A distributed energy cluster operation method and device, equipment and medium

Abstract

The application discloses a distributed energy cluster operation method and device, equipment and medium, and belongs to the field of distributed energy cluster operation. The method is as follows: obtaining a power source energy benefit function, a power source energy strategy space, a load energy benefit function and a load energy strategy subspace set; obtaining a total load energy strategy space based on a load aggregation direction vector and the load energy strategy subspace set; constructing a total energy constraint condition based on a preset power grid game sequence, a preset target total energy power, the power source energy strategy space and the total load energy strategy space; constructing a total energy target subspace function set based on a benefit penalty function, a benefit penalty coefficient, the power source energy benefit function and the load energy benefit function; and obtaining a to-be-implemented energy object power set based on the total energy constraint condition and the total energy target subspace function set, so as to operate a power source energy set and a load energy set, thereby improving the reliability of distributed energy cluster operation.

Description

Technical Field

[0001] This invention relates to the field of distributed energy cluster operation, and in particular to a method, apparatus, equipment and medium for operating a distributed energy cluster. Background Technology

[0002] Distributed energy cluster managers utilize advanced information and communication technologies and energy management systems to aggregate and optimize distributed energy sources, energy storage devices, and controllable loads, forming a unified and dispatchable virtual power energy cluster pool. This aims to enhance grid operational flexibility and improve the absorption of new energy sources. Distributed energy clusters are characterized by small capacity, large number, complex types, and wide geographical distribution. Furthermore, the output of new energy sources such as wind power and photovoltaics is intermittent and random. These factors pose challenges to the aggregation and coordinated operation of distributed energy clusters.

[0003] Existing technologies primarily employ a centralized scheduling method, where a central controller collects information from the entire network, performs global optimization calculations, and then issues control commands. While this method can theoretically achieve global optimization, it suffers from the following significant drawbacks in practical applications: For large-scale, high-dimensional nonlinear mixed-integer optimization problems, the computational load of the centralized method increases exponentially with the number of devices; in intraday scheduling, the centralized algorithm struggles to effectively handle real-time fluctuations in renewable energy output; and a failure of the central controller can paralyze the entire system, posing a single point of failure risk. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for operating a distributed energy cluster, which can solve the above-mentioned problems in the prior art and improve the reliability of distributed energy cluster operation.

[0005] This invention provides a method for operating a distributed energy cluster, comprising: Obtain the power energy revenue function and power energy strategy space of the power energy set; Obtain the load energy revenue function of the load energy set, and obtain the load energy strategy subspace of each load energy in the load energy set to obtain the load energy strategy subspace set; Obtain the total number of load aggregation directions, and obtain the load aggregation direction vector based on the total number of load aggregation directions; The total load energy strategy space is obtained based on the load aggregation direction vector and the set of load energy strategy subspaces. The total energy constraint is constructed based on the preset power grid game order, the preset target total energy power, the power supply energy strategy space and the total load energy strategy space. Obtain the revenue penalty function and revenue penalty coefficient, and construct a set of total energy target sub-functions based on the revenue penalty function, the revenue penalty coefficient, the power energy revenue function, and the load energy revenue function; Based on the total energy constraints and the total energy objective sub-function set, a power set of energy objects to be implemented is obtained, and the power supply energy set and the load energy set are operated based on the power set of energy objects to be implemented.

[0006] In the above scheme, by obtaining the power energy revenue function and power energy strategy space of the power energy set, and aggregating the load energy strategy subspace of each load energy in the load energy set, the total load energy strategy space is obtained based on the load aggregation direction vector. This achieves effective integration and unified representation of massive distributed load energy, significantly reducing the computational complexity of distributed collaborative optimization. By constructing total energy constraints based on a preset grid game order, a preset target total energy power, and the power energy strategy space and the total load energy strategy space, and introducing a revenue penalty function and revenue penalty coefficient to construct a total energy target sub-function set, each energy can autonomously coordinate and iteratively optimize in the distributed decision-making process. Finally, a set of energy objects to be implemented that satisfies global power balance and takes into account individual economic benefits is obtained. Then, the power energy set and the load energy set are run based on the set of energy objects to be implemented, thereby effectively improving the operational reliability of the distributed energy cluster without the need for a centralized controller.

[0007] Further, obtaining the total number of load aggregation directions and obtaining the load aggregation direction vector based on the total number of load aggregation directions includes: The load aggregation direction number is determined based on the total number of load aggregation directions. The load aggregation angle vector is determined based on the load aggregation direction number and the total number of load aggregation directions. The load aggregation angle vector is processed by trigonometric functions to obtain the load aggregation direction vector.

[0008] In the above scheme, by generating a load aggregation direction vector based on the total number of load aggregation directions, a standard set of detection directions is provided for subsequent multi-directional optimization scanning of the feasible domain boundary, ensuring the comprehensiveness and unbiasedness of boundary detection and improving the accuracy of total load energy strategy space acquisition.

[0009] Further, the step of performing trigonometric function processing on the load aggregation angle vector to obtain the load aggregation direction vector includes: Obtain the sine value of the load aggregation angle vector, and use it as the load aggregation angle sine value; Obtain the cosine value of the load aggregation angle vector as the load aggregation angle cosine value; The load aggregation direction vector is obtained based on the sine and cosine values ​​of the load aggregation angle.

[0010] In the above scheme, the load aggregation direction vector is obtained by performing a sine-cosine transformation on the load aggregation angle vector, which realizes an accurate mapping from angle space to vector space and provides a reliable geometric basis for constructing the total load energy strategy space.

[0011] Further, obtaining the total load energy strategy space based on the load aggregation direction vector and the load energy strategy subspace set includes: Based on the preset strategy selection matrix and the set of load energy strategy subspaces, a load energy strategy mapping vector is obtained; A load energy objective function is constructed with the goal of maximizing the product of the load energy strategy mapping vector and the load aggregation direction vector. Using the load energy strategy subspace set as the load energy constraint, the load energy objective function is solved to obtain the total load energy strategy space.

[0012] In the above scheme, the high-dimensional load energy strategy subspace is projected onto a two-dimensional space by a preset strategy selection matrix, and the linear programming problem is solved with the goal of maximizing the product of the load energy strategy mapping vector and the load aggregation direction vector. Finally, the total load strategy space composed of the convex hull of the boundary points is obtained, which realizes the efficient aggregation of massive load feasible regions and greatly reduces the computational complexity of subsequent games.

[0013] Furthermore, the power energy set includes photovoltaic energy, wind turbine energy, diesel energy, and energy storage energy. The process of obtaining the power energy revenue function and power energy strategy space of the power energy set includes: Obtain the photovoltaic energy revenue sub-function and photovoltaic energy strategy subspace of the photovoltaic energy; Obtain the wind turbine energy revenue subfunction and wind turbine energy strategy subspace; Obtain the wind turbine energy revenue sub-function and diesel energy strategy subspace of the diesel energy; Obtain the energy storage revenue subfunction and energy storage strategy subspace of the energy storage energy; Based on the photovoltaic energy revenue sub-function, the wind turbine energy revenue sub-function, the wind turbine energy revenue sub-function, and the energy storage energy revenue sub-function, the power supply energy revenue function is obtained; Based on the photovoltaic energy strategy subspace, the wind turbine energy strategy subspace, the diesel energy strategy subspace, and the energy storage energy strategy subspace, the power supply energy strategy space is obtained.

[0014] In the above scheme, by modeling four types of power sources—photovoltaics, wind turbines, diesel, and energy storage—the revenue functions and energy strategy spaces of each type of power source are accurately characterized, so as to realize the targeted construction of revenue functions and energy strategy spaces adapted to the characteristics of different power source categories.

[0015] Furthermore, the step of obtaining the photovoltaic energy revenue sub-function and photovoltaic energy strategy subspace of the photovoltaic energy includes: Obtain the predicted output power of the photovoltaic system; The photovoltaic energy strategy subspace is constructed based on the preset initial value and the predicted output power of the photovoltaic system.

[0016] In the above scheme, by constructing a strategy space from the preset initial value to the predicted output power of photovoltaic power based on the maximum predicted output power of photovoltaic power, the physical constraints of photovoltaic power output are accurately characterized, providing a clear feasible domain boundary for subsequent revenue calculation and game iteration.

[0017] Furthermore, the step of obtaining the wind turbine energy revenue subfunction and the diesel energy strategy subspace for the diesel energy includes: Obtain the minimum allowable output power and the maximum allowable output power of the diesel engine, and construct a first diesel subspace based on the minimum allowable output power and the maximum allowable output power of the diesel engine; Obtain the rise rate limit value and the fall rate limit value, and construct a second diesel subspace based on the rise rate limit value and the fall rate limit value; The diesel energy strategy subspace is constructed based on the first diesel subspace and the second diesel subspace.

[0018] In the above scheme, the diesel engine strategy space is constructed by simultaneously considering the upper and lower limits of the output power corresponding to the minimum and maximum allowable output power of the diesel engine, as well as the ramping constraints corresponding to the rise rate limit and fall rate limit. This ensures the physical feasibility of the diesel engine output plan in terms of temporal variation, making the optimization results both economical and engineering feasible.

[0019] Another embodiment of the present invention provides a distributed energy cluster operation device, comprising: The power basic data acquisition module acquires the power energy revenue function and power energy strategy space of the power energy set. The load basic data acquisition module is used to acquire the load energy revenue function of the load energy set and acquire the load energy strategy subspace of each load energy in the load energy set, thereby obtaining a load energy strategy subspace set. The load aggregation direction vector acquisition module is used to acquire the total number of load aggregation directions and obtain the load aggregation direction vector based on the total number of load aggregation directions; The total load energy strategy space construction module is used to obtain the total load energy strategy space based on the load aggregation direction vector and the set of load energy strategy subspaces. The total energy constraint construction module is used to construct total energy constraints based on a preset power grid game order, a preset target total energy power, the power supply energy strategy space and the total load energy strategy space. The total energy target sub-function set construction module is used to obtain the revenue penalty function and the revenue penalty coefficient, and construct the total energy target sub-function set based on the revenue penalty function, the revenue penalty coefficient, the power energy revenue function and the load energy revenue function; The running module is used to obtain the power set of energy objects to be implemented based on the total energy constraints and the total energy target sub-function set, and to run the power energy set and the load energy set based on the power set of energy objects to be implemented.

[0020] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of a distributed energy cluster operation method as described in the present invention.

[0021] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of a distributed energy cluster operation method of the present invention. Attached Figure Description

[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a distributed energy cluster operation method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a distributed energy cluster operation device provided in one embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0030] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0031] See Figure 1 To address the aforementioned problems in the prior art and improve the reliability of distributed energy cluster operation, an embodiment of the present invention provides a distributed energy cluster operation method, comprising: Step S1: Obtain the power energy revenue function and power energy strategy space of the power energy set; Step S2: Obtain the load energy revenue function of the load energy set, and obtain the load energy strategy subspace of each load energy in the load energy set to obtain the load energy strategy subspace set; Step S3: Obtain the total number of load aggregation directions, and obtain the load aggregation direction vector based on the total number of load aggregation directions; Step S4: Obtain the total load energy strategy space based on the load aggregation direction vector and the set of load energy strategy subspaces; Step S5: Construct total energy constraints based on the preset grid game sequence, preset target total energy power, power source energy strategy space, and total load energy strategy space; Step S6: Obtain the revenue penalty function and revenue penalty coefficient, and construct a set of total energy target sub-functions based on the revenue penalty function, revenue penalty coefficient, power energy revenue function and load energy revenue function; Step S7: Based on the total energy constraints and the total energy objective sub-function set, obtain the power set of the energy objects to be implemented, and then operate the power supply energy set and load energy set based on the power set of the energy objects to be implemented.

[0032] In the above scheme, by obtaining the power energy revenue function and power energy strategy space of the power energy set, and aggregating the load energy strategy subspace of each load energy in the load energy set, the total load energy strategy space is obtained based on the load aggregation direction vector. This achieves effective integration and unified representation of massive distributed load energy, significantly reducing the computational complexity of distributed collaborative optimization. By constructing total energy constraints based on a preset grid game order, a preset target total energy power, and the power energy strategy space and the total load energy strategy space, and introducing a revenue penalty function and revenue penalty coefficient to construct a total energy target sub-function set, each energy can autonomously coordinate and iteratively optimize in the distributed decision-making process. Finally, a set of energy objects to be implemented that satisfies global power balance and takes into account individual economic benefits is obtained. Then, the power energy set and the load energy set are run based on the set of energy objects to be implemented, thereby effectively improving the operational reliability of the distributed energy cluster without the need for a centralized controller.

[0033] It should be noted that, firstly, the energy revenue functions and energy strategy spaces of various energy resources in the energy set, such as photovoltaic energy, wind turbine energy, diesel engine energy, and energy storage energy, are obtained. It's understood that although energy storage energy can be charged and discharged, it is categorized as part of the energy set. Secondly, the energy revenue functions of the load energy set, i.e., the adjustable load group, are obtained, and the operational constraints of each individual load energy (i.e., the adjustable load) are collected as its load energy strategy subspace. Finally, the load energy strategy subspaces corresponding to all load energy are aggregated to obtain the load energy strategy subspace set. The energy strategy space is defined by the physical constraints of each energy source, such as output upper and lower limits, ramp rate, and state of charge boundary. For load energy, a feasible region model, i.e., its load energy strategy subspace, needs to be established for each individual adjustable load to form the load energy strategy subspace set corresponding to the load energy set. Next, the total number of load aggregation directions is set, and a load aggregation direction vector is obtained based on this total number of directions. Then, based on the load aggregation direction vector and the load energy strategy subspace set, a series of linear programming problems are solved to obtain the total load energy strategy space. After constructing the power energy strategy space corresponding to the power energy set and the total energy strategy space corresponding to the load energy set, total energy constraints are established based on the dispatch instructions issued by the upper-level power grid, i.e., the preset target total energy power, and the preset power grid game order. It is important to note that the preset target total energy power is used to clarify the overall power exchange target that the distributed energy cluster needs to complete within the dispatch cycle, providing target constraints for subsequent coordinated dispatch and optimized operation within the cluster. Simultaneously, a payoff penalty function and payoff penalty coefficient are introduced, adding these terms to the payoff function of each player to form a set of total energy target sub-functions. Here, "players" refers to the power energy set and the load energy set. Finally, multiple rounds of iterative game are conducted: in each round, each player solves the maximization problem within its own strategy space based on the strategies of other players, obtaining the optimal output plan for this round and updating its strategy. This iteration is repeated until the preset Nash equilibrium condition is met. The hourly output plans obtained at this point constitute the power sets of the energy objects to be implemented, used for the actual operation of the power energy set and the load energy set. It should be noted that the final output power set is the output value of each time period within 24 hours (or within the scheduling cycle), ensuring that the power grid command is met in each time period and that the benefits of each bureau are balanced.

[0034] In another embodiment, the power energy set includes photovoltaic energy, wind turbine energy, diesel energy, and energy storage energy. The step of obtaining the power energy revenue function and power energy strategy space of the power energy set includes: Obtain the photovoltaic energy revenue sub-function and photovoltaic energy strategy subspace of the photovoltaic energy; Obtain the wind turbine energy revenue subfunction and wind turbine energy strategy subspace; Obtain the wind turbine energy revenue sub-function and diesel energy strategy subspace of the diesel energy; Obtain the energy storage revenue subfunction and energy storage strategy subspace of the energy storage energy; Based on the photovoltaic energy revenue sub-function, the wind turbine energy revenue sub-function, the wind turbine energy revenue sub-function, and the energy storage energy revenue sub-function, the power supply energy revenue function is obtained; Based on the photovoltaic energy strategy subspace, the wind turbine energy strategy subspace, the diesel energy strategy subspace, and the energy storage energy strategy subspace, the power supply energy strategy space is obtained.

[0035] It should be noted that the photovoltaic energy revenue sub-function is mainly divided into two parts: electricity sales revenue and operation and maintenance costs. The photovoltaic energy revenue sub-function is as follows: ; In the formula, For photovoltaic energy revenue; Indicates electricity price; For time step; Photovoltaic output power; This represents the preset maintenance cost coefficient for photovoltaic systems. What is understandable is that... This refers to the variable that needs to be solved, and after solving, it becomes one of the elements in the power set of the energy object to be implemented.

[0036] In another embodiment, obtaining the photovoltaic energy revenue sub-function and photovoltaic energy strategy subspace of the photovoltaic energy includes: Obtain the predicted output power of the photovoltaic system; The photovoltaic energy strategy subspace is constructed based on the preset initial value and the predicted output power of the photovoltaic system.

[0037] It should be noted that, The photovoltaic energy strategy subspace is constructed based on the photovoltaic predicted output power, with a preset initial value of 0. ; That is, in the process of obtaining the power set of the energy objects to be implemented, the photovoltaic output power The solution needs to be found within the photovoltaic energy strategy subspace.

[0038] Furthermore, specifically regarding the wind turbine energy revenue subfunction and wind turbine energy strategy subspace for obtaining the wind turbine energy: The wind turbine energy revenue sub-function is mainly divided into two parts: electricity sales revenue and operation and maintenance costs. The specific details of the wind turbine energy revenue sub-function are as follows: ; In the formula, For wind turbine revenue; Indicates electricity price; This refers to the output power of the fan. This represents the maintenance cost coefficient of the wind turbine. What is understandable is that... This refers to the variable that needs to be solved, and after solving, it becomes one of the elements in the power set of the energy object to be implemented.

[0039] The wind turbine energy strategy subspace is: ; in, Predict the output power of the wind turbine.

[0040] In another embodiment, obtaining the wind turbine energy revenue subfunction and the diesel energy strategy subspace of the diesel energy includes: Obtain the minimum allowable output power and the maximum allowable output power of the diesel engine, and construct a first diesel subspace based on the minimum allowable output power and the maximum allowable output power of the diesel engine; Obtain the rise rate limit and the preset fall rate limit, and construct a second diesel subspace based on the rise rate limit and the fall rate limit; The diesel energy strategy subspace is constructed based on the first diesel subspace and the second diesel subspace.

[0041] It should be noted that the revenue sub-function for diesel-powered wind turbines is mainly the revenue from electricity sales minus the cost of electricity generation. Specifically, the revenue sub-function for wind turbines is as follows: ; In the formula, Indicates the revenue of the diesel engine; This refers to the output power of the diesel engine. This represents the maintenance cost coefficient for diesel engines. What is understandable is that... This refers to the variable that needs to be solved, and after solving, it becomes one of the elements in the power set of the energy object to be implemented.

[0042] The diesel energy strategy subspace must satisfy its mechanical output requirements, namely: ; in This is the minimum permissible output power of the diesel engine. The maximum permissible output power of the diesel engine is determined; a first diesel subspace is constructed based on the above formula. In addition, the rate of ascent limit is obtained. and the rate of decline limit A second diesel subspace is constructed based on the rise rate limit and the fall rate limit. This second diesel subspace represents the unit's ramp power constraint. ; in, and All The value in, because It is sequential, therefore and These represent the output power of the diesel engine at time t and the output power of the diesel engine at time t-1, respectively. This represents the sum of the diesel engine's output power at all times. The diesel energy strategy subspace is constructed based on the first and second diesel subspaces. The solution needs to follow the diesel energy strategy subspace.

[0043] Furthermore, specifically regarding the energy storage revenue subfunction and energy storage strategy subspace for obtaining the aforementioned energy storage energy: The energy storage revenue sub-function mainly consists of electricity price arbitrage and operation and maintenance costs. Specifically, the energy storage revenue sub-function is as follows: ; In the formula, For energy storage revenue; Indicates electricity price; This refers to the output power of energy storage. What is understandable is that... This refers to the variable that needs to be solved, and after solving, it becomes one of the elements in the power set of the energy object to be implemented.

[0044] The energy storage strategy subspace is specifically as follows: ; ; ; in, for The state of charge of the stored energy at time +1 for The state of charge that stores energy at all times. for Energy storage output power at all times The self-discharge rate of the stored energy; This refers to the rated capacity of the energy storage. For energy storage discharge efficiency; This refers to the charging efficiency of energy storage. It's understandable that... It is also temporal, representing different moments. constitute .

[0045] In summary, based on the photovoltaic energy revenue sub-function, the wind turbine energy revenue sub-function, the wind turbine energy revenue sub-function, and the energy storage energy revenue sub-function, the power supply energy revenue function is obtained; based on the photovoltaic energy strategy subspace, the wind turbine energy strategy subspace, the diesel energy strategy subspace, and the energy storage energy strategy subspace, the power supply energy strategy space is obtained.

[0046] Furthermore, regarding step S2: obtaining the load energy revenue function of the load energy set, and obtaining the load energy strategy subspace for each load energy in the load energy set, to obtain the load energy strategy subspace set, specifically: For a large number of dispersed and adjustable load energy sources, the power feasible region model of each individual load energy source is first constructed, i.e., the load energy strategy subspace. Then, the Minkowski algorithm is used to aggregate the load energy strategy subspaces of each adjustable load energy source to obtain the overall power feasible region of the load energy set, i.e., the set of load energy strategy subspaces. Specifically, obtaining the load energy revenue function of the load energy set includes: constructing the load energy revenue function of the load energy set, which is the sum of electricity cost and regulation cost. The load energy revenue function is as follows: ; In the formula, For adjustable load energy benefits; Indicates electricity price; The actual power consumption of the adjustable load; Adjustable load planning power consumption; To adjust the cost coefficient. What is understandable is that... This refers to the variable that needs to be solved, and after solving, it becomes one of the elements in the power set of the energy object to be implemented.

[0047] The process of obtaining the load energy strategy subspace for each load energy source in the load energy set specifically includes: constructing a power feasible region model for each load energy source based on its type (e.g., shiftable, transferable, or interruptible load) and user-declared operating parameters (e.g., baseline power, adjustment limits, adjustable time periods, and durations). This model serves as the load energy strategy subspace for each individual load energy source. For shiftable loads, their power feasible region model, i.e., the load energy strategy subspace, is defined by the following constraints: ; ; ; In the formula, The actual power of the load that can be shifted at time t; The rated power of the transferable load; Let its working state at time t be a 0-1 variable, where 0 means standby and 1 means working; For its movable time period, This is the start time of the shiftable time period. This is the end time of the shiftable time period; Its total runtime; This refers to the start and end times of the load that can be shifted after adjustment. The adjusted start time. This is the adjusted end time; Let t be its starting state at time t, which is a 0-1 variable, and there is only one starting time in the entire start-end period; This allows for any time within the transferable period to ensure the continuous operation of transferable loads.

[0048] Transferable load is defined as a load whose total electricity consumption remains constant within an allowable dispatch interval, but whose user electricity consumption can be flexibly adjusted at different times, such as charging equipment for electric vehicles. For transferable load, its power feasible region model, i.e., the load energy strategy subspace, is defined by the following constraints: ; ; in, The actual power of the transferable load. and These are the lower and upper limits of transferable load, respectively; The schedulable time period for transferable loads, i.e., the range of values ​​for time t; The total charge used for transferable loads.

[0049] Interruptible loads are defined as loads whose power can be adjusted during use but whose usage period is fixed, such as air conditioners. The power feasible region model, i.e., the load energy strategy subspace, is defined by the following constraints: ; in, The actual power of the interruptible load. and These are the lower and upper limits of interruptible load, respectively, and their usage periods are fixed, requiring no additional time constraints.

[0050] What is understandable is that , , These are the variables that need to be solved; each adjustable load energy unit has the above variables.

[0051] In summary, for any adjustable load energy individual, all the aforementioned load energy strategy subspaces, including the respective load energy strategy subspaces for the three types of loads—shiftable loads, transferable loads, and interruptible loads—can be uniformly expressed as a system of linear inequalities, namely: ; in, , This refers to the number of individual load energy sources within the load energy set. Indicates the first The power feasible region of an adjustable load energy individual, wherein the power feasible region is the set of power vectors that satisfy the above-mentioned adjustable load energy operation constraints, that is, the power vectors that satisfy the corresponding load energy strategy subspace. , They represent the first The coefficient matrix and vector of an adjustable load energy individual. The matrix is ​​the first The decision variables for the adjustable load energy individual, i.e., the first... The load energy strategy subspace of each adjustable load energy individual. By summing the load energy strategy subspaces of each load energy in the aforementioned load energy set, the load energy strategy subspace set can be obtained. Taking interruptible loads as an example, That is, there are two inequalities: and It conforms to the form of a system of linear inequalities, that is... The forms of expression. Different types of load rules are translated into different numbers and filled into this container.

[0052] In another embodiment, step S3: obtaining the total number of load aggregation directions and obtaining the load aggregation direction vector based on the total number of load aggregation directions includes: The load aggregation direction number is determined based on the total number of load aggregation directions. The load aggregation angle vector is determined based on the load aggregation direction number and the total number of load aggregation directions. The load aggregation angle vector is processed by trigonometric functions to obtain the load aggregation direction vector.

[0053] It should be noted that, to obtain the total load energy strategy space after load energy aggregation, an aggregation algorithm based on multi-directional optimization and hyperplane approximation is adopted. The specific process is as follows: First, the search directions are set by defining a set of uniformly distributed direction vectors on a two-dimensional plane. That is, the total number of load aggregation directions K is first obtained, and the load aggregation direction indices k = 1, 2, ..., K are determined based on this. It should be noted that the value of K is usually determined according to the accuracy requirements to balance computational efficiency and boundary approximation accuracy. Then, for each load aggregation direction indices k, the corresponding load aggregation angle sub-vector is calculated: ; in, The load aggregation angle sub-vector is the k-th load aggregation angle sub-vector. By summing all the load aggregation angle sub-vectors, the load aggregation angle vector is obtained. Finally, trigonometric functions are applied to the load aggregation angle vector to obtain the load aggregation direction vector.

[0054] In another embodiment, the step of performing trigonometric function processing on the load aggregation angle vector to obtain the load aggregation direction vector includes: Obtain the sine value of the load aggregation angle vector, and use it as the load aggregation angle sine value; Obtain the cosine value of the load aggregation angle vector as the load aggregation angle cosine value; The load aggregation direction vector is obtained based on the sine and cosine values ​​of the load aggregation angle.

[0055] It should be noted that for each load aggregation angle subvector, its sine value is obtained. and obtain the remaining chord values. Based on sine value Sum of cosine values Obtain the load aggregation direction sub-vector corresponding to the load aggregation angle sub-vector. T is the transformation matrix. The sine values ​​corresponding to all load aggregation angle sub-vectors. The summation is the sine value of the load aggregation angle, and the cosine value corresponding to all load aggregation angle sub-vectors. The sum of all load aggregation angle cosine values ​​is the load aggregation direction vector, and the sum of all load aggregation direction sub-vectors is the load aggregation direction vector. Therefore, the load aggregation direction vector is obtained based on the load aggregation angle sine value and the load aggregation angle cosine value.

[0056] In another embodiment, step S4: obtaining the total load energy strategy space based on the load aggregation direction vector and the load energy strategy subspace set includes: Based on the preset strategy selection matrix and the set of load energy strategy subspaces, a load energy strategy mapping vector is obtained; A load energy objective function is constructed with the goal of maximizing the product of the load energy strategy mapping vector and the load aggregation direction vector. Using the load energy strategy subspace set as the load energy constraint, the load energy objective function is solved to obtain the total load energy strategy space.

[0057] It should be noted that the defined preset strategy selection matrix S serves to set the load energy strategy subspace. Projecting onto a two-dimensional space, the elements in x are... In Where i takes different values, the load energy strategy mapping vector is obtained. ,in for The mapping vector in the projection space is the load energy strategy mapping vector. Next, for each load aggregation direction sub-vector, a load energy objective sub-function is constructed with the objective of maximizing the product of the load energy strategy mapping vector and the load aggregation direction sub-vector. ,in, Indicates the first For each load energy objective sub-function, the load energy objective function is obtained by summing these sub-functions, specifically, by maximizing the product of the load energy strategy mapping vector and the load aggregation direction vector. The load energy strategy subspace set serves as the load energy constraint condition. "st" means "constrained by...". Based on this, the objective function of the load energy is solved to obtain the total load energy strategy space. In other words... Solving this optimization problem yields the optimal solution, which is a boundary point in that direction. Traversing all K directions yields the set of boundary points, and then the convex hull of these points is calculated. This convex hull is the total load energy strategy space, which is the overall power feasible region of the load energy set.

[0058] For step S5: Constructing total energy constraints based on the preset grid game order, the preset target total energy power, the power supply energy strategy space, and the total load energy strategy space, specifically: Set upper-level power grid dispatch instructions As a preset target total energy output, this instruction represents the power exchange target for each time period within the scheduling cycle (typically an hourly value over 24 hours). Simultaneously, based on the strategy spaces of each participant—photovoltaic energy, wind turbine energy, load energy collection, energy storage energy, and diesel engine energy—namely, the power supply strategy space and the total load energy strategy space, and a preset game order (e.g., photovoltaic energy, wind turbine energy, load energy collection, energy storage energy, diesel engine energy), a power balance constraint is established as the core total energy constraint condition. This requires the sum of the contributions from all participants to meet a certain threshold. , Let be the power output corresponding to the a-th player. This holds true for all time periods t. There are A players, where A is the number of power source sets and load sets. It's understood that the load set is considered as a whole; that is, the sum of the power sets of the energy objects to be implemented is required to be close to... It should be noted that this constraint is introduced through a payoff penalty function rather than being rigidly enforced, in order to facilitate distributed solution.

[0059] For step S6: Obtain the revenue penalty function and revenue penalty coefficient, and based on the revenue penalty function, the revenue penalty coefficient, the power source energy revenue function, and the load energy revenue function, construct a set of total energy target sub-functions, specifically: First, define the payoff penalty function: ; in, The first is the revenue penalty value, used to quantify the deviation between the actual total output and the grid dispatch instructions; the second is to set the revenue penalty coefficient. Also known as the penalty factor, this coefficient can be dynamically adjusted during the iteration process; then, for each player (photovoltaic energy, wind turbine energy, load energy collection, energy storage energy, diesel engine energy), its original payoff function is... That is, the power source energy revenue function or the load energy revenue function is combined with the revenue penalty function to form a revenue function with a revenue penalty function. This serves as the overall energy objective sub-function for all players in the game. It should be noted that all players... This constitutes a set of sub-functions representing the overall energy objective, with each player making decisions to maximize their own goals. The goal is to construct a potential function. G is the potential function value. The potential function is used as a theoretical analysis tool to prove the convergence of the game. The potential function is the sum of the original payoffs of all players minus the penalty function term. Its property guarantees that the game model is a complete potential game, and thus there exists a Nash equilibrium solution.

[0060] For step S7: Based on the total energy constraint and the total energy objective sub-function set, obtain the power set of energy objects to be implemented, and run the power supply energy set and the load energy set based on the power set of energy objects to be implemented, specifically: Following a predetermined game order, such as prioritizing photovoltaic and wind turbine energy, followed by load energy, energy storage energy, and diesel engine energy, multiple iterations are conducted. In each iteration, each player, based on the publicly available strategy spaces of other players, solves for maximizing their own payoff with a penalty function within their own strategy space. The optimization problem is to obtain the optimal output decision (hourly output plan) for the current round and update the strategy. Specifically, the photovoltaic energy player solves the problem while satisfying the upper limit constraint of the predicted output. Soon Substitute into In Then, iterative solutions are performed; the player in the wind turbine energy sector solves the problem while satisfying the upper limit constraint of the predicted output. Soon Substitute into In Then, iterative solutions are performed; the adjustable load energy set, as the player, uses the total load energy strategy space constructed above as its feasible region, and solves within this region. Soon Substitute into In Then, iterative solutions are performed; the energy storage player uses energy storage power and state of charge as decision variables, and solves the problem under the conditions of satisfying charging and discharging power constraints, energy balance constraints, and state of charge boundary constraints. Soon Substitute into In Then, iterative solutions are performed; the diesel engine system solves the problem while satisfying the minimum / maximum output constraints and the climbing constraint. Soon Substitute into In Then, the solution is iteratively obtained. After each round of the game, the change in the effort decisions of each player in adjacent rounds is calculated, and the power deficit is also calculated. A game is considered to have reached Nash equilibrium when all three of the following conditions are met: the change in effort decisions made by each player is less than a preset threshold; the power deficit approaches zero; and so on. Approximately equal to 0; under the current strategy combination, no player in any round can increase their own payout by unilaterally adjusting their strategy. If the above conditions are not met, the payout penalty coefficient is increased based on preset rules. For example, the scale can be increased proportionally, and the iteration can continue according to the preset power grid game sequence. By continuously increasing the payout penalty coefficient, players are forced to pay more attention to power balance constraints, eventually causing the payout penalty function term to approach zero, thus obtaining a Nash equilibrium solution that satisfies power balance. When the game reaches Nash equilibrium, the iteration process ends, and the power set of the energy objects to be implemented is output, which is the hourly output plan of each player in the equilibrium state. Specifically, this includes: the hourly output plan power of photovoltaic energy and wind turbine energy, the optimized power consumption plan power of adjustable load energy combination, the charging and discharging power and state of charge change trajectory of energy storage, and the start-stop and output plan power of diesel engines. All the above plans are summarized to obtain the execution result of the distributed energy cluster on the upper-level power grid dispatch instructions, which is used for the actual operating power energy set and load energy set. It should be noted that the power set of the energy objects to be implemented is the power value of each time period within the dispatch cycle, ensuring hourly power balance, and that each player achieves the unity of individual income and global coordination under this scheme.

[0061] Based on the above method embodiments, corresponding apparatus embodiments are provided; like Figure 2 As shown, one embodiment of the present invention provides a distributed energy cluster operation device, comprising: The power basic data acquisition module acquires the power energy revenue function and power energy strategy space of the power energy set. The load basic data acquisition module is used to acquire the load energy revenue function of the load energy set and acquire the load energy strategy subspace of each load energy in the load energy set, thereby obtaining a load energy strategy subspace set. The load aggregation direction vector acquisition module is used to acquire the total number of load aggregation directions and obtain the load aggregation direction vector based on the total number of load aggregation directions; The total load energy strategy space construction module is used to obtain the total load energy strategy space based on the load aggregation direction vector and the set of load energy strategy subspaces. The total energy constraint construction module is used to construct total energy constraints based on a preset power grid game order, a preset target total energy power, the power supply energy strategy space and the total load energy strategy space. The total energy target sub-function set construction module is used to obtain the revenue penalty function and the revenue penalty coefficient, and construct the total energy target sub-function set based on the revenue penalty function, the revenue penalty coefficient, the power energy revenue function and the load energy revenue function; The running module is used to obtain the power set of energy objects to be implemented based on the total energy constraints and the total energy target sub-function set, and to run the power energy set and the load energy set based on the power set of energy objects to be implemented.

[0062] In the above scheme, by obtaining the power energy revenue function and power energy strategy space of the power energy set, and aggregating the load energy strategy subspace of each load energy in the load energy set, the total load energy strategy space is obtained based on the load aggregation direction vector. This achieves effective integration and unified representation of massive distributed load energy, significantly reducing the computational complexity of distributed collaborative optimization. By constructing total energy constraints based on a preset grid game order, a preset target total energy power, and the power energy strategy space and the total load energy strategy space, and introducing a revenue penalty function and revenue penalty coefficient to construct a total energy target sub-function set, each energy can autonomously coordinate and iteratively optimize in the distributed decision-making process. Finally, a set of energy objects to be implemented that satisfies global power balance and takes into account individual economic benefits is obtained. Then, the power energy set and the load energy set are run based on the set of energy objects to be implemented, thereby effectively improving the operational reliability of the distributed energy cluster without the need for a centralized controller.

[0063] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the distributed energy cluster operation method provided by any of the above-described method embodiments of the present invention.

[0064] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0065] In another embodiment, the load base data acquisition module is further configured to: The load aggregation direction number is determined based on the total number of load aggregation directions. The load aggregation angle vector is determined based on the load aggregation direction number and the total number of load aggregation directions. The load aggregation angle vector is processed by trigonometric functions to obtain the load aggregation direction vector.

[0066] In the above scheme, by generating a load aggregation direction vector based on the total number of load aggregation directions, a standard set of detection directions is provided for subsequent multi-directional optimization scanning of the feasible domain boundary, ensuring the comprehensiveness and unbiasedness of boundary detection and improving the accuracy of total load energy strategy space acquisition.

[0067] In another embodiment, the load base data acquisition module is further configured to: Obtain the sine value of the load aggregation angle vector, and use it as the load aggregation angle sine value; Obtain the cosine value of the load aggregation angle vector as the load aggregation angle cosine value; The load aggregation direction vector is obtained based on the sine and cosine values ​​of the load aggregation angle.

[0068] In the above scheme, the load aggregation direction vector is obtained by performing a sine-cosine transformation on the load aggregation angle vector, which realizes an accurate mapping from angle space to vector space and provides a reliable geometric basis for constructing the total load energy strategy space.

[0069] In another embodiment, the total load energy strategy space construction module is also used for: Based on the preset strategy selection matrix and the set of load energy strategy subspaces, a load energy strategy mapping vector is obtained; A load energy objective function is constructed with the goal of maximizing the product of the load energy strategy mapping vector and the load aggregation direction vector. Using the load energy strategy subspace set as the load energy constraint, the load energy objective function is solved to obtain the total load energy strategy space.

[0070] In the above scheme, the high-dimensional load energy strategy subspace is projected onto a two-dimensional space by a preset strategy selection matrix, and the linear programming problem is solved with the goal of maximizing the product of the load energy strategy mapping vector and the load aggregation direction vector. Finally, the total load strategy space composed of the convex hull of the boundary points is obtained, which realizes the efficient aggregation of massive load feasible regions and greatly reduces the computational complexity of subsequent games.

[0071] In another embodiment, the power source collection includes photovoltaic energy, wind turbine energy, diesel energy, and energy storage energy, and the power source basic data acquisition module is further used for: Obtain the photovoltaic energy revenue sub-function and photovoltaic energy strategy subspace of the photovoltaic energy; Obtain the wind turbine energy revenue subfunction and wind turbine energy strategy subspace; Obtain the wind turbine energy revenue sub-function and diesel energy strategy subspace of the diesel energy; Obtain the energy storage revenue subfunction and energy storage strategy subspace of the energy storage energy; Based on the photovoltaic energy revenue sub-function, the wind turbine energy revenue sub-function, the wind turbine energy revenue sub-function, and the energy storage energy revenue sub-function, the power supply energy revenue function is obtained; Based on the photovoltaic energy strategy subspace, the wind turbine energy strategy subspace, the diesel energy strategy subspace, and the energy storage energy strategy subspace, the power supply energy strategy space is obtained.

[0072] In the above scheme, by modeling four types of power sources—photovoltaics, wind turbines, diesel, and energy storage—the revenue functions and energy strategy spaces of each type of power source are accurately characterized, so as to realize the targeted construction of revenue functions and energy strategy spaces adapted to the characteristics of different power source categories.

[0073] In another embodiment, the power supply basic data acquisition module is further configured to: Obtain the predicted output power of the photovoltaic system; The photovoltaic energy strategy subspace is constructed based on the preset initial value and the predicted output power of the photovoltaic system.

[0074] In the above scheme, by constructing a strategy space from the preset initial value to the predicted output power of photovoltaic power based on the maximum predicted output power of photovoltaic power, the physical constraints of photovoltaic power output are accurately characterized, providing a clear feasible domain boundary for subsequent revenue calculation and game iteration.

[0075] In another embodiment, the power supply basic data acquisition module is further configured to: Obtain the minimum allowable output power and the maximum allowable output power of the diesel engine, and construct a first diesel subspace based on the minimum allowable output power and the maximum allowable output power of the diesel engine; Obtain the rise rate limit value and the fall rate limit value, and construct a second diesel subspace based on the rise rate limit value and the fall rate limit value; The diesel energy strategy subspace is constructed based on the first diesel subspace and the second diesel subspace.

[0076] In the above scheme, the diesel engine strategy space is constructed by simultaneously considering the upper and lower limits of the output power corresponding to the minimum and maximum allowable output power of the diesel engine, as well as the ramping constraints corresponding to the rise rate limit and fall rate limit. This ensures the physical feasibility of the diesel engine output plan in terms of temporal variation, making the optimization results both economical and engineering feasible.

[0077] Based on the above-described embodiment of a distributed energy cluster operation method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a distributed energy cluster operation method according to any embodiment of the present invention.

[0078] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0079] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory.

[0080] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0081] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a distributed energy cluster operation method as described in any of the above-described method embodiments of the present invention.

[0082] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0083] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for operating a distributed energy cluster, characterized in that, include: Obtain the power energy revenue function and power energy strategy space of the power energy set; Obtain the load energy revenue function of the load energy set, and obtain the load energy strategy subspace of each load energy in the load energy set to obtain the load energy strategy subspace set; Obtain the total number of load aggregation directions, and obtain the load aggregation direction vector based on the total number of load aggregation directions; The total load energy strategy space is obtained based on the load aggregation direction vector and the set of load energy strategy subspaces. The total energy constraint is constructed based on the preset power grid game order, the preset target total energy power, the power supply energy strategy space and the total load energy strategy space. Obtain the revenue penalty function and revenue penalty coefficient, and construct a set of total energy target sub-functions based on the revenue penalty function, the revenue penalty coefficient, the power energy revenue function, and the load energy revenue function; Based on the total energy constraints and the total energy objective sub-function set, a power set of energy objects to be implemented is obtained, and the power supply energy set and the load energy set are operated based on the power set of energy objects to be implemented.

2. The distributed energy cluster operation method according to claim 1, characterized in that, The step of obtaining the total number of load aggregation directions and obtaining the load aggregation direction vector based on the total number of load aggregation directions includes: The load aggregation direction number is determined based on the total number of load aggregation directions. The load aggregation angle vector is determined based on the load aggregation direction number and the total number of load aggregation directions. The load aggregation angle vector is processed by trigonometric functions to obtain the load aggregation direction vector.

3. The distributed energy cluster operation method according to claim 2, characterized in that, The process of performing trigonometric function processing on the load aggregation angle vector to obtain the load aggregation direction vector includes: Obtain the sine value of the load aggregation angle vector, and use it as the load aggregation angle sine value; Obtain the cosine value of the load aggregation angle vector as the load aggregation angle cosine value; The load aggregation direction vector is obtained based on the sine and cosine values ​​of the load aggregation angle.

4. The distributed energy cluster operation method according to claim 1, characterized in that, The process of obtaining the total load energy strategy space based on the load aggregation direction vector and the set of load energy strategy subspaces includes: Based on the preset strategy selection matrix and the set of load energy strategy subspaces, a load energy strategy mapping vector is obtained; A load energy objective function is constructed with the goal of maximizing the product of the load energy strategy mapping vector and the load aggregation direction vector. Using the load energy strategy subspace set as the load energy constraint, the load energy objective function is solved to obtain the total load energy strategy space.

5. The distributed energy cluster operation method according to claim 1, characterized in that, The power source set includes photovoltaic energy, wind turbine energy, diesel energy, and energy storage energy. The process of obtaining the power source set's revenue function and strategy space includes: Obtain the photovoltaic energy revenue sub-function and photovoltaic energy strategy subspace of the photovoltaic energy; Obtain the wind turbine energy revenue subfunction and wind turbine energy strategy subspace; Obtain the wind turbine energy revenue sub-function and diesel energy strategy subspace of the diesel energy; Obtain the energy storage revenue subfunction and energy storage strategy subspace of the energy storage energy; Based on the photovoltaic energy revenue sub-function, the wind turbine energy revenue sub-function, the wind turbine energy revenue sub-function, and the energy storage energy revenue sub-function, the power supply energy revenue function is obtained; Based on the photovoltaic energy strategy subspace, the wind turbine energy strategy subspace, the diesel energy strategy subspace, and the energy storage energy strategy subspace, the power supply energy strategy space is obtained.

6. A distributed energy cluster operation method according to claim 5, characterized in that, The process of obtaining the photovoltaic energy revenue sub-function and photovoltaic energy strategy subspace includes: Obtain the predicted output power of the photovoltaic system; The photovoltaic energy strategy subspace is constructed based on the preset initial value and the predicted output power of the photovoltaic system.

7. A distributed energy cluster operation method according to claim 5, characterized in that, The sub-function for obtaining the wind turbine energy revenue from the diesel energy and the diesel energy strategy subspace include: Obtain the minimum allowable output power and the maximum allowable output power of the diesel engine, and construct a first diesel subspace based on the minimum allowable output power and the maximum allowable output power of the diesel engine; Obtain the rise rate limit value and the fall rate limit value, and construct a second diesel subspace based on the rise rate limit value and the fall rate limit value; The diesel energy strategy subspace is constructed based on the first diesel subspace and the second diesel subspace.

8. A distributed energy cluster operation device, characterized in that, include: The power basic data acquisition module acquires the power energy revenue function and power energy strategy space of the power energy set. The load basic data acquisition module is used to acquire the load energy revenue function of the load energy set and acquire the load energy strategy subspace of each load energy in the load energy set, thereby obtaining a load energy strategy subspace set. The load aggregation direction vector acquisition module is used to acquire the total number of load aggregation directions and obtain the load aggregation direction vector based on the total number of load aggregation directions; The total load energy strategy space construction module is used to obtain the total load energy strategy space based on the load aggregation direction vector and the set of load energy strategy subspaces. The total energy constraint construction module is used to construct total energy constraints based on a preset power grid game order, a preset target total energy power, the power supply energy strategy space and the total load energy strategy space. The total energy target sub-function set construction module is used to obtain the revenue penalty function and the revenue penalty coefficient, and construct the total energy target sub-function set based on the revenue penalty function, the revenue penalty coefficient, the power energy revenue function and the load energy revenue function; The running module is used to obtain the power set of energy objects to be implemented based on the total energy constraints and the total energy target sub-function set, and to run the power energy set and the load energy set based on the power set of energy objects to be implemented.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a distributed energy cluster operation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a distributed energy cluster operation method as described in any one of claims 1-7.

Images