A method and system for distributed optimal dispatching of AC-DC power distribution network

Abstract

The application provides a kind of AC-DC distribution network distributed optimization scheduling method and system, the method comprises: based on the load data of first local intelligent controller acquisition local node, distributed power output data and energy storage state data;Based on the local optimization objective function of first local intelligent controller according to local optimization decision-making, obtain preliminary local scheduling decision-making, combined with load data, distributed power output data and energy storage state data;Based on each first local intelligent controller according to the consistency of collaborative optimization, determine target local scheduling decision-making, combined with the second interaction information of second local intelligent controller according to first interaction information;Based on each regional coordinator according to the node scheduling decision of target local intelligent controller, determine the node scheduling decision of the node corresponding to target local intelligent controller, realize the peak-valley difference optimization scheduling of AC-DC distribution network.The application improves the overall operation efficiency and stability of power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer technology, and in particular to a kind of AC-DC distribution network distributed optimization scheduling method and system thereof. BACKGROUND

[0002] With the continuous development of power system and the wide access of various distributed power, energy storage devices, DC load in distribution network, the operation of AC-DC distribution network becomes increasingly complex. In the process of power system operation, the peak-valley difference of load brings many challenges to the safe, stable and economic operation of power grid.

[0003] The current AC-DC distribution network distributed peak-valley difference method mainly has a scheduling method based on centralized control, that is, relying on the control center of the power grid, collecting various types of information in the entire distribution network area, including load data of each node, power generation of distributed power and state of energy storage device, etc., and then formulating a unified scheduling plan through a complex optimization algorithm to regulate the output of distributed power, charging and discharging behavior of energy storage, etc., trying to smooth the peak-valley difference. On the one hand, it highly depends on massive and accurate real-time information transmission, which requires high bandwidth and stability of communication network. Once there is a communication failure, data packet loss or delay, etc., the information obtained by the control center will be distorted, and then the scheduling strategy formulated will be invalid, which cannot effectively cope with the peak-valley difference change. On the other hand, the centralized control mode lacks detailed consideration of the characteristics of each local area and different nodes, which is prone to "one-size-fits-all" situation, and it is difficult to fully exert the advantages of each distributed power and energy storage in different scenarios, and it is difficult to realize fine peak-valley difference optimization. SUMMARY

[0004] In order to solve the technical problems of the existing method, the present application provides an AC-DC distribution network distributed optimization scheduling method and system, which realizes accurate and fine optimization scheduling of the peak-valley difference of AC-DC distribution network, better cope with load changes, realize reasonable allocation and utilization of resources, and improve the overall operation efficiency and stability of AC-DC distribution network.

[0005] In the first aspect, the present application provides an AC-DC distribution network distributed optimization scheduling method, which is realized based on an AC-DC distribution network distributed optimization scheduling system. The AC-DC distribution network distributed optimization scheduling system at least includes a local intelligent controller and a regional coordinator. Each regional coordinator is connected with the local intelligent controller within a predetermined range through a communication link. The local intelligent controller is deployed at each node of the AC-DC distribution network. The node includes a distributed power node, an energy storage device node and a load node. The method comprises:

[0006] Based on the load data, distributed power output data and energy storage state data of the local node where each first local intelligent controller is located;

[0007] Based on each first local intelligent controller making a local optimization decision according to a local optimization objective function combined with load data, distributed power output data and energy storage status data, a preliminary local dispatch decision of each first local intelligent controller is obtained;

[0008] Determining a target local scheduling decision of each first local intelligent controller based on consistency collaborative optimization performed by each first local intelligent controller according to the first interaction information and the second interaction information of the second local intelligent controller;

[0009] Based on the distributed collaborative optimization of each regional coordinator according to the target local intelligent controller's target local scheduling decision, the node scheduling decision of the node corresponding to the target local intelligent controller is determined to achieve peak-valley difference optimization scheduling of the AC / DC distribution network;

[0010] The local optimization objective function aims to minimize the peak-to-valley difference, maximize the local energy self-sufficiency rate and minimize the energy storage life loss. It is a function constructed by combining the load characteristics of the local load node, the power regulation characteristics of the distributed power supply node and the charging and discharging characteristics of the energy storage device node; the second local intelligent controller is the controller corresponding to the node adjacent to the local node; the interaction information includes preliminary local scheduling decisions and local resource margin information; the target local intelligent controller is the first local intelligent controller connected to each regional coordinator.

[0011] In a second aspect, the present invention further provides a distributed optimization scheduling system for an AC / DC distribution network, the system comprising at least a local intelligent controller and a regional coordinator, each regional coordinator being connected to a local intelligent controller within a preset range via a communication link; the local intelligent controller is deployed at each node of the AC / DC distribution network, the node comprising a distributed power supply node, an energy storage device node, and a load node, for implementing the distributed optimization scheduling method for the AC / DC distribution network as described in the first aspect;

[0012] The local intelligent controller is used to:

[0013] Collect load data, distributed power output data and energy storage status data of the local node;

[0014] According to the local optimization objective function, combined with load data, distributed power output data and energy storage status data, local optimization decision is made to obtain the preliminary local dispatch decision of each first local intelligent controller.

[0015] performing consistency collaborative optimization based on the first interaction information and the second interaction information of the second local intelligent controller to determine a target local scheduling decision of each first local intelligent controller;

[0016] The regional coordinator is used to:

[0017] Distributed collaborative optimization is performed based on the target local dispatching decision of the target local intelligent controller to determine the node dispatching decision of the node corresponding to the target local intelligent controller, thus achieving peak-valley difference optimized dispatching of the AC / DC distribution network.

[0018] The local optimization objective function aims to minimize the peak-to-valley difference, maximize the local energy self-sufficiency rate and minimize the energy storage life loss. It is a function constructed by combining the load characteristics of the local load node, the power regulation characteristics of the distributed power supply node and the charging and discharging characteristics of the energy storage device node; the second local intelligent controller is the controller corresponding to the node adjacent to the local node; the interaction information includes preliminary local scheduling decisions and local resource margin information; the target local intelligent controller is the first local intelligent controller connected to each regional coordinator.

[0019] In a third aspect, the present invention further provides an electronic device comprising: a memory for storing a computer software program; and a processor for reading and executing the computer software program, thereby implementing any of the above-described methods for distributed optimization scheduling of AC and DC distribution networks.

[0020] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, wherein the storage medium stores a computer software program, and when the computer software program is executed by a processor, it implements any of the above-mentioned distributed optimization scheduling methods for AC and DC distribution networks.

[0021] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for distributed optimization scheduling of AC and DC distribution networks.

[0022] In the distributed optimization and scheduling method for AC / DC distribution networks provided by the embodiments of the present invention, the first local intelligent controller is able to comprehensively consider the various characteristics of the local nodes and make optimization decisions based on the constructed local optimization objective function and the load data, distributed power output data, and energy storage status data of the local nodes. Therefore, it can provide reasonable local scheduling decisions for the efficient and stable operation of the AC / DC distribution network based on its actual situation. Furthermore, the regional coordinator performs distributed collaborative optimization on the local scheduling decisions of the local intelligent controllers to determine the optimal scheduling decision for each local intelligent controller, achieving accurate and precise optimization and scheduling of the peak-to-valley difference of the AC / DC distribution network, better responding to load changes, achieving reasonable allocation and utilization of resources, and improving the overall operating efficiency and stability of the AC / DC distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a distributed optimization scheduling method for AC and DC distribution networks provided by an embodiment of the present invention;

[0024] Figure 21 is a structural diagram of a distributed optimization scheduling system for AC and DC distribution networks provided by an embodiment of the present invention;

[0025] Figure 3 An embodiment diagram of an electronic device provided by an embodiment of the present invention;

[0026] Figure 4 An embodiment diagram of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0029] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0030] See Figure 1 , Figure 1This is a flow chart of the distributed optimization scheduling method for AC / DC distribution networks provided by the present invention. In the embodiments of the present invention, the distributed optimization scheduling method for AC / DC distribution networks is executed by an optimization scheduling system. The optimization scheduling system includes at least a local intelligent controller and a regional coordinator. Each regional coordinator is connected to a local intelligent controller within a preset range via a communication link. The local intelligent controller is deployed at each node of the AC / DC distribution network, which includes a distributed power supply node, an energy storage device node, and a load node. Therefore, the distributed optimization scheduling method for AC / DC distribution networks includes:

[0031] Step 10: Based on each first local intelligent controller, the load data, distributed power output data and energy storage status data of the local node are collected.

[0032] Optionally, for each first local intelligent controller, the first local intelligent controller collects key data on the local node where it is located, and the key data specifically includes load data , indicating the load situation at different times, in kilowatts; distributed power output data , represents the power generation of distributed power at different times, in kilowatts; energy storage status data , indicating the amount of energy stored, in kilowatt-hours.

[0033] Furthermore, in order to ensure the stability of the AC / DC distribution network, it is necessary to monitor the stability of each node at different times. Therefore, the first local intelligent controller performs node energy balance analysis based on the collected data to obtain the node energy balance coefficient, that is, to analyze the energy dynamic balance of the local node at different times. Among them, the node energy balance coefficient The specific analysis formula is as follows:

[0034] .

[0035] in, Indicates the rate of change of energy storage capacity over time.

[0036] Furthermore, a comprehensive evaluation and analysis of energy storage life and efficiency is conducted based on the energy storage status data to obtain the energy storage comprehensive impact factor. The specific analysis formula is as follows:

[0037] .

[0038] in, Indicates the charging efficiency of the energy storage device (the value range is between 0 and 1), Indicates the discharge efficiency of the energy storage device (value range is between 0 and 1).

[0039] Furthermore, the distributed power output stability and load matching analysis is performed based on the distributed power output data to obtain the distributed power output stability coefficient , the specific analysis formula is as follows:

[0040] .

[0041] in, Indicates the length of the time window of a preset length, such as 1 hour; Indicates the average output of distributed generation within this time window.

[0042] Furthermore, the first local intelligent controller is configured to generate a point energy balance coefficient , comprehensive impact factors of energy storage and distributed power output stability coefficient Calculate the comprehensive stability coefficient , where the comprehensive stability coefficient The specific formula is as follows:

[0043] .

[0044] If the comprehensive stability coefficient , then the current node is determined to be in a stable state. If the comprehensive stability coefficient , it is determined that the current node is in an unstable state and requires further maintenance.

[0045] Step 20: Each first local intelligent controller makes a local optimization decision based on the local optimization objective function combined with the load data, the distributed power output data and the energy storage status data to obtain a preliminary local scheduling decision of each first local intelligent controller.

[0046] Optionally, a pre-constructed local optimization objective function is stored in the first local intelligent controller of an embodiment of the present invention, wherein the local optimization objective function is a function constructed with the goals of minimizing the peak-to-valley difference, maximizing the local energy self-sufficiency rate, and minimizing the energy storage life loss, combined with the load characteristics of the local load node, the power regulation characteristics of the distributed power supply node, and the charging and discharging characteristics of the energy storage device node. The specific process of constructing the local optimization objective function is as described in steps 201 to 204.

[0047] The load characteristics of load nodes include those in areas with concentrated industrial and residential users. The power regulation characteristics of distributed power generation nodes include the output of photovoltaic and wind power sources. The charge and discharge characteristics of energy storage device nodes include the charge and discharge efficiency of lithium batteries and flow batteries.

[0048] Further, the first local intelligent controller utilizes a MINLP (mixed integer nonlinear programming) solver to make a local optimization decision according to a local optimization objective function combined with the collected load data, distributed power output data and energy storage state data, and the MINLP solver searches for a set of decision variable values that makes the local optimization objective function optimal in a feasible solution space satisfying all constraint conditions, to obtain a preliminary local scheduling decision of the first local intelligent controller.

[0049] Step 30, based on the consistency collaborative optimization of each first local intelligent controller according to the first interaction information and the second interaction information of the second local intelligent controller, a target local scheduling decision of each first local intelligent controller is determined.

[0050] Optionally, the second local intelligent controller in the embodiment of the application is a controller corresponding to a node adjacent to the local node, and the interaction information includes the preliminary local scheduling decision and local resource margin information such as a remaining adjustable power margin of the distributed power and a remaining charge-discharge capacity margin of the energy storage.

[0051] Further, the first local intelligent controller performs consistency collaborative optimization according to its own first preliminary local scheduling decision and first local resource margin information, and the second preliminary local scheduling decision and the second local resource margin information of the second local intelligent controller, to determine the target local scheduling decision of the first local intelligent controller, which is specifically described in steps 301-303.

[0052] Step 40, based on the distributed collaborative optimization of each regional coordinator according to the target local scheduling decision of the target local intelligent controller, a node scheduling decision of the node corresponding to the target local intelligent controller is determined, to realize the peak-valley difference optimization scheduling of the AC-DC distribution network.

[0053] Further, for each regional coordinator, the regional coordinator takes the first local intelligent controller connected thereto as the target local intelligent controller, and performs distributed collaborative optimization according to the target local scheduling decision of the target local intelligent controller, to determine the node scheduling decision of the node corresponding to the target local intelligent controller, wherein the node scheduling decision includes the output behavior of the distributed power and the charge-discharge behavior of the energy storage, and therefore, the regional coordinator controls the target local intelligent controller to execute the output behavior of the distributed power and the charge-discharge behavior of the energy storage in the node scheduling decision, to realize the peak-valley difference optimization scheduling of the AC-DC distribution network, which is specifically described in steps 401-405.

[0054] The first local intelligent controller of the embodiment of the application can comprehensively consider various characteristics of the local node, make optimization decisions according to the constructed local optimization objective function and load data, distributed power output data and energy storage state data of the local node, and provide reasonable local scheduling decisions for efficient and stable operation of the AC-DC power distribution network according to the self condition. The further regional coordinator performs distributed collaborative optimization on the local scheduling decisions of the local intelligent controller, determines the optimal scheduling decisions of each local intelligent controller, accurately and finely optimizes scheduling of the peak-valley difference of the AC-DC power distribution network, better responds to load changes, realizes reasonable allocation and utilization of resources, and improves the overall operation efficiency and stability of the AC-DC power distribution network.

[0055] In an embodiment, steps 201 to 204 are described as follows:

[0056] Step 201, based on the first load curve corresponding to the load characteristics of the industrial user concentrated area and the second load curve corresponding to the load characteristics of the residential user concentrated area, determining the total load characteristics.

[0057] Optionally, for the load characteristics of the industrial user concentrated area, taking 24 hours as a period, the time variable , unit: hour, the first load curve can be expressed as:

[0058] .

[0059] Wherein, represents the reference capacity of industrial load, representing the approximate scale of industrial load in the area; represents the parameter of the night drop rate of industrial load, representing the characteristics of the rapid reduction of industrial load caused by the night shutdown of industrial users.

[0060] Optionally, for the load characteristics of the residential user concentrated area, the second load curve can be expressed as: .

[0061] Wherein, represents the reference capacity of residential load, represents the influence parameter of residential load at noon and night low period.

[0062] Therefore, the total load characteristics in the area can be expressed as:

[0063] .

[0064] Wherein, and are the weight coefficients of industrial load and residential load in the total load, respectively.

[0065] Step 202 : Determine the output of the distributed power source based on a first power regulation characteristic corresponding to the output of the photovoltaic power source and a second power regulation characteristic corresponding to the output of the wind power source.

[0066] Optionally, the distributed power supply node characteristics of the embodiment of the present invention include the output of the photovoltaic power supply and the output of the wind power supply, wherein the output of the photovoltaic power supply and light intensity (Unit is ), ambient temperature (Unit is ) and other factors, its power first rate regulation characteristics can be expressed as:

[0067] .

[0068] in, Indicates the rated power of the photovoltaic module under standard test conditions (STC). and Respectively represent the light intensity and temperature under standard test conditions, Indicates the light intensity threshold.

[0069] Optional, wind power output Depends on wind speed (Unit is ) and the fan cut-in speed , rated wind speed , cut-out wind speed Therefore, the second power regulation characteristic of the wind power output can be expressed as:

[0070] .

[0071] Therefore, the comprehensive distributed power output It can be expressed as:

[0072] .

[0073] in, and They are the weight coefficients of photovoltaic power and wind power in the distributed power supply combination, satisfying .

[0074] Step 203 : determining the energy capacity change rate based on the lithium battery capacity change corresponding to the charge and discharge efficiency of the lithium battery and the energy storage capacity change rate corresponding to the charge and discharge efficiency of the flow battery.

[0075] Optionally, the node characteristics of the energy storage device in the embodiment of the present invention include the charge and discharge efficiency of the lithium battery and the charge and discharge efficiency of the flow battery, wherein the charge and discharge efficiency of the lithium battery varies under different charge and discharge powers and states of charge (SOC). It can be expressed as:

[0076] .

[0077] in, It indicates the benchmark charging efficiency of lithium batteries when they are charged at an ideal low power and in the best state of charge. Indicates the charging power of the lithium battery. Indicates the maximum charging power of the lithium battery. Indicates the real-time charge status of the lithium battery. Indicates the optimal state of charge of the lithium battery. and The maximum and minimum state of charge allowed for lithium batteries are shown respectively.

[0078] Similarly, the discharge efficiency can be obtained .

[0079] Therefore, the charge of the lithium battery changes It can be expressed as:

[0080] .

[0081] in, Indicates discharge power.

[0082] Optionally, for the charge and discharge efficiency of the flow battery, the charge efficiency of the flow battery and charging electrolyte flow (Unit is ), charging current density (Unit is ) is related to charging efficiency It can be expressed as:

[0083] .

[0084] in, represents the benchmark charging efficiency of the flow battery, and represent the optimal electrolyte flow rate and optimal current density of the flow battery, 、 、 and is the corresponding limit parameter.

[0085] Similarly, the discharge efficiency can be obtained .

[0086] Therefore, the energy storage power change rate may be expressed as:

[0087] .

[0088] wherein, represents the charging power of the flow battery, represents the discharging power of the flow battery, represents the discharging efficiency of the flow battery.

[0089] Therefore, the comprehensive charging and discharging characteristics, i.e. the energy storage power change rate may be expressed as:

[0090] .

[0091] wherein, and respectively represent the weight coefficients of the lithium battery energy storage and the flow battery energy storage in the total energy storage, satisfying .

[0092] Step 204, constructing a local optimization objective function based on the total load characteristics, the distributed power output and the energy storage power change rate.

[0093] Further, according to the total load characteristics, a peak-valley difference minimization objective function is constructed, according to the distributed power output and the total load characteristics, a local energy self-sufficiency rate maximization objective function is constructed, and according to the energy storage power change rate, a energy storage life loss minimization objective function is constructed. Further, according to the peak-valley difference minimization objective function, the local energy self-sufficiency rate maximization objective function and the energy storage life loss minimization objective function, a local optimization objective function is constructed, which is specifically described in steps 2041 to 2044.

[0094] The first local intelligent controller according to the embodiment of the application can comprehensively consider various characteristics of the local node, make optimization decisions according to the constructed local optimization objective function and the load data, the distributed power output data and the energy storage state data of the local node, and thus can provide reasonable local scheduling decisions for efficient and stable operation of the AC-DC power distribution network according to the actual situation of the local node.

[0095] In an embodiment, steps 2041 to 2044 are described as follows:

[0096] Step 2041, determining the peak-valley difference based on the total load characteristics, and constructing a peak-valley difference minimization objective function based on the peak-valley difference.

[0097] Optionally, for the peak-valley difference minimization objective function, first, the peak-valley difference of the load curve in a certain time period (e.g. one day) is calculated , the specific formula is as follows:

[0098] .

[0099] Furthermore, in order to better integrate the optimization objective function, the peak-to-valley difference Perform normalization processing and construct the peak-to-valley difference minimization objective function. The specific formula is:

[0100] .

[0101] in, Indicates the time period The average load within.

[0102] Step 2042: Based on the total load characteristics and the output of the distributed power sources, the total energy generated by the local distributed power sources and the energy consumed by the local total load are determined respectively, and based on the total energy generated by the local distributed power sources and the energy consumed by the local total load, a local energy self-sufficiency rate maximization objective function is constructed.

[0103] Optionally, for the objective function of maximizing the local energy self-sufficiency rate, the local energy self-sufficiency rate is the ratio of the total energy generated by the local distributed power generation to the energy consumed by the local total load. The total energy generated by local distributed power generation is The calculation formula is as follows:

[0104] .

[0105] Local total load energy consumption The calculation formula is as follows:

[0106] .

[0107] Therefore, the objective function for maximizing local energy self-sufficiency is constructed as follows:

[0108] .

[0109] in, Represents the adjustment coefficient.

[0110] Step 2043: Determine a first life loss index of lithium battery energy storage and a second life loss index of flow battery energy storage based on the energy and charge change rate, and construct an energy storage life loss minimization objective function based on the first life loss index and the second life loss index.

[0111] Optionally, for the objective function of minimizing the energy storage life loss, it is considered that the energy storage life loss is related to the number of charge and discharge times, charge and discharge depth, and characteristics of different energy storage technologies.

[0112] For lithium battery energy storage, the first life loss indicator of lithium battery energy storage is It can be expressed as:

[0113] .

[0114] in, Indicates that lithium batteries are The change in charge and discharge power at each moment, Indicates the rated capacity of the lithium battery. Indicates the coefficient that affects the relationship between the life loss and state of charge of lithium batteries. represents the average state of charge, Indicates that lithium batteries are The cumulative value of charge and discharge times at the moment.

[0115] For liquid flow battery energy storage, the second life loss index of liquid flow battery energy storage It can be expressed as:

[0116] .

[0117] in, Indicates that flow batteries The change in charge and discharge power at each moment, Indicates the rated capacity of the flow battery, The coefficient that affects the relationship between the life loss and state of charge of the flow battery, represents the average state of charge, Indicates that flow batteries The cumulative value of charge and discharge times at the moment.

[0118] Therefore, the comprehensive energy storage life loss index It can be expressed as:

[0119] .

[0120] in, and They are the weight coefficients of lithium battery energy storage and flow battery energy storage when considering life loss, satisfying .

[0121] Therefore, the objective function of minimizing energy storage life loss is constructed for:

[0122] .

[0123] Step 2044: construct a local optimization objective function based on the peak-to-valley difference minimization objective function, the local energy self-sufficiency rate maximization objective function, and the energy storage life loss minimization objective function.

[0124] Combining the above three objective functions, the local optimization objective function is constructed for:

[0125] .

[0126] Optionally, the embodiment of the present invention sets constraints in the local optimization decision process, wherein, for the power balance constraint: at any time , the sum of the total output of the distributed power source and the charging and discharging power changes of the energy storage device must be equal to the local load demand, that is:

[0127] .

[0128] The power regulation range constraints of distributed power sources include photovoltaic power and wind power constraints. The photovoltaic power constraint is: For wind power sources: .in, and They are the maximum allowable power outputs of photovoltaic power sources and wind power sources respectively.

[0129] The charging and discharging power and capacity constraints of energy storage devices include lithium battery energy storage constraints and flow battery energy storage constraints. The constraints for lithium battery energy storage are: , , The constraints for flow battery energy storage are: , , .in, 、 、 、 、 、 、 and Respectively represent the corresponding maximum and minimum charge and discharge power and capacity limits.

[0130] Furthermore, the embodiment of the present invention adopts the mixed integer nonlinear programming MINLP method to solve the above constrained optimization problem. 、wind power supply etc.), the charge and discharge power value of the energy storage device (such as the 、 , flow battery 、 etc.) and related discrete control variables (such as the start and stop status of distributed power sources) as decision variables.

[0131] By constructing a local optimization objective function The inequality and equality constraints corresponding to the above constraints are solved using a MINLP solver (such as Bonmin, Couenne, etc.). The solver searches for a local optimization objective function in the feasible solution space that satisfies all constraints. The optimal set of decision variable values ​​is achieved. The initial power output value of the distributed power supply and the initial charge and discharge power setting of the energy storage device corresponding to this set of values ​​are the preliminary local scheduling decisions of each first local intelligent controller. .

[0132] The first local intelligent controller of the embodiment of the present invention can comprehensively consider the various characteristics of the local node, and make optimization decisions based on the constructed local optimization objective function and the load data, distributed power output data and energy storage status data of the local node. Therefore, it can provide reasonable local scheduling decisions for the efficient and stable operation of the AC and DC distribution network according to its own actual situation.

[0133] In one embodiment, steps 301 to 303 are described as follows:

[0134] Step 301: Determine the consistency of decision variables between the first local intelligent controller and the second local intelligent controller based on the first decision variable vector and the first preliminary local scheduling decision vector of the first local intelligent controller, and the second decision variable vector and the second preliminary local scheduling decision vector of the second local intelligent controller.

[0135] Optionally, in the embodiment of the present invention, there are a total of nodes, each node corresponds to a local intelligent controller, so the local node set corresponding to the first local intelligent controller can be expressed as , the second local intelligent controller set corresponding to the nodes adjacent to the local node can be expressed as , local node set and node collection There is an association, and the node set The node corresponding to the element in the local node set For each first local intelligent controller , and its corresponding first decision variable vector can be expressed as ,in, represents the number of decision variables, such as It can be the output setting value of the distributed power supply, It can be the energy storage charging and discharging power setting value.

[0136] Optionally, the first preliminary local scheduling decision vector of the first local intelligent controller obtained based on the local optimization objective function in the embodiment of the present invention can be expressed as , the first local resource margin information vector can be expressed as , Indicates the number of dimensions contained in the resource margin information, such as the remaining adjustable power margin of the distributed power generation, the remaining charge and discharge capacity margin of the energy storage, etc. , and its corresponding second decision variable vector can be expressed as , the second preliminary local scheduling decision vector can be expressed as , the second local resource margin information vector can be expressed as .

[0137] Furthermore, based on the first decision variable vector and the first preliminary local scheduling decision vector of the first local intelligent controller and the second decision variable vector and the second preliminary local scheduling decision vector of the second local intelligent controller, the consistency degree of the decision variables between the first local intelligent controller and the second local intelligent controller is determined. The specific formula for the consistency degree of the decision variables is as follows:

[0138] .

[0139] in, Indicates the first local intelligent controller With a second local smart controller The degree of consistency between decision variables, represents the first decision variable in the vector elements, represents the first elements, represents the first preliminary local scheduling decision vector elements, representing the first element in the second preliminary local scheduling decision vector elements.

[0140] Step 302 : Determine the degree of correlation between the decision variables and the local resource margin information in the first local intelligent controller based on the first decision variable vector and the first local resource margin information of the first local intelligent controller.

[0141] Furthermore, based on the first decision variable vector and the first local resource margin information of the first local intelligent controller, a correlation degree between the decision variable and the local resource margin information in the first local intelligent controller is determined, wherein a specific formula for the correlation degree is as follows:

[0142] .

[0143] in, Indicates the first local intelligent controller a degree of correlation between the local resource margin information and the decision variables, denotes an element in the first decision variable vector, denotes an element in the first decision variable vector, denotes an upper limit value of an element in the first decision variable vector.

[0144] In step 303, a target decision function is constructed based on the consistency degree and the degree of correlation, and the target decision function is consistency-collaboratively optimized based on the second local resource margin information of the second local intelligent controller, to obtain a target local scheduling decision of each first local intelligent controller.

[0145] Further, in order to measure whether the entire regional power grid reaches a consistency state of overall optimization, the embodiment of the application constructs a target decision function wherein, denotes a total vector covering decision variables of all node intelligent controllers, and a specific formula of the target decision function is as follows:

[0146] .

[0147] wherein, denotes a preset weight coefficient between the first local intelligent controller and the second local intelligent controller .

[0148] Further, the embodiment of the application optimizes the target decision function by a distributed gradient descent algorithm, so that the target decision function is calculated according to the second local resource margin information of the second local intelligent controller, and a gradient of each decision variable of the first local intelligent controller is calculated, and the gradient of each decision variable of the first local intelligent controller is iterated to obtain an optimal iteration result, to obtain the target local scheduling decision of the first local intelligent controller, which is specifically described in steps 3031 to 3033.

[0149] According to the first interaction information of the first local intelligent controller and the second interaction information of the second local intelligent controller, the embodiment of the application performs consistency-collaborative optimization, so that reasonable local scheduling decisions can be provided for efficient and stable operation of the AC-DC distribution network according to actual conditions of the self and adjacent nodes, thereby improving the overall operation efficiency and stability of the AC-DC distribution network.

[0150] In an embodiment, steps 3031 to 3033 are described as follows:

[0151] ​In step 3031, a gradient of each decision variable in the first decision variable vector of the first local intelligent controller is calculated according to the second local resource margin information and the target decision function.

[0152] Optionally, the second local resource margin information of the second local intelligent controller is used to calculate the gradient of each decision variable in the first decision variable vector of the first local intelligent controller. The target decision function is calculated. The gradient of each decision variable in the first decision variable vector of the first local intelligent controller is calculated. The gradient of each decision variable in the first decision variable vector of the first local intelligent controller is calculated. The gradient of each decision variable in the first decision variable vector of the first local intelligent controller is calculated. The gradient of each decision variable in the first decision variable vector of the first local intelligent controller is calculated. The gradient of each decision variable in the first decision variable vector of the first local intelligent controller is calculated. The gradient of each decision variable in the first decision variable vector of the first local intelligent controller is calculated. The gradient of each decision variable in the first decision variable vector of the first local intelligent controller is calculated.

[0153] .

[0154] Wherein, w represents a preset weight coefficient.

[0155] In step 3032, the gradient of each decision variable is iteratively optimized, and a convergence index in each iteration optimization process is obtained. The convergence index represents the consistency of whether each decision variable gradually tends to the overall optimal state of the AC / DC distribution network.

[0156] Further, based on the above gradient calculation, in each iteration, the update formula of the decision variable vector of the first local intelligent controller is as follows:

[0157] .

[0158] Wherein, represents a learning rate, represents the decision variable vector at the i th iteration, represents the gradient vector of the decision variable vector of the i th node of the target decision function at the i th iteration, and the element is calculated by The element of the constraint vector, which is used to ensure that the updated decision variable meets the local resource margin and other actual constraint conditions, can be represented as:

[0159] .

[0160] ​​​​​​​​​​​​​In an embodiment, if the updated distributed power output set value exceeds the adjustable power range allowed by the local resource margin or exceeds its own maximum power limit, etc. constraints, the corresponding constraint vector element is 0, preventing this update, ensuring that the update of the decision variable is always within a reasonable and feasible range.

[0161] Optionally, in the initial stage , each first local intelligent controller starts iteration with its preliminary local scheduling decision vector as the initial value. In each iteration process, each first local intelligent controller first exchanges interaction information, i.e. the preliminary local scheduling decision vector and the local resource margin information vector, with the adjacent second local intelligent controller , and then calculates the weight coefficient with each adjacent node in the current iteration according to the received information. The calculation of the weight coefficient can take into account multiple factors, such as the electrical connection strength between nodes (measured by electrical parameters such as conductance), communication link quality (measured by indicators such as signal strength and packet loss rate), and the relevance of historical interaction information, etc. Therefore, the calculation formula of the weight coefficient is as follows:

[0162] .

[0163] wherein represents the electrical connection strength related parameter between node and node , represents the communication link quality related parameter between node and node , represents the relevance index calculated based on historical interaction information, , and represent the corresponding weight coefficients, .

[0164] Further, according to the above distributed gradient descent update formula, the decision variable vector of itself is calculated and updated, and the constraint vector is checked and adjusted to ensure that the updated decision variable meets the local resource margin and other actual constraint conditions. In an embodiment, if the remaining adjustable power margin of the distributed power source is insufficient, the distributed power output set value cannot be updated to exceed the margin range, which can be realized by adding a margin constraint condition in the update formula, for example, when the distributed power output set value is updated, it needs to satisfy , It is the margin corresponding to the distributed power source in the margin information.

[0165] Furthermore, in order to judge whether the iteration has converged, that is, whether the key decision variables of each node gradually tend to the optimal consistency state of the regional power grid as a whole, the convergence index is calculated, and its calculation formula is as follows:

[0166] .

[0167] in, Represents the convergence index.

[0168] Step 3033: If the convergence index is less than the preset convergence threshold, the decision variable vector obtained after the current iteration is determined as the target local scheduling decision of the first local intelligent controller.

[0169] Further, it is determined whether the convergence index is less than a preset convergence threshold, wherein the preset convergence threshold in the embodiment of the present invention is If the convergence index is determined Less than the preset convergence threshold When the iteration is determined to have converged, the decision variable vector of each first local intelligent controller is This is the final target local scheduling decision.

[0170] The embodiment of the present invention performs consistent collaborative optimization of the target decision function based on the second local resource margin information of the second local intelligent controller, thereby being able to provide reasonable local scheduling decisions for the efficient and stable operation of the AC / DC distribution network based on the actual conditions of adjacent nodes, thereby improving the overall operating efficiency and stability of the AC / DC distribution network.

[0171] In one embodiment, steps 401 to 405 are described as follows:

[0172] Step 401: Obtain the total duration of the entire scheduling process and divide the total duration into multiple time segments.

[0173] The embodiment of the present invention divides the AC / DC distribution network into Each region is managed by a regional coordinator. The first local intelligent controller set connected to the regional coordinator is , that is, Areas include The target local intelligent controller can be understood as the Areas connected Target local intelligent controller .

[0174] Optionally, the embodiment of the present application sets the total time length considered in the whole scheduling process as The total time length is divided into equal interval time segments, each time segment has a time length of , i.e. , i.e. For each time segment , the corresponding time point is .

[0175] Step 402, based on the time segment and the load data of the local node of the target local intelligent controller at the current time point, determine the total peak-valley difference in the minimized area.

[0176] Optionally, each target local intelligent controller corresponds to a local node, and the information of the local node includes: the load data of the local node at the current time point , the output data of the distributed power at the current time point , and the energy storage state data of the energy storage device at the current time point .

[0177] Among them, the load data of the local node at the current time point is composed of a plurality of different types of load components, such as a load curve including a basic load and a peak load, so the load data can be expressed as , wherein represents the basic load, represents the peak load, and the peak load is related to different electricity characteristics periods of residents and industries. Further, according to the time segment and the load data of the local node of the target local intelligent controller at the current time point, the total peak-valley difference in the minimized area is determined, and the specific formula is as follows:

[0178]

[0179] . Among them,

[0180] represents the total peak-valley difference in the minimized area. Step 403, based on the time segment and the output data of the distributed power of the target local intelligent controller at the current time point, determine the deviation between the actual output of the distributed power and the output setting value in the target local scheduling decision.

[0181] Optionally, the output data of the distributed power at the current time point

[0182] ​​​​​Including distributed power generation at the current moment Output setting value (from target local dispatch decision) and actual output The output characteristics are affected by many factors, such as external environment such as light intensity and wind speed, as well as the limitations of its own adjustment ability.

[0183] Furthermore, based on the time segment and the output data of the distributed power source of the target local intelligent controller at the current moment, the deviation between the actual output of the distributed power source and the output set value in the target local scheduling decision is determined. The specific formula is as follows:

[0184] .

[0185] It represents the deviation between the actual output of the distributed generation and the output set value in the target local dispatch decision.

[0186] Step 404 : Based on the time segment and the energy storage status data of the energy storage device of the target local intelligent controller at the current moment, determine the degree of deviation between the actual power of the energy storage device and the power set value in the target local scheduling decision.

[0187] Optionally, the energy storage device at the current moment Energy storage status data Including the energy storage device at the current moment Charge and discharge power setting value (from target local dispatch decision) and actual charge and discharge power , actual charge and discharge power The power state is , and has minimum power and maximum power limit.

[0188] Furthermore, based on the time segment and the energy storage status data of the target local intelligent controller's energy storage device at the current moment, the degree of deviation between the actual power of the energy storage device and the power set value in the target local scheduling decision is determined. The specific formula is as follows:

[0189] .

[0190] in, Indicates the degree of deviation between the actual power of the energy storage device and the power set value in the target local scheduling decision.

[0191] Step 405: construct a comprehensive objective function based on the total peak-to-valley difference, deviation, and degree of deviation, and update the target local scheduling decision of the target local intelligent controller based on the distributed collaborative optimization algorithm to minimize the comprehensive objective function and obtain the node scheduling decision of the node corresponding to the target local intelligent controller.

[0192] Furthermore, in order to achieve peak-valley difference optimization scheduling of AC / DC distribution networks, the embodiment of the present invention constructs a comprehensive objective function based on the total peak-valley difference, deviation and deviation degree. , therefore, the comprehensive objective function Taking into account multiple factors such as peak-valley difference reduction, rational resource utilization and scheduling stability, the comprehensive objective function The formula is as follows:

[0193] .

[0194] in, represents the total peak-to-valley difference in the minimized region, Indicates the deviation between the actual output of the distributed generation and the output set value in the target local dispatch decision, Indicates the degree of deviation between the actual power of the energy storage device and the power setting value in the target local scheduling decision, Indicates the coordination of scheduling behaviors between different target local intelligent controllers, Indicates the preset weight coefficient.

[0195] The embodiment of the present invention takes into account the coordination of scheduling behaviors between different target local intelligent controllers. , the specific formula is as follows:

[0196] .

[0197] in, Indicates the The target local intelligent controller and the The association weights between the target local intelligent controllers can be calculated based on the The target local intelligent controller and the The target local intelligent controllers are determined by factors such as the electrical distance and resource complementarity between them.

[0198] Optionally, the regional coordinator in the embodiment of the present invention uses a distributed collaborative optimization algorithm to update the target local scheduling decision of each target local intelligent controller (node) to minimize the comprehensive objective function , obtain the node scheduling decision of the node corresponding to the target local intelligent controller, as described in steps 4051 to 4053.

[0199] The regional coordinator of the embodiment of the present invention performs distributed collaborative optimization on the local scheduling decisions of the local intelligent controllers, determines the optimal scheduling decision for each local intelligent controller, and realizes accurate and precise optimization and scheduling of the peak-to-valley differences of the AC / DC distribution network, better responds to load changes, realizes the rational allocation and utilization of resources, and improves the overall operating efficiency and stability of the AC / DC distribution network.

[0200] In one embodiment, steps 4051 to 4053 are described as follows:

[0201] Step 4051: Update each scheduling decision variable in the target local scheduling decision of the target local intelligent controller, and obtain the first convergence indicator in each updating process.

[0202] Optionally, in each iteration, the embodiment of the present invention When, for the region The target local intelligent controller (node), The update rules for the scheduling decision variables (such as the actual output of distributed generation, the actual charging and discharging power of energy storage, etc.) in the target local scheduling decision of the target local intelligent controller are as follows:

[0203] The update rule for distributed power generation output is:

[0204] .

[0205] in, Indicates the The distributed power supply of the target local intelligent controller is Iterations, time slices The actual output at represents the learning rate, Represents the comprehensive objective function About The distributed power supply of the target local intelligent controller is Iterations, time slices The partial derivative of the output when Indicates the area The target local intelligent controller and the The coordinated weight of the distributed power output between the target local intelligent controllers, Indicates A target local intelligent controller is a set of nodes that have specific relationships (such as electrical adjacency, resource complementarity requirements, etc.), Represents the associated weight of the distributed generation output.

[0206] The update rule for energy storage charging and discharging power is:

[0207] .

[0208] in, Indicates the The distributed power supply of the target local intelligent controller is Iterations, time slices The actual charge and discharge power when represents the learning rate, Represents the comprehensive objective function About The distributed power supply of the target local intelligent controller is Iterations, time slices The partial derivative of the charge and discharge power when Indicates the area The target local intelligent controller and the The coordinated weight of energy storage charging and discharging power between the target local intelligent controllers, Indicates A set of nodes with specific associations (such as energy storage resource complementarity, load characteristics association, etc.) for each target local intelligent controller. Represents the associated weight of energy storage charging and discharging power.

[0209] Furthermore, the first convergence index is obtained in each update process, wherein the first convergence index The calculation formula is as follows:

[0210] .

[0211] Step 4052: Obtain the constraints in the distributed collaborative optimization process.

[0212] Optionally, in the distributed collaborative optimization process of the embodiment of the present invention, the following constraints need to be met to ensure the feasibility and rationality of the scheduling decision:

[0213] 1. Power balance constraints

[0214] For each target local intelligent controller (node ) at any time segment ,have:

[0215] .

[0216] in, Indicates the target local intelligent controller In time segments The power that interacts with the outside world (such as adjacent nodes, upper-level power grid, etc.) is positive when it flows in and negative when it flows out.

[0217] 2. Distributed power output range constraints

[0218] The actual output of the distributed power source must be within its allowed minimum and maximum output range, that is:

[0219] .

[0220] in, and They are the minimum and maximum output limits of distributed power sources respectively.

[0221] 3. Energy storage charging and discharging power and capacity constraints

[0222] The charging and discharging power of the energy storage device must meet its rated charging and discharging power limit. When charging:

[0223] .

[0224] When discharging:

[0225] .

[0226] At the same time, the amount of energy stored must always be between its minimum and maximum capacity, that is:

[0227] .

[0228] in, and are the rated charging power and rated discharging power of the energy storage device, and are its minimum and maximum charge capacities respectively.

[0229] In step 4053, if the constraints are met and the first convergence indicator is less than the first preset convergence threshold, the node scheduling decision of the node corresponding to the target local intelligent controller is determined based on the actual output of the distributed power source and the actual charge and discharge power of the energy storage of the target local intelligent controller when the distributed collaborative optimization algorithm converges.

[0230] Optionally, the convergence judgment mechanism of the embodiment of the present invention is to judge whether the convergence index of the distributed collaborative optimization algorithm is converged, that is, whether the convergence index is less than a preset convergence threshold .

[0231] when Less than the preset convergence threshold When , it is determined that the distributed collaborative optimization algorithm converges.

[0232] Furthermore, after the convergence of the distributed collaborative optimization algorithm is determined, the actual output of the distributed power supply of each target local intelligent controller is and actual charging and discharging power of energy storage That is the final node scheduling decision of the target local intelligent controller. Therefore, the node scheduling decision includes the output behavior of the distributed power supply and the charging and discharging behavior of the energy storage.

[0233] Furthermore, the regional coordinator controls each target local intelligent controller to perform corresponding scheduling behavior according to the output behavior of the corresponding distributed power source and the charging and discharging behavior of the energy storage. That is, the distributed power source generates electricity according to the determined output behavior, and the energy storage performs charging and discharging operations according to the determined charging and discharging power, thereby realizing peak-valley difference optimization scheduling of the AC / DC distribution network.

[0234] The regional coordinator of the embodiment of the present invention performs distributed collaborative optimization on the local scheduling decisions of the local intelligent controllers, determines the optimal scheduling decision for each local intelligent controller, and realizes accurate and precise optimization and scheduling of the peak-to-valley differences of the AC / DC distribution network, better responds to load changes, realizes the rational allocation and utilization of resources, and improves the overall operating efficiency and stability of the AC / DC distribution network.

[0235] Furthermore, the distributed optimization scheduling system for AC / DC distribution networks provided by the present invention is described below. The distributed optimization scheduling system for AC / DC distribution networks described below and the distributed optimization scheduling method for AC / DC distribution networks described above can refer to each other.

[0236] Optional, see Figure 2 , Figure 2 This is a structural diagram of the distributed optimized dispatching system for AC / DC distribution networks provided by the present invention. This system includes at least an optimized dispatching management center, local intelligent controllers, and regional coordinators. Each regional coordinator is connected to local intelligent controllers within a preset range via a communication link. The optimized dispatching management center is connected to all regional coordinators, providing unified management for them. Local intelligent controllers are deployed at various nodes in the AC / DC distribution network, including distributed power supply nodes, energy storage device nodes, and load nodes.

[0237] Local intelligent controller for:

[0238] Collect load data, distributed power output data and energy storage status data of the local node;

[0239] According to the local optimization objective function, combined with load data, distributed power output data and energy storage status data, local optimization decision is made to obtain the preliminary local dispatch decision of each first local intelligent controller.

[0240] performing consistency collaborative optimization based on the first interaction information and the second interaction information of the second local intelligent controller to determine a target local scheduling decision of each first local intelligent controller;

[0241] Regional Coordinator for:

[0242] Distributed collaborative optimization is performed based on the target local scheduling decision of the target local intelligent controller to determine the node scheduling decision of the node corresponding to the target local intelligent controller, thereby realizing peak-valley difference optimal scheduling of the AC / DC distribution network.

[0243] The first local intelligent controller of the embodiment of the present invention is able to comprehensively consider the various characteristics of local nodes, make optimization decisions based on a constructed local optimization objective function and the load data, distributed power output data, and energy storage status data of the local nodes, and can provide reasonable local scheduling decisions for the efficient and stable operation of the AC / DC distribution network based on its own conditions. Furthermore, the regional coordinator performs distributed collaborative optimization on the local scheduling decisions of the local intelligent controllers, determining the optimal scheduling decision for each local intelligent controller. This enables accurate and precise optimization and scheduling of the peak-to-valley differences of the AC / DC distribution network, better responding to load changes, achieving reasonable allocation and utilization of resources, and improving the overall operating efficiency and stability of the AC / DC distribution network.

[0244] See also Figure 3 , Figure 3 This is a diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented:

[0245] Based on each first local intelligent controller, load data, distributed power output data and energy storage status data of the local node are collected;

[0246] Based on each first local intelligent controller making a local optimization decision according to a local optimization objective function combined with load data, distributed power output data and energy storage status data, a preliminary local dispatch decision of each first local intelligent controller is obtained;

[0247] Determining a target local scheduling decision of each first local intelligent controller based on consistency collaborative optimization performed by each first local intelligent controller according to the first interaction information and the second interaction information of the second local intelligent controller;

[0248] Based on the distributed collaborative optimization of each regional coordinator according to the target local scheduling decision of the target local intelligent controller, the node scheduling decision of the node corresponding to the target local intelligent controller is determined to achieve peak-valley difference optimal scheduling of the AC / DC distribution network.

[0249] See also Figure 4 , Figure 4An embodiment of a computer readable storage medium provided for an embodiment of the present application is shown in FIG. Figure 4 As shown in the figure, the embodiment provides a computer readable storage medium 400, which stores a computer program 311, and the computer program 311 is executed by a processor to implement the following steps:

[0250] Collecting, by each first local intelligent controller, load data, distributed power output data and energy storage state data of a local node where the first local intelligent controller is located;

[0251] Making, by each first local intelligent controller, a preliminary local scheduling decision according to a local optimization objective function in combination with the load data, the distributed power output data and the energy storage state data;

[0252] Making, by each first local intelligent controller, a target local scheduling decision according to the first interaction information and second interaction information of a second local intelligent controller;

[0253] Making, by each regional coordinator, a node scheduling decision of a node corresponding to the target local intelligent controller according to the target local scheduling decision of the target local intelligent controller, to implement peak-valley difference optimization scheduling of the AC / DC distribution network.

[0254] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, so that the computer can execute the AC / DC distribution network distributed optimization scheduling method provided by each method, and the method includes:

[0255] Collecting, by each first local intelligent controller, load data, distributed power output data and energy storage state data of a local node where the first local intelligent controller is located;

[0256] Making, by each first local intelligent controller, a preliminary local scheduling decision according to a local optimization objective function in combination with the load data, the distributed power output data and the energy storage state data;

[0257] Making, by each first local intelligent controller, a target local scheduling decision according to the first interaction information and second interaction information of a second local intelligent controller;

[0258] Making, by each regional coordinator, a node scheduling decision of a node corresponding to the target local intelligent controller according to the target local scheduling decision of the target local intelligent controller, to implement peak-valley difference optimization scheduling of the AC / DC distribution network.

[0259] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0260] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0261] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distributed optimization scheduling method for AC / DC distribution networks, characterized in that: It is implemented based on the distributed optimization dispatching system of AC and DC distribution networks, which includes at least a local intelligent controller and a regional coordinator. Each regional coordinator is connected to the local intelligent controller within a preset range through a communication link. The local intelligent controller is deployed at each node of the AC / DC distribution network, including a distributed power supply node, an energy storage device node, and a load node. The method includes: Based on each first local intelligent controller, load data, distributed power output data and energy storage status data of the local node are collected; Based on each first local intelligent controller making a local optimization decision according to a local optimization objective function combined with load data, distributed power output data and energy storage status data, a preliminary local dispatch decision of each first local intelligent controller is obtained; Determining a target local scheduling decision of each first local intelligent controller based on consistency collaborative optimization performed by each first local intelligent controller according to the first interaction information and the second interaction information of the second local intelligent controller; Based on the distributed collaborative optimization of each regional coordinator according to the target local intelligent controller's target local scheduling decision, the node scheduling decision of the node corresponding to the target local intelligent controller is determined to achieve peak-valley difference optimization scheduling of the AC / DC distribution network; The local optimization objective function aims to minimize the peak-to-valley difference, maximize the local energy self-sufficiency rate, and minimize the energy storage life loss. It is constructed by combining the load characteristics of the local load node, the power regulation characteristics of the distributed power supply node, and the charge and discharge characteristics of the energy storage device node. The second local intelligent controller is the controller corresponding to the node adjacent to the local node. The interaction information includes preliminary local scheduling decisions and local resource margin information. The target local intelligent controller is the first local intelligent controller connected to each regional coordinator. The performing consistency collaborative optimization based on the first interaction information and the second interaction information of the second local intelligent controller to determine the target local scheduling decision of each first local intelligent controller includes: determining a degree of consistency of decision variables between the first local intelligent controller and the second local intelligent controller based on a first decision variable vector and a first preliminary local scheduling decision vector of the first local intelligent controller, and a second decision variable vector and a second preliminary local scheduling decision vector of the second local intelligent controller; determining, based on a first decision variable vector and first local resource margin information of the first local intelligent controller, a correlation degree between the decision variable and the local resource margin information in the first local intelligent controller; A target decision function is constructed based on the consistency degree and the association degree, and the target decision function is collaboratively optimized for consistency based on the second local resource margin information of the second local intelligent controller to obtain a target local scheduling decision of each first local intelligent controller.

2. The distributed optimization scheduling method for AC / DC power distribution network according to claim 1, characterized in that: The distributed collaborative optimization is performed according to the target local scheduling decision of the target local intelligent controller to determine the node scheduling decision of the node corresponding to the target local intelligent controller, including: Obtain the total duration of the entire scheduling process and divide the total duration into multiple time segments; Based on the time slice and the load data of the local node of the target local intelligent controller at the current moment, the total peak-to-valley difference in the minimized area is determined; Based on the time slice and the output data of the distributed power source of the target local intelligent controller at the current moment, the deviation between the actual output of the distributed power source and the output set value in the target local scheduling decision is determined; Determine the degree of deviation between the actual power of the energy storage device and the power setpoint in the target local scheduling decision based on the time segment and the energy storage status data of the target local intelligent controller at the current moment; A comprehensive objective function is constructed based on the total peak-to-valley difference, the deviation and the degree of deviation, and the target local scheduling decision of the target local intelligent controller is updated based on a distributed collaborative optimization algorithm to minimize the comprehensive objective function and obtain a node scheduling decision of the node corresponding to the target local intelligent controller.

3. The distributed optimization scheduling method for AC / DC power distribution network according to claim 2, characterized in that: The target local scheduling decision of the target local intelligent controller is updated based on the distributed collaborative optimization algorithm to minimize the comprehensive objective function, thereby obtaining the node scheduling decision of the node corresponding to the target local intelligent controller, including: updating each scheduling decision variable in the target local scheduling decision of the target local intelligent controller, and obtaining a first convergence indicator in each updating process; Obtaining constraints in the distributed collaborative optimization process; If the constraints are met and the first convergence indicator is less than a first preset convergence threshold, the node scheduling decision of the node corresponding to the target local intelligent controller is determined based on the actual output of the distributed power source and the actual charging and discharging power of the energy storage of the target local intelligent controller when the distributed collaborative optimization algorithm converges; the node scheduling decision includes the output behavior of the distributed power source and the charging and discharging behavior of the energy storage.

4. The distributed optimization scheduling method for AC / DC power distribution network according to claim 1, characterized in that: The performing consistency collaborative optimization on the target decision function based on the second local resource margin information of the second local intelligent controller to obtain the target local scheduling decision of each first local intelligent controller includes: Calculating the gradient of the target decision function with respect to each decision variable in the first decision variable vector of the first local intelligent controller according to the second local resource margin information; Iteratively optimizing the gradient of each decision variable and obtaining a second convergence indicator during each iterative optimization process; the second convergence indicator indicates whether each decision variable is gradually tending towards an optimal consistency state of the AC / DC distribution network as a whole; If the second convergence indicator is less than a second preset convergence threshold, the decision variable vector obtained after the current iteration is determined as the target local scheduling decision of the first local intelligent controller.

5. The distributed optimization scheduling method for AC / DC power distribution network according to claim 1, characterized in that: The specific process of constructing the local optimization objective function includes: determining a total load characteristic based on a first load curve corresponding to a load characteristic of an industrial user concentrated area and a second load curve corresponding to a load characteristic of an residential user concentrated area; Determining the output of the distributed power source based on a first power regulation characteristic corresponding to the output of the photovoltaic power source and a second power regulation characteristic corresponding to the output of the wind power source; Determine the energy capacity change rate based on the lithium battery capacity change corresponding to the charge and discharge efficiency of the lithium battery and the energy storage capacity change rate corresponding to the charge and discharge efficiency of the flow battery; The local optimization objective function is constructed based on the total load characteristics, the distributed power output and the energy and power change rate.

6. The distributed optimization scheduling method for AC / DC power distribution network according to claim 5, characterized in that: The constructing of the local optimization objective function based on the total load characteristics, the output of the distributed power source and the energy and quantity change rate includes: determining a peak-to-valley difference based on the total load characteristic, and constructing a peak-to-valley difference minimization objective function based on the peak-to-valley difference; Based on the total load characteristics and the output of the distributed power sources, respectively determining the total energy generated by the local distributed power sources and the energy consumed by the local total load, and constructing a local energy self-sufficiency maximization objective function based on the total energy generated by the local distributed power sources and the energy consumed by the local total load; Determining a first life loss index of lithium battery energy storage and a second life loss index of flow battery energy storage based on the energy and charge change rate, and constructing an energy storage life loss minimization objective function based on the first life loss index and the second life loss index; The local optimization objective function is constructed based on the peak-to-valley difference minimization objective function, the local energy self-sufficiency rate maximization objective function and the energy storage life loss minimization objective function.

7. A distributed optimization dispatching system for AC and DC distribution networks, characterized in that: The system includes at least a local intelligent controller and a regional coordinator, each regional coordinator is connected to the local intelligent controllers within a preset range via a communication link; the local intelligent controllers are deployed at each node of the AC / DC distribution network, the nodes including distributed power supply nodes, energy storage device nodes and load nodes, and are used to implement the distributed optimization scheduling method of the AC / DC distribution network according to any one of claims 1 to 6; The local intelligent controller is used to: Collect load data, distributed power output data and energy storage status data of the local node; According to the local optimization objective function, combined with load data, distributed power output data and energy storage status data, local optimization decision is made to obtain the preliminary local dispatch decision of each first local intelligent controller. performing consistency collaborative optimization based on the first interaction information and the second interaction information of the second local intelligent controller to determine a target local scheduling decision of each first local intelligent controller; The regional coordinator is used to: Distributed collaborative optimization is performed based on the target local dispatching decision of the target local intelligent controller to determine the node dispatching decision of the node corresponding to the target local intelligent controller, thus achieving peak-valley difference optimized dispatching of the AC / DC distribution network. The local optimization objective function aims to minimize the peak-to-valley difference, maximize the local energy self-sufficiency rate, and minimize the energy storage life loss. It is constructed by combining the load characteristics of the local load node, the power regulation characteristics of the distributed power node, and the charge and discharge characteristics of the energy storage device node. The second local intelligent controller is a controller corresponding to a node adjacent to the local node; the interaction information includes preliminary local scheduling decisions and local resource margin information; the target local intelligent controller is the first local intelligent controller connected to each regional coordinator; The performing consistency collaborative optimization based on the first interaction information and the second interaction information of the second local intelligent controller to determine the target local scheduling decision of each first local intelligent controller includes: determining a degree of consistency of decision variables between the first local intelligent controller and the second local intelligent controller based on a first decision variable vector and a first preliminary local scheduling decision vector of the first local intelligent controller, and a second decision variable vector and a second preliminary local scheduling decision vector of the second local intelligent controller; determining, based on a first decision variable vector and first local resource margin information of the first local intelligent controller, a correlation degree between the decision variable and the local resource margin information in the first local intelligent controller; A target decision function is constructed based on the consistency degree and the association degree, and the target decision function is collaboratively optimized for consistency based on the second local resource margin information of the second local intelligent controller to obtain a target local scheduling decision of each first local intelligent controller.

8. An electronic device comprising: Memory for storing computer software programs; A processor, configured to read and execute the computer software program, wherein when the processor executes the computer software program, the processor implements the distributed optimization scheduling method for AC / DC distribution networks according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing a computer software program, wherein: When the computer software program is executed by a processor, the distributed optimization scheduling method for AC / DC power distribution networks according to any one of claims 1 to 6 is implemented.

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