A two-stage collaborative dispatching method for multi-microgrids based on GBS-ADMM and generalized Nash equilibrium

Abstract

The present invention discloses a two-stage collaborative dispatching method for multiple microgrids based on GBS‑ADMM and generalized Nash equilibrium, wherein a microgrid day-ahead low-carbon dispatching model is constructed with the goal of minimizing the day-ahead operation cost, and the alternating direction multiplier method is used to solve the problem, and the day-ahead dispatching result is obtained; a microgrid intraday low-carbon dispatching model is constructed with the goal of minimizing the intraday operation cost, and the improved alternating direction multiplier method with Gaussian back substitution is used to solve the problem, and the intraday dispatching result is obtained; a profit distribution model is constructed with the goal of maximizing the microgrid distribution profit, and the NSGA‑III algorithm is used to solve the problem, and the final intraday dispatching result and profit distribution result are obtained. The present invention can provide a dispatching result with the best system operation economy in the two stages of day-ahead and intraday, as well as a fair and reasonable microgrid operation profit distribution result, thereby providing support for multi-time scale dispatching of new power systems, and has certain engineering use value.

Description

Technical Field

[0001] The invention relates to a two-stage collaborative dispatching method for multiple microgrids based on GBS-ADMM and generalized Nash equilibrium, belonging to the technical field of power system dispatching. Background Art

[0002] Traditional thermal power generation has prominent environmental pollution and resource consumption issues. Improving the operating efficiency of existing units, realizing equipment expansion, or promoting the efficient use of renewable energy are the key development directions for low-carbon and clean power systems. Small-scale microgrids can well integrate resources within a certain range, realize flexible dispatching and control, and are also reliable carriers of emerging technologies. However, the margin for a single microgrid to cope with uncertainty is limited. Allowing multiple microgrids to operate in coordination not only takes into account changes in load demand, but also fully explores the potential of various equipment for auxiliary regulation. When multiple microgrids operate in cooperation, since they belong to different stakeholders, a reasonable trading model is needed to coordinate the interests of all parties. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a two-stage coordinated scheduling method for multiple microgrids based on GBS-ADMM and generalized Nash equilibrium, so as to realize two-stage coordinated scheduling of multiple microgrids taking into account renewable energy, load output uncertainty, system economy and environmental protection.

[0004] The present invention adopts the following technical solutions to solve the above technical problems:

[0005] A two-stage coordinated dispatching method for multiple microgrids based on GBS-ADMM and generalized Nash equilibrium includes the following steps:

[0006] Step 1: With the goal of minimizing the day-ahead operation cost, a multi-microgrid day-ahead low-carbon dispatch model is constructed, and the alternating direction multiplier method is used to solve it to obtain the day-ahead dispatch result;

[0007] Step 2: Based on the day-ahead dispatch results, the uncertainty of renewable energy output and load is additionally considered, and the intraday low-carbon dispatch model of multiple microgrids is constructed with the goal of minimizing the intraday operation cost. The model is then solved using the improved alternating direction multiplier method with Gaussian back substitution to obtain the intraday dispatch results.

[0008] Step 3: Based on the intraday dispatch results in step 2, with the goal of maximizing the distributed revenue of each microgrid, a revenue distribution model based on generalized Nash equilibrium is constructed, and the NSGA-III algorithm is used to solve it to obtain the final intraday dispatch results and revenue distribution results.

[0009] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0010] 1. The present invention takes the uncertainty fluctuations of renewable energy and load into consideration, and after the two-stage scheduling of the day-ahead and the day-intraday, considers the distribution of benefits, fully taps the flexible scheduling potential of the source, storage, and load sides within the microgrid, explores the economic benefits and low-carbon environmental benefits of the coordinated operation between microgrids, and constructs a two-stage multi-microgrid coordinated scheduling model of the day-ahead and the day-intraday. The day-ahead stage belongs to a distributed optimization problem, and the day-intraday stage, based on the day-ahead stage, additionally considers the fluctuations of renewable energy and load, changes in the operating status of equipment, and the scheduling of different demand response resources. After the intraday scheduling is completed, the benefits brought by the cooperative operation of the microgrid are distributed according to the concept of generalized Nash equilibrium.

[0011] 2. The present invention can provide the dispatching results with the best system operation economy in the two stages of day-ahead and day-intraday, as well as the fair and reasonable distribution results of microgrid operation income, thereby providing support for the multi-time scale dispatching of new power systems and having certain engineering use value. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of a two-stage collaborative dispatching method of multi-microgrids based on GBS-ADMM and generalized Nash equilibrium of the present invention;

[0013] Figure 2 It is the flowchart of the improved GBS-ADMM for solving the scheduling problem;

[0014] Figure 3 This is the flow chart of NSGA-III solving the revenue allocation problem;

[0015] Figure 4 is the load data considering uncertainty fluctuations in the embodiment, where (a) is the load of microgrid 1, (b) is the load of microgrid 2, and (c) is the load of microgrid 3;

[0016] Figure 5 is the renewable energy output data considering uncertainty fluctuations in the embodiment;

[0017] Figure 6 is the power interaction result of each microgrid during the intraday dispatching stage in the embodiment;

[0018] Figure 7 It is the dispatch result of a microgrid in the intraday stage;

[0019] Figure 8 It is a high-quality solution set of revenue distribution obtained using the NSGA-III algorithm. DETAILED DESCRIPTION

[0020] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0021] like Figure 1 As shown, the present invention proposes a two-stage collaborative scheduling method for multiple microgrids based on GBS-ADMM and generalized Nash equilibrium, and the specific steps are as follows:

[0022] Step 1: Considering the operating characteristics of different units, the time characteristics of different types of demand response resources, as well as the uncertainty of renewable energy and load fluctuations, a day-ahead low-carbon scheduling model of the microgrid is constructed, and the day-ahead scheduling is solved using ADMM to output the day-ahead scheduling results.

[0023] A day-ahead low-carbon dispatch model for microgrids is constructed, and the objective function is set to minimize the operating cost, which takes into account the power sharing between microgrids and the interaction between microgrids and the upper grid:

[0024]

[0025] In the formula, is the demand response cost of the microgrid in the day-ahead stage; The degradation cost of the energy storage system; For carbon emissions and carbon trading costs; Operation and maintenance costs for renewable energy; is the cost of interaction between the microgrid and the external world; Penalty costs for curtailing wind and solar power; The transmission fee required for transmitting electricity between microgrids; is the operating cost of the generator set. For microgrids containing different power generation equipment, the cost calculation method is different.

[0026] For the microgrid set I1 using CHP units, its generator sets are running The cost is:

[0027]

[0028] Where a is the unit power generation cost coefficient of the CHP unit, It is the power generation capacity of CHP unit.

[0029] For the microgrid set I2 containing the carbon capture power plant, its generator sets are running The cost is:

[0030]

[0031] Where b, c, d, and e are the corresponding cost coefficients. is the total output of the carbon capture power plant in the present invention, and its net output Carbon capture operation energy consumption and fixed energy consumption It consists of three parts:

[0032]

[0033] The loads in the model can be divided into rigid loads and flexible loads. Flexible loads participate in demand response, which is divided into price-based demand response (PDR) and incentive-based demand response (IDR) based on the response mechanism. IDR adjusts the output of units with different carbon emission intensities by optimizing the load curve; PDR reduces the output of high-carbon emission units by shaving peaks and filling valleys. Taking into account the differences in response speed between electricity and heat systems, IDR resources are divided into two categories. The response time of Class A IDR is about 1 hour, and the call plan is determined in the day-ahead scheduling stage; the response time of Class B IDR is about 10-20 minutes, and its call plan is determined in the intraday scheduling stage.

[0034] The electrical load in the model Translable electrical load Reduce electrical load And rigid electrical load At the same time, the heat load Heat load reduction and rigid heat load

[0035]

[0036] Therefore, the demand response cost in the day-ahead phase is:

[0037]

[0038] In the formula, is the amount of Class A IDR participating in day-ahead scheduling, that is π ptran , π pDRA , π hcut They are the unit power compensation costs corresponding to the shiftable electrical load (PDR), the removable electrical load (IDR), and the reducible thermal load.

[0039] The operational degradation cost of the energy storage system can be expressed as:

[0040]

[0041] In the formula, π ESS is the unit charge and discharge loss cost, and are the charging and discharging power of the energy storage system respectively.

[0042] Carbon emissions and carbon trading costs can be expressed as:

[0043]

[0044] In the formula, π CO2 is the unit carbon transaction cost coefficient, is the carbon emission of the microgrid, is the carbon quota, which can be expressed as follows according to the selected units:

[0045]

[0046]

[0047] The model also takes into account the operation and maintenance costs of renewable energy in daily operation:

[0048]

[0049] In the formula, is the actual renewable energy output value used in operation, π res is the unit RES operation and maintenance cost coefficient.

[0050] In order to encourage microgrids to increase the renewable energy consumption rate and promote low-carbon operation, the model sets a penalty cost for wind and solar power abandonment:

[0051]

[0052] In the formula, and are wind turbine output and photovoltaic power generation in the day-ahead phase, π waste is the unit penalty cost coefficient.

[0053] When a microgrid is operating, if it purchases electricity or natural gas from the upper grid, it needs to pay the corresponding fees:

[0054]

[0055] In the formula, and are the power purchased and sold by the microgrid to the upper grid respectively; gas is the gas purchase cost per unit volume; and are the purchase and sale prices of electricity between the microgrid and the parent grid, respectively; and are the volumes of natural gas consumed by the CHP unit and gas boiler in the microgrid, respectively.

[0056] Given a unit power grid cost π ex , the interaction cost between microgrids is:

[0057]

[0058] Where P ij,t is the power transmitted from microgrid i to microgrid j.

[0059] Step 2: Based on the day-ahead dispatch results and with the goal of minimizing the intraday operating cost, a microgrid intraday low-carbon dispatch model is constructed, and the improved GBS-ADMM is used to solve it to obtain the intraday dispatch results.

[0060] Construct a daily low-carbon dispatch model for microgrids, and set the objective function to minimize the daily operating cost. In the daily stage, it is necessary to consider the impact of uncertainty on the source and load sides on the measured data:

[0061]

[0062] Where: is the demand response cost during the intraday period, is the Class B IDR participating in intraday scheduling, π pDRB is the corresponding unit power compensation cost. and Adjusted to the actual wind and solar output during the day.

[0063] The GBS-ADMM algorithm is designed to reduce the complexity of solving the multi-MG coordinated dispatch model and improve the model efficiency. For the day-ahead and intra-day stages, the main problem of multi-microgrid coordinated dispatch can be expressed as:

[0064]

[0065] Where I is the set of microgrid groups.

[0066] Assuming that the multi-microgrid is three microgrids, there are multiple couplings in the power balance between the microgrids, and it is necessary to introduce an auxiliary variable P ji,t , decompose the main problem at three microgrids and construct the sub-problems of augmented Lagrangian function:

[0067]

[0068] In each iteration, only the decision variables of a single microgrid are updated, the decision variables of other microgrids are fixed, and the Lagrange multiplier λ is updated during the iterative interpretation. ij .

[0069]

[0070] To simplify the calculation and prepare for the Gaussian back-substitution process, the Lagrange multipliers are degenerated, the number of which is reduced by half, the computational complexity of the iterative process is reduced, and the solution efficiency is improved:

[0071]

[0072] Considering the multiple couplings of different operating constraints and balance constraints in the model, if the direct generalization of the ADMM method is used to solve the intraday scheduling model, there may be non-convergence. Therefore, appropriate compensation is made for variables with unfair information. After updating the Lagrange multiplier, add a Gaussian back-substitution process to correct it if necessary:

[0073]

[0074] In the formula, an appropriately small amount μ∈[0.9,0.95] is the correction coefficient of Gaussian back substitution, which can ensure the convergence of the algorithm.

[0075] After Gaussian back iteration, the residual needs to be calculated to determine whether it has converged. The traditional ADMM sets the penalty factor to a fixed value. If it is not selected properly, it will lead to slow convergence or even no convergence; the solution mechanism of existing commercial solvers will also prolong the single iteration time as the number of iterations increases. Therefore, it is necessary to improve the iteration process and set a dynamic convergence accuracy to segment the overall iteration process. It is not necessary to start the iteration from a fixed point every time. For example, initially set a larger convergence accuracy, stop the iteration when the iterative residual is less than the current accuracy, reduce the current convergence accuracy by 10 times, and use the current result as the initial value of the next iteration, and restart the iteration until the convergence accuracy is met. In this way, the multi-segment hot start of the algorithm is realized, which effectively avoids the problem of too long single-round iteration time in the later stage. The solution process using the improved GBS-ADMM is as follows: Figure 2 shown.

[0076] Step 3: Based on the intraday dispatch results in step 2, with the goal of maximizing the microgrid distribution revenue, a revenue distribution model based on generalized Nash equilibrium is constructed, and the NSGA-III algorithm is used to solve it to obtain the final intraday dispatch results and revenue distribution results.

[0077] Setting W i In order to measure the weight coefficient of each microgrid's contribution, the coefficient is quantified based on the natural logarithm, which effectively amplifies the contribution of a single microgrid to the microgrid group, so that the microgrid gains corresponding voice in the distribution of benefits:

[0078]

[0079]

[0080] In the formula, and They are the net output power and net absorbed power of the microgrid respectively. This ensures that all microgrids participating in the collaborative operation will obtain corresponding weights, and the output power behavior will obtain a higher weight.

[0081] On this basis, the results of revenue distribution are simulated by using the electricity trading behavior between microgrids, and the revenue distribution is referenced to the peak-valley-flat electricity price, so that the microgrids that still transmit power to the outside during the peak electricity price period are allocated more revenue. Daily electricity trading also avoids the impact of electricity price changes on the strategies of microgrids in cooperative operation. Microgrids can only participate in cooperation if the revenue after redistribution is not lower than the revenue during independent operation. Assuming that each microgrid entity knows the bidding strategy of other microgrids, no microgrid can increase its own revenue by changing its own strategy. In this way, the Nash equilibrium state is achieved, which can ensure the fairness and persuasiveness of revenue distribution. Therefore, the objective function is set to maximize the revenue allocated to each microgrid:

[0082]

[0083] In the formula, and are the operating costs in independent operation and intraday phase respectively. ij,t is the power transfer amount between microgrids determined in step 1. It is the transaction price between two microgrids during the trading period.

[0084] Using NSGA-III to solve multi-objective optimization problems such as revenue distribution can introduce widely distributed reference points to avoid falling into local optimal situations. While ensuring population diversity during the iteration process, it also balances convergence well, so as to find high-quality solutions distributed on the Pareto frontier. Figure 3 shown.

[0085] The present invention is introduced with an example below:

[0086] First, three microgrids connected to the distribution network are selected. Figure 4 (a)-(c) and Figure 5 The forecast data of renewable energy and load in a typical day in a typical area is shown in the figure. A multi-microgrid cooperative operation model is constructed in the day-ahead stage to carry out day-ahead dispatch. ADMM is used for distributed solution of day-ahead dispatch.

[0087] Secondly, based on the day-ahead dispatch, assuming that the uncertainty fluctuations of renewable energy and load satisfy the normal distribution with a mean of 0 and a standard deviation of 8% of the predicted value, a multi-microgrid cooperative operation model is constructed in the intraday stage for intraday dispatch. The improved GBS-ADMM solution is used for intraday dispatch, and the dynamic convergence accuracy is set to realize the multi-stage hot start of the algorithm. The dispatch results are as follows: Figure 6 As shown, the power received from other microgrids is positive, and the power transmitted is negative.

[0088] Finally, after the intraday dispatch is completed, the revenue distribution method based on generalized Nash equilibrium is used to redistribute the revenue generated by the cooperative operation of each microgrid. The results of the NSGA-III algorithm are as follows: Figure 8 As shown in the figure, the coordinates represent the amount of revenue that needs to be adjusted for each microgrid during revenue distribution. It can be seen that the results obtained by using the NSGA-III algorithm are concentrated, and a very reasonable distribution result can be obtained after screening.

[0089] The final dispatch result of a microgrid is as follows: Figure 7 As shown in the figure, it can be seen that the proposed multi-microgrid two-stage coordinated dispatching method can effectively absorb renewable energy; reduce the output of high-carbon units in the microgrid, reduce carbon emissions; and improve the independence of the microgrid itself. Cooperation with other microgrids helps to give full play to the flexible dispatching capabilities of the equipment to smooth out the uncertain fluctuations from the source and load sides in actual operation.

[0090] The following table shows the costs of microgrids at different operating stages. It can be seen that cooperative operation reduces the operating costs of microgrids. The profit distribution method proposed based on the concept of generalized Nash equilibrium distributes profits based on the contribution of each microgrid to the whole, ensuring that the profits of each microgrid in the cooperation are improved while taking into account fairness.

[0091]

[0092] The above simulation results verify the effectiveness and practicality of the method proposed in this invention. It shows that a reasonable and fair scheduling scheme can be obtained through a two-stage collaborative scheduling method of multi-microgrids based on GBS-ADMM and generalized Nash equilibrium. It can well guide the scheduling of new power systems under the consideration of source-load uncertainty and has certain engineering practical value.

[0093] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aforementioned two-stage collaborative scheduling method for multi-microgrids based on GBS-ADMM and generalized Nash equilibrium are implemented.

[0094] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the aforementioned multi-microgrid two-stage collaborative scheduling method based on GBS-ADMM and generalized Nash equilibrium.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] The above 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 two-stage collaborative dispatching method for multiple microgrids based on GBS-ADMM and generalized Nash equilibrium, characterized in that: The steps include: Step 1: With the goal of minimizing the day-ahead operation cost, a multi-microgrid day-ahead low-carbon dispatch model is constructed, and the alternating direction multiplier method is used to solve it to obtain the day-ahead dispatch result; Step 2: Based on the day-ahead dispatch results, the uncertainty of renewable energy output and load is additionally considered, and the intraday low-carbon dispatch model of multiple microgrids is constructed with the goal of minimizing the intraday operation cost. The model is then solved using the improved alternating direction multiplier method with Gaussian back substitution to obtain the intraday dispatch results. Step 3: Based on the intraday dispatch results in step 2, with the goal of maximizing the distributed revenue of each microgrid, a revenue distribution model based on generalized Nash equilibrium is constructed, and the NSGA-III algorithm is used to solve it to obtain the final intraday dispatch results and revenue distribution results.

2. The two-stage coordinated dispatching method of multiple microgrids based on GBS-ADMM and generalized Nash equilibrium according to claim 1 is characterized in that: The microgrid day-ahead low-carbon dispatch model in step 1 is specifically as follows: In the formula, is the day-ahead operating cost; is the demand response cost of the microgrid in the day-ahead stage; The degradation cost of the energy storage system; For carbon emissions and carbon trading costs; Operation and maintenance costs for renewable energy; is the cost of interaction between the microgrid and the external world; Penalty costs for curtailing wind and solar power the day before; The transmission fee required for transmitting electricity between microgrids; is the operating cost of the generator set; i represents microgrid i; Based on the response mechanism, the flexible loads in the microgrid are divided into price-based demand response PDR and incentive-based demand response IDR. According to the response speed, IDR is divided into Class A IDR and Class B IDR. The response time of Class A IDR is 1 hour, and the response time of Class B IDR is 10-20 minutes. The demand response cost of the microgrid in the day-ahead stage is expressed as: Where T is the entire scheduling period, are the movable electric loads, is the amount of Class A IDRs participating in day-ahead scheduling, To reduce the heat load, π ptran is the unit power compensation cost of the movable electric load, π pDRA is the unit power compensation fee of Class A IDR participating in day-ahead scheduling, π hcut Compensation cost per unit of power that can reduce heat load; The degradation cost of the energy storage system is expressed as: In the formula, π ESS is the unit charge and discharge loss cost, and are the charging and discharging power of the energy storage system respectively; Carbon emissions and carbon trading costs are expressed as: In the formula, π CO2 is the unit carbon transaction cost coefficient, is the carbon emission of the microgrid, For carbon quotas; For the microgrid set I1 using combined heat and power units, and Respectively expressed as: In the formula, The power generated by the combined heat and power unit is is the heat generation power of the combined heat and power unit, ω1 is the thermal-electric coupling coefficient of the combined heat and power unit, and are the carbon emission coefficients of the combined heat and power unit and the gas boiler, is the heating power of the gas boiler, is the carbon emission coefficient corresponding to the microgrid electricity purchasing behavior, is the power purchased by the microgrid from the upper grid, and are the carbon quotas corresponding to the combined heat and power units and the unit electricity generation of renewable energy, It is the output value of renewable energy actually used in operation; For the microgrid set I2 containing a carbon capture power plant, and Respectively expressed as: In the formula, Total output of carbon capture power plants, is the unit carbon emission intensity, η ccs is the carbon capture efficiency, γ i,t is the flue gas split ratio, The carbon quota corresponding to the unit electricity generated by the carbon capture power plant; The operation and maintenance cost of renewable energy is expressed as: In the formula, π res is the unit renewable energy operation and maintenance cost coefficient; The interaction cost between microgrid and external environment is expressed as: In the formula, π gas is the gas purchase cost per unit volume, and are the volumes of natural gas consumed by the combined heat and power unit and gas boiler in the microgrid, and are the purchase and sale prices of electricity between the microgrid and the upper grid, The power sold by the microgrid to the upper grid; The penalty cost for wind and solar curtailment on the day before is expressed as: In the formula, π waste is the unit penalty cost coefficient, and They are wind turbine output and photovoltaic power generation in the day-ahead phase respectively; The transmission fee to be paid when transmitting electric energy between microgrids is expressed as: In the formula, π ex is the unit power transmission fee, P ij,t is the power transmitted from microgrid i to microgrid j; For the microgrid set I1 using combined heat and power units, the operating cost of the generator set is expressed as: In the formula, a is the unit power generation cost coefficient of the combined heat and power unit; For the microgrid set I2 containing a carbon capture power plant, the operating cost of the generator set is expressed as: Where b, c, d, and e are the corresponding cost coefficients. Energy consumption for carbon capture operation.

3. The two-stage coordinated dispatching method of multiple microgrids based on GBS-ADMM and generalized Nash equilibrium according to claim 2 is characterized in that: In step 2, the microgrid intraday low-carbon dispatch model is specifically as follows: In the formula, is the daily operating cost; is the demand response cost of the microgrid during the intraday phase; The penalty cost for curtailing wind and solar power within the day; The demand response cost of the microgrid during the intraday phase is expressed as: In the formula, is the amount of Class B IDRs participating in intraday scheduling, π pDRB Unit power compensation fee for Class B IDRs participating in intraday dispatch; The penalty cost for curtailing wind and solar power within a day is expressed as: In the formula, and They are the actual outputs of wind turbines and photovoltaic power generation during the day; When using the improved alternating direction multiplier method with Gaussian back substitution to solve the intraday low-carbon scheduling model, set the dynamic convergence accuracy to divide the entire iterative process into segments, set the initial convergence accuracy, stop the iteration when the iterative residual is less than the current convergence accuracy, reduce the current convergence accuracy by 10 times, use the decision variable value and Lagrange multiplier value obtained in the current iteration as the initial value of the next iteration, and restart the iteration until the convergence accuracy meets the standard.

4. The two-stage coordinated dispatching method of multiple microgrids based on GBS-ADMM and generalized Nash equilibrium according to claim 3 is characterized in that: In step 3, the profit distribution model based on generalized Nash equilibrium is as follows: For microgrids containing combined heat and power units or carbon capture power plants, a weight coefficient W is set to measure the contribution of each microgrid. i ,as follows: In the formula, and are the net output power and net absorbed power of the microgrid, respectively, I=I1∪I2; The objective function f of the income distribution model i for: In the formula, is the operating cost when running independently, It is the transaction price between two microgrids during the trading period.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the two-stage collaborative scheduling method of multiple microgrids based on GBS-ADMM and generalized Nash equilibrium as described in any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the two-stage collaborative scheduling method of multiple microgrids based on GBS-ADMM and generalized Nash equilibrium as described in any one of claims 1 to 4 are implemented.

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