Cloud edge cooperative control method and device considering participation of energy storage system in power grid dispatching

Abstract

The invention discloses a cloud-side cooperative control method and device considering participation of an energy storage system in power grid scheduling, and relates to the technical field of power system operation and scheduling, and the method comprises the steps: constructing a cloud-side cooperative control architecture of the energy storage system in power grid scheduling; based on a cloud-side cooperative control architecture, a cooperative scheduling model is constructed. The collaborative scheduling model comprises a cloud collaborative scheduling model, an edge collaborative scheduling model and constraint conditions; solving an optimal scheduling solution of the collaborative scheduling model by using an improved sparrow search algorithm; the improved sparrow search algorithm is obtained by introducing an inertia weight mechanism into the sparrow search algorithm; and completing power grid dispatching based on the optimal dispatching solution. The improved sparrow search algorithm is obtained after the inertia weight mechanism is introduced into the sparrow search algorithm and is used for power grid dispatching, and the stability and creativity of power grid dispatching can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system operation and dispatching, and in particular to a cloud-edge collaborative control method and device that considers the participation of energy storage systems in power grid dispatching. Background Art

[0002] As the penetration rate of renewable energy in the power grid continues to increase, its inherent intermittent and volatile nature poses a huge challenge to the stable operation of the power grid. Energy storage, as a key technology that can effectively smooth out the fluctuations of renewable energy and improve the flexibility and reliability of the power grid, has been widely used. At present, common types of energy storage include battery energy storage, pumped storage, supercapacitor energy storage, etc. Each type of energy storage has its own unique charging and discharging characteristics, cost and life. In traditional power grid dispatching and control, the management of energy storage is often relatively simple, and the comprehensive advantages of multiple types of energy storage are not fully considered. At the same time, centralized control methods are difficult to meet the needs of real-time power grid and distributed energy access. Although cloud computing technology can provide powerful data processing and analysis capabilities, there are problems with transmission delay and reliability. Edge computing has fast response and real-time processing capabilities locally. The cloud-edge collaborative control mode that combines cloud computing with edge computing provides a new idea for solving the problem of multiple types of energy storage participating in power grid dispatching. Summary of the invention

[0003] The purpose of this application is to provide a cloud-edge collaborative control method and device that takes into account the participation of energy storage systems in power grid dispatching, which can improve the stability and creativity of power grid dispatching.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a cloud-edge collaborative control method considering the participation of energy storage systems in power grid dispatching, including:

[0006] Build a cloud-edge collaborative control architecture for energy storage systems to participate in grid dispatching;

[0007] Based on the cloud-edge collaborative control architecture, a collaborative scheduling model is constructed; the collaborative scheduling model includes a cloud collaborative scheduling model, an edge collaborative scheduling model and constraints;

[0008] Using an improved sparrow search algorithm, the optimal scheduling solution of the collaborative scheduling model is solved; the improved sparrow search algorithm is obtained after introducing an inertia weight mechanism into the sparrow search algorithm;

[0009] The power grid dispatching is completed based on the optimal dispatching solution.

[0010] Optionally, the cloud-edge collaborative control architecture includes a cloud end and an edge end;

[0011] The cloud integrates a power grid dispatching center, a data center and a cloud platform center; the cloud is used to collect global data of the power grid and formulate long-term energy storage dispatching strategies from a global perspective using big data analysis technology and optimization algorithms; the global data of the power grid includes: long-term operation data of the power grid, various load forecast data, weather change data and basic information of different energy storage devices;

[0012] The edge end is based on the edge computing server and combines data processing and 5G technology to achieve real-time transmission, secure storage and reliable analysis of massive power grid data.

[0013] Optionally, the objective function of the cloud-based collaborative scheduling model includes: an overall grid operation scheduling energy consumption function and an overall grid load fluctuation function;

[0014] The overall operation and dispatching energy consumption function of the power grid is:

[0015]

[0016] Among them, f i represents the operation and dispatching energy consumption of the i-th multi-type energy storage that can be controlled in the power grid; n represents the number of multi-type energy storage that can be controlled in the power grid; C i represents the power generation and consumption function of the controllable multi-type energy storage in the power grid; P t g,i represents the power generation of the i-th multi-type energy storage that can be controlled in the power grid at time t, E i represents the dispatching energy consumption function of the i-th multi-type energy storage that can be controlled in the power grid;

[0017] The overall load fluctuation function of the power grid is:

[0018]

[0019] Among them, f s represents the overall load fluctuation of the power grid; T represents the entire dispatching cycle; P t represents the electricity consumption plan at time t; Indicates the average value of the total required power.

[0020] Optionally, the objective function of the edge collaborative scheduling model includes: a scheduling time consumption function, a node scheduling flexibility function and a network scheduling flexibility function;

[0021] The scheduling time-consuming function is:

[0022]

[0023] Among them, f1 represents the scheduling time; Indicates the average transmission delay of the edge when scheduling;

[0024] The node scheduling flexibility function is:

[0025]

[0026] Among them, f2 represents the node scheduling flexibility; It represents the flexibility margin of the node when transmitting upward, B t - represents the flexibility margin of the node when transmitting downward, and ζ represents the state variable of the flexibility margin of the node when transmitting;

[0027] The network scheduling flexibility function is:

[0028]

[0029] Among them, f3 represents the network scheduling flexibility; It represents the transmission margin rate of line ij in the power grid at time t, and M represents the number of lines in the power grid.

[0030] Optionally, the constraints include: power grid common coupling node output constraints, controllable multi-type energy storage power generation power output constraints, base station power supply must meet communication demand constraints, long-term transmission energy consumption constraints and flexibility constraints.

[0031] Optionally, the power grid common coupling node output constraint is:

[0032]

[0033] Among them, P t Z represents the exchange rate of the common coupling node at time t; P t L represents the load demand of the power grid at time t;

[0034] The controllable multi-type energy storage power generation power output constraint is:

[0035]

[0036] in, represents the power generation of the i-th multi-type energy storage that can be controlled in the power grid at time t+1;

[0037] The power supply of the base station needs to meet the communication demand constraint:

[0038]

[0039] Among them, P i 5G represents the power supply power of the i-th base station node; represents the power load of the i-th base station node;

[0040] The long-term transmission energy consumption constraint is:

[0041]

[0042] in, Indicates long-term transmission energy consumption; Indicates the maximum limit of energy consumption;

[0043] The flexibility constraint is:

[0044]

[0045] Optionally, using an improved sparrow search algorithm to solve the optimal scheduling solution of the collaborative scheduling model includes:

[0046] Initialize the sparrow population;

[0047] Calculate the fitness value of each individual in the sparrow population;

[0048] Arrange all individuals in the sparrow population in descending order based on the fitness values;

[0049] Based on the inertia weight mechanism and the descending order results, the weight factor of each individual is determined;

[0050] Based on the weight factor and the warning position update formula, the individual positions in the population are updated to obtain an updated population;

[0051] Calculate and update the fitness value of each individual in the population to determine the pending global optimal solution;

[0052] When the pending global optimal solution does not satisfy the constraint condition, returning to step “initializing the sparrow population”;

[0053] When the pending global optimal solution satisfies the constraint condition, the pending global optimal solution is determined to be the global optimal solution.

[0054] Optionally, the weight factor is:

[0055]

[0056] in, represents the optimized weight factor after k iterations; w max represents the maximum value of the optimized weight factor after k iterations; w min It represents the minimum value of the optimized weight factor after k iterations; K represents the maximum number of iterations.

[0057] Optionally, the warning source position update formula is:

[0058]

[0059] in, Represents population The i-th The updated position of the alerter in the j-dimensional space; represents the global optimum of the i-th early warning person in the population in the j-dimensional space; β represents a random number under normal distribution; represents the position of the population before the early warning update in the j-dimensional space; f g Represents the optimal fitness value of the population; A express Random numbers with uniform distribution; represents the worst position of the i-th early warning person in the population in the j-dimensional space; ε represents a very small number; f w Indicates the worst fitness value of the population.

[0060] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned cloud-edge collaborative control method considering the participation of the energy storage system in power grid scheduling.

[0061] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0062] The present application provides a cloud-edge collaborative control method and device that considers the participation of energy storage systems in grid dispatching, constructs an overall cloud-edge collaborative control architecture for the participation of energy storage systems in grid dispatching, so as to clarify the control structure and regulation scheme of cloud-edge collaborative control, and then establishes a cloud-side collaborative dispatching model and an edge-side collaborative dispatching model based on the established cloud-edge collaborative control architecture, and then imposes constraints on the established models. Subsequently, based on the improved sparrow search algorithm, the cloud-side collaborative dispatching model and the edge-side collaborative dispatching model are solved to obtain the optimal dispatching solution, thereby achieving the cloud-edge collaborative control effect for the participation of energy storage systems in grid dispatching, so as to give full play to the advantages of the energy storage system, improve the grid's ability to absorb renewable energy, and enhance the stability and reliability of grid operation. By dispatching multiple types of energy storage to adjust electricity supply and demand, improve operational efficiency and balance reliability, give full play to its advantages to achieve efficient operation, and integrate cloud computing and edge computing technologies. Cloud computing is responsible for massive data processing and long-term optimization in the background, and edge computing achieves local real-time response, achieving remote monitoring and local real-time control; relying on the power communication network to realize data transmission between cloud platforms, edge nodes and energy storage equipment, ensuring information security and timeliness, supporting control decisions, assisting the development of smart grids, improving the acceptance of renewable energy, and promoting the intelligent transformation of power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 This is an application environment diagram of a cloud-edge collaborative control method considering the participation of an energy storage system in power grid dispatching in one embodiment of the present application;

[0065] Figure 2 This is an overall architecture diagram of cloud-edge collaborative control of energy storage system participating in power grid dispatching in one embodiment of the present application;

[0066] Figure 3 This is a solution flow chart based on the improved sparrow search algorithm in one embodiment of the present application. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0068] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0069] In an exemplary embodiment, Figure 1 As shown, a cloud-edge collaborative control method considering the participation of energy storage system in power grid dispatch is provided, including:

[0070] Step 101: Construct a cloud-edge collaborative control architecture for energy storage systems to participate in grid dispatching.

[0071] Step 102: Based on the cloud-edge collaborative control architecture, a collaborative scheduling model is constructed. The collaborative scheduling model includes a cloud collaborative scheduling model, an edge collaborative scheduling model, and constraints.

[0072] Step 103: Use the improved sparrow search algorithm to solve the optimal scheduling solution of the collaborative scheduling model. The improved sparrow search algorithm is obtained by introducing the inertia weight mechanism into the sparrow search algorithm.

[0073] Step 104: Complete grid dispatch based on the optimal dispatch solution.

[0074] like Figure 2The cloud-edge collaborative control architecture includes the cloud and the edge. The cloud integrates the power grid dispatching center, data center, and cloud platform center. The cloud is used to collect global data of the power grid and use big data analysis technology and optimization algorithms to formulate long-term energy storage dispatching strategies from a global perspective. Global data of the power grid includes: long-term operation data of the power grid, various load forecast data, weather change data, and basic information of different energy storage devices. The edge is based on the edge computing server, combined with data processing and 5G technology, to achieve real-time transmission, secure storage, and reliable analysis of massive power grid data.

[0075] Determine the scheduling process of cloud-edge for power terminals, and provide an overall framework for the entire control method. The control architecture of the present invention designs two parts for the power terminal, namely the cloud and the edge. The cloud is like the brain center of the entire system, integrating the power grid dispatching center, data center and cloud platform center, and has powerful data processing and analysis capabilities. It widely collects massive data such as long-term operation data of the power grid, various load forecast data, weather change data, and basic information of various energy storage devices, and uses advanced big data analysis technology and optimization algorithms to formulate long-term energy storage scheduling strategies from a global perspective. The edge is closely attached to the local operating environment of the energy storage equipment and the power grid, and collects real-time operating data such as voltage, current, and power of the local power grid, as well as detailed status information such as the state of charge (SOC) and charging and discharging efficiency of the local energy storage equipment, providing strong support for the cloud. Based on these real-time data, the edge node can quickly respond to local sudden changes, execute the dispatching instructions issued by the cloud platform, and control the local energy storage equipment in real time to ensure the real-time response and processing capabilities of the power grid. The cloud performs a comprehensive analysis of the grid operation data calculated by the edge end, realizes the integrated coordinated optimization of grid energy dispatch, sends dispatch instructions to each execution end of the grid, promotes the coordinated interaction of source, grid, load and storage, so as to comprehensively monitor and grasp the operation status of the grid. The edge end takes the edge computing server as the core, combines data processing and 5G technology to realize the real-time transmission, secure storage and reliable analysis of massive grid data. Utilizing the powerful computing power of edge computing, the edge end can calculate the operation status of the grid in each area of ​​the grid, providing important support for cloud-based coordinated dispatch. Then, based on the cloud-edge collaborative control architecture, a cloud-edge collaborative dispatch model is established. The establishment of the cloud-edge dispatch model aims at the overall safety and stability of the grid, and sets the objective function as the minimum operation dispatch energy consumption of the overall grid. Minimum load fluctuation minf s .

[0076] The objective functions of the cloud-based collaborative scheduling model include: the overall grid operation scheduling energy consumption function and the overall grid load fluctuation function.

[0077] The overall operation and dispatching energy consumption function of the power grid is:

[0078]

[0079] Among them, f i represents the operation and dispatching energy consumption of the i-th multi-type energy storage that can be controlled in the power grid. n represents the number of multi-type energy storage that can be controlled in the power grid. i represents the power generation and consumption function of the controllable multi-type energy storage in the power grid. t g,i represents the power generation of the i-th multi-type energy storage that can be controlled in the power grid at time t, E i Represents the dispatching energy consumption function of the i-th controllable multi-type energy storage in the power grid.

[0080] The overall load fluctuation function of the power grid is:

[0081]

[0082] Among them, f s represents the overall load fluctuation of the power grid. T represents the entire dispatching cycle. t Represents the electricity consumption plan at time t. Indicates the average value of the total required power.

[0083] The edge-end collaborative scheduling model can calculate the grid operation equipment and energy storage system equipment within the coverage area of ​​the end, obtain the optimal operation plan and transmit it to the cloud. The objective function of the model is to minimize the scheduling time minf1, maximize the node scheduling flexibility (minimum time consumption) minf2, and maximize the network scheduling flexibility (minf3).

[0084] The objective functions of the edge collaborative scheduling model include: scheduling time function, node scheduling flexibility function and network scheduling flexibility function.

[0085] The scheduling time-consuming function is:

[0086]

[0087] Among them, f1 represents the scheduling time. Indicates the average transmission delay of the edge during scheduling.

[0088] The node scheduling flexibility function is:

[0089]

[0090] Among them, f2 represents the node scheduling flexibility. It indicates the flexibility margin of the node when transmitting upward. It represents the flexibility margin of the node when transmitting downward, and ζ represents the state variable of the flexibility margin of the node when transmitting.

[0091] The network scheduling flexibility function is:

[0092]

[0093] Among them, f3 represents the network scheduling flexibility. It represents the transmission margin rate of line ij in the power grid at time t, and M represents the number of lines in the power grid.

[0094] The constraints include: power output constraints of the common coupling nodes of the power grid, power output constraints of controllable multi-type energy storage power generation, constraints on the power supply of base stations to meet communication needs, constraints on long-term transmission energy consumption, and constraints on sufficient flexibility.

[0095] The output constraint of the common coupling node of the power grid is:

[0096]

[0097] Among them, P t Z P represents the exchange rate of the common coupling node at time t. t L It represents the load demand of the power grid at time t.

[0098] The power output constraints of controllable multi-type energy storage power generation are:

[0099]

[0100] in, It represents the power generation of the i-th multi-type energy storage that can be controlled in the power grid at time t+1.

[0101] The power supply of the base station must meet the communication demand constraints:

[0102]

[0103] Among them, P i 5G represents the power supply of the i-th base station node. represents the power load of the i-th base station node.

[0104] The long-term transmission energy consumption constraint is:

[0105]

[0106] in, Indicates long-term transmission energy consumption. Indicates the maximum limit of energy consumption.

[0107] The flexibility constraint is:

[0108]

[0109] like Figure 3 , step 103 comprises:

[0110] Step 103-1: Initialize the sparrow population.

[0111] Step 103-2: Calculate the fitness value of each individual in the sparrow population.

[0112] Step 103-3: Arrange all individuals in the sparrow population in descending order based on fitness values.

[0113] Step 103-4: Determine the weight factor of each individual based on the inertia weight mechanism and the descending order results.

[0114] Step 103-5: Based on the weight factor and the warning source position update formula, update the individual positions in the population to obtain an updated population.

[0115] Step 103-6: Calculate and update the fitness value of each individual in the population to determine the pending global optimal solution.

[0116] Step 103-7: When the pending global optimal solution does not satisfy the constraint conditions, return to step 103-1.

[0117] Step 103 - 8 : When the pending global optimal solution satisfies the constraint conditions, the pending global optimal solution is determined to be the global optimal solution.

[0118] The sparrow search algorithm is mainly based on the foraging behavior and anti-predator behavior of the sparrow group. Sparrows are divided into discoverers, joiners and early warning. Discoverers usually have good foraging ability. They look for food sources in the search space (corresponding to the optimal solution in the optimization problem), and their position update formula will guide the entire group to move to the possible optimal area. Joiners update their positions according to the positions of discoverers to obtain food. Early warning mainly avoids danger by changing positions when it perceives danger (in the algorithm, it may be that the search falls into a local optimum, etc.), so that the group can continue to search effectively. In the original algorithm, the proportion of discoverers is fixed. The improved algorithm can dynamically adjust the proportion of discoverers according to indicators such as the fitness variance of the population. For example, when the fitness variance is large, it means that the differences between individuals in the population are large and there is a large search space to explore. At this time, the proportion of discoverers can be appropriately increased to speed up the global search. The improved algorithm can also adaptively adjust the step size of individual position updates. In the early stage of iteration, a larger step size is set so that individuals can search in a wider space to prevent them from falling into a local optimum too early. As the number of iterations increases, when the algorithm gradually converges, the step size is reduced to improve the accuracy of the algorithm in searching for the optimal solution in the local area.

[0119] The improved sparrow search algorithm is used to solve the cloud collaborative scheduling model and the edge collaborative scheduling model. Based on the basic algorithm, the algorithm introduces an inertia weight mechanism to optimize the global optimization capability in order to improve the diversity of the algorithm population and the global convergence speed, so as to ensure a better solution effect. In the solution process of the improved sparrow search algorithm, each solution is regarded as a potential "food", and the goal of the algorithm is to find the best "food", that is, the optimal solution, so as to achieve the coordinated scheduling of the power grid. Before starting the calculation, the solution process based on the improved sparrow search algorithm first sets and initializes the required parameters to provide basic data for subsequent calculations. Then, according to the set parameters and rules, the fitness value of each individual parameter is calculated, and the calculated individual fitness values ​​are sorted for subsequent processing. Then, the weight inertia mechanism is used to optimize these values. Based on the optimized information, the individual position is updated, the fitness value is calculated again, and the global optimal solution is found. The optimal solution is matched with the above constraints. If the constraints are met, the optimal scheduling result is obtained. If the constraints are not met, the process after parameter initialization is repeated until the conditions are met. The main content of weight inertia is to change the nonlinear degree of weight through the cosine function cos() to increase the disturbance degree, so as to meet the randomness and disturbance of power grid dispatching. The calculation formula is:

[0120]

[0121] in, represents the optimized weight factor after k iterations. max Indicates the maximum value of the weight factor optimized after k iterations. min It represents the minimum value of the optimized weight factor after k iterations. K represents the maximum number of iterations.

[0122] Optimized It can better adapt to the nonlinear and random changes in the grid dispatching process. Update the positions of the discoverer, follower, and alerter to ensure that they can better adapt to the changes in the objective function and obtain the optimal solution. If the current individual fitness value is represented by f, the alerter position update formula is:

[0123]

[0124] in, Represents population The i-th The updated position of the alerter in the j-dimensional space; represents the global optimum of the i-th early warning person in the population in the j-dimensional space; β represents a random number under normal distribution; represents the position of the population before the early warning update in the j-dimensional space; f gRepresents the optimal fitness value of the population; A express Random numbers with uniform distribution; represents the worst position of the i-th early warning person in the population in the j-dimensional space; ε represents a very small number; f w Indicates the worst fitness value of the population.

[0125] The position update formula of the warner is used to update the position, so that it can continuously adjust its position during the search process, maintain connection with the population, and share information with other sparrows, so as to ensure that the entire population can better adapt to changes in the search environment and improve the efficiency and quality of the solution.

[0126] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a cloud-edge collaborative control method considering the participation of an energy storage system in power grid scheduling is implemented.

[0127] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0128] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0131] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0132] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A cloud-edge collaborative control method considering energy storage system participation in power grid dispatching, characterized in that: include: Build a cloud-edge collaborative control architecture for energy storage systems to participate in grid dispatching; Based on the cloud-edge collaborative control architecture, a collaborative scheduling model is constructed; the collaborative scheduling model includes a cloud collaborative scheduling model, an edge collaborative scheduling model and constraints; Using an improved sparrow search algorithm, the optimal scheduling solution of the collaborative scheduling model is solved; the improved sparrow search algorithm is obtained after introducing an inertia weight mechanism into the sparrow search algorithm; The power grid dispatching is completed based on the optimal dispatching solution.

2. The cloud-edge collaborative control method considering the energy storage system participating in power grid dispatching according to claim 1 is characterized in that: The cloud-edge collaborative control architecture includes a cloud end and an edge end; The cloud integrates a power grid dispatching center, a data center and a cloud platform center; the cloud is used to collect global data of the power grid and formulate long-term energy storage dispatching strategies from a global perspective using big data analysis technology and optimization algorithms; The global power grid data includes: long-term power grid operation data, various load forecast data, weather change data and basic information of different energy storage devices; The edge end is based on the edge computing server and combines data processing and 5G technology to achieve real-time transmission, secure storage and reliable analysis of massive power grid data.

3. The cloud-edge collaborative control method considering the energy storage system participating in power grid dispatching according to claim 1 is characterized in that: The objective functions of the cloud-based collaborative scheduling model include: the overall operation and scheduling energy consumption function of the power grid and the overall load fluctuation function of the power grid; The overall operation and dispatching energy consumption function of the power grid is: Among them, f i represents the operation and dispatching energy consumption of the i-th multi-type energy storage that can be controlled in the power grid; n represents the number of multi-type energy storage that can be controlled in the power grid; C i represents the power generation and consumption function of the i-th multi-type energy storage that can be controlled in the power grid; represents the power generation of the i-th multi-type energy storage that can be controlled in the power grid at time t, E i represents the dispatching energy consumption function of the i-th multi-type energy storage that can be controlled in the power grid; The overall load fluctuation function of the power grid is: Among them, f s represents the overall load fluctuation of the power grid; T represents the entire dispatching cycle; P t represents the electricity consumption plan at time t; Indicates the average value of the total required power.

4. The cloud-edge collaborative control method considering the energy storage system participating in power grid dispatching according to claim 3 is characterized in that: The objective functions of the edge collaborative scheduling model include: scheduling time function, node scheduling flexibility function and network scheduling flexibility function; The scheduling time-consuming function is: Among them, f1 represents the scheduling time; Indicates the average transmission delay of the edge when scheduling; The node scheduling flexibility function is: Among them, f2 represents the node scheduling flexibility; It indicates the flexibility margin of the node when transmitting upward. represents the flexibility margin of the node when transmitting downward, and ζ represents the state variable of the flexibility margin of the node when transmitting; The network scheduling flexibility function is: Among them, f3 represents the network scheduling flexibility; It represents the transmission margin rate of line ij in the power grid at time t, and M represents the number of lines in the power grid.

5. The cloud-edge collaborative control method considering the energy storage system participating in power grid dispatching according to claim 4 is characterized in that: The constraints include: power grid common coupling node output constraints, controllable multi-type energy storage power generation output constraints, base station power supply must meet communication demand constraints, long-term transmission energy consumption constraints and flexibility constraints.

6. The cloud-edge collaborative control method considering the energy storage system participating in power grid dispatching according to claim 5 is characterized in that: The output constraint of the common coupling node of the power grid is: in, represents the exchange rate of the common coupling node at time t; represents the load demand of the power grid at time t; The controllable multi-type energy storage power generation power output constraint is: in, represents the power generation of the i-th multi-type energy storage that can be controlled in the power grid at time t+1; The power supply of the base station needs to meet the communication demand constraint: in, represents the power supply power of the i-th base station node; represents the power load of the i-th base station node; The long-term transmission energy consumption constraint is: in, Indicates long-term transmission energy consumption; Indicates the maximum limit of energy consumption; The flexibility constraint is:

7. The cloud-edge collaborative control method considering the energy storage system participating in power grid dispatching according to claim 6 is characterized in that: The optimal scheduling solution of the collaborative scheduling model is solved by using the improved sparrow search algorithm, including: Initialize the sparrow population; Calculate the fitness value of each individual in the sparrow population; Arrange all individuals in the sparrow population in descending order based on the fitness values; Based on the inertia weight mechanism and the descending order results, the weight factor of each individual is determined; Based on the weight factor and the warning position update formula, the individual positions in the population are updated to obtain an updated population; Calculate and update the fitness value of each individual in the population to determine the pending global optimal solution; When the pending global optimal solution does not satisfy the constraint condition, return to step "initializing the sparrow population"; When the pending global optimal solution satisfies the constraint condition, the pending global optimal solution is determined to be the global optimal solution.

8. The cloud-edge collaborative control method considering the energy storage system participating in power grid dispatching according to claim 7 is characterized in that: The weighting factor is: in, represents the optimized weight factor after k iterations; w max represents the maximum value of the optimized weight factor after k iterations; w min It represents the minimum value of the optimized weight factor after k iterations; K represents the maximum number of iterations.

9. The cloud-edge collaborative control method considering the energy storage system participating in power grid dispatching according to claim 8 is characterized in that: The update formula of the warning person's position is: in, Represents population The i-th The updated position of the alerter in the j-dimensional space; represents the global optimum of the i-th early warning person in the population in the j-dimensional space; β represents a random number under normal distribution; represents the position of the population before the early warning update in the j-dimensional space; f g Represents the optimal fitness value of the population; A express Random numbers with uniform distribution; represents the worst position of the i-th early warning person in the population in the j-dimensional space; ε represents a very small number; f w Indicates the worst fitness value of the population.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cloud-edge collaborative control method according to any one of claim 9 that considers the participation of the energy storage system in grid scheduling.

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