Distributed resource optimal regulation and control method and system considering multi-factor uncertainty

Abstract

The invention relates to a distributed resource optimal regulation and control method and system considering multi-factor uncertainty, and belongs to the technical field of distributed resource regulation and control. The method and the system comprise an uncertainty evaluation model considering variable correlation, and the method comprises the following steps of: firstly, measuring a conditional probability density distribution function of an error between a point prediction value and an actual value of a variable on the premise of considering the variable correlation; then, the uncertainty range of the difference or sum of variables is measured through discrete convolution with complex correlation; and finally, a distributed robust optimal resource regulation and control strategy considering multi-factor uncertainty is provided, different game strategies are constructed for different distributed heterogeneous components, personal optimization is achieved through strategy communication and updating between game supply and demand bodies, and consistent optimization of local and overall target functions is ensured through a potential function.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed resource control, and relates to a distributed resource optimal control method and system taking into account the uncertainty of multiple factors. Background Art

[0002] In recent years, due to the use of a large amount of fossil fuels, environmental pollution problems have gradually worsened. Renewable energy has received greater attention due to its environmentally friendly characteristics and higher economic benefits. Renewable energy, including light, is driving the power system to shift to a model where renewable energy is the basis and traditional fossil fuels are the supplement, and it is gradually beginning to get rid of its dependence on traditional fossil fuels. However, with the large-scale access of renewable energy, its output characteristics depend on the output uncertainty caused by unstable meteorological factors (such as light, wind speed, etc.), which has become the main obstacle to the large-scale popularization of renewable energy. Researchers integrate distributed heterogeneous components such as renewable energy, energy storage systems, distributed dispatchable generators, and demand response plans into distribution networks to solve the above problems. These initiatives have led to the concept of microgrids, which is to provide power to local loads based on smaller-scale power grids, achieve high penetration of renewable energy access, and combine local renewable energy or distributed generation to reduce transmission losses and maximize output. Microgrids can be connected to the grid as part of the distribution network in traditional power grids, or they can be operated off-grid. However, the components of a microgrid have different distributed heterogeneous characteristics, and there are coupled or independent relationships between the distributed heterogeneous components. In addition, the uncertainty of renewable energy output and the uncertainty of demand for multiple loads need to be considered in the optimal regulation of distributed heterogeneous components, which brings obstacles to the optimal regulation of distributed microgrids. How to effectively manage distributed energy, energy storage systems and loads through optimized regulation and intelligent control technology to improve the performance, efficiency and flexibility of microgrids is the focus of microgrid research.

[0003] Game theory has been widely used in microgrid optimization scheduling due to its distributed control and optimization capabilities. By establishing the strategy model of each participant, the mutual influence between different strategies is analyzed. However, considering the multi-coupling characteristics of distributed resources, its convergence is very difficult to guarantee. Not every game has a Nash equilibrium, and there is a conflict between the optimal individual interests of some components and the optimal path of global interests. The potential game proposed by Monderer et al. provides a new idea for the optimization of distributed systems, that is, through the self-optimization of each distributed game supply and demand body member, it converges to a Nash equilibrium with limited improvement, and the utility functions of the game supply and demand body members are combined through the potential function to express the overall function of the system, showing its unique advantages in the multi-objective optimization scheduling of distributed resources. However, the game strategy of the game supply and demand body members in the existing work is formulated under the assumption that the output and supply of distributed resources are stable, ignoring the uncertainty of the supply and demand of distributed resources.

[0004] Copula theory, as an effective method for modeling nonlinear dependencies between various uncertainties, has been introduced into random dependency modeling in uncertainty analysis. Copula theory transforms random variables into a common uniform domain through a reversible cumulative distribution function, and uses the Copula function in this domain to model multivariate random dependencies. Gaussian Copula function has been applied in modeling the impact of wind power grid connection on system load in the Netherlands, laying the foundation for random correlation modeling of power systems. Summary of the invention

[0005] In view of this, the technical problem to be solved by the present invention is: in the optimal control of distributed resources, the present invention designs a distributed resource optimal control method and system that considers multi-factor uncertainty. In view of the impact of the complex uncertainty of distributed resources, the conditional probability distribution of the error between the point prediction value and the actual value is first measured under the premise of considering the correlation, and then the uncertainty range of the variable is measured by a discrete convolution method with a correlation measurement. And considering that in a distributed environment, the formulation of the objective functions between different devices may be inconsistent with each other and the overall system, resulting in inconsistency between the individual optimization path and the overall system optimization, and due to the different characteristics of distributed resources, the system may not reach the optimal state of convergence. A distributed optimal resource control method considering related uncertainty is proposed. In a distributed environment, different game strategy sets are constructed for different distributed heterogeneous resources, and the individual optimum is achieved through strategy communication and update optimization between the game supply and demand bodies, and the individual and the overall optimal consistency of the microgrid are guaranteed through the potential function.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A distributed resource optimal control method considering multi-factor uncertainty, the method comprising the following steps:

[0008] S1: Use the Copula function to measure the relationship between the predicted value and the actual value of the uncertain variable, and calculate the range of the prediction error of the uncertain variable;

[0009] S2: Using the Copula function and discrete convolution method, the problem that discrete convolution requires variable independence is solved by adding the Copula function that measures the correlation of different variables in the discrete convolution calculation. The uncertainty range of different coupling relationship variables is measured through the above steps.

[0010] S3: Using the potential game method with distributed characteristics and mapping relationships, the rationality of individual interests is constructed through utility functions and benefit functions, and the overall importance is expressed through potential functions;

[0011] S4: Based on the constructed game strategy set and the set priority sequence, the strategy iteration and optimization algorithm is completed using the local information of the game supply and demand body members and the strategies of the neighbors.

[0012] Further, in step S1, the Copula function is used to measure the relationship between the predicted value and the actual value of the uncertainty variable, and the range of the prediction error of the uncertainty variable is calculated, which specifically includes:

[0013] S11: The output in a distributed system is affected by many factors. Let the measured value X and the point prediction value Y of the uncertainty variable be solved for the reversible cumulative distribution function F of the measured value and the point prediction value. X (x) = P (x < x) and F Y (y) = P(Y<y), where if F X (x) and F Y (y) are considered as random variables, then they are all considered to be uniformly distributed:

[0014]

[0015] S12: Use the Copula function to construct the uniform marginal distribution of the measured values ​​and point prediction values ​​of the uncertainty variable into a multivariate cumulative distribution function F X,Y (x,y)=C(F X (x),F Y (y)), and rewrite the multivariate cumulative distribution function of the uncertain variable into the form of Copula function

[0016] S13: Use the Gaussian Copula function to obtain the joint probability density function f of the uncertain variables through the given point prediction data X,Y (x, y), the joint probability density function can be used to evaluate the performance of the prediction model and reveal the dependency between the actual value and the predicted value of the variable;

[0017] S14: Given a point prediction value, obtain the reversible cumulative distribution function of the actual output, and transform it to obtain the conditional probability density function of the uncertainty variable prediction error value. The conditional probability density function of the uncertainty variable prediction error is calculated by the actual output distribution and the Copula density function, where the Copula function contains the correlation between the prediction value and the actual value. Through the above formula, a method for quantifying the variable conditional prediction error using point prediction values ​​is provided.

[0018] Further, in step S2, it specifically includes:

[0019] S21: Taking the conditional probability function of the predicted variable error as input, calculate the joint probability distribution function f of the variables and X,Y(x,y), and rewrite it as a discrete convolution of the variables and;

[0020] S22: Considering the complex coupling relationship between variables, the Copula function is introduced to measure the complex correlation between the conditional probability density functions of the variables, and it is introduced into the convolution calculation of the difference or sum of the variables to solve the premise problem that the convolution requires the variables to be independent; through the Copula theory, it is rewritten into a related convolution form with a Copula function;

[0021] S23: Discretize the related convolution form with variable correlation into a discrete convolution form, which has a similar structure to the convolution formula, but has an additional Copula function to construct the dependency relationship between variables.

[0022] S24: According to the relationship between the sum or difference of the variables, the expansion of the relevant discrete convolution described in S23 is applied to calculate the variable prediction error distribution to measure the uncertainty of the complex coupling relationship between different uncertainty variables.

[0023] Further, in step S3, the following steps are specifically included:

[0024] S31: Determine the device members participating in resource regulation and collect input and output variables of different members existing in the system;

[0025] S32: Define the basic types of game supply and demand members in the system. The individuals participating in the game are not limited to the following basic individuals, but can also be any combination of these basic constituent individuals;

[0026] S33: Each member of the supply and demand entity participating in the distributed resource regulation has a corresponding profit function, that is, the profit generated by its strategy, as follows:

[0027]

[0028] S34: According to different members, various constraints of members participating in distributed resource regulation are converted into functions of game strategy concentration. The strategies of game supply and demand entities participating in the game mainly refer to the output and input demand range of each basic component, where P i is the strategy of the participants, and its strategy set is expressed as:

[0029] S35: Determine Γ=<N,{Y i} i∈N ,{U i} i∈N > is a strategic game with limited participants, where N = {1, 2, ..., n} is the number of participants in the game, Y i is the strategy set of participating member i, U irepresents the benefit / utility function of member i;

[0030] S36: The basis of the game model is to satisfy the utility and benefits of each player under the premise of satisfying the supply and demand balance of distributed resource regulation and control, and set balance constraints;

[0031] S37: Each member of the game supply and demand body in the game should be responsible for the supply and demand balance constraints of distributed resource regulation, but the strategy set goal of each member of the game supply and demand body is only to maximize its own utility function. The penalty function is introduced to deal with the global constraints of the game supply and demand body members:

[0032] Further, in step S4, it specifically includes:

[0033] S41: Determine the member constraint function, benefit function and utility function participating in the optimal regulation of distributed resources;

[0034] S42: Initialize optimization algorithm parameters, build individual game models of game supply and demand body members according to different constraints described in S2, and set priority queues in the game according to importance;

[0035] S43: Send a request to the previous game supply and demand body member in the priority queue; evaluate the game supply and demand body member's own strategy and select the best strategy; the game supply and demand body member updates the strategy and calculates the strategy change rate and sets the current convergence status;

[0036] S44: Through strategy iteration and penalty system constraints, all members of the game supply and demand body communicate with neighbor members to determine the current game status;

[0037] S45: Determine whether the supply and demand constraint balance is satisfied. If not, optimize by iteratively increasing the penalty coefficient. If satisfied, all members of the game supply and demand body retain the current strategy.

[0038] S46: Output the game strategy and optimization results ({P i,t}i=1,2,...,N; t=1,2,...,T).

[0039] The present invention also provides a distributed resource optimal control system taking into account the uncertainties of multiple factors.

[0040] The beneficial effects of the present invention are:

[0041] The present invention designs an optimal control method for distributed resources considering multi-factor uncertainty. In view of the fact that the complex coupling uncertainty of different distributed resources is not fully considered in a distributed environment, the uncertainty range of distributed resource variables is measured by fully considering the variables themselves and the complex coupling relationship between different resources, and a conditional probability density function of the prediction error is constructed. In view of the fact that its convergence is very difficult to ensure under the premise of considering the multi-coupling characteristics of distributed resources, not every game has a Nash equilibrium, and there is a conflict between the optimal individual interests of some components and the optimal global interest path, all distributed resource heterogeneous components are regarded as independent members of the game supply and demand body through the distributed potential game method, and the rationality of individual interests is constructed through utility functions and benefit functions, and the overall importance is expressed through potential functions. Finally, according to the constructed game strategy set and the set priority sequence, the local information of the game supply and demand body members and the strategies of neighbors are used to complete the strategy iteration and optimization method.

[0042] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0044] Figure 1 A diagram of a joint uncertainty assessment method considering variable correlation according to the present invention;

[0045] Figure 2 A distributed optimal resource control method diagram designed for the present invention taking into account multi-factor uncertainty;

[0046] Figure 3 A flow chart of a distributed optimal resource control method considering multi-factor uncertainty is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0048] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0049] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0050] like Figure 1 Shown is a diagram of a multi-factor uncertainty measurement approach.

[0051] in Figure 2 What is shown is a distributed resource optimal control method that takes into account multi-factor uncertainty.

[0052] Figure 3 The figure shows a distributed resource optimal control method process considering multi-factor uncertainty, including the following steps:

[0053] Step S1: Use the Copula function to measure the relationship between the predicted value and the actual value of the uncertain variable, and calculate the range of the prediction error of the uncertain variable.

[0054] Step S2: Using the Copula function and discrete convolution method, the problem that discrete convolution requires variable independence is solved by adding the Copula function that measures the correlation between different variables in the discrete convolution calculation. The uncertainty range of different coupling relationship variables is measured through the above steps.

[0055] Step S3: Use the uncertainty range of variables measured in steps S1 and S2 and the utility function and benefit function of distributed resources to construct different game strategies and use the potential game method with distributed characteristics and mapping relationships to construct the rationality of individual interests through utility functions and benefit functions, and express the overall importance through potential functions.

[0056] Step S4: According to the constructed game strategy set and the set priority sequence, the local information of the game supply and demand body members and the strategies of the neighbors are used to complete the strategy iteration and optimization algorithm.

[0057] Step S11: Let X and Y be random variables of measured values ​​and point prediction values, then the CDF of the distribution of measured values ​​and point prediction values ​​can be expressed as: F X (x) = P (x < x) and F Y (y) = P(Y<y), where if F X (x) and F Y (y) are considered as random variables, then they are all considered to be uniformly distributed:

[0058]

[0059] where u X It follows a uniform distribution in the interval [0,1].

[0060] Step S12: Copula function C can construct the uniform marginal distribution of the measured value and the point predicted value of the variable into a multivariate cumulative distribution function: F X,Y (x,y)=C(F X (x),F Y (y)). where F X,Y (x,y) is the multivariate cumulative distribution function of X,Y. It can be rewritten as

[0061] Step S13: Use the Gaussian Copula function to obtain the joint probability density function through the given point prediction data: f X,Y (x, y), the joint probability density function can evaluate the performance of the prediction model, reveal the dependency between the actual value and the predicted value, and provide important information for optimizing the energy management strategy. The details are as follows:

[0062]

[0063] Where c is the Copula density function of C, f X (x) and f Y (y) are the marginal probability density functions of X and Y respectively.

[0064]

[0065] Where N is N∈[-1,1] 2×2 is the correlation matrix of , and I is the identity matrix.

[0066] Step S14: Given a point prediction value y=r, the conditional probability density function f of the actual output can be obtained X|Y , that is, the conditional probability density function of the actual output value under the condition of the predicted value:

[0067]

[0068] The conditional probability density function of the prediction error value is obtained by transforming the above formula:

[0069]

[0070] Where E = XY, e = xy, e∈[-r, max(x)-r]. The above formula shows that the distribution of prediction errors can be calculated by the actual power output distribution and the Copula density function, where c(·) contains the correlation between variables. Through the above formula, a method is provided to quantify the conditional prediction error of the b variable using point prediction values.

[0071] Step S2: Using the Copula function and discrete convolution method, the problem that discrete convolution requires variable independence is solved by adding the Copula function that measures the correlation of different variables in the discrete convolution calculation, and the uncertainty range of different coupling relationship variables is measured through the above steps. Specifically, the following steps are included:

[0072] Step S21: Assume that X and Y are different sub-resources with marginal distribution functions, and the marginal distribution function f X (x) and f Y The joint probability distribution function f of (y) X,Y (x,y). The conditional probability density function of the sum X+Y can be expressed as f X+Y (x,y) is written as:

[0073]

[0074] Step S22: The convolution form of its discrete form is:

[0075]

[0076] Step S23: Considering the complex coupling relationship between variables, the present invention introduces the Copula function to measure the complex correlation between the conditional probability density function of the sub-resource prediction error and the conditional probability density function of the sub-resource prediction error, and introduces it into the convolution calculation of the sum or difference of the sub-resources to solve the premise problem that the convolution requires the variables to be independent. Discrete correlation convolution is as follows

[0077]

[0078] Step S24: Based on the modeling in steps S21 and S22, the probability distribution of the prediction error of the sum or difference of the probability sub-resources is calculated by applying the extension of the relevant discrete convolution as:

[0079]

[0080] Step S3: Use the uncertainty range of variables measured in steps S1 and S2 and the utility function and benefit function of distributed resources to construct different game strategies and use the potential game method with distributed characteristics and mapping relationships to construct the rationality of individual interests through utility functions and benefit functions, and express the overall importance through potential functions.

[0081] S31: Determine the device members participating in resource regulation and collect input and output variables of different members existing in the system.

[0082] S32: Define the basic types of game supply and demand members in the system. The individuals participating in the game are not limited to the following basic individuals, but can also be any combination of these basic constituent individuals: N = {X1, X2, ...X n1 ,Y1,Y2,...,Y n2 ,Z1,Z2,...,Z n3 ···}.

[0083] S33: Each member of the supply and demand entity participating in the distributed resource regulation has a corresponding profit function, that is, the profit generated by its strategy, as follows:

[0084]

[0085] S34: According to different members, various constraints of members participating in distributed resource regulation are converted into functions of game strategy concentration. The strategies of game supply and demand entities participating in the game mainly refer to the output and input demand range of each basic component, where P i is the strategy of the participants, and its strategy set is expressed as:

[0086] S35: Determine Γ=<N,{Y i} i∈N ,{Ui} i∈N > is a strategic game with limited participants, where N = {1, 2, ..., n} is the number of participants in the game, Y i is the strategy set of participating member i, U i Represents the payoff / utility function of member i.

[0087] S36: The basis of the game model is to satisfy the utility and benefits of each player under the premise of satisfying the supply and demand balance of distributed resource regulation, and set balance constraints:

[0088] S37: Each member of the game supply and demand body in the game should be responsible for the supply and demand balance constraints of distributed resource regulation, but the strategy set goal of each member of the game supply and demand body is only to maximize its own utility function. The penalty function is introduced to deal with the global constraints of the game supply and demand body members:

[0089] Step S4: Based on the constructed game strategy set and the set priority sequence, the local information of the game supply and demand body members and the strategies of the neighbors are used to complete the strategy iteration and optimization algorithm. Specifically, the following steps are included:

[0090] S41: Determine the member constraint function, benefit function and utility function involved in the optimal regulation of distributed resources.

[0091] S42: Initialize optimization algorithm parameters, construct individual game models of game supply and demand body members according to the different constraints described in S2, and set priority queues in the game according to importance.

[0092] S43: Send a request to the previous gaming supply and demand member in the priority queue. Evaluate the gaming supply and demand member's own strategy and select the best strategy: Game supply and demand members update their strategies, calculate the strategy change rate, and set the current convergence status:

[0093] S44: Through strategy iteration and penalty system constraints, all members of the game supply and demand body determine the current game status by communicating with neighbor members.

[0094] S45: Determine whether the supply and demand constraints are balanced: ( For power generation, If the penalty coefficient is not met, the optimization is performed by iteratively increasing the penalty coefficient. If satisfied, all members of the game supply and demand body retain the current strategy.

[0095] S46: Output the game strategy and optimization results ({P i,t}i=1,2,...,N; t=1,2,...,T).

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A distributed resource optimal control method considering multi-factor uncertainty, characterized by: The method comprises the following steps: S1: Use the Copula function to measure the relationship between the predicted value and the actual value of the uncertain variable, and calculate the range of the prediction error of the uncertain variable; S2: Using the Copula function and discrete convolution method, the problem that discrete convolution requires variable independence is solved by adding the Copula function that measures the correlation of different variables in the discrete convolution calculation. The uncertainty range of different coupling relationship variables is measured through the above steps. S3: Using the potential game method with distributed characteristics and mapping relationships, the rationality of individual interests is constructed through utility functions and benefit functions, and the overall importance is expressed through potential functions; S4: Based on the constructed game strategy set and the set priority sequence, the strategy iteration and optimization algorithm is completed using the local information of the game supply and demand body members and the strategies of the neighbors.

2. The distributed resource optimal control method considering multiple factors of uncertainty according to claim 1 is characterized by: In step S1, the Copula function is used to measure the relationship between the predicted value and the actual value of the uncertainty variable, and the range of the prediction error of the uncertainty variable is calculated, which specifically includes: S11: The output in a distributed system is affected by many factors. Let the measured value X and the point prediction value Y of the uncertainty variable be solved for the reversible cumulative distribution function F of the measured value and the point prediction value. X (x) = P (x < x) and F Y (y) = P(Y<y), where if F X (x) and F Y (y) are considered as random variables, then they are all considered to be uniformly distributed: where u X and u Y It follows a uniform distribution in the interval [0,1]; S12: Use the Copula function C(·) to construct the uniform marginal distribution of the measured values ​​and point forecast values ​​of the uncertainty variable into a multivariate cumulative distribution function F X,Y (x,y)=C(F X (x),F Y (y)), and rewrite the multivariate cumulative distribution function of the uncertain variable into the form of Copula function S13: Use the Gaussian Copula function C(·) to obtain the joint probability density function f of the uncertain variables through the given point prediction data X,Y (x, y), the joint probability density function can be used to evaluate the performance of the prediction model and reveal the dependency between the actual value and the predicted value of the variable; S14: Given a point prediction value, the reversible cumulative distribution function of the actual output is obtained, and the conditional probability density function of the uncertainty variable prediction error value is obtained by transforming it. The conditional probability density function of the uncertainty variable prediction error is calculated by the actual output distribution and the Copula density function, where the Copula function contains the correlation between the predicted value and the actual value.

3. The distributed resource optimal control method considering multi-factor uncertainty according to claim 2 is characterized in that: In step S2, it specifically includes: S21: Taking the conditional probability function of the predicted variable error as input, calculate the joint probability distribution function f of the variables and X,Y (x,y), and rewrite it as a discrete convolution of the variables and; S22: Considering the complex coupling relationship between variables, the Copula function is introduced to measure the complex correlation between the conditional probability density functions of the variables, and it is introduced into the convolution calculation of the difference or sum of the variables to solve the premise problem that the convolution requires the variables to be independent; through the Copula theory, it is rewritten into a related convolution form with a Copula function; S23: Discretize the related convolution form with variable correlation into a discrete convolution form, which has a similar structure to the convolution formula, but has an additional Copula function to construct the dependency relationship between variables. S24: According to the relationship between the sum or difference of the variables, the expansion of the relevant discrete convolution described in S23 is applied to calculate the variable prediction error distribution to measure the uncertainty of the complex coupling relationship between different uncertainty variables.

4. The distributed resource optimal control method considering multi-factor uncertainty according to claim 3 is characterized by: In step S3, the following steps are specifically included: S31: Determine the device members participating in resource regulation and collect input and output variables of different members in the system; S32: Define the basic types of game supply and demand members in the system. The individuals participating in the game are not limited to the following basic individuals, but can also be any combination of these basic constituent individuals; S33: Each member of the supply and demand entity participating in the distributed resource regulation has a corresponding profit function, that is, the profit generated by its strategy, as follows: U i =F i Among them U i is the utility function of the distributed heterogeneous component i participating in the distributed potential game, F i is the payoff function of the distributed heterogeneous component i participating in the distributed potential game, is the payoff function of the Jth distributed heterogeneous component X participating in the distributed potential game; S34: According to different members, various constraints of members participating in distributed resource regulation are converted into functions of game strategy concentration. The strategies of game supply and demand entities participating in the game mainly refer to the output and input demand range of each basic component, where P i is the strategy of the participants, and its strategy set is expressed as: S35: Determine Γ=<N,{Y i } i∈N ,{U i } i∈N > is a strategic game with limited participants, where N = {1, 2, ..., n} is the number of participants in the game, Y i is the strategy set of participating member i, U i represents the benefit / utility function of member i; S36: The basis of the game model is to satisfy the utility and benefits of each player under the premise of satisfying the supply and demand balance of distributed resource regulation and control, and set balance constraints; S37: Each member of the supply and demand body in the game should be responsible for the supply and demand balance constraints of distributed resource regulation, but the strategy set goal of each member of the supply and demand body is only to maximize its own utility function, so the penalty function is introduced. To handle the global constraints of the game supply and demand members: Where P Z is the difference between the supply and demand of distributed resources.

5. The distributed resource optimal control method considering multi-factor uncertainty according to claim 4 is characterized in that: In step S4, it specifically includes: S41: Determine the member constraint function, benefit function and utility function participating in the optimal regulation of distributed resources; S42: Initialize optimization algorithm parameters, build individual game models of game supply and demand body members according to different constraints described in S2, and set priority queues in the game according to importance; S43: Send a request to the previous game supply and demand body member in the priority queue; evaluate the game supply and demand body member's own strategy and select the best strategy; the game supply and demand body member updates the strategy and calculates the strategy change rate and sets the current convergence status; S44: Through strategy iteration and penalty system constraints, all members of the game supply and demand body communicate with neighbor members to determine the current game status; S45: Determine whether the supply and demand constraint balance is satisfied. If not, optimize by iteratively increasing the penalty coefficient. If satisfied, all members of the game supply and demand body retain the current strategy. S46: Output the game strategy and optimization results ({P i,t }i=1,2,...,N; t=1,2,...,T).

6. A distributed resource optimal control system considering multi-factor uncertainty, characterized by: The system adopts the method as claimed in any one of claims 1 to 5.

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