as the control target in the creation of the heat source control plan. A management device characterized by the following features.

8. A control device according to claim 1, The aforementioned storage device is The facility stores information about its waste heat recovery function, which is a function that provides heat supply using waste heat from the facility's power generation system. The aforementioned processing apparatus is Furthermore, based on the information of the waste heat recovery function, multiple heat source unit groups are created, each containing one or more heat source units from the multiple heat source units. Furthermore, based on the information on the amount of waste heat, one or more heat source unit groups are identified from the plurality of heat source unit groups to be adopted as the control target corresponding to the heat demand. A management device characterized by the following features.

9. A control device according to claim 8, The aforementioned processing apparatus is A first heat source control plan relating to the heat source control plan when the heat recovery function and heat output information are not used, and a second heat source control plan relating to the heat source control plan when the heat recovery function and heat output information are used can be created separately. The information of the first heat source control plan and the information of the second heat source control plan are output in a comparable manner. A management device characterized by the following features.

10. A management method using a processing device and a memory device, The aforementioned storage device is Information on heat demand in an energy plant, and equipment characteristic information showing the correlation between the load on each of the multiple heat source units included in the energy plant and energy consumption, I remember, The aforementioned equipment characteristic information includes information on the heat source unit width, which is information on the range of values ​​that can be taken for energy consumption efficiency when a predetermined range of load is applied to the heat source. The processing apparatus performs the steps of creating a plurality of heat source unit groups, each of which contains one or more heat source units included in the plurality of heat source units, based on the heat source unit width, The processing device includes the step of identifying one or more heat source machine groups from among the multiple heat source machine groups to be adopted as the control target corresponding to the heat demand, based on the heat demand information stored in the storage device and the information of the multiple heat source machine groups. The process includes the steps of creating a heat source control plan which is a plan for the processing device to supply heat to meet the heat demand using heat source machines included in the identified heat source machine group, A management method characterized by including the following.

11. A device comprising a processing unit and a storage device, The aforementioned storage device is Information on heat demand in an energy plant, and equipment characteristic information showing the correlation between the load on each of the multiple heat source units included in the energy plant and energy consumption, Remember this, The aforementioned equipment characteristic information includes information on the heat source unit width, which is information on the range of values ​​that can be taken for energy consumption efficiency when a predetermined range of load is applied to the heat source. The aforementioned processing apparatus is Based on the heat source unit width, multiple heat source unit groups are created, each containing one or more heat source units from the multiple heat source units. A management device characterized by identifying one or more heat source unit groups from among the multiple heat source unit groups to be adopted as a control target corresponding to the heat demand, based on the heat demand information stored in the memory device and the information of the multiple heat source unit groups.

12. A management method using a processing device and a storage device, The aforementioned storage device is Information on heat demand in an energy plant, and equipment characteristic information showing the correlation between the load on each of the multiple heat source units included in the energy plant and energy consumption, I remember, The aforementioned equipment characteristic information includes information on the heat source unit width, which is information on the range of values ​​that can be taken for energy consumption efficiency when a predetermined range of load is applied to the heat source. The processing apparatus performs the steps of creating a plurality of heat source unit groups, each of which contains one or more heat source units included in the plurality of heat source units, based on the heat source unit width, The processing device includes the step of identifying one or more heat source machine groups from among the multiple heat source machine groups to be adopted as the control target corresponding to the heat demand, based on the heat demand information stored in the storage device and the information of the multiple heat source machine groups. A management method characterized by including the following.

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