Methods, devices and terminal equipment for coordinated regulation of power generation, grid, load and storage in power distribution networks

Abstract

This application provides a method, device, and terminal equipment for coordinated regulation of power generation, grid, load, and storage in a distribution network, relating to the field of distribution network dispatching and control technology. The method includes: acquiring the day-ahead dispatch plan for each distribution area, which includes the planned operation data of the current distribution area during each day-ahead dispatch period; acquiring the real-time operation data of each distribution area; determining the dispatch target for each distribution area based on the real-time operation data; solving a pre-constructed intraday dispatching optimization model based on the dispatch target, pre-established coordination constraints, and power balance constraints to obtain the intraday dispatch plan; and revising the day-ahead dispatch plan using the intraday dispatch plan, which includes the optimized operation data of the current distribution area during each day-ahead dispatch period. This application effectively ensures the coordination between the short-term local rolling optimization of intraday dispatching and the global optimization results of day-ahead dispatching, achieving efficient utilization of power between distribution areas.

Description

Technical Field

[0001] This application relates to the field of distribution network dispatch and control technology, specifically to a method for coordinated regulation of distribution network sources, grid, load and storage, a device for coordinated regulation of distribution network sources, grid, load and storage, a machine-readable storage medium and a terminal device. Background Technology

[0002] With the rapid development of distributed resources such as distributed power sources, energy storage, and electric vehicles, distribution networks are gradually exhibiting new characteristics of "high proportion of clean energy and high proportion of power electronic devices." Along with the massive influx of distributed resources, problems have arisen in distribution networks, including difficulties in renewable energy consumption, resource aggregation, and coordinated control. The efficient and convenient access and clustered control of distributed resources have become major challenges in the construction of new distribution networks.

[0003] The power characteristics of distributed photovoltaic (PV), energy storage, electric vehicles, and flexible adjustable loads in distribution network areas are easily affected by environmental, time, residential constraints, and market incentives, exhibiting heterogeneous, multi-period coupling, and significant uncertainty. Their characteristics are difficult to grasp, and their operational regulation capabilities are difficult to calculate. Furthermore, distributed resources are characterized by massive quantity, heterogeneity, and uncertainty. Simple aggregation cannot meet the needs of the power grid for frequency regulation, voltage regulation, peak shaving, and inertial support. Therefore, dynamic aggregation based on the type, capacity, and complementary characteristics of distributed resources is necessary. Currently, distribution cloud master stations cannot efficiently and flexibly control the aggregated distributed resources in distribution areas, lacking relevant distributed resource cluster control strategies, making it difficult to fully realize the potential of massive distributed adjustable resources to support the safe, stable, and economical operation of the power grid. Summary of the Invention

[0004] The purpose of this application is to provide a method for coordinated regulation of power generation, grid, load and storage in a distribution network, a device for coordinated regulation of power generation, grid, load and storage in a distribution network, a machine-readable storage medium and a terminal device, so as to solve the above problems.

[0005] To achieve the above objectives, the first aspect of this application provides a method for coordinated regulation of power generation, grid, load, and storage in a distribution network, comprising:

[0006] Obtain the day-ahead scheduling plan for each transformer area. The day-ahead scheduling plan includes the planned operation data of the current transformer area in each day-ahead scheduling period. The day-ahead scheduling plan is obtained by solving a pre-constructed day-ahead scheduling optimization model based on the historical operation data of the current transformer area.

[0007] The system acquires real-time operation data for each transformer substation, determines scheduling objectives for each substation based on the real-time operation data, solves a pre-built intraday scheduling optimization model based on the scheduling objectives, pre-established coordination constraints, and power balance constraints to obtain an intraday scheduling plan, and corrects the previous day scheduling plan using the intraday scheduling plan. The intraday scheduling plan includes optimized operation data for the current transformer substation during each previous day scheduling period.

[0008] The coordination constraints and the power balance constraints are constructed based on the planned operation data of the current transformer area during each day-ahead scheduling period and the optimized operation data of the current transformer area during each day-ahead scheduling period.

[0009] Optionally, each substation includes at least one power generation unit, an energy storage unit, an external discharge unit, and an electricity load unit, and a day-ahead dispatch optimization model is constructed, including:

[0010] Determine at least one cost coefficient of the power generation unit, and perform a quadratic function fitting on the output power of the power generation unit using at least one cost coefficient of the power generation unit to construct the cost function of the power generation unit;

[0011] The revenue coefficient of the energy storage unit is determined, and the charging and discharging power of the energy storage unit is fitted with the revenue coefficient of the energy storage unit to construct the cost function of the energy storage unit.

[0012] The cost coefficient of the external discharge unit is determined, and the discharge power of the external discharge unit is fitted with a quadratic function using the cost coefficient of the external discharge unit to construct the cost function of the external discharge unit.

[0013] Determine the cost coefficient and revenue coefficient of the power load unit, and fit the power consumption of the power load unit to the cost coefficient and revenue coefficient of the power load unit to construct the cost function of the power load unit.

[0014] Based on the cost functions of the power generation unit, the energy storage unit, the external discharge unit, and the power load unit, a day-ahead scheduling optimization model for each power distribution area is constructed.

[0015] Optionally, a day-ahead scheduling optimization model for each distribution area is constructed based on the cost function of the power generation unit, the cost function of the energy storage unit, the cost function of the external discharge unit, and the cost function of the power load unit, including:

[0016] With the goal of minimizing the cost of all power generation units in each distribution area during all day-ahead scheduling periods, a day-ahead scheduling optimization model for each distribution area is constructed by calculating the difference between the cost function of the power generation unit, the cost function of the energy storage unit, the cost function of the external discharge unit, and the cost function of the power load unit.

[0017] Optionally, the real-time operating data of each distribution area includes the power status of each distribution area, which includes surplus power (indicating that power generation in each distribution area is greater than power consumption) and deficit power (indicating that power generation in each distribution area is less than power consumption); acquiring the real-time operating data of each distribution area and determining the scheduling target of each distribution area based on the real-time operating data includes:

[0018] Identify the areas with surplus power and the areas with deficit power, and sort the areas with surplus power and deficit power from highest to lowest.

[0019] According to the sorting order of the remaining power and the power deficit of each transformer area, the remaining power of each transformer area with the power deficit of each transformer area are matched in turn.

[0020] If the remaining power of the current surplus power station is greater than the current deficit power of the current deficit power station, the deficit power of the current deficit power station is used as the target power. If the remaining power of the current surplus power station is not greater than the deficit power of the current deficit power station, the target power is allocated from the current surplus power station to the current deficit power station, until the remaining power of all surplus power stations is allocated to the deficit power station, or until the deficit power of any deficit power station is not greater than the preset deficit power threshold.

[0021] If the power deficit of any power-deficient power station is not greater than the preset power deficit threshold, and the power surplus of any power-remaining power station is not less than the preset power surplus threshold, the energy storage capacity of each power station is obtained, and the power surplus of each power-remaining power station is sequentially allocated to each power station for energy storage until the power surplus of each power-remaining power station is less than the power surplus threshold, thus obtaining the scheduling target of each power station.

[0022] Optionally, an intraday scheduling optimization model is constructed, including:

[0023] Determine the different load categories and at least one incentive compensation cost coefficient for each electrical load unit;

[0024] The adjustment amount of the power load unit under different load categories is fitted with at least one incentive compensation cost coefficient to construct the incentive compensation cost function of the power load unit;

[0025] With the objective of minimizing the incentive compensation cost of all power load units in each distribution area during all intraday scheduling periods, an intraday scheduling optimization model for each distribution area is constructed by calculating the difference between the cost function of the power generation unit, the cost function of the energy storage unit, and the incentive compensation cost function of the power load unit.

[0026] Optionally, the planned operation data of the current distribution area during each day-ahead scheduling period includes: the charging power or discharging power of each energy storage unit during each day-ahead scheduling period; the optimized operation data of the current distribution area during each day-ahead scheduling period includes: the optimized charging power or optimized discharging power of each energy storage unit during each day-ahead scheduling period; the coordination constraints include:

[0027] The absolute value of the difference between the charging power of the current energy storage unit during any given day-ahead scheduling period and the optimized charging power of the current energy storage unit during the corresponding day-ahead scheduling period shall not be greater than the product of the preset coordination coefficient and the charging power of the current energy storage unit during that day-ahead scheduling period; and

[0028] The absolute value of the difference between the discharge power of the current energy storage unit during any given day-ahead scheduling period and the optimized discharge power of the current energy storage unit during the corresponding day-ahead scheduling period is not greater than the product of the coordination coefficient and the discharge power of the current energy storage unit during that day-ahead scheduling period.

[0029] Optionally, the method further includes:

[0030] The sum of the first charge and discharge power of all energy storage units during the daily scheduling period is determined; the sum of the second charge and discharge power of each energy storage unit during the daily scheduling period is determined; and the sum of the incentive compensation costs of each power load unit is determined based on the incentive compensation cost function of the power load unit.

[0031] The power balance constraints include:

[0032] The scheduling target is equal to the sum of the first charging and discharging power, the sum of the second charging and discharging power, and the sum of the incentive compensation costs of each power load unit.

[0033] Optionally, the cost function of the power generation unit includes:

[0034]

[0035] in, Let i be the cost function of the i-th power generation unit. Let i be the output power of the i-th power generation unit during the dispatch period on the t-th day. , , This is the cost coefficient.

[0036] Optionally, the cost function of the energy storage unit includes:

[0037]

[0038] in, Let be the discharge power of the j-th energy storage unit during the t-th day-ahead scheduling period. is the revenue coefficient of the energy storage unit.

[0039] Optionally, the cost function of the external discharge unit includes:

[0040]

[0041] in, Let i be the cost function of the i-th external discharge unit. The cost coefficient of the external discharge unit. Let be the discharge power of the i-th external discharge unit during the t-th day-ahead scheduling period. This represents the maximum discharge power of the i-th external discharge unit.

[0042] Optionally, the cost function of the electrical load unit includes:

[0043]

[0044] in, Let j be the cost function of the j-th electrical load unit. Let j be the power consumption of the j-th electrical load unit during the t-th day-ahead dispatch period. and These are the cost coefficient and revenue coefficient for the j-th electrical load unit, respectively.

[0045] Optionally, the day-ahead scheduling optimization model includes:

[0046]

[0047] Where N is the total number of power generation units, L is the total number of energy storage units, M is the total number of external discharge units, Q is the total number of power load units, and t = 1, 2, ..., T.

[0048] Optionally, the planned operation data of the current distribution area during each day-ahead scheduling period also includes the predicted power value of the power load unit during each day-ahead scheduling period, and the incentive compensation cost function of the power load unit, including:

[0049]

[0050]

[0051] in, Let m be the incentive compensation cost for the m-th power load unit during the t-th day-ahead scheduling period. These represent load categories as day-ahead load reduction, day-ahead load transfer, and intraday load reduction, respectively. and These are the incentive compensation cost coefficients for each load category. This represents the adjustment amount for the m-th electrical load unit within the k-th load category. Let m be the predicted power value of the m-th electrical load unit in the k-th load category. This represents the current power value of the m-th electrical load unit in the k-th load category.

[0052] Optionally, the intraday scheduling optimization model includes:

[0053] .

[0054] Optionally, the power balance constraint includes:

[0055]

[0056] in, Indicates the scheduling target. This represents the charging power of the energy storage unit during the day-ahead scheduling period in the t-th day-ahead scheduling period. This represents the discharge power of the energy storage unit during the day-ahead scheduling period in the t-th day-ahead scheduling period. This represents the optimized charging power of the energy storage unit during the scheduling period on the t-th day. This represents the discharge power of the energy storage unit during the scheduling period on the t-th day.

[0057] A second aspect of this application provides a distribution network source-grid-load-storage coordinated control device, which applies the above-mentioned distribution network source-grid-load-storage coordinated control method, including:

[0058] The day-ahead scheduling plan module is configured to obtain the day-ahead scheduling plan for each transformer area. The day-ahead scheduling plan includes the planned operation data of the current transformer area in each day-ahead scheduling period. The day-ahead scheduling plan is obtained by solving a pre-built day-ahead scheduling optimization model based on the historical operation data of the current transformer area.

[0059] The intraday scheduling planning module is configured to acquire real-time operating data of each transformer substation, determine the scheduling objectives of each substation based on the real-time operating data of each substation, and solve the pre-constructed intraday scheduling optimization model based on the scheduling objectives, pre-established coordination constraints, and power balance constraints to obtain the intraday scheduling plan. The intraday scheduling plan is then used to correct the previous day scheduling plan. The intraday scheduling plan includes the optimized operating data of the current transformer substation during each previous day scheduling period.

[0060] The coordination constraints and the power balance constraints are constructed based on the planned operation data of the current transformer area during each day-ahead scheduling period and the optimized operation data of the current transformer area during each day-ahead scheduling period.

[0061] In a third aspect, this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the above-described distribution network source-grid-load-storage coordinated control method.

[0062] In a fourth aspect, this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described distribution network source-grid-load-storage coordinated control method.

[0063] This application obtains the day-ahead scheduling plan for each distribution area through a pre-constructed day-ahead scheduling optimization model. Using the day-ahead scheduling plan of each distribution area as its baseline scheduling plan, the application acquires real-time operational data of each distribution area during operation. Based on this real-time data, the application dynamically determines the scheduling objectives for each distribution area and, based on these objectives, obtains the current day-ahead scheduling plan through a pre-constructed intraday scheduling optimization model. This allows for adjustments to the day-ahead scheduling plan. Thus, this application achieves optimized control within a single distribution area and overall coordinated control between multiple distribution areas through two-layer coordinated regulation of single and multiple distribution areas. Furthermore, during the adjustment of the day-ahead scheduling plan, this application effectively ensures the coordination between the short-term local rolling optimization of intraday scheduling and the global optimization results of day-ahead scheduling through coordination constraints and power balance constraints. This achieves efficient utilization of power between distribution areas, thereby realizing the coordinated and optimized operation of the distribution network's "source-grid-load-storage" system.

[0064] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0065] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0066] Figure 1 A system schematic diagram of a power distribution network source-grid-load-storage coordinated control system provided in a preferred embodiment of this application;

[0067] Figure 2 This is a schematic diagram of the functional modules of the intelligent fusion terminal provided in a preferred embodiment of this application;

[0068] Figure 3A flowchart illustrating the method for coordinated regulation of power distribution network sources, grid, load, and storage provided in a preferred embodiment of this application;

[0069] Figure 4 A schematic block diagram of a power distribution network source-grid-load-storage coordinated control device provided in a preferred embodiment of this application;

[0070] Figure 5 A schematic diagram of a terminal device provided for a preferred embodiment of this application.

[0071] Explanation of reference numerals in the attached figures

[0072] 10 - Terminal device, 100 - Processor, 101 - Memory, 102 - Computer program. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0074] It should be noted that the technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this application.

[0075] Currently, distributed resources in distribution substations are mostly connected to devices manually. This results in a large number of devices of various types, with diverse physical interfaces and communication protocols, lacking a unified and standardized information interaction model. The connection process requires a significant amount of on-site debugging and maintenance work. Furthermore, seamless replacement is not possible after equipment failure, leading to errors, heavy debugging workload, poor scalability, and high construction and maintenance costs, severely restricting the potential of distributed resources for collaborative control. To address these issues, the distribution network source-grid-load-storage collaborative control system proposed in this application uses the distribution substation as an intermediate management layer between the terminal load and the distribution cloud platform. A single substation can achieve distributed optimization control of massive, dispersed, adjustable loads and distributed renewable energy. For multi-substation collaborative control, the operating status of the substation is reported to the distribution cloud platform through intelligent fusion terminals. The cloud platform then issues control commands, controlling flexible interconnection devices to achieve interconnection and mutual supply between substations, promoting efficient absorption of distributed power sources and flexible access of various sources and loads. Figure 1As shown, the power grid-source-load-storage coordinated control system of this application includes a cloud platform and multiple distribution areas. Each distribution area is a micro-autonomous system formed by intelligent fusion terminals, flexible interconnection devices, and their connected distributed resources such as photovoltaics, energy storage, and charging piles. Thus, the power grid-source-load-storage coordinated control system of this application includes two levels of control. For a single distribution area, by controlling the charging and discharging power of energy storage, the output power of photovoltaic inverters, and the charging power of charging piles, the optimal selection between distributed devices is achieved under the premise of ensuring the safe operation of equipment, thereby realizing the economic optimization of the distribution area. For multiple distribution areas, if there is unabsorbable electricity in a certain distribution area, it is reported to the cloud master station, and matched with other autonomous systems through flexible interconnection devices to realize mutual assistance scheduling between distribution areas, improve the access capacity of distributed resources, and enhance the safe operation level of the power grid.

[0076] like Figure 2 As shown, the control software module of this application can be deployed in the intelligent converged terminal of each distribution area, including a configuration file import module, an MQTT message module, a data center interaction module, a data acquisition module, a control module, and an MBUS / TCP protocol module.

[0077] The configuration file import module is used to configure the configuration file, which contains information about each terminal device in the low-voltage distribution area and its communication protocol. The communication protocols of terminal devices from different manufacturers may differ. Terminal device information includes device address, protocol type, port, data model, etc.; communication protocols include various protocols, protocols from different manufacturers, and data item identifiers within the protocols. The configuration file allows for the flexible addition or removal of distributed devices and adaptation to different manufacturers' communication protocols.

[0078] The MQTT module is used to establish and maintain connections with the MQTT server, subscribe to MQTT messages, and send, receive, parse, and forward messages. The intelligent converged terminal uses this module to send the operating status data of the distribution area equipment to the cloud platform, and simultaneously forwards control commands issued by the cloud platform to the control module to achieve optimized control of massive, distributed, adjustable loads and distributed renewable energy sources.

[0079] The data center interaction module is used to set up and register the models of the end devices in the data center based on the end device profile information in the configuration file, and write the collected end device data into the data center. This module provides interface for model registration and device registration, interface for querying various data in the data center, and interface for interaction with other apps.

[0080] The data acquisition module is used to collect data from the peer devices. This module can periodically collect data from the peer devices sequentially according to the device profile and protocol information in the configuration file, and then write the collected data to the data center. The acquisition period is adjustable; for example, a default value of 5 minutes can be set.

[0081] The control module reads information such as voltage, frequency, and load from the data center, integrates the big data of the distribution area with the control commands issued by the main control station, and adjusts the photovoltaic and energy storage systems to ensure that the new energy equipment outputs or absorbs reactive power, thereby stabilizing the voltage. Furthermore, it dynamically controls the end devices according to the control algorithm, periodically adjusting the target to achieve the desired effect. The control period is adjustable; for example, a default value of 5 minutes can be set.

[0082] The MBUS / TCP protocol module handles protocol-related tasks, such as framing and sending, receiving response frames, and parsing to obtain data. During operation, it first initializes the communication channel, establishes a communication channel with the end device, sends the required messages through the communication channel, and inserts the response message into the send message queue upon receiving a correct response. This module also includes framing and parsing functions to process protocol-related messages, which are then called by the data acquisition and control modules.

[0083] It is understood that the distribution network source-grid-load-storage coordinated control method of this application is applied to the distribution network source-grid-load-storage coordinated control system of this application, specifically, it can be applied to the intelligent fusion terminal of the distribution network source-grid-load-storage coordinated control system. For example... Figure 3 As shown, the first aspect of this application provides a method for coordinated regulation and control of power generation, grid, load, and storage in a distribution network, comprising:

[0084] S100. Obtain the day-ahead scheduling plan for each transformer area. The day-ahead scheduling plan includes the planned operation data of the current transformer area in each day-ahead scheduling period. The day-ahead scheduling plan is obtained by solving the pre-built day-ahead scheduling optimization model based on the historical operation data of the current transformer area.

[0085] S200. Obtain real-time operation data for each transformer substation, determine the scheduling objectives for each substation based on the real-time operation data, and solve the pre-built intraday scheduling optimization model based on the scheduling objectives, pre-established coordination constraints, and power balance constraints to obtain the intraday scheduling plan. The intraday scheduling plan is used to correct the daytime scheduling plan. The intraday scheduling plan includes the optimized operation data of the current transformer substation during each daytime scheduling period. The coordination constraints and power balance constraints are constructed based on the planned operation data and optimized operation data of the current transformer substation during each daytime scheduling period.

[0086] Thus, this application obtains the day-ahead scheduling plan for each distribution area through a pre-constructed day-ahead scheduling optimization model. Using the day-ahead scheduling plan of each distribution area as its baseline scheduling plan, the application acquires real-time operational data of each distribution area during operation. Based on this real-time data, the application dynamically determines the scheduling objectives for each distribution area and, based on these objectives, obtains the current day-ahead scheduling plan through a pre-constructed intraday scheduling optimization model. This allows for adjustments to the day-ahead scheduling plan. Therefore, this application achieves optimized control within a single distribution area and overall coordinated control between multiple distribution areas through two-layer coordinated regulation of single and multiple distribution areas. Simultaneously, during the adjustment of the day-ahead scheduling plan, this application effectively ensures the coordination between the short-term local rolling optimization of intraday scheduling and the global optimization results of day-ahead scheduling through coordinated constraints and power balance constraints. This achieves efficient utilization of power between distribution areas, thereby realizing the coordinated and optimized operation of the distribution network's "source-grid-load-storage" system.

[0087] In this application, each transformer substation includes at least one power generation unit, an energy storage unit, an external discharge unit, and an electrical load unit. The power generation unit can be a distributed new energy system such as distributed photovoltaic, the energy storage unit is an energy storage battery, the external discharge unit is a charging pile, and the electrical load unit is the various load equipment in the transformer substation, such as household or commercial load equipment such as lighting systems and air conditioning systems.

[0088] This application pre-constructs a day-ahead scheduling optimization model for each transformer substation, thereby enabling the generation of day-ahead scheduling plans for each substation based on its historical operational data. Step S100 involves constructing the day-ahead scheduling optimization model, including:

[0089] S110. Determine at least one cost coefficient for the power generation unit, and perform a quadratic function fitting on the output power of the power generation unit using at least one cost coefficient to construct the cost function of the power generation unit, wherein the cost function of the power generation unit includes:

[0090]

[0091] in, Let i be the cost function of the i-th power generation unit. Let i be the output power of the i-th power generation unit during the dispatch period on the t-th day. , , This is a cost coefficient, which can be determined in advance based on actual circumstances.

[0092] S120. Determine the revenue coefficient of the energy storage unit, and fit the charging and discharging power of the energy storage unit to the revenue coefficient to construct the cost function of the energy storage unit. The cost function of the energy storage unit includes:

[0093]

[0094] in, Let j be the cost function of the j-th energy storage unit. Let be the discharge power of the j-th energy storage unit during the t-th day-ahead scheduling period. Let be the revenue coefficient of the energy storage unit, where the revenue coefficient of the energy storage unit can be determined in advance.

[0095] S130. Determine the cost coefficient of the external discharge unit, and use the cost coefficient of the external discharge unit to perform a quadratic function fitting on the discharge power of the external discharge unit to construct the cost function of the external discharge unit.

[0096] The cost function of the external discharge unit includes:

[0097]

[0098] in, Let i be the cost function of the i-th external discharge unit. The cost coefficient for the external discharge unit, Let be the discharge power of the i-th external discharge unit during the scheduling period on the t-th day, which is the charging power of the charging pile during the scheduling period on the t-th day. Let be the maximum discharge power of the i-th external discharge unit, which is the maximum charging power of the charging pile. The cost coefficient of the external discharge unit can be determined in advance.

[0099] S140. Determine the cost coefficient and revenue coefficient of the power load unit, and fit the power consumption of the power load unit with the cost coefficient and revenue coefficient of the power load unit to construct the cost function of the power load unit.

[0100] Optionally, the cost function of the electrical load unit includes:

[0101]

[0102] in, Let j be the cost function of the j-th electrical load unit. Let j be the power consumption of the j-th electrical load unit during the t-th day-ahead dispatch period. and These are the cost coefficient and revenue coefficient of the j-th power load unit, respectively. The cost coefficient and revenue coefficient of the power load unit can be predetermined. For example, the revenue coefficient can be predetermined based on the reduction in power costs caused by the power load unit using the power of the energy storage unit.

[0103] S150. Based on the cost functions of power generation units, energy storage units, external discharge units, and electricity load units, a day-ahead dispatch optimization model is constructed for each distribution area. This includes: aiming to minimize the cost of all power generation units in each distribution area during all day-ahead dispatch periods, and constructing a day-ahead dispatch optimization model for each distribution area by calculating the difference between the cost functions of power generation units and the cost functions of energy storage units, external discharge units, and electricity load units. Specifically, this application treats the control arrangements issued by the cloud to each edge distribution area as the overall control task for the distribution area, aiming to maximize photovoltaic absorption, minimize voltage fluctuations, and reduce equipment operating costs. A day-ahead dispatch optimization model is established, which includes:

[0104]

[0105] Where N is the total number of power generation units, L is the total number of energy storage units, M is the total number of external discharge units, Q is the total number of power load units, t=1,2,...,T, where t represents the t-th day-ahead scheduling period, and T represents the scheduling cycle. For example, if the day-ahead scheduling cycle is 24 hours and the resolution is 1 hour, then T=24.

[0106] Understandably, in step S100, the day-ahead scheduling plan can be obtained by solving a pre-constructed day-ahead scheduling optimization model based on the historical operating data of the current transformer area. For example, historical operating data of the current transformer area can be obtained in advance, such as the historical power generation of photovoltaic units in each day-ahead scheduling period on different days. The predicted power generation of the current transformer area in each day-ahead scheduling period on the next day, i.e., the photovoltaic output power, can be predicted by using existing optimization algorithms such as linear programming, particle swarm optimization, and gray wolf algorithm to solve the day-ahead scheduling optimization model to obtain the day-ahead scheduling plan. That is, the charging and discharging power of the energy storage unit, the discharge power of the external discharge unit, and the power consumption of the load unit in each day-ahead scheduling period, i.e., the predicted power value, etc. Understandably, when solving the day-ahead scheduling optimization model, it is also necessary to determine the constraints. For example, the constraints may include that the energy storage capacity of the energy storage unit is not less than the minimum energy storage capacity and not more than the maximum energy storage capacity, and that the photovoltaic output power is not less than the minimum output power and not more than the maximum output power, etc. The process of solving the day-ahead scheduling model using existing optimization algorithms is existing technology and is not limited here.

[0107] During the dispatching process, the day-ahead dispatching plan for each distribution area is first obtained, along with real-time operational data. Based on this data, the distributed resources of each distribution area are then regulated. The real-time operational data for each distribution area includes its power status, which is divided into surplus power (representing power generation exceeding power consumption) and deficit power (representing power generation falling short of power consumption). For example, the current and voltage status parameters of the photovoltaic inverters, energy storage devices, and electrical appliances in each distribution area are collected to determine the current power status. The specific values ​​of the power consumption current, photovoltaic power generation current, and energy storage power supply current are statistically analyzed. If the photovoltaic power generation current and energy storage power supply current are greater than the power consumption current, the current power status of the distribution area is considered surplus, allowing for self-sufficiency. If these parameters are less than the power consumption current, the current power status is considered deficit, requiring power from the grid. Simultaneously, when the grid power generation current exceeds the power consumption current, the energy storage devices are activated to begin energy storage.

[0108] In step S200, real-time operating data of each distribution area is obtained, and the scheduling target of each distribution area is determined based on the real-time operating data, including:

[0109] S210. Determine whether a transformer area has surplus power or deficit power, and sort the surplus power and deficit power of each transformer area from high to low. For example, determine whether each transformer area is a surplus power area or a deficit power area based on the real-time power generation and power consumption of each transformer area, and sort the surplus power and deficit power of each transformer area from high to low.

[0110] S211. According to the sorting order of the remaining power and the power deficit of each transformer substation, match the remaining power of each substation with the power deficit of each substation in turn. For example, match the substation with the substation in turn in descending order of power, until all matches are completed.

[0111] S212. If the remaining power of the current surplus power station is greater than the current deficit power of the current deficit power station, the deficit power of the current deficit power station is used as the target power. If the remaining power of the current surplus power station is not greater than the deficit power of the current deficit power station, the target power is allocated from the current surplus power station to the current deficit power station, until the remaining power of all surplus power stations is allocated to the deficit power station, or until the deficit power of any deficit power station is not greater than the preset deficit power threshold. For example, taking a surplus power station area including surplus power station area 1 and surplus power station area 2, and a deficit power station area including deficit power station area 1 and deficit power station area 2 as an example, if the surplus power of surplus power station area 1 is 20kW, the surplus power of surplus power station area 2 is 15kW, the deficit power of deficit power station area 1 is 15kW, and the deficit power of deficit power station area 2 is 10kW, then during matching, surplus power station area 1 and deficit power station area 1 are matched first, with a target power of 15kW. 15kW is allocated from surplus power station area 1 to deficit power station area 1, and then surplus power station area 1 and deficit power station area 2 are matched... In Zone 2, matching is performed. Since the remaining power of Zone 1 is 5kW, which is less than the power deficit of Zone 2, the remaining 5kW is allocated from Zone 1 to Zone 2. Then, Zone 2 is matched with Zone 2, where the power deficit is 5kW. The target power is 5kW, so 5kW is allocated from Zone 2 to Zone 2. At this point, the power deficit of all zones is greater than the power deficit threshold, which can be set to 0kW. In another scenario, where the power deficit of all zones is greater than the remaining power of all zones, the remaining power of all zones is allocated to the corresponding zones, thus completing the power scheduling between zones.

[0112] S213. If the power deficit of any power-deficient area is not greater than a preset power deficit threshold, and the remaining power of any power-retaining area is not less than a preset remaining power threshold, obtain the energy storage capacity of each area, and allocate the remaining power of each power-retaining area to the corresponding area for energy storage in sequence until the remaining power of each power-retaining area is less than the remaining power threshold, thereby obtaining the scheduling target of each area. If, after completing the power adjustment between areas, the power-retaining areas still have remaining power, and the remaining power of the power-retaining areas is not less than a preset remaining power threshold (e.g., 0 kW), then, according to the current energy storage capacity of the energy storage units in each area, the remaining power of each power-retaining area can be allocated to the corresponding area for energy storage in sequence until the power allocation of each area is completed, thereby obtaining the scheduling target of each area.

[0113] In step S200, an intraday scheduling optimization model is constructed, including:

[0114] S220. Determine the different load categories and at least one incentive compensation cost coefficient for each power load unit, wherein the different load categories include day-ahead loads, day-ahead loads that can be transferred, and intraday loads that can be reduced.

[0115] S221. Fit the adjustment amount of the power load unit under different load categories with at least one incentive compensation cost coefficient to construct the incentive compensation cost function of the power load unit.

[0116] Specifically, the planned operation data of the current distribution area during each day-ahead dispatch period also includes the predicted power values ​​of the electricity load units during each day-ahead dispatch period, and the incentive compensation cost function of the electricity load units, including:

[0117]

[0118]

[0119] in, Let m be the incentive compensation cost for the m-th power load unit during the t-th day-ahead scheduling period. These represent load categories as day-ahead load reduction, day-ahead load transfer, and intraday load reduction, respectively. and These are the incentive compensation cost coefficients for each load category. This represents the adjustment amount for the m-th electrical load unit within the k-th load category. Let m be the predicted power value of the m-th electrical load unit in the k-th load category. This represents the current power value of the m-th electrical load unit in the k-th load category.

[0120] S222. With the goal of minimizing the incentive compensation cost of all power load units in each distribution area during all daily scheduling periods, an intraday scheduling optimization model for each distribution area is constructed by calculating the difference between the cost function of the power generation unit, the cost function of the energy storage unit, and the incentive compensation cost function of the power load unit.

[0121] This application, based on the optimized scheduling of multiple microautonomous systems, transforms the target of the optimized scheduling of the distribution area from the response quantities of each distributed device unit to the adjustment quantities of each microautonomous system group. While considering the differences in the controllable capabilities of each device unit, it reduces the computational scale of the optimized control unit in the distribution area, thereby lowering the difficulty of centralized management of large-scale end-side resources. In this application, the daily scheduling plan for each cluster can be set to be formulated every 1 hour, with a resolution of 15 minutes. The daily scheduling optimization model can be expressed as follows: .

[0122] To ensure that the short-term local rolling optimization of intraday scheduling is consistent with the global optimization results of day-ahead scheduling, this application uses the pre-scheduling plan under the day-ahead global optimization as the baseline for formulating the actual scheduling plan of each microautonomous body during the day. This ensures that the intraday scheduling of the system tracks the day-ahead global optimization results. Specifically, this is reflected in establishing a coordination constraint on the total charging and discharging amount of the energy storage unit within a rolling cycle. Based on the hourly charging and discharging amount determined in the day-ahead scheduling plan based on the conservation of the energy storage unit's 24-hour charging and discharging power, and based on the coordination constraint conditions, the charging and discharging amount in each rolling cycle during the day is coordinated with the day-ahead pre-scheduling instructions. Specifically, the planned operation data of the current distribution area during each day-ahead scheduling period includes: the charging or discharging power of each energy storage unit during each day-ahead scheduling period; the optimized operation data of the current distribution area during each day-ahead scheduling period includes: the optimized charging or discharging power of each energy storage unit during each day-ahead scheduling period; and the coordination constraints include: the absolute value of the difference between the charging power of the current energy storage unit during any day-ahead scheduling period and the optimized charging power of the current energy storage unit during the corresponding day-ahead scheduling period is not greater than the product of the preset coordination coefficient and the charging power of the current energy storage unit during that day-ahead scheduling period; and the absolute value of the difference between the discharging power of the current energy storage unit during any day-ahead scheduling period and the optimized discharging power of the current energy storage unit during the corresponding day-ahead scheduling period is not greater than the product of the coordination coefficient and the discharging power of the current energy storage unit during that day-ahead scheduling period. The coordination constraints can be expressed as follows:

[0123]

[0124] in, , For the t-th day-ahead scheduling period, respectively, the energy storage unit The charging and discharging power is a known quantity; , These are the energy storage units to be optimized during the scheduling period on the t-th day of the day. The charging and discharging power is the optimization variable to be adjusted within the day; The day-ahead-intraday coordination coefficient is empirically set at 0.3. The coordination constraints constructed in this application combine the long-term global pre-scheduling results of the day-ahead, avoiding the situation where energy storage cannot fully utilize its regulation capabilities due to the limitation of charging and discharging power conservation within a short period when there are only power down-regulation or up-regulation tasks within a short rolling cycle.

[0125] Before determining the power balance constraints, the method in this application further includes: determining the sum of the first charging and discharging power of all energy storage units during each day-ahead scheduling period; determining the sum of the second charging and discharging power of each energy storage unit during each day-intra-day scheduling period; and determining the sum of the incentive compensation costs of each power load unit based on the incentive compensation cost function of the power load unit. The power balance constraints include: the scheduling target equals the sum of the first charging and discharging power, the sum of the second charging and discharging power, and the sum of the incentive compensation costs of each power load unit. Specifically, in this application, power balance requires that the total regulation task issued by the upper-level cloud platform to the distribution area after intra-day adjustment equals the sum of the regulation power of all resources within the micro-autonomous region before and during the day. At this time, the total regulation task of the cloud platform after the day-ahead adjustment of the autonomous region... The power balance constraint can be expressed as:

[0126]

[0127] in, Indicates the scheduling target. This represents the charging power of the energy storage unit during the scheduling period on the t-th day. This represents the discharge power of the energy storage unit during the dispatch period on the t-th day. This represents the optimized charging power of the energy storage unit during the scheduling period on the t-th day. This represents the optimized discharge power of the energy storage unit during the scheduling period on the t-th day.

[0128] Understandably, the calculation of the intraday scheduling optimization model can also be achieved using existing optimization algorithms, and this is not a limitation here.

[0129] like Figure 4 As shown, in a second aspect, this application provides a distribution network source-grid-load-storage coordinated control device, which applies the above-mentioned distribution network source-grid-load-storage coordinated control method, including:

[0130] The day-ahead scheduling plan module is configured to obtain the day-ahead scheduling plan for each transformer area. The day-ahead scheduling plan includes the planned operation data of the current transformer area in each day-ahead scheduling period. The day-ahead scheduling plan is obtained by solving the pre-built day-ahead scheduling optimization model based on the historical operation data of the current transformer area.

[0131] The intraday scheduling planning module is configured to acquire real-time operating data of each transformer area, determine the scheduling objectives of each transformer area based on the real-time operating data of each transformer area, and solve the pre-built intraday scheduling optimization model based on the scheduling objectives, pre-established coordination constraints and power balance constraints to obtain the intraday scheduling plan, so as to correct the day-ahead scheduling plan through the intraday scheduling plan. The intraday scheduling plan includes the optimized operating data of the current transformer area in each day-ahead scheduling period.

[0132] The coordination constraints and power balance constraints are constructed based on the planned operation data of the current transformer area during each day-ahead scheduling period and the optimized operation data of the current transformer area during each day-ahead scheduling period.

[0133] It is understood that those skilled in the art will clearly recognize that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0134] In a third aspect, this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned power generation, grid, load, and storage coordinated control method for a distribution network.

[0135] In a fourth aspect, this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described distribution network source-grid-load-storage coordinated control method.

[0136] like Figure 5 The diagram shown is a schematic representation of a terminal device provided in an embodiment of this application. Figure 5 As shown, the terminal device 10 of this embodiment includes a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the steps in the above method embodiments. Alternatively, when the processor 100 executes the computer program 102, it implements the functions of each module / unit in the above device embodiments.

[0137] For example, computer program 102 may be divided into one or more modules / units, one or more of which are stored in memory 101 and executed by processor 100 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 102 in terminal device 10.

[0138] Terminal device 10 may be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will understand that... Figure 5 This is merely an example of terminal device 10 and does not constitute a limitation on terminal device 10. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device may also include input / output devices, network access devices, buses, etc.

[0139] Processor 100 can be a Central Processing Unit (CPU), or 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. The general-purpose processor can be a microprocessor or any conventional processor.

[0140] The memory 101 can be an internal storage unit of the terminal device 10, such as a hard disk or RAM of the terminal device 10. The memory 101 can also be an external storage device of the terminal device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or FlashCard equipped on the terminal device 10. Furthermore, the memory 101 can include both internal and external storage units of the terminal device 10. The memory 101 is used to store computer programs and other programs and data required by the terminal device 10. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0141] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] In summary, this application achieves internal autonomy within a single transformer substation and localized consumption of distributed renewable energy through a two-tiered collaborative control system involving both single-substation micro-autonomous systems and multiple substations. Simultaneously, by modeling distributed resources within the micro-autonomous system, including photovoltaics, energy storage, charging piles, and loads, and by setting objective functions and optimizing the day-ahead scheduling model through a two-tiered distributed resource control strategy, it proposes coordination constraints and power balance constraints to ensure consistency between short-term local rolling optimization of intraday scheduling and global optimization results of day-ahead scheduling. This achieves efficient utilization of electricity between substations, thereby realizing coordinated and optimized operation of the "source-grid-load-storage" system.

[0143] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0144] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for coordinated regulation of power grid source, network, load and storage, characterized in that, The method comprises the following steps: obtaining a day-ahead scheduling plan of each substation, wherein the day-ahead scheduling plan comprises planned operation data of the current substation in each day-ahead scheduling period, and the day-ahead scheduling plan is obtained by solving a pre-constructed day-ahead scheduling optimization model based on historical operation data of the current substation; obtaining real-time operation data of each substation, determining a scheduling target of each substation based on the real-time operation data of each substation, solving a pre-constructed day-ahead scheduling optimization model based on the scheduling target, pre-established coordination constraints and power balance constraints, and obtaining a day-ahead scheduling plan to correct the day-ahead scheduling plan through the day-ahead scheduling plan, wherein the day-ahead scheduling plan comprises optimized operation data of the current substation in each day-ahead scheduling period; wherein the coordination constraints and the power balance constraints are constructed based on the planned operation data of the current substation in each day-ahead scheduling period and the optimized operation data of the current substation in each day-ahead scheduling period; The real-time operation data of each substation comprises an electricity state of each substation, wherein the electricity state comprises surplus electricity and deficit electricity, and the surplus electricity represents that the power generation of each substation is greater than the power consumption, and the deficit electricity represents that the power generation of each substation is less than the power consumption; obtaining the real-time operation data of each substation, and determining the scheduling target of each substation based on the real-time operation data of each substation, comprises the following steps: determining surplus substation and deficit substation, and sorting the surplus electricity and the deficit electricity of each substation from high to low; sequentially matching the surplus electricity of each surplus substation with the deficit electricity of each deficit substation according to the sorting order of the surplus electricity and the sorting order of the deficit electricity; if the surplus electricity of the current surplus substation is greater than the deficit electricity of the current deficit substation, taking the deficit electricity of the current deficit substation as a target electricity, if the surplus electricity of the current surplus substation is not greater than the deficit electricity of the current deficit substation, taking the surplus electricity of the current surplus substation as the target electricity, and distributing the target electricity from the current surplus substation to the current deficit substation until the surplus electricity of all surplus substations is distributed to the deficit substations, or until the deficit electricity of any deficit substation is not greater than a preset deficit electricity threshold; in the case that the deficit electricity of any deficit substation is not greater than the preset deficit electricity threshold, if the surplus electricity of any surplus substation is not less than a preset surplus electricity threshold, obtaining the energy storage capacity of each substation, and sequentially distributing the surplus electricity of each surplus substation to each substation for energy storage until the surplus electricity of each surplus substation is less than the surplus electricity threshold, to obtain the scheduling target of each substation.

2. The power distribution network source-network-load-storage collaborative regulation method of claim 1, wherein, Each substation comprises at least one power generation unit, an energy storage unit, an external discharging unit and a power consumption load unit, and the day-ahead scheduling optimization model is constructed by the following steps: determining at least one cost coefficient of the power generation unit, performing quadratic function fitting on the output power of the power generation unit by using the at least one cost coefficient of the power generation unit, and constructing a cost function of the power generation unit; determining a benefit coefficient of the energy storage unit, fitting the charging and discharging power of the energy storage unit by using the benefit coefficient of the energy storage unit, and constructing a cost function of the energy storage unit; determining a cost coefficient of the external discharging unit, fitting a quadratic function of the discharging power of the external discharging unit with the cost coefficient of the external discharging unit, and constructing a cost function of the external discharging unit; determining a cost coefficient and a benefit coefficient of the power consumption load unit, fitting the consumption power of the power consumption load unit with the cost coefficient and the benefit coefficient of the power consumption load unit, and constructing a cost function of the power consumption load unit; constructing a day-ahead scheduling optimization model of each substation based on the cost function of the power generation unit, the cost function of the energy storage unit, the cost function of the external discharging unit, and the cost function of the power consumption load unit.

3. The power distribution network source-network-load-storage collaborative regulation method according to claim 2, characterized in that, constructing a day-ahead scheduling optimization model of each substation based on the cost function of the power generation unit, the cost function of the energy storage unit, the cost function of the external discharging unit, and the cost function of the power consumption load unit, comprising: constructing a day-ahead scheduling optimization model of each substation based on the cost function of the power generation unit, the cost function of the energy storage unit, the cost function of the external discharging unit, and the cost function of the power consumption load unit, comprising:

4. The power distribution network source-network-load-storage collaborative regulation method according to claim 3, characterized in that, constructing a day-ahead scheduling optimization model of each substation based on the cost function of the power generation unit, the cost function of the energy storage unit, the cost function of the external discharging unit, and the cost function of the power consumption load unit, comprising: constructing a day-ahead scheduling optimization model of each substation based on the cost function of the power generation unit, the cost function of the energy storage unit, the cost function of the external discharging unit, and the cost function of the power consumption load unit, comprising: determining different load categories and at least one incentive compensation cost coefficient of each power consumption load unit; fitting the adjustment amount of the power consumption load unit under different load categories with the at least one incentive compensation cost coefficient to construct an incentive compensation cost function of the power consumption load unit; 5. The power distribution network source-network-load-storage collaborative regulation method according to claim 3, characterized in that, constructing a day-ahead scheduling optimization model of each substation based on the cost function of the power generation unit, the cost function of the energy storage unit, the cost function of the external discharging unit, and the cost function of the power consumption load unit. the planned operation data of the current substation in each day-ahead scheduling period, comprising: the charging power or the discharging power of each energy storage unit in each day-ahead scheduling period; the optimized operation data of the current substation in each day-ahead scheduling period, comprising: the optimized charging power or the optimized discharging power of each energy storage unit in each day-ahead scheduling period; the coordination constraint condition, comprising: the absolute value of the difference between the charging power of the current energy storage unit in any day-ahead scheduling period and the optimized charging power of the current energy storage unit in the corresponding day-ahead scheduling period is not greater than the product of a preset coordination coefficient and the charging power of the current energy storage unit in the day-ahead scheduling period; and 6. The power distribution network source-network-load-storage collaborative regulation method according to claim 5, characterized in that, the absolute value of the difference between the discharging power of the current energy storage unit in any day-ahead scheduling period and the optimized discharging power of the current energy storage unit in the corresponding day-ahead scheduling period is not greater than the product of the coordination coefficient and the discharging power of the current energy storage unit in the day-ahead scheduling period. the method further comprises: determining the first sum of the charging power and the discharging power of all energy storage units in each day-ahead scheduling period, determining the second sum of the charging power and the discharging power of each energy storage unit in each day-ahead scheduling period, and determining the sum of the incentive compensation costs of each power consumption load unit based on the incentive compensation cost function of the power consumption load unit; the power balance constraint condition, comprising: The scheduling target is equal to the sum of the first charging and discharging power, the sum of the second charging and discharging power, and the sum of the incentive compensation cost of each power consumption load unit.

7. The power distribution network source-network-load-storage collaborative regulation method according to claim 4, characterized in that, The cost function of the power generation unit comprises: wherein, is the cost function for the i-th generation unit, is the output power of the i-th generation unit at the t-th day-ahead dispatch period, , , is the cost coefficient.

8. The power distribution network source-network-load-storage collaborative regulation method according to claim 7, characterized in that, The cost function of the energy storage unit comprises: wherein, is the discharge power of the jth energy storage unit at the tth day-ahead dispatch period, is the benefit coefficient of the energy storage unit.

9. The power distribution network source-network-load-storage collaborative regulation method according to claim 8, characterized in that, The cost function of the external discharging unit comprises: wherein, is a cost function for the ith external discharging unit, is a cost coefficient for the external discharging unit, is a discharging power of the ith external discharging unit at the tth day-ahead dispatch period, is a maximum discharging power of the ith external discharging unit.

10. The power distribution network source-network-load-storage collaborative regulation method according to claim 9, characterized in that, The cost function of the power consumption load unit comprises: wherein, is a cost function of the jth electricity load unit, is a consumed power of the jth electricity load unit at the tth day-ahead dispatch period, and are a cost coefficient and a benefit coefficient of the jth electricity load unit, respectively.

11. The power distribution network source-network-load-storage collaborative regulation method according to claim 10, characterized in that, The day-ahead scheduling optimization model comprises: Wherein, N is the total number of power generation units, L is the total number of energy storage units, M is the total number of external discharging units, Q is the total number of power consumption load units, t=1, 2,..., T.

12. The power distribution network source-network-load-storage collaborative regulation method according to claim 11, characterized in that, The scheduled operation data of the current substation in each day-ahead scheduling period also includes the predicted power value of the power consumption load unit in each day-ahead scheduling period, and the incentive compensation cost function of the power consumption load unit comprises: wherein, is the incentive compensation cost of the mth electricity consumption load unit in the tth day-ahead scheduling period, respectively represent the load categories of day-ahead reducible load, day-ahead transferable load, and intra-day reducible load, and are the incentive compensation cost coefficients of the respective load categories, is the adjustment amount of the mth electricity consumption load unit in the kth load category, is the predicted power value of the mth electricity consumption load unit in the kth load category, is the current power value of the mth electricity consumption load unit in the kth load category.

13. The power distribution network source-network-load-storage collaborative regulation method according to claim 12, characterized in that, The day-ahead scheduling optimization model comprises: 。 14. The power distribution network source-network-load-storage collaborative regulation method of claim 12, wherein, The power balance constraint condition comprises: wherein, denotes the dispatch target, denotes the charging power of the energy storage unit at the tth day-ahead dispatch period, denotes the discharging power of the energy storage unit at the tth day-ahead dispatch period, denotes the optimized charging power of the energy storage unit at the tth day-ahead dispatch period, denotes the optimized discharging power of the energy storage unit at the tth day-ahead dispatch period.

15. A power distribution network source-network-load-storage collaborative regulation device, applying the power distribution network source-network-load-storage collaborative regulation method of any one of claims 1-14, characterized in that, Comprise: The day-ahead scheduling plan module is configured to obtain the day-ahead scheduling plan of each substation, the day-ahead scheduling plan comprising the scheduled operation data of the current substation in each day-ahead scheduling period, the day-ahead scheduling plan being obtained by solving the pre-constructed day-ahead scheduling optimization model based on the historical operation data of the current substation; The day-ahead scheduling plan module is configured to obtain the real-time operation data of each substation, determine the scheduling target of each substation based on the real-time operation data of each substation, solve the pre-constructed day-ahead scheduling optimization model based on the scheduling target, the pre-established coordination constraint condition and the power balance constraint condition, and obtain the day-ahead scheduling plan to correct the day-ahead scheduling plan through the day-ahead scheduling plan, the day-ahead scheduling plan comprising the optimized operation data of the current substation in each day-ahead scheduling period. Wherein, the coordination constraint condition and the power balance constraint condition are constructed based on the scheduled operation data of the current substation in each day-ahead scheduling period and the optimized operation data of the current substation in each day-ahead scheduling period.

16. A machine-readable storage medium having stored thereon instructions, the instructions being executable by a machine to cause the machine to: The instructions, when executed by the processor, cause the processor to be configured to perform the power distribution network source network load storage collaborative regulation method according to any one of claims 1-14.

17. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the power distribution network source network load storage collaborative regulation method according to any one of claims 1-14.

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