A Control Method and System for the Power at the Point of Common Connection of a Multi - Microgrid System

Abstract

The present invention provides a control method and system for the power at the connection point of a multi - microgrid system. The method includes: obtaining the real - time operation data of the multi - microgrid system, where the real - time operation data includes the real - time new - energy power generation output status, real - time load level status, and real - time energy - storage state of charge status of each microgrid in the multi - microgrid system; controlling the charging and discharging actions of each microgrid according to the real - time operation data and a preset energy - storage scheduling strategy, thereby reducing the power fluctuation of the multi - microgrid system and improving the stability of the operation of the connection point of the multi - microgrid system; wherein, the energy - storage scheduling strategy is to construct a stochastic team - game model of the multi - microgrid system based on the historical operation data of the multi - microgrid system and an initial energy - storage scheduling strategy, and then iteratively optimize the initial energy - storage scheduling strategy using a preset stochastic team - game algorithm based on the stochastic team - game model.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed power generation systems, and particularly to a control method and system for the power at the grid connection point of a multi-microgrid system. Background Art

[0002] Nowadays, the large-scale integration of new energy into the grid has become a prominent feature of the new power system. However, the intermittency and uncertainty of new energy power generation have brought difficulties to the safe and stable operation and economic dispatching of the power system. Microgrids with energy storage devices can effectively promote the consumption of renewable energy and improve the reliability of the power system, and are widely introduced into the distribution system. Energy can be coordinately dispatched among different microgrids through distribution operators to form a multi-microgrid system, improving energy utilization efficiency and enhancing the stability of the power system.

[0003] Existing technologies generally only consider promoting the consumption of new energy, maximizing the power generation benefit of new energy, or suppressing the output fluctuations of new energy in a single way, lacking technologies that can balance both power generation benefits and power smoothing. At the same time, for the problem of the output fluctuations of new energy, most of the existing methods for smoothing the output fluctuations of new energy smooth the power output on the time scale of seconds or minutes to make the microgrid meet the grid connection specifications. However, such methods only consider the real-time regulation or the smoothing effect based on short-term power prediction. Due to the limited capacity of energy storage devices, it is difficult for them to effectively suppress the long-term, large-scale, and frequent fluctuations of new energy, so it is necessary to consider on a long time scale.

[0004] On the other hand, for the control problem of the grid connection of multi-microgrid systems, existing methods mostly adopt the method of centralized control optimization, where a central controller performs global optimization and issues dispatching instructions to each microgrid system. However, in actual situations, the decision variable space of the central controller will increase exponentially with the number of microgrid systems, making centralized optimization calculations difficult. Summary of the Invention

[0005] The present invention provides a control method and system for the power at the grid connection point of a multi-microgrid system. By controlling the multi-microgrid main body to perform energy storage scheduling according to the real-time state of the system, the power mean-variance value at the common grid connection point is reduced, and the stability of the operation of the grid connection point of the multi-microgrid system is improved.

[0006] In a first aspect, an embodiment of the present invention provides a control method for the power at the grid connection point of a multi-microgrid system, including:

[0007] Obtain the real-time operation data of the multi-microgrid system, where the real-time operation data includes the real-time new energy power generation output state, real-time load level state, and real-time energy storage state of charge of each microgrid in the multi-microgrid system;

[0008] Control the charging and discharging actions of each microgrid according to the real-time operation data and a preset energy storage scheduling strategy, so as to reduce the power fluctuation of the multi-microgrid system;

[0009] Among them, the energy storage scheduling strategy is to construct a stochastic team game model of the multi-microgrid system based on the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, and then iteratively optimize the initial energy storage scheduling strategy based on the stochastic team game model using a preset stochastic team game algorithm.

[0010] The embodiment of the present invention provides a method for controlling the power at the connection point of a multi-microgrid system. During the operation of the multi-microgrid system, according to the real-time operation data of the multi-microgrid system, the charging and discharging actions of each microgrid are controlled through a preset energy storage scheduling strategy, so as to reduce the power fluctuation of the multi-microgrid system. Different from the prior art that uses energy storage to smooth the fluctuation of new energy output on the second or minute time scale, the method of the present invention aims at the limited capacity characteristic of energy storage and considers the energy storage scheduling method on the long-term time scale. At the same time, when each microgrid conducts energy storage scheduling, it can take into account the states and strategies of other microgrids, realize the coordination between the energy storage scheduling strategies of multiple microgrids, so that the generated energy storage scheduling strategy can more accurately control the charging and discharging behaviors of each microgrid and improve the stability of the operation of the connection point of the multi-microgrid system.

[0011] Further, the construction of the stochastic team game model of the multi-microgrid system according to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system is specifically as follows:

[0012] According to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, construct the steady-state distribution function of the stochastic team game model, where the historical operation data includes the new energy generation output state, load level state, and energy storage state of charge of each microgrid in the multi-microgrid system, and the steady-state distribution function represents the probability distribution of the microgrid system being in various different states during long-term operation;

[0013] According to the current state and charging and discharging actions of each microgrid in the multi-microgrid system, construct the reward function of the stochastic team game model, and the reward function represents the sum of the powers output by each microgrid after performing the corresponding charging and discharging actions;

[0014] Construct the optimization objective function and the system mean-variance value function of the stochastic team game model according to the steady-state distribution function and the reward function, and the system mean-variance value function is used to evaluate the performance of the multi-microgrid system.

[0015] An embodiment of the present invention provides a method for a stochastic team game model of a multi - microgrid system. Based on the historical operation data of the multi - microgrid system and the initial energy storage scheduling strategy, a stochastic team game model of the multi - microgrid system is constructed by considering the long - term output and fluctuations of the grid - connected point power. The optimization objective function and the system mean - variance value function in the stochastic team game model are used to determine the optimization direction of the stochastic team game model and accurately evaluate the performance of the multi - microgrid system during the optimization process, so that the energy storage scheduling strategy generated by the stochastic team game model based on the initial energy storage scheduling strategy can effectively reduce the power mean - variance value of the common grid - connected point and improve the operation stability of the grid - connected point of the multi - microgrid system.

[0016] In a possible implementation manner, constructing the steady - state distribution function of the stochastic team game model according to the historical operation data and the initial energy storage scheduling strategy of the multi - microgrid system is specifically as follows:

[0017] According to the frequencies of different states of each microgrid in the historical operation data, construct the new - energy output state transition probability matrix and the load - level state transition probability matrix of each microgrid;

[0018] According to the new - energy output state transition probability matrix and the load - level state transition probability matrix of each microgrid, construct the state transition probability matrix of the multi - microgrid system;

[0019] Under the long - term operation state of the multi - microgrid system, construct the steady - state distribution function of the stochastic team game model according to the state transition probability matrix of the multi - microgrid system and the initial energy storage scheduling strategy.

[0020] An embodiment of the present invention provides a method for constructing the steady - state distribution function of a stochastic team game model. According to the historical operation data, the frequencies of different new - energy output states and load - level states of each microgrid can be counted, and then the new - energy output state transition probability matrix and the load - level state transition probability matrix of each microgrid can be obtained, which are used to represent the probabilities of each microgrid in various different states; according to these two probability matrices, the state probability matrix of the multi - microgrid system can be further summarized, and combined with the initial energy storage scheduling strategy, the steady - state distribution function of the stochastic team game model is constructed. Through the steady - state distribution function, the state change law of the multi - microgrid system under the guidance of the energy storage scheduling strategy can be predicted, providing data support for continuously optimizing the energy storage scheduling strategy of the subsequent model.

[0021] Furthermore, constructing the state transition probability matrix of the multi - microgrid system according to the new - energy output state transition probability matrix and the load - level state transition probability matrix of each microgrid, the specific formula is:

[0022]

[0023] Among them, represents the probability that the multi - microgrid system changes from state s to state , N represents the number of microgrids, is the new - energy output state - transition probability matrix of the i - th microgrid, is the load - level state - transition probability matrix of the i - th microgrid, and represent two different new - energy output states of the i - th microgrid, and represent two different load - level states of the i - th microgrid, and represent two different energy - storage state - of - charge states of the i - th microgrid, is the probability that the i - th microgrid takes charge - discharge actions under the condition of the energy - storage state - of - charge .

[0024] Furthermore, the steady - state distribution function of the stochastic team - game model is constructed according to the state - transition probability matrix and the initial energy - storage scheduling strategy of the multi - microgrid system. The specific formula is:

[0025] d π =(d(s1),…,d(s h ))

[0026] Among them, the d π is the steady - state distribution function of the stochastic team - game model, h is the number of states of the multi - microgrid system, π is the joint energy - storage scheduling strategy of the multi - microgrid system, d(s h ) is the probability that state s h appears, and is calculated according to the state - transition probability matrix.

[0027] In a possible implementation manner, the reward function of the stochastic team - game model is constructed according to the current states of each microgrid in the multi - microgrid system and the charge - discharge actions. The specific formula is:

[0028]

[0029] Among them, r t is the reward function of the stochastic team - game model at time t, N is the number of microgrids, is the charge - discharge action of the i - th microgrid at time t, corresponds to the charging action when corresponds to the discharging action when is the new - energy output state of the i - th microgrid at time t, is the load level state of the i-th microgrid at time t.

[0030] Furthermore, the optimization objective function and the system mean-variance value function of the stochastic team game model are constructed according to the steady-state distribution function and the reward function. The specific formula is as follows:

[0031]

[0032] where η π is the long-term average electrical energy of the multi-microgrid system under the control of the energy storage joint scheduling strategy π, T represents the operation time of the microgrid system, r(s t , a t ) represents the reward function of the microgrid system at time t, s t represents the state of the microgrid system at time t, and a t represents the charging and discharging action taken by the microgrid system at time t;

[0033] ζ π is the long-term electrical energy fluctuation variance of the multi-microgrid system under the control of the energy storage joint scheduling strategy π;

[0034] is the optimization objective function of the stochastic team game model, β is the mean-variance balance coefficient, and the specific values of η π and J π can be calculated in combination with the steady-state distribution function and the reward function of the multi-microgrid system;

[0035] V π (s) is the system mean-variance value function of the multi-microgrid system in state s, and the specific value of V π (s) can be calculated in combination with the Poisson equation.

[0036] In a possible implementation manner, the initial energy storage scheduling strategy is iteratively optimized based on the stochastic team game model using a preset stochastic team game algorithm to obtain the energy storage scheduling strategy. Specifically:

[0037] Randomly generate the permutation order of each microgrid;

[0038] According to the optimization objective function and the system mean-variance value function in the stochastic team game model, the first energy storage scheduling strategy of each microgrid is updated in sequence according to the permutation order of each microgrid to obtain the second energy storage scheduling strategy;

[0039] Determine whether the change in the value of the optimization objective function of the multi - microgrid system caused by the second energy storage scheduling strategy is less than a preset threshold. If the change in the optimization objective function value is less than the preset threshold, stop the iterative optimization, and the second energy storage scheduling strategy is the energy storage scheduling strategy; otherwise, continue the iterative optimization of the second energy storage scheduling strategy.

[0040] The embodiment of the present invention provides a method for iteratively optimizing the initial energy storage scheduling strategy using the stochastic team - game model. Based on the optimization objective function and the system mean - variance value function, the energy storage scheduling strategies of each microgrid are continuously updated, and by calculating the impact of the updated energy storage scheduling strategy on the value of the optimization objective function, as the basis for determining whether to stop the iteration, finally, an energy storage scheduling strategy that can optimize the performance of the multi - microgrid system is obtained. During the iterative optimization of the initial energy storage scheduling strategy, the strategy search space at each strategy update corresponds to the action space of a single microgrid entity, and the computational complexity increases linearly with the number of microgrid entities. Compared with the existing centralized control optimization technology, the computational complexity at each strategy update is reduced, and the optimization efficiency of the energy storage scheduling strategy is improved.

[0041] In a possible implementation manner, according to the optimization objective function and the system mean - variance value function in the stochastic team - game model, the first energy storage scheduling strategies of each microgrid are sequentially updated in the arrangement order of each microgrid to obtain a second energy storage scheduling strategy. Specifically:

[0042] Determine the energy storage scheduling strategy update function of each microgrid according to the optimization objective function and the system mean - variance value function in the stochastic team - game model;

[0043] Update the first energy storage scheduling strategies of each microgrid in sequence according to the energy storage scheduling strategy update function of each microgrid to obtain a second energy storage scheduling strategy.

[0044] The embodiment of the present invention further illustrates the specific process of optimizing the energy storage scheduling strategy according to the optimization objective function. Determine the energy storage scheduling strategy update function of each microgrid according to the relevant functions in the stochastic team - game model, and then update the energy storage scheduling strategies of each microgrid based on the update function, determining the direction of each update, so that the energy storage scheduling strategy after each update can further reduce the power fluctuation of the microgrid and improve the stability of the multi - microgrid system at the grid - connection point.

[0045] Further, the energy storage scheduling strategy update function of each microgrid, the specific formula is:

[0046]

[0047] Where For the energy storage scheduling strategy of the j n -th microgrid in state s, k is the number of algorithm iterations, and is the action of other microgrids except microgrid j n in state s.

[0048] In a second aspect, correspondingly, an embodiment of the present invention provides a control system for the grid-connected point power of a multi-microgrid system, including an acquisition module and a control module;

[0049] Among them, the acquisition module is used to acquire the real-time operation data of the multi-microgrid system, and the real-time operation data includes the real-time new energy power generation output state, real-time load level state, and real-time energy storage state of charge of each microgrid in the multi-microgrid system;

[0050] The control module is used to control the charge and discharge actions of each microgrid according to the real-time operation data and a preset energy storage scheduling strategy, so as to reduce the power fluctuation of the multi-microgrid system;

[0051] Among them, the energy storage scheduling strategy is to construct a stochastic team game model of the multi-microgrid system according to the historical operation data of the multi-microgrid system and an initial energy storage scheduling strategy, and then iteratively optimize the initial energy storage scheduling strategy based on the stochastic team game model using a preset stochastic team game algorithm.

[0052] In a possible implementation manner, the control system further includes a model construction module, and the model construction module is used to construct a stochastic team game model of the multi-microgrid system according to the historical operation data of the multi-microgrid system and an initial energy storage scheduling strategy, including a steady-state distribution function construction unit, a reward function construction unit, and an optimization target and performance evaluation unit;

[0053] Among them, the steady-state distribution function construction unit is used to construct a steady-state distribution function of the stochastic team game model according to the historical operation data of the multi-microgrid system and an initial energy storage scheduling strategy, where the historical operation data includes the new energy power generation output state, load level state, and energy storage state of charge of each microgrid in the multi-microgrid system at different times, and the steady-state distribution function represents the probability distribution of the microgrid system being in each different state during long-term operation;

[0054] The reward function construction unit is used to construct a reward function of the stochastic team game model according to the current state and charge and discharge actions of each microgrid in the multi-microgrid system, and the reward function represents the sum of the powers output by each microgrid after performing the corresponding charge and discharge actions;

[0055] The optimization objective and performance evaluation unit is used to construct an optimization objective function and a system mean-variance value function of the stochastic team game model according to the steady-state distribution function and the reward function, and the system mean-variance value function is used to evaluate the performance of the multi-microgrid system.

[0056] Further, the steady-state distribution function construction unit constructs the steady-state distribution function of the stochastic team game model according to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, specifically:

[0057] Construct the new energy output state transition probability matrix and the load level state transition probability matrix of each microgrid according to the frequencies of different states of each microgrid in the historical operation data;

[0058] Construct the state transition probability matrix of the multi-microgrid system according to the new energy output state transition probability matrix and the load level state transition probability matrix of each microgrid;

[0059] Under the long-term operation state of the multi-microgrid system, construct the steady-state distribution function of the stochastic team game model according to the state transition probability matrix and the initial energy storage scheduling strategy of the multi-microgrid system.

[0060] In a possible implementation manner, the control system further includes an optimization module, and the optimization module is used to iteratively optimize the initial energy storage scheduling strategy based on the stochastic team game model using a preset stochastic team game algorithm to obtain the energy storage scheduling strategy, including a random sorting unit, a strategy update unit, and a judgment unit;

[0061] Among them, the random sorting unit is used to randomly generate the arrangement order of each microgrid;

[0062] The strategy update unit is used to sequentially update the first energy storage scheduling strategy of each microgrid according to the optimization objective function and the system mean-variance value function in the stochastic team game model to obtain a second energy storage scheduling strategy;

[0063] The judgment unit is used to judge whether the change in the optimization objective function value of the multi-microgrid system caused by the second energy storage scheduling strategy is less than a preset threshold. If the change in the optimization objective function value is less than the preset threshold, stop the iterative optimization, and the second energy storage scheduling strategy is the energy storage scheduling strategy; otherwise, continue the iterative optimization of the second energy storage scheduling strategy.

[0064] In a possible implementation manner, the policy update unit updates the first energy storage scheduling policies of the respective microgrids in sequence according to the optimization objective function and the system mean-variance value function in the stochastic team game model to obtain second energy storage scheduling policies. Specifically:

[0065] Determine the energy storage scheduling policy update functions of the respective microgrids according to the optimization objective function and the system mean-variance value function in the stochastic team game model;

[0066] Update the first energy storage scheduling policies of the respective microgrids in sequence according to the energy storage scheduling policy update functions of the respective microgrids to obtain second energy storage scheduling policies. Description of the Drawings

[0067] Figure 1 : is a schematic flow chart of a method for controlling the power at the grid connection point of a multi-microgrid system provided by the present invention.

[0068] Figure 2 : is a schematic flow chart of constructing a stochastic team game model in a method for controlling the power at the grid connection point of a multi-microgrid system provided by the present invention.

[0069] Figure 3 : is a schematic flow chart of constructing a steady-state distribution function of a stochastic team game model in a method for controlling the power at the grid connection point of a multi-microgrid system provided by the present invention.

[0070] Figure 4 : is a schematic flow chart of iteratively optimizing an initial energy storage scheduling policy in a method for controlling the power at the grid connection point of a multi-microgrid system provided by the present invention.

[0071] Figure 5 : is a schematic structural diagram of a control system for the power at the grid connection point of a multi-microgrid system provided by the present invention.

[0072] Figure 6 : is a schematic structural diagram of a model construction module in a control system for the power at the grid connection point of a multi-microgrid system provided by the present invention.

[0073] Figure 7 : is a schematic structural diagram of an optimization module in a control system for the power at the grid connection point of a multi-microgrid system provided by the present invention. Detailed Embodiments

[0074] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0075] It should be noted that the step numbers in the text are only for the convenience of explaining specific embodiments and do not serve to limit the execution order of the steps.

[0076] Throughout this specification, the ultimate power supply source of the microgrid described in the embodiments of the present invention is new energy power generation (including photovoltaic power generation, wind power generation, etc.). For example, by configuring a photovoltaic power generation system, the direct current generated by the solar photovoltaic panels is converted into alternating current and input into the power grid, and then the power grid supplies power to the load. In some scenarios, the microgrid will also be matched with some energy storage systems. When there is surplus power in photovoltaic power generation, the surplus power is stored by the energy storage system. When the photovoltaic power generation is lacking or zero, the energy storage system supplies power to the microgrid to achieve stable power supply of the microgrid. The mutual scheduling of this power supply requires a control center (such as a supervision and management system) to allocate. The normal operation of the microgrid requires the normal operation of the control center as the basis. The scenarios of the multi-microgrid system grid connection described in the embodiments of the present invention include, but are not limited to, the scenarios of connecting to the power grid.

[0077] Embodiment 1:

[0078] As Figure 1 shown, Embodiment 1 provides a method for controlling the power at the grid connection point of a multi-microgrid system, including steps S1 to S2:

[0079] Step S1, obtain the real-time operation data of the multi-microgrid system, where the real-time operation data includes the real-time new energy power generation output status, real-time load level status, and real-time energy storage state of charge of each microgrid in the multi-microgrid system;

[0080] Step S2, control the charge and discharge actions of each microgrid according to the real-time operation data and a preset energy storage scheduling strategy, thereby reducing the power fluctuation of the multi-microgrid system; wherein, the energy storage scheduling strategy is to construct a stochastic team game model of the multi-microgrid system based on the historical operation data of the multi-microgrid system and an initial energy storage scheduling strategy, and then iteratively optimize the initial energy storage scheduling strategy based on the stochastic team game model using a preset stochastic team game algorithm.

[0081] An embodiment of the present invention provides a method for controlling the power at the connection point of a multi - microgrid system. During the operation of the multi - microgrid system, according to the real - time operation data of the multi - microgrid system, the charge - discharge actions of each microgrid are controlled through a preset energy storage scheduling strategy, thereby reducing the power fluctuation of the multi - microgrid system. Different from the prior art that uses energy storage to smooth the output fluctuation of new energy on the second or minute time scale, the method of the present invention aims at the limited capacity characteristic of energy storage and considers the energy storage scheduling method in the long - term time scale. At the same time, when each microgrid conducts energy storage scheduling, it can take into account the states and strategies of other microgrids, realize the coordination between the energy storage scheduling strategies of multiple microgrids, so that the generated energy storage scheduling strategy can more accurately control the charge - discharge behaviors of each microgrid and improve the stability of the operation of the multi - microgrid system at the connection point.

[0082] Further, the stochastic team - game model of the multi - microgrid system is constructed according to the historical operation data and the initial energy storage scheduling strategy of the multi - microgrid system, as Figure 2 shown, including steps S31 - S33:

[0083] Step S31: According to the historical operation data and the initial energy storage scheduling strategy of the multi - microgrid system, construct the steady - state distribution function of the stochastic team - game model, where the historical operation data includes the new - energy power generation output state, load - level state, and energy - storage state - of - charge of each microgrid in the multi - microgrid system at different times, and the steady - state distribution function represents the probability distribution of the microgrid system being in each different state during long - term operation;

[0084] Step S32: According to the current state and charge - discharge actions of each microgrid in the multi - microgrid system, construct the reward function of the stochastic team - game model, and the reward function represents the sum of the powers output by each microgrid after performing the corresponding charge - discharge actions;

[0085] Step S33: According to the steady - state distribution function and the reward function, construct the optimization objective function and the system mean - variance value function of the stochastic team - game model, and the system mean - variance value function is used to evaluate the performance of the multi - microgrid system.

[0086] An embodiment of the present invention provides a method for a stochastic team game model of a multi-microgrid system. Based on the historical operation data of the multi-microgrid system and the initial energy storage scheduling strategy, a stochastic team game model of the multi-microgrid system is constructed by considering the long-term output and fluctuations of the grid-connected point power. The optimization objective function and the system mean-variance value function in the stochastic team game model are used to determine the optimization direction of the stochastic team game model and accurately evaluate the performance of the multi-microgrid system during the optimization process, so that the energy storage scheduling strategy generated by the stochastic team game model based on the initial energy storage scheduling strategy can effectively reduce the power mean-variance value of the common grid-connected point and improve the operation stability of the grid-connected point of the multi-microgrid system.

[0087] In a possible implementation manner, in step S31, according to the historical operation data of the multi-microgrid system and the initial energy storage scheduling strategy, the steady-state distribution function of the stochastic team game model is constructed, as Figure 3 shown, including steps S311 to S313:

[0088] Step S311: Construct the new energy output state transition probability matrix and the load level state transition probability matrix of each microgrid according to the frequencies of different states of each microgrid in the historical operation data;

[0089] Step S312: Construct the state transition probability matrix of the multi-microgrid system according to the new energy output state transition probability matrix and the load level state transition probability matrix of each microgrid;

[0090] Step S313: Under the long-term operation state of the multi-microgrid system, construct the steady-state distribution function of the stochastic team game model according to the state transition probability matrix of the multi-microgrid system and the initial energy storage scheduling strategy.

[0091] An embodiment of the present invention provides a method for constructing the steady-state distribution function of a stochastic team game model. According to the historical operation data, the frequencies of different new energy output states and load level states of each microgrid can be counted, and then the new energy output state transition probability matrix and the load level state transition probability matrix of each microgrid can be obtained, which are used to represent the probabilities of various different states of each microgrid; according to these two probability matrices, the state probability matrix of the multi-microgrid system can be further summarized, and combined with the initial energy storage scheduling strategy, the steady-state distribution function of the stochastic team game model is constructed. Through the steady-state distribution function, the state change law of the multi-microgrid system under the guidance of the energy storage scheduling strategy can be predicted, providing data support for continuously optimizing the energy storage scheduling strategy of the subsequent model.

[0092] Further, in step S312, the state transition probability matrix of the multi-microgrid system is constructed according to the new energy output state transition probability matrix and the load level state transition probability matrix of each microgrid. The specific formula is as follows:

[0093]

[0094] Wherein, represents the probability that the multi-microgrid system changes from state s to state , N represents the number of microgrids, is the new energy output state transition probability matrix of the i-th microgrid, is the load level state transition probability matrix of the i-th microgrid, and represent two different new energy output states of the i-th microgrid, and represent two different load level states of the i-th microgrid, and represent two different energy storage charge states of the i-th microgrid, is the probability that the i-th microgrid takes charge and discharge actions under the condition of the energy storage charge state .

[0095] In a preferred embodiment, first, for a system containing N microgrid entities, according to the technical parameters of the new energy and energy storage devices of each microgrid entity i and the historical data of the electrical load, a finite number of new energy output states energy storage charge states and load demand states are divided, and based on the frequencies of the occurrence of each output state in the historical data, a new energy output state transition probability matrix and an electrical load state transition matrix are established. By combining the new energy output states electrical load states energy storage charge states of all microgrid entities, the team stochastic game model is constructed, where i = {1,..., N} is the microgrid number. The system state transition probability P π is jointly determined by the new energy output state transition probability matrix electrical load state transition probability matrix and the energy storage scheduling strategy π = (π 1 ,..., π N ). Specifically, for any two states and , the corresponding element in its P π ​

[0096] Further, in step S313, constructing the steady-state distribution function of the stochastic team game model according to the state transition probability matrix and the initial energy storage scheduling strategy of the multi-microgrid system, the specific formula is:

[0097] d π =(d(s1),…,d(s h ))

[0098] where d π is the steady-state distribution function of the stochastic team game model, h is the number of states of the multi-microgrid system, π is the joint energy storage scheduling strategy of the multi-microgrid system, d(s h ) is the probability of the occurrence of state s h , which is calculated according to the state transition probability matrix.

[0099] In a possible implementation manner, in step S32, constructing the reward function of the stochastic team game model according to the current states and charge / discharge actions of each microgrid in the multi-microgrid system, the specific formula is:

[0100]

[0101] where r t is the reward function of the stochastic team game model at time t, N is the number of microgrids, is the charge / discharge action of the i-th microgrid at time t, corresponds to the charging action when corresponds to the discharging action when is the new energy output state of the i-th microgrid at time t, is the load level state of the i-th microgrid at time t.

[0102] Specifically, the state of the multi-microgrid system at time t is The microgrid entity i selects the action t according to the system state s and its own strategy corresponding to the discharging power of the energy storage, corresponds to the energy storage charging when where is the maximum capacity of the energy storage of microgrid i, and are the minimum and maximum charge / discharge powers allowed for microgrid i. Thus, the energy storage state at the next moment is When all microgrids take actions After that, the power of the grid connection point is This power is used as the common reward function r(s) for all microgrid entities. t ,a t ).

[0103] Further, in step S33, the optimization objective function and system mean-variance value function of the random team game model are constructed according to the steady-state distribution function and the reward function, and the specific formula is:

[0104]

[0105] Among them, η π is the long-term average electric energy of the multi-microgrid system under the control of the energy storage joint dispatch strategy π, T represents the operating time of the microgrid system, r(s t ,a t ) represents the reward function of the microgrid system at time t, s t represents the state of the microgrid system at time t, a t represents the charging and discharging actions taken by the microgrid system at time t;

[0106] ζ π is the long-term electric energy fluctuation variance of the multi-microgrid system under the control of the energy storage joint dispatch strategy π;

[0107] is the optimization objective function of the random team game model, β is the mean-variance balance coefficient, η π and J π The specific value of can be calculated by combining the steady-state distribution function and reward function of the multi-microgrid system;

[0108] V π (s) is the system mean-variance function of the multi-microgrid system in state s, V π The specific value of (s) can be calculated using the Poisson equation.

[0109] Specifically, the long-term electric energy mean of the multi-microgrid system under the control of the energy storage joint scheduling strategy π can be expressed as η π =∑ s∈S d π (s)∑ a∈A r(s,a)π(a|s) calculation, d π (s) is the steady-state distribution function of the multi-microgrid system under the control of the energy storage joint dispatch strategy π, which can be solved by the simultaneous equation d π P π =P π and∑ s d π(s) = 1 is solved to obtain; the corresponding objective function J π can be obtained from J π = ∑ s∈S d π (s)∑ a∈A (r(s,a) - β(r(s,a) - η π )) 2 )π(a|s) is calculated; the specific value of the system mean - variance value function of the multi - microgrid system in state s can be obtained from the Poisson equation V π = r π - J π 1 + P π V π is calculated, where r π = (r π (s1), …, r π (s h )) and for any state s, r π (s) = ∑ a r(s,a)π(a|s). 1 is a unit vector of length s h .

[0110] In a possible implementation manner, the initial energy storage scheduling strategy is iteratively optimized using a preset stochastic team game algorithm based on the stochastic team game model to obtain the energy storage scheduling strategy. As Figure 4 shown, it includes steps S41 - S43:

[0111] Step S41: Randomly generate the permutation order of each microgrid;

[0112] Step S42: According to the optimization objective function and the system mean - variance value function in the stochastic team game model, update the first energy storage scheduling strategy of each microgrid in sequence according to the permutation order of each microgrid to obtain the second energy storage scheduling strategy;

[0113] Step S43: Determine whether the change in the optimization objective function value of the multi - microgrid system caused by the second energy storage scheduling strategy is less than a preset threshold. If the change in the optimization objective function value is less than the preset threshold, stop the iterative optimization, and the second energy storage scheduling strategy is the energy storage scheduling strategy; otherwise, continue the iterative optimization for the second energy storage scheduling strategy.

[0114] An embodiment of the present invention provides a method for iteratively optimizing an initial energy storage scheduling strategy using the stochastic team game model. Based on the optimization objective function, the energy storage scheduling strategies of each microgrid are continuously updated, and the impact of the updated energy storage scheduling strategy on the performance of the multi-microgrid system is calculated through the system mean-variance value function as the basis for determining whether to stop the iteration, and finally an energy storage scheduling strategy that can optimize the performance of the multi-microgrid system is obtained. During the iterative optimization of the initial energy storage scheduling strategy, the strategy search space at each strategy update corresponds to the action space of a single microgrid entity, and the computational complexity increases linearly with the number of microgrid entities. Compared with the centralized control optimization technology in the prior art, the computational complexity at each strategy update is reduced, and the optimization efficiency of the energy storage scheduling strategy is improved.

[0115] Further, in S42, according to the optimization objective function and the system mean-variance value function in the stochastic team game model, the first energy storage scheduling strategies of the respective microgrids are sequentially updated in the arranged order of the respective microgrids to obtain second energy storage scheduling strategies, specifically:

[0116] Determine the energy storage scheduling strategy update function of each microgrid according to the optimization objective function and the system mean-variance value function in the stochastic team game model;

[0117] Update the first energy storage scheduling strategies of the respective microgrids in sequence according to the energy storage scheduling strategy update function of each microgrid to obtain second energy storage scheduling strategies.

[0118] Further, the energy storage scheduling strategy update function of each microgrid has the following specific formula:

[0119]

[0120] Where is the energy storage scheduling strategy of the j n -th microgrid in state s, k is the number of algorithm iterations, is the action of other microgrids except microgrid j n in state s.

[0121] Specifically, in the process of steps S41 to S43, first initialize the energy storage scheduling strategy π (0) =(π 1,(0) ,…,π N,(0) ) of the microgrid system, and the number of polling times k = 0; calculate the state transition matrix (0) corresponding to the initial strategy π Long-term electrical energy mean and the system mean-variance value function

[0122] For the k-th loop, let randomly generate a permutation j1, …, j consisting of numbers from 1 to N N . First, use the energy storage scheduling strategy update function to update the energy storage scheduling strategy of microgrid j1 The energy storage scheduling strategies of other microgrids remain unchanged.

[0123] When the energy storage scheduling strategy of microgrid j1 is updated to then the combined energy storage scheduling strategy of the microgrid system needs to be updated to and recalculate as well as

[0124] Successively update the energy storage scheduling strategies of microgrids j1, …, j N . After completing the update of the energy storage scheduling strategies of the permutation j1, …, j N , let At this time, the optimization objective function corresponding to the energy storage scheduling strategy of the microgrid system has

[0125] Judge whether it holds, where ∈ is the algorithm convergence accuracy, which is set to a small value. If it holds, stop the iteration and obtain the energy storage scheduling strategy π of the multi-microgrid system (k+1) . If it does not hold, let k = k + 1 and continue to iteratively update the energy storage scheduling strategy.

[0126] Embodiment 2:

[0127] Correspondingly, as Figure 5 shown, Embodiment 2 provides a control system for the grid-connected power of a multi-microgrid system, including an acquisition module 10 and a control module 20;

[0128] Among them, the acquisition module 10 is used to acquire the real-time operation data of the multi-microgrid system, and the real-time operation data includes the real-time new energy power generation output status, real-time load level status, and real-time energy storage state of charge of each microgrid in the multi-microgrid system;

[0129] The control module 20 is used to control the charge and discharge actions of each microgrid according to the real-time operation data and a preset energy storage scheduling strategy, so as to reduce the power fluctuation of the multi-microgrid system; among them, the energy storage scheduling strategy is to construct a stochastic team game model of the multi-microgrid system based on the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, and then iteratively optimize the initial energy storage scheduling strategy based on the stochastic team game model using a preset stochastic team game algorithm.

[0130] In a possible implementation manner, the control system further includes a model construction module 30, and the model construction module 30 is configured to construct a stochastic team game model of the multi-microgrid system according to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, as Figure 6 shown, including a steady-state distribution function construction unit 301, a reward function construction unit 302, and an optimization objective and performance evaluation unit 303;

[0131] Among them, the steady-state distribution function construction unit 301 is configured to construct a steady-state distribution function of the stochastic team game model according to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, where the historical operation data includes the new energy generation output states, load level states, and energy storage state of charge states of each microgrid in the multi-microgrid system at different times, and the steady-state distribution function represents the probability distribution of the microgrid system being in various different states during long-term operation;

[0132] The reward function construction unit 302 is configured to construct a reward function of the stochastic team game model according to the current states and charge and discharge actions of each microgrid in the multi-microgrid system, and the reward function represents the sum of the powers output by each microgrid after performing the corresponding charge and discharge actions;

[0133] The optimization objective and performance evaluation unit 303 is configured to construct an optimization objective function and a system mean-variance value function of the stochastic team game model according to the steady-state distribution function and the reward function, and the system mean-variance value function is used to evaluate the performance of the multi-microgrid system.

[0134] Further, the steady-state distribution function construction unit 301 constructs the steady-state distribution function of the stochastic team game model according to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, specifically:

[0135] Construct a new energy output state transition probability matrix and a load level state transition probability matrix of each microgrid according to the frequencies of different states of each microgrid in the historical operation data;

[0136] Construct a state transition probability matrix of the multi-microgrid system according to the new energy output state transition probability matrix and the load level state transition probability matrix of each microgrid;

[0137] Construct the steady-state distribution function of the stochastic team game model according to the state transition probability matrix and the initial energy storage scheduling strategy of the multi-microgrid system under the long-term operation state of the multi-microgrid system.

[0138] In a possible implementation manner, the control system further includes an optimization module 40. The optimization module 40 is configured to iteratively optimize an initial energy storage scheduling strategy based on the stochastic team game model using a preset stochastic team game algorithm to obtain the energy storage scheduling strategy. As Figure 7 shown, it includes a random sorting unit 401, a strategy update unit 402, and a judgment unit 403;

[0139] Among them, the random sorting unit 401 is configured to randomly generate the arrangement order of the respective microgrids;

[0140] The strategy update unit 402 is configured to sequentially update the first energy storage scheduling strategy of the respective microgrids according to the optimization objective function and the system mean-variance value function in the stochastic team game model according to the arrangement order of the respective microgrids to obtain a second energy storage scheduling strategy;

[0141] The judgment unit 403 is configured to judge whether the change in the optimization objective function value of the multi-microgrid system caused by the second energy storage scheduling strategy is less than a preset threshold. If the change in the optimization objective function value is less than the preset threshold, the iterative optimization is stopped, and the second energy storage scheduling strategy is the energy storage scheduling strategy; otherwise, the iterative optimization is continued for the second energy storage scheduling strategy.

[0142] In a possible implementation manner, the strategy update unit 402 sequentially updates the first energy storage scheduling strategy of the respective microgrids according to the optimization objective function and the system mean-variance value function in the stochastic team game model according to the arrangement order of the respective microgrids to obtain a second energy storage scheduling strategy. Specifically:

[0143] Determine the energy storage scheduling strategy update function of the respective microgrids according to the optimization objective function and the system mean-variance value function in the stochastic team game model;

[0144] Sequentially update the first energy storage scheduling strategy of the respective microgrids according to the energy storage scheduling strategy update function of the respective microgrids to obtain a second energy storage scheduling strategy.

[0145] An embodiment of the present invention provides a control system for the power at the connection point of a multi-microgrid system. During the operation of the multi-microgrid system, according to the real-time operation data of the multi-microgrid system, the charging and discharging actions of each microgrid are controlled through a preset energy storage scheduling strategy, thereby reducing the power fluctuation of the multi-microgrid system. Different from the prior art that uses energy storage to smooth the output fluctuation of new energy on the second or minute time scale, the method of the present invention, in view of the limited capacity characteristic of energy storage, considers the energy storage scheduling method on the long-term time scale. At the same time, when each microgrid performs energy storage scheduling, it can take into account the states and strategies of other microgrids, realize the coordination between the energy storage scheduling strategies of multiple microgrids, so that the generated energy storage scheduling strategy can more accurately control the charging and discharging behaviors of each microgrid, and improve the stability of the operation of the connection point of the multi-microgrid system.

[0146] The more detailed working principle and step flow of this embodiment can, but are not limited to, refer to the relevant records in Embodiment 1.

[0147] The specific embodiments described above have further detailed the purpose, technical solution and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A control method for the power at the connection point of a multi - micro - grid system, characterized in that, Including: Obtain the real-time operation data of the multi-microgrid system, where the real-time operation data includes the real-time new energy power generation output status, real-time load level status, and real-time energy storage state of charge of each microgrid in the multi-microgrid system; Control the charge and discharge actions of each microgrid according to the real-time operation data and a preset energy storage scheduling strategy, so as to reduce the power fluctuation of the multi-microgrid system; Among them, the energy storage scheduling strategy is to construct a stochastic team game model of the multi-microgrid system based on the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, and then use a preset stochastic team game algorithm to iteratively optimize the initial energy storage scheduling strategy based on the stochastic team game model; The construction of the stochastic team game model of the multi-microgrid system according to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system is specifically as follows: According to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system, construct the steady-state distribution function of the stochastic team game model, where the historical operation data includes the new energy power generation output status, load level status, and energy storage state of charge of each microgrid in the multi-microgrid system at different times, and the steady-state distribution function represents the probability distribution of the microgrid system in various different states during long-term operation; According to the current state and charge and discharge actions of each microgrid in the multi-microgrid system, construct the reward function of the stochastic team game model, and the reward function represents the sum of the powers output by each microgrid after performing the corresponding charge and discharge actions; Construct the optimization objective function and the system mean-variance value function of the stochastic team game model according to the steady-state distribution function and the reward function, and the system mean-variance value function is used to evaluate the performance of the multi-microgrid system.

2. The control method for the power at the connection point of a multi - micro - grid system according to claim 1, characterized in that, The construction of the steady-state distribution function of the stochastic team game model according to the historical operation data and the initial energy storage scheduling strategy of the multi-microgrid system is specifically as follows: Construct the new energy output state transition probability matrix and load level state transition probability matrix of each microgrid according to the frequencies of different states of each microgrid in the historical operation data; Construct the state transition probability matrix of the multi-microgrid system according to the new energy output state transition probability matrix and load level state transition probability matrix of each microgrid; Under the long-term operation state of the multi-microgrid system, construct the steady-state distribution function of the stochastic team game model according to the state transition probability matrix and the initial energy storage scheduling strategy of the multi-microgrid system.

3. The control method for the power at the connection point of a multi - micro - grid system according to claim 2, characterized in that, The construction of the state transition probability matrix of the multi-microgrid system according to the new energy output state transition probability matrix and load level state transition probability matrix of each microgrid, the specific formula is: Among them, represents the probability that the multi - microgrid system changes from state s to state , N represents the number of microgrids, is the new - energy output state - transition probability matrix of the i - th microgrid, is the load - level state - transition probability matrix of the i - th microgrid, and represent two different new - energy output states of the i - th microgrid, and represent two different load - level states of the i - th microgrid, and represent two different energy - storage state - of - charge states of the i - th microgrid, is the probability that the i - th microgrid takes charge - and - discharge actions under the condition of the energy - storage state - of - charge .

4. The control method for the power at the connection point of a multi - micro - grid system according to claim 1, characterized in that, The construction of the steady-state distribution function of the stochastic team game model according to the state transition probability matrix and the initial energy storage scheduling strategy of the multi-microgrid system, the specific formula is: d π = (d(s1), …, d(s h )) where d π is the steady-state distribution function of the random team game model, h is the number of states of the multi-microgrid system, π is the joint energy storage scheduling strategy of the multi-microgrid system, and d(s h ) is the probability of the occurrence of state s h , which is obtained by calculating according to the state transition probability matrix.

5. The control method for the power at the connection point of a multi - micro - grid system according to claim 1, characterized in that, Construct the reward function of the stochastic team game model according to the current states of each microgrid and the charge and discharge actions in the multi - microgrid system. The specific formula is as follows: where r t is the reward function of the stochastic team game model at time t, N is the number of microgrids, is the charging and discharging action of the i-th microgrid at time t, corresponds to the charging action when corresponds to the discharging action when is the new energy output state of the i-th microgrid at time t, is the load level state of the i-th microgrid at time t.

6. The control method for the power at the connection point of a multi - micro - grid system according to claim 1, characterized in that, Construct the optimization objective function and the system mean - variance value function of the stochastic team game model according to the steady - state distribution function and the reward function. The specific formula is as follows: Among them, η π is the long-term average electrical energy of the multi-microgrid system under the control of the energy storage joint scheduling strategy π, T represents the operation time of the microgrid system, r(s t , a t ) represents the reward function of the microgrid system at time t, s t represents the state of the microgrid system at time t, a t represents the charge and discharge action taken by the microgrid system at time t; ζ π is the long-term variance of the electrical energy fluctuation of the multi-microgrid system under the control of the energy storage joint dispatching strategy π; is the optimization objective function of the random team game model, β is the mean-variance balance coefficient, η π and J π The specific values of can be calculated in combination with the steady-state distribution function and the reward function of the multi-microgrid system; V π V(s) is the system mean-variance value function of the multi-microgrid system in state s π The specific value of V(s) can be calculated by combining with Poisson's equation.

7. The control method for the power at the connection point of a multi - micro - grid system according to claim 1, characterized in that, Based on the stochastic team game model, use a preset stochastic team game algorithm to iteratively optimize the initial energy storage scheduling strategy to obtain the energy storage scheduling strategy. Specifically: Randomly generate the permutation order of each microgrid; According to the optimization objective function and the system mean - variance value function in the stochastic team game model, sequentially update the first energy storage scheduling strategy of each microgrid according to the permutation order of each microgrid to obtain the second energy storage scheduling strategy; Judge whether the change in the value of the optimization objective function of the multi - microgrid system caused by the second energy storage scheduling strategy is less than a preset threshold. If the change in the optimization objective function value is less than the preset threshold, stop the iterative optimization, and the second energy storage scheduling strategy is the energy storage scheduling strategy; otherwise, continue the iterative optimization for the second energy storage scheduling strategy.

8. A control method for the power at the grid connection point of a multi - microgrid system, characterized in that, According to the optimization objective function and the system mean - variance value function in the stochastic team game model, sequentially update the first energy storage scheduling strategy of each microgrid according to the permutation order of each microgrid to obtain the second energy storage scheduling strategy. Specifically: Determine the energy storage scheduling strategy update function of each microgrid according to the optimization objective function and the system mean - variance value function in the stochastic team game model; Update the first energy storage scheduling strategy of each microgrid in sequence according to the energy storage scheduling strategy update function of each microgrid to obtain the second energy storage scheduling strategy.

9. A control method for the power at the grid connection point of a multi - microgrid system according to claim 8, characterized in that, The energy storage scheduling strategy update function of each microgrid. The specific formula is as follows: where is the energy storage scheduling strategy of the j-th n microgrid in state s, k is the number of algorithm iterations, is the action of other microgrids except microgrid j n in state s.

10. A control system for the power at the grid connection point of a multi - microgrid system, characterized in that, It includes an acquisition module, a control module, and a model construction module; Among them, the acquisition module is used to acquire the real - time operation data of the multi - microgrid system. The real - time operation data includes the real - time new energy power generation output state, the real - time load level state, and the real - time energy storage state of charge of each microgrid in the multi - microgrid system; The control module is used to control the charge and discharge actions of each microgrid according to the real - time operation data and a preset energy storage scheduling strategy, thereby reducing the power fluctuation of the multi - microgrid system; Among them, the energy storage scheduling strategy is obtained by constructing a stochastic team game model of the multi - microgrid system according to the historical operation data and the initial energy storage scheduling strategy of the multi - microgrid system, and then using a preset stochastic team game algorithm to iteratively optimize the initial energy storage scheduling strategy based on the stochastic team game model. The model construction module is used to construct the stochastic team game model of the multi-microgrid system according to the historical operation data of the multi-microgrid system and the initial energy storage scheduling strategy, including a steady-state distribution function construction unit, a reward function construction unit, and an optimization objective and performance evaluation unit; wherein, the steady-state distribution function construction unit is used to construct the steady-state distribution function of the stochastic team game model according to the historical operation data of the multi-microgrid system and the initial energy storage scheduling strategy, where the historical operation data includes the new energy generation output state, load level state, and energy storage state of charge of each microgrid in the multi-microgrid system at different times, and the steady-state distribution function represents the probability distribution of the microgrid system being in various different states during long-term operation; the reward function construction unit is used to construct the reward function of the stochastic team game model according to the current state and charge and discharge actions of each microgrid in the multi-microgrid system, and the reward function represents the sum of the powers output by each microgrid after performing the corresponding charge and discharge actions; the optimization objective and performance evaluation unit is used to construct the optimization objective function and the system mean-variance value function of the stochastic team game model according to the steady-state distribution function and the reward function, and the system mean-variance value function is used to evaluate the performance of the multi-microgrid system.

11. A control system for the power at the grid connection point of a multi - microgrid system according to claim 10, characterized in that, The steady-state distribution function construction unit constructs the steady-state distribution function of the stochastic team game model according to the historical operation data of the multi-microgrid system and the initial energy storage scheduling strategy, specifically as follows: Construct the new energy output state transition probability matrix and load level state transition probability matrix of each microgrid according to the frequencies of different states of each microgrid in the historical operation data; Construct the state transition probability matrix of the multi-microgrid system according to the new energy output state transition probability matrix and load level state transition probability matrix of each microgrid; Under the long-term operation state of the multi-microgrid system, construct the steady-state distribution function of the stochastic team game model according to the state transition probability matrix of the multi-microgrid system and the initial energy storage scheduling strategy.

12. A control system for the power at the grid connection point of a multi - microgrid system according to claim 10, characterized in that, The control system further includes an optimization module, and the optimization module is used to iteratively optimize the initial energy storage scheduling strategy using a preset stochastic team game algorithm based on the stochastic team game model to obtain the energy storage scheduling strategy, including a random sorting unit, a strategy update unit, and a judgment unit; wherein, the random sorting unit is used to randomly generate the arrangement order of each microgrid; The strategy update unit is used to sequentially update the first energy storage scheduling strategy of each microgrid according to the optimization objective function and the system mean-variance value function in the stochastic team game model according to the arrangement order of each microgrid to obtain the second energy storage scheduling strategy; The determination unit is used to determine whether the change in the value of the optimization objective function of the multi - microgrid system caused by the second energy storage scheduling strategy is less than a preset threshold. If the change in the optimization objective function value is less than the preset threshold, the iterative optimization is stopped, and the second energy storage scheduling strategy is the energy storage scheduling strategy; otherwise, the iterative optimization of the second energy storage scheduling strategy is continued.

13. A control system for the power at the grid connection point of a multi - microgrid system according to claim 12, characterized in that, The strategy update unit updates the first energy storage scheduling strategy of each microgrid in sequence according to the optimization objective function and the system mean - variance value function in the stochastic team game model to obtain a second energy storage scheduling strategy. Specifically: Determine the energy storage scheduling strategy update function of each microgrid according to the optimization objective function and the system mean - variance value function in the stochastic team game model; Update the first energy storage scheduling strategy of each microgrid in sequence according to the energy storage scheduling strategy update function of each microgrid to obtain a second energy storage scheduling strategy.

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