Energy management system

Abstract

This energy management system is provided with at least one of a power generation facility and a power storage facility. An operation plan creation system of the energy management system comprises: a storage unit that stores feature amount data of each of a plurality of products that have been manufactured in the past; an extraction unit that extracts data of a product from a production plan; a first determination unit that determines whether or not the extracted product matches one of the plurality of products; a second determination unit that, if it is determined that the extracted product does not match any of the plurality of products, determines that the product is a specific product and determines whether or not a physical model corresponding to the specific product can be generated; a machine learning model generation unit that, if it is determined that such a physical model cannot be generated, generates a machine learning model corresponding to the specific product using at least a part of the feature amount data of the plurality of products; a power consumption prediction unit that predicts the power consumption of the specific product using the machine learning model for the specific product; and an operation plan creation unit that uses data of the predicted power consumption of the specific product to create an operation plan to maintain, in a prescribed period, the power consumption of the specific product within a desired power range.

Description

Energy Management System

[0001] The present disclosure relates to an energy management system.

[0002] 2. Description of the Related Art A technology relating to an operation plan creation support device that supports the creation of an operation plan for manufacturing equipment has been disclosed (see, for example, Patent Document 1).

[0003] Japanese Patent Application Laid-Open No. 2019-21211

[0004] Conventionally, there are cases where an operation plan is created to plan the operation status of equipment in a production plan when manufacturing multiple types of products. It is required to efficiently manage the supply and demand of electricity based on the operation plans of multiple pieces of equipment.

[0005] Therefore, one of the objects is to provide an energy management system that can more efficiently manage the supply and demand of electricity based on an operation plan for the equipment.

[0006] An energy management system according to the present disclosure manages the supply and demand of electricity during the operation of a plurality of facilities. The energy management system includes at least one of a power generation facility that supplies power to the plurality of facilities by transmitting power and a power storage facility that supplies power to the plurality of facilities by discharging power. The plurality of facilities are operated based on an operation plan created by an operation plan creation system that creates an operation plan so that a desired power range is achieved within a predetermined period when a plurality of products are manufactured using the plurality of facilities. the operation plan creation system includes: a memory unit that stores feature data for each of a plurality of products that have been manufactured in the past, including data on the power consumption of each of the plurality of products when manufacturing each of the plurality of products; a reception unit that receives a request for creation of an operation plan; an extraction unit that, when the reception unit receives a request for creation of an operation plan, extracts product data from a production plan including data on the product to be manufactured; a first judgment unit that determines whether the product extracted by the extraction unit matches one of the plurality of products stored in the memory unit; a second judgment unit that, when the first judgment unit determines that there is no match, determines whether a corresponding physical model can be generated for the product as a specific product; a machine learning model generation unit that, when the second judgment unit determines that a corresponding physical model cannot be generated, generates a machine learning model corresponding to the specific product using at least a portion of the feature data for the plurality of products stored in the memory unit; a power consumption prediction unit that predicts the power consumption of the specific product using the machine learning model of the specific product generated by the machine learning model generation unit; an operation plan creation unit that creates an operation plan using the power consumption data of the specific product predicted by the power consumption prediction unit to keep the power consumption within a desired power range within a predetermined period; and an output unit that outputs the operation plan created by the operation plan creation unit.

[0007] Such an energy management system makes it possible to more efficiently manage the supply and demand of electricity based on an operation plan for the facility.

[0008] FIG. 1 is a diagram schematically illustrating the configuration of an energy management system according to a first embodiment. FIG. 2 is a block diagram illustrating a schematic configuration of the energy management system according to the first embodiment. FIG. 3 is a flowchart illustrating a process flow when an operation plan is created using an operation plan creation system. FIG. 4 is a graph illustrating the relationship between the total power of all equipment and the power of an electric kiln and the elapsed time when a product is manufactured in a conventional manner. FIG. 5 is a graph illustrating the relationship between the total power of all equipment and the power of an electric kiln and the elapsed time when a product is manufactured based on an operation plan created by the operation plan creation system. FIG. 6 is a graph illustrating the difference in power consumption of an electric kiln before and after degradation. FIG. 7 is a flowchart illustrating a control flow when equipment is operated based on a created operation plan. Graphs illustrating the total equipment power curve and each equipment power curve for a conventional system and the energy management system according to the first embodiment.

[0009] [Overview of the Embodiments] First, embodiments of the present disclosure will be listed and described. An energy management system according to the present disclosure manages the supply and demand of electricity during the operation of a plurality of facilities. The energy management system includes at least one of a power generation facility that supplies power to the plurality of facilities by transmitting power and a power storage facility that supplies power to the plurality of facilities by discharging power. When a plurality of products are manufactured using the plurality of facilities, the plurality of facilities are operated based on an operation plan created by an operation plan creation system that creates an operation plan so that the power consumption falls within a desired power range within a predetermined period. the operation plan creation system includes: a memory unit that stores feature data for each of a plurality of products that have been manufactured in the past, including data on the power consumption of each of the plurality of products when manufacturing each of the plurality of products; a reception unit that receives a request for creation of an operation plan; an extraction unit that, when the reception unit receives a request for creation of an operation plan, extracts product data from a production plan including data on the product to be manufactured; a first judgment unit that determines whether the product extracted by the extraction unit matches one of the plurality of products stored in the memory unit; a second judgment unit that, when the first judgment unit determines that there is no match, determines whether a corresponding physical model can be generated for the product as a specific product; a machine learning model generation unit that, when the second judgment unit determines that a corresponding physical model cannot be generated, generates a machine learning model corresponding to the specific product using at least a portion of the feature data for the plurality of products stored in the memory unit; a power consumption prediction unit that predicts the power consumption of the specific product using the machine learning model of the specific product generated by the machine learning model generation unit; an operation plan creation unit that creates an operation plan using the power consumption data of the specific product predicted by the power consumption prediction unit to keep the power consumption within a desired power range within a predetermined period; and an output unit that outputs the operation plan created by the operation plan creation unit.

[0010] According to the energy management system disclosed herein, when a production plan includes a specific product that has never been manufactured and a physical model cannot be generated, a machine learning model corresponding to the specific product is generated using at least a portion of the feature data of a product that has been manufactured in the past. The generated machine learning model can then be used to predict power consumption. This makes it possible to more accurately predict power consumption and generate an operation plan that falls within a desired power range, even for a product that has never been manufactured and for which a physical model cannot be generated. Therefore, this operation plan generation system can generate a more appropriate operation plan even when a production plan includes a product that has never been manufactured in the past. Then, based on the appropriately generated operation plan, power can be transmitted from at least one of a power generation facility and a power storage facility to multiple facilities, thereby managing the power supply and demand of the multiple facilities. As described above, this energy management system can more efficiently manage power supply and demand based on the facility operation plans. In other words, this energy management system facilitates optimizing the management of power supply and demand.

[0011] In the energy management system of the above aspect, the plurality of facilities may include both power generation facilities and power storage facilities. In this way, power can be transmitted from both the power generation facilities and the power storage facilities to the plurality of facilities based on the created operation plan. In addition, power generated by the power generation facilities can be stored in the power storage facilities. Therefore, power supply and demand can be managed more efficiently.

[0012] In the energy management system of the above aspect, the power generated by the power generation equipment and not used by the plurality of equipment based on the operation plan may be controlled to be charged to the power storage equipment. In this way, the risk of the power generated by the power generation equipment being wasted can be significantly reduced, and the power generated by the power generation equipment can be effectively used.

[0013] In the energy management system according to any one of the above aspects, if it is determined that the amount of power consumed by the plurality of facilities is greater than the amount of power generated by the power generation facility, the system may be controlled to supply power to the plurality of facilities by discharging power from the power storage facility. In this way, even if the power consumption of the plurality of facilities increases, it is possible to respond by generating power from the power generation facility and discharging power from the power storage facility. This makes it possible to reduce power purchases and manage power supply and demand based on an operation plan. This facilitates cost reduction.

[0014] In the energy management system according to any one of the above aspects, if the amount of stored electricity in the electricity storage equipment is greater than a predetermined value, the operation plan may be prioritized to generate a peak in power consumption, and the amount of discharge from the electricity storage equipment may be increased to cut the peak in total power consumption of the multiple equipment. This makes it easier to manage the supply and demand of electricity based on the operation plan.

[0015] In the energy management system according to any one of the above aspects, the power generation facility may generate power using renewable energy. Such a power generation facility does not require fuel or the like for power generation, thereby reducing costs.

[0016] In the energy management system according to any one of the above aspects, the power storage device may include a NAS battery. Such a power storage device is capable of storing a large amount of power and efficiently repeating charging and discharging.

[0017] [Specific Example of Embodiment] Next, specific examples of the operation plan creation system of the present disclosure will be described with reference to the drawings. In the following drawings, the same or corresponding parts are designated by the same reference characters, and description thereof will not be repeated.

[0018] (First embodiment) An energy management system according to the present disclosure will be described. Fig. 1 is a diagram schematically showing the configuration of an energy management system in the first embodiment. In Fig. 1, arrows shown in thick lines indicate the flow of power, and arrows shown in thin lines indicate the flow of control. Fig. 2 is a block diagram showing a schematic configuration of the energy management system in the first embodiment.

[0019] 1 and 2 , an energy management system 13 manages the supply and demand of electricity during the operation of a plurality of pieces of equipment. The energy management system 13 includes a server 11, and the supply and demand of electricity during the operation of the plurality of pieces of equipment is managed substantially under the control of the server 11. In this embodiment, the plurality of pieces of equipment includes a plurality of electric kilns 12a, 12b, and 12c included in a factory facility 14. The plurality of pieces of equipment also includes a power generation facility 15 that transmits power to ultimately supply power to the electric kilns 12a, 12b, and 12c of the factory facility 14, and a power storage facility 16 that discharges power to ultimately supply power to the electric kilns 12a, 12b, and 12c of the factory facility 14. The factory facility 14 may also be supplied with power by purchasing power from a power grid 17. That is, the plurality of pieces of equipment includes both the power generation facility 15 and the power storage facility 16. Therefore, power can be transmitted to the plurality of pieces of equipment from both the power generation facility 15 and the power storage facility 16 based on the created operation plan. Furthermore, the power generated by the power generation facility 15 can also be stored in the power storage facility 16. Therefore, the supply and demand of power can be managed more efficiently.

[0020] The power generation facility 15 generates electricity using renewable energy. The power generation facility 15 is, for example, a solar power generation system. Note that the power generation facility 15 may also be a wind power generation facility. Such a power generation facility 15 is effectively used as a power generation facility that uses renewable energy. Such a power generation facility 15 does not require fuel or the like for power generation, which allows for cost reduction.

[0021] In this embodiment, the power storage facility 16 may be, for example, a storage battery, specifically, a sodium-sulfur (NAS) battery, which is suitable for industrial use, because it has a large capacity for long-term charging and discharging and can be efficiently charged and discharged over a long period of time. Alternatively, a lithium-ion battery, which is a secondary battery, may be used as the power storage facility 16. Such a power storage facility 16 can store a large amount of power and can repeatedly and efficiently charge and discharge the power.

[0022] In the energy management system 13, the plurality of pieces of equipment, specifically the electric kilns 12a, 12b, and 12c, are operated based on operation plans created by an operation plan creation system that creates operation plans so that the power consumption falls within a desired range within a predetermined period when the plurality of electric kilns 12a, 12b, and 12c are used to manufacture a plurality of products. Here, "falling within the desired power range" means controlling the power within the desired range.

[0023] Here, when manufacturing multiple products using multiple pieces of equipment, the operation plan creation system 10 creates operation plans for the multiple pieces of equipment so that the power consumption falls within a desired power range within a predetermined period. One example of an operation plan is to adjust the operation timing of the multiple pieces of equipment to minimize peak power. Examples of the multiple pieces of equipment include manufacturing equipment directly used in manufacturing ceramic products and lighting equipment that is not directly used in the manufacture of products but is used indirectly. In this embodiment, the electric sintering furnaces 12a, 12b, and 12c are used in parallel within the factory facility 14. The electric sintering furnaces 12a, 12b, and 12c may be used in a batch format, where one batch is processed from the start to the end of heat treatment. In this embodiment, the desired power range is defined as a range in which peak power is minimized. In this case, control is performed to minimize peak power within the contracted power range.

[0024] The operation plan creation system 10 is provided in the control unit of a single computer, such as a server or a laptop PC. The control unit includes a CPU, a volatile memory, etc. Of course, each component may be included across two or more computers. The operation plan creation system 10 includes a server 11 and multiple, specifically, three, electric kilns 12a, 12b, and 12c. Note that wireless communication is possible between the server 11 and each of the electric kilns 12a, 12b, and 12c. The server 11 and each of the electric kilns 12a, 12b, and 12c may also be configured to enable wired communication or communication via other components.

[0025] The power consumption of each electric kiln 12a, 12b, 12c during operation is not always constant but varies depending on the temperature, elapsed time, etc. Because the manufacturing conditions differ for each of the multiple types of products being manufactured, the power consumption of the equipment varies even when the same heat treatment is performed. For example, because the manufacturing conditions differ for each product, the heat curve, which is the progression of the baking temperature, differs, and the power consumption also varies accordingly. The electric kilns 12a, 12b, 12c have kilns made of carbon. The carbon-made portion is located on the inner wall side of the kiln.

[0026] The server 11 includes a database 21 as a storage unit, a reception unit 22, an extraction unit 23, a first determination unit 24, a second determination unit 25, a machine learning model generation unit 26, a power consumption prediction unit 27, an operation plan creation unit 28, and an output unit 29. That is, in the present embodiment, the server 11 includes all of the components included in the operation plan creation system 10.

[0027] The database 21 stores various types of data. The data stored in the database 21 includes historical data on products that have been manufactured in the past, such as data on the power consumption, heating temperature, heating time, and heat curve of products manufactured in the past. The heating temperature, heating time, and heat curve data may be used as feature data that characterizes the manufacturing of the product. In this embodiment, the product feature data includes data on temperature transitions during product manufacturing, data on pressure transitions during product manufacturing, and data on the time required for manufacturing the product. The database 21 stores feature data for each of multiple products that have been manufactured in the past, including data on the power consumption during the manufacturing of each of the multiple products. Each piece of feature data is stored in association with each product. Even if the same product is manufactured at different times, each piece of feature data is stored at the respective timing. In other words, when manufacturing a product under a new production plan, feature data from the most recent manufacturing run can be selected and used.

[0028] The receiving unit 22 receives a request for creating an operation plan. When the receiving unit 22 receives the request for creating an operation plan, the extraction unit 23 extracts product data from a production plan including data on the products to be manufactured. The first determination unit 24 determines whether the product extracted by the extraction unit 23 matches one of the multiple products stored in the database 21. If the first determination unit 24 determines that there is no match, the second determination unit 25 determines whether a corresponding physical model can be generated for the product as a specific product. If the second determination unit 25 determines that a corresponding physical model cannot be generated, the machine learning model generation unit 26 generates a machine learning model corresponding to the specific product using at least a portion of the feature data of the multiple products stored in the database 21. In this embodiment, the machine learning in the machine learning model generation unit 26 is performed using a random forest. The power consumption prediction unit 27 predicts the power consumption of the specific product using the machine learning model of the specific product generated by the machine learning model generation unit 26. The operation plan creation unit 28 uses the power consumption data of the specific product predicted by the power consumption prediction unit 27 to create an operation plan such that the power consumption is within a desired power range within a predetermined period. The output unit 29 outputs the operation plan created by the operation plan creation unit 28. These components will be described in detail later.

[0029] Next, a description will be given of a case where an operation plan is created using the operation plan creation system 10. Fig. 3 is a flowchart showing a processing flow when an operation plan is created using the operation plan creation system 10.

[0030] Referring also to FIG. 3 , the server 11 first receives a request for creating an operation plan through the reception unit 22 (YES in step S11; hereinafter, the term "step" will be omitted). The reception unit 22 receives the request for creating an operation plan, for example, based on input information input to the server 11. At this time, the reception unit 22 also receives input of production plan data including the operation plan. That is, the reception unit 22 also receives input of data on products to be manufactured in the production plan and data on equipment that is the target of the operation plan. The reception unit 22 also receives requests for the operation plan, for example, a request to set a desired power range within which peak power should be minimized. The reception unit 22 then receives data on a predetermined period, for example, one week. That is, the reception unit 22 requests that an operation plan be created that minimizes peak power over a one-week period.

[0031] Next, the extraction unit 23 extracts product data from the production plan including data on the products to be manufactured when the request for creating an operation plan is received by the receiving unit 22. Specifically, for example, when the request for creating an operation plan is received by the receiving unit 22, the extraction unit 23 first accesses the database 21 (S12) and extracts product data included in the production plan (S13).

[0032] The first determination unit 24 then determines whether the products extracted by the extraction unit 23 include any products stored in the database 21 that have not been manufactured in the past (S14). That is, it determines whether the data of products included in the production plan is included in the data of products that have been manufactured in the past stored in the database 21. In this case, the determination is made for all products included in the production plan. If it is determined that no products that have not been manufactured in the past are included (NO in S14), the operation plan creation unit 28 creates an operation plan based on past power consumption (S20). In this case, an operation plan is created that minimizes peak power within a desired power range based on the data of products that have been manufactured in the database 21 and the feature data corresponding to the products. The created operation plan is then output, and the process ends (S19).

[0033] On the other hand, if the first judgment unit 24 determines that there is no match (YES in S14), the second judgment unit 25 determines whether a corresponding physical model can be generated for the product as a specific product (S15). That is, a product that has not been manufactured in the past is defined as a specific product, and if the first judgment unit 24 determines that a product that has not been manufactured in the past is included, the second judgment unit 25 determines whether a corresponding physical model can be generated for the product as a specific product. Examples of criteria for determining whether a physical model can be generated include whether the manufacturing of the product does not include a firing process or the manufacturing of the product does not involve complex changes in composition.

[0034] If it is determined that a physical model can be created (YES in S15), the operation plan creation unit 28 creates a physical model for the specific product (S21) and predicts power consumption using the created physical model (S22). Then, an operation plan is created based on the predicted power consumption of the specific product and the power consumption of past products (S22). In this case, an operation plan is created that minimizes peak power within a desired power range based on power consumption data of products that have been manufactured in the past and have a track record in the database 21, power consumption data predicted for the specific product for which a physical model has been created, and feature data corresponding to each product. The created operation plan is then output, and the process ends (S19).

[0035] Next, if the second judgment unit 25 determines that a physical model cannot be created (NO in S15), the machine learning model generation unit 26 generates a machine learning model for the specific product (S16). In this case, the machine learning model generation unit 26 performs machine learning using random forest, and therefore the generated machine learning model is a machine learning model based on random forest. Here, an example of generating a machine learning model is described. First, data on the power consumption, temperature, pressure inside the electric kiln, etc. of various products baked in the past is obtained from the database 21. Then, feature data such as the amount of temperature change, the state of the electric kiln (how the temperature is rising, how the temperature is maintained, and how the temperature is falling), and the elapsed time in the state of the electric kiln are generated from the temperature and pressure inside the electric kiln. Then, a model for predicting power consumption is generated using machine learning using the generated feature data.

[0036] Next, the power consumption prediction unit 27 predicts the power consumption of the specific product using the machine learning model generated for the specific product by the machine learning model generation unit 26 (S17). To predict the power consumption of the specific product, the set temperature and set pressure inside the electric baking furnace of the specific product that is being manufactured for the first time are input into the prediction model described above to predict the power consumption.

[0037] Next, the operation plan creation unit 28 uses the power consumption data of the specific product predicted by the power consumption prediction unit to create an operation plan so that the power consumption falls within a desired power range within a predetermined period (S18). In this embodiment, the predetermined period is one week, and the operation plan is created so that the desired power range falls within a range that minimizes peak power. As a specific example, mathematical optimization is used to create an operation plan that minimizes power peaks based on the types of products manufactured within one week, the number of processing times, and the power consumption. Furthermore, in this embodiment, the operation plan creation unit 28 creates an operation plan that reflects the degree of deterioration of the equipment, specifically the electric kilns 12a, 12b, and 12c.

[0038] Finally, the output unit 29 outputs the operation plan created by the operation plan creation unit 28 (S19). The output by the output unit 29 is performed, for example, by displaying the data of the operation plan on a display connected to the server 11, or by transmitting the data of the operation plan to a mobile terminal owned by the planner who created the production plan.

[0039] According to the operation plan creation system 10, when a production plan includes a characteristic product that has not been manufactured in the past and a physical model cannot be generated, a machine learning model corresponding to the specific product is generated using at least a portion of the feature data of a product that has been manufactured in the past. The generated machine learning model can then be used to predict power consumption. This makes it possible to more accurately predict power consumption even for a product that has not been manufactured in the past, and to create an operation plan that falls within a desired power range. Therefore, with this operation plan creation system, a more appropriate operation plan can be created even when a production plan includes a product that has not been manufactured in the past.

[0040] FIG. 4 is a graph showing the relationship between the total power consumption of all the equipment and the power consumption of the electric kilns and the elapsed time when a product is manufactured in a conventional manner. In FIG. 4, the upper part shows a graph of the total power consumption of all the equipment, and the lower part shows a graph of the power consumption of each electric kiln. FIG. 5 is a graph showing the relationship between the total power consumption of all the equipment and the power consumption of each electric kiln and the elapsed time when a product is manufactured based on an operation plan created by the operation plan creation system 10. FIG. 5 is a graph of a simulation. In FIG. 5, the upper part also shows a graph of the total power consumption of all the equipment, and the lower part shows a graph of the power consumption of each electric kiln. In FIGS. 4 and 5, the vertical axis represents power, and the horizontal axis represents the elapsed time. The graphs at the bottom of each of Figures 4 and 5 show graphs for the electric kilns 12a, 12b, and 12c, as well as graphs for the other electric kilns 12d, 12e, 12f, 12g, 12h, 12i, 12j, 12k, 12l, and 12m included in the entire facility (the total number of electric kilns is 13).

[0041] 4 and 5, the power consumption of the electric kiln is not so different from the peak power value of each electric kiln when a product is manufactured in the conventional manner. However, the peak power value of the electric kiln when a product is manufactured based on the operation plan created by the operation plan creation system 10 is not so different from the peak power value of the total power of all the equipment when a product is manufactured in the conventional manner. １ In comparison with the peak power value X of the total power of all the facilities when manufacturing products based on the operation plan created by the operation plan creation system 10 ２ is greatly reduced.

[0042] In this embodiment, the product feature data includes data on temperature transitions during product manufacturing, data on pressure transitions during product manufacturing, and data on time during product manufacturing. Therefore, more effective data can be used as product feature data for machine learning of power consumption. This allows for more accurate machine learning models to be generated, and more appropriate operation plans to be created.

[0043] In this embodiment, the operation plan creation unit 28 creates an operation plan that reflects the degree of deterioration of the equipment. Equipment deteriorates with use, and power consumption changes along with this deterioration. With this configuration, an operation plan can be created that takes into account changes in power consumption that change depending on the degree of deterioration of the equipment, thereby creating a more appropriate operation plan.

[0044] 6 is a graph showing the difference in power consumption before and after deterioration of an electric kiln. In FIG. 6, the vertical axis represents power consumption, and the horizontal axis represents elapsed time. Line 41 shows the change in power consumption before deterioration of the electric kiln, and line 42 shows the change in power consumption after deterioration of the electric kiln.

[0045] 6, even if the temperature of the electric kiln is increased at the same rate, the power consumption varies depending on the degree of deterioration, as shown by lines 41 and 42. In this embodiment, the operation plan creation unit 28 creates an operation plan that reflects the degree of deterioration of the electric kiln. Such an operation plan is more appropriate in line with the actual situation.

[0046] In this embodiment, the machine learning is performed by a random forest in the machine learning model generation unit 26. Such machine learning is more suitable for creating an operation plan.

[0047] In this embodiment, the facility includes an electric kiln having a kiln formed using carbon. With such an electric kiln, the amount of carbon decreases with continued use, and the power consumption varies and is not constant. Therefore, the operation plan creation system 10 configured as described above makes it possible to create an operation plan that corresponds to changes in power consumption.

[0048] In the present embodiment, in the operation plan creation system 10, the feature data includes data on heat curves during product manufacturing. The heat curve data during product manufacturing is very important as feature data accompanying changes in power consumption during product manufacturing. Therefore, by adopting such a configuration, a more appropriate operation plan can be created.

[0049] Next, a description will be given of a flow of operating equipment based on an operation plan created by the above-described operation plan creation system 10. Fig. 7 is a flowchart showing a control flow when equipment is operated based on the created operation plan.

[0050] Referring to FIG. 7 , the operation of the equipment is started based on the operation plan created by the operation plan creation system 10 (S31). Then, power generated by the power generation equipment 15 based on the operation plan is transmitted to the electric kilns 12a, 12b, and 12c. Multiple products are then manufactured. After the operation, if the server 11 determines that the amount of power generated by the power generation equipment 15 is greater than the amount of power consumed by the electric kilns 12a, 12b, and 12c operating based on the operation plan (YES in S32), the server 11 controls the storage equipment 16 to store the unused power, i.e., surplus power (S33). Furthermore, if the server 11 determines that the power consumed by the electric kilns 12a, 12b, and 12c is greater than the amount of power generated by the power generation equipment 15 (YES in S34), the server 11 controls the storage equipment 16 to discharge power to supply power to the electric kilns 12a, 12b, and 12c (S35). This control is performed until the predetermined period ends (S36). In this manner, the energy management system 13 performs control.

[0051] The energy management system 13 transmits power from at least one of the power generation facility and the power storage facility to multiple facilities based on an appropriately created operation plan, and can manage the power supply and demand of the multiple facilities. As described above, the energy management system 13 can more efficiently manage the power supply and demand based on the facility operation plans.

[0052] FIG. 8 is a graph showing the total facility power curve and each facility power curve for a conventional system and the energy management system according to the first embodiment. In FIG. 8 , the left side shows the case of the conventional system, and the right side shows the case of the energy management system according to the first embodiment. In each graph, the vertical axis represents power, and the horizontal axis represents the elapsed time. Referring to FIG. 8 , for each facility power curve, the maximum height of the power peak is almost the same for the energy management system according to the first embodiment as for the conventional system, but the number of times the maximum height is reached is greater for the energy management system according to the first embodiment. It can also be seen that the peak power of the energy management system according to the first embodiment is reduced by 50% compared to the case of the conventional system. In this way, it is easy to minimize peak power and optimize management of power supply and demand.

[0053] In this embodiment, the electric power generated by the power generation equipment 15 and not used by the plurality of equipment based on the operation plan is controlled to be charged to the power storage equipment 16. Therefore, it is possible to significantly reduce the risk of the electric power generated by the power generation equipment 15 being wasted, and it is possible to effectively use the electric power generated by the power generation equipment 15.

[0054] In this embodiment, if it is determined that the amount of power consumed by the multiple facilities is greater than the amount of power generated by the power generation facility 15, control is performed to supply power to the multiple facilities by discharging from the power storage facility 16. Therefore, even if the power consumption by the multiple facilities increases, it is possible to respond by generating power from the power generation facility 15 and discharging from the power storage facility 16. This makes it possible to reduce power purchases and manage the supply and demand of power based on the operation plan. This makes it easy to reduce costs.

[0055] In the above embodiment, if the amount of stored electricity in the power storage facility is greater than a predetermined value, the operation plan may be prioritized to generate a peak in power consumption, and the amount of discharge from the power storage facility may be increased to cut the peak in the total power consumption of multiple facilities. This makes it easier to manage the supply and demand of power based on the operation plan.

[0056] (Other Embodiments) In the above embodiment, the desired power range is a range in which peak power is minimized, but this is not limiting. The desired power range may be, for example, a range of a certain fixed power value, or may be set to several percent of the peak power, or the desired power may be different between the first half and second half of a week or between daytime and nighttime on a single day. Furthermore, the predetermined period is not limited to one week, and may be, for example, two weeks or one month.

[0057] Furthermore, in the above embodiment, the machine learning in the machine learning model generation unit is performed using a random forest, but this is not limited to this, and the machine learning in the machine learning model generation unit may use other methods such as a support vector machine or a neural network.

[0058] In the above embodiment, the product feature data includes all of the data on temperature transitions during product manufacturing, data on pressure transitions during product manufacturing, and data on the time taken to manufacture the product. However, the data is not limited to this and may include at least one of the data on temperature transitions during product manufacturing, data on pressure transitions during product manufacturing, and data on the time taken to manufacture the product. This allows more effective data to be used as product feature data for machine learning of power consumption. This allows for more accurate machine learning models to be generated, and more appropriate operation plans to be created.

[0059] It should be understood that the embodiments disclosed herein are illustrative in all respects and are not limiting in any respect. The scope of the present invention is defined not by the above description but by the claims, and it is intended to include all modifications within the meaning and scope of the claims.

[0060] 10 Operation plan creation system, 11 Server, 12a, 12b, 12c, 12d, 12e, 12f, 12g, 12h, 12i, 12j, 12k, 12l, 12m Electric kiln, 13 Energy management system, 14 Factory equipment, 15 Power generation equipment, 16 Power storage equipment, 17 Power system, 21 Database, 22 Reception unit, 23 Extraction unit, 24 First judgment unit, 25 Second judgment unit, 26 Machine learning model generation unit, 27 Power consumption prediction unit, 28 Operation plan creation unit, 29 Output unit, 41, 42 Lines.

Claims

1. An energy management system that manages the supply and demand of electricity during the operation of a plurality of facilities, comprising at least one of a power generation facility that supplies power to the plurality of facilities by transmitting power and a power storage facility that supplies power to the plurality of facilities by discharging power, wherein the plurality of facilities operate based on an operation plan created by an operation plan creation system that creates an operation plan so that a desired power range is maintained within a predetermined period when a plurality of products are manufactured using the plurality of facilities, the operation plan creation system comprising: a storage unit that stores feature data of each of the plurality of products that have been manufactured in the past, including data on the power consumption of each of the plurality of products when manufacturing each of the plurality of products; a reception unit that receives a request for creation of an operation plan; an extraction unit that, when the reception unit receives the request for creation of an operation plan, extracts data of the product from a production plan including data on the product to be manufactured; a first judgment unit that judges whether the product extracted by the extraction unit matches one of the plurality of products stored in the storage unit; and, when the first judgment unit judges that there is no match, a second judgment unit that judges whether a corresponding physical model can be generated for the product as a specific product. an energy management system comprising: a machine learning model generation unit that generates a machine learning model corresponding to the specific product by using at least a portion of the feature data of the plurality of products stored in the memory unit if the second determination unit determines that the specific product cannot be generated; a power consumption prediction unit that predicts the power consumption of the specific product using the machine learning model of the specific product generated by the machine learning model generation unit; an operation plan creation unit that creates the operation plan so that the desired power is within the specified period by using data on the power consumption of the specific product predicted by the power consumption prediction unit; and an output unit that outputs the operation plan created by the operation plan creation unit.

2. The energy management system according to claim 1, wherein the plurality of facilities include both the power generation facilities and the power storage facilities.

3. An energy management system as described in claim 2, which controls the storage equipment to store the electricity generated by the power generation equipment and not used by the plurality of equipment based on the operation plan.

4. An energy management system as described in claim 2 or claim 3, which controls the supply of power to said plurality of facilities by discharging power from said storage facility if it is determined that the amount of power consumed by said plurality of facilities is greater than the amount of power generated by said power generation facility.

5. An energy management system according to any one of claims 2 to 4, wherein if the amount of stored electricity in the power storage equipment is greater than a predetermined value, the operation plan is given priority to cause a peak in power consumption, and the amount of discharge from the power storage equipment is increased to cut the peak in the total power consumption of the multiple equipment.

6. An energy management system according to any one of claims 1 to 5, wherein the power generation facility generates electricity using renewable energy.

7. An energy management system according to any one of claims 1 to 6, wherein the storage facility includes a NAS battery.

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