Regional source load balance architecture based on edge-edge coordination, scheduling method and equipment

Abstract

The invention discloses a regional source load balance scheduling method, architecture and equipment based on edge collaboration, and the method comprises the steps: carrying out the edge collaboration region division of an intelligent fusion terminal through a cloud master station, selecting one intelligent fusion terminal as master equipment, selecting other intelligent fusion terminals as slave equipment, transmitting the IP and port number of the master equipment to the slave equipment, and carrying out the edge interaction data configuration; the method comprises the following steps: monitoring operation parameters of low-voltage equipment connected with master equipment through the master equipment, generating an operation instruction of the low-voltage equipment in combination with the operation parameters of the low-voltage equipment uploaded by each slave equipment and a pre-constructed multi-target model, and controlling the low-voltage equipment or transmitting the operation instruction to the slave equipment based on the operation instruction of the low-voltage equipment; the slave equipment monitors the operation parameters of the low-voltage equipment connected with the slave equipment and uploads the operation parameters to the master equipment, and the low-voltage equipment is controlled based on the operation instruction of the low-voltage equipment. According to the invention, source-load balance is realized, large-range power flow is reduced, and line loss and communication and calculation pressure of the cloud master station are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power Internet of Things, and particularly relates to a regional source-load balance architecture, a scheduling method and a device based on edge-edge collaboration. Background Art

[0002] In order to better manage distributed resources such as distributed photovoltaics, charging vehicles, and energy storage, based on the distribution Internet of Things architecture, an intelligent fusion terminal (edge computing platform) is installed on the low-voltage side of the distribution transformer to perform intelligent acquisition and control on the distribution substation area (the power supply range or area of the distribution transformer).

[0003] However, the resources within a single substation area are limited and it is difficult to meet the demand for local consumption of distributed resources. Collaborative management of multiple substation areas is an effective way to achieve nearby consumption of distributed photovoltaics, which can reduce large-scale power flow and line losses.

[0004] If each joint area is managed through the cloud platform, the huge amount of regional collaboration requirements will bring great pressure on the communication and computing of the cloud platform. Edge-edge collaboration is the key to solving this problem. Summary of the Invention

[0005] In order to solve the problem in the prior art that if each joint area is managed through the cloud platform, the huge amount of regional collaboration requirements will bring great pressure on the communication and computing of the cloud platform, on the other hand, the present application also provides a regional source-load balance scheduling method based on edge-edge collaboration, including:

[0006] Dividing the edge-edge collaboration area for the intelligent fusion terminal through the cloud master station, selecting one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices, sending the IP and port number of the master device to the slave devices, and also performing edge-edge interaction data configuration;

[0007] Monitoring, by the master device, the operating parameters of the low-voltage devices connected to the master device, combining the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, and a pre-constructed multi-objective model to generate an operating instruction for the low-voltage devices connected to the master device and an operating instruction for the low-voltage devices connected to each slave device, and controlling the low-voltage devices connected to the master device based on the operating instruction for the low-voltage devices connected to the master device, and sending the operating instruction for the low-voltage devices connected to each slave device to each slave device;

[0008] Monitoring, by the slave device, the operating parameters of the low-voltage devices connected to the slave device, uploading the operating parameters to the master device, and at the same time controlling the low-voltage devices based on the operating instruction for the low-voltage devices connected to each slave device sent by the master device;

[0009] Among them, the multi-objective model is constructed by taking the minimum power exchanged between the region and the outside world as the first objective and the maximum total photovoltaic output of the region as the second objective, combined with the constraint conditions.

[0010] Optionally, the construction of the multi-objective model includes:

[0011] Taking the minimum power exchanged between the region and the outside world as the first objective, constructing the first objective function;

[0012] Taking the maximum total photovoltaic output of the region as the second objective, constructing the second objective function;

[0013] Setting constraint conditions for the first objective function and the second objective function.

[0014] Optionally, the first objective function is shown as the following formula:

[0015]

[0016] In the formula, P chg represents the power exchanged between the region and the outside world; P i represents the power exchanged between each substation area and the 10kV feeder; m represents the number of substation areas in the region; i is a counting unit.

[0017] Optionally, the second objective function is shown as the following formula:

[0018]

[0019] In the formula, P pv represents the photovoltaic output, n represents the number of photovoltaics, j is a counting unit, P pv·j represents the output of the j-th photovoltaic.

[0020] Optionally, the constraint conditions are shown as the following formula:

[0021]

[0022] In the formula, P i represents the power exchanged between each substation area and the 10kV feeder, P i·min represents the minimum power limit of the feeder flowing to the i-th substation area, P i·max represents the maximum power limit of the feeder flowing to the i-th substation area; P pv·j represents the output of the j-th photovoltaic; P pv·j·max represents the maximum value of the output of the j-th photovoltaic, P st·x represents the power of the x-th energy storage, P st·x·min represents the minimum energy storage power of the x-th energy storage, P st·x·max represents the maximum energy storage power of the x-th energy storage; S st·x represents the energy storage capacity of the x-th energy storage, S st·x·minrepresents the minimum energy storage capacity of the x-th energy storage, S st·x·max represents the maximum energy storage capacity of the x-th energy storage; V pv.j represents the voltage of the j-th photovoltaic interface, V pv.j·min represents the minimum voltage of the j-th photovoltaic interface, V pv.j·max represents the maximum voltage of the j-th photovoltaic interface, m represents the number of substations in the area, q represents the number of energy storages, n represents the number of photovoltaics, i represents the substation counting unit, j represents the photovoltaic counting unit, and x represents the energy storage counting unit.

[0023] Optionally, the cloud master station performs edge-edge collaborative area division on the intelligent fusion terminals, selects one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices, sends the IP and port number of the master device to the slave devices, and also performs edge-edge interactive data configuration, including:

[0024] The cloud master station's area division module uses the 10kV bus, tie switch, ring main unit, and transformer as boundaries to perform edge-edge collaborative area division on the 10kV feeder and the connected substations, and selects one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices;

[0025] The cloud master station's edge-edge communication module sends the IP and port number of the master device to the slave devices;

[0026] The cloud master station's edge-edge data configuration module configures the slave devices to send data to the master device regularly, or when an emergency occurs, informs the master device through immediate reporting;

[0027] Among them, the data sent by the slave devices to the master device includes: the power flow direction of the monitored distribution transformer, the remaining capacity, the energy storage power and the remaining capacity, and the photovoltaic output;

[0028] The emergency events include: overload of the distribution transformer and voltage over-limit at the distributed photovoltaic connection point.

[0029] Optionally, the master device monitors the operating parameters of the low-voltage devices connected to the master device, combines the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, and based on the pre-constructed multi-objective model, generates the operating instructions for the low-voltage devices connected to the master device and the operating instructions for the low-voltage devices connected to each slave device, and controls the low-voltage devices connected to the master device based on the operating instructions for the low-voltage devices connected to the master device, and issues the operating instructions for the low-voltage devices connected to each slave device to each slave device, including:

[0030] The master device monitors the operating parameters of the low-voltage devices connected to it, and receives the operating parameters of the low-voltage devices connected to the slave devices uploaded by each slave device; based on the operating parameters of the low-voltage devices connected to the master device and the operating parameters of the low-voltage devices connected to the slave devices, a primary calculation is performed in combination with a pre-constructed multi-objective model. According to the calculation results, adjustments are made in the adjustment order of the energy storage device and the distributed photovoltaic, and operating instructions for the low-voltage devices connected to the master device and operating instructions for the low-voltage devices connected to the slave devices are generated. Then, based on the operating instructions for the low-voltage devices connected to the master device, the low-voltage devices connected to the master device are controlled, and the operating instructions for the low-voltage devices connected to the slave devices are sent to the slave devices.

[0031] Optionally, the cloud master station divides the edge-edge collaboration area of the intelligent fusion terminals, selects one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices. At the same time, the IP and port number of the master device are sent to the slave devices, and edge-edge interaction data configuration is also performed, including:

[0032] The cloud master station's area division module divides the 10kV feeder and the connected substations with the 10kV busbar, tie switch, ring main unit, and transformer as boundaries for edge-edge collaboration area division, and selects one intelligent fusion terminal as the master device and other intelligent fusion terminals as slave devices;

[0033] The cloud master station's edge-edge communication module sends the IP and port number of the master device to the slave devices;

[0034] The cloud master station's edge-edge data configuration module configures the slave devices to send data to the master device regularly, or when an emergency occurs, inform the master device through timely reporting;

[0035] Among them, the data sent by the slave devices to the master device includes: the power flow direction of the monitored distribution transformer, the remaining capacity, the energy storage power and the remaining capacity, and the photovoltaic output;

[0036] The emergencies include: overload of the distribution transformer and voltage over-limit at the distributed photovoltaic connection point.

[0037] Optionally, the master device monitors the operating parameters of the connected low-voltage devices, generates operating instructions for the low-voltage devices based on the monitoring data uploaded by the slave devices in combination with a pre-constructed multi-objective model, and sends them to the slave devices, including:

[0038] The master device monitors the operating parameters of the connected low-voltage devices, receives the monitoring data uploaded by the slave devices, performs a primary calculation based on the monitoring data in combination with a pre-constructed multi-objective model, makes adjustments according to the calculation results in the adjustment order of the energy storage device and the distributed photovoltaic, generates operating instructions for the energy storage device and the distributed photovoltaic, and sends them to the slave devices.

[0039] Based on the same inventive concept, the present invention also provides a regional source-load balance architecture based on edge-edge collaboration, including: a cloud master station, multiple intelligent fusion terminals, and low-voltage devices respectively connected to each intelligent fusion terminal;

[0040] The cloud master station is used for dividing the edge-edge collaboration area of the intelligent fusion terminals, selecting one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices, sending the IP and port number of the master device to the slave devices, and also performing edge-edge interaction data configuration;

[0041] The master device and the slave devices are communicatively connected;

[0042] The master device is used for monitoring the operating parameters of the low-voltage devices connected to the master device, and also for receiving the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, and combining with a pre-constructed multi-objective model to generate an operating instruction for the low-voltage devices connected to the master device and an operating instruction for the low-voltage devices connected to each slave device, and controlling the low-voltage devices connected to the master device based on the operating instruction for the low-voltage devices connected to the master device, and sending the operating instruction for the low-voltage devices connected to each slave device to each slave device;

[0043] The slave device is used for monitoring the operating parameters of the low-voltage devices connected to the slave device, uploading them to the master device, and simultaneously controlling the low-voltage devices connected to the slave device based on the operating instruction for the low-voltage devices connected to the slave device issued by the master device;

[0044] Among them, the multi-objective model is constructed by taking the minimum power exchanged between the region and the outside world as the first objective and the maximum total photovoltaic output of the region as the second objective, in combination with constraint conditions.

[0045] Optionally, the low-voltage devices include: energy storage devices, distributed photovoltaics, charging piles, and circuit breakers.

[0046] Optionally, it further includes a model construction module, which is used for:

[0047] Taking the minimum power exchanged between the region and the outside world as the first objective, constructing a first objective function;

[0048] Taking the maximum total photovoltaic output of the region as the second objective, constructing a second objective function;

[0049] Setting constraint conditions for the first objective function and the second objective function.

[0050] Optionally, the first objective function is shown as the following formula:

[0051]

[0052] In the formula, Pchg Power exchanged between the representative area and the outside world; P i Power exchanged between each substation area and the 10kV feeder. The direction from the feeder to the substation area is positive, and the direction from the substation area to the feeder is negative; m represents the number of substation areas in the region; i is the counting unit.

[0053] Optionally, the second objective function is shown as follows:

[0054]

[0055] In the formula, P pv Represents the photovoltaic output. n represents the number of photovoltaics, j is the counting unit, and P pv·j Represents the output of the j-th photovoltaic.

[0056] Optionally, the constraint conditions are shown as follows:

[0057]

[0058] In the formula, P i Represents the power exchanged between each substation area and the 10kV feeder. P i·min Represents the minimum power limit of the feeder flowing to the i-th substation area. P i·max Represents the maximum power limit of the feeder flowing to the i-th substation area; P pv·j Represents the output of the j-th photovoltaic; P pv·j·max Represents the maximum value of the output of the j-th photovoltaic. P st·x Represents the power of the x-th energy storage. P st·x·min Represents the minimum energy storage power of the x-th energy storage. P st·x·max Represents the maximum energy storage power of the x-th energy storage; S st·x Represents the energy storage capacity of the x-th energy storage. S st·x·min Represents the minimum energy storage capacity of the x-th energy storage. S st·x·max Represents the maximum energy storage capacity of the x-th energy storage; V pv.j Represents the interface voltage of the j-th photovoltaic. V pv.j·min Represents the minimum interface voltage of the j-th photovoltaic. V pv.j·max Represents the maximum interface voltage of the j-th photovoltaic. m represents the number of substation areas in the region, q represents the number of energy storages, n represents the number of photovoltaics, i represents the substation area counting unit, j represents the photovoltaic counting unit, and x represents the energy storage counting unit.

[0059] Optionally, the cloud master station includes:

[0060] A regional division module for performing edge-edge collaborative regional division on the 10kV feeder and the connected substation areas with the 10kV busbar, tie switches, ring main units, and transformers as boundaries, and selecting one intelligent fusion terminal as the main device and other intelligent fusion terminals as slave devices;

[0061] The edge-to-edge communication module is used to send the IP and port number of the master device to the slave device;

[0062] The edge-to-edge data configuration module is used to configure the slave device to send data to the master device regularly, or when an emergency occurs, inform the master device by means of timely reporting;

[0063] Among them, the slave device sending data to the master device regularly includes: the power flow direction of the monitored distribution transformer, the remaining capacity, the energy storage power and the remaining capacity, and the photovoltaic output;

[0064] The emergency events include: overload of the distribution transformer and voltage over-limit at the grid connection point of the distributed photovoltaic.

[0065] Optionally, the master device is specifically used for:

[0066] Monitoring the operating parameters of the low-voltage devices connected to the master device, receiving the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, performing a first calculation based on the operating parameters of the low-voltage devices connected to the master device and the operating parameters of the low-voltage devices connected to each slave device in combination with a pre-constructed multi-objective model, and according to the calculation results, making adjustments in the adjustment order of the energy storage device and the distributed photovoltaic, generating the operating instructions for the low-voltage devices connected to the master device and the operating instructions for the low-voltage devices connected to each slave device, controlling the low-voltage devices connected to the master device based on the operating instructions for the low-voltage devices connected to the master device, and sending the operating instructions for the low-voltage devices connected to each slave device to each slave device.

[0067] On the other hand, the present application also provides a computer device, including: at least one processor and a memory;

[0068] The memory is used to store one or more programs;

[0069] When the one or more programs are executed by the at least one processor, the above-mentioned method for regional source-load balance scheduling based on edge-to-edge collaboration is implemented.

[0070] On the other hand, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the above-mentioned method for regional source-load balance scheduling based on edge-to-edge collaboration is implemented.

[0071] Compared with the prior art, the beneficial effects of the present invention are:

[0072] The present invention provides a method for balancing regional source and load based on edge-edge collaboration, including: dividing the edge-edge collaboration area for intelligent fusion terminals through a cloud master station, selecting one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices, sending the IP and port number of the master device to the slave devices, and also configuring edge-edge interaction data; monitoring, by the master device, the operating parameters of the low-voltage devices connected to the master device, combining the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, and a pre-constructed multi-objective model to generate operating instructions for the low-voltage devices connected to the master device and operating instructions for the low-voltage devices connected to each slave device, and controlling the low-voltage devices connected to the master device based on the operating instructions for the low-voltage devices connected to the master device, and sending the operating instructions for the low-voltage devices connected to each slave device to each slave device; monitoring, by the slave devices, the operating parameters of the low-voltage devices connected to the slave devices, uploading the operating parameters to the master device, and simultaneously controlling the low-voltage devices based on the operating instructions for the low-voltage devices connected to each slave device sent by the master device; wherein the multi-objective model is constructed with the minimum power exchanged between the region and the outside as the first objective and the maximum total photovoltaic output of the region as the second objective, in combination with constraint conditions. The present invention uses the pre-constructed multi-objective model by the master device to generate operating instructions for low-voltage devices, schedule distributed resources within the region, achieve source-load balance, reduce large-scale power flow, reduce line losses, and simultaneously reduce the communication and computing pressure on the cloud master station. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a flowchart of a method for scheduling regional source-load balance based on edge-edge collaboration according to the present invention;

[0074] Figure 2 It is a schematic diagram of an edge-edge interaction architecture according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] The present invention proposes a method for scheduling regional source-load balance based on edge-edge collaboration. The present invention schedules distributed resources within the region by constructing an edge-edge collaboration architecture, achieves source-load balance, reduces large-scale power flow, reduces line losses, and simultaneously reduces the communication and computing pressure on the cloud master station.

[0076] Embodiment 1:

[0077] The present invention based on the same inventive concept also provides a method for scheduling regional source-load balance based on edge-edge collaboration, as Figure 1 shown, including:

[0078] Dividing the edge-edge collaboration area for intelligent fusion terminals through a cloud master station, selecting one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices, sending the IP and port number of the master device to the slave devices, and also configuring edge-edge interaction data;

[0079] By the master device monitoring the operating parameters of the low-voltage devices connected to the master device, combining the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, a multi-objective model is pre-constructed to generate an operating instruction for the low-voltage device connected to the master device and an operating instruction for the low-voltage device connected to each slave device, and controlling the low-voltage device connected to the master device based on the operating instruction for the low-voltage device connected to the master device, and sending the operating instruction for the low-voltage device connected to each slave device to each slave device;

[0080] By the slave device monitoring the operating parameters of the low-voltage device connected to the slave device, uploading the operating parameters to the master device, and at the same time controlling the low-voltage device based on the operating instruction for the low-voltage device connected to each slave device issued by the master device;

[0081] Among them, the multi-objective model is constructed with the minimum power exchanged between the region and the outside as the first objective and the maximum total photovoltaic output in the region as the second objective, in combination with constraint conditions.

[0082] Optionally, the construction of the multi-objective model includes:

[0083] Taking the minimum power exchanged between the region and the outside as the first objective, constructing a first objective function;

[0084] Taking the maximum total photovoltaic output in the region as the second objective, constructing a second objective function;

[0085] Setting constraint conditions for the first objective function and the second objective function.

[0086] Optionally, the first objective function is shown as the following formula:

[0087]

[0088] In the formula, P chg represents the power exchanged between the region and the outside; P i represents the power exchanged between each substation area and the 10kV feeder; m represents the number of substation areas in the region; i is a counting unit.

[0089] Optionally, the second objective function is shown as the following formula:

[0090]

[0091] In the formula, P pv represents the photovoltaic output, n represents the number of photovoltaics, j is a counting unit, and P pv·j represents the output of the jth photovoltaic.

[0092] Optionally, the constraint conditions are shown as the following formula:

[0093]

[0094] In the formula, P i represents the power exchanged between each substation area and the 10kV feeder. P i·min represents the minimum power limit of the feeder flowing to the i-th substation area. P i·max represents the maximum power limit of the feeder flowing to the i-th substation area; P pv·j represents the output power of the j-th photovoltaic; P pv·j·max represents the maximum value of the output power of the j-th photovoltaic. P st·x represents the power of the x-th energy storage; P st·x·min represents the minimum energy storage power of the x-th energy storage. P st·x·max represents the maximum energy storage power of the x-th energy storage; S st·x represents the energy storage capacity of the x-th energy storage. S st·x·min represents the minimum energy storage capacity of the x-th energy storage. S st·x·max represents the maximum energy storage capacity of the x-th energy storage; V pv.j represents the interface voltage of the j-th photovoltaic. V pv.j·min represents the minimum interface voltage of the j-th photovoltaic. V pv.j·max represents the maximum interface voltage of the j-th photovoltaic. m represents the number of substation areas in the region, q represents the number of energy storages, n represents the number of photovoltaics, and i, j, and x are all counting units.

[0095] Optionally, the cloud master station performs edge-edge collaborative area division on the intelligent fusion terminal, selects one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices. At the same time, the IP and port number of the master device are sent to the slave devices, and edge-edge interaction data configuration is also performed, including:

[0096] The cloud master station's area division module uses the 10kV bus, tie switch, ring main unit, and transformer as boundaries to perform edge-edge collaborative area division on the 10kV feeder and the connected substation areas, and selects one intelligent fusion terminal as the master device and other intelligent fusion terminals as slave devices;

[0097] The cloud master station's edge-edge communication module sends the IP and port number of the master device to the slave devices;

[0098] The cloud master station's edge-edge data configuration module configures the slave devices to send data to the master device regularly, or when an emergency occurs, informs the master device through immediate reporting;

[0099] Among them, the data sent by the slave devices to the master device includes: the power flow direction of the monitored distribution transformer, the remaining capacity, the energy storage power and the remaining capacity, and the photovoltaic output;

[0100] The emergency events include: overload of the distribution transformer and voltage over-limit at the distributed photovoltaic connection point.

[0101] Optionally, the master device monitors the operating parameters of the low-voltage devices connected to the master device, combines the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, and a pre-constructed multi-objective model to generate an operating instruction for the low-voltage device connected to the master device and an operating instruction for the low-voltage device connected to each slave device, and controls the low-voltage device connected to the master device based on the operating instruction for the low-voltage device connected to the master device, and issues the operating instruction for the low-voltage device connected to each slave device to the slave device, including:

[0102] The master device monitors the operating parameters of the low-voltage devices connected to the master device, receives the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, performs a first calculation based on the operating parameters of the low-voltage devices connected to the master device, the operating parameters of the low-voltage devices connected to each slave device, and a pre-constructed multi-objective model, and adjusts according to the adjustment sequence of the energy storage device and the distributed photovoltaic according to the calculation result, generates an operating instruction for the low-voltage device connected to the master device and an operating instruction for the low-voltage device connected to each slave device, and controls the low-voltage device connected to the master device based on the operating instruction for the low-voltage device connected to the master device, and issues the operating instruction for the low-voltage device connected to each slave device to each slave device.

[0103] Embodiment 2:

[0104] A regional source-load balance framework based on edge-edge collaboration, as Figure 2 shown, includes:

[0105] A cloud master station, a plurality of intelligent fusion terminals, and low-voltage devices respectively connected to each intelligent fusion terminal;

[0106] The cloud master station is used for performing edge-edge collaboration area division on the intelligent fusion terminals, selecting one intelligent fusion terminal as the master device, and other intelligent fusion terminals as slave devices, sending the IP and port number of the master device to the slave devices, and also performing edge-edge interaction data configuration;

[0107] The master device and the slave devices are communicatively connected;

[0108] The master device is used for monitoring the operating parameters of the low-voltage devices connected to the master device, and is also used for receiving the operating parameters of the low-voltage devices connected to each slave device uploaded by each slave device, and combining a pre-constructed multi-objective model to generate an operating instruction for the low-voltage device connected to the master device and an operating instruction for the low-voltage device connected to each slave device, and controlling the low-voltage device connected to the master device based on the operating instruction for the low-voltage device connected to the master device, and issuing the operating instruction for the low-voltage device connected to each slave device to each slave device;

[0109] The slave device is used to monitor the operating parameters of the low-voltage device connected to the slave device, upload them to the master device, and at the same time control the low-voltage device connected to the slave device based on the operation instruction of the low-voltage device connected to the slave device issued by the master device;

[0110] Among them, the multi-objective model is constructed with the minimum power exchanged between the region and the outside world as the first objective and the maximum total photovoltaic output of the region as the second objective, combined with constraint conditions.

[0111] The present invention realizes the system scheduling of distributed resources within the region by designing an edge-edge collaboration architecture. The realization of edge-edge collaboration also depends on the cloud master station to complete the edge-edge collaboration region division and edge-edge interaction data configuration.

[0112] First, the cloud master station performs edge-edge collaboration region division. The division basis is: 10kV feeders and connected substations are divided with the 10kV bus, tie switch, ring main unit, and transformer as the boundaries.

[0113] After completing the division of the region, the cloud master station selects an edge computing platform as the master device, and the remaining edge computing terminals as slave devices. The cloud master station sends the IP and port number of the master device to the slave devices. All slave devices are connected to the master device through 4G / 5G, and the slave devices are not connected to each other. Here, the edge computing platform and the intelligent fusion terminal have the same meaning.

[0114] Then, according to the business needs, the cloud master station performs edge-edge interaction data configuration. The slave devices regularly send data to the master device, or when an emergency occurs, they inform the master device through immediate reporting.

[0115] The slave devices regularly sending data to the master device includes: the power flow direction, remaining capacity, energy storage power and remaining capacity, and photovoltaic output of the monitored distribution transformer.

[0116] Emergencies include: overload of the distribution transformer and overvoltage of distributed photovoltaics. Each time data or event information is collected, a calculation and adjustment are performed, and the adjustment order is the energy storage device and distributed photovoltaics.

[0117] A regional source-load balance framework based on edge-edge collaboration further includes: a model construction module, which is used for:

[0118] Taking the minimum power exchanged between the region and the outside world as the first objective, constructing a first objective function;

[0119] Taking the maximum total photovoltaic output of the region as the second objective, constructing a second objective function;

[0120] Setting constraint conditions for the first objective function and the second objective function.

[0121] The goal of achieving regional source-load balance through edge-edge collaboration is to minimize energy interaction with the outside of the region without voltage violation and power overload. The multi-objective equation includes the first objective function, the second objective function, and the constraint conditions, specifically:

[0122]

[0123]

[0124]

[0125] In the formula, P i represents the power exchanged between each substation area and the 10 kV feeder. The direction from the feeder to the substation area is positive, and the direction from the substation area to the feeder is negative. Therefore, P i·min represents the minimum power limit of the feeder flowing to the i-th substation area, which is the smallest as a negative number, and can also be expressed as the maximum power limit of the i-th substation area flowing to the feeder. P i·max represents the maximum power limit of the feeder flowing to the i-th substation area; P pv·j·max represents the maximum value of the j-th photovoltaic output. P st·x represents the power of the x-th energy storage. P st·x·min represents the minimum energy storage power of the x-th energy storage. P st·x·max represents the maximum energy storage power of the x-th energy storage; S st·x represents the energy storage capacity of the x-th energy storage. S st·x·min represents the minimum energy storage capacity of the x-th energy storage. S st·x·max represents the maximum energy storage capacity of the x-th energy storage; V pv.j represents the voltage of the j-th photovoltaic interface. V pv.j·min represents the minimum voltage of the j-th photovoltaic interface. V pv.j·max represents the maximum voltage of the j-th photovoltaic interface. m represents the number of substation areas in the region, q represents the number of energy storages, n represents the number of photovoltaics, i represents the substation area counting unit, j represents the photovoltaic counting unit, and x represents the energy storage counting unit.

[0126] A regional source-load balance architecture based on edge-edge collaboration proposed by the present invention realizes in-situ control of distributed resources within the region by establishing an edge-edge collaboration architecture. And by constructing a multi-objective optimization equation for distributed resources, in-situ distributed consumption is realized, large-scale power flow is reduced, and line loss is decreased.

[0127] Example 3:

[0128] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for regional source-load balance scheduling based on edge-edge collaboration in the above embodiments.

[0129] Embodiment 4:

[0130] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the steps of a method for regional source-load balance scheduling based on edge-edge collaboration in the above embodiments.

[0131] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0132] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0133] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realize the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0135] The above are only embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A regional source-load balancing scheduling method based on edge-to-edge collaboration, characterized in that: include: The cloud master station divides the edge-to-edge collaboration area for the intelligent fusion terminals, selects one intelligent fusion terminal as the master device, and the other intelligent fusion terminals as slave devices. The master device's IP and port number are sent to the slave devices, and the edge-to-edge interaction data is configured. Monitoring the operating parameters of the low-voltage equipment connected to the master device through the master device, combining the operating parameters of the low-voltage equipment connected to the slave devices uploaded by each slave device and the pre-built multi-objective model, generating operating instructions for the low-voltage equipment connected to the master device and operating instructions for the low-voltage equipment connected to the slave devices, and controlling the low-voltage equipment connected to the master device based on the operating instructions of the low-voltage equipment connected to the master device, and issuing the operating instructions of the low-voltage equipment connected to the slave devices to each slave device; The operating parameters of the low-voltage devices connected to the slave devices are monitored by the slave devices and uploaded to the master device, and the low-voltage devices are controlled based on the operating instructions of the low-voltage devices connected to the slave devices issued by the master device; Among them, the multi-objective model is constructed with the minimum power exchanged between the region and the outside world as the first goal and the maximum total regional photovoltaic output as the second goal, combined with constraints.

2. The method according to claim 1, characterized in that The construction of the multi-objective model includes: Taking the minimum power exchanged between the region and the outside world as the first goal, the first objective function is constructed; Taking the maximum total regional photovoltaic output as the second goal, the second objective function is constructed; Constraints are set for the first objective function and the second objective function.

3. The method according to claim 2, characterized in that The first objective function is shown as follows: Where P chg Represents the power exchanged between the region and the outside world; P i Represents the power exchanged between each substation and the 10kV feeder, with the power flowing from the feeder to the substation as positive and the power flowing from the substation to the feeder as negative; m represents the number of substations in the area; i is the counting unit.

4. The method according to claim 2, characterized in that The second objective function is shown as follows: Where P pv represents the photovoltaic output, n represents the number of photovoltaics, j is the counting unit, P pv·j Represents the output of the jth photovoltaic.

5. The method according to claim 2, characterized in that The constraint condition is as follows: Where P i Represents the power exchanged between each substation and the 10kV feeder, P i·min represents the minimum power limit of the feeder to the ith substation, P i·max represents the maximum power limit of the feeder flowing to the i-th substation; P pv·j represents the output of the jth photovoltaic; P pv·j·max represents the maximum value of the jth photovoltaic output, P st·x represents the power of the xth energy storage, P st·x·min represents the minimum energy storage power of the xth energy storage, P st·x·max represents the maximum energy storage power of the xth energy storage; S st·x represents the energy storage capacity of the xth energy storage, S st·x·min represents the minimum energy storage capacity of the xth energy storage, S st·x·max represents the maximum energy storage capacity of the xth energy storage; V pv.j Represents the voltage of the jth photovoltaic interface, V pv.j·min Represents the minimum voltage of the jth photovoltaic interface, V pv.j·max represents the maximum voltage of the jth PV interface, m represents the number of substations in the area, q represents the number of energy storage units, n represents the number of PV units, i represents the substation counting unit, j represents the PV counting unit, and x represents the energy storage counting unit.

6. The method according to claim 1, characterized in that The cloud master station divides the edge-to-edge collaborative area of ​​the intelligent fusion terminal, selects one intelligent fusion terminal as the master device, and the other intelligent fusion terminals as slave devices, sends the IP and port number of the master device to the slave device, and performs edge-to-edge interaction data configuration, including: The regional division module of the cloud master station is used to divide the 10kV feeder and the connected substation into edge-to-edge collaborative regions with 10kV busbar, tie switch, ring main unit, and transformer as the boundaries, and selects one intelligent fusion terminal as the master device and other intelligent fusion terminals as slave devices; The IP and port number of the master device are sent to the slave device through the edge-to-edge communication module of the cloud master station; Use the edge data configuration module of the cloud master to configure the slave device to send data to the master device regularly, or inform the master device through timely reporting when an emergency occurs; The data sent by the slave device to the master device include: the power flow direction, remaining capacity, energy storage power and remaining capacity, and photovoltaic output of the monitored distribution transformer; The emergency events include: overload of distribution transformer and voltage exceeding the limit of distributed photovoltaic grid connection point.

7. The method according to claim 1, characterized in that The method comprises: monitoring the operating parameters of the low-voltage device connected to the master device through the master device, combining the operating parameters of the low-voltage device connected to the slave devices uploaded by each slave device and the pre-built multi-objective model, generating the operating instructions of the low-voltage device connected to the master device and the operating instructions of the low-voltage device connected to the slave devices, and controlling the low-voltage device connected to the master device based on the operating instructions of the low-voltage device connected to the master device, and sending the operating instructions of the low-voltage device connected to the slave devices to each slave device, including: The main device monitors the operating parameters of the low-voltage device connected to the main device, and accepts the operating parameters of the low-voltage device connected to the slave device uploaded by each slave device; based on the operating parameters of the low-voltage device connected to the main device and the operating parameters of the low-voltage device connected to the slave device, a calculation is performed in combination with a pre-constructed multi-objective model; according to the calculation result, adjustments are made in accordance with the adjustment sequence of the energy storage device and the distributed photovoltaic, and operating instructions of the low-voltage device connected to the main device and the operating instructions of the low-voltage device connected to the slave device are generated; based on the operating instructions of the low-voltage device connected to the main device, the low-voltage device connected to the main device is controlled, and the operating instructions of the low-voltage device connected to the slave device are sent to the slave device.

8. A regional source-load balancing architecture based on edge-to-edge collaboration, characterized in that: include: A cloud master station, multiple intelligent fusion terminals, and low-voltage equipment respectively connected to each intelligent fusion terminal; The cloud master is used to divide the edge-to-edge collaborative areas of the intelligent fusion terminals, select one intelligent fusion terminal as the master device and the other intelligent fusion terminals as slave devices, send the IP and port number of the master device to the slave devices, and perform edge-to-edge interaction data configuration; The master device is in communication connection with the slave device; The master device is used to monitor the operating parameters of the low-voltage device connected to the master device, and is also used to accept the operating parameters of the low-voltage device connected to the slave devices uploaded by each slave device, and generate operating instructions for the low-voltage device connected to the master device and operating instructions for the low-voltage devices connected to the slave devices in combination with a pre-built multi-objective model, and control the low-voltage device connected to the master device based on the operating instructions of the low-voltage device connected to the master device, and send the operating instructions of the low-voltage device connected to the slave devices to each slave device; The slave device is used to monitor the operating parameters of the low-voltage device connected to the slave device and upload them to the master device, and control the low-voltage device connected to the slave device based on the operating instructions of the low-voltage device connected to the slave device issued by the master device; Among them, the multi-objective model is constructed with the minimum power exchanged between the region and the outside world as the first goal and the maximum total regional photovoltaic output as the second goal, combined with constraints.

9. The architecture of claim 8, wherein: The low-voltage equipment includes: at least one of an energy storage device, distributed photovoltaics, a charging pile and a circuit breaker.

10. The architecture of claim 8, wherein: Also included are model building modules for: Taking the minimum power exchanged between the region and the outside world as the first goal, the first objective function is constructed; Taking the maximum total regional photovoltaic output as the second goal, the second objective function is constructed; Constraints are set for the first objective function and the second objective function.

11. The architecture of claim 10, wherein: The first objective function is shown as follows: Where P chg Represents the power exchanged between the region and the outside world; P i Represents the power exchanged between each substation and the 10kV feeder, with the power flowing from the feeder to the substation as positive and the power flowing from the substation to the feeder as negative; m represents the number of substations in the area; i is the counting unit.

12. The architecture of claim 10, wherein: The second objective function is shown as follows: Where P pv represents the photovoltaic output, n represents the number of photovoltaics, j is the counting unit, P pv·j Represents the output of the jth photovoltaic.

13. The architecture of claim 10, wherein: The constraint condition is as follows: Where P i Represents the power exchanged between each substation and the 10kV feeder, P i·min represents the minimum power limit of the feeder to the ith substation, P i·max represents the maximum power limit of the feeder flowing to the i-th substation; P pv·j represents the output of the jth photovoltaic; P pv·j·max represents the maximum value of the jth photovoltaic output, P st·x represents the power of the xth energy storage, P st·x·min represents the minimum energy storage power of the xth energy storage, P st·x·max represents the maximum energy storage power of the xth energy storage; S st·x represents the energy storage capacity of the xth energy storage, S st·x·min represents the minimum energy storage capacity of the xth energy storage, S st·x·max represents the maximum energy storage capacity of the xth energy storage; V pv.j Represents the voltage of the jth photovoltaic interface, V pv.j·min Represents the minimum voltage of the jth photovoltaic interface, V pv.j·max represents the maximum voltage of the jth PV interface, m represents the number of substations in the area, q represents the number of energy storage units, n represents the number of PV units, i represents the substation counting unit, j represents the PV counting unit, and x represents the energy storage counting unit.

14. The architecture of claim 8, wherein: The cloud master station includes: The regional division module is used to divide the 10kV feeder and the connected substation into edge-to-edge collaborative regions based on the 10kV busbar, tie switch, ring main unit, and transformer, and select one intelligent fusion terminal as the master device and other intelligent fusion terminals as slave devices; The edge-to-edge communication module is used to send the IP and port number of the master device to the slave device; The edge data configuration module is used to configure the slave device to send data to the master device regularly, or to inform the master device in a timely manner when an emergency occurs; The slave device periodically sends data to the master device including: the monitored power flow direction, remaining capacity, energy storage power and remaining capacity, and photovoltaic output of the distribution transformer; The emergency events include: overload of distribution transformer and voltage exceeding the limit of distributed photovoltaic grid connection point.

15. The architecture of claim 8, wherein: The main device is specifically used for: Monitor the operating parameters of the low-voltage equipment connected to the master device, accept the operating parameters of the low-voltage equipment connected to the slave devices uploaded by the slave devices, perform a calculation based on the operating parameters of the low-voltage equipment connected to the master device and the operating parameters of the low-voltage equipment connected to the slave devices in combination with a pre-built multi-objective model, and make adjustments according to the calculation results in the adjustment order of the energy storage device and the distributed photovoltaic, generate operating instructions for the low-voltage equipment connected to the master device and operating instructions for the low-voltage equipment connected to the slave devices, control the low-voltage equipment connected to the master device based on the operating instructions of the low-voltage equipment connected to the master device, and send the operating instructions of the low-voltage equipment connected to the slave devices to each slave device.

16. A computer device, characterized in that: include: at least one processor and memory; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a regional source-load balancing scheduling method based on edge-to-edge collaboration as described in any one of claims 1 to 7 is implemented.

17. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, a regional source-load balancing scheduling method based on edge-to-edge collaboration as described in any one of claims 1 to 7 is implemented.

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