A park energy optimization method, device, electronic equipment and storage medium

Abstract

The present invention provides a park energy optimization method, device, electronic device and storage medium, which relates to the technical field of power system. The park energy optimization method includes: obtaining the load rate of output nodes and status information of power nodes in the park, the status information includes the load power and running time of the equipment; determining the energy consumption coefficient corresponding to the equipment according to the load power and running time corresponding to the equipment; determining the node weight of the power node according to the load power and corresponding energy consumption coefficient of all equipment in the power node; when the node weight of the power node is greater than the first preset threshold, judging whether the load rate of the output node corresponding to the power node is greater than the second preset threshold; if so, determining the migration device according to the comparison result of the energy consumption coefficient of all equipment, and determining the migration output node according to the comparison result of the load rate of all output nodes; migrating the migration device to be powered by the migration output node. The present invention can effectively improve the utilization rate of park energy.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a park energy optimization method, device, electronic equipment and storage medium. Background Art

[0002] In the process of modernization, electricity, as the cornerstone of industrial park development, is irreplaceable. As areas of concentrated production and services, industrial parks require a stable and reliable power supply to ensure the smooth operation of businesses, improve production efficiency, and reduce operating costs.

[0003] However, the power distribution method of traditional industrial parks is relatively fixed. When the power demand of equipment changes, the fixed power distribution may not be able to adjust the power distribution in time with the changes in the power demand of equipment, resulting in poor adaptability of the park to changes in the power demand of equipment, affecting the utilization rate of electricity. Summary of the Invention

[0004] The problem solved by the present invention is how to improve the utilization rate of energy in the park.

[0005] To solve the above problems, the present invention provides a park energy optimization method, device, electronic equipment and storage medium.

[0006] In a first aspect, the present invention provides a park energy optimization method, comprising:

[0007] Obtaining the load rate of the output node and the status information of the corresponding power-consuming node in the park, wherein the status information includes the load power and corresponding operating time of each device in the power-consuming node;

[0008] Determining an energy consumption coefficient corresponding to the device according to a product of the load power corresponding to the device and the operating time;

[0009] Determining the node weight of the power-consuming node by a preset weight relationship according to the load power and the corresponding energy consumption coefficient of all the devices in the power-consuming node;

[0010] When the node weight of the power node is greater than a first preset threshold, determining whether the load rate of the output node corresponding to the power node is greater than a second preset threshold;

[0011] If yes, determining the migration device according to the comparison result of the energy consumption coefficients of all the devices, and determining the migration output node according to the comparison result of the load rates of all the output nodes;

[0012] The migration device is migrated to be powered by the migration output node.

[0013] Optionally, the weight relationship satisfies:

[0014] ;

[0015] Among them, β is the node weight, α i is the energy consumption coefficient of the i-th device in the power node, q i is the load power of the i-th device in the power-consuming node, and N is the number of devices in the power-consuming node.

[0016] Optionally, determining the migration device according to the comparison result of the energy consumption coefficients of all the devices includes:

[0017] Obtaining the output power of the output node;

[0018] Determining the deviation power by a preset deviation relationship according to the output power, the load rate and the second preset threshold corresponding to the output node;

[0019] All the devices are sorted from small to large according to the corresponding energy consumption coefficients, and the load powers of the devices are superimposed one by one according to the sorting results to obtain the superimposed total power, until the superimposed total power is greater than or equal to the deviation power, and all the superimposed devices are determined as the migration devices.

[0020] Optionally, the deviation relationship satisfies:

[0021] ;

[0022] Wherein, A is the deviation power corresponding to the output node, Q is the output power of the output node, p is the load rate corresponding to the output node, and γ is the second preset threshold corresponding to the output node.

[0023] Optionally, determining the migration output node according to the comparison result of the load rates of all the output nodes includes:

[0024] Determine all the output nodes whose load rates are less than the second preset threshold as target nodes;

[0025] Obtaining target node output power and target node load rate corresponding to all the target nodes;

[0026] Determining the surplus power corresponding to each target node through a preset surplus relationship according to the target node output power, the target node load rate and a second preset threshold corresponding to the target node;

[0027] The migration output node is determined according to a comparison result of the surplus powers corresponding to all the target nodes.

[0028] Optionally, the determining the migration output node according to a comparison result of the surplus power corresponding to all the target nodes includes:

[0029] determining the largest surplus power among all the surplus powers as the migration power;

[0030] When the migration power is greater than the superposition total power, determining the corresponding target node as the migration output node;

[0031] When the migration power is less than or equal to the superimposed total power, the maximum surplus power is selected from the remaining surplus powers as the migration power and compared with the superimposed total power. The above steps are repeated until the determined migration power is greater than the superimposed total power, and the target node corresponding to the determined migration power is determined as the migration output node.

[0032] Optionally, the surplus relationship satisfies:

[0033] ;

[0034] Wherein, B is the surplus power, M is the output power of the target node, m is the load rate of the target node, and γ is the second preset threshold.

[0035] In a second aspect, the present invention provides a park energy optimization device, comprising:

[0036] An acquisition module is used to obtain the load rate of the output node in the park and the status information of the corresponding power-consuming node, wherein the status information includes the load power and corresponding operating time of each device in the power-consuming node;

[0037] A first processing module, configured to determine an energy consumption coefficient corresponding to the device according to a product of the load power and the corresponding operating time;

[0038] A second processing module is configured to determine a node weight of the power-consuming node according to a preset weight relationship based on the load power and the corresponding energy consumption coefficient of all the devices in the power-consuming node;

[0039] a judgment module, configured to judge whether the load rate of the output node corresponding to the power consumption node is greater than a second preset threshold when the node weight of the power consumption node is greater than a first preset threshold;

[0040] a comparison module, configured to determine a migration device based on a comparison result of the energy consumption coefficients of all the devices, and to determine a migration output node based on a comparison result of the load rates of all the output nodes;

[0041] A migration module is used to migrate the migration device to be powered by the migration output node.

[0042] In a third aspect, the present invention provides an electronic device comprising a memory and a processor;

[0043] The memory is used to store computer programs;

[0044] The processor is used to implement the campus energy optimization method as described in the first aspect when executing the computer program.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the campus energy optimization method as described in the first aspect is implemented.

[0046] The beneficial effects of the campus energy optimization method, device, electronic device, and storage medium of the present invention are as follows: by obtaining the load rate of the output node and the status information of the power consumption node, the operation status of the entire campus power system can be accurately grasped. The energy consumption coefficient is determined based on the product of the load power and operating time of the equipment in the output node. The actual energy demand of each device in the output node can be accurately judged through the energy consumption coefficient. Based on the energy demand of all devices and their corresponding load power, the node weight of the power consumption node is calculated through a pre-set weight relationship, so that the node weight includes both the actual power demand information of the device and the power information of the device, thereby accurately judging the power demand of the power consumption node. When the node weight of a power-consuming node is greater than a first preset threshold, it indicates that the power consumption of the power-consuming node has exceeded the set standard value, that is, the current power demand of the power-consuming node is high, and the output node that supplies it may be under great power supply pressure. Therefore, by comparing the load rate of the output node corresponding to the power-consuming node with a second preset threshold, it is determined whether the power-consuming node needs to undergo equipment migration, that is, whether the load power of the power-consuming node exceeds the power supply capacity of the corresponding output node. If the load power is also greater than the second preset threshold, it indicates that the devices in the power-consuming node need to be migrated, thereby reducing the power supply pressure of the corresponding output node. The energy consumption coefficient of each device in the output node is compared, and a suitable device is selected as the migration device. At the same time, a comprehensive comparison is made based on the load rates of different output nodes, and a suitable output node is selected as the migration output node. The migration device that needs to be migrated is migrated to the selected migration output node for power supply. Through equipment migration, load balancing is achieved, the risk of overload of a single output node is reduced, the stable operation of the power system is ensured, power outages and equipment damage caused by overload are avoided, energy is rationally allocated, excessive concentration of energy consumption areas is avoided, and energy utilization is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic flow chart of a park energy optimization method according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic structural diagram of a campus energy optimization device according to an embodiment of the present invention;

[0049] Figure 3 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0051] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0052] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0053] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0054] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0055] In related technologies, in industrial parks, multiple output nodes and multiple power consumption nodes together form a complex power transmission and distribution network. Output nodes, as the source of power supply, play a crucial role. The output end of a transformer is a common output node. It converts high-voltage power to a voltage level suitable for various equipment within the park, ensuring safe and stable power transmission to power consumption nodes. Energy storage devices can also serve as output nodes. For example, in the event of a power grid shortage or failure, energy storage devices can release power to provide emergency power to critical equipment, ensuring basic production and operation of the park. However, due to objective factors such as park construction, the power supply within the park is relatively fixed. As equipment power demand fluctuates, power consumption nodes may be oversupplied during low loads and undersupplied during high loads. This results in low power resource utilization and energy waste, impacting production efficiency and even damaging equipment. Furthermore, it can cause voltage instability and increase harmonic interference, negatively impacting equipment performance and accelerating equipment aging. At the system level, this reduces system reliability and flexibility, making it prone to failures and power outages. It also fails to meet the diverse power needs of the park, affecting power resource utilization.

[0056] In response to the problems existing in the above-mentioned related technologies, this embodiment provides a campus energy optimization method, device, electronic device and storage medium.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a campus energy optimization method, including:

[0058] S100 , obtaining the load rate of the output node and the status information of the corresponding power-consuming node in the park, wherein the status information includes the load power and the corresponding operating time of each device in the power-consuming node.

[0059] Specifically, an industrial park includes multiple output nodes that provide continuous and stable power to the electrical equipment in various power-consuming nodes. The load factor of these output nodes is a key indicator of their operating status and efficiency. By installing intelligent monitoring equipment, such as high-precision power sensors and data acquisition devices, real-time monitoring of parameters such as current and voltage at the output nodes can be performed. Based on these parameters, the actual output power of the output nodes can be calculated. The load factor is the ratio of actual output power to the rated power of the output node. For example, if an output node has a rated power of 1000 kilowatts and its current actual output power is 800 kilowatts, its load factor is 80%. Obtaining the load factor of output nodes helps to understand the level of power supply constraints across the entire park. When the load factor is too high, it may mean that the output node is approaching or exceeding its carrying capacity, requiring measures such as load adjustment or increased power supply to prevent failures and power outages caused by overload. When the load factor is too low, it may indicate energy waste, and consideration should be given to optimizing power distribution and improving energy efficiency.

[0060] Furthermore, each power-consuming node includes multiple power-consuming devices. The power nodes distribute the power received from the corresponding output nodes to each corresponding power-consuming device. For each device in a power-consuming node, accurate load power (i.e., the power of the device) can be obtained by installing power sensors on the devices or by using their built-in monitoring systems to obtain real-time power data. For example, the load power of an industrial motor during operation may vary with production tasks. By monitoring these changes, abnormally high power consumption can be detected, enabling fault diagnosis and energy-saving optimization. Recording the operating time of power-consuming devices can help analyze their power usage patterns. For example, some devices may only operate during specific time periods, while others require continuous operation. Understanding the operating time of a device can help understand the power consumption of that device within the power-consuming node. Obtaining the load factor of the output nodes and the status information of the corresponding power-consuming nodes within the park provides important data support for park power management and optimization, helping to improve energy efficiency, ensure power supply reliability, and reduce operating costs.

[0061] S200: Determine an energy consumption coefficient corresponding to the device according to a product of the load power corresponding to the device and the operating time.

[0062] Specifically, the load power of a device reflects the rate at which it actually consumes electrical energy during operation. Different types of equipment have varying load powers due to their functions, performance, and operating principles. For example, large mechanical equipment typically has a higher load power, while small electronic equipment has a relatively lower load power. The magnitude of the load power directly affects the energy consumption level of the device. The operating time of a device indicates how long the device is in operation within a certain period of time. The operating time of a device is affected by various factors, including production demand, workflow, and equipment maintenance schedules. Some critical equipment may need to run continuously to ensure production continuity, while other equipment may only operate during specific time periods.

[0063] Furthermore, the energy consumption coefficient of the device is obtained by multiplying the load power of the device by the corresponding operating time. This energy consumption coefficient can be used to measure the energy consumption of the corresponding device during the actual production process, that is, the actual energy consumption required by the device during the entire operating cycle. Therefore, the energy consumption coefficient corresponding to each device can accurately measure the operating conditions of different devices at the power node and the degree of power consumption, thereby formulating more scientific and reasonable energy management strategies. For example, devices with low energy consumption coefficients can be optimized. When a device needs to run for a long time, in order to maintain the stability of the device operation, it is not advisable to migrate it. When the load power of the device is high, such equipment is usually more important and is also not suitable for migration. Therefore, the energy consumption coefficient can be used to comprehensively consider the operating time and power of the device. Based on this energy consumption coefficient, devices with relatively good operating time and power consumption can be comprehensively selected as migration devices.

[0064] S300 , determining a node weight of the power-consuming node according to a preset weight relationship based on the load power and the corresponding energy consumption coefficients of all the devices in the power-consuming node.

[0065] Specifically, based on the load power and energy consumption coefficient corresponding to each device in the power node, the node weight of the power node is calculated through a weighted relationship. The node weight reflects the importance of the power node, allowing for a more reasonable allocation of power resources based on the node weight. For power nodes with higher node weights, the devices in these nodes are more important, and power supply to these devices can be prioritized to ensure the normal operation of critical equipment and smooth production. At the same time, power allocation strategies can be adjusted based on node weights to avoid over-supplying low-weight power nodes, thereby improving power utilization efficiency. Node weights can provide an important basis for energy management decisions in industrial parks, allowing for the formulation of reasonable energy management strategies based on the node weights of power nodes. For example, for power nodes with higher node weights, devices with lower power consumption can be migrated to other output nodes, thereby reducing the energy consumption of the current output node and ensuring the ionization supply of remaining power devices, thereby improving the energy utilization efficiency of the output nodes in the park. Determining the node weight of the power-consuming node based on the load power and corresponding energy consumption coefficient of all equipment in the power-consuming node through a preset weight relationship can provide effective decision-making support for the power management and energy management of the industrial park, help improve power utilization efficiency, reduce energy costs and ensure smooth production.

[0066] S400 : When the node weight of the power consumption node is greater than a first preset threshold, determine whether the load rate of the output node corresponding to the power consumption node is greater than a second preset threshold.

[0067] Specifically, the node weight of a power-consuming node is a value determined by a preset weight relationship after comprehensively considering the load power and corresponding energy consumption coefficients of all devices in the node. Thus, the node's load power and energy consumption reflect its relative importance in the entire park's power system. A larger node weight indicates a relatively higher power demand and consumption, i.e., a higher load and longer operating time, or a more critical role for the park's production and operations. For example, in an industrial park, power-consuming nodes on certain production lines may have higher node weights due to their high power consumption, long operating time, and critical importance to product quality and production progress. The first preset threshold is a value determined based on the actual situation of all power-consuming nodes in the park and the power management strategy. It is typically set after considering factors such as the park's power supply capacity, the importance classification of each power-consuming node's equipment, and historical power consumption data. The first preset threshold serves to screen out relatively important power-consuming nodes, i.e., those with higher loads and longer operating times, so that their power supply can be more strictly monitored and allocated. For example, after analyzing and evaluating the power consumption of the park, the first preset threshold is determined to be 80. When the node weight of a power consumption node is greater than 80, it is considered that the power consumption node has high importance in the park.

[0068] Furthermore, output nodes are key components of a campus power supply system that provide power to various power-consuming nodes. Their load factor reflects the current level of utilization at the output node. The load factor is the ratio of the output node's actual output power to its rated power. By monitoring the load factor of an output node, we can understand its power supply stress and potential overload risks. For example, if an output node has a rated power of 1000 kilowatts and its current actual output power is 700 kilowatts, its load factor is 700 ÷ 1000 = 0.7, or 70%. The second preset threshold is a critical value set to determine whether an output node is overloaded. Its determination takes into account factors such as the output node's equipment capacity, reliability requirements, and grid stability. If the output node's load factor exceeds the second preset threshold, it may lead to degraded power quality, equipment damage, or even a power outage. For example, based on the output node's equipment characteristics and grid requirements, the second preset threshold is determined to be 85%. When the output node's load factor exceeds 85%, it is considered to be at risk of overload. When the node weight of a power node is greater than the first preset threshold, it means that the power node is relatively important. At this time, it is necessary to further determine whether the load rate of the output node corresponding to the power node is greater than the second preset threshold. This judgment process is to ensure the power supply stability and reliability of important power nodes. First, determine the node weight of the power node. If the node weight is greater than the first preset threshold, obtain the load rate of the output node corresponding to the power node. Then, compare the load rate of the output node with the second preset threshold. If the power node is important and the load rate of its corresponding output node exceeds the second preset threshold, it means that the power node may face the risk of insufficient or unstable power supply. In this case, timely measures need to be taken to ensure its power supply, such as adjusting power distribution, starting backup power supply, etc. This judgment process helps park managers to promptly discover potential power supply problems and take corresponding measures to reasonably distribute electricity and improve the utilization efficiency of electricity in the park.

[0069] S500: If yes, determine the migration device according to the comparison result of the energy consumption coefficients of all the devices, and determine the migration output node according to the comparison result of the load rates of all the output nodes.

[0070] S600: Migrate the migration device to be powered by the migration output node.

[0071] Specifically, when the node weight of a power-consuming node is greater than a first preset threshold and the load rate of its corresponding output node exceeds a second preset threshold, it means that the power-consuming node is relatively important and the output node to which it is currently connected may face an overload risk. In this case, measures need to be taken to optimize power distribution to ensure stable power supply to important power-consuming nodes. First, the migration device is determined based on the energy consumption coefficient. The energy consumption coefficient can be determined based on the product of the load power and the corresponding operating time of each device in the power-consuming node. It reflects the energy consumption of the device during the operating time, that is, the continuous operating time and energy consumption of the device. By comparing the energy consumption coefficients of different devices, it can be understood which devices are the main energy-consuming devices and which devices are devices that need to run for a long time. For example, if the load power of device A is 5 kilowatts and the operating time is 8 hours, its energy consumption coefficient is 5×8=40; while the load power of device B is 3 kilowatts and the operating time is 10 hours, its energy consumption coefficient is 3×10=30. By comparison, it can be seen that device A consumes more power, so even if the operating time is short, its energy consumption coefficient is relatively high. Therefore, the energy consumption coefficient can be used to comprehensively evaluate the energy demand of the equipment. When it is necessary to optimize the power nodes, the energy consumption coefficients of all devices can be compared. In order to maintain stable operation of the equipment, it is not advisable to migrate devices with high energy consumption coefficients. Devices with low energy consumption coefficients can be selected for migration. Devices with low energy consumption coefficients can be shut down for a short time, and the load on the original power node after migration will not be too invisible, which can effectively avoid fluctuations in the power grid during the migration process.

[0072] Furthermore, the output node for migration is determined based on its load ratio. The load ratio of an output node reflects the node's usage and power supply pressure. A higher load ratio indicates a greater load on the output node, potentially facing overload risk. By comparing the load ratios of different output nodes, a relatively lightly loaded output node can be identified as the target node for device migration. For example, if the load ratio of output node A is 80%, the load ratio of output node B is 60%, and the load ratio of output node C is 70%, this comparison indicates that output node B is relatively lightly loaded. When device migration is determined to be necessary, the load ratios of all output nodes are compared and the output node with the lower load ratio is selected as the migration output node. This allows some load to be transferred from the more heavily loaded output node to the less heavily loaded node, optimizing power distribution. For example, if, after determining the device to be migrated, a comparison reveals that output node B has the lowest load ratio, the migration device can be connected to output node B, with output node B providing power to the migrated device. This reduces the load pressure on the original output node, avoids the impact on the power grid caused by uneven load distribution among output nodes due to changes in device power demand, and improves energy utilization across the campus output nodes. When determining the migration output node, factors such as the output node's capacity, reliability, and proximity to power-consuming nodes must also be considered. Selecting an output node with sufficient capacity, high reliability, and close proximity to the migrating equipment can reduce migration costs and the impact on the power system.

[0073] In this embodiment, by obtaining the load rate of the output node and the status information of the power-consuming node, the operating status of the entire park power system can be accurately grasped. The energy consumption coefficient is determined by multiplying the load power and operating time of the equipment in the output node. This energy consumption coefficient can accurately determine the actual energy demand of each device in the output node. Based on the energy demand of all devices and their corresponding load power, the node weight of the power-consuming node is calculated through a pre-set weight relationship. This node weight includes both the actual power demand information of the device and the power information of the device, thereby accurately determining the power demand of the power-consuming node. When the node weight of a power-consuming node is greater than a first preset threshold, it indicates that the power consumption of the power-consuming node has exceeded the set standard value, that is, the current power demand of the power-consuming node is high, and the output node that supplies it may be under great power supply pressure. Therefore, by comparing the load rate of the output node corresponding to the power-consuming node with a second preset threshold, it is determined whether the power-consuming node needs to undergo equipment migration, that is, whether the load power of the power-consuming node exceeds the power supply capacity of the corresponding output node. If the load power is also greater than the second preset threshold, it indicates that the devices in the power-consuming node need to be migrated, thereby reducing the power supply pressure of the corresponding output node. The energy consumption coefficient of each device in the output node is compared, and a suitable device is selected as the migration device. At the same time, a comprehensive comparison is made based on the load rates of different output nodes, and a suitable output node is selected as the migration output node. The migration device that needs to be migrated is migrated to the selected migration output node for power supply. Through equipment migration, load balancing is achieved, the risk of overload of a single output node is reduced, the stable operation of the power system is ensured, power outages and equipment damage caused by overload are avoided, energy is rationally allocated, excessive concentration of energy consumption areas is avoided, and energy utilization is improved.

[0074] Optionally, the weight relationship satisfies:

[0075] ;

[0076] Among them, β is the node weight, α i is the energy consumption coefficient of the i-th device in the power node, q i is the load power of the i-th device in the power-consuming node, and N is the number of devices in the power-consuming node.

[0077] Specifically, for each device in the power node, the product of its energy consumption coefficient and load power is first calculated. This product can be understood as a quantitative contribution of the device to the overall importance of the power node. For example, if a device has a high energy consumption coefficient and a large load power, then its contribution to the power node is relatively large. The above calculation is performed on all devices in the power node, and the results are accumulated to obtain the node weight of the power node. The node weight reflects the comprehensive energy consumption and load conditions of all devices in the entire power node. Through the node weight, you can quickly understand the importance of different power nodes in the entire power system. Power nodes with higher node weights may be key production areas, areas where important equipment is concentrated, or areas with higher requirements for power supply stability. They need to be given priority in power distribution and management. The power supply of equipment in power nodes with higher node weights can be prioritized to ensure the stable operation of the power system of the entire park.

[0078] Optionally, determining the migration device according to the comparison result of the energy consumption coefficients of all the devices includes:

[0079] Obtaining the output power of the output node;

[0080] Determining the deviation power by a preset deviation relationship according to the output power, the load rate and the second preset threshold corresponding to the output node;

[0081] All the devices are sorted from small to large according to the corresponding energy consumption coefficients, and the load powers of the devices are superimposed one by one according to the sorting results to obtain the superimposed total power, until the superimposed total power is greater than or equal to the deviation power, and all the superimposed devices are determined as the migration devices.

[0082] Optionally, the deviation relationship satisfies:

[0083] ;

[0084] Wherein, A is the deviation power corresponding to the output node, Q is the output power of the output node, p is the load rate corresponding to the output node, and γ is the second preset threshold corresponding to the output node.

[0085] Specifically, the output power of the output node is a key indicator for measuring the power supply capacity of the output node. It reflects the actual power that the output node can provide to the power-consuming nodes connected to it in the current state. The output power of the output node can be obtained in real time by installing power monitoring equipment at the output node, such as smart meters, power sensors, etc. These devices can measure current and voltage and calculate the output power based on the product of the two. Then, the measured output power data is transmitted to the central control system through the data transmission system for subsequent analysis and processing.

[0086] Furthermore, based on the output power, load factor, and a second preset threshold, the deviation power of the output node is calculated using a deviation relationship. This deviation power is then used to determine the power deviation between the output node's current load and its safe operating state. This deviation power serves as an important basis for determining the appropriate device to migrate. The deviation relationship can be expressed as: deviation power = output power × (load factor - second preset threshold), where the load factor is the ratio of the output node's current actual load to its rated load, and the second preset threshold is a preset upper limit for the safe load factor. First, the obtained output power of the output node, the known load factor, and the second preset threshold are substituted into the deviation relationship formula for calculation. Because the output node's load factor exceeds the second preset threshold, the calculated deviation power will be positive, indicating that the output node is overloaded or nearing overload and requires measures to reduce the load. For example, if the output power of an output node is 1000 kilowatts, the current load factor is 80%, and the second preset threshold is 70%, then the deviation power = 1000 × (80% - 70%) = 1000 × 0.1 = 100 kilowatts. This means that the output node needs to reduce the load by 100 kilowatts to return to a safe operating state.

[0087] To determine the devices to be migrated, first, all devices are sorted from smallest to largest according to their corresponding energy consumption coefficient. The energy consumption coefficient generally reflects the energy efficiency of the device during operation. A smaller coefficient means that the device consumes relatively less energy during its operation cycle. Through this sorting, low-energy-consuming devices can be prioritized for subsequent operations to optimize energy utilization and reduce overall energy costs. Next, the load powers corresponding to the devices are added one by one according to the sorted order to obtain the superimposed total power. The purpose of adding one by one is to gradually increase the superimposed total power to find a device combination that meets the requirements. That is, until the superimposed total power is greater than or equal to the deviation power, all superimposed devices are determined to be migrated. For example, the load power of the device with the smallest energy consumption coefficient, ranked first, is determined as the superimposed total power. When this superimposed total power is less than the deviation power, the load power of the second-ranked device is added to the load power of the first-ranked device to obtain a new superimposed total power. This process is repeated until the superimposed total power is greater than or equal to the deviation power for the first time, at which point all superimposed devices are determined to be migrated.

[0088] It should be noted that the greater the number of migrated devices, the better. It is necessary to ensure that the total load power of the migrated devices is greater than or equal to the deviation power while minimizing the number of migrated devices. This avoids large-scale device migration and improves the stability of campus equipment operation. For example, assuming the deviation power is 18 kW, the device with the smallest energy consumption coefficient is first selected, with a load power of 5 kW, which does not meet the requirement. The device with the next smallest energy consumption coefficient is then selected, with a load power of 8 kW, resulting in a total cumulative power of 13 kW, which still does not meet the requirement. The device with the next smallest energy consumption coefficient is then selected, with a load power of 10 kW. At this point, the sum of the load power of the first three devices is 5 + 8 + 10 = 23 kW, meaning the total cumulative power is 23 kW, which is greater than the deviation power of 18 kW. Therefore, no new devices need to be selected for migration, and the first three devices are determined to be migrated.

[0089] In this optional embodiment, the output power of the output node is obtained, combined with the load rate of the output node and a second preset threshold, and a preset deviation relationship is used to determine the deviation power. All devices are sorted in ascending order of energy consumption coefficient, and the load power of each device is then added one by one to obtain the total superimposed power. This is done until the superimposed total power meets the deviation power condition. This allows accurate identification of devices that require migration. This avoids over- or under-migration and improves the accuracy and efficiency of device migration decisions.

[0090] Optionally, determining the migration output node according to the comparison result of the load rates of all the output nodes includes:

[0091] Determine all the output nodes whose load rates are less than the second preset threshold as target nodes;

[0092] Obtaining target node output power and target node load rate corresponding to all the target nodes;

[0093] Determining the surplus power corresponding to each target node through a preset surplus relationship according to the target node output power, the target node load rate and a second preset threshold corresponding to the target node;

[0094] The migration output node is determined according to a comparison result of the surplus powers corresponding to all the target nodes.

[0095] Specifically, by comparing the load rate of each output node with the second preset threshold, all output nodes with a load rate less than the second preset threshold are determined as target nodes, thereby screening out output nodes with relatively light loads. These nodes may have excess energy supply capacity or lower usage demand, providing potential resources for subsequent operations, and when the load rate is lower than the second preset threshold, it means that the node is not fully utilized in the current state or has a certain margin that can be adjusted.

[0096] Furthermore, the corresponding target node output power and target node load rate are obtained. The target node output power reflects the actual amount of energy output by the node, while the target node load rate can help understand the relationship between the node's utilization level and potential capacity. Based on the target node output power, target node load rate, and a second preset threshold, the surplus power corresponding to each target node is determined through a preset surplus relationship, thereby quantifying the power that each target node can still output after meeting the power supply needs of the current load device. The migration output node is determined based on the comparison results of the surplus power corresponding to all target nodes. This may involve sorting the surplus power of each target node or other comparison methods to find the target node that is most suitable as the migration output node. The migration output node is typically a node with higher surplus power. This node has more energy to provide the power required by other migration devices or can bear more load transfer. Selecting the appropriate migration output node can achieve optimal energy distribution and balanced system operation.

[0097] In this optional embodiment, by identifying output nodes with load rates less than a second preset threshold as target nodes and obtaining parameters such as their output power and load rate, nodes with potential energy surpluses can be accurately screened. The preset surplus relationship is used to calculate the surplus power of each target node, providing a quantitative basis for optimizing energy allocation. Finally, based on the surplus power comparison results, the output node to be migrated is determined, helping to achieve reasonable system load distribution and efficient energy utilization.

[0098] Optionally, the determining the migration output node according to a comparison result of the surplus power corresponding to all the target nodes includes:

[0099] determining the largest surplus power among all the surplus powers as the migration power;

[0100] When the migration power is greater than the superposition total power, determining the corresponding target node as the migration output node;

[0101] When the migration power is less than or equal to the superimposed total power, the maximum surplus power is selected from the remaining surplus powers as the migration power and compared with the superimposed total power. The above steps are repeated until the determined migration power is greater than the superimposed total power, and the target node corresponding to the determined migration power is determined as the migration output node.

[0102] Specifically, the largest surplus power is first identified from the surplus power of all target nodes and determined as the migration power. The target node with the largest energy surplus can provide the most allocable energy. When the migration power is greater than the total combined power, it indicates that the currently determined target node with the largest surplus power is sufficient to meet the system's energy adjustment needs. The corresponding target node is then designated as the migration output node, becoming the source of energy transfer and providing energy to the migrated devices. If the migration power is less than or equal to the superimposed total power, it means that the current target node with the largest surplus power cannot meet the energy adjustment requirements of the system. At this time, it is necessary to further select the largest surplus power from the remaining surplus power as the new migration power, and repeatedly select the largest surplus power from the remaining surplus power as the migration power until the obtained migration power is greater than the superimposed total power. This process is to ensure that the final determined migration output node combination can provide sufficient energy to meet the needs of the migration device. Each time a new migration power is selected and the corresponding target node is determined as the migration output node, an energy transfer plan that can meet the requirements of the migration device is gradually constructed. In this way, the surplus energy of each target node in the system can be maximized to achieve optimal energy allocation and stable operation of the system.

[0103] In this optional embodiment, the migration power is determined by the maximum surplus power, and the migration output node is selected accordingly, which can achieve efficient energy allocation, quickly transfer excess energy to where it is needed, and improve energy utilization efficiency. Moreover, when the migration power does not meet the requirements, the largest one is continuously selected from the remaining surplus power as the new migration power until it is greater than the superimposed total power. This can accurately match the system energy demand, help optimize the system load distribution and energy allocation, improve system stability, reliability and performance, adapt to different system operating states and energy demand changes, and has strong adaptability.

[0104] Optionally, the surplus relationship satisfies:

[0105] ;

[0106] Wherein, B is the surplus power, M is the output power of the target node, m is the load rate of the target node, and γ is the second preset threshold.

[0107] like Figure 2 As shown, an embodiment of the present invention provides a campus energy optimization device 700, including:

[0108] An acquisition module 701 is configured to acquire the load rate of an output node in a park and status information of a corresponding power-consuming node, wherein the status information includes the load power and corresponding operating time of each device in the power-consuming node;

[0109] A first processing module 702 is configured to determine an energy consumption coefficient corresponding to the device according to a product of the load power and the corresponding operating time;

[0110] The second processing module 703 is configured to determine the node weight of the power consumption node according to the load power and the corresponding energy consumption coefficient of all the devices in the power consumption node using a preset weight relationship;

[0111] The judgment module 704 is configured to judge whether the load rate of the output node corresponding to the power consumption node is greater than a second preset threshold when the node weight of the power consumption node is greater than a first preset threshold;

[0112] A comparison module 705 is configured to determine a migration device based on a comparison result of the energy consumption coefficients of all the devices, and to determine a migration output node based on a comparison result of the load rates of all the output nodes;

[0113] Migration module 706, configured to migrate the migration device to a node powered by the migration output node.

[0114] The campus energy optimization device of this embodiment is used to implement the campus energy optimization method described above. Its advantages over the existing technology are the same as the advantages of the above-mentioned campus energy optimization method over the existing technology, and will not be repeated here.

[0115] like Figure 3 As shown, an electronic device 800 provided by an embodiment of the present invention includes a memory 810 and a processor 820; the memory 810 is used to store computer programs; the processor 820 is used to implement the above-mentioned campus energy optimization method when executing the computer program.

[0116] In other words, an electronic device 800 includes a memory 810 and a processor 820 coupled to the memory 810; the memory 810 is configured to store a computer program; and the processor 820 is configured to perform the following operations when executing the computer program:

[0117] Obtaining the load rate of the output node and the status information of the corresponding power-consuming node in the park, wherein the status information includes the load power and corresponding operating time of each device in the power-consuming node;

[0118] Determining an energy consumption coefficient corresponding to the device according to a product of the load power corresponding to the device and the operating time;

[0119] Determining the node weight of the power-consuming node by a preset weight relationship according to the load power and the corresponding energy consumption coefficient of all the devices in the power-consuming node;

[0120] When the node weight of the power node is greater than a first preset threshold, determining whether the load rate of the output node corresponding to the power node is greater than a second preset threshold;

[0121] If yes, determining the migration device according to the comparison result of the energy consumption coefficients of all the devices, and determining the migration output node according to the comparison result of the load rates of all the output nodes;

[0122] The migration device is migrated to be powered by the migration output node.

[0123] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the campus energy optimization method described above is implemented.

[0124] In other words, a non-volatile computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the following operations:

[0125] Obtaining the load rate of the output node and the status information of the corresponding power-consuming node in the park, wherein the status information includes the load power and corresponding operating time of each device in the power-consuming node;

[0126] Determining an energy consumption coefficient corresponding to the device according to a product of the load power corresponding to the device and the operating time;

[0127] Determining the node weight of the power-consuming node by a preset weight relationship according to the load power and the corresponding energy consumption coefficient of all the devices in the power-consuming node;

[0128] When the node weight of the power node is greater than a first preset threshold, determining whether the load rate of the output node corresponding to the power node is greater than a second preset threshold;

[0129] If yes, determining the migration device according to the comparison result of the energy consumption coefficients of all the devices, and determining the migration output node according to the comparison result of the load rates of all the output nodes;

[0130] The migration device is migrated to be powered by the migration output node.

[0131] An electronic device 800 that can serve as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device 800 is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 800 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0132] Electronic device 800 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. An input / output (I / O) interface is also connected to the bus.

[0133] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM). In this application, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network elements. Some or all of these units can be selected based on actual needs to achieve the objectives of the embodiments of the present invention. Furthermore, the functional units in the various embodiments of the present invention can be integrated into a single processing unit, each unit can exist physically separately, or two or more units can be integrated into a single unit. These integrated units can be implemented in either hardware or software functional units.

[0134] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A park energy optimization method, characterized in that: include: Obtaining the load rate of the output node in the park and the status information of the corresponding power consumption node, wherein the status information includes the load power and the corresponding operating time of each device in the power consumption node; Determining an energy consumption coefficient corresponding to the device according to the product of the load power corresponding to the device and the operating time; Determine the node weight of the power consumption node through a preset weight relationship according to the load power of all the devices in the power consumption node and the corresponding energy consumption coefficient; When the node weight of the power consumption node is greater than a first preset threshold, determining whether the load rate of the output node corresponding to the power consumption node is greater than a second preset threshold; If yes, determining the migration device according to the comparison result of the energy consumption coefficients of all the devices, and determining the migration output node according to the comparison result of the load rates of all the output nodes; Migrating the migration device to be powered by the migration output node; The determining of the migration device according to the comparison result of the energy consumption coefficients of all the devices includes: obtaining the output power of the output node; determining the deviation power through a preset deviation relationship according to the output power, load rate and second preset threshold corresponding to the output node; sorting all the devices from small to large according to the corresponding energy consumption coefficients, and superimposing the load powers of the devices one by one according to the sorting result to obtain the superimposed total power, until the superimposed total power is greater than or equal to the deviation power, and determining all the superimposed devices as the migration devices; The deviation relationship satisfies: ； Wherein, A is the deviation power corresponding to the output node, Q is the output power of the output node, p is the load rate corresponding to the output node, and γ is the second preset threshold corresponding to the output node; The step of determining the migration output node according to the comparison result of the load rates of all the output nodes includes: determining all the output nodes whose load rates are less than the second preset threshold as target nodes; obtaining the target node output power and the target node load rate corresponding to the target node; determining the surplus power corresponding to each target node through a preset surplus relationship according to the target node output power, the target node load rate and the second preset threshold corresponding to the target node; and determining the migration output node according to the comparison result of the surplus power corresponding to all the target nodes; The method of determining the migration output node according to the comparison result of the surplus powers corresponding to all the target nodes includes: determining the largest surplus power among all the surplus powers as the migration power; when the migration power is greater than the superimposed total power, determining the corresponding target node as the migration output node; when the migration power is less than or equal to the superimposed total power, selecting the largest surplus power among the remaining surplus powers as the migration power, and comparing it with the superimposed total power, repeating the above steps until the determined migration power is greater than the superimposed total power, and determining the target node corresponding to the determined migration power as the migration output node.

2. The park energy optimization method according to claim 1, characterized in that: The weight relationship satisfies: ； Among them, β is the node weight, α i is the energy consumption coefficient of the i-th device in the power-consuming node, q i is the load power of the i-th device in the power-consuming node, and N is the number of devices in the power-consuming node.

3. The park energy optimization method according to claim 1, characterized in that: The surplus relationship satisfies: ； Among them, B is the surplus power, M is the output power of the target node, m is the load rate of the target node, and γ is the second preset threshold.

4. A park energy optimization device, characterized in that: include: An acquisition module, used to acquire the load rate of the output node in the park and the status information of the corresponding power consumption node, wherein the status information includes the load power and the corresponding operating time of each device in the power consumption node; A first processing module, used for determining an energy consumption coefficient corresponding to the device according to the product of the load power and the corresponding operating time; A second processing module, configured to determine the node weight of the power consumption node according to the load power of all the devices in the power consumption node and the corresponding energy consumption coefficient through a preset weight relationship; A judgment module, configured to judge whether the load rate of the output node corresponding to the power consumption node is greater than a second preset threshold value when the node weight of the power consumption node is greater than a first preset threshold value; A comparison module, configured to determine a migration device according to a comparison result of the energy consumption coefficients of all the devices, and to determine a migration output node according to a comparison result of the load rates of all the output nodes; Wherein, the determining of the migration device according to the comparison result of the energy consumption coefficients of all the devices includes: obtaining the output power of the output node; determining the deviation power through a preset deviation relationship according to the output power, load rate and second preset threshold corresponding to the output node; sorting all the devices from small to large according to the corresponding energy consumption coefficients, and superimposing the load powers of the devices one by one according to the sorting results to obtain the superimposed total power, until the superimposed total power is greater than or equal to the deviation power, and determining all the devices to be superimposed as the migration devices; The deviation relationship satisfies: ； Wherein, A is the deviation power corresponding to the output node, Q is the output power of the output node, p is the load rate corresponding to the output node, and γ is the second preset threshold corresponding to the output node; The step of determining the migration output node according to the comparison result of the load rates of all the output nodes includes: determining all the output nodes whose load rates are less than the second preset threshold as target nodes; obtaining the target node output power and the target node load rate corresponding to the target node; determining the surplus power corresponding to each target node through a preset surplus relationship according to the target node output power, the target node load rate and the second preset threshold corresponding to the target node; and determining the migration output node according to the comparison result of the surplus power corresponding to all the target nodes; The step of determining the migration output node according to the comparison result of the surplus powers corresponding to all the target nodes includes: determining the largest surplus power among all the surplus powers as the migration power; when the migration power is greater than the superposition total power, determining the corresponding target node as the migration output node; when the migration power is less than or equal to the superposition total power, selecting the largest surplus power among the remaining surplus powers as the migration power, and comparing it with the superposition total power, repeating the above steps until the determined migration power is greater than the superposition total power, and determining the target node corresponding to the determined migration power as the migration output node; A migration module is used to migrate the migration device to be powered by the migration output node.

5. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the campus energy optimization method as described in any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the campus energy optimization method according to any one of claims 1 to 3 is implemented.

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