Optimal dispatching method and system for distribution network including photovoltaic, energy storage and electric heating loads

Abstract

The present invention belongs to the technical field of distribution network operation optimization and scheduling, and specifically relates to a distribution network optimization and scheduling method and system containing photovoltaic, energy storage and electric heating loads. The method is based on a cloud-edge computing network architecture, which includes a cloud computing layer, an edge computing layer and a terminal device layer. By establishing a cloud-edge-end collaborative processing data volume model and corresponding constraints, the cloud-edge-end collaborative processing data volume is maximized; a distribution network optimization model is established in the cloud computing layer to minimize node voltage deviation and system network loss; an optimization model is established in the edge computing layer to maximize photovoltaic absorption, optimize electric heating user comfort and minimize energy storage operation costs. Through the cloud-edge computing network architecture, computing tasks are reasonably allocated to the cloud computing layer, the edge computing layer and the terminal device layer, solving the problems of data transmission delay and high computing resource consumption in traditional centralized computing methods.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network operation optimization and scheduling, and specifically relates to a distribution network optimization and scheduling method and system containing photovoltaic, energy storage and electric heating loads. Background Art

[0002] The output of distributed photovoltaics is affected by weather conditions and seasonal changes. The power of electric heating equipment is also dynamically related to changes in building and outdoor temperature. When the distribution network is connected to a large-scale distributed source-load storage, it will induce voltage fluctuations and frequency changes at the grid connection point, greatly affecting the quality of electricity and complicating the optimization and scheduling of the distribution network, posing a huge challenge to the safe and stable power supply to users.

[0003] Traditional distribution network optimization dispatching methods mainly rely on centralized cloud computing platforms, which collect and process large amounts of data through central servers to make global optimization decisions. These methods can achieve optimized dispatching to a certain extent and have been widely applied and verified, but they also have significant disadvantages such as data transmission delays, high consumption of computing resources, and insufficient local optimization. Summary of the invention

[0004] The object of the present invention is to provide a method and system for optimizing the scheduling of a distribution network including photovoltaic, energy storage and electric heating loads, so as to solve the defects of the traditional method for optimizing the scheduling of a distribution network in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for optimizing the dispatching of a distribution network including photovoltaic, energy storage and electric heating loads, comprising:

[0007] Determine a pre-built cloud-edge-end computing network architecture; wherein the cloud-edge-end computing network architecture includes a cloud computing layer, an edge computing layer, and a terminal device layer;

[0008] Based on the cloud-edge computing network architecture, a cloud-edge-end collaborative processing data volume model and a corresponding first constraint condition are established, and the objective function of the cloud-edge-end collaborative processing data volume model is to maximize the cloud-edge-end collaborative processing data volume; a cloud computing layer distribution network optimization model and a corresponding second constraint condition are established at the cloud computing layer, and the objective function of the cloud computing layer distribution network optimization model is to minimize the node voltage deviation and the system network loss in the distribution network area; an edge layer distribution network optimization model and a corresponding third constraint condition are established at the edge computing layer, and the objective function of the edge layer distribution network optimization model is to maximize photovoltaic absorption, optimize the comfort of electric heating users, and minimize the energy storage operation cost;

[0009] Based on the first constraint condition, the cloud-edge-terminal collaborative processing data volume model is solved to obtain the optimal solution for the cloud-edge-terminal collaborative processing data volume; based on the optimal solution for the cloud-edge-terminal collaborative processing data volume, the cloud computing layer sends a global optimization strategy to each edge computing layer, and the terminal device layer sends the collected device operation data to the corresponding edge computing layer. The edge computing layer solves the edge layer distribution network optimization model based on the third constraint condition, the received device operation data and the global optimization strategy to obtain local optimization results, and the edge computing layer uploads the local optimization results and the device operation data to the cloud computing layer. The cloud computing layer solves the cloud computing layer distribution network optimization model based on the second constraint condition, each local optimization result and each device operation data to obtain a global optimization result.

[0010] Furthermore, the objective function of the cloud-edge-end collaborative processing data volume model is:

[0011] Specifically include:

[0012]

[0013]

[0014] in, T Indicates the total number of time periods throughout the day. I Indicates the number of terminals, Indicates the amount of data processed by the cloud-edge collaborative process. represents the data computation decision variable of the edge computing layer, Indicates the amount of data that can be calculated locally at the terminal device layer, represents the data computing decision variables of the cloud computing layer, Indicates the amount of data that the edge computing layer can calculate, Indicates the amount of data that the cloud computing layer can calculate.

[0015] Furthermore, the first constraint condition is

[0016] Specifically include:

[0017]

[0018]

[0019] In the formula, T Indicates the total number of time periods throughout the day. Indicates the task upload delay threshold. is the maximum delay, Q i (t) is the data of the local computing task queue, U i,min Collect the minimum amount of data for the terminal device layer.

[0020] Furthermore, the objective function of the edge layer distribution network optimization model specifically includes:

[0021]

[0022] In the formula, min F 1 represents the objective function of the edge layer distribution network optimization model; f 1 means photovoltaic consumption, f 2 indicates the comfort level of electric heating users. f 3 represents the energy storage operation cost; They are the weight coefficients for maximum photovoltaic consumption, optimal electric heating user comfort, and minimum energy storage operation cost; is the target value of photovoltaic consumption, is the actual absorption value; T is the total number of time periods throughout the day; T best For the best indoor temperature, T in,t is the indoor temperature at time t; N ESS is the amount of energy storage, pch t,j and PDIs t,j are the charging power and discharging power of energy storage j respectively.

[0023] Furthermore, the third constraint condition specifically includes photovoltaic operation output constraint, energy storage upper and lower limit constraints, electric heating operation constraint and temperature constraint.

[0024] Furthermore, the objective function of the cloud computing layer distribution network optimization model specifically includes:

[0025]

[0026] In the formula, min F 2 represents the objective function of the distribution network optimization model at the cloud computing layer; f 4 means the node voltage deviation is the smallest, f 5 means the system network loss is minimal. are the weight coefficients for minimizing node voltage deviation and minimizing system network loss respectively; T is the total number of time periods throughout the day; U i,t is the actual voltage of node i at time t, U N is the reference voltage, U i，t，max and U i，t，min are the maximum voltage and minimum voltage of node i at time t respectively; ij is the branch connecting node i and node j; E is the set of distribution network branches; Iij,t is the branch at time t ij The current; r ij For branch ij resistance.

[0027] Furthermore, the second constraint condition specifically includes node voltage constraint and branch power flow constraint.

[0028] In a second aspect, the present invention provides a distribution network optimization dispatching device including photovoltaic, energy storage and electric heating loads, comprising:

[0029] An architecture determination module is used to determine a pre-built cloud-edge-end computing network architecture; wherein the cloud-edge-end computing network architecture includes a cloud computing layer, an edge computing layer, and a terminal device layer;

[0030] A model building module is used to establish a cloud-edge-end collaborative processing data volume model and a corresponding first constraint condition based on the cloud-edge-end computing network architecture, wherein the objective function of the cloud-edge-end collaborative processing data volume model is to maximize the cloud-edge-end collaborative processing data volume; establish a cloud computing layer distribution network optimization model and a corresponding second constraint condition at the cloud computing layer, wherein the objective function of the cloud computing layer distribution network optimization model is to minimize node voltage deviation and system network loss within the distribution network area; establish an edge layer distribution network optimization model and a corresponding third constraint condition at the edge computing layer, wherein the objective function of the edge layer distribution network optimization model is to maximize photovoltaic absorption, optimize electric heating user comfort, and minimize energy storage operation cost;

[0031] A solving module is used to solve the cloud-edge collaborative processing data volume model based on the first constraint condition, and obtain the optimal solution for cloud-edge collaborative processing data volume; based on the optimal solution for cloud-edge collaborative processing data volume, the cloud computing layer sends a global optimization strategy to each edge computing layer, and the terminal device layer sends the collected device operation data to the corresponding edge computing layer. The edge computing layer solves the edge layer distribution network optimization model based on the third constraint condition, the received device operation data and the global optimization strategy to obtain local optimization results, and the edge computing layer uploads the local optimization results and the device operation data to the cloud computing layer. The cloud computing layer solves the cloud computing layer distribution network optimization model based on the second constraint condition, each local optimization result and each device operation data to obtain a global optimization result.

[0032] According to a third aspect of the present invention, there is provided an electronic device, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the distribution network optimization scheduling method as described above.

[0033] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the distribution network optimization scheduling method as described above is implemented.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] Through the cloud-edge computing network architecture, computing tasks are reasonably allocated to the cloud computing layer, edge computing layer and terminal device layer, solving the problems of data transmission delay and high computing resource consumption in traditional centralized computing methods. The cloud computing layer uses high-performance servers and optimization algorithms to perform global optimization calculations and generate optimal scheduling strategies to ensure the overall optimal performance of the system. The edge computing layer performs local optimization based on the global optimization strategy issued by the cloud computing layer and local actual conditions, thereby improving the accuracy and adaptability of the optimization scheduling; the edge computing layer can also process local data in real time and respond quickly to changes in equipment status, thereby improving the real-time and flexibility of the system. The distribution network optimization scheduling device, electronic device and computer-readable storage medium provided by the present invention, which contain photovoltaic, energy storage and electric heating loads, also solve the problems raised in the background technology section. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0037] Figure 1 This is a flow chart of a method for optimizing the dispatching of a distribution network including photovoltaic, energy storage and electric heating loads according to an embodiment of the present invention;

[0038] Figure 2 This is a structural block diagram of a distribution network optimization dispatching device containing photovoltaic, energy storage and electric heating loads according to an embodiment of the present invention;

[0039] Figure 3 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0041] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0042] Example 1

[0043] The present invention proposes a method for optimizing the dispatching of a distribution network containing photovoltaic, energy storage and electric heating loads. First, an objective function including maximum photovoltaic absorption, optimal electric heating user comfort and minimum energy storage operation cost is established in the edge computing layer, and photovoltaic operation output constraints, energy storage upper and lower limit constraints, electric heating operation constraints and temperature constraints are set. Secondly, an objective function of minimum node voltage deviation and minimum system network loss in the distribution network area is established in the cloud computing layer, and node voltage constraints and branch flow constraints are set. Finally, through the cloud-edge computing network architecture, the terminal device layer is responsible for data collection and execution of control instructions, the edge computing layer is responsible for local data processing and real-time optimization, and the cloud computing layer is responsible for global data processing, optimization calculation and decision making; at the same time, a cloud-edge collaborative processing data volume model and its constraints are established, with the goal of maximizing the cloud-edge collaborative processing data volume. Through multi-level collaborative computing, the entire system realizes efficient management and optimized dispatching of distributed photovoltaic, energy storage and electric heating loads, improves the operating efficiency and economy of the system, and ensures the comfort of users and the safe and stable operation of the distribution network.

[0044] like Figure 1 As shown, a method for optimizing the dispatching of a distribution network including photovoltaic, energy storage and electric heating loads comprises the following steps:

[0045] S1. Determine a pre-built cloud-edge-end computing network architecture; wherein the cloud-edge-end computing network architecture includes a cloud computing layer, an edge computing layer, and a terminal device layer.

[0046] It should be noted that the cloud-edge computing network architecture constructed in this solution is a distributed computing architecture, which is used to optimize the scheduling and management of distributed photovoltaic, energy storage and electric heating loads to ensure efficient and stable operation of the system.

[0047] Specifically, the cloud-edge computing network architecture includes the following:

[0048] 1) Terminal equipment layer

[0049] The terminal device layer is responsible for data collection and execution of control instructions. The terminal device layer can include terminals such as photovoltaic inverters, energy storage management systems, and electric heating controllers. The main tasks of the terminal include real-time collection of equipment operation data and ambient temperature data, reporting the collected data to the edge computing layer, and executing control instructions from the edge computing layer.

[0050] Optionally, 5G communication is used as the communication mode between the terminal and the edge computing layer.

[0051] Specifically, the terminal includes a local computing part and a data uploading part. The data of the local computing task queue isQ i ( t ), the amount of local computing data is determined by the maximum processing capacity of the terminal and the amount of data in the task queue, expressed as:

[0052]

[0053] In the formula, U i,1 ( t ) is the amount of local computing data, f i，1 ( t ) is the maximum processing capacity of the terminal, λ i To calculate the complexity, the terminal processor calculation frequency can be used to represent it, and τ is a moment. After the data and tasks are calculated at each moment, the local calculation queue data is updated:

[0054]

[0055] In the formula, U i ( t ) is the amount of data processed by cloud-edge-end collaboration, A i ( t ) is the amount of data collected by the terminal at time t.

[0056] At time t, the terminal uses channel J for end-to-end data transmission at a rate of R i,j (t):

[0057]

[0058] In the formula, B j is the transmission bandwidth; γ i,j (t) is the signal-to-noise ratio, which is related to the channel transmission power and channel gain. Meanwhile, the co-channel interference and electromagnetic interference that may be generated during data transmission are ignored here.

[0059] 2) Edge computing layer

[0060] The edge computing layer is responsible for local data processing and real-time optimization. The edge computing layer includes communication base stations, edge servers and other equipment. The main tasks of the edge computing layer include real-time monitoring of the status of photovoltaic, energy storage and electric heating equipment in the area under its jurisdiction, performing local optimization calculations, and regularly uploading data and local optimization results to the cloud computing layer.

[0061] Specifically, the amount of data that can be calculated by the edge computing layer is determined by the edge data transmission capacity, the edge server computing capacity, and the amount of data in the terminal local task queue, which can be expressed as:

[0062]

[0063] In the formula, U i,2 ( t ) is the amount of data that can be calculated by the edge computing layer; f i，2 ( t ) is the maximum computing capacity of the edge server, F i,1 (t) is the decision variable for data calculation, i.e., the optimization objective of the edge layer.

[0064] 3) Cloud computing layer

[0065] The cloud computing layer is responsible for global data processing, optimization calculations and decision-making. It is composed of cloud servers built by the power grid company itself or rented from trusted third parties. Its main tasks include: collecting data uploaded by each edge computing layer; performing global optimization calculations and generating scheduling strategies; and sending optimized scheduling instructions to each edge computing layer.

[0066] Specifically, the amount of data that can be calculated by the cloud computing layer is determined by the transmission capacity of the wireless channel between the terminal and the edge, the transmission capacity of the communication link between the cloud and the edge, and the amount of data in the terminal local task queue, which can be expressed as:

[0067]

[0068] In the formula, U i,3 ( t ) is the amount of data that can be calculated by the edge computing layer; Λ( t ) is the transmission rate of the cloud-edge communication link, which is affected by network delay, network rate, etc. f i，3 ( t ) is the maximum computing capacity of the cloud server, F i,2 (t) is the data calculation decision variable, that is, the optimization objective of the cloud computing layer.

[0069] S2. Based on the cloud-edge computing network architecture, a cloud-edge-end collaborative data processing model and the corresponding first constraint condition are established, and the objective function of the cloud-edge-end collaborative data processing model is to maximize the cloud-edge-end collaborative data processing volume; a cloud computing layer distribution network optimization model and the corresponding second constraint condition are established in the cloud computing layer, and the objective function of the cloud computing layer distribution network optimization model is to minimize the node voltage deviation and the system network loss in the distribution network area; an edge layer distribution network optimization model and the corresponding third constraint condition are established in the edge computing layer, and the objective function of the edge layer distribution network optimization model is to maximize the photovoltaic absorption, optimize the comfort of electric heating users and minimize the energy storage operation cost.

[0070] In one embodiment, the objective function of the cloud-edge-end collaborative processing data volume model specifically includes:

[0071]

[0072] Considering the differentiated model construction of local computing, edge processing, and cloud processing, the amount of data processed by cloud-edge-end collaboration is expressed as:

[0073]

[0074] in, T Indicates the total number of time periods throughout the day. I Indicates the number of business data collection terminals. Indicates the amount of data processed by the cloud-edge collaborative process. represents the data computation decision variable of the edge computing layer, Indicates the amount of data that can be calculated locally at the terminal device layer, represents the data computing decision variables of the cloud computing layer, Indicates the amount of data that the edge computing layer can calculate, Indicates the amount of data that the cloud computing layer can calculate.

[0075] In one embodiment, when the terminal needs to process and upload too much data, data transmission delay will occur, and its impact on the distribution network optimization model cannot be ignored. At the same time, other distribution network services, such as power consumption information collection, distribution automation, and power quality monitoring, all have requirements for the amount of collected data. Therefore, the cloud-edge collaborative optimization architecture also needs to consider task upload time constraints and collected data volume constraints. The first constraint condition specifically includes:

[0076]

[0077]

[0078] In the formula, T Indicates the total number of time periods throughout the day. Indicates the task upload delay threshold. is the maximum delay, Q i (t) is the data of the local computing task queue, U i,min Collect the minimum amount of data for the terminal device layer.

[0079] In one embodiment, the objective function of the edge layer distribution network optimization model is:

[0080] Specifically include:

[0081]

[0082] In the formula, min F 1 represents the objective function of the edge layer distribution network optimization model; f 1 means photovoltaic consumption, f 2 indicates the comfort level of electric heating users. f 3 represents the energy storage operation cost; They are the weight coefficients for maximum photovoltaic consumption, optimal electric heating user comfort, and minimum energy storage operation cost; is the target value of photovoltaic consumption, is the actual absorption value; T is the total number of time periods throughout the day; T best For the best indoor temperature, T in,t is the indoor temperature at time t; N ESS is the amount of energy storage, pch t,j and PDIs t,j are the charging and discharging powers of energy storage j respectively.

[0083] In one embodiment, the third constraint condition specifically includes photovoltaic operation output constraint, energy storage upper and lower limit constraints, electric heating operation constraint and temperature constraint.

[0084] Specifically, the third constraint condition includes the following:

[0085] 1) PV operation output constraints

[0086]

[0087] In the formula, is the actual output of the photovoltaic power added at node i at time t, The maximum value of the photovoltaic output allowed. In this scheme, it is assumed that the photovoltaic output is continuously adjustable.

[0088] 2) Energy storage upper and lower limit constraints

[0089]

[0090] In the formula, P ch i,t and P dis i,t are the actual charging and discharging power of energy storage, P ESS i,t is the rated operating power of the energy storage, η ch and η dis are the energy storage charging and discharging efficiency, δ It is the energy storage working state variable, and its value is 0 or 1, representing that the energy storage is working in the charging and discharging states respectively.

[0091] 3) Electric heating operation constraints

[0092]

[0093] In the formula, is the actual power of the electric heating equipment added at node i at time t, To ensure the maximum power of the electric heating equipment to meet the highest heating temperature requirements.

[0094] 4) Indoor temperature constraints

[0095] Buildings contain structures such as walls, doors and windows, which have a certain storage effect on heat. It is necessary to consider the operating power of electric heating equipment, indoor and outdoor temperatures, and house thermodynamic parameters, and obtain the second-order differential equation of temperature change and power:

[0096]

[0097] In the formula, T in 、T wall 、T out Represent the indoor temperature, wall temperature and outdoor temperature respectively. C 1. C 2 represent the air heat capacity parameters and the wall heat capacity parameters, R 1. R 2 represent the thermal resistance parameters between indoor air and wall and between outdoor air and wall respectively.

[0098] According to the national standard "Design Code for Heating, Ventilation and Air Conditioning of Civil Buildings", the heating temperature range is within [18℃, 22℃], and the user's comfort level is relatively good. Therefore, when using electric heating equipment, the indoor temperature constraints are:

[0099]

[0100] In the formula, T min and T max They are the lower limit and upper limit of indoor temperature obtained by the operation settings of the electric heating equipment.

[0101] In one embodiment, the objective function of the cloud computing layer distribution network optimization model is:

[0102] Specifically include:

[0103]

[0104] In the formula, min F 2 represents the objective function of the distribution network optimization model at the cloud computing layer; f 4 means the node voltage deviation is the smallest, f 5 means the system network loss is minimal. are the weight coefficients for minimizing node voltage deviation and minimizing system network loss respectively; T is the total number of time periods throughout the day; U i,t is the actual voltage of node i at time t, U N is the reference voltage, U i，t，max and U i，t，min are the maximum voltage and minimum voltage of node i at time t respectively; ij is the branch connecting node i and node j, t is the time period mark; E is the set of distribution network branches; I ij,t is the branch at time t ij The current; r ij For branch ij resistance.

[0105] In one embodiment, the second constraint condition specifically includes a node voltage constraint and a branch power flow constraint.

[0106] Specifically, the second constraint condition includes the following:

[0107] 1) Node voltage constraints

[0108]

[0109] Where U max and U min are the maximum and minimum values ​​of the voltage at node i, U i,t is the node voltage value at time t.

[0110] 2) Branch flow constraints

[0111] Taking the radial distribution network as the research object, the operating state of one branch at time t is selected to establish the branch flow model. The constraints that the branch flow should meet are:

[0112]

[0113]

[0114]

[0115]

[0116] In the formula, i, j Number the node; U i,t , U j,t Node i, j Voltage; r ij +jx ij For branch ij Impedance; P ij,t and Q ij,t Branch ij The active and reactive power at the head end; I ij,t For branch ij The current; p j,t For Node i, j Active injection power of k:j→k Represents a node j It is the set of child nodes of the parent node; P jk,t and Q jk,t are respectively the active and reactive power at the head end of branch jk; q j,t For Node j The reactive injection power.

[0117] S3. Based on the first constraint condition, solve the cloud-edge collaborative processing data volume model to obtain the optimal solution for cloud-edge collaborative processing data volume; based on the optimal solution for cloud-edge collaborative processing data volume, the cloud computing layer sends a global optimization strategy to each edge computing layer, and the terminal device layer sends the collected device operation data to the corresponding edge computing layer. The edge computing layer solves the edge layer distribution network optimization model based on the third constraint condition, the received device operation data and the global optimization strategy to obtain local optimization results, and the edge computing layer uploads the local optimization results and the device operation data to the cloud computing layer. The cloud computing layer solves the cloud computing layer distribution network optimization model based on the second constraint condition, each local optimization result and each device operation data to obtain a global optimization result.

[0118] In one embodiment, step S3 specifically includes the following:

[0119] Step a: The photovoltaic inverter, energy storage management system and electric heating controller of the terminal device layer collect data in real time, including equipment operation data and ambient temperature, etc. The terminal device layer uploads the collected data to the database of the edge computing layer. Specifically, the collected data may include photovoltaic power generation, energy storage charging and discharging status, electric heating equipment operating power and indoor temperature, etc.

[0120] Step b: The edge server of the edge computing layer receives the data uploaded from the terminal device layer and pre-processes the data; the pre-processing may include data cleaning, normalization, and anomaly detection. Based on the global optimization strategy issued by the cloud computing layer, local optimization calculations are performed in combination with the actual conditions of various places. Among them, the optimization goals of the edge computing layer include maximizing photovoltaic absorption, minimizing energy storage operation costs, and satisfying the user's indoor temperature comfort level. According to the local optimization results, control instructions are generated and sent to the terminal to adjust the operating parameters of the equipment, such as the output power of the photovoltaic inverter, the charge and discharge rate of the energy storage battery, and the heating power of the electric heating equipment. At the same time, the edge computing layer uploads the local optimization results, ambient temperature, and equipment operation data to the cloud computing layer for global optimization.

[0121] Step c: The cloud computing layer receives the data uploaded from the edge computing layer. The cloud computing layer performs further preprocessing operations such as cleaning and normalization on the collected data. Based on the preprocessed data, the cloud computing layer performs global optimization calculations. The optimization goals of the cloud computing layer include minimizing node voltage deviation and minimizing system network loss. At the same time, the cloud computing layer generates a global optimization strategy based on the optimization calculation results, including the operating parameters and control instructions of each device. The cloud computing layer sends the optimized global optimization strategy to each edge computing layer through the network interface to guide it to perform local optimization.

[0122] Step d: The terminal device layer receives control instructions from the edge computing layer; the terminal device layer adjusts the operating parameters of the device according to the control instructions; in addition, the terminal device layer continues to collect device operating data and ambient temperature in real time, and regularly reports to the edge computing layer to form a closed-loop feedback mechanism.

[0123] Step e: The terminal device layer continuously uploads new data, the edge computing layer performs local optimization and feedbacks the results, and the cloud computing layer performs global optimization and issues new scheduling instructions. Based on real-time data and feedback results, the cloud computing layer and the edge computing layer continuously adjust the optimization strategy to achieve dynamic optimization of the system. Through multiple iterations of optimization, the optimal control scheme for photovoltaic, energy storage and electric heating loads is finally output to ensure efficient and stable operation of the system.

[0124] Step f: In the above steps a~e, data calculation and transmission are implemented based on the cloud-edge computing network architecture of this solution. The amount of data transmitted and calculated is executed according to the optimal solution for cloud-edge collaborative processing of data to ensure that the cloud-edge collaborative processing of data in the entire cloud-edge computing network architecture is maximized.

[0125] Model Application

[0126] The present invention is mainly intended to achieve accurate calculation of photovoltaic consumption and electric heating scheduling, improve the problems of traditional optimization scheduling methods such as data transmission delay and large consumption of computing resources, and achieve more refined and effective optimization scheduling.

[0127] The specific applications can be divided into the following two cases:

[0128] 1) When the photovoltaic power generation is sufficient within a certain period of time, the cloud computing layer generates control instructions based on the global optimization model, and sends them to the terminal device layer through the edge computing layer to appropriately increase the set value of the electric heating equipment temperature, for example, from 20°C to 22°C, to better meet the heating needs of residents. If there is still a surplus of photovoltaic power, the cloud computing layer will generate instructions to control the energy storage through the edge computing layer to store the excess photovoltaic power. For example, the excess photovoltaic power is stored in the energy storage battery for use at night or on cloudy days.

[0129] 2) When the photovoltaic power generation power is insufficient within a certain period of time, the cloud computing layer generates control instructions based on the global optimization model, and sends them to the terminal device layer through the edge computing layer to appropriately reduce the set value of the electric heating equipment temperature, for example, from 20°C to 18°C, to ensure that the photovoltaic power is consumed to the maximum extent within the comfort range of residents. The cloud computing layer generates instructions, and controls the energy storage to release the stored power through the edge computing layer to supplement the insufficient photovoltaic output. For example, at night or on cloudy days, the energy storage battery can discharge to supply the electric heating equipment. In special weather conditions such as rain, snow, etc., the photovoltaic output may be small or even zero. The cloud computing layer will generate power purchase instructions, and control the terminal device layer through the edge computing layer to purchase electricity from the power grid to meet the heating needs and ensure that the heating of residents is not affected.

[0130] Example 2

[0131] like Figure 2 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a distribution network optimization scheduling device containing photovoltaic, energy storage and electric heating loads, including:

[0132] An architecture determination module is used to determine a pre-built cloud-edge-end computing network architecture; wherein the cloud-edge-end computing network architecture includes a cloud computing layer, an edge computing layer, and a terminal device layer;

[0133] A model building module is used to establish a cloud-edge-end collaborative processing data volume model and a corresponding first constraint condition based on the cloud-edge-end computing network architecture, wherein the objective function of the cloud-edge-end collaborative processing data volume model is to maximize the cloud-edge-end collaborative processing data volume; establish a cloud computing layer distribution network optimization model and a corresponding second constraint condition at the cloud computing layer, wherein the objective function of the cloud computing layer distribution network optimization model is to minimize node voltage deviation and system network loss within the distribution network area; establish an edge layer distribution network optimization model and a corresponding third constraint condition at the edge computing layer, wherein the objective function of the edge layer distribution network optimization model is to maximize photovoltaic absorption, optimize electric heating user comfort, and minimize energy storage operation cost;

[0134] A solving module is used to solve the cloud-edge collaborative processing data volume model based on the first constraint condition, and obtain the optimal solution for cloud-edge collaborative processing data volume; based on the optimal solution for cloud-edge collaborative processing data volume, the cloud computing layer sends a global optimization strategy to each edge computing layer, and the terminal device layer sends the collected device operation data to the corresponding edge computing layer. The edge computing layer solves the edge layer distribution network optimization model based on the third constraint condition, the received device operation data and the global optimization strategy to obtain local optimization results, and the edge computing layer uploads the local optimization results and the device operation data to the cloud computing layer. The cloud computing layer solves the cloud computing layer distribution network optimization model based on the second constraint condition, each local optimization result and each device operation data to obtain a global optimization result.

[0135] Example 3

[0136] like Figure 3 As shown, the present invention also provides an electronic device 100 for realizing a method for optimizing the dispatching of a distribution network including photovoltaic, energy storage and electric heating loads;

[0137] The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0138] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of a method for optimizing the scheduling of a distribution network containing photovoltaic, energy storage and electric heating loads in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0139] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0140] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0141] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for optimizing the dispatching of a distribution network including photovoltaic, energy storage and electric heating loads, and the processor 102 can execute the plurality of instructions to implement:

[0142] Determine a pre-built cloud-edge-end computing network architecture; wherein the cloud-edge-end computing network architecture includes a cloud computing layer, an edge computing layer, and a terminal device layer;

[0143] Based on the cloud-edge computing network architecture, a cloud-edge-end collaborative processing data volume model and a corresponding first constraint condition are established, and the objective function of the cloud-edge-end collaborative processing data volume model is to maximize the cloud-edge-end collaborative processing data volume; a cloud computing layer distribution network optimization model and a corresponding second constraint condition are established at the cloud computing layer, and the objective function of the cloud computing layer distribution network optimization model is to minimize the node voltage deviation and the system network loss in the distribution network area; an edge layer distribution network optimization model and a corresponding third constraint condition are established at the edge computing layer, and the objective function of the edge layer distribution network optimization model is to maximize photovoltaic absorption, optimize the comfort of electric heating users, and minimize the energy storage operation cost;

[0144] Based on the first constraint condition, the cloud-edge-terminal collaborative processing data volume model is solved to obtain the optimal solution for the cloud-edge-terminal collaborative processing data volume; based on the optimal solution for the cloud-edge-terminal collaborative processing data volume, the cloud computing layer sends a global optimization strategy to each edge computing layer, and the terminal device layer sends the collected device operation data to the corresponding edge computing layer. The edge computing layer solves the edge layer distribution network optimization model based on the third constraint condition, the received device operation data and the global optimization strategy to obtain local optimization results, and the edge computing layer uploads the local optimization results and the device operation data to the cloud computing layer. The cloud computing layer solves the cloud computing layer distribution network optimization model based on the second constraint condition, each local optimization result and each device operation data to obtain a global optimization result.

[0145] Example 4

[0146] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0147] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented 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.

[0148] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0152] 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the dispatching of a distribution network containing photovoltaic, energy storage and electric heating loads, characterized in that: include: Determine a pre-built cloud-edge-end computing network architecture; wherein the cloud-edge-end computing network architecture includes a cloud computing layer, an edge computing layer, and a terminal device layer; Based on the cloud-edge computing network architecture, a cloud-edge-end collaborative processing data volume model and a corresponding first constraint condition are established, and the objective function of the cloud-edge-end collaborative processing data volume model is to maximize the cloud-edge-end collaborative processing data volume; a cloud computing layer distribution network optimization model and a corresponding second constraint condition are established at the cloud computing layer, and the objective function of the cloud computing layer distribution network optimization model is to minimize the node voltage deviation and the system network loss in the distribution network area; an edge layer distribution network optimization model and a corresponding third constraint condition are established at the edge computing layer, and the objective function of the edge layer distribution network optimization model is to maximize photovoltaic absorption, optimize the comfort of electric heating users, and minimize the energy storage operation cost; Based on the first constraint, the cloud-edge-terminal collaborative processing data volume model is solved to obtain the optimal solution for the cloud-edge-terminal collaborative processing data volume; based on the optimal solution for the cloud-edge-terminal collaborative processing data volume, the cloud computing layer sends a global optimization strategy to each edge computing layer, the terminal device layer sends the collected device operation data to the corresponding edge computing layer, and the edge computing layer solves the edge layer distribution network optimization model based on the third constraint, the received device operation data and the global optimization strategy to obtain a local optimization result, and the edge computing layer uploads the local optimization result and the device operation data to the cloud computing layer, and the cloud computing layer solves the cloud computing layer distribution network optimization model based on the second constraint, each local optimization result and each device operation data to obtain a global optimization result; The objective function of the cloud-edge-end collaborative data processing model specifically includes: in, T Indicates the total number of time periods throughout the day. I Indicates the number of terminals, Indicates the amount of data processed by the cloud-edge collaborative process. represents the data computation decision variable of the edge computing layer, Indicates the amount of data that can be calculated locally at the terminal device layer, represents the data computing decision variables of the cloud computing layer, Indicates the amount of data that the edge computing layer can calculate, Indicates the amount of data that the cloud computing layer can calculate.

2. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: The first constraint condition specifically includes: In the formula, T Indicates the total number of time periods throughout the day. Indicates the task upload delay threshold. is the maximum delay, Q i (t) is the data of the local computing task queue, U i,min Collect the minimum amount of data for the terminal device layer.

3. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: The objective function of the edge layer distribution network optimization model specifically includes: In the formula, min F 1 represents the objective function of the edge layer distribution network optimization model; f 1 means photovoltaic consumption, f 2 indicates the comfort level of electric heating users. f 3 represents the energy storage operation cost; They are the weight coefficients for maximum photovoltaic consumption, optimal electric heating user comfort, and minimum energy storage operation cost; is the target value of photovoltaic consumption, is the actual absorption value; T is the total number of time periods throughout the day; T best For the best indoor temperature, T in,t is the indoor temperature at time t; N ESS is the amount of energy storage, pch t,j and PDIs t,j are the charging power and discharging power of energy storage j respectively.

4. The method for optimizing the dispatching of a distribution network according to claim 3, characterized in that: The third constraint condition specifically includes photovoltaic operation output constraint, energy storage upper and lower limit constraints, electric heating operation constraint and temperature constraint.

5. The distribution network optimization dispatching method according to claim 1, characterized in that: The objective function of the cloud computing layer distribution network optimization model specifically includes: In the formula, min F 2 represents the objective function of the distribution network optimization model at the cloud computing layer; f 4 means the node voltage deviation is the smallest, f 5 means the system network loss is minimal. are the weight coefficients for minimizing node voltage deviation and minimizing system network loss respectively; T is the total number of time periods throughout the day; U i,t is the actual voltage of node i at time t, U N is the reference voltage, U i，t，max and U i，t，min are the maximum voltage and minimum voltage of node i at time t respectively; ij is the branch connecting node i and node j; E is the set of distribution network branches; I ij,t is the branch at time t ij The current; r ij For branch ij resistance.

6. The method for optimizing and dispatching a distribution network according to claim 5, characterized in that: The second constraint condition specifically includes node voltage constraint and branch power flow constraint.

7. A distribution network optimization dispatching device containing photovoltaic, energy storage and electric heating loads, characterized in that: include: An architecture determination module is used to determine a pre-built cloud-edge-end computing network architecture; wherein the cloud-edge-end computing network architecture includes a cloud computing layer, an edge computing layer, and a terminal device layer; A model building module is used to establish a cloud-edge-end collaborative processing data volume model and a corresponding first constraint condition based on the cloud-edge-end computing network architecture, wherein the objective function of the cloud-edge-end collaborative processing data volume model is to maximize the cloud-edge-end collaborative processing data volume; establish a cloud computing layer distribution network optimization model and a corresponding second constraint condition at the cloud computing layer, wherein the objective function of the cloud computing layer distribution network optimization model is to minimize node voltage deviation and system network loss within the distribution network area; establish an edge layer distribution network optimization model and a corresponding third constraint condition at the edge computing layer, wherein the objective function of the edge layer distribution network optimization model is to maximize photovoltaic absorption, optimize electric heating user comfort, and minimize energy storage operation cost; A solution module is used to solve the cloud-edge collaborative processing data volume model based on the first constraint condition, and obtain the optimal solution for the cloud-edge collaborative processing data volume; based on the optimal solution for the cloud-edge collaborative processing data volume, the cloud computing layer sends a global optimization strategy to each edge computing layer, the terminal device layer sends the collected device operation data to the corresponding edge computing layer, and the edge computing layer solves the edge layer distribution network optimization model based on the third constraint condition, the received device operation data and the global optimization strategy to obtain a local optimization result, and the edge computing layer uploads the local optimization result and the device operation data to the cloud computing layer, and the cloud computing layer solves the cloud computing layer distribution network optimization model based on the second constraint condition, each local optimization result and each device operation data to obtain a global optimization result; The objective function of the cloud-edge-end collaborative data processing model specifically includes: in, T Indicates the total number of time periods throughout the day. I Indicates the number of terminals, Indicates the amount of data processed by the cloud-edge collaborative process. represents the data computation decision variable of the edge computing layer, Indicates the amount of data that can be calculated locally at the terminal device layer, represents the data computing decision variables of the cloud computing layer, Indicates the amount of data that the edge computing layer can calculate, Indicates the amount of data that the cloud computing layer can calculate.

8. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the distribution network optimization scheduling method as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the distribution network optimization scheduling method according to any one of claims 1 to 6 is implemented.

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