CN-117055489-B - Equipment operation control method and device, cloud server and storage medium

CN117055489BCN 117055489 BCN117055489 BCN 117055489BCN-117055489-B

Abstract

The invention discloses a device operation control method, a device, a cloud server and a storage medium, wherein the method comprises the steps that the cloud server obtains first electric quantity in a target period and a first available electric quantity predicted value in the target period from a target industrial user side; and inputting the first available electric quantity predicted value and the first electric quantity into a dispatching cost optimization model to generate a first pre-dispatching curve, a second pre-dispatching curve and a third pre-dispatching curve of the target industrial user terminal in a target period, wherein the first pre-dispatching curve, the second pre-dispatching curve and the third pre-dispatching curve are respectively used for controlling metallurgical equipment, power generation equipment and energy storage equipment to operate by the target industrial user terminal. By implementing the method, the actual electricity consumption requirement of metallurgical equipment is met according to the first electric quantity sent by the target industrial user terminal, and the problem of an electric power gap generated by a factory with special requirements under an energy management system is solved.

Inventors

FU MING

Assignees

深圳海辰储能控制技术有限公司

Dates

Publication Date: 20260508
Application Date: 20230830

Claims (9)

1. The utility model provides a control method for equipment operation, which is characterized in that the cloud server of an energy management system is applied, the energy management system also comprises a plurality of industrial clients, each industrial client in the plurality of industrial clients manages metallurgical equipment, power generation equipment and energy storage equipment, the energy management system comprises a first power supply network formed by the power generation equipment managed by the plurality of industrial clients, the energy storage equipment and the metallurgical equipment managed by the plurality of industrial clients are connected with the first power supply network, and the method comprises the following steps: The cloud server acquires first electric quantity in a target period from a target industrial user side, wherein the first electric quantity comprises electric quantity required by metallurgical equipment managed by the target industrial user side in the target period and power generation equipment managed by the target industrial user side; The cloud server acquires the utilization rate of key metallurgical equipment in a target period from the target industrial user side, judges whether the metallurgical equipment managed by the target industrial user side is in a production peak period according to the utilization rate of the key metallurgical equipment, and the key metallurgical equipment is metallurgical equipment which needs to continuously run in the target period; if the metallurgical equipment managed by the target industrial user side is not in the production peak period and the first power supply network is in the electricity consumption valley period, the cloud server acquires the electric quantity which is required to be acquired from the first power supply network by all the energy storage equipment in the energy management system in the target period; the cloud server inputs the first available electric quantity predicted value and the first electric quantity into a scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve and a third pre-scheduling curve of the target period; The first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve of the target period are respectively used for controlling the metallurgical equipment at the target period by the target industrial user side, and the power generation equipment and the energy storage equipment operate.
2. The method according to claim 1, wherein the method further comprises: If the metallurgical equipment managed by the target industrial user side is in the production peak period and the first power supply network is in the electricity consumption valley period, the cloud server acquires the electric quantity required to be acquired from a second power supply network by all the energy storage equipment in the energy management system in the target period; And the cloud server obtains the first available electric quantity predicted value according to the electric quantity required to be obtained from the second power supply network in the target period and the electric quantity which can be stored by the energy storage equipment managed by the target industrial user side in the target period.
3. The method according to claim 1, wherein the method further comprises: The cloud server acquires the electricity consumption of a target industrial user terminal cluster in the target period, and determines a second available electricity consumption predicted value of the target industrial user terminal cluster according to the electricity consumption of the target industrial user terminal cluster in the target period and the electricity consumption required by the first power supply network, wherein the electricity consumption of the target industrial user terminal cluster in the target period comprises the electricity consumption required by metallurgical equipment managed by all industrial user terminals in the target industrial user terminal cluster in the target period and the electricity consumption which can be provided by power generation equipment managed by all industrial user terminals in the target period; the target industrial user terminal cluster comprises the target industrial user terminal and an industrial user terminal, wherein the distance between the industrial user terminal and the target industrial user terminal is smaller than a preset distance; The cloud server inputs the first available electric quantity predicted value and the first electric quantity into the scheduling cost optimization model, and generates a first pre-scheduling curve, a second pre-scheduling curve and a third pre-scheduling curve of the target period, wherein the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve comprise: and the cloud server inputs the second available electric quantity predicted value, the first available electric quantity predicted value and the first electric quantity into the dispatching cost optimization model to generate a first pre-dispatching curve, a second pre-dispatching curve and a third pre-dispatching curve of the target period.
4. The method according to claim 2, wherein the method further comprises: The cloud server acquires the electricity consumption of a target industrial user terminal cluster in the target period, and determines a third electricity consumption predicted value of the target industrial user terminal cluster according to the electricity consumption of the target industrial user terminal cluster in the target period and the electricity consumption required by the power generation equipment managed by all industrial user terminals in the target industrial user terminal cluster in the target period, wherein the electricity consumption of the target industrial user terminal cluster in the target period comprises the electricity consumption required by the metallurgical equipment managed by all industrial user terminals in the target industrial user terminal cluster in the target period and the electricity consumption which can be provided by the power generation equipment managed by all industrial user terminals in the target period; the target industrial user terminal cluster comprises the target industrial user terminal and an industrial user terminal, wherein the distance between the industrial user terminal and the target industrial user terminal is smaller than a preset distance; The cloud server inputs the first available electric quantity predicted value and the first electric quantity into the scheduling cost optimization model, and generates a first pre-scheduling curve, a second pre-scheduling curve and a third pre-scheduling curve of the target period, wherein the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve comprise: And the cloud server inputs the third available electric quantity predicted value, the first available electric quantity predicted value and the first electric quantity into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve and a third pre-scheduling curve of the target period.
5. The method according to claim 2, wherein the method further comprises: the cloud server obtains the total electricity consumption of all metallurgical equipment in a target period of the energy management system; if the first available electric quantity predicted value is smaller than the total electric quantity of all the metallurgical equipment, the cloud server determines the electric priority of the target industrial user side, wherein the higher the utilization rate of the key metallurgical equipment corresponding to the industrial user side is, the higher the electric priority is; The cloud server inputs the first available electric quantity predicted value and the first electric quantity into the scheduling cost optimization model, and generates a first pre-scheduling curve, a second pre-scheduling curve and a third pre-scheduling curve of the target period, wherein the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve comprise: and the cloud server inputs the power utilization priority, the first available electric quantity predicted value and the first electric quantity into the dispatching cost optimization model to generate a first pre-dispatching curve, a second pre-dispatching curve and a third pre-dispatching curve of the target period.
6. The method of any one of claims 1-5, wherein said determining whether said metallurgical equipment managed by said industrial user is at a peak production period based on said critical metallurgical equipment usage comprises: the cloud server acquires the equipment type of the metallurgical equipment managed by the industrial user side from the industrial user side, and confirms the metallurgical equipment with the equipment type of continuous heating equipment or discharge equipment as the key metallurgical equipment; The cloud server acquires the number of the key metallurgical equipment and the number of the key metallurgical equipment which the target industrial user side needs to operate in the target period from the target industrial user side; The cloud server determines the ratio of the number of the key metallurgical equipment to be operated to the number of the key metallurgical equipment as the key metallurgical equipment utilization rate; If the utilization rate of the key metallurgical equipment is smaller than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user side is not in the production peak period; And if the utilization rate of the key metallurgical equipment is not less than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user side is in the production peak period.
7. The utility model provides a device operation controlling means, its characterized in that is used for carrying out device operation control method, is applied to energy management system, energy management system still includes a plurality of industry ues, each industry ue in a plurality of industry ues manages metallurgical equipment, power generation equipment and energy storage equipment, the first power supply network that the power generation equipment that a plurality of industry ues that energy management system included constitutes, a plurality of industry ues manage energy storage equipment and metallurgical equipment with first power supply network connection, device operation controlling means includes: The cloud server is used for acquiring first electric quantity in a target period from a target industrial user side, wherein the first electric quantity comprises electric quantity required by metallurgical equipment managed by the target industrial user side in the target period and power generation equipment managed by the target industrial user side; The cloud server acquires the utilization rate of key metallurgical equipment in a target period from the target industrial user side, judges whether the metallurgical equipment managed by the target industrial user side is in a production peak period according to the utilization rate of the key metallurgical equipment, and the key metallurgical equipment is metallurgical equipment which needs to continuously run in the target period; if the metallurgical equipment managed by the target industrial user side is not in the production peak period and the first power supply network is in the electricity consumption valley period, the cloud server acquires the electric quantity which is required to be acquired from the first power supply network by all the energy storage equipment in the energy management system in the target period; the cloud server inputs the first available electric quantity predicted value and the first electric quantity into a scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve and a third pre-scheduling curve of the target period; The first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve of the target period are respectively used for controlling the metallurgical equipment at the target period by the target industrial user side, and the power generation equipment and the energy storage equipment operate.
8. A cloud server comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-6.
9. A computer readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method of any one of claims 1-6.

Description

Equipment operation control method and device, cloud server and storage medium Technical Field The present invention relates to the field of general data processing technologies in energy management systems, and in particular, to a device operation control method and apparatus, a cloud server, and a storage medium. Background The energy management system is a comprehensive system integrating software and hardware and is used for monitoring, controlling and optimizing the operation of the energy system. A large number of electric equipment, power generation equipment and energy storage equipment are managed in the energy management system, the energy management system stores electric energy in the electricity consumption valley period, and the problems of insufficient electric power, unstable energy supply and the like in the electricity consumption peak period system are solved by releasing the stored electric energy in the electricity consumption peak period. However, the scheme of simply storing electric energy in the electricity consumption valley period and discharging in the electricity consumption peak period cannot meet the actual electricity consumption requirement of a specific industrial scene, and therefore a part of factories with special requirements can generate electric power gaps and the like under an energy management system. Disclosure of Invention In view of the above problems, the embodiment of the application provides a device operation control method, a device, a cloud server and a storage medium, wherein a first available electric quantity predicted value and first electric quantity are obtained through the cloud server, and a first pre-dispatching curve, a second pre-dispatching curve and a third pre-dispatching curve are obtained by inputting the first available electric quantity predicted value and a first electric quantity dispatching cost optimization model, so that the operation of metallurgical equipment, power generation equipment and energy storage equipment managed by an industrial user side is controlled, the actual power consumption requirements of the metallurgical equipment are met, and the problem that a factory with special requirements generates a power gap under an energy management system is solved. In order to achieve the above object, in a first aspect, an embodiment of the present application provides an apparatus operation control method, which is applied to a cloud server of an energy management system, where the energy management system further includes a plurality of industrial clients, each of the plurality of industrial clients manages a metallurgical apparatus, a power generation apparatus, and an energy storage apparatus, and the method includes the cloud server obtaining a first power amount from a target industrial client in a target period, the first power amount including a power consumption required by the metallurgical apparatus managed by the target industrial client in the target period and a power generation apparatus managed by the target industrial client, the cloud server obtaining a first available power amount predicted value in the target period, the cloud server inputting the first available power amount predicted value and the first power amount into a scheduling cost optimization model, generating a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve of the target period, the second pre-scheduling curve and the third pre-scheduling curve being used for the target industrial client to control the metallurgical apparatus, the power generation apparatus, and the energy storage apparatus, respectively, in the target period. It can be seen that the cloud server acquires the first electricity quantity and the first available electricity quantity predicted value in the target period from the target industrial user side, the first electricity quantity and the first available electricity quantity predicted value are input into the scheduling cost optimization model, and the scheduling cost optimization model distributes electricity quantity for the target industrial user side according to the first available electricity quantity predicted value, so that the actual electricity consumption requirement of metallurgical equipment is met according to the first electricity quantity sent by the target industrial user side, and the problem that a factory with special requirements generates an electricity gap under an energy management system is solved. In combination with the first aspect, the energy management system comprises a first power supply network formed by a plurality of power generation devices managed by industrial clients, the plurality of energy storage devices and the metallurgical devices managed by the industrial clients are connected with the first power supply network, the cloud server acquires a first available electric quantity predicted value in a target period, the cloud server acquires the u