Control method for multiple micro-grids to jointly participate in secondary frequency modulation of distribution network

Abstract

The invention discloses a control method for multiple micro-grids to jointly participate in secondary frequency modulation of a distribution network, which comprises the following steps of: 1, establishing a mathematical model for photovoltaic, energy storage and charging stations to participate in secondary frequency modulation, and further deducing an overall control model of the micro-grids in combination with active power-voltage sensitivity of a system; 2, if uncontrollable photovoltaic output fluctuates, generating a scene tree model of photovoltaic uncertain power according to real-time measurement data based on a Markov chain principle, so as to obtain a series of typical scenes and corresponding probabilities in a prediction time domain; and step 3, based on the control model and the scene tree model, taking the minimum AGC instruction tracking deviation and the highest auxiliary frequency modulation net income as targets, comprehensively considering the output constraint of each device and the voltage constraint of the system, and designing a secondary frequency modulation control strategy based on SMPC, so that the rapid and accurate tracking of the power grid AGC instruction and the optimal economical efficiency and safety of the adjustment process are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation control, and in particular to a control method for multiple microgrids to jointly participate in secondary frequency modulation of a distribution network. Background Art

[0002] With the rapid development of my country's economy and the increase in electricity demand, the depletion of non-renewable energy has been accelerated, and the environment has also been polluted. Therefore, distributed power sources have been widely used. The large-scale access of renewable energy has brought greater challenges to the voltage and frequency control of the power grid. The microgrid composed of source, load and storage on the distribution side, as a new type of subject participating in auxiliary services, has brought new possibilities for supplementing the voltage and frequency regulation capabilities on the power generation side. However, there are still problems that need to be solved, such as insufficient exploration of dynamic response capabilities of resources, insufficient utilization of the advantages of resource complementarity and mutual assistance, difficulty in predicting real-time photovoltaic output fluctuations, and difficulty in balancing the safety and economy of microgrid regulation. Summary of the invention

[0003] The purpose of the present invention is to provide a control method for multiple microgrids jointly participating in secondary frequency modulation of distribution network, so as to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a control method for multiple microgrids to jointly participate in secondary frequency modulation of distribution network, comprising the following steps:

[0005] Step 1: Establish a microgrid-assisted secondary frequency regulation control architecture, establish a mathematical model for photovoltaic, energy storage and charging stations to participate in secondary frequency regulation, and establish a microgrid overall control model based on the system active power-voltage sensitivity;

[0006] When the output of uncontrollable photovoltaic power fluctuates, based on the Markov chain principle, a scenario tree model of photovoltaic uncertain power is generated according to real-time measurement data to obtain several typical scenarios and corresponding probabilities in the prediction time domain;

[0007] Based on the control model and scenario tree model, with the goal of minimizing the AGC (automatic generation control, used to maintain grid frequency stability) command tracking deviation and maximizing the auxiliary frequency regulation net benefit, the output constraints of each device and the system voltage constraints are calculated, and a secondary frequency regulation control strategy based on SMPC (predictive control based on a stochastic model, used to optimize grid frequency regulation operation) is established;

[0008] Step 2: Establish a secondary frequency regulation model based on the AGC command sent from the regional power grid to the microgrid at a certain time as ΔP AGC ;

[0009] Based on the simplification of the detailed control model of the photovoltaic converter, within the acceptable error range, the first-order inertia link is used to represent its active dynamic transfer characteristics;

[0010] Based on the energy storage converter control model, the energy storage active reference value of the Pf droop characteristic is obtained; based on the adjustable capacity of a single charging pile, all adjustable charging piles connected to the same node of the distribution network are aggregated into a charging station to jointly participate in the secondary frequency regulation; the microgrid node active power-voltage model is calculated;

[0011] Step 3: Establish a secondary frequency regulation control strategy. Based on the resource models within the microgrid and the node active power-voltage model, obtain the discretized state space model of DDVPP participating in secondary frequency regulation; calculate the compensation fee for AGC ancillary services and the active power regulation cost.

[0012] Furthermore, in step 2, the AGC instruction sent from the regional power grid to the microgrid at a certain moment is ΔP AGC , the power allocation equation is:

[0013]

[0014] Where: N pv,c 、N b and N e are the number of controllable photovoltaic, energy storage and charging stations, ΔP PV,p is the active output regulation of the pth controllable photovoltaic, ΔP BESS,b is the active output regulation of the b-th energy storage, ΔP CP,e is the active output regulation of the e-th charging station, ΔP PV,un is the active output fluctuation of all uncontrollable photovoltaics, ΔP d Indicates the deviation between the sum of the actual active adjustment amounts of each adjustable device and the AGC instruction.

[0015] Furthermore, in step 2, the active power dynamic transfer characteristic is:

[0016]

[0017] Where: ΔP solar,p is the change in active power input to the DC side of the pth PV converter, T pv,p is its inertia time constant.

[0018] In step 2, the energy storage active reference value P of the Pf droop characteristic BESS,ref It is expressed as:

[0019]

[0020] in: It is the active power regulation instruction sent by the secondary frequency regulation controller to the energy storage; k bess is the inverse of the energy storage droop coefficient, i.e. k bess =1 / k dr,P ;ω *As the reference angular frequency, take ω * =100π; ω is the actual angular frequency.

[0021] In step 2, the power output model after the charging pile converter model is aggregated is expressed as:

[0022]

[0023] Where: ΔP CP is the change in active output of all charging piles in working state in the charging station. is the overall active power regulation command received by the charging station, T cp is the response time constant of the charging pile.

[0024] Furthermore, in step 3, the frequency regulation revenue and the compensation cost C of AGC ancillary services are calculated. r It can be expressed as:

[0025] C r =C AGC ·ln(K ddvpp +1);

[0026] Where: C AGC is the frequency modulation compensation coefficient, K ddvpp It is a comprehensive performance evaluation index of microgrid participating in secondary frequency regulation auxiliary service. ddvpp It consists of three indicators: adjustment rate K1, adjustment accuracy K2 and response time K3, and its expression is as follows:

[0027] K ddvpp =K1·K2·K3;

[0028] The specific expressions of K1, K2, and K3 are:

[0029]

[0030] Where: P N Adjust the power for a given AGC; P t1 , P t2 They represent the regulated power actually emitted by the microgrid at time t1 and t2 respectively, ΔP is the size of the power tracking deviation, and ΔT is the sampling time interval.

[0031] Calculate the regulation cost, the objective function of the secondary frequency modulation controller is:

[0032]

[0033] Among them, β m is the probability of each scene in the scene tree, R f2 and G f2 is the weight matrix of each part in the objective function, where:

[0034]

[0035] Where: G pv , G bess and G cp is the weighting coefficient matrix.

[0036] The active regulation cost of photovoltaic energy storage is:

[0037] C bess =c bess ·P bess Δt;

[0038] Where: C bess is the active regulation cost of energy storage in the period of Δt; c bess is the unit active power regulation cost coefficient of energy storage;

[0039] Analogous to the comprehensive regulation cost of photovoltaic power generation, the comprehensive cost of energy storage frequency regulation is expressed as:

[0040]

[0041] in: is the comprehensive frequency regulation cost of energy storage, α bess is the comprehensive cost coefficient of energy storage;

[0042] Analogous to the comprehensive regulation cost of photovoltaic power generation, the comprehensive cost of charging stations participating in auxiliary frequency regulation is:

[0043]

[0044] in: is the comprehensive frequency regulation cost of the charging station, α cp is the comprehensive cost coefficient of the charging station.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] (1) A mathematical model of photovoltaic, energy storage and charging stations participating in secondary frequency regulation is established, and then the overall control model of the microgrid is derived in combination with the system active power-voltage sensitivity, laying the model foundation for subsequent strategy design: If the output of uncontrollable photovoltaic fluctuates, based on the Markov chain principle, a scenario tree model of photovoltaic uncertain power is generated according to real-time measurement data, thereby obtaining a series of typical scenarios and corresponding probabilities in the prediction time domain; Based on the above control model and scenario tree model, with the goal of minimizing the AGC command tracking deviation and maximizing the net benefit of auxiliary frequency regulation, the output constraints of each device and the system voltage constraints are comprehensively considered, and a secondary frequency regulation control strategy based on SMPC is designed, so as to achieve rapid and accurate tracking of the power grid AGC command and the optimal economy and safety of the regulation process.

[0047] (2) In view of the volatility and randomness of the output of renewable energy on the power supply side, and the time-varying and certain randomness of the energy demand of the load-side energy-consuming equipment, the control of the joint auxiliary secondary frequency regulation of multiple microgrids; the compensation cost is positively correlated with the frequency regulation performance index, that is, it is positively correlated with the regulation rate, accuracy and response time index. Therefore, for the power tracking control in the real-time stage, the frequency regulation performance index can be improved by improving the regulation accuracy, that is, minimizing the power tracking error, thereby achieving the goal of maximizing the auxiliary frequency regulation benefits.

[0048] (3) In terms of regulation cost, the cost of microgrid participating in secondary frequency regulation mainly includes active network loss cost and active output cost of each device. The regulation cost can be reduced by reasonably setting the weighted matrix parameters in the objective function. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a control flow chart of the present invention;

[0050] Figure 2 This is a block diagram of the controlled model of the microgrid participating in secondary frequency regulation of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example:

[0053] See also Figure 1-2 ,The present invention provides a technical solution: a control method for multiple microgrids to jointly participate in secondary frequency modulation of distribution network;

[0054] 1. Step 1: Design the microgrid-assisted secondary frequency regulation control architecture.

[0055] Step 2: Establish a secondary frequency modulation model.

[0056] Step 3: Propose a secondary frequency modulation control strategy.

[0057] 2. Design the microgrid auxiliary secondary frequency regulation control architecture, including:

[0058] Step 1: Establish a mathematical model for the participation of photovoltaic, energy storage and charging stations in secondary frequency regulation, and then derive the overall control model of the microgrid in combination with the system active power-voltage sensitivity, laying the foundation for the subsequent strategy design:

[0059] Step 2: If the output of uncontrollable photovoltaic power fluctuates, based on the Markov chain principle, a scenario tree model of photovoltaic uncertain power is generated according to real-time measurement data, thereby obtaining a series of typical scenarios and corresponding probabilities in the prediction time domain;

[0060] Step 3: Based on the above control model and scenario tree model, with the goal of minimizing the AGC instruction tracking deviation and maximizing the auxiliary frequency regulation net benefit, the secondary frequency regulation control strategy based on SMPC is designed by comprehensively considering the output constraints of each device and the system voltage constraints, so as to achieve fast and accurate tracking of the power grid AGC instructions and the optimal economy and safety of the regulation process.

[0061] 3. Establish a secondary frequency modulation model, the method is:

[0062] (1) AGC instruction allocation model

[0063] Assume that the AGC command sent by the regional power grid to the microgrid at a certain time is ΔP AGC , we can get the following power allocation equation:

[0064]

[0065] Where: N pv,c 、N b and N e are the number of controllable photovoltaic, energy storage and charging stations, ΔP PV,p is the active output regulation of the pth controllable photovoltaic, ΔP BESS,b is the active output regulation of the b-th energy storage, ΔP CP,e is the active output regulation of the e-th charging station, ΔP PV,un is the active output fluctuation of all uncontrollable photovoltaics, ΔP d Indicates the deviation between the sum of the actual active adjustment amounts of each adjustable device and the AGC instruction.

[0066] (2) Photovoltaic secondary frequency modulation model

[0067] Based on the simplification of the detailed control model of the photovoltaic converter, within the acceptable error range, the first-order inertia link can be used to express its active dynamic transfer characteristics as follows:

[0068]

[0069] Where: ΔP solar,p is the change in active power input to the DC side of the pth PV converter, T pv,p is its inertia time constant.

[0070] For controllable photovoltaics working in load reduction control mode, its active input can be considered as the active reference value issued by the controller, that is, Therefore, its secondary frequency modulation model can be further expressed as:

[0071]

[0072] For uncontrollable photovoltaics, in order to reduce the order of the control model, the impact of its spatial distribution on system control can be ignored, and all uncontrollable photovoltaic units in the microgrid are aggregated into an equivalent photovoltaic power station and an aggregate model is established. At the same time, given the inertia time constant T of the photovoltaic converter pv,p Compared with the secondary frequency modulation control cycle, it is a fast dynamic process. To simplify the analysis, it can be assumed that all photovoltaic converters have the same time constant, that is, T pv,p =T pv The active power transfer model of the equivalent uncontrollable photovoltaic power station after aggregation is:

[0073]

[0074] Where: ΔP solar,un It is the change in active input of all uncontrollable photovoltaics.

[0075] (3) Energy storage secondary frequency modulation model

[0076] From the energy storage converter control model, it can be seen that the energy storage active reference value P with Pf droop characteristic BESS,ref It can be expressed as:

[0077]

[0078] Where: It is the active power regulation instruction sent by the secondary frequency regulation controller to the energy storage; k bess is the inverse of the energy storage droop coefficient, i.e. k bess =1 / k dr,P ;ω * As the reference angular frequency, take ω * =100π; ω is the actual angular frequency.

[0079] Similar to the above analysis, the active input-output relationship of energy storage is approximately simplified to a first-order inertia link. Based on equation (5), the secondary frequency modulation model of energy storage can be obtained as follows:

[0080]

[0081] Where: ΔP BESS is the change in active output of the energy storage power station, T bess is the inertia time constant of the energy storage converter.

[0082] (4) Charging station secondary frequency modulation model

[0083] Since the adjustable capacity of a single charging pile is small, all adjustable charging piles connected to the same node of the distribution network can be aggregated into a charging station to jointly participate in secondary frequency regulation. The charging pile converter model mostly adopts constant power control, so the power output model after aggregation can be simplified as follows by analogy with photovoltaics:

[0084]

[0085] Where: ΔP CP is the change in active output of all charging piles in working state in the charging station, is the overall active power regulation command received by the charging station, T cp is the response time constant of the charging pile.

[0086] (5) Microgrid node active power-voltage model

[0087] In order to reduce the impact of active power regulation of the microgrid on the voltage in the system, the voltage of important nodes is added to the control model as a state variable. This embodiment represents the relationship between the node voltage change and the active power regulation amount based on voltage sensitivity. If the impact of reactive power change is ignored, the voltage of the i-th node can be expressed as:

[0088]

[0089] Where: U i,0 is the voltage of node i in steady state; is the active voltage sensitivity matrix J UP The (i,j)th element in the system, N is the number of nodes in the system, ΔP j is the active power change of node j.

[0090] 4. A secondary frequency modulation control strategy is proposed, and the method is as follows:

[0091] 4.1 Microgrid secondary frequency regulation prediction model

[0092] According to the resource models within the microgrid and the active power-voltage models of important nodes (4), (6), (7), (8), and discretizing the above equation, the discretized state space model of DDVPP participating in secondary frequency regulation can be obtained as follows:

[0093]

[0094] Where: x f2 (k) is the overall state variable of the system where DDVPP participates in secondary frequency modulation, u f2 (k) is the system control variable, w f2 (k) is the system disturbance variable, z f2 (k) is the system control output variable, y f2 (k) is the system constraint output variable, Af2 , B f2 、E f2 , C f2,c , C f2,b is the parameter matrix.

[0095] 4.2 Objective function design considering the net benefit of auxiliary frequency regulation

[0096] One of the important goals of microgrids participating in ancillary services is to maximize the net benefits of ancillary services. Therefore, the goals of microgrids participating in secondary frequency regulation can be summarized as follows: (1) maximizing the benefits of auxiliary frequency regulation, and (2) minimizing the active power regulation cost. Next, the two goals are analyzed separately, and then the objective function of the microgrid secondary frequency regulation controller is designed.

[0097] In terms of frequency regulation revenue, the compensation cost of AGC ancillary services C r It can be expressed as:

[0098] C r =C AGC ·ln(K ddvpp +1) (10)

[0099] Where: C AGC is the frequency modulation compensation coefficient, K ddvpp It is a comprehensive performance evaluation index of microgrid participating in secondary frequency regulation auxiliary service. ddvpp It consists of three indicators: adjustment rate K1, adjustment accuracy K2 and response time K3, and its expression is as follows:

[0100] K ddvpp =K1·K2·K3 (11)

[0101] The specific expressions of K1, K2, and K3 are:

[0102]

[0103] Where: P N Adjust the power for a given AGC; P t1 , P t2 They represent the regulated power actually emitted by the microgrid at time t1 and t2 respectively, ΔP is the magnitude of the power tracking deviation, ΔT is the sampling time interval, which is 0.2s in this embodiment; T N The standard response time can be 60s. The value range of the above indicators is [0.1, 2.0]. If the actual calculated value is less than 0.1, the lower limit 0.1 is taken.

[0104] From the above analysis, we can see that the compensation cost is related to the frequency modulation performance index K ddvppIt is positively correlated with the regulation rate, accuracy and response time indicators. Therefore, for the power tracking control in the real-time stage, the regulation accuracy can be improved, that is, the power tracking error ΔP can be minimized. d Ways to increase K ddvpp , thereby achieving the goal of maximizing the auxiliary frequency modulation benefits.

[0105] In terms of regulation cost, the cost of microgrid participating in secondary frequency regulation mainly includes active network loss cost and active output cost of each device. The regulation cost can be reduced by reasonably setting the weighted matrix parameters in the objective function.

[0106] Based on the above analysis, the objective function of the secondary frequency modulation controller can be expressed as follows:

[0107]

[0108] Where: β m is the probability of each scene in the scene tree, R f2 and G f2 is the weight matrix of each part in the objective function, where:

[0109]

[0110] Where: G pv , G bess and G cp is the weighting coefficient matrix.

[0111] In order to achieve the lowest adjustment cost of power output, it is necessary to minimize the output cost of controllable photovoltaic, energy storage and charging stations participating in frequency regulation, and reduce the increase in network loss during the secondary frequency regulation process. The network loss can be represented by the network loss sensitivity model. Therefore, based on the active output cost and network loss sensitivity of photovoltaic, energy storage and charging participating in frequency regulation, the improved weighting coefficient matrix G is designed. f2 .

[0112] The cost of controllable photovoltaics participating in active power regulation is mainly the cost of abandoned light, which can be expressed as: C pv =c pv ΔP PV Δt;

[0113] Where: C pv is the cost of photovoltaic power abandonment during the Δt period; c pv It is the unit cost coefficient of photovoltaic power abandonment, and its value is the same as the electricity price.

[0114] Assume that the grid loss sensitivity of photovoltaic access node i is The cost of active network loss caused by photovoltaic active power regulation can be expressed as:

[0115]

[0116] Where: is the grid loss cost of PV active power regulation, c ep For electricity price, is the network loss cost coefficient. On this basis, the comprehensive regulation cost of controllable photovoltaic per unit time is defined as for:

[0117]

[0118] Where: α pv is the comprehensive cost coefficient of controllable photovoltaics,

[0119] The cost of energy storage participating in active power regulation is mainly depreciation cost. Since this embodiment focuses on studying the coordinated regulation of multiple resources in a microgrid, the active power regulation cost of energy storage is simplified as follows:

[0120] C bess =c bess ·P bess ·Δt (19)

[0121] Where: C bess is the active regulation cost of energy storage in the period of Δt; c bess It is the unit active power regulation cost coefficient of energy storage. Its value is related to the construction cost of energy storage, etc. and is a fixed value.

[0122] Analogous to the comprehensive regulation cost of photovoltaic power generation, the comprehensive cost of energy storage frequency regulation can be expressed as:

[0123]

[0124] Where: is the comprehensive frequency regulation cost of energy storage, α bess is the comprehensive cost coefficient of energy storage.

[0125] Similarly, the comprehensive cost of the charging station participating in auxiliary frequency regulation can be deduced as shown in formula (21):

[0126]

[0127] Where: is the comprehensive frequency regulation cost of the charging station, α cp is the comprehensive cost coefficient of the charging station.

[0128] In summary, the improved weighting coefficient matrix G pv , G bess , G cp They are respectively pv , α bess , α cp A diagonal matrix composed of .

[0129] 4.3 Constraint Setting

[0130] When designing the controller, it is necessary to fully consider the adjustable characteristics of photovoltaics, energy storage and charging stations, and determine their output constraints for participating in frequency regulation.

[0131] The active power P of the photovoltaic system working under load reduction control is pv It can be adjusted in one direction within a certain range. The adjustment range can be expressed as follows:

[0132]

[0133] Where: k pv is the load reduction factor, ΔP pv Regulate power for photovoltaics.

[0134] Active output of energy storage system P bess It can be adjusted bidirectionally, and its charge and discharge power constraints can be expressed as:

[0135]

[0136] Where: is the upper limit of the discharge power of energy storage, It is the upper limit of charging power of energy storage.

[0137] The state of charge constraint of the energy storage system is:

[0138] SOC min ≤SOC≤SOC max (twenty four)

[0139] Where: SOC max , SOC min They are the upper and lower limits of the energy storage charge state, respectively.

[0140] Since the charging pile only participates in system regulation by reducing the charging power, its active power can only be adjusted in one direction. Based on this, its active power adjustable range can be expressed as:

[0141] 0≤ΔP cp ≤k cp P cp,max (25)

[0142] Where: ΔP cp is the adjustment amount of the charging pile output power, P cp,max is the upper limit of the charging power of the charging pile, k cp is the adjustment coefficient.

[0143] At the same time, in order to ensure the safety of the voltage in the microgrid system during power regulation, the voltage constraints of important nodes are set as follows:

[0144] U min ≤U i ≤U max (26)

[0145] Where: U max and U min They are the upper and lower limits of the voltage safety range respectively.

[0146] In addition, the method proposed in this embodiment also has certain limitations in some aspects and needs to be improved. In the actual distribution network, if the configuration of distributed power sources, energy storage and adjustable loads is different in type, quantity, capacity, etc., based on the ideas proposed in this embodiment, a multi-type resource secondary frequency regulation model and a scenario tree model of uncertain output of distributed power sources can be established in this scenario, and a corresponding secondary frequency regulation controller can be designed to improve the engineering adaptability of the proposed strategy.

[0147] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for multiple microgrids to jointly participate in secondary frequency modulation of distribution network, characterized in that: The following steps are involved: Step 1: Establish a microgrid-assisted secondary frequency regulation control architecture, establish a mathematical model for photovoltaic, energy storage and charging stations to participate in secondary frequency regulation, and establish a microgrid overall control model based on the system active power-voltage sensitivity; When the output of uncontrollable photovoltaic power fluctuates, based on the Markov chain principle, a scenario tree model of photovoltaic uncertain power is generated according to real-time measurement data to obtain several typical scenarios and corresponding probabilities in the prediction time domain; Based on the control model and scenario tree model, with the goal of minimizing the AGC command tracking deviation and maximizing the auxiliary frequency regulation net benefit, the output constraints of each device and the system voltage constraints are calculated, and a secondary frequency regulation control strategy based on SMPC is established; Step 2: Establish a secondary frequency regulation model based on the AGC command sent from the regional power grid to the microgrid at a certain time as ΔP AGC ; Based on the simplification of the detailed control model of the photovoltaic converter, the first-order inertia link is used to represent its active dynamic transfer characteristics; Based on the energy storage converter control model, the energy storage active reference value of the Pf droop characteristic is obtained; based on the adjustable capacity of a single charging pile, all adjustable charging piles connected to the same node of the distribution network are aggregated into a charging station to jointly participate in secondary frequency regulation; Calculate the active power-voltage model of microgrid nodes; Step 3: Establish a secondary frequency regulation control strategy. Based on the resource models within the microgrid and the node active power-voltage model, obtain the discretized state space model of DDVPP participating in secondary frequency regulation; calculate the compensation fee for AGC ancillary services and the active power regulation cost.

2. A control method for multiple microgrids jointly participating in distribution network secondary frequency modulation according to claim 1, characterized in that: In step 2, the AGC command sent from the regional power grid to the microgrid at a certain time is ΔP AGC , the power allocation equation is: Where: N pv,c 、N b and N e are the number of controllable photovoltaic, energy storage and charging stations, ΔP PV,p is the active output regulation of the pth controllable photovoltaic, ΔP BESS,b is the active output regulation of the b-th energy storage, ΔP CP,e is the active output regulation of the e-th charging station, ΔP PV,un is the active output fluctuation of all uncontrollable photovoltaics, ΔP d Indicates the deviation between the sum of the actual active adjustment amounts of each adjustable device and the AGC instruction.

3. A control method for multiple microgrids jointly participating in distribution network secondary frequency modulation according to claim 1, characterized in that: In step 2, the active dynamic transfer characteristic is: Where: ΔP solar,p is the change in active power input to the DC side of the pth PV converter, T pv,p is its inertia time constant.

4. A control method for multiple microgrids jointly participating in distribution network secondary frequency modulation according to claim 1, characterized in that: In step 2, the energy storage active reference value P of the Pf droop characteristic BESS,ref It is expressed as: in: It is the active power regulation instruction sent by the secondary frequency regulation controller to the energy storage; k bess is the inverse of the energy storage droop coefficient, k bess =1 / k dr,P ;ω * As the reference angular frequency, take ω * =100π; ω is the actual angular frequency.

5. A control method for multiple microgrids jointly participating in distribution network secondary frequency modulation according to claim 1, characterized in that: In step 2, the power output model after the charging pile converter model is aggregated is expressed as: Where: ΔP CP is the change in active output of all charging piles in working state in the charging station, is the overall active power regulation command received by the charging station, T cp is the response time constant of the charging pile.

6. A control method for multiple microgrids jointly participating in distribution network secondary frequency modulation according to claim 1, characterized in that: In step 3, calculate the frequency regulation revenue and the compensation cost C of AGC ancillary services r It is expressed as: C r =C AGC ·ln(K ddvpp +1); Where: C AGC is the frequency modulation compensation coefficient, K ddvpp is the comprehensive performance evaluation index of microgrid participating in secondary frequency regulation auxiliary service, among which K ddvpp It consists of three indicators: adjustment rate K1, adjustment accuracy K2 and response time K3, and its expression is as follows: K ddvpp =K1·K2·K3; The specific expressions of K1, K2, and K3 are: Where: P N Adjust the power for a given AGC; P t1 , P t2 They represent the regulated power actually emitted by the microgrid at time t1 and t2 respectively, ΔP is the size of the power tracking deviation, and ΔT is the sampling time interval.

7. A control method for multiple microgrids jointly participating in distribution network secondary frequency modulation according to claim 1, characterized in that: In step 3, the regulation cost is calculated, and the objective function of the secondary frequency modulation controller is: Among them, β m is the probability of each scene in the scene tree, R f2 and G f2 is the weight matrix of each part in the objective function, where: Where: G pv , G bess and G cp is the weighting coefficient matrix.

8. A control method for multiple microgrids jointly participating in distribution network secondary frequency modulation according to claim 1, characterized in that: In step 3, the active regulation cost of photovoltaic energy storage is: C bess =c bess ·P bess ·Δt; Where: C bess is the active regulation cost of energy storage in the period of Δt; c bess is the unit active power adjustment cost coefficient of energy storage; Analogous to the comprehensive regulation cost of photovoltaic power generation, the comprehensive cost of energy storage frequency regulation is expressed as: in: is the comprehensive frequency regulation cost of energy storage, α bess is the comprehensive cost coefficient of energy storage; Analogous to the comprehensive regulation cost of photovoltaic power generation, the comprehensive cost of charging stations participating in auxiliary frequency regulation is: in: is the comprehensive frequency regulation cost of the charging station, α cp is the comprehensive cost coefficient of the charging station.

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