Multi-new energy base interaction control method, device, equipment and medium

Abstract

The invention discloses a multi-new energy base interaction control method, device, equipment and medium, and belongs to the field of new energy base power optimizing.The method comprises the steps that electric energy interaction relations between new energy bases and load centers and between the new energy bases and the load centers and between the new energy bases are collected, and according to all the electric energy interaction relations, the electric energy interaction relations between the new energy bases and the load centers and between the new energy bases and the load centers are obtained; constructing a cooperative game model by embedding a frequency stability constraint; constructing a Nash bargaining model based on a Nash bargaining theory in combination with the cooperative game model; sequentially iteratively solving augmented Lagrange functions corresponding to the first sub-model and the second sub-model through an alternating direction multiplier algorithm to obtain electric energy interaction schemes between the new energy bases and the load centers and between the new energy bases; and controlling each new energy base and each load center to perform electric energy interaction according to the electric energy interaction scheme. According to the invention, the overall frequency response capability of the new energy base cluster can be improved while the frequency safety is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy base power optimization, and in particular to a multi-new energy base interaction control method, device, equipment and medium. BACKGROUND

[0002] With the transformation of global energy structure to low carbonization, the proportion of new energy power generation represented by wind power and photovoltaic power in the power system is rapidly increasing. However, new energy power generation has significant intermittency and volatility, and large-scale grid connection poses a serious challenge to the frequency stability of the power system. In particular, for multi-new energy bases, due to similar resource endowments, the spatio-temporal correlation of new energy output is strong, which can easily cause regional frequency fluctuations and even instability problems. In particular, in the typical power supply architecture formed by new energy bases and load centers, the power fluctuation of the sending end new energy base will directly affect the power supply stability and frequency safety of the receiving end load center, so it is necessary to optimize the power transmission process between the new energy base and the load center.

[0003] Existing new energy base power optimization strategies mainly target a single base or a local power grid, and lack systematic consideration of multi-base cluster collaborative optimization. Due to the large fluctuation of the output of a single new energy base, it is difficult to completely suppress power fluctuations by relying solely on local regulation, so it is necessary to consider power mutual aid between multi-new energy bases to achieve complementary advantages and improve overall power supply stability. However, the existing mutual aid mechanism faces three key problems: first, the traditional economic dispatching model aims to minimize cost and does not fully consider frequency stability constraints, resulting in low grid safety when performing power interaction; second, under the electricity market architecture, new energy bases participate in power interaction as independent individuals, but the pricing mechanism for multi-agent power interaction is not perfect at this stage, and the interests of participants are not fully considered, thereby reducing the enthusiasm of participants, ultimately affecting the overall power interaction amount in the new energy base cluster and reducing the overall frequency response capability of the new energy base cluster.

[0004] Therefore, how to improve the overall frequency response capability of the new energy base cluster while ensuring power supply stability and frequency safety is a technical problem that needs to be solved at present. SUMMARY

[0005] The present application provides a multi-new energy base interaction control method, device, equipment and medium, which can solve the problem of how to improve the overall frequency response capability of the new energy base cluster while ensuring power supply stability and frequency safety in the prior art.

[0006] Some embodiments of the present application provide a multi-new energy base interaction control method, comprising: Collecting power interaction relationships between each new energy base and each load center and between each of the energy bases, constructing a cooperative game model by embedding a frequency stability constraint according to all the power interaction relationships, wherein the frequency stability constraint is obtained by constructing a preset system frequency response model through analysis; Based on the Nash bargaining theory, a Nash bargaining model is constructed in combination with the cooperative game model, wherein the Nash bargaining model includes a first sub-model for optimizing power interaction quantity and a second sub-model for optimizing power interaction unit price; The first sub-model and the second sub-model are iteratively solved in turn by an alternating direction multiplier algorithm to obtain a power interaction scheme between each new energy base and each load center and between each new energy base, wherein the first sub-model and the second sub-model update a penalty factor in the augmented Lagrangian function corresponding to each of them according to a residual value corresponding to each of them in each iteration solving process; According to the power interaction scheme, each new energy base and each load center are controlled to interact with each other.

[0007] Compared with the prior art, the above embodiment has the following beneficial effects: by obtaining the power interaction relationships between each new energy base and the load center and other new energy bases, and embedding the frequency stability constraint to construct the cooperative game model, the power fluctuation of the multi-base cluster is effectively associated with the system frequency stability, the systemic constraint of frequency safety is realized, and the stability and safety in the power supply process of the new energy base are improved. On this basis, the double sub-model design based on the Nash bargaining theory is introduced, the power interaction quantity and the power interaction unit price are optimized respectively, the power complementarity and benefit distribution among each new energy base are taken into account, and the power interaction quantity of the multi-new energy base cluster is maximized, thereby effectively improving the frequency response capability of the multi-new energy base cluster and solving the problem of imperfect multi-agent power mutual aid pricing mechanism. At the same time, the augmented Lagrangian function is iteratively solved by the alternating direction multiplier algorithm, and the penalty factor is dynamically updated according to the residual value in the iteration process, so that each sub-model can quickly converge and obtain the optimal power interaction scheme between each new energy base and the load center and between the bases, thereby improving the frequency response speed of the multi-new energy base cluster.

[0008] Further, the step of constructing the frequency stability constraint comprises: Based on the virtual synchronization technology, a virtual inertia model of each new energy power station is constructed, and the system frequency response model is constructed according to each virtual inertia model; The input of the system frequency response model is set as a step disturbance with fixed amplitude, and the frequency response model is analyzed in time domain by using the complex frequency domain analysis method to obtain a frequency fluctuation function; The frequency fluctuation function is analyzed to obtain first function expressions corresponding to a plurality of safety indexes, and the frequency stability constraint is constructed according to the first function expressions.

[0009] Compared with the prior art, the above-mentioned embodiments have the following beneficial effects: the virtual inertia model of each new energy power station is constructed based on the virtual synchronization technology, and the system frequency response model is established, so that the frequency stability constraint has a clear physical basis. By applying a step disturbance to the model and using the complex frequency domain analysis method to analyze the frequency fluctuation function, the dynamic frequency characteristics of the system under disturbance can be quantitatively reflected. Further, by extracting key safety indexes such as the maximum frequency change rate, the maximum frequency deviation and the steady-state deviation from the frequency fluctuation function, the frequency stability constraint is constructed, so that the frequency constraint is directly related to the new energy output characteristics, so that the dynamic safety margin of the system can be reflected in real time in the process of energy interaction optimization, and the interaction strategy that may cause frequency instability can be identified in advance in the dispatching stage, so as to suppress the system oscillation caused by power fluctuation from the source, and significantly improve the frequency stability and system safety under the coordinated operation of multiple new energy bases.

[0010] Further, the cooperation game model is constructed by embedding the frequency stability constraint according to all the energy interaction relationships, including: According to the energy interaction relationship between each new energy base and each load center and between each energy base, a first optimization objective function is constructed to maximize the operation income of all the new energy bases; According to the energy interaction relationship between each new energy base and each load center, a second optimization objective function is constructed to minimize the electricity purchase cost of all the load centers; According to the frequency stability constraint, the first optimization objective function and the second optimization objective function, the cooperation game model is constructed.

[0011] Compared with the prior art, the above-mentioned embodiments have the following beneficial effects: the cooperation game model containing the operation income of the new energy base and the electricity purchase cost of the load center is constructed, the first optimization objective function is used to maximize the income of the new energy base group, the second optimization objective function is used to minimize the electricity purchase cost of the load center, and the two are jointly solved under the frequency stability constraint, so that the optimization result considers the economic benefits and system operation safety on both supply and demand sides, thereby balancing the benefit distribution relationship between the new energy bases and the load centers, and forming a power complementary mechanism between different bases, so that each subject cooperates in the energy interaction in the game process, which improves the overall operation efficiency of the cluster and enhances the ability of the system to resist large-scale power fluctuation.

[0012] Furthermore, the construction of the Nash bargaining model based on Nash bargaining theory and combined with the cooperative game model includes: Based on Nash bargaining theory, a basic Nash bargaining model is constructed by quantifying the changes in interests of each new energy base and each load center and maximizing the changes in interests. The cooperative game model is simplified by merging the first optimization objective function and the second optimization objective function, and the simplified cooperative game model is substituted into the basic Nash bargaining model to obtain the first sub-model; After setting the optimization objective variable of the first sub-model in the basic Nash bargaining model to a constant, and then transforming it into a convex function through logarithmic transformation, the second sub-model is obtained.

[0013] Compared with existing technologies, the above embodiments have the following beneficial effects: Based on Nash bargaining theory, this application further transforms the cooperative game model into a quantifiable Nash bargaining model. By quantifying the changes in the interests of each participant and maximizing their product, the overall benefit optimization and distribution fairness under multi-party participation conditions are guaranteed. Simultaneously, by merging the dual objective functions in the cooperative game model and substituting them into the basic Nash bargaining model, a first sub-model for optimizing the electrical energy interaction quantity is obtained. This model is then transformed into a convex function form through logarithmic transformation, thereby significantly reducing the solution complexity, improving global convergence, and ultimately increasing the response speed in the electrical energy interaction control process.

[0014] Further, the step of iteratively solving for the augmented Lagrangian functions corresponding to the first sub-model and the second sub-model using the alternating direction multiplier algorithm includes: The first augmented Lagrangian function corresponding to the first sub-model is initialized by the initial value of the energy interaction quantity and iteratively solved until the preset first iteration termination condition is met, so as to obtain the optimal energy interaction quantity. The optimal energy interaction unit price is obtained by initializing the energy interaction unit price and iteratively solving the second augmented Lagrangian function corresponding to the second sub-model until the preset second iteration termination condition is met.

[0015] Compared with existing technologies, the above embodiments have the following beneficial effects: This application uses an alternating direction multiplier algorithm to iteratively solve the two augmented Lagrangian functions corresponding to the energy interaction quantity and the interaction unit price, respectively, achieving a combination of distributed optimization and efficient convergence. By using the energy interaction quantity and the energy interaction unit price as independent variables for alternating optimization, each subproblem can be solved independently, reducing the overall computational complexity. Furthermore, by setting the first and second iteration termination conditions, it is ensured that the next stage of optimization is entered only after the subproblem converges, avoiding numerical oscillations caused by excessive iteration, thereby effectively shortening the solution time and improving the real-time performance of scheduling.

[0016] Furthermore, each iteration of solving for the first augmented Lagrangian function corresponding to the first sub-model includes: Substitute the first energy interaction quantity obtained so far into the solution and solve the first augmented Lagrangian function to obtain the second energy interaction quantity required for the next iteration. Based on the first electrical energy interaction quantity, the currently obtained first penalty factor, and the currently obtained first Lagrange multiplier, determine the second Lagrange multiplier required for the next iteration; wherein, the first penalty factor is obtained by updating based on the currently obtained first residual value; Based on the second electrical energy interaction quantity, determine the second residual value required for the next iteration solution.

[0017] Compared with existing technologies, the above embodiments have the following beneficial effects: In the process of solving the first sub-model, this application introduces an adaptive update mechanism based on residual feedback, using a penalty factor and Lagrange multipliers, to achieve dynamic convergence control of the energy interaction optimization process. By determining the initial value of the next round of optimization based on the current energy interaction, penalty factor, and Lagrange multipliers in each iteration, the solution direction can be corrected in real time, enabling the algorithm to quickly approach the global optimum. When the residual is large, the penalty factor is automatically increased to strengthen constraint convergence; when the residual approaches zero, the penalty factor is decreased to avoid numerical oscillations. This mechanism effectively overcomes the problem that traditional algorithms with fixed penalty parameters are prone to oscillations or slow convergence, making the optimization of energy interaction more stable and accurate. Through this dynamic update mechanism, the system can maintain a stable iterative process under complex power fluctuations, thereby improving the coordination and robustness of power allocation among multiple new energy bases.

[0018] Furthermore, each iteration of solving for the second augmented Lagrangian function corresponding to the second sub-model includes: Substitute the currently obtained first energy interaction unit price into the solution and solve for the second augmented Lagrangian function to obtain the second energy interaction unit price required for the next iteration. Based on the first energy interaction unit price, the currently obtained third penalty factor, and the currently obtained third Lagrange multiplier, determine the third Lagrange multiplier required for the next iteration; wherein, the third penalty factor is obtained by updating based on the currently obtained third residual value; Based on the second energy interaction unit price, determine the fourth residual value required for the next iteration.

[0019] Compared with existing technologies, the above embodiments have the following beneficial effects: This application employs an adaptive penalty and residual update mechanism similar to that used in interaction quantity optimization during the solution of the second sub-model, achieving stable convergence of the energy interaction unit price optimization. By using the current interaction unit price, penalty factor, and Lagrange multipliers to jointly update the parameters for the next round in each iteration, the algorithm convergence can be accelerated while maintaining economic optimality. When the residual is too large, the penalty factor is automatically adjusted to increase the constraint strength, enabling the price optimization process to approach the equilibrium point more quickly; when the residual converges, the penalty strength is reduced to maintain numerical stability, thereby improving market coordination efficiency and enhancing the economic sustainability of the system's frequency response.

[0020] Another embodiment of this application also provides a multi-new energy base interactive control device, including: a cooperative game model construction module, a Nash bargaining model construction module, an energy interaction scheme solving module, and an energy interaction control module; The cooperative game model construction module is used to collect the power interaction relationships between each new energy base and each load center, as well as between each energy base. Based on all the power interaction relationships, a cooperative game model is constructed by embedding frequency stability constraints. The frequency stability constraints are constructed and obtained by analyzing a preset system frequency response model. The Nash bargaining model construction module is used to construct a Nash bargaining model based on Nash bargaining theory and combined with the cooperative game model; wherein, the Nash bargaining model includes: a first sub-model for optimizing the amount of electricity interaction and a second sub-model for optimizing the unit price of electricity interaction; The power interaction scheme solution module is used to iteratively solve the augmented Lagrangian functions corresponding to the first sub-model and the second sub-model through an alternating direction multiplier algorithm to obtain the power interaction schemes between each new energy base and each load center, as well as between each new energy base; wherein, in each iteration, the first sub-model and the second sub-model update the penalty factor in their respective augmented Lagrangian functions according to their respective residual values. The power interaction control module is used to control each of the new energy bases and each of the load centers to perform power interaction according to the power interaction scheme.

[0021] Another embodiment of this application also 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 the multi-new energy base interactive control method of this application.

[0022] Another embodiment of this application also 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 the multi-new energy base interactive control method of this application. Attached Figure Description

[0023] 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.

[0024] Figure 1 This is a flowchart illustrating an interactive control method for multiple new energy bases provided in some embodiments of this application; Figure 2 This is a framework diagram of a frequency response model for a new energy base provided in some embodiments of this application; Figure 3 This is a framework diagram of a cooperative game model between multiple new energy bases and load centers provided in some embodiments of this application; Figure 4 Here is a flowchart illustrating the solution process for a Nash bargaining model provided in some embodiments of this application; Figure 5 The image shows the convergence effect of solving the Nash bargaining model based on an improved method provided in some embodiments of this application. Figure 6 The image shows the convergence effect of solving the Nash bargaining model based on a traditional method in some embodiments of this application. Figure 7 This is a schematic diagram of the structure of a multi-new energy base interactive control device provided in some embodiments of this application. Detailed Implementation

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] Existing power optimization strategies for renewable energy bases primarily target single bases or local grids, lacking a systematic consideration of coordinated optimization across multiple base clusters. Due to significant output fluctuations in individual renewable energy bases, relying solely on local regulation is insufficient to completely mitigate these fluctuations. Therefore, it is necessary to consider power mutual assistance among multiple renewable energy bases to achieve surplus-deficit complementarity and improve overall power supply stability. However, existing mutual assistance mechanisms face three key problems: First, traditional economic dispatch models prioritize cost minimization and fail to adequately consider frequency stability constraints, resulting in lower grid security during power exchange. Second, under the electricity market architecture, renewable energy bases participate in power exchange as independent entities; however, the current pricing mechanism for multi-entity power mutual assistance is inadequate, failing to fully consider the distribution of benefits among participants, thus reducing their participation enthusiasm and ultimately impacting the overall power exchange volume within the renewable energy base cluster, thereby reducing the overall frequency response capability of the cluster.

[0033] Please refer to Figure 1 To address the problem in existing technologies of how to improve the overall frequency response capability of a new energy base cluster while ensuring power supply stability and frequency security, this application provides a multi-new energy base interactive control method, including S101 to S104, specifically as follows: S101: Collect the power interaction relationships between each new energy base and each load center, as well as between each of the energy bases. Based on all the power interaction relationships, construct a cooperative game model by embedding frequency stability constraints. The frequency stability constraints are constructed and obtained by analyzing a preset system frequency response model.

[0034] Preferably, in some embodiments of this application, the power interaction relationship can be understood as which new energy bases can interact with which load centers, and which new energy bases can interact with each other.

[0035] Furthermore, in some embodiments of this application, the step of constructing the frequency stability constraint includes: Based on virtual synchronization technology, a virtual inertia model for each new energy power station is constructed, and based on each virtual inertia model, a system frequency response model is constructed. The input to the system frequency response model is set to a step disturbance with a fixed amplitude, and the frequency response model is analyzed in the time domain using the complex frequency domain analysis method to obtain the frequency fluctuation function. The frequency fluctuation function is analyzed to obtain first function expressions corresponding to several safety indicators, and the frequency stability constraints are constructed based on each of the first function expressions; wherein, the safety indicators include: maximum frequency change rate, maximum frequency deviation, and steady-state frequency deviation.

[0036] Preferably, in some embodiments of this application, the step of constructing a virtual inertia model for each new energy power station based on virtual synchronization technology, and constructing the system frequency response model according to each virtual inertia model, includes S1011 to S1012: wherein, the system frequency response model is obtained by constructing a system frequency response model that considers the frequency safety indicators such as the maximum frequency change rate, the maximum frequency deviation, and the steady-state frequency deviation, taking into account the frequency regulation characteristics of new energy sources. See [specific reference] for details. Figure 2 The frequency response model framework diagram is shown below.

[0037] S1011: Based on virtual synchronous control technology, simulate the inertia characteristics of synchronous units and construct virtual inertia models for offshore wind farms, onshore photovoltaic power stations, and energy storage power stations respectively.

[0038] S1012: Construct a system frequency response model by combining the inertia support and primary frequency regulation characteristics of thermal power plants, wind farms, photovoltaic power plants, and energy storage power plants.

[0039] Specifically, in S1011, based on the law of conservation of energy, a virtual inertia model can be derived that includes the virtual inertia of offshore wind farms, onshore photovoltaic power stations, energy storage power stations, and the synchronous inertia of thermal power plants, as shown in the following equation (1): (1) in, , , These represent the virtual inertial time constants of wind farms, photovoltaic power stations, and energy storage power stations, respectively. This represents the inertial time constant of a thermal power plant; This represents the system's equivalent inertial time constant. , , , These represent the rated power of wind farms, photovoltaic power stations, energy storage power stations, and thermal power plants, respectively. This indicates the total capacity of the new energy base; , , , These represent the equivalent rotational kinetic energy of wind farms, photovoltaic power stations, energy storage power stations, and thermal power plants, respectively.

[0040] Specifically, in S1012, the complex frequency domain model of the system dynamic frequency response considering the inertia support and primary frequency regulation characteristics of thermal power plants, wind farms, photovoltaic power stations and energy storage power stations is shown in the following equation (2): (2) in, Indicates system frequency deviation; Indicates the system damping coefficient; , , , These represent the response power of thermal power plants, wind farms, photovoltaic power stations, and energy storage power stations, respectively. As a system disturbance power, it reflects the prediction deviation of new energy output; is the complex frequency variable in the Laplace transform.

[0041] The response power of thermal power plants, wind farms, photovoltaic power stations and energy storage power stations is proportional to the frequency deviation. The expression for the response power is as follows (3): (3) in, , , These represent the percentage of the rated power of wind farms, photovoltaic power stations, and energy storage power stations in the total system capacity, respectively. , , This represents the primary frequency regulation coefficient of wind farms, photovoltaic power stations, and energy storage power stations. It can be understood that formulas (2) and (3) together form the system frequency response model.

[0042] Preferably, in some embodiments of this application, after obtaining the system frequency response model, the method further includes: deriving the frequency response transfer function with disturbance power as input and frequency fluctuation as output based on the system frequency response model; specifically, according to the definition of the transfer function, the frequency response transfer function is derived with disturbance power as input and frequency fluctuation as output. Input and system frequency deviation Transfer function for the output frequency response Equal to disturbance power Divide by frequency deviation Substituting equations (1) and (3) into equation (2) and rearranging, we can obtain the frequency response transfer function, as shown in the following equations (4) and (5): (4) (5) In the formula, This represents the frequency regulation coefficient of the governor in a thermal power plant; Indicates the reheat time constant; This indicates the proportion of high-pressure boilers; and These represent the natural frequency and damping ratio of the generalized second-order model, respectively. It represents the negative number of the zeros of the generalized second-order model.

[0043] Preferably, in some embodiments of this application, the input to the system frequency response model is set to a step disturbance with a fixed amplitude, and the frequency response model is analyzed in the time domain using the complex frequency domain analysis method to obtain the frequency fluctuation function, as shown in the following formulas (6) and (7): (6) (7) In the formula, Indicates the change in frequency; Indicates intermediate variables; Indicates the oscillation frequency; Indicates additional phase; Frequency response time; This is the phase angle.

[0044] Preferably, in some embodiments of this application, the step of parsing the frequency fluctuation function to obtain first function expressions corresponding to several safety indicators, and constructing the frequency stability constraints based on each of the first function expressions, includes: based on the definitions of frequency safety indicators such as maximum frequency change rate, maximum frequency deviation, and steady-state frequency deviation, it can be known that the initial moment when the maximum frequency change rate occurs is known, the maximum frequency deviation is obtained at the frequency extreme point, and the steady-state frequency deviation can be obtained by taking the limit of the frequency change amount. Therefore, through the frequency fluctuation function shown in formulas (6) and (7), the first function expressions corresponding to the maximum frequency change rate, maximum frequency deviation, and steady-state frequency deviation can be further obtained, as shown in the following formulas (8) to (10): (8) (9) (10) in, , , These represent the maximum rate of frequency change, the maximum frequency deviation, and the steady-state frequency deviation, respectively. This indicates the moment when the maximum frequency deviation occurs. Once the expressions for each of the first functions are obtained, the corresponding constraints can be determined based on the fluctuation range of each safety index, thus obtaining the frequency stability constraints.

[0045] This application constructs virtual inertia models for each new energy power station based on virtual synchronization technology, and establishes a system frequency response model accordingly, thus providing a clear physical basis for frequency stability constraints. By applying a step disturbance to this model and analyzing the frequency fluctuation function using complex frequency domain analysis, the dynamic frequency characteristics of the system under disturbance can be quantitatively reflected. Furthermore, by extracting key safety indicators such as the maximum rate of frequency change, maximum frequency deviation, and steady-state deviation from the frequency fluctuation function, frequency stability constraints are constructed, directly linking the frequency constraints to the power output characteristics of new energy sources. This allows for real-time reflection of the system's dynamic safety margin during power interaction optimization and enables early identification of interaction strategies that may lead to frequency instability during the scheduling phase. This suppresses system oscillations caused by power fluctuations at the source, significantly improving frequency stability and system security under the coordinated operation of multiple new energy bases.

[0046] Furthermore, in some embodiments of this application, the step of constructing a cooperative game model based on all the stated electrical energy interaction relationships by embedding frequency stability constraints includes: Based on the power interaction relationship between each new energy base and each load center, as well as between each of the energy bases, a first optimization objective function is constructed to maximize the operating revenue of all the new energy bases. Based on the power interaction relationship between each new energy base and each load center, a second optimization objective function is constructed to minimize the power purchase cost of all the load centers. The cooperative game model is constructed based on the frequency stability constraint, the first optimization objective function, and the second optimization objective function.

[0047] Preferably, in some embodiments of this application, reference is made to Figure 3 Before constructing the cooperative game model, a cooperative alliance consisting of multiple new energy bases and multiple load centers is first constructed. Under the cooperative operation framework of the alliance, the power interaction relationship between the new energy bases and the load centers is defined as being carried out by DC power transmission, and the power interaction relationship between each new energy base is defined as being carried out by mutual assistance of DC power. The defined power interaction relationship is then characterized by the subsequent cooperative game model.

[0048] Preferably, in some embodiments of this application, the mathematical expression of the first optimization objective function is as shown in formulas (11) and (12), specifically: (11) (12) in, Let i represent the operating profit of base i, and formula (11) indicates that the operating profit needs to be maximized. This represents the operating revenue of base i; The operating cost of the units at base i; Transmission costs between base i and other bases; Transmission costs between base station i and load center; These represent the power exchange costs between base i and other bases, respectively. Indicates the number of new energy bases; This represents a scheduling cycle; Indicates the scheduling time; Let represent the energy exchange prices between base i and load center at time t, respectively; This represents the electrical energy delivered by base i to the load center at time t; The unit transmission cost of electrical energy; Let the mutual support power between base i and base j be denoted as . Let be the electricity exchange price between base i and base j.

[0049] Preferably, in some embodiments of this application, the mathematical expression of the second optimization objective function is as shown in formula (13), specifically: (13) in, Let represent the electricity purchase cost of the load center, and formula (13) represents minimizing this electricity purchase cost.

[0050] Specifically, the operating cost of the base's units mentioned in formula (11) includes the fuel combustion cost of the thermal power units and the battery charging and discharging cost of the energy storage system. Based on the operating characteristics of the thermal power units, the fuel combustion cost is positively correlated with their electrical power output. The battery charging and discharging cost of the energy storage system has a linear relationship with the amount of electricity stored within a scheduling cycle. Therefore, the calculation expression for the base's unit operating cost is shown in the following formula (14): (14) in, This refers to the electrical power output of the thermal power unit. To improve the operating efficiency of thermal power units; It represents the lower heating value of coal; For coal prices; and , respectively, represent the charging and discharging power of the battery at time t; Cost per unit power of battery charge / discharge.

[0051] Preferably, in some embodiments of this application, the frequency stability constraints obtained based on each first function expression are specifically as shown in the following formula (15): (15) In the formula, , , These represent the maximum allowable frequency deviation, maximum frequency change rate, and upper limit of steady-state frequency deviation for the new energy base, respectively. It is understandable that substituting formulas (8) to (10) into formula (15) will yield the frequency stability constraint.

[0052] This application constructs a cooperative game model that simultaneously incorporates the operating revenue of new energy bases and the electricity purchase cost of load centers. It employs a first optimization objective function aimed at maximizing the revenue of the new energy base cluster, and a second optimization objective function aimed at minimizing the electricity purchase cost of load centers. The two are solved jointly under frequency stability constraints, so that the optimization results take into account the economic benefits on both the supply and demand sides and the system's operational safety. This balances the distribution of benefits between each new energy base and the load center, and also forms a power complementarity mechanism between different bases. This encourages all parties to participate in the interaction of electricity during the game, thereby improving the overall operating efficiency of the cluster and enhancing the system's ability to withstand large-scale power fluctuations.

[0053] S102: Based on Nash bargaining theory and combined with the cooperative game model, construct a Nash bargaining model; wherein, the Nash bargaining model includes: a first sub-model for optimizing the amount of electricity exchange and a second sub-model for optimizing the unit price of electricity exchange.

[0054] Furthermore, in some embodiments of this application, the construction of the Nash bargaining model based on Nash bargaining theory and combined with the cooperative game model includes: Based on Nash bargaining theory, a basic Nash bargaining model is constructed by quantifying the changes in interests of each new energy base and each load center and maximizing the changes in interests. The cooperative game model is simplified by merging the first optimization objective function and the second optimization objective function, and the simplified cooperative game model is substituted into the basic Nash bargaining model to obtain the first sub-model; After setting the optimization objective variable of the first sub-model in the basic Nash bargaining model to a constant, and then transforming it into a convex function through logarithmic transformation, the second sub-model is obtained.

[0055] Preferably, in some embodiments of this application, the mathematical expression of the basic Nash bargaining model is specifically as follows: (16) in, and This can be represented as the point of breakdown in negotiations between load centers and new energy bases in Nash bargaining, and is usually taken as the interest of the former two in a cooperative game. and This represents the increase in benefits for the load center and the new energy base after the cooperative game.

[0056] Furthermore, it needs to be proven that the basic Nash bargaining model corresponding to formula (16) has a maximum value. Therefore, by scaling formula (16) using the mean inequality, we can obtain formula (17): (17) Among them, when When the equality holds, the interests of all parties can increase in an equal manner, thus satisfying Pareto optimality. Therefore, the basic Nash bargaining model has a maximum value.

[0057] Preferably, in some embodiments of the application, the step of simplifying the cooperative game model by merging the first optimization objective function and the second optimization objective function, and substituting the simplified cooperative game model into the basic Nash bargaining model to obtain the first sub-model, includes: As can be seen from formula 17, since and Since all variables are constants, finding the maximum value in the basic Nash bargaining model is equivalent to finding the maximum value in the coalition interest. Therefore, by substituting formulas (11) and (13) into the above formula for simplification, we can obtain the first sub-model as shown in formula (18): (18) Preferably, in some embodiments of this application, the process of obtaining the second sub-model includes: when solving for the sum of the interests of each subject, the interaction costs between the subjects can be offset against each other. Therefore, equation (18) can only obtain the optimal energy interaction amount of each subject. It is necessary to substitute it into equation (16) to further solve for the optimal energy interaction price between the subjects, so as to obtain the Nash bargaining form of the second sub-model, as shown in equation (19): (19) in, , , This represents the optimal value obtained by solving the first sub-model.

[0058] Since the logarithmic function is a strictly convex function, the product form of equation (19) can be transformed into a convex function through logarithmic transformation, and the transformed function is shown in equation (20). Equation (20) reflects the optimal allocation of interests among the subjects, and it is defined as the second sub-model. By solving the second sub-model, the optimal interaction price among the subjects can be obtained.

[0059] (20) This application, based on Nash bargaining theory, further transforms the cooperative game model into a quantifiable Nash bargaining model. By quantifying the changes in the interests of each participant and maximizing their product, it ensures optimal overall interests and fair distribution under multi-participant conditions. Simultaneously, by merging the bi-objective functions in the cooperative game model and substituting them into the basic Nash bargaining model, a first sub-model for optimizing energy interaction quantities is obtained. This model is then transformed into a convex function form through logarithmic transformation, significantly reducing solution complexity, improving global convergence, and ultimately enhancing the response speed in the energy interaction control process.

[0060] S103: The augmented Lagrangian functions corresponding to the first sub-model and the second sub-model are solved iteratively by alternating direction multiplier algorithm to obtain the power interaction scheme between each new energy base and each load center, as well as between each new energy base; wherein, the first sub-model and the second sub-model update the penalty factor in their respective augmented Lagrangian functions according to their respective residual values ​​during each iteration.

[0061] Preferably, in some embodiments of this application, the penalty factor in the augmented Lagrangian function is updated using the following formula (21): (twenty one) In the formula, Indicates the number of iterations; Let represent the penalty factor for the d-th iteration; and Let represent the original residual and the dual residual of the (d+1)th iteration; Scaling factor This is the proportionality coefficient; It uses the L2 norm. In each iteration, this application dynamically updates the penalty factor based on the changes in the original residual and the dual residual, thereby reducing the impact of the initial value of the penalty factor on the convergence of the algorithm.

[0062] Furthermore, in some embodiments of this application, the step of iteratively solving for the augmented Lagrangian functions corresponding to the first sub-model and the second sub-model using the alternating direction multiplier algorithm includes: The first augmented Lagrangian function corresponding to the first sub-model is initialized by the initial value of the energy interaction quantity and iteratively solved until the preset first iteration termination condition is met, so as to obtain the optimal energy interaction quantity. The optimal energy interaction unit price is obtained by initializing the energy interaction unit price and iteratively solving the second augmented Lagrangian function corresponding to the second sub-model until the preset second iteration termination condition is met.

[0063] Specifically, refer to Figure 4The flowchart shown is for solving the Nash bargaining model. Subproblem 1 represents the problem solved by the first sub-model, and subproblem 2 represents the problem solved by the second sub-model. When the amount of electrical energy interaction is maximized, the overall benefit of the cooperative alliance is maximized, that is, the alliance benefit is maximized. When the unit price of electrical energy interaction is maximized, the individual distribution of benefits among the participants in the cooperative alliance is optimal, that is, the optimal distribution of benefits is achieved.

[0064] This application employs an alternating direction multiplier algorithm to iteratively solve the two augmented Lagrangian functions corresponding to the energy interaction quantity and interaction unit price, achieving a combination of distributed optimization and efficient convergence. By using energy interaction quantity and interaction unit price as independent variables for alternating optimization, each subproblem can be solved independently, reducing the overall computational complexity. Furthermore, by setting termination conditions for the first and second iterations, it ensures that the next stage of optimization only begins after the subproblem converges, avoiding numerical oscillations caused by excessive iteration, thereby effectively shortening the solution time and improving the real-time performance of scheduling.

[0065] Furthermore, in some embodiments of this application, each iteration of solving the first augmented Lagrangian function corresponding to the first sub-model includes: Substitute the first energy interaction quantity obtained so far into the solution and solve the first augmented Lagrangian function to obtain the second energy interaction quantity required for the next iteration. Based on the first electrical energy interaction quantity, the currently obtained first penalty factor, and the currently obtained first Lagrange multiplier, determine the second Lagrange multiplier required for the next iteration; wherein, the first penalty factor is obtained by updating based on the currently obtained first residual value; Based on the second electrical energy interaction quantity, determine the second residual value required for the next iteration solution.

[0066] Preferably, in some embodiments of this application, before solving the augmented Lagrangian function corresponding to the first sub-model, since the energy interaction quantities among the entities in the cooperative alliance are coupled variables, they satisfy the coupling relationship of equal purchase and sales quantities. To achieve distributed solving, auxiliary variables need to be introduced to decouple them. The relationship between the auxiliary variables and the coupled variables is as follows: (twenty two) In the formula, and This serves as an auxiliary variable for the energy interaction between the new energy base i and the load center. This indicates the amount of electricity purchased by the load center from the new energy base i. This represents the electrical energy sold by the new energy base i to the load center; This is an auxiliary variable for the electrical energy interaction between new energy bases i and j.

[0067] Furthermore, by calculating the augmented Lagrangian functions of the load center and the new energy base with respect to the first sub-model online, the electrical energy interaction quantities between each new energy base and between each base and the load center are updated. The augmented Lagrangian functions of the load center and the new energy base with respect to the first sub-model are shown in Equation (23), which are used to update the electrical energy interaction quantities between each new energy base and the load center, as well as between new energy bases. The update equation for the electrical energy interaction quantities is shown in Equation (24), the calculation formulas for the original residual and the dual residual are shown in Equation (25), the iterative formula for the Lagrange multiplier is shown in Equation (26), and the update equation for the penalty factor is consistent with Equation (21).

[0068] (twenty three) (twenty four) (25) (26) In the formula, Indicates the first The Lagrange multiplier of the electrical energy interaction between the new energy base i and the load center in the next iteration; Indicates the first The Lagrange multiplier of the electrical energy interaction between new energy bases in the next iteration; , , Let i and j represent the augmented Lagrangian functions of the load center, new energy base i, and new energy base j with respect to the first sub-model, respectively. and Let be the original residual and dual residual of the augmented Lagrangian function at iteration d+1 corresponding to the first sub-model.

[0069] In solving the first sub-model, this application introduces an adaptive update mechanism based on residual feedback, using a penalty factor and Lagrange multipliers, to achieve dynamic convergence control of the energy interaction optimization process. By determining the initial value for the next round of optimization in each iteration based on the current energy interaction, penalty factor, and Lagrange multipliers, the solution direction can be corrected in real time, enabling the algorithm to quickly approach the global optimum. When the residual is large, the penalty factor is automatically increased to strengthen constraint convergence; when the residual approaches zero, the penalty factor is decreased to avoid numerical oscillations. This mechanism effectively overcomes the problem of traditional algorithms with fixed penalty parameters easily getting stuck in oscillations or slow convergence, making the optimization of energy interaction more stable and accurate. Through this dynamic update mechanism, the system can maintain a stable iterative process under complex power fluctuations, thereby improving the coordination and robustness of power allocation among multiple new energy bases.

[0070] Furthermore, in some embodiments of this application, each iteration of solving the second augmented Lagrangian function corresponding to the second sub-model includes: Substitute the currently obtained first energy interaction unit price into the solution and solve for the second augmented Lagrangian function to obtain the second energy interaction unit price required for the next iteration. Based on the first energy interaction unit price, the currently obtained third penalty factor, and the currently obtained third Lagrange multiplier, determine the third Lagrange multiplier required for the next iteration; wherein, the third penalty factor is obtained by updating based on the currently obtained third residual value; Based on the second energy interaction unit price, determine the fourth residual value required for the next iteration.

[0071] Preferably, in some embodiments of this application, before solving the augmented Lagrangian function corresponding to the second sub-model, since the electricity exchange prices among the entities in the cooperative alliance are coupled variables, satisfying the coupling relationship that the electricity purchase price is equal to the electricity sales price, an auxiliary variable needs to be introduced to decouple it in order to achieve distributed solution. The relationship between the auxiliary variable and the coupled variable is as follows: (27) In the formula, and This serves as an auxiliary variable for the electricity exchange price between the new energy base i and the load center. This indicates the electricity purchase price from the load center to the new energy base i. This indicates the electricity price sold from the new energy base i to the load center; This serves as an auxiliary variable for the electricity interaction price between new energy bases i and j.

[0072] Furthermore, by calculating the augmented Lagrangian functions of the load center and the new energy base with respect to the second sub-model online, the electricity interaction prices between each new energy base and between each base and the load center are updated. The augmented Lagrangian functions of the load center and the new energy base with respect to the second sub-model are shown in Equation (23), which are used to update the electricity interaction prices between each new energy base and the load center, as well as between new energy bases. The update equation for the interaction price is shown in Equation (29), the calculation formulas for the original residual and the dual residual are shown in Equation (30), the iterative formula for the Lagrange multiplier is shown in Equation (31), and the update equation for the penalty factor is consistent with Equation (21).

[0073] (28) (29) (30) (31) In the formula, Indicates the first The Lagrange multiplier of the interactive electricity price between the new energy base and the load center in the next iteration; Indicates the first The Lagrange multiplier of the interactive electricity price between new energy bases in the next iteration; , , Let i and j represent the augmented Lagrangian functions of the load center, new energy base i, and new energy base j with respect to the second sub-model, respectively. and These are the original and dual residuals of the augmented Lagrangian function at iteration d+1 corresponding to the second sub-model.

[0074] This application employs an adaptive penalty and residual update mechanism similar to that used in interaction quantity optimization during the solution of the second sub-model, achieving stable convergence of the energy interaction unit price optimization. By updating the parameters for the next round using the current interaction unit price, penalty factor, and Lagrange multipliers in each iteration, the algorithm convergence is accelerated while maintaining economic optimality. When the residual is too large, the penalty factor is automatically adjusted to increase the constraint strength, allowing the price optimization process to approach the equilibrium point more quickly; when the residual converges, the penalty strength is reduced to maintain numerical stability, thereby improving market coordination efficiency and enhancing the economic sustainability of the system's frequency response.

[0075] S104: According to the power interaction scheme, control each of the new energy bases and each of the load centers to perform power interaction.

[0076] Specifically, refer to Figure 5 The improved method in the embodiments of this application is used to solve the convergence result of the Nash bargaining model. Figure 6 To solve the Nash bargaining model using traditional algorithms, the traditional algorithm approaches the optimal value after approximately 90 iterations and meets the convergence accuracy requirement after 134 iterations. In contrast, the improved method in this embodiment only requires 53 iterations to meet the convergence accuracy requirement for all new energy bases. The improved convergence speed is due to the fact that the improved method in this embodiment can rapidly change its penalty factor during iteration based on the convergence of the original residual and the dual residual, thereby reducing the dependence on the initial value of the penalty factor. Therefore, the improved method proposed in this embodiment can effectively solve the DC power mutual assistance problem for multiple new energy bases, and it has a significant advantage in convergence performance compared to traditional algorithms.

[0077] In summary, the multi-new energy base interactive control method provided in this application has the following advantages compared with the prior art: By acquiring the power interaction relationship between each new energy base and the load center and other new energy bases, and embedding frequency stability constraints to construct a cooperative game model, the power fluctuation of the multi-base cluster is effectively linked to the system frequency stability, realizing a systematic constraint on frequency security and improving the stability and security of the power supply process of the new energy bases. On this basis, a twin model design based on Nash bargaining theory is introduced to optimize the power interaction quantity and power interaction unit price respectively. While taking into account the power complementarity and benefit distribution among the new energy bases, it also maximizes the power interaction quantity of the multi-new energy base cluster, thereby effectively improving the frequency response capability of the new energy base cluster and solving the problem of the imperfection of the existing multi-entity power mutual assistance pricing mechanism. At the same time, by iteratively solving the augmented Lagrangian function through the alternating direction multiplier algorithm, and dynamically updating the penalty factor according to the residual value during the iteration process, each sub-model can quickly converge and obtain the optimal power interaction scheme between each new energy base and the load center and between bases, thereby improving the frequency response speed of the multi-new energy base cluster.

[0078] like Figure 7 As shown, based on the above-mentioned method embodiments, an embodiment of this application provides a multi-new energy base interactive control device, including: a cooperative game model construction module 201, a Nash bargaining model construction module 202, an energy interaction scheme solving module 203, and an energy interaction control module 204.

[0079] Further, in some embodiments of this application, the cooperative game model construction module 201 is used to collect the power interaction relationships between each new energy base and each load center, as well as between each energy base, and to construct a cooperative game model based on all the power interaction relationships by embedding frequency stability constraints; wherein, the frequency stability constraints are constructed and obtained by analyzing a preset system frequency response model; the Nash bargaining model construction module 202 is used to construct a Nash bargaining model based on Nash bargaining theory and combined with the cooperative game model; wherein, the Nash bargaining model includes: a first sub-model for optimizing the power interaction quantity and a sub-model for optimizing the power interaction unit price. The second sub-model; the power interaction scheme solution module 203 is used to iteratively solve the augmented Lagrangian functions corresponding to the first sub-model and the second sub-model through an alternating direction multiplier algorithm to obtain the power interaction schemes between each new energy base and each load center, as well as between each new energy base; wherein, in each iteration of the solution process, the first sub-model and the second sub-model update the penalty factor in their respective augmented Lagrangian functions according to their respective residual values; the power interaction control module 204 is used to control each new energy base and each load center to perform power interaction according to the power interaction scheme.

[0080] Furthermore, in some embodiments of this application, the step of constructing the frequency stability constraint includes: constructing a virtual inertia model for each new energy power station based on virtual synchronization technology, and constructing the system frequency response model according to each virtual inertia model; setting the input of the system frequency response model as a step disturbance with a fixed amplitude, and performing time-domain analysis on the frequency response model using complex frequency domain analysis to obtain a frequency fluctuation function; analyzing the frequency fluctuation function to obtain first function expressions corresponding to several safety indicators, and constructing the frequency stability constraint according to each first function expression; wherein, the safety indicators include: maximum frequency change rate, maximum frequency deviation, and steady-state frequency deviation.

[0081] Further, in some embodiments of this application, the cooperative game model construction module 201 includes: a first optimization objective function construction unit, a second optimization objective function construction unit, and a model construction unit; the cooperative game model construction module 201 is used to construct a cooperative game model based on all the power interaction relationships by embedding frequency stability constraints, including: the first optimization objective function construction unit is used to construct a first optimization objective function that maximizes the operating revenue of all the new energy bases based on the power interaction relationships between each new energy base and each load center and between each of the energy bases; the second optimization objective function construction unit is used to construct a second optimization objective function that minimizes the electricity purchase cost of all the load centers based on the power interaction relationships between each new energy base and each load center; the model construction unit is used to construct the cooperative game model based on the frequency stability constraints, the first optimization objective function, and the second optimization objective function.

[0082] Further, in some embodiments of this application, the Nash bargaining model construction module 202 includes: a Nash bargaining basic model construction unit, a first sub-model construction unit, and a second sub-model construction unit; the Nash bargaining model construction module 202 is used to construct a Nash bargaining model based on Nash bargaining theory and combined with the cooperative game model, including: the Nash bargaining basic model construction unit is used to construct a Nash bargaining basic model by quantifying the change in interests of each of the new energy bases and each of the load centers and maximizing the change in interests of each; the first sub-model construction unit is used to simplify the cooperative game model by merging the first optimization objective function and the second optimization objective function, and substitute the simplified cooperative game model into the Nash bargaining basic model to obtain the first sub-model; the second sub-model construction unit is used to set the optimization objective variable of the first sub-model in the Nash bargaining basic model as a constant, and then transform it into a convex function through logarithmic transformation to obtain the second sub-model.

[0083] Further, in some embodiments of this application, the energy interaction scheme solving module 203 includes: a first iteration unit and a second iteration unit; the energy interaction scheme solving module 203 is used to iteratively solve the augmented Lagrangian functions corresponding to the first sub-model and the second sub-model respectively through an alternating direction multiplier algorithm, including: the first iteration unit is used to initialize and iteratively solve the first augmented Lagrangian function corresponding to the first sub-model with an initial value of energy interaction quantity until a preset first iteration termination condition is met to obtain the optimal energy interaction quantity; the second iteration unit is used to initialize and iteratively solve the second augmented Lagrangian function corresponding to the second sub-model with an initial value of energy interaction unit price until a preset second iteration termination condition is met to obtain the optimal energy interaction unit price.

[0084] Furthermore, in some embodiments of this application, each iteration of solving the first augmented Lagrange function corresponding to the first sub-model includes: substituting the currently obtained first energy interaction quantity into and solving the first augmented Lagrange function to obtain the second energy interaction quantity required for the next iteration; determining the second Lagrange multiplier required for the next iteration based on the first energy interaction quantity, the currently obtained first penalty factor, and the currently obtained first Lagrange multiplier; wherein the first penalty factor is updated based on the currently obtained first residual value; and determining the second residual value required for the next iteration based on the second energy interaction quantity.

[0085] Furthermore, in some embodiments of this application, each iteration of solving the second augmented Lagrange function corresponding to the second sub-model includes: substituting the currently obtained first energy interaction unit price into and solving the second augmented Lagrange function to obtain the second energy interaction unit price required for the next iteration; determining the third Lagrange multiplier required for the next iteration based on the first energy interaction unit price, the currently obtained third penalty factor, and the currently obtained third Lagrange multiplier; wherein the third penalty factor is obtained by updating based on the currently obtained third residual value; and determining the fourth residual value required for the next iteration based on the second energy interaction unit price.

[0086] In summary, the multi-new energy base interactive control device provided in this application has the following advantages compared with the prior art: By acquiring the power interaction relationship between each new energy base and the load center and other new energy bases, and embedding frequency stability constraints to construct a cooperative game model, the power fluctuation of the multi-base cluster is effectively linked to the system frequency stability, realizing a systematic constraint on frequency security and improving the stability and security of the power supply process of the new energy bases. On this basis, a twin model design based on Nash bargaining theory is introduced to optimize the power interaction quantity and power interaction unit price respectively. While taking into account the power complementarity and benefit distribution among the new energy bases, it also maximizes the power interaction quantity of the multi-new energy base cluster, thereby effectively improving the frequency response capability of the new energy base cluster and solving the problem of the imperfection of the existing multi-entity power mutual assistance pricing mechanism. At the same time, by iteratively solving the augmented Lagrangian function through the alternating direction multiplier algorithm, and dynamically updating the penalty factor according to the residual value during the iteration process, each sub-model can quickly converge and obtain the optimal power interaction scheme between each new energy base and the load center and between bases, thereby improving the frequency response speed of the multi-new energy base cluster.

[0087] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can realize the multi-new energy base interactive control method provided by any of the above-described method embodiments of this application.

[0088] 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 in this application, 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.

[0089] Based on the above embodiments of the multi-new energy base interactive control method, another embodiment of this application 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 the multi-new energy base interactive control method of any embodiment of this application.

[0090] 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 this application. The one or more module units 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.

[0091] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0092] 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 various parts of the terminal device via various interfaces and lines.

[0093] Based on the above-described method embodiments, another embodiment of this application 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 the multi-new energy base interactive control method described in any of the above-described method embodiments of this application.

[0094] 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-described embodiments 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.

Claims

1. A method for interactive control of multiple new energy bases, characterized in that, include: The power interaction relationships between each new energy base and each load center, as well as between each of the energy bases, are collected. Based on all the power interaction relationships, a cooperative game model is constructed by embedding frequency stability constraints. The frequency stability constraints are constructed and obtained by analyzing a preset system frequency response model. Based on Nash bargaining theory and combined with the aforementioned cooperative game model, a Nash bargaining model is constructed; wherein, the Nash bargaining model includes: a first sub-model for optimizing the amount of electricity exchange and a second sub-model for optimizing the unit price of electricity exchange; The augmented Lagrangian functions corresponding to the first sub-model and the second sub-model are solved iteratively by alternating direction multiplier algorithm to obtain the power interaction scheme between each new energy base and each load center, as well as between each new energy base; wherein, in each iteration, the first sub-model and the second sub-model update the penalty factor in their respective augmented Lagrangian functions according to their respective residual values. According to the aforementioned power interaction scheme, each of the new energy bases and each of the load centers is controlled to perform power interaction.

2. The multi-new energy base interactive control method as described in claim 1, characterized in that, The steps for constructing the frequency stability constraints include: Based on virtual synchronization technology, a virtual inertia model for each new energy power station is constructed, and based on each virtual inertia model, a system frequency response model is constructed. The input to the system frequency response model is set to a step disturbance with a fixed amplitude, and the frequency response model is analyzed in the time domain using the complex frequency domain analysis method to obtain the frequency fluctuation function. The frequency fluctuation function is analyzed to obtain first function expressions corresponding to several safety indicators, and the frequency stability constraints are constructed based on each of the first function expressions; wherein, the safety indicators include: maximum frequency change rate, maximum frequency deviation, and steady-state frequency deviation.

3. The multi-new energy base interactive control method as described in claim 1, characterized in that, The step of constructing a cooperative game model based on all the aforementioned electrical energy interaction relationships, by embedding frequency stability constraints, includes: Based on the power interaction relationship between each new energy base and each load center, as well as between each of the energy bases, a first optimization objective function is constructed to maximize the operating revenue of all the new energy bases. Based on the power interaction relationship between each new energy base and each load center, a second optimization objective function is constructed to minimize the power purchase cost of all the load centers. The cooperative game model is constructed based on the frequency stability constraint, the first optimization objective function, and the second optimization objective function.

4. The multi-new energy base interactive control method as described in claim 3, characterized in that, The Nash bargaining model, constructed based on Nash bargaining theory and combined with the cooperative game model, includes: Based on Nash bargaining theory, a basic Nash bargaining model is constructed by quantifying the changes in interests of each new energy base and each load center and maximizing the changes in interests. The cooperative game model is simplified by merging the first optimization objective function and the second optimization objective function, and the simplified cooperative game model is substituted into the basic Nash bargaining model to obtain the first sub-model; After setting the optimization objective variable of the first sub-model in the basic Nash bargaining model to a constant, and then transforming it into a convex function through logarithmic transformation, the second sub-model is obtained.

5. The multi-new energy base interactive control method as described in claim 1, characterized in that, The step of iteratively solving for the augmented Lagrangian functions corresponding to the first sub-model and the second sub-model using the alternating direction multiplier algorithm includes: The first augmented Lagrangian function corresponding to the first sub-model is initialized by the initial value of the energy interaction quantity and iteratively solved until the preset first iteration termination condition is met, so as to obtain the optimal energy interaction quantity. The optimal energy interaction unit price is obtained by initializing the energy interaction unit price and iteratively solving the second augmented Lagrangian function corresponding to the second sub-model until the preset second iteration termination condition is met.

6. The multi-new energy base interactive control method as described in claim 5, characterized in that, Each iteration of solving for the first augmented Lagrangian function corresponding to the first sub-model includes: Substitute the first energy interaction quantity obtained so far into the solution and solve the first augmented Lagrangian function to obtain the second energy interaction quantity required for the next iteration. Based on the first electrical energy interaction quantity, the currently obtained first penalty factor, and the currently obtained first Lagrange multiplier, determine the second Lagrange multiplier required for the next iteration; wherein, the first penalty factor is obtained by updating based on the currently obtained first residual value; Based on the second electrical energy interaction quantity, determine the second residual value required for the next iteration solution.

7. The multi-new energy base interactive control method as described in claim 5, characterized in that, Each iteration of solving for the second augmented Lagrangian function corresponding to the second sub-model includes: Substitute the currently obtained first energy interaction unit price into the solution and solve for the second augmented Lagrange function to obtain the second energy interaction unit price required for the next iteration. Based on the first energy interaction unit price, the currently obtained third penalty factor, and the currently obtained third Lagrange multiplier, determine the third Lagrange multiplier required for the next iteration; wherein, the third penalty factor is obtained by updating based on the currently obtained third residual value; Based on the second energy interaction unit price, determine the fourth residual value required for the next iteration.

8. A multi-energy base interactive control device, characterized in that, include: The module includes a cooperative game theory model construction module, a Nash bargaining model construction module, an energy interaction solution module, and an energy interaction control module. The cooperative game model construction module is used to collect the power interaction relationships between each new energy base and each load center, as well as between each energy base. Based on all the power interaction relationships, a cooperative game model is constructed by embedding frequency stability constraints. The frequency stability constraints are constructed and obtained by analyzing a preset system frequency response model. The Nash bargaining model construction module is used to construct a Nash bargaining model based on Nash bargaining theory and combined with the cooperative game model; wherein, the Nash bargaining model includes: a first sub-model for optimizing the amount of electricity interaction and a second sub-model for optimizing the unit price of electricity interaction; The power interaction scheme solution module is used to iteratively solve the augmented Lagrangian functions corresponding to the first sub-model and the second sub-model through an alternating direction multiplier algorithm to obtain the power interaction schemes between each new energy base and each load center, as well as between each new energy base; wherein, in each iteration, the first sub-model and the second sub-model update the penalty factor in their respective augmented Lagrangian functions according to their respective residual values. The power interaction control module is used to control each of the new energy bases and each of the load centers to perform power interaction according to the power interaction scheme.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a multi-new energy base interactive control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a multi-new energy base interactive control method as described in any one of claims 1 to 7.

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