Multi-average field control method and device for virtual power plant participating in secondary frequency modulation

Abstract

The invention relates to a multi-average field control method and device for a virtual power plant participating in secondary frequency modulation. The method comprises the following steps: evaluating adjustable space and comprehensive frequency modulation performance of heterogeneous distributed resources in the virtual power plant by adopting a unified modeling method; dividing different types of distributed resources into a plurality of homogeneous clusters with the same response characteristics by adopting a dynamic granulation method; describing dynamic interaction between a single homogeneous cluster and an average field item in the same kind of distributed resources through the average field item and an HJB equation; and solving the average field term and the equilibrium solution of the HJB equation. According to the method, on the basis of a multi-average field control framework model of interactive iteration between an HJB equation and average field items, pairwise interaction between homogeneous DERs is converted into interaction between a single DER cluster and an average field composed of other DERs, a multi-body problem is decomposed into a plurality of single body problems for distributed solving, rapid decomposition of a regulation and control instruction in a VPP is achieved, and the control and control efficiency of the VPP is improved. And the regulation and control speed of the VPP is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant resource regulation and control, and in particular to a multi-average field control method and device for a virtual power plant participating in secondary frequency regulation. Background Art

[0002] As the fusion point of energy supply and energy consumption, virtual power plant (VPP) can aggregate distributed energy resources (DERs) to flexibly participate in the frequency regulation ancillary service market. When VPP participates in the ancillary service market, it needs to report the quotation curve and adjustable capacity to the power grid on the day before. Existing studies only model the scenarios under the aggregation of certain specific resources (such as energy storage, users, etc.), which makes the model particular and the model itself has poor scalability, which makes it difficult for virtual power plant operators (VPPOs) to formulate operation strategies for flexibly aggregating different types of resources on the day before. In addition, the frequency regulation ancillary service market places high requirements on the speed of VPP command decomposition: on the one hand, since the DERs aggregated by VPP usually belong to different stakeholders, the command decomposition within VPP involves interactions between a large number of stakeholders; on the other hand, DERs need to respond to the control commands of VPP while pursuing their own interests. In this case, there are a large number of interactions between DERs, but the computational complexity of the traditional control framework will increase with the number of participants. When the number of participants is quite large, it may fall into the "dimensionality disaster" problem. Therefore, it is urgent to build a reasonable and efficient control framework that can evaluate the adjustable capacity of VPP according to its different aggregation situations, formulate scheduling strategies for virtual power plants' day-ahead bidding, and establish a more accurate control instruction decomposition model to reduce scheduling errors and ensure maximum individual benefits, thereby achieving rapid decomposition of control instructions within the VPP. Summary of the invention

[0003] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a multi-average field control method and device for a virtual power plant participating in secondary frequency modulation, which specifically adopts the following technical solutions:

[0004] A multi-average field control method for a virtual power plant participating in secondary frequency regulation comprises the following steps:

[0005] A unified modeling approach is used to evaluate the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within a virtual power plant.

[0006] A dynamic granulation method is used to divide different types of distributed resources into multiple homogeneous clusters with the same response characteristics;

[0007] A multi-mean field control framework model is constructed, and the dynamic interaction between a single homogeneous cluster and mean field terms in the same distributed resource is described by using mean field terms and HJB equations;

[0008] The mean field term and the equilibrium solution of the HJB equation are solved based on the distributed acceleration method.

[0009] Optionally, the step of evaluating the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within the virtual power plant using a unified modeling method includes:

[0010] According to the operating characteristics of heterogeneous distributed resources, a convex set in T-dimensional space is used Unified modeling of the aggregate power feasible domain of distributed resources:

[0011]

[0012] Where u is the type of distributed resource, which includes renewable energy, distributed energy storage or temperature control load; F u is a coefficient matrix, and Where K represents the number of constraints in a time interval; b u is a constant matrix, and Where T represents the number of scheduling time intervals; is the power of distributed resource i of type u, is the charge vector of u-type distributed resource i, where

[0013] Using the Minkowski sum, Mapping to the aggregate power feasible region

[0014]

[0015] Where n is the number of distributed resources; is the sum of the points in Euclidean space; is the aggregate power of u-type distributed resources;

[0016] The embedded rectangular pyramid method is used to solve the operating power boundaries of different time periods to achieve time decoupling of aggregated adjustable power:

[0017]

[0018] in and are the upper and lower bounds of the operating power; is the adjustable power range of u-type distributed resources;

[0019] Finally, the three indicators of regulation rate, regulation accuracy and response time are selected to form the comprehensive indicators for the performance evaluation of distributed resources participating in frequency regulation:

[0020]

[0021] in, is a comprehensive indicator; To adjust the rate; To adjust the accuracy; is the response time; K i,1 is the adjustment rate of a single distributed resource i; K i,2 is the adjustment accuracy of a single distributed resource i; K i,3 is the response time of a single distributed resource i.

[0022] Optionally, the step of using a dynamic granulation method to divide different types of distributed resources into a plurality of homogeneous clusters with the same response characteristics includes:

[0023] First, a four-dimensional information system containing detailed information on distributed resources is constructed:

[0024] S = (U, D, V, f);

[0025] Where U = {DER u,1 ,DER u,2 ,…,DER u,n} is a domain, and DER u,i (i=1,2,…,n) represents the i-th distributed resource in the u-type distributed resource set; D is the attribute set; V is the attribute value set; f is the information function, which represents the mapping between each distributed resource and the corresponding attribute value;

[0026] Randomly aggregate the distributed resources in the domain U into cluster A u,c , where cluster A u,c The conditional attributes are:

[0027]

[0028] in For cluster A u,c The aggregate power can be adjusted downwards within the range; For cluster A u,c The aggregate power can be adjusted upwards within the range; For cluster A u,c Comprehensive frequency modulation performance indicators;

[0029] According to the time decoupling method of aggregate adjustable power and the comprehensive index calculation method, cluster A is obtained. u,c The value set of the attribute

[0030]

[0031] According to cluster A u,c The attribute value set V and the corresponding information function to obtain the value of each attribute are:

[0032]

[0033] in For cluster A u,c The upper limit of aggregate power; For cluster A u,c The lower limit of aggregate power; For cluster A u,c Base value of aggregate power;

[0034] Determine cluster A u,c and standard cluster A u,s Is there an indistinguishable relationship between

[0035]

[0036] where Euc[f(A u,c ,D),f(A u,s ,D)] is cluster A u,c and standard cluster A u,s The Euclidean distance between them; To meet the frequency modulation accuracy requirements, standard cluster A u,s The constraints are as follows,

[0037]

[0038] will be with standard cluster A u,s Clusters A with indistinguishable relationships between u,c As a homogeneous cluster A u,d :

[0039]

[0040] Where E u,d A is a homogeneous cluster u,d The charge of A is a homogeneous cluster u,d The power increase; A is a homogeneous cluster u,d The power reduction of Standard cluster A u,s Increase the power limit; Standard cluster A u,s Lower the power limit; Standard cluster A u,s The upper limit of the charge; Standard cluster A u,s The lower limit of charge; A is a homogeneous cluster u,d Comprehensive frequency modulation performance indicators; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance indicators.

[0041] Optionally, the step of describing the dynamic interaction between a single homogeneous cluster and the mean field term in the same type of distributed resources by means of the mean field term and the HJB equation comprises:

[0042] Based on the purpose of seeking the optimal frequency modulation instructions and economic benefit distribution, a dynamic random control model is constructed according to the interaction between distributed resource clusters:

[0043]

[0044] in is the set of participants, which represents the number of u-type distributed resource clusters participating in period t, and is the state space, which represents the joint state of all clusters, and is the decision vector space, which represents the joint control strategy of all clusters, and {J u,d} is the utility function, which represents the degree of preference of the participants in the interaction;

[0045] Obtaining homogeneous cluster A based on dynamic random control model u,d The profit function is:

[0046]

[0047] Among them J u,d () is the profit function; s u,d is the decision vector of u-type distributed resources; S u,-d It is a distributed resource cluster of type u, excluding homogeneous cluster A u,d The joint decision vector after ; A is a homogeneous cluster u,d The unit incentive price of frequency modulation power; A is a homogeneous cluster u,d FM power; is the standard charge, μ u Deviating from the standard charge The penalty coefficient, is the standard FM power, and The deviation from the standard FM power is The penalty coefficient of

[0048] According to the regulation capability and frequency regulation requirements of distributed resources, a corresponding dynamic incentive mechanism is constructed:

[0049]

[0050] in is the unit incentive price for frequency regulation power of a homogeneous cluster of renewable energy sources; is the unit incentive price of frequency regulation power of a homogeneous cluster in distributed energy storage; is the unit incentive price of frequency modulation power of homogeneous clusters in temperature control loads; h RES,d is a piecewise linear function describing the relationship between renewable energy regulation and incentive price; h ESS,d is a piecewise linear function describing the relationship between the distributed energy storage regulation and the incentive price; h TCL,d is a piecewise linear function describing the relationship between temperature control load adjustment and incentive price; N RES (t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL (t) is the number of temperature control load clusters participating in period t; To reflect the homogeneous cluster A in u-type distributed resources u,d The linear parameter of the adjustment amount, To reflect the homogeneous cluster A in u-type distributed resources u,d The linear parameters of the mediating positivity effects; is the ratio of the total incentives received by renewable energy to the market clearing price, is the ratio of the total incentives received by distributed energy storage to the market clearing price; is the ratio of the total incentives obtained by the temperature-controlled load to the market clearing price; f(t) is the system frequency during period t, f0 is the rated frequency during period t; p FR (t) is the FM market clearing price in period t;

[0051] Combining homogeneous cluster A u,d The first optimal control strategy that maximizes the frequency modulation benefit is obtained by using its own state and dynamic incentive mechanism:

[0052]

[0053] in is the optimal control strategy at the equilibrium point in the first optimal control strategy;

[0054] Based on the multi-mean field control framework model, the first optimal control strategy is transformed to obtain the second optimal control strategy, and the HJB equation of the multi-mean field control framework model is obtained based on the second optimal control strategy.

[0055] Optional: The mean field term includes:

[0056]

[0057] where y RES (t) is the mean field term describing the collective effect of RES; y ESS (t) is the mean field term describing the collective effect of ESS; y TCL (t) is the mean field term describing the collective effect of TCL; N RES (t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL (t) is the number of temperature control load clusters participating in period t; is the frequency regulation power of a single cluster of renewable energy participating in period t; is the frequency regulation power of a single cluster in the distributed energy storage participating in period t; is the frequency regulation power of a single cluster in the temperature control load participating in period t.

[0058] Optionally, the step of converting the first optimal control strategy to obtain the second optimal control strategy based on the multi-mean field control framework model includes:

[0059] According to the average field term and dynamic incentive mechanism, the relationship between the incentive price and the corresponding average field term in the homogeneous cluster is obtained:

[0060]

[0061] in is the unit incentive price for frequency regulation power of a homogeneous cluster of renewable energy sources; is the unit incentive price of frequency regulation power of a homogeneous cluster in distributed energy storage; is the unit incentive price of frequency modulation power of homogeneous clusters in temperature control loads; y RES (t) is the mean field term describing the collective effect of RES; y ESS (t) is the mean field term describing the collective effect of ESS; y TCL (t) is the average field term describing the collective effect of TCL; h RES,d is a piecewise linear function describing the relationship between renewable energy regulation and incentive price; h ESS,d is a piecewise linear function describing the relationship between the distributed energy storage regulation and the incentive price; h TCL,d is a piecewise linear function describing the relationship between temperature control load adjustment and incentive price; N RES (t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL(t) is the number of temperature control load clusters participating in period t;

[0062] Optimizing homogeneous cluster A based on the relationship between incentive price and mean field term in homogeneous clusters u,d The profit function is:

[0063]

[0064] Among them J u,d () is the profit function; s u,d is the decision vector of u-type distributed resources; y u is the mean field term describing the collective effect of type u DER; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance index; h u,d is a piecewise linear function describing the relationship between the regulation amount and incentive price of u-type distributed resources; N u (t) is the number of u-type distributed resource clusters participating in time period t; y u (t) is the average field term describing the collective effect of type u DER in period t; is the frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the standard frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the penalty coefficient for deviation from the standard FM power; μ u is the penalty coefficient for deviation from the standard charge; E u,d (t) is the homogeneous cluster A in period t u,d The charge of A is a homogeneous cluster in time period t u,d The standard charge of

[0065] Based on the multi-mean field control framework model, the first optimal control strategy is optimized to obtain the second optimal control strategy:

[0066]

[0067] Based on Bellman's optimal value principle, the value function of the second optimal control strategy is obtained:

[0068]

[0069] Based on the value function, the HJB equation of the second optimal control strategy under the multi-mean field control framework model is obtained:

[0070]

[0071] in Indicates u,d Operator for partial differentiation; v u,d() is the value function of the optimal control strategy; t is the frequency modulation period; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance index; h u,d is a piecewise linear function describing the relationship between the regulation amount and incentive price of u-type distributed resources; N u (t) is the number of u-type distributed resource clusters participating in time period t; is the frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the standard frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the penalty coefficient for deviation from the standard FM power; μ u is the penalty coefficient for deviation from the standard charge; E u,d (t) is the homogeneous cluster A in period t u,d The charge of A is a homogeneous cluster in time period t u,d The standard charge.

[0072] Optionally: the step of solving the mean field term and the equilibrium solution of the HJB equation comprises:

[0073] Select a solution algorithm and set the maximum number of iterations and convergence parameters of the solution algorithm;

[0074] Each homogeneous cluster A u,d Combined with the HJB equation of the multi-mean field control framework model, the homogeneous cluster A corresponding to the kth iteration is obtained u,d FM power;

[0075] And according to the homogeneous cluster A corresponding to the kth iteration u,d The frequency modulation power calculation obtains the homogeneous cluster A corresponding to the kth iteration u,d The mean field term of

[0076] When the kth iteration corresponds to the homogeneous cluster A u,d When the average field term result satisfies the convergence parameter, the homogeneous cluster A corresponding to the kth iteration is output u,d Otherwise, the current iteration number is incremented by 1 and the homogeneous cluster A corresponding to the k+1th iteration is calculated in sequence u,d The FM power and mean field term results.

[0077] Optional: The distributed acceleration algorithm adopts the Mann iterative algorithm:

[0078]

[0079] in, The kth iteration corresponds to the homogeneous cluster A u,d The mean field term of The k+1th iteration corresponds to the homogeneous cluster A u,d The mean field term of is the coefficient sequence of the Mann iterative algorithm at the k+1th iteration; N u is the number of DER clusters of type u; The k+1th iteration corresponds to the homogeneous cluster A u,d FM power.

[0080] Furthermore, the present invention also discloses a multi-average field control device for a virtual power plant to participate in secondary frequency regulation, the device comprising:

[0081] A modeling and evaluation module is used to evaluate the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within the virtual power plant using a unified modeling method;

[0082] The cluster partitioning module is used to divide different types of distributed resources into multiple homogeneous clusters with the same response characteristics using a dynamic granulation method;

[0083] Dynamic analysis module, which is used to construct a multi-mean field control framework model and describe the dynamic interaction between a single homogeneous cluster and mean field terms in the same distributed resource through mean field terms and HJB equations;

[0084] The result solving module is used to solve the mean field terms and the equilibrium solution of the HJB equations based on the distributed acceleration method.

[0085] Beneficial Effects

[0086] The technical solution of the present invention achieves the following beneficial effects:

[0087] (1) The method of the present invention can be used for the frequency regulation auxiliary service market to uniformly model and evaluate the adjustable space of heterogeneous distributed resources, providing a basis for VPP to declare aggregated adjustable capacity in advance, and effectively improving the scalability of VPP to aggregate different types of DERs.

[0088] (2) The method of the present invention constructs a multi-mean field control framework model based on the interactive iteration between the HJB equation and the mean field term (MFT), transforms the pairwise interaction between homogeneous DERs into the interaction between a single DER cluster and the mean field composed of the remaining DERs, and decomposes the multi-body problem into multiple single-body problems for distributed solution, thereby realizing the rapid decomposition of the control instructions inside the VPP and improving the VPP control speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 FIG. 4 is a control framework diagram of the VPP in an embodiment of the present invention.

[0090] Figure 2It is a flow chart of a multi-average field control method for a virtual power plant participating in secondary frequency regulation in an embodiment of the present invention.

[0091] Figure 3 This is a flow chart of a distributed acceleration algorithm in an embodiment of the present invention.

[0092] Figure 4 Schematic diagram of valley filling power of different DER clusters under the optimal control strategy in an embodiment of the present invention.

[0093] Figure 5 Schematic diagram of peak power shaving of different DER clusters under the optimal control strategy in an embodiment of the present invention.

[0094] Figure 6 Schematic diagram of net benefits of different DER clusters in an embodiment of the present invention. DETAILED DESCRIPTION

[0095] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention. It should be pointed out that the following detailed descriptions are all exemplary and are intended to provide further explanation of the present application.

[0096] In the present invention, the distributed energy resources (DERs) aggregated by the virtual power plant (VPP) include at least renewable energy sources (RESs), distributed energy storage systems (ESSs) and temperature control loads (TCLs), and when the above distributed resources participate in the frequency regulation market (FRM), the control framework inside the VPP is as follows: Figure 1 shown.

[0097] Combination Figure 1 and Figure 2 As shown, the present invention specifically discloses a multi-average field control method for a virtual power plant to participate in secondary frequency regulation, which includes the following steps:

[0098] Step 1: Use a unified modeling approach to evaluate the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within the virtual power plant;

[0099] Specifically, the specific process of evaluating the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within the virtual power plant using a unified modeling method in step 1 includes:

[0100] First, according to the operating characteristics of heterogeneous distributed resources, a convex set in T-dimensional space is used Unified modeling of the aggregate power feasible domain of distributed resources:

[0101]

[0102] Where u is the type of distributed resource, which includes renewable energy, distributed energy storage or temperature control load; F u is a coefficient matrix, and Where K represents the number of constraints in a time interval; b u is a constant matrix, and Where T represents the number of scheduling time intervals; is the power of distributed resource i of type u, is the charge vector of u-type distributed resource i, where

[0103] Using the Minkowski sum, Mapping to the aggregate power feasible region

[0104]

[0105] Where n is the number of distributed resources; is the sum of the points in Euclidean space; is the aggregate power of u-type distributed resources.

[0106] In the present invention, since the operation model of some DERs is time-varying, the aggregate power flexibility has time coupling, which can be abstracted as a high-dimensional aggregate power polyhedron from a geometric perspective, making it more difficult to obtain an accurate operating power boundary. Therefore, this step uses the embedded right-angle pyramid method to solve the operating power boundary of different time periods to achieve time decoupling of aggregate adjustable power:

[0107]

[0108] in and are the upper and lower bounds of the operating power; It is the adjustable power range of class u distributed resources.

[0109] Finally, the three indicators of regulation rate, regulation accuracy and response time are selected to form the comprehensive indicators for the performance evaluation of distributed resources participating in frequency regulation:

[0110]

[0111] in, is a comprehensive indicator; To adjust the rate; To adjust the accuracy; is the response time; K i,1 is the adjustment rate of a single distributed resource i; K i,2 is the adjustment accuracy of a single distributed resource i; K i,3 is the response time of a single distributed resource i.

[0112] Step 2: Use a dynamic granulation method to divide different types of distributed resources into multiple homogeneous clusters with the same response characteristics. In practical applications, a large number of DERs in a VPP are rarely completely homogeneous, and they are difficult to manage uniformly. Therefore, in step 2, a dynamic granulation method is used to divide large-scale DERs into smaller subsets with similar regulation characteristics, namely, DER clusters, for the convenience of subsequent management. Specifically, the specific method of using a dynamic granulation method in step 2 to divide different types of distributed resources into multiple homogeneous clusters with the same response characteristics includes:

[0113] First, a four-dimensional information system containing detailed information of distributed resources is constructed. From the perspective of granular computing, the four-dimensional information system containing detailed information of DERs can be defined as follows:

[0114] S = (U, D, V, f);

[0115] Where U = {DER u,1 ,DER u,2 ,…,DER u,n} is a domain, and DER u,i (i=1,2,…,n) represents the i-th distributed resource in the u-type distributed resource set; D is the attribute set; V is the attribute value set, generally in It is attribute D m value; f is the information function, which represents the mapping between each distributed resource and the value of the corresponding attribute, that is, f:U×D→V.

[0116] Then, the distributed resources DERs in the domain U are randomly aggregated into cluster A. u,c Since the incentives for DER clusters should depend on their output and service quality in frequency regulation ancillary services, the upper and lower adjustable power ranges define cluster A. u,c Conditional attributes:

[0117]

[0118] in For cluster A u,c The aggregate power can be adjusted downwards within the range; For cluster A u,c The aggregate power can be adjusted upwards within the range; For cluster A u,c Comprehensive frequency modulation performance indicators;

[0119] According to the time decoupling method of aggregate adjustable power and the comprehensive index calculation method, cluster A is obtained. u,c The value set of the attribute

[0120]

[0121] in For cluster A u,c The upper limit of aggregate power; For cluster A u,c The lower limit of aggregate power; is the comprehensive index of u-type distributed resources, For cluster A u,c The base value of the aggregate power.

[0122] According to cluster A u,c The attribute value set V and the corresponding information function to obtain the value of each attribute are:

[0123]

[0124] Determine cluster A u,c and standard cluster A u,s Is there an indistinguishable relationship between

[0125]

[0126] where Euc[f(A u,c ,D),f(A u,s ,D)] is cluster A u,c and standard cluster A u,s The Euclidean distance between

[0127] It should be noted that in order to meet the frequency modulation accuracy requirements, standard cluster A u,s The constraints are as follows:

[0128]

[0129] will be with standard cluster A u,s Clusters A with indistinguishable relationships between u,c As a homogeneous cluster A u,d , which makes a large number of heterogeneous DERs in the domain U be divided into homogeneous DER clusters A u,d , where homogeneous cluster A u,d It is expressed as:

[0130]

[0131] Where E u,d A is a homogeneous cluster u,d The charge of A is a homogeneous cluster u,d The power increase; A is a homogeneous cluster u,d The power reduction of Standard cluster A u,s Increase the power limit; Standard cluster A u,s Lower the power limit; Standard cluster A u,s The upper limit of the charge; Standard cluster A u,s The lower limit of charge; A is a homogeneous cluster u,d Comprehensive frequency modulation performance indicators; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance indicators.

[0132] Step 3: Construct a multi-mean field control framework model and describe the dynamic interaction between a single homogeneous cluster and mean field terms in the same distributed resource through mean field terms and HJB equations;

[0133] Specifically, the process in which the mean field term and the HJB equation in step 3 describe the dynamic interaction between a single homogeneous cluster and the mean field term in the same type of distributed resources includes:

[0134] The clearing results of various DERs in FRM should be further distributed to DER clusters. While pursuing their own interests, DER clusters are obliged to undertake the frequency regulation tasks of VPP. In order to seek the optimal frequency regulation instructions and the distribution of economic benefits, interactions are formed between DER clusters. Based on the purpose of seeking the optimal frequency regulation instructions and the distribution of economic benefits, a dynamic random control model can be constructed according to the interaction between distributed resource clusters:

[0135]

[0136] in is the set of participants, which represents the number of u-type distributed resource clusters participating in period t, and is the state space, which represents the joint state of all clusters, and is the decision vector space, which represents the joint control strategy of all clusters, and {J u,d} is the utility function, which represents the degree of preference of the participants in the interaction;

[0137] Because each homogeneous cluster A u,dIn pursuit of maximizing benefits, it is necessary to respond to the control instructions of VPP, so the utility function is defined as net benefit. Therefore, based on the dynamic random control model, the homogeneous cluster A is obtained. u,d The profit function is:

[0138]

[0139] Among them J u,d () is the profit function; s u,d is the decision vector of u-type distributed resources; S u,-d It is a distributed resource cluster of type u, excluding homogeneous cluster A u,d The joint decision vector after ; A is a homogeneous cluster u,d The unit incentive price of frequency modulation power; A is a homogeneous cluster u,d FM power; is the standard charge, μ u Deviating from the standard charge The penalty coefficient, is the standard FM power, and The deviation from the standard FM power is The penalty coefficient of

[0140] It should be noted that, considering the homogeneous cluster A u,d With similar response characteristics, the standard FM power The clearing power Calculation results:

[0141]

[0142] According to the regulation capability and frequency regulation requirements of distributed resources, a corresponding dynamic incentive mechanism is constructed:

[0143]

[0144] in is the unit incentive price for frequency regulation power of a homogeneous cluster of renewable energy sources; is the unit incentive price of frequency regulation power of a homogeneous cluster in distributed energy storage; is the unit incentive price of frequency modulation power of homogeneous clusters in temperature control loads; h RES,d is a piecewise linear function describing the relationship between renewable energy regulation and incentive price; h ESS,d is a piecewise linear function describing the relationship between the distributed energy storage regulation and the incentive price; h TCL,d is a piecewise linear function describing the relationship between temperature control load adjustment and incentive price; N RES(t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL (t) is the number of temperature control load clusters participating in period t; To reflect the homogeneous cluster A in u-type distributed resources u,d The linear parameter of the adjustment amount, To reflect the homogeneous cluster A in u-type distributed resources u,d The linear parameters of the mediating positivity effects; is the ratio of the total incentives received by renewable energy to the market clearing price, is the ratio of the total incentives received by distributed energy storage to the market clearing price; is the ratio of the total incentives obtained by the temperature-controlled load to the market clearing price; f(t) is the system frequency during period t, f0 is the rated frequency during period t; p FR (T) is the FM market clearing price in period t;

[0145] Under the dynamic incentive mechanism, homogeneous cluster A u,d The optimal control strategy to maximize the frequency regulation benefit can be sought based on its own status and leveling incentive price. Therefore, combined with homogeneous cluster A u,d The state of itself and the dynamic incentive mechanism can define the above dynamic stochastic control problem, that is, to obtain the first optimal control strategy that maximizes the frequency modulation benefit:

[0146]

[0147] in is the optimal control strategy at the equilibrium point in the first optimal control strategy;

[0148] Based on Bellman's optimal value principle, the value function v of the above first optimal control strategy is u,d is defined as:

[0149]

[0150] Furthermore, the HJB equation (Hamilton-Jacobi-Bellman equation) of the above first optimal control strategy is:

[0151]

[0152] in To express s u,d Operator for partial differentiation; v u,d () is the value function of the optimal control strategy; t is the frequency modulation period; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance index; h u,dis a piecewise linear function describing the relationship between the regulation amount and incentive price of u-type distributed resources; N u (t) is the number of u-type distributed resource clusters participating in time period t; is the frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the standard frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the penalty coefficient for deviation from the standard FM power; μ u is the penalty coefficient for deviation from the standard charge; E u,d (t) is the homogeneous cluster A in period t u,d The charge of A is a homogeneous cluster in time period t u,d The standard charge.

[0153] It should be noted that in order to find the optimal strategy point of the first optimal control strategy, all DER clusters need to interact in pairs. Therefore, as the number of clusters increases, searching for equilibrium points becomes an NPhard problem. In order to overcome this problem, the present invention constructs a multi-mean field control framework model to approximate the solution. In the multi-mean field control framework model, DER clusters are no longer paired interactions, but interactions between a single participant in each DER type and the mean field formed by the remaining N-1 participants. When large-scale clusters are involved, the impact of a single cluster's decision on other clusters becomes negligible. However, the impact of the mean field on the decision of each cluster is significant, and it can be simulated as a collective effect.

[0154] Based on the multi-mean field control framework model, the first optimal control strategy is transformed to obtain the second optimal control strategy, and the HJB equation of the multi-mean field control framework model is obtained based on the second optimal control strategy.

[0155] In more detail, the specific process of transforming the first optimal control strategy to obtain the second optimal control strategy based on the multi-mean field control framework model includes:

[0156] According to the average field term and dynamic incentive mechanism, the relationship between the incentive price and the corresponding average field term in the homogeneous cluster is obtained:

[0157]

[0158] in is the unit incentive price for frequency regulation power of a homogeneous cluster of renewable energy sources; is the unit incentive price of frequency regulation power of a homogeneous cluster in distributed energy storage; is the unit incentive price of frequency modulation power of homogeneous clusters in temperature control loads; y RES (t) is the mean field term describing the collective effect of RES; y ESS(t) is the mean field term describing the collective effect of ESS; y TCL (t) is the average field term describing the collective effect of TCL; h RES,d is a piecewise linear function describing the relationship between renewable energy regulation and incentive price; h ESS,d is a piecewise linear function describing the relationship between the distributed energy storage regulation and the incentive price; h TCL,d is a piecewise linear function describing the relationship between temperature control load adjustment and incentive price; N RES (t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL (t) is the number of temperature control load clusters participating in period t;

[0159] The mean field term in the above steps describes the mapping from the control strategy of a single homogeneous particle to the mean field effect, and the mean field term includes:

[0160]

[0161] where y RES (t) is the mean field term describing the collective effect of RES; y ESS (t) is the mean field term describing the collective effect of ESS; y TCL (t) is the mean field term describing the collective effect of TCL; N RES (t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL (t) is the number of temperature control load clusters participating in period t; is the frequency regulation power of a single cluster of renewable energy participating in period t; is the frequency regulation power of a single cluster in the distributed energy storage participating in period t; is the frequency regulation power of a single cluster in the temperature control load participating in period t.

[0162] Then, the homogeneous cluster A is optimized based on the relationship between the incentive price and the mean field term in the homogeneous cluster. u,d The profit function is:

[0163]

[0164] Among them J u,d () is the profit function; s u,d is the decision vector of u-type distributed resources; y u is the mean field term describing the collective effect of u-type distributed resources; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance index; h u,dis a piecewise linear function describing the relationship between the regulation amount and incentive price of u-type distributed resources; N u (t) is the number of u-type distributed resource clusters participating in time period t; y u (t) is the average field term describing the collective effect of u-type distributed resources in period t; is the frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the standard frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the penalty coefficient for deviation from the standard FM power; μ u is the penalty coefficient for deviation from the standard charge; E u,d (t) is the homogeneous cluster A in period t u,d The charge of A is a homogeneous cluster in time period t u,d The standard charge of

[0165] Based on the multi-mean field control framework model, the first optimal control strategy is optimized to obtain the second optimal control strategy:

[0166]

[0167] Based on Bellman's optimal value principle, the value function of the second optimal control strategy is obtained:

[0168]

[0169] Based on the value function, the HJB equation of the second optimal control strategy under the multi-mean field control framework model is obtained:

[0170]

[0171] in To express s u,d Operator for partial differentiation; v u,d () is the value function of the optimal control strategy; t is the frequency modulation period; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance index; h u,d is a piecewise linear function describing the relationship between the regulation amount and incentive price of u-type distributed resources; N u (t) is the number of u-type distributed resource clusters participating in time period t; is the frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the standard frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the penalty coefficient for deviation from the standard FM power; μ u is the penalty coefficient for deviation from the standard charge; E u,d (t) is the homogeneous cluster A in period tu,d The charge of A is a homogeneous cluster in time period t u,d The standard charge.

[0172] Step 4: Solve the equilibrium solution of the mean field term and the HJB equation based on the distributed acceleration method.

[0173] Specifically, Figure 3 As shown, the specific method for solving the mean field term and the equilibrium solution of the HJB equation in the above steps is:

[0174] First, a solution algorithm is selected and the maximum number of iterations and convergence parameters of the solution algorithm are set; in this embodiment, the Mann iteration algorithm is preferably used. The Mann iteration algorithm is a variational inclusion group (VICG) algorithm, which is mainly used to solve optimization problems in Banach space. The basic idea of ​​the algorithm is to update the current iteration point by solving a variational inclusion group (VICG) in each iteration step, which has good convergence performance.

[0175] Each homogeneous cluster A u,d Combined with the HJB equation of the multi-mean field control framework model, the homogeneous cluster A corresponding to the kth iteration is obtained u,d FM power;

[0176] And according to the homogeneous cluster A corresponding to the kth iteration u,d The frequency modulation power calculation obtains the homogeneous cluster A corresponding to the kth iteration u,d The mean field term of

[0177] When the kth iteration corresponds to the homogeneous cluster A u,d When the average field term result satisfies the convergence parameter, the homogeneous cluster A corresponding to the kth iteration is output u,d Otherwise, the current iteration number is incremented by 1 and the homogeneous cluster A corresponding to the k+1th iteration is calculated in sequence u,d The FM power and mean field term results.

[0178] Optional: The distributed acceleration algorithm adopts the Mann iterative algorithm:

[0179]

[0180] in, The kth iteration corresponds to the homogeneous cluster A u,d The mean field term of The k+1th iteration corresponds to the homogeneous cluster A u,d The mean field term of is the coefficient sequence of the Mann iterative algorithm at the k+1th iteration; N uis the number of DER clusters of type u; The k+1th iteration corresponds to the homogeneous cluster A u,d FM power.

[0181] Furthermore, in order to verify the effect of the above method, this embodiment performs simulation. The device for the simulation is an HPZ600 machine, which is configured with an Intel Xeon CPU (frequency 2.4GHz) and 12GB RAM. The above device calls the Gurobi 9.5.2 solver in the MATLABR2023a environment to run the simulation model, and the time step is set to 5min.

[0182] First, taking N = 100 as an example, the optimal frequency control strategy of the DER cluster at the MFE point is as follows: Figure 4 and Figure 5 As shown: the growth trends of the incentive price and the frequency modulation power are the same, which indicates that the multi-average field control method proposed in the present invention has incentive compatibility and can well incentivize DER clusters to participate in frequency modulation. When the incentive price rises, the DER cluster will actively respond to the frequency modulation instructions of the VPP, and the incentive price will also increase with the improvement of the frequency modulation performance until the upper limit is reached. In the time dimension, the changing trends of the frequency modulation power and incentive price of different clusters are similar. The above results show that the dynamic granulation method adopted in the present invention can effectively divide DERs into homogeneous clusters with similar external regulation characteristics.

[0183] Secondly, the net benefits of different DER clusters are as follows: Figure 6 As shown in the figure: the net income of the cluster gradually increases with the increase of d, and reaches a peak of 4427.74 at d = 72, at which time the cluster's incentive sensitivity to the frequency reduction demand is 0.034, and the cluster's incentive sensitivity to the frequency increase demand is 0.052. This is because the frequency regulation power and the corresponding incentive price increase with the increase of d, while the net income always increases. When d∈[73,100], the net income will drop sharply because the VPP has limited income and frequency regulation power that can be allocated. If the total output of the DER cluster exceeds the frequency regulation power, it will be economically penalized. This shows that the penalty factor can effectively limit the response frequency regulation power of the DER cluster so that it does not exceed the winning bid frequency regulation power of the VPP.

[0184] Finally, the average computation time of the multi-mean field control method of the present invention and the traditional dynamic random control under different numbers of DER clusters are shown in Table I. When N = 100, the computation time of the multi-mean field control method is 0.129% and 0.017% shorter than that of the dynamic random control. As N increases, the computation time of the dynamic random control increases sharply, and exceeds 24 hours when N = 700, which is no longer suitable for the revenue distribution of the frequency modulation market. The computation time of the multi-mean field control increases slowly with the increase of the number of clusters N, because the proposed distributed acceleration algorithm based on granular computing allows cluster parallel computing and accelerates the convergence speed.

[0185] Table I Comparison of computation time of different game modes

[0186]

[0187] In addition, the present invention also discloses a multi-average field control device for a virtual power plant to participate in secondary frequency modulation, the device comprising:

[0188] A modeling and evaluation module is used to evaluate the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within the virtual power plant using a unified modeling method;

[0189] The cluster partitioning module is used to divide different types of distributed resources into multiple homogeneous clusters with the same response characteristics using a dynamic granulation method;

[0190] Dynamic analysis module, which is used to construct a multi-mean field control framework model and describe the dynamic interaction between a single homogeneous cluster and mean field terms in the same distributed resource through mean field terms and HJB equations;

[0191] The result solving module is used to solve the mean field terms and the equilibrium solution of the HJB equations based on the distributed acceleration method.

[0192] Furthermore, the present invention also discloses a non-volatile storage medium, which includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the above-mentioned multi-average field control method.

[0193] In addition, the present invention also discloses a device including a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the multi-average field control method is executed when the computer-readable instructions are executed.

[0194] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0195] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices, and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0196] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0197] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0198] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A multi-average field control method for virtual power plants participating in secondary frequency modulation, characterized in that: The following steps are involved: A unified modeling approach is used to evaluate the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within a virtual power plant. A dynamic granulation method is used to divide different types of distributed resources into multiple homogeneous clusters with the same response characteristics; A multi-mean field control framework model is constructed, and the dynamic interaction between a single homogeneous cluster and mean field terms in the same distributed resource is described by using mean field terms and HJB equations; The mean field term and the equilibrium solution of the HJB equation are solved based on the distributed acceleration method.

2. The multi-mean field control method according to claim 1, characterized in that: The steps of evaluating the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within the virtual power plant by using a unified modeling method include: According to the operating characteristics of heterogeneous distributed resources, a convex set in T-dimensional space is used Unified modeling of the aggregate power feasible domain of distributed resources: Where u is the type of distributed resource, which includes renewable energy, distributed energy storage or temperature control load; F u is a coefficient matrix, and Where K represents the number of constraints in a time interval; b u is a constant matrix, and Where T represents the number of scheduling time intervals; is the power of distributed resource i of type u, is the charge vector of u-type distributed resource i, where Using the Minkowski sum, Mapping to the aggregate power feasible region Where n is the number of distributed resources; ⊕ is the sum of the point sets in Euclidean space; is the aggregate power of u-type distributed resources; The embedded rectangular pyramid method is used to solve the operating power boundaries of different time periods to achieve time decoupling of aggregated adjustable power: in and are the upper and lower bounds of the operating power; is the adjustable power range of u-type distributed resources; Finally, the three indicators of regulation rate, regulation accuracy and response time are selected to form the comprehensive indicators for the performance evaluation of distributed resources participating in frequency regulation: in, is a comprehensive indicator; To adjust the rate; To adjust the accuracy; is the response time; K i,1 is the adjustment rate of a single distributed resource i; K i,2 is the adjustment accuracy of a single distributed resource i; K i,3 is the response time of a single distributed resource i.

3. The multi-mean field control method according to claim 1, characterized in that: The steps of using the dynamic granulation method to divide different types of distributed resources into multiple homogeneous clusters with the same response characteristics include: First, a four-dimensional information system containing detailed information on distributed resources is constructed: S = (U, D, V, f); Where U = {DER u,1 ,DER u,2 ,…,DER u,n } is a domain, and DER u,i (i=1,2,…,n) represents the i-th distributed resource in the u-type distributed resource set; D is the attribute set; V is the attribute value set; f is the information function, which represents the mapping between each distributed resource and the corresponding attribute value; Randomly aggregate the distributed resources in the domain U into cluster A u,c , where cluster A u,c The conditional attributes are: in For cluster A u,c The aggregate power can be adjusted downwards within the range; For cluster A u,c The aggregate power can be adjusted upwards within the range; For cluster A u,c Comprehensive frequency modulation performance indicators; According to the time decoupling method of aggregate adjustable power and the comprehensive index calculation method, cluster A is obtained. u,c The value set of the attribute According to cluster A u,c The attribute value set V and the corresponding information function to obtain the value of each attribute are: in For cluster A u,c The upper limit of aggregate power; For cluster A u,c The lower limit of aggregate power; For cluster A u,c Base value of aggregate power; K i,1 is the adjustment rate of a single distributed resource i; K i,2 is the adjustment accuracy of a single distributed resource i; K i,3 is the response time of a single distributed resource i; Determine cluster A u,c and standard cluster A u,s Is there an indistinguishable relationship between where Euc[f(A u,c ,D),f(A u,s ,D)] is cluster A u,c and standard cluster A u,s The Euclidean distance between the standard cluster A u,s The constraints are as follows, will be with standard cluster A u,s Clusters A with indistinguishable relationships between u,c As a homogeneous cluster A u,d : Where E u,d A is a homogeneous cluster u,d The charge of A is a homogeneous cluster u,d The power increase; A is a homogeneous cluster u,d The power reduction of Standard cluster A u,s Increase the power limit; Standard cluster A u,s Lower the power limit; Standard cluster A u,s The upper limit of the charge; Standard cluster A u,s The lower limit of charge; A is a homogeneous cluster u,d Comprehensive frequency modulation performance indicators; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance indicators.

4. The multi-mean field control method according to claim 1, characterized in that: The step of describing the dynamic interaction between a single homogeneous cluster and the mean field term in the same type of distributed resources by using the mean field term and the HJB equation comprises: Based on the purpose of seeking the optimal frequency modulation instructions and economic benefit distribution, a dynamic random control model is constructed according to the interaction between distributed resource clusters: in is the set of participants, which represents the number of u-type distributed resource clusters participating in period t, and is the state space, which represents the joint state of all clusters, and {S u,d } is the decision vector space, which represents the joint control strategy of all clusters, and is the utility function, which represents the degree of preference of the participants in the interaction; Obtaining homogeneous cluster A based on dynamic random control model u,d The profit function is: Among them J u,d () is the profit function; s u,d is the decision vector of u-type distributed resources; S u,-d It is a distributed resource cluster of type u, excluding homogeneous cluster A u,d The joint decision vector after ; A is a homogeneous cluster u,d The unit incentive price of FM power; A is a homogeneous cluster u,d FM power; is the standard charge, μ u Deviating from the standard charge The penalty coefficient, is the standard FM power, and The deviation from the standard FM power is The penalty coefficient of According to the regulation capability and frequency regulation requirements of distributed resources, a corresponding dynamic incentive mechanism is constructed: in is the unit incentive price for frequency regulation power of a homogeneous cluster of renewable energy sources; is the unit incentive price of frequency regulation power of a homogeneous cluster in distributed energy storage; is the unit incentive price of frequency modulation power of homogeneous clusters in temperature control loads; h RES,d is a piecewise linear function describing the relationship between renewable energy regulation and incentive price; h ESS,d is a piecewise linear function describing the relationship between the distributed energy storage regulation and the incentive price; h TCL,d is a piecewise linear function describing the relationship between temperature control load adjustment and incentive price; N RES (t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL (t) is the number of temperature control load clusters participating in period t; To reflect the homogeneous cluster A in u-type distributed resources u,d The linear parameter of the adjustment amount, To reflect the homogeneous cluster A in u-type distributed resources u,d The linear parameters of the mediating positivity effects; is the ratio of the total incentives received by renewable energy to the market clearing price, is the ratio of the total incentives received by distributed energy storage to the market clearing price; is the ratio of the total incentives obtained by the temperature-controlled load to the market clearing price; f(t) is the system frequency during period t, f0 is the rated frequency during period t; p FR (t) is the FM market clearing price in period t; Combining homogeneous cluster A u,d The first optimal control strategy that maximizes the frequency modulation benefit is obtained by using its own state and dynamic incentive mechanism: in is the optimal control strategy at the equilibrium point in the first optimal control strategy; Based on the multi-mean field control framework model, the first optimal control strategy is transformed to obtain the second optimal control strategy, and the HJB equation of the multi-mean field control framework model is obtained based on the second optimal control strategy.

5. The multi-mean field control method according to claim 4, characterized in that: The mean field term includes: where y RES (t) is the average field term describing the collective effect of renewable energy; y ESS (t) is the average field term describing the collective effect of distributed energy storage; y TCL (t) is the average field term describing the collective effect of temperature control load; N RES (t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL (t) is the number of temperature control load clusters participating in period t; is the frequency regulation power of a single cluster of renewable energy participating in period t; is the frequency regulation power of a single cluster in the distributed energy storage participating in period t; is the frequency regulation power of a single cluster in the temperature control load participating in period t.

6. The multi-mean field control method according to claim 4, characterized in that: The step of converting the first optimal control strategy to obtain the second optimal control strategy based on the multi-mean field control framework model comprises: According to the average field term and dynamic incentive mechanism, the relationship between the incentive price and the corresponding average field term in the homogeneous cluster is obtained: in is the unit incentive price for frequency regulation power of a homogeneous cluster of renewable energy sources; is the unit incentive price of frequency regulation power of a homogeneous cluster in distributed energy storage; is the unit incentive price of frequency modulation power of homogeneous clusters in temperature control loads; y RES (t) is the average field term describing the collective effect of renewable energy; y ESS (t) is the average field term describing the collective effect of distributed energy storage; y TCL (t) is the average field term describing the collective effect of temperature control loads; h RES,d is a piecewise linear function that describes the relationship between renewable energy regulation and incentive price; h ESS,d is a piecewise linear function describing the relationship between the distributed energy storage regulation and the incentive price; h TCL,d is a piecewise linear function describing the relationship between temperature control load adjustment and incentive price; N RES (t) is the number of renewable energy clusters participating in period t; N ESS (t) is the number of distributed energy storage clusters participating in period t; N TCL (t) is the number of temperature control load clusters participating in period t; Optimizing homogeneous cluster A based on the relationship between incentive price and mean field term in homogeneous clusters u,d The profit function is: Among them J u,d () is the profit function; s u,d is the decision vector of u-type distributed resources; y u is the mean field term describing the collective effect of u-type distributed resources; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance index; h u,d is a piecewise linear function describing the relationship between the regulation amount and incentive price of u-type distributed resources; N u (t) is the number of u-type distributed resource clusters participating in time period t; y u (t) is the average field term describing the collective effect of u-type distributed resources in period t; is the frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the standard frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the penalty coefficient for deviation from the standard FM power; μ u is the penalty coefficient for deviation from the standard charge; E u,d (t) is the homogeneous cluster A in period t u,d The charge of A is a homogeneous cluster in time period t u,d The standard charge of Based on the multi-mean field control framework model, the first optimal control strategy is optimized to obtain the second optimal control strategy: Based on Bellman's optimal value principle, the value function of the second optimal control strategy is obtained: Based on the value function, the HJB equation of the second optimal control strategy under the multi-mean field control framework model is obtained: in Indicates u,d Operator for partial differentiation; v u,d () is the value function of the optimal control strategy; t is the frequency modulation period; A is a homogeneous cluster u,d Standard comprehensive frequency modulation performance index; h u,d is a piecewise linear function describing the relationship between the regulation amount and incentive price of u-type distributed resources; N u (t) is the number of u-type distributed resource clusters participating in time period t; y u (t) is the average field term describing the collective effect of u-type distributed resources in period t; is the frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the standard frequency modulation power of a single cluster of u-type distributed resources participating in period t; is the penalty coefficient for deviation from the standard FM power; μ u is the penalty coefficient for deviation from the standard charge; E u,d (t) is the homogeneous cluster A in period t u,d The charge of A is a homogeneous cluster in time period t u,d The standard charge.

7. The multi-mean field control method according to claim 1, characterized in that: The step of solving the mean field term and the equilibrium solution of the HJB equation comprises: Select a solution algorithm and set the maximum number of iterations and convergence parameters of the solution algorithm; Each homogeneous cluster A u,d Combined with the HJB equation of the multi-mean field control framework model, the homogeneous cluster A corresponding to the kth iteration is obtained u,d FM power; And according to the homogeneous cluster A corresponding to the kth iteration u,d The frequency modulation power calculation obtains the homogeneous cluster A corresponding to the kth iteration u,d The mean field term of When the kth iteration corresponds to the homogeneous cluster A u,d When the average field term result satisfies the convergence parameter, the homogeneous cluster A corresponding to the kth iteration is output u,d Otherwise, the current iteration number is incremented by 1 and the homogeneous cluster A corresponding to the k+1th iteration is calculated in sequence u,d The FM power and mean field term results.

8. The multi-mean field control method according to claim 7, characterized in that: The distributed acceleration algorithm adopts the Mann iterative algorithm: in, The kth iteration corresponds to the homogeneous cluster A u,d The mean field term of The k+1th iteration corresponds to the homogeneous cluster A u,d The mean field term of is the coefficient sequence of the Mann iterative algorithm at the k+1th iteration; N u is the number of DER clusters of type u; The k+1th iteration corresponds to the homogeneous cluster A u,d FM power.

9. A multi-average field control device for a virtual power plant participating in secondary frequency modulation, characterized in that: The device comprises: A modeling and evaluation module is used to evaluate the adjustable space and comprehensive frequency regulation performance of heterogeneous distributed resources within the virtual power plant using a unified modeling method; The cluster partitioning module is used to divide different types of distributed resources into multiple homogeneous clusters with the same response characteristics using a dynamic granulation method; Dynamic analysis module, which is used to construct a multi-mean field control framework model and describe the dynamic interaction between a single homogeneous cluster and mean field terms in the same distributed resource through mean field terms and HJB equations; The result solving module is used to solve the equilibrium solution of the multi-mean field control framework model and the HJB equation based on the distributed acceleration method.

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