Aggregation control method, system, device and medium for distributed energy storage power station

Abstract

The present invention relates to the field of electric power technology, and discloses a method, device, electronic device and medium for aggregate control of distributed energy storage power stations. The specific scheme is: determine a first function for evaluating the peak-shaving and valley-filling capability of the distributed energy storage power station, determine a second function for evaluating the total power purchase cost of the distributed energy storage power station, and determine a third function for the system profitability of the distributed energy storage power station; determine an objective function based on the first function, the second function and the third function; solve the objective function for the optimal solution based on some constraints to obtain an energy storage control scheme for the distributed energy storage power station; and control the distributed energy storage power station based on the energy storage control scheme.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method, system, device and medium for aggregate control of a distributed energy storage power station. Background Art

[0002] Under the new power system, the scale and capacity of new energy storage power stations are growing rapidly, but the use of electricity is not ideal. The contradiction of "high growth and low use" continues to intensify. The existing power regulation method cannot fully utilize the support capabilities of new energy storage for new energy and power grids. The scientific regulation and operation of multiple new energy storages is imminent.

[0003] Distributed energy storage can effectively alleviate the pressure on the power grid caused by the access of new energy generation and the rapid growth of load. Different from traditional centralized energy storage power stations, distributed energy storage has the advantages of small footprint and low investment cost, and can be flexibly installed in various locations on the user side.

[0004] However, the charging and discharging power of a single distributed energy storage is small and its capacity is limited, which makes it difficult to attract the interest of the power system for independent regulation and management. In contrast, the potential for distributed energy storage regulation after aggregation is greater. However, the existing distributed energy storage regulation after aggregation is not perfect in terms of scheduling and lacks efficient and reasonable scheduling means. Summary of the invention

[0005] The technical problem to be solved by the present invention is to solve the problem of low efficiency of the aggregation control method of the existing distributed energy storage power station.

[0006] In order to solve the above technical problems, the present invention provides an aggregate control method for a distributed energy storage power station, comprising:

[0007] Determine a first function for evaluating the peak-shaving and valley-filling capability of the distributed energy storage power station based on the difference between the maximum and minimum purchased power of each node in the distributed energy storage power station at each time;

[0008] Based on the power purchase power of each node in the distributed energy storage power station at each time and the electricity price at each time, determine a second function for evaluating the total power purchase cost of the distributed energy storage power station;

[0009] Determine a third function for the system profitability of the distributed energy storage power station based on the charging power, discharging power, non-storage load power, and purchased power of each node in the distributed energy storage power station at each time;

[0010] Determine a target function based on the first function, the second function and the third function;

[0011] Based on the energy storage capacity constraint condition, the power balance constraint condition, and the purchased power constraint condition of the distributed energy storage power station, the objective function is solved for the optimal solution to obtain the energy storage control scheme of the distributed energy storage power station;

[0012] Based on the energy storage control scheme, the distributed energy storage power station is controlled.

[0013] According to another aspect of the present invention, there is provided an aggregate control device for a distributed energy storage power station, comprising:

[0014] A first function determination module, used to determine a first function for evaluating the peak shaving and valley filling capability of the distributed energy storage power station based on the difference between the maximum and minimum purchased power of each node in the distributed energy storage power station at each time;

[0015] A second function determination module, used to determine a second function for evaluating the total power purchase cost of the distributed energy storage power station based on the power purchase power of each node in the distributed energy storage power station at each time and the electricity price at each time;

[0016] A third function determination module, configured to determine a third function for the system profitability of the distributed energy storage power station based on the charging power, discharging power, non-storage load power, and purchased power of each node in the distributed energy storage power station at each time;

[0017] An objective function determination module, configured to determine an objective function based on the first function, the second function and the third function;

[0018] A function solving module, used to solve the objective function for the optimal solution based on the energy storage capacity constraint condition, the power balance constraint condition, and the purchased power constraint condition of the distributed energy storage power station, and obtain the energy storage control scheme of the distributed energy storage power station;

[0019] A control module is used to control the distributed energy storage power station based on the energy storage control scheme.

[0020] According to another aspect of the present invention, there is provided an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the aggregated control method of any distributed energy storage power station in the embodiments of the present invention.

[0021] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the aggregation control method of any distributed energy storage power station in the embodiments of the present invention.

[0022] By adopting the technical solution of the present invention, the aggregation target is determined by adopting a multidimensional objective function, taking into account the peak shaving and valley filling capability, the total electricity purchase cost and the total system benefit level, thereby realizing the comprehensive optimization of the energy storage power station dispatching strategy, making the energy storage power station operation more economical and efficient. Secondly, the embodiment of the present invention constrains the multidimensional objective function to obtain the optimal objective function, which can better achieve the optimization balance of multiple objectives under various constraints, such as energy storage capacity constraints, power balance constraints, and slack variable constraints, and obtain the equilibrium solution under different optimization objectives, thereby improving the feasibility and applicability of the dispatching strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a method for aggregated control of a distributed energy storage power station according to an embodiment of the present disclosure;

[0024] Figure 2 is an algorithm framework diagram of an embodiment of the present disclosure;

[0025] Figure 3 It is a structural block diagram of an aggregation control device of a distributed energy storage power station according to an embodiment of the present disclosure;

[0026] Figure 4 The block diagram is a block diagram of an electronic device for implementing the method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0028] Figure 1 FIG. 1 is a flow chart of a method for controlling the aggregation of a distributed energy storage power station according to an embodiment of the present disclosure. Figure 1 As shown, the method includes:

[0029] S110, determining a first function for evaluating the peak shaving and valley filling capability of the distributed energy storage power station based on the difference between the maximum and minimum purchased power of each node in the distributed energy storage power station at each time;

[0030] S120, determining a second function for evaluating the total power purchase cost of the distributed energy storage power station based on the power purchase power of each node in the distributed energy storage power station at each time and the electricity price at each time;

[0031] S130, determining a third function for the system revenue capacity of the distributed energy storage power station based on the charging power, discharging power, non-storage load power, and purchased power of each node in the distributed energy storage power station at each time;

[0032] S140, determining a target function based on the first function, the second function, and the third function;

[0033] S150, based on the energy storage capacity constraint condition, the power balance constraint condition, and the purchased power constraint condition of the distributed energy storage power station, solving the optimal solution of the objective function to obtain an energy storage control plan of the distributed energy storage power station;

[0034] S160, based on the energy storage control scheme, the distributed energy storage power station is controlled.

[0035] It can be understood that the maximum value of the purchased power of all nodes at each time is summed, the minimum value of the purchased power of all nodes at each time is summed, and then the two sums are subtracted to obtain the first function;

[0036] It can be understood that the peak-shaving and valley-filling capability of a distributed energy storage power station is related to the difference between the maximum and minimum total power purchase of the distributed energy storage power station. The better the peak-shaving and valley-filling capability, the smaller the difference between the maximum and minimum total power purchase of the distributed energy storage power station. Therefore, to optimize the peak-shaving and valley-filling capability of a distributed energy storage power station, it is to solve the minimum value of the difference between the maximum and minimum total power purchase of the distributed energy storage power station.

[0037] Exemplarily, the first function is:

[0038] ;

[0039] in, represents the first function, It represents the maximum value of the total purchased power of each node in the distributed energy storage power station at time t. It represents the minimum value of the total purchased power of each node in the distributed energy storage power station at time t.

[0040] Exemplarily, the first function may also be standardized, and the standardized first function is as follows: .

[0041] in, It represents the first function after standardization, which indicates the peak-to-valley difference of the purchased power when the distributed energy storage power station has energy storage relative to the peak-to-valley difference when there is no energy storage. represents the load power without energy storage of the i-th node at time t.

[0042] It can be understood that by solving the minimum value of the first function above, the optimal peak shaving and valley filling capability can be obtained. The formula is:

[0043] .

[0044] Exemplarily, the second function is:

[0045] ;

[0046] in, represents the second function, represents the total power purchase of each node in the distributed energy storage power station at time t, represents the electricity price at time t.

[0047] Exemplarily, the first function may also be standardized, and the standardized second function is as follows:

[0048] ;

[0049] in, It represents the standardized second function, which indicates the electricity purchasing cost of the distributed energy storage power station with energy storage relative to the electricity purchasing cost without energy storage.

[0050] It can be understood that by solving the minimum of the second function above, the minimum electricity purchase cost can be obtained. The formula is: .

[0051] In one embodiment, the third function is:

[0052] ;

[0053] in, represents the third function, represents the discharge power of the i-th node at time t, represents the charging power of the i-th node at time t, represents the load power without energy storage of the i-th node at time t, Represents the total purchased power of each node in the distributed energy storage power station at time t.

[0054] Among them, the third function is standardized, and the standardized third function is expressed as:

[0055]

[0056] in, It represents the standardized third function, which expresses the difference between the electricity purchase cost and the energy storage charging and discharging income when there is energy storage relative to the system electricity purchase cost when there is no energy storage.

[0057] Since the third function is used for the system profitability of a distributed energy storage power station, by solving the maximum value of the third function, the optimal system profitability of the distributed energy storage power station can be obtained.

[0058] Therefore, the maximum value of the third function is solved as follows:

[0059] ;

[0060] At this time, in order to calculate the objective function, the minimum value of the first and second functions is combined, and the maximum value of the third function is converted into the following equivalent formula:

[0061] .

[0062] Therefore, by solving the minimum value of the above formula, the optimal system profitability of the distributed energy storage power station can be obtained.

[0063] In one embodiment, the objective function is determined based on the first function, the second function and the third function, including: solving the minimum value of the first function and the second function respectively; solving the maximum value of the third function, and converting the third function for solving the maximum value into an equivalent fourth function for solving the minimum value; performing weighted summation of the first function for solving the minimum value, the second function for solving the minimum value and the fourth function for solving the minimum value to obtain the objective function.

[0064] In one implementation, determining the target function based on the first function, the second function, and the third function includes:

[0065] The first function, the second function and the third function are weighted averaged, and the minimum value of the weighted averaged function is solved to obtain the objective function.

[0066] Exemplarily, the weights of various functions may be the same or different.

[0067] For example, the weight of each function may be 0.3, and the objective function may be as follows:

[0068]

[0069] .

[0070] According to the above implementation, by adopting a multi-dimensional objective function to determine the aggregate target, taking into account the peak shaving and valley filling capability, the total electricity purchase cost and the total system benefit level, the energy storage power station dispatching strategy is fully optimized, making the operation of the energy storage power station more economical and efficient.

[0071] In one implementation, based on the energy storage capacity constraint, power balance constraint, and purchased power constraint of the distributed energy storage power station, the objective function is solved for an optimal solution to obtain an energy storage control scheme for the distributed energy storage power station, including:

[0072] Determine a first basis matrix based on the values ​​of each variable in the objective function;

[0073] Determining a first coefficient matrix based on coefficients of each variable value in the objective function;

[0074] A deep deterministic policy gradient algorithm is used to solve the minimum value of the product of the first basis matrix and the first coefficient matrix to obtain a second basis matrix;

[0075] When the difference between the product of the second base matrix and the first coefficient matrix and the product of the first base matrix and the first coefficient matrix is ​​less than a preset threshold, an energy storage control scheme of the distributed energy storage power station is determined based on the second base matrix.

[0076] For example, the deep deterministic policy gradient algorithm (DDPG) can be pre-trained or can be trained and updated during the process of solving the optimal value.

[0077] Exemplarily, the first base matrix and the first coefficient matrix are initial values ​​at the first iteration.

[0078] Exemplarily, the first basis matrix and the first coefficient matrix are input into the deep deterministic policy gradient algorithm, the deep deterministic policy gradient algorithm performs a process of solving the minimum value of the product of the first basis matrix and the first coefficient matrix, and outputs the second basis matrix.

[0079] Exemplarily, when the difference between the product of the second basis matrix and the first coefficient matrix and the product of the first basis matrix and the first coefficient matrix is ​​greater than a preset threshold, the first basis matrix is ​​updated based on the second basis matrix, and based on the updated first basis matrix, the following steps are continued: a deep deterministic policy gradient algorithm is used to solve the minimum value of the product of the first basis matrix and the first coefficient matrix to obtain the second basis matrix.

[0080] like Figure 2 As shown in the figure, the optimization process and the training process are shown. In the training process, the coefficient matrix and basic variables in the optimization problem solving process are used as the state space data set, the entry and exit of the basic variables are used as the decision space data set, and the energy storage resource optimization scheduling target and the number of optimization iterations are used as the reward function. Reinforcement learning is used for training to obtain a deep deterministic policy gradient algorithm for energy storage resource scheduling optimization solution.

[0081] According to the above implementation, the deep deterministic policy gradient algorithm is used to solve the objective function, which can accelerate the solution efficiency.

[0082] In one embodiment, the energy storage capacity constraints include:

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] in, represents the charging power of the i-th node at time t, represents the discharge power of the i-th node at time t, represents the upper limit of the energy storage power of the i-th node, represents the load state of the i-th node at time t, represents the load state of the i-th node at time t-1, represents the upper limit of the energy storage capacity of the i-th node, The coefficient that represents the regression of the energy storage capacity of the i-th node to the initial level in both the initial period t=0 and the final period t=T.

[0088] It can be understood that the first formula in the above energy storage capacity constraint condition is used to limit the charging and discharging power of the i-th energy storage node to not exceed the energy storage power upper limit value of the i-th energy storage node.

[0089] It can be understood that the second formula in the above energy storage capacity constraint condition is the energy balance equation of the i-th energy storage node in adjacent time periods.

[0090] It can be understood that the third formula in the above energy storage capacity constraint condition is used to limit the load state of the i-th node at time t to not exceed the upper limit value of the energy storage capacity of the i-th node.

[0091] It can be understood that the fourth formula in the above energy storage capacity constraint condition is used to limit the energy storage capacity of the i-th node to return to the initial level in both the initial period t=0 and the final period t=T.

[0092] In one embodiment, the power balance constraints include:

[0093] ;

[0094] in, represents the load power without energy storage of the i-th node at time t, Represents the total purchased power of each node in the distributed energy storage power station at time t.

[0095] It can be understood that the above-mentioned power balance constraints are used to limit the purchase of grid electricity and the energy storage charging and discharging of each node to meet the total system load demand at each moment.

[0096] In one embodiment, the power purchase power constraint condition includes:

[0097] ;

[0098] ;

[0099] in, It represents the difference between the maximum and minimum total power purchased by each node in the distributed energy storage power station at time t.

[0100] It can be understood that the first formula in the above power purchase power constraint condition is used to limit the power purchase power of the i-th node at each moment to be greater than the maximum system load of the node and less than the minimum system load.

[0101] It can be understood that the second formula in the above power purchase constraint is used to limit the sum of the minimum system load and the slack variable of the i-th node at each time to be less than the maximum system load of the node. Among them, the slack variable is , which represents the difference between the maximum and minimum total purchased power of each node in the distributed energy storage power station at time t.

[0102] According to the above implementation mode, by constraining the multidimensional objective function to obtain the optimal objective function, it is possible to better achieve the optimal balance of multiple objectives under various constraints, such as energy storage capacity constraints, power balance constraints, and slack variable constraints, and obtain equilibrium solutions under different optimization objectives, thereby improving the feasibility and applicability of the scheduling strategy.

[0103] The embodiments of the present disclosure have the following beneficial effects:

[0104] 1. Introduce slack variables. When optimizing the maximum peak-to-valley capacity, the objective function has the problem of double maximum and minimum values, which leads to high computational complexity and greatly slows down the calculation speed. This model innovatively introduces slack variables as the maximum peak-to-valley difference, converts the inequality constraints in the objective function into equality constraints, and successfully converts the double maximum and minimum value problem into a single minimization problem, which reduces the calculation level and improves the calculation efficiency.

[0105] 2. Achieve multi-objective optimization. Under the constraints of battery installation and power balance, the model adopts a multi-objective optimization method to normalize the multiple objectives of maximizing peak-shaving and valley-filling capacity, minimizing aggregation cost, maximizing benefit level and minimizing calculation time, and achieves optimal calculation according to proportion in the specified area to obtain equilibrium solutions under different optimization objectives.

[0106] 3. Intelligent accelerated solution. The AI-based preprocessing strategy is used to accelerate the linear programming solution algorithm based on the simplex method, greatly improving the solution efficiency, which is suitable for solving the optimization problems of the aggregation and regulation of larger-scale massive energy storage resources.

[0107] 4. Minimizing the number of charging times is the optimization goal. The model takes into account the rationality of actual operation, adds conditions for reducing the number of charging and discharging times, and sets the cost of enabling energy storage equipment once, making the calculation results more in line with the actual application scenarios.

[0108] Figure 3 It is a structural block diagram of an aggregation control device of a distributed energy storage power station according to an embodiment of the present disclosure.

[0109] like Figure 3 As shown, the aggregation control device of the distributed energy storage power station includes:

[0110] A first function determination module 310 is used to determine a first function for evaluating the peak shaving and valley filling capability of the distributed energy storage power station based on the difference between the maximum and minimum purchased power of each node in the distributed energy storage power station at each time;

[0111] A second function determination module 320, configured to determine a second function for evaluating the total power purchase cost of the distributed energy storage power station based on the power purchase power of each node in the distributed energy storage power station at each time and the electricity price at each time;

[0112] A third function determination module 330, for determining a third function for the system profitability of the distributed energy storage power station based on the charging power, discharging power, non-storage load power and purchased power of each node in the distributed energy storage power station at each time;

[0113] An objective function determination module 340, configured to determine an objective function based on the first function, the second function and the third function;

[0114] A function solving module 350 is used to solve the objective function for an optimal solution based on the energy storage capacity constraint condition, the power balance constraint condition, and the purchased power constraint condition of the distributed energy storage power station, so as to obtain an energy storage control scheme of the distributed energy storage power station;

[0115] The control module 360 ​​is used to control the distributed energy storage power station based on the energy storage control scheme.

[0116] In one implementation, the objective function determination module 340 is specifically configured to:

[0117] The first function, the second function and the third function are weighted averaged, and a minimum value is solved for the weighted averaged function to obtain the objective function.

[0118] In one embodiment, the first function is:

[0119] ;

[0120] in, represents the first function, represents the maximum value of the total purchased power of each node in the distributed energy storage power station at time t, It represents the minimum value of the total purchased power of each node in the distributed energy storage power station at time t.

[0121] In one embodiment, the second function is:

[0122] ;

[0123] in, represents the second function, represents the total power purchase of each node in the distributed energy storage power station at time t, represents the electricity price at time t.

[0124] In one implementation, the third function is:

[0125]

[0126] in, represents the third function, represents the discharge power of the i-th node at time t, represents the charging power of the i-th node at time t, represents the load power without energy storage of the i-th node at time t, Represents the total purchased power of each node in the distributed energy storage power station at time t.

[0127] In one embodiment, based on the energy storage capacity constraint condition, the power balance constraint condition, and the purchased power constraint condition of the distributed energy storage power station, solving the optimal solution of the objective function to obtain the energy storage control scheme of the distributed energy storage power station includes:

[0128] Determine a first basis matrix based on the values ​​of each variable in the objective function;

[0129] Determining a first coefficient matrix based on coefficients of the values ​​of the variables in the objective function;

[0130] Using a deep deterministic policy gradient algorithm, solving a minimum value of a product of the first basis matrix and the first coefficient matrix to obtain a second basis matrix;

[0131] When the difference between the product of the second base matrix and the first coefficient matrix and the product of the first base matrix and the first coefficient matrix is ​​less than a preset threshold, the energy storage control scheme of the distributed energy storage power station is determined based on the second base matrix.

[0132] In one embodiment, the energy storage capacity constraint condition includes:

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] in, represents the charging power of the i-th node at time t, represents the discharge power of the i-th node at time t, represents the upper limit of the energy storage power of the i-th node, represents the load state of the i-th node at time t, represents the load state of the i-th node at time t-1, represents the upper limit of the energy storage capacity of the i-th node, The coefficient representing the regression of the energy storage capacity of the i-th node to the initial level in both the initial period t=0 and the final period t=T;

[0138] The power balance conditions include:

[0139] ;

[0140] in, represents the load power without energy storage of the i-th node at time t, Represents the total power purchase of each node in the distributed energy storage power station at time t;

[0141] The power purchase power constraint conditions include:

[0142] ;

[0143] ;

[0144] in, It represents the difference between the maximum and minimum total purchased power of each node in the distributed energy storage power station at time t.

[0145] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0146] Figure 4 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0147] like Figure 4 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0148] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0149] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as a method for aggregated regulation of a distributed energy storage power station. For example, in some embodiments, a method for aggregated regulation of a distributed energy storage power station may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method for aggregated regulation of a distributed energy storage power station described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute a method for collectively controlling a distributed energy storage power station in any other appropriate manner (for example, by means of firmware).

[0150] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0151] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0152] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0153] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0154] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0155] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0156] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0157] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for aggregate control of a distributed energy storage power station, characterized in that: include: Determine a first function for evaluating the peak-shaving and valley-filling capability of the distributed energy storage power station based on the difference between the maximum and minimum purchased power of each node in the distributed energy storage power station at each time; Based on the power purchase power of each node in the distributed energy storage power station at each time and the electricity price at each time, determine a second function for evaluating the total power purchase cost of the distributed energy storage power station; Determine a third function for evaluating the system profitability of the distributed energy storage power station based on the charging power, discharging power, non-storage load power, and purchased power of each node in the distributed energy storage power station at each time; Determine a target function based on the first function, the second function and the third function; Based on the energy storage capacity constraint condition, power balance constraint condition, and purchased power constraint condition of the distributed energy storage power station, the objective function is solved for an optimal solution to obtain an energy storage control scheme for the distributed energy storage power station, which specifically includes: Determine a first basis matrix based on the values ​​of each variable in the objective function; Determining a first coefficient matrix based on coefficients of the values ​​of the variables in the objective function; Using a deep deterministic policy gradient algorithm, solving a minimum value of a product of the first basis matrix and the first coefficient matrix to obtain a second basis matrix; and When the difference between the product of the second base matrix and the first coefficient matrix and the product of the first base matrix and the first coefficient matrix is ​​less than a preset threshold, determining an energy storage control scheme of the distributed energy storage power station based on the second base matrix; Based on the energy storage control scheme, regulating the distributed energy storage power station; Wherein, determining the objective function based on the first function, the second function and the third function includes: Solving the minimum values ​​of the first function and the second function respectively; Solving the third function for the maximum value, and converting the third function for the maximum value into an equivalent fourth function for the minimum value; Performing weighted summation on the first function for solving the minimum value, the second function for solving the minimum value, and the fourth function for solving the minimum value to obtain the objective function; The energy storage capacity constraints include: ； ； ； ； in, represents the charging power of the i-th node at time t, represents the discharge power of the i-th node at time t, represents the upper limit of the energy storage power of the i-th node, represents the load state of the i-th node at time t, represents the load state of the i-th node at time t-1, represents the upper limit of the energy storage capacity of the i-th node, The coefficient representing the regression of the energy storage capacity of the i-th node to the initial level in both the initial period t=0 and the final period t=T; The power balance constraints include: ； in, represents the load power without energy storage of the i-th node at time t, Represents the total power purchase of each node in the distributed energy storage power station at time t; The power purchase power constraint conditions include: ； ； in, It represents the difference between the maximum and minimum total purchased power of each node in the distributed energy storage power station at time t.

2. The method according to claim 1, characterized in that The first function is: ； in, represents the first function, represents the maximum value of the total purchased power of each node in the distributed energy storage power station at time t, It represents the minimum value of the total purchased power of each node in the distributed energy storage power station at time t.

3. The method according to claim 1, characterized in that The second function is: ； in, represents the second function, represents the total power purchase of each node in the distributed energy storage power station at time t, represents the electricity price at time t.

4. The method according to claim 1, characterized in that: The third function is: in, represents the third function, represents the discharge power of the i-th node at time t, represents the charging power of the i-th node at time t, represents the load power without energy storage of the i-th node at time t, Represents the total purchased power of each node in the distributed energy storage power station at time t.

5. An aggregate control device for a distributed energy storage power station, characterized in that: include: A first function determination module, configured to determine a first function for evaluating the peak shaving and valley filling capability of the distributed energy storage power station based on the difference between the maximum and minimum purchased power of each node in the distributed energy storage power station at each time; A second function determination module, used to determine a second function for evaluating the total power purchase cost of the distributed energy storage power station based on the power purchase power of each node in the distributed energy storage power station at each time and the electricity price at each time; A third function determination module, configured to determine a third function for the system profitability of the distributed energy storage power station based on the charging power, discharging power, non-storage load power, and purchased power of each node in the distributed energy storage power station at each time; An objective function determination module, configured to determine an objective function based on the first function, the second function and the third function; The function solving module is used to solve the objective function for the optimal solution based on the energy storage capacity constraint condition, power balance constraint condition, and purchased power constraint condition of the distributed energy storage power station, and obtain the energy storage control scheme of the distributed energy storage power station, specifically including: Determine a first basis matrix based on the values ​​of each variable in the objective function; Determining a first coefficient matrix based on coefficients of the values ​​of the variables in the objective function; Using a deep deterministic policy gradient algorithm, solving a minimum value of a product of the first basis matrix and the first coefficient matrix to obtain a second basis matrix; and When a difference between a product of the second base matrix and the first coefficient matrix and a product of the first base matrix and the first coefficient matrix is ​​less than a preset threshold; A control module, used to control the distributed energy storage power station based on the energy storage control scheme; Wherein, determining the objective function based on the first function, the second function and the third function includes: Solving the minimum values ​​of the first function and the second function respectively; Solving the third function for the maximum value, and converting the third function for the maximum value into an equivalent fourth function for the minimum value; Performing weighted summation on the first function for solving the minimum value, the second function for solving the minimum value, and the fourth function for solving the minimum value to obtain the objective function; The energy storage capacity constraints include: ； ； ； ； in, represents the charging power of the i-th node at time t, represents the discharge power of the i-th node at time t, represents the upper limit of the energy storage power of the i-th node, represents the load state of the i-th node at time t, represents the load state of the i-th node at time t-1, represents the upper limit of the energy storage capacity of the i-th node, The coefficient representing the regression of the energy storage capacity of the i-th node to the initial level in both the initial period t=0 and the final period t=T; The power balance constraints include: ； in, represents the load power without energy storage of the i-th node at time t, Represents the total power purchase of each node in the distributed energy storage power station at time t; The power purchase power constraint conditions include: ； ； in, It represents the difference between the maximum and minimum total purchased power of each node in the distributed energy storage power station at time t.

6. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

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