Park energy sharing and distributed scheduling method based on cooperative game

Abstract

The invention discloses a park energy sharing and distributed scheduling method based on a cooperative game, and the method comprises the steps: 1), constructing an energy storage model, an electric vehicle model, an air conditioning system model and a photovoltaic model, and forming a park equipment model; 2) in combination with the park equipment model, constructing a park energy sharing and distributed scheduling model based on a cooperative game; and 3) solving the park energy sharing and distributed scheduling model by adopting an ADMM algorithm to obtain a park energy sharing and distributed scheduling strategy. According to the invention, a park energy sharing and distributed scheduling model based on a cooperative game is established, and an ADMM algorithm is adopted to solve and obtain a park energy sharing and distributed scheduling strategy, so that efficient scheduling and cooperative operation of resources are realized, nearby consumption of distributed energy is facilitated, the advantage of resource sharing is exerted, and the energy utilization efficiency is improved. The operation stability and reliability of the park are improved, information interaction in the scheduling process is reduced, and privacy protection is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy technology, and in particular relates to a park energy sharing and distributed scheduling method based on cooperative game. Background Art

[0002] The promotion of the "dual carbon" strategic goal is promoting the rapid development of renewable energy. However, the current low efficiency of renewable energy utilization has become one of the main obstacles in the transformation of the power grid to a new power system. At the same time, with the increasing proportion of parks in global energy consumption, the problem of park energy management has gradually become prominent and has attracted widespread attention. In this context, the park integrates diversified resources such as distributed renewable energy, flexible loads and energy storage, and its importance in energy management and utilization is becoming increasingly prominent. With its advanced energy management system, the park can deeply tap the regulation potential of internal distributed resources.

[0003] The park is a new type of entity that aggregates multiple distributed resources such as photovoltaics, energy storage, electric vehicles and air-conditioning systems. It integrates electricity production and consumption. Through unified coordination and regulation of the park's internal energy management system, it realizes functions such as data analysis, prediction and decision optimization. Under the background of "dual carbon", people pay more attention to the use of clean energy. More and more parks choose to deploy distributed power generation resources such as wind power and photovoltaics to save electricity costs, reduce carbon emissions and enhance environmental friendliness. On the other hand, the development of Internet of Things technology has promoted the transformation of the park, enabling it to directly and effectively control and manage equipment such as air-conditioning systems, electric vehicles and electric energy storage. The energy management of the park can help it improve energy efficiency, save energy costs, reduce dependence on traditional energy, and achieve energy diversification and sustainable use, which is crucial to the sustainable development of the energy society.

[0004] At present, how to more efficiently manage distributed resources within the park, deeply tap their regulatory potential, and promote the development and utilization of renewable energy has become a key issue that needs to be solved urgently. Summary of the invention

[0005] In order to solve the technical problems mentioned in the above background technology, the technical solution of the present invention is:

[0006] A park energy sharing and distributed scheduling method based on cooperative game includes the following steps:

[0007] S1. Build energy storage model, electric vehicle charging and discharging model, air conditioning system model and photovoltaic model respectively, and then form a park equipment model;

[0008] S2. Combined with the park equipment model, a park energy sharing and distributed scheduling model based on cooperative game is constructed;

[0009] S3. Use the ADMM algorithm to solve the park energy sharing and distributed scheduling model and obtain the park energy sharing and distributed scheduling strategy.

[0010] The construction of the energy storage model in S1 includes:

[0011] S1.1. Constructing energy storage model:

[0012]

[0013] In the formula, t represents the time period; i represents the park; T represents the total scheduling period; represents the operating cost of energy storage in park i; They represent the energy storage charging and discharging power in period t in park i respectively; i ES is the unit charging and discharging loss cost of energy storage in park i; P i ch,max , P i dis,max represents the maximum charging and discharging power of energy storage in park i; and They represent the energy storage levels in period t and period t-1 in park i respectively; They represent the charging and discharging efficiency of energy storage in park i respectively; are the minimum and maximum storage levels of energy storage in park i, respectively.

[0014] The construction of the air conditioning system model in S1 includes:

[0015] S1.2. Construct air conditioning system model:

[0016]

[0017] In the formula, They represent the outdoor temperature and indoor temperature of park i during period t; C i is the equivalent heat capacity of park i; R i is the equivalent thermal resistance of park i; is the cooling capacity of the air conditioning system during period t in park i; represents the indoor temperature in park i during period t-1; and They represent the upper and lower limits of the indoor temperature allowed in period t in park i respectively; is the cooling power of the air conditioner during time period t in park i; is the cooling efficiency of the air conditioner during period t in park i.

[0018] The construction of the electric vehicle model in S1 includes:

[0019] S1.3. Constructing the charging and discharging model of electric vehicles:

[0020]

[0021]

[0022] Where, v represents electric vehicle; N v represents the total number of electric vehicles; represents the charging and discharging loss cost of electric vehicles in park i; The battery cost of the vth electric car in the park; They are the number of charge and discharge cycles and the battery discharge depth of the vth electric vehicle in the park i; is the battery capacity of the vth electric car in the park; They represent the charging and discharging efficiency of the vth electric vehicle in park i respectively; They represent the charging and discharging power of the vth electric vehicle in park i during period t; are the maximum charging and discharging power of the vth electric vehicle in park i during period t; t a ,t l Respectively indicate the start and end time of access; They represent the charge state of the vth electric vehicle in park i at time period t and time period t-1 respectively; They represent the initial state of charge and the state of charge when the vth electric vehicle leaves the park i respectively; They respectively represent the upper and lower limits of the state of charge of the vth electric car in park i during period t.

[0023] The construction of the photovoltaic model in S1 includes:

[0024] S1.4. Constructing photovoltaic model:

[0025]

[0026] In the formula, is the photovoltaic output of park i at time t; is the unit photovoltaic power generation coefficient of park i at time t; Configure the PV capacity for park i.

[0027] The specific process of S2 is as follows:

[0028] S2.1. Construct the objective function of the park energy sharing and distributed scheduling model based on cooperative game:

[0029] S2.2. Construct power balance constraints:

[0030] S2.3. The constraints of the park energy sharing and distributed scheduling model based on cooperative game include equations (1)-(16) and (20)-(21).

[0031] S2.1. The specific steps of constructing the objective function of the park energy sharing and distributed scheduling model based on cooperative game are as follows:

[0032]

[0033] In the formula, min means taking the minimum value; j means the park; N means the number of parks; They represent the electricity purchase and sales costs and grid access costs of park i respectively; is the amount of electricity purchased and sold; t grid,buy , t grid,sell Respectively represent the electricity purchase cost and electricity sales cost; α i,j represents the unit electricity transmission cost between park i and park j; represents the transmission power between park i and park j at time t; |·| represents the absolute value.

[0034] S2.2. The specific steps of constructing power balance constraints are:

[0035]

[0036] In the formula, is the base load of park i at time t; Represents the transmission power between park j and park i at time t.

[0037] The specific process of S3 is as follows:

[0038] S3.1: Set the initial number of iterations k = 0 and the initial value of the dual variable and penalty factor τ p =0;

[0039] S3.2: Express the form of the standard ADMM algorithm;

[0040] S3.3: Construct the augmented Lagrangian function of equation (22);

[0041] S3.4: Update the objective function;

[0042] S3.5: Update dual variables;

[0043] S3.6: Calculate the primal residual and dual residual at the kth iteration;

[0044] S3.7: Compare with the set residual threshold.

[0045] S3.2: The standard ADMM algorithm is expressed as follows:

[0046]

[0047] Where f(x) and g(z) are convex functions; x and z are variables to be optimized; A, B and c are coefficient matrices.

[0048] S3.3: Construct the augmented Lagrangian function of equation (22):

[0049]

[0050] Where λ is the dual variable, i.e., the Lagrange multiplier; ρ is the penalty factor; L ρ (x, z, λ) represents the Lagrangian function; ||·||2 represents the binary norm.

[0051] S3.4: Update the objective function as follows:

[0052]

[0053] In the formula, x i is the variable to be optimized in park i; l i represents the constraint set of park i; τ is the time constant; and are the transmission power between park i and park j, and the transmission power between park j and park i at time t in the kth iteration process; λ i,j,t (k) is the Lagrange multiplier of park i and park j at time t in the kth iteration process.

[0054] S3.5: Update the dual variables:

[0055]

[0056] In the formula, λ i,j,t (k+1) is the Lagrange multiplier of park i and park j at time t during the k+1th iteration; and They are respectively the transmission power between park i and park j at time t, and the transmission power between park j and park i during the k+1th iteration.

[0057] S3.6: The primal residual and dual residual at the kth iteration are calculated as follows:

[0058]

[0059] λ k+1 =λ k +ρ(Ax k+1 +Bz k+1 -c) (28)

[0060] In the formula, x k+1 and z k+1 are the variables to be optimized in the k+1th iteration process; k+1 and λ k are the Lagrange multipliers in the k+1th iteration and the kth iteration respectively; z k is the variable to be optimized in the kth iteration process; arg represents the complex phase angle.

[0061] S3.7: Compare with the set residual threshold, as shown below. If the residual is less than the threshold, the model converges and outputs the result; if the residual is greater than the threshold, the number of iterations k=k+1, and return to step 4 until the convergence condition is met.

[0062] ||r k ||2=||Ax k +Bz k -c||2≤ε pri (29)

[0063] ||s k ||2=||ρA T B(z k -z k-1 )||2≤ε dual (30)

[0064] In the formula, r k and k are the original residual and dual residual in the kth iteration process respectively; x k is the variable to be optimized in the kth iteration; z k-1 is the variable to be optimized in the k-1th iteration; ε pri , ε dual are the convergence thresholds for the primal residual and dual residual respectively.

[0065] The present invention provides an electronic device, comprising one or more processors for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors execute the aforementioned park energy sharing and distributed scheduling method based on cooperative game.

[0066] The present invention provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the aforementioned park energy sharing and distributed scheduling method based on cooperative game when running.

[0067] The beneficial effects brought by adopting the above technical solution are:

[0068] The present invention establishes a park energy sharing and distributed scheduling model based on cooperative game, which can obtain the distributed scheduling strategy of the park, which is conducive to the nearby consumption of distributed energy, gives play to the advantages of resource sharing and mutual assistance, realizes efficient sharing and complementary coordination of resources, and can more flexibly respond to changes in the external environment and fluctuations in internal demand. The system can dynamically adjust the use of photovoltaic power generation, energy storage system and air-conditioning system, thereby optimizing energy utilization efficiency to the greatest extent. The ADMM algorithm adopted by the present invention can effectively solve the model and quickly converge to the optimal value, ensuring the accuracy of the results, while reducing information interaction in the scheduling process and realizing privacy protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The present invention is a flow chart of the method.

[0070] Figure 2 This is the residual change diagram of the ADMM algorithm.

[0071] Figure 3 This is a model convergence analysis diagram under different numbers of parks. DETAILED DESCRIPTION

[0072] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0073] The present invention designs a park energy sharing and distributed scheduling method based on cooperative game, such as Figure 1 As shown, the specific steps are as follows:

[0074] S1: Build energy storage model, electric vehicle model, air conditioning system model and photovoltaic model respectively, and then form the park equipment model;

[0075] S2: Combined with the park equipment model, a park energy sharing and distributed scheduling model based on cooperative game is constructed;

[0076] S3: The ADMM algorithm is used to solve the park energy sharing and distributed scheduling model and obtain the park energy sharing and distributed scheduling strategy.

[0077] The specific process of S1 is as follows:

[0078] S1.1. Constructing energy storage model:

[0079]

[0080] In the formula, t represents the time period; i represents the park; T represents the total scheduling period; represents the operating cost of energy storage in park i; They represent the energy storage charging and discharging power in period t in park i respectively; i ESis the unit charging and discharging loss cost of energy storage in park i; P i ch,max , P i dis,max represents the maximum charging and discharging power of energy storage in park i; and They represent the energy storage levels in period t and period t-1 in park i respectively; They represent the charging and discharging efficiency of energy storage in park i respectively; are the minimum and maximum storage levels of energy storage in park i, respectively.

[0081] S1.2. Construct air conditioning system model:

[0082]

[0083] In the formula, They represent the outdoor temperature and indoor temperature of park i during period t; C i is the equivalent heat capacity of park i; R i is the equivalent thermal resistance of park i; is the cooling capacity of the air conditioning system during period t in park i; represents the indoor temperature in park i during period t-1; and They represent the upper and lower limits of the indoor temperature allowed in period t in park i respectively; is the cooling power of the air conditioner during time period t in park i; is the cooling efficiency of the air conditioner during period t in park i.

[0084] S1.3. Constructing the charging and discharging model of electric vehicles:

[0085]

[0086]

[0087] Where, v represents electric vehicle; N v represents the total number of electric vehicles; represents the charging and discharging loss cost of electric vehicles in park i; The battery cost of the vth electric car in the park; They are the number of charge and discharge cycles and the battery discharge depth of the vth electric vehicle in the park i; is the battery capacity of the vth electric car in the park; They represent the charging and discharging efficiency of the vth electric vehicle in park i respectively; They represent the charging and discharging power of the vth electric vehicle in park i during period t; are the maximum charging and discharging power of the vth electric vehicle in park i during period t; ta ,t l Respectively indicate the start and end time of access; They represent the charge state of the vth electric vehicle in park i at time period t and time period t-1 respectively; They represent the initial state of charge and the state of charge when the vth electric vehicle leaves the park i respectively; They respectively represent the upper and lower limits of the state of charge of the vth electric car in park i during period t.

[0088] S1.4. Constructing photovoltaic model:

[0089]

[0090] In the formula, is the photovoltaic output of park i at time t; is the unit photovoltaic power generation coefficient of park i at time t; Configure the PV capacity for park i.

[0091] The specific process of S2 is as follows:

[0092] S2.1. Construct the objective function of the park energy sharing and distributed scheduling model based on cooperative game:

[0093]

[0094] In the formula, min means taking the minimum value; j means the park; N means the number of parks; They represent the electricity purchase and sales costs and grid access costs of park i respectively; is the amount of electricity purchased and sold; t grid,buy , t grid,sell Respectively represent the electricity purchase cost and electricity sales cost; α i,j represents the unit electricity transmission cost between park i and park j; represents the transmission power between park i and park j at time t; |·| represents the absolute value.

[0095] S2.2. Construct power balance constraints:

[0096]

[0097] In the formula, is the base load of park i at time t; Represents the transmission power between park j and park i at time t.

[0098] S2.3. The constraints of the park energy sharing and distributed scheduling model based on cooperative game include equations (1)-(16) and (20)-(21).

[0099] The specific process of S3 is as follows:

[0100] S3.1: Set the initial number of iterations k = 0 and the initial value of the dual variable and penalty factor τ p =0;

[0101] S3.2: The standard ADMM algorithm is expressed as follows:

[0102]

[0103] Where f(x) and g(z) are convex functions; x and z are variables to be optimized; A, B and c are coefficient matrices.

[0104] S3.3: Construct the augmented Lagrangian function of equation (22):

[0105]

[0106] Where λ is the dual variable, i.e., the Lagrange multiplier; ρ is the penalty factor; L ρ (x, z, λ) represents the Lagrangian function; ||·||2 represents the binary norm.

[0107] S3.4: Update the objective function as follows:

[0108]

[0109] In the formula, x i is the variable to be optimized in park i; l i represents the constraint set of park i; τ is the time constant; and are the transmission power between park i and park j, and the transmission power between park j and park i at time t in the kth iteration process; λ i,j,t (k) is the Lagrange multiplier of park i and park j at time t in the kth iteration process.

[0110] S3.5: Update the dual variables:

[0111]

[0112] In the formula, λ i,j,t (k+1) is the Lagrange multiplier of park i and park j at time t during the k+1th iteration; and They are respectively the transmission power between park i and park j at time t, and the transmission power between park j and park i during the k+1th iteration.

[0113] S3.6: The primal residual and dual residual at the kth iteration are calculated as follows:

[0114]

[0115] λ k+1 =λ k +ρ(Ax k+1 +Bz k+1 -c) (28)

[0116] In the formula, x k+1 and z k+1 are the variables to be optimized in the k+1th iteration process; k+1 and λ k are the Lagrange multipliers in the k+1th iteration and the kth iteration respectively; z k is the variable to be optimized in the kth iteration process; arg represents the complex phase angle.

[0117] S3.7: Compare with the set residual threshold, as shown below. If the residual is less than the threshold, the model converges and outputs the result; if the residual is greater than the threshold, the number of iterations k=k+1, and return to step 4 until the convergence condition is met.

[0118] ||r k ||2=||Ax k +Bz k -c||2≤ε pri (29)

[0119] ||s k ||2=||ρA T B(z k -z k-1 )||2≤ε dual (30) In the formula, r k and k are the original residual and dual residual in the kth iteration process respectively; x k is the variable to be optimized in the kth iteration; z k-1 is the variable to be optimized in the k-1th iteration; ε pri , ε dual are the convergence thresholds for the primal residual and dual residual respectively.

[0120] The present invention provides an electronic device, comprising one or more processors for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors execute the aforementioned park energy sharing and distributed scheduling method based on cooperative game.

[0121] The present invention provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the aforementioned park energy sharing and distributed scheduling method based on cooperative game when running.

[0122] The three selected parks built a park cooperation alliance to conduct case tests to verify the effectiveness of the proposed method. It is assumed that the three parks all contain photovoltaic, energy storage, HVAC, electric vehicles and other equipment. Each park is assumed to contain 100 electric vehicles, and the grid connection time of electric vehicles is stipulated to be 20:00-8:00 the next day. The maximum charging and discharging power of the energy storage is set to 100kW, the energy storage capacity is limited to [60,300]kWh, and the charging efficiency and discharge efficiency are 0.9 and 0.9. The above model is solved by calling the MOSEK solver on the GAMS software. The initial value and penalty factor of the dual multiplier in the ADMM algorithm are set to 0 and 0.01 respectively, and the upper limit of the original residual and the dual residual is set to 10 -3 .

[0123] In order to verify the effectiveness of the ADMM algorithm used in this invention to solve the park energy sharing and distributed scheduling model, the convergence of the ADMM algorithm is analyzed. The residual change is as follows: Figure 2 As shown in the figure. After 52 iterations, the model successfully reached a convergence state, and the entire operation process took 52.1 seconds, which fully met the day-ahead scheduling and requirements. Therefore, the total cost of the park alliance under energy sharing and distributed scheduling can converge quickly, ensuring the accuracy of scheduling and verifying the effectiveness of the proposed algorithm.

[0124] To further test the applicability of the ADMM algorithm, we tested the park alliances with different numbers of parks. The test results are shown in Figure 3 .Depend on Figure 3 It can be seen that for park alliances with different numbers of parks, the model can converge to the optimal value after a certain number of iterations. The more parks there are, the more time it takes in each iteration, and the slower the model converges, which also leads to an increase in the number of iterations. However, it can be found that for an alliance with 15 parks, the model can converge after 78 iterations, which takes 488.4 seconds and still meets the energy sharing and distributed scheduling requirements of the parks.

[0125] The embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A park energy sharing and distributed scheduling method based on cooperative game, characterized in that: The following steps are involved: S1. Build energy storage model, electric vehicle charging and discharging model, air conditioning system model and photovoltaic model respectively, and then form a park equipment model; S2. Combined with the park equipment model, a park energy sharing and distributed scheduling model based on cooperative game is constructed; S3. Use the ADMM algorithm to solve the park energy sharing and distributed scheduling model and obtain the park energy sharing and distributed scheduling strategy.

2. According to claim 1, a park energy sharing and distributed scheduling method based on cooperative game is characterized in that: The construction of the energy storage model in S1 includes: S1.

1. Constructing energy storage model: In the formula, t represents the time period; i represents the park; T represents the total scheduling period; represents the operating cost of energy storage in park i; They represent the energy storage charging and discharging power in period t in park i respectively; i ES is the unit charging and discharging loss cost of energy storage in park i; P i ch,max , P i dis,max represents the maximum charging and discharging power of energy storage in park i; and They represent the energy storage levels in period t and period t-1 in park i respectively; They represent the charging and discharging efficiency of energy storage in park i respectively; are the minimum and maximum storage levels of energy storage in park i, respectively.

3. According to the cooperative game-based park energy sharing and distributed scheduling method of claim 1, it is characterized in that: The construction of the air conditioning system model in S1 includes: S1.

2. Construct air conditioning system model: In the formula, They represent the outdoor temperature and indoor temperature of park i during period t; C i is the equivalent heat capacity of park i; R i is the equivalent thermal resistance of park i; is the cooling capacity of the air conditioning system during period t in park i; represents the indoor temperature in park i during period t-1; and They represent the upper and lower limits of the indoor temperature allowed in period t in park i respectively; is the cooling power of the air conditioner during time period t in park i; is the cooling efficiency of the air conditioner during period t in park i.

4. According to the cooperative game-based park energy sharing and distributed scheduling method of claim 1, it is characterized in that: The construction of the electric vehicle model in S1 includes: S1.

3. Constructing the charging and discharging model of electric vehicles: Where, v represents electric vehicle; N v represents the total number of electric vehicles; represents the charging and discharging loss cost of electric vehicles in park i; The battery cost of the vth electric car in the park; They are the number of charge and discharge cycles and the battery discharge depth of the vth electric vehicle in the park i; is the battery capacity of the vth electric car in the park; They represent the charging and discharging efficiency of the vth electric vehicle in park i respectively; They represent the charging and discharging power of the vth electric vehicle in park i during period t; are the maximum charging and discharging power of the vth electric vehicle in park i during period t; t a ,t l Respectively indicate the start and end time of access; They represent the charge state of the vth electric vehicle in park i at time period t and time period t-1 respectively; They represent the initial state of charge and the state of charge when the vth electric vehicle leaves the park i respectively; They respectively represent the upper and lower limits of the state of charge of the vth electric car in park i during period t.

5. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 1, characterized in that: The construction of the photovoltaic model in S1 includes: S1.

4. Constructing photovoltaic model: In the formula, is the photovoltaic output of park i at time t; is the unit photovoltaic power generation coefficient of park i at time t; Configure the PV capacity for park i.

6. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 1, characterized in that: The specific process of S2 is as follows: S2.

1. Construct the objective function of the park energy sharing and distributed scheduling model based on cooperative game: S2.

2. Construct power balance constraints: S2.

3. The constraints of the park energy sharing and distributed scheduling model based on cooperative game include equations (1)-(16) and (20)-(21).

7. A method for park energy sharing and distributed scheduling based on cooperative game according to claim 5, characterized in that: S2.

1. The specific steps of constructing the objective function of the park energy sharing and distributed scheduling model based on cooperative game are as follows: In the formula, min means taking the minimum value; j means the park; N means the number of parks; They represent the electricity purchase and sales costs and grid access costs of park i respectively; is the amount of electricity purchased and sold; t grid,buy , t grid,sell Respectively represent the electricity purchase cost and electricity sales cost; α i,j represents the unit electricity transmission cost between park i and park j; represents the transmission power between park i and park j at time t; |·| represents the absolute value.

8. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 5 is characterized in that: S2.

2. The specific steps of constructing power balance constraints are: In the formula, is the base load of park i at time t; Represents the transmission power between park j and park i at time t.

9. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 1, characterized in that: The specific process of S3 is as follows: S3.1: Set the initial number of iterations k = 0 and the initial value of the dual variable and penalty factor τ p =0; S3.2: Express the form of the standard ADMM algorithm; S3.3: Construct the augmented Lagrangian function of equation (22); S3.4: Update the objective function; S3.5: Update dual variables; S3.6: Calculate the primal residual and dual residual at the kth iteration; S3.7: Compare with the set residual threshold.

10. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 8, characterized in that: S3.2: The standard ADMM algorithm is expressed as follows: Where f(x) and g(z) are convex functions; x and z are variables to be optimized; A, B and c are coefficient matrices.

11. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 8, characterized in that: S3.3: Construct the augmented Lagrangian function of equation (22): Where λ is the dual variable, i.e., the Lagrange multiplier; ρ is the penalty factor; L ρ (x, z, λ) represents the Lagrangian function; ||·||2 represents the binary norm.

12. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 8, characterized in that: S3.4: Update the objective function as follows: In the formula, x i is the variable to be optimized in park i; l i represents the constraint set of park i; τ is the time constant; and are the transmission power between park i and park j, and the transmission power between park j and park i at time t in the kth iteration process; λ i,j,t (k) is the Lagrange multiplier of park i and park j at time t in the k-th iteration process.

13. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 8, characterized in that: S3.5: Update the dual variables: In the formula, λ i,j,t (k+1) is the Lagrange multiplier of park i and park j at time t during the k+1th iteration; and They are respectively the transmission power between park i and park j at time t, and the transmission power between park j and park i during the k+1th iteration.

14. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 8, characterized in that: S3.6: The primal residual and dual residual at the kth iteration are calculated as follows: l k+1 =λ k +ρ(Ax k+1 +Bz k+1 -c) (28) In the formula, x k+1 and z k+1 are the variables to be optimized in the k+1th iteration process; k+1 and λ k are the Lagrange multipliers in the k+1th iteration and the kth iteration respectively; z k is the variable to be optimized in the kth iteration process; arg represents the complex phase angle.

15. The method for park energy sharing and distributed scheduling based on cooperative game according to claim 8, characterized in that: S3.7: Compare with the set residual threshold, as shown below. If the residual is less than the threshold, the model converges and the result is output; if the residual is greater than the threshold, the number of iterations k = k + 1, and return to step 4 until the convergence condition is met; ||r k ||2=||Ax k +Bz k -c||2≤ε pri (29) ||s k ||2=||ρA T B(z k -z k-1 )||2≤ε dual (30) In the formula, r k and k are the original residual and dual residual in the kth iteration process respectively; x k is the variable to be optimized in the kth iteration; z k-1 is the variable to be optimized in the k-1th iteration; ε pri , ε dual are the convergence thresholds for the primal residual and dual residual respectively.

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