A multi-area power grid dispatching optimization method and related device

Abstract

The application belongs to a dispatching optimization method, and aims at the problems of low resource utilization efficiency, low calculation efficiency, high privacy leakage risk and unreasonable incentive mechanism of the existing multi-region dispatching method, and provides a multi-region power grid dispatching optimization method and related device, which solves a multi-energy collaborative optimization model between regions to obtain transaction power and transaction price of multi-region power grid cooperation. In the application, the target of the multi-energy collaborative optimization model between regions is to minimize the operation cost when the regions cooperate, and when the multi-energy collaborative optimization model between regions is solved, an asymmetric Nash bargaining inter-regional cooperation game model is constructed to divide the solution problem of the multi-energy collaborative optimization model between regions into subproblem 1 and subproblem 2. The subproblem 1 is a cost minimization problem of the cooperative alliance composed of the multi-region power grid, and the subproblem 2 is a cooperative alliance revenue distribution subproblem of the cooperative alliance composed of the multi-region power grid, thereby forming a complete cross-region power grid collaborative optimization and revenue distribution mechanism.

Description

TECHNICAL FIELD

[0001] The application belongs to a dispatch optimization method, and particularly relates to a multi-regional power grid dispatch optimization method and related devices. BACKGROUND

[0002] The output characteristics of new energy such as wind power and photovoltaic power have volatility and uncertainty, and large-scale access of the new energy causes problems such as difficulty in power balance and increased complexity of dispatching for the operation of a traditional regional power grid. In order to improve the flexibility of a power system, energy storage technology has been widely applied in the power system. At present, main energy storage technology modes include electrochemical energy storage and pumped storage. Different modes of energy storage technology have significant differences in capacity scale, response speed, life cycle and operation cost, and show obvious heterogeneity. Existing dispatch methods mostly focus on energy storage optimization in a single region, and less consider the complementary characteristics of different modes of energy storage in a cross-regional power grid, resulting in low resource utilization efficiency. In addition, in the process of cross-regional power trading and collaborative operation, different regional power grids also have differences in resource endowment, load characteristics and economic benefits. How to ensure the overall benefit improvement while realizing the fair benefit distribution among participating regions is a key problem affecting whether cooperation can be long-term and stable. SUMMARY

[0003] The application provides a multi-regional power grid dispatch optimization method and related devices to solve the technical problems of low resource utilization efficiency, low calculation efficiency, high privacy leakage risk and unreasonable incentive mechanism in the existing multi-regional dispatch method.

[0004] In order to achieve the above purpose, the application adopts the following technical solutions:

[0005] In a first aspect, the application provides a multi-regional power grid dispatch optimization method, comprising:

[0006] obtaining regional power grid operation data;

[0007] According to the regional power grid operation data, the inter-regional multi-energy collaborative optimization model is solved to obtain the transaction power and transaction price of the multi-regional power grid cooperation as the dispatch optimization scheme; wherein, the objective of the inter-regional multi-energy collaborative optimization model is to minimize the operation cost when the inter-regional cooperation is operated; when the inter-regional multi-energy collaborative optimization model is solved, the inter-regional cooperation game model of asymmetric Nash bargaining is constructed, the solving problem of the inter-regional multi-energy collaborative optimization model is divided into sub-problem 1 and sub-problem 2, and sub-problem 1 and sub-problem 2 are optimized and solved respectively, sub-problem 1 is the cost minimization problem of the cooperative alliance composed of multi-regional power grids, and sub-problem 2 is the revenue distribution sub-problem of the cooperative alliance composed of multi-regional power grids.

[0008] Further, the multi-regional power grid includes a regional power grid 1 and a regional power grid 2; the regional power grid 1 includes a thermal power unit, a photovoltaic power station, a wind power unit, an industrial high-load energy load and an electrochemical energy storage system; the regional power grid 2 includes a thermal power unit, a photovoltaic power station, a wind power unit, a civil load and a pumped storage unit.

[0009] Further, the objective of the inter-regional multi-energy collaborative optimization model is represented as:

[0010]

[0011] Wherein, is the operation cost when the inter-regional cooperation is operated, is the number of thermal power units, is the serial number of the thermal power unit, is the total number of time periods, is the time period thermal power unit operation cost, is the time period new energy unit abandoned wind and light cost, is the time period load side demand response cost, is the time period electrochemical energy storage system operation cost, is the time period pumped storage unit operation cost, is the time period regional power grid and the upper large power grid interaction power purchase and sale cost, is the time period regional power grid interaction power cost.

[0012] Further, the constraints of the inter-regional multi-energy collaborative optimization model include: power balance constraint, thermal power unit operation constraint, electrochemical energy storage system constraint, pumped storage unit constraint, direct current tie line transmission constraint, reducible load constraint, regional power interaction constraint, and power interaction constraint between regional power grid and upper large power grid.

[0013] Further, the inter-regional cooperation game model is represented as:

[0014]

[0015] wherein, is the number of participants in the Nash negotiation subject, is the operation cost of the regional power grid when running independently, that is, the Nash negotiation breaking point, is the operation cost of the regional power grid after participating in the Nash negotiation.

[0016] Further, the sub-problem 1 is represented as:

[0017]

[0018] The sub-problem 2 is represented as:

[0019]

[0020] wherein, is the optimal solution of the regional power grid after participating in the cooperation, is the cross-regional transaction cost of the regional power grid after participating in the Nash negotiation.

[0021] Further, when solving the sub-problem 1 and the sub-problem 2, an accelerated adaptive alternating direction multiplier algorithm is adopted.

[0022] In a second aspect, the application provides a multi-regional power grid dispatching optimization system, comprising:

[0023] a data module configured to acquire regional power grid operation data;

[0024] an optimization module configured to solve an inter-regional multi-energy collaborative optimization model according to the regional power grid operation data, to obtain transaction electricity and transaction price of inter-regional cooperation of the multi-regional power grid as a dispatching optimization scheme; wherein the objective of the inter-regional multi-energy collaborative optimization model is to minimize the operation cost when the inter-regional cooperation is running; when solving the inter-regional multi-energy collaborative optimization model, an asymmetric Nash bargaining inter-regional cooperation game model is constructed to split the solving problem of the inter-regional multi-energy collaborative optimization model into a sub-problem 1 and a sub-problem 2, and the sub-problem 1 and the sub-problem 2 are solved respectively; the sub-problem 1 is a cost minimization problem of a cooperation alliance composed of the multi-regional power grid, and the sub-problem 2 is a cooperation alliance revenue distribution sub-problem of the cooperation alliance composed of the multi-regional power grid.

[0025] In a third aspect, the present application provides an electronic device, comprising: a memory, one or more processors; the memory is coupled with the processor; wherein the memory has computer program code stored therein, the computer program code comprising computer instructions, when the computer instructions are executed by the processor, the electronic device executes the steps of the multi-region power grid dispatch optimization method.

[0026] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium has a computer program stored therein, the computer program is executed by a processor to implement the steps of the multi-region power grid dispatch optimization method.

[0027] Compared with the prior art, the present application has the following beneficial effects:

[0028] The present application provides a multi-region power grid dispatch optimization method, according to the grid operation data, the inter-regional multi-energy collaborative optimization model is solved, and the transaction power and the transaction price of the multi-region power grid cooperation are obtained as the dispatch optimization scheme. In the present application, the objective of the inter-regional multi-energy collaborative optimization model is to minimize the operation cost when the regions cooperate, and when the inter-regional multi-energy collaborative optimization model is solved, the solving problem of the inter-regional multi-energy collaborative optimization model is divided into sub-problem 1 and sub-problem 2 by constructing an asymmetric Nash bargaining inter-regional cooperation game model. Sub-problem 1 is a cost minimization problem of a cooperative alliance composed of multi-region power grids, and sub-problem 2 is a cooperative alliance revenue distribution sub-problem of a cooperative alliance composed of multi-region power grids. In view of the problems of complex operation, low efficiency of energy storage utilization and unstable cooperation mechanism of multi-region power grids under high proportion of new energy access, the present application introduces the cooperation game theory to realize the economic and efficient operation and fair income distribution of the cross-region power system, and divides the solving problem of the inter-regional multi-energy collaborative optimization model into two sub-problems. One is a social cost minimization problem, which is used to optimize the inter-regional energy exchange and energy storage scheduling, and the other is a revenue distribution maximization problem, which is used to determine the economic incentives of the cooperation parties. A complete cross-region power grid collaborative optimization and revenue distribution mechanism is formed, which can output the optimal dispatch strategy and distribution result of each regional power grid under the condition of limited information interaction. The method effectively improves the overall economy and new energy consumption capacity of the power system, realizes the complementary utilization of different types of energy storage, and at the same time ensures the fairness of the income distribution, enhances the stability and sustainability of the multi-region power grid cooperation.

[0029] The present application also provides a multi-region power grid dispatch optimization system, an electronic device and a computer readable storage medium, which have all the advantages of the multi-region power grid dispatch optimization method provided by the present application. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0031] Figure 1 The first flowchart of the multi-region power grid dispatching optimization method of the present application;

[0032] Figure 2 The second flowchart of the multi-region power grid dispatching optimization method of the present application;

[0033] Figure 3 The first flowchart of the multi-region power grid dispatching optimization method of the present application; DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0035] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0036] It should be noted that: similar numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0037] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship of the product of the present application when it is usually placed, which is only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0038] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly inclined. As "horizontal" only means that it is more horizontal than "vertical", it does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0039] In the description of the embodiments of the present application, it should also be noted that, unless otherwise explicitly specified and limited, if the terms "arrange", "mount", "connect", "connect" appear, they should be understood in a broad sense, for example, they can be fixedly connected, or can be detachably connected, or integrally connected; can be mechanically connected, or can be electrically connected; can be directly connected, or indirectly connected through an intermediate medium; can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0040] Under the guidance of the global "carbon peak and carbon neutral" goal, energy transformation has become the core strategic direction for countries to respond to climate change and ensure energy security. As the core carrier of energy consumption and conversion, the power system is undergoing a profound transformation from traditional fossil energy dominance to renewable energy dominance. New energy, with its outstanding advantages of clean and low carbon, renewable resources, and wide distribution, has a continuously increasing installed capacity and power generation proportion in the power system, gradually becoming an important part of power supply, and is widely used in centralized large-scale wind farms, photovoltaic power stations, and distributed household photovoltaic and industrial and commercial distributed wind power scenarios. At the same time, cross-regional power grid interconnection, as an important means of optimizing energy resource allocation, has realized the complementation and mutual aid between different regions through backbone networks such as ultra-high voltage transmission lines, effectively solving the problem of geographical distribution mismatch between new energy resources and load centers, further promoting the large-scale development and consumption of new energy, and becoming an important trend of modern power system development.

[0041] However, the output characteristics of new energy are greatly affected by natural conditions, and there is significant volatility and uncertainty. This characteristic is continuously amplified as the penetration rate of new energy increases, posing a serious challenge to the safe and stable operation of traditional regional power grids. Although the interconnection of cross-regional power grids improves resource allocation efficiency, it also enhances the coupling of power grid operation and further increases the complexity of dispatching. The superposition of new energy fluctuations in different regions poses higher requirements for power balance and security and stability control of the entire interconnected power grid. In addition, during the cross-regional power trading and collaborative operation, there are significant differences among the participants in terms of resource endowment, load characteristics, and economic interest demands. How to coordinate the interests of all parties, while ensuring the maximization of the overall benefits of the interconnected power grid, and achieving a fair and reasonable income distribution among the participating regions, is a key problem affecting the long-term and stable progress of cross-regional cooperation. If the income distribution mechanism is unreasonable, it is easy to cause cooperation contradictions and hinder the smooth development of cross-regional power grid collaborative optimization.

[0042] To cope with the power balance problem brought by large-scale access of new energy and improve the flexibility of power system operation, energy storage technology as a key supporting technology is widely used in the generation, transmission, distribution and use of power system. At present, the mainstream energy storage methods in power system mainly include two categories of electrochemical energy storage and pumped storage. In addition to these two mainstream technologies, there are other energy storage methods such as compressed air energy storage and flywheel energy storage, but they are still not as mature as the first two in terms of application scale and technology. At the same time, for the problem of collaborative optimization of cross-regional power grid, the traditional centralized optimization method is widely used. This method collects all the operation data of regional power grids by establishing a unified optimization model, and makes centralized calculation and formulates cross-regional dispatching scheme and power trading plan to realize the power balance and resource optimization configuration of the whole interconnected power grid.

[0043] Although the existing energy storage technology and centralized optimization method can alleviate some problems faced by power system to a certain extent, there are still many deficiencies, which are difficult to meet the complex needs of cross-regional power grid collaborative operation under high penetration of new energy: from the perspective of energy storage utilization, different types of energy storage have significant differences in capacity scale, response speed, cycle life, operation cost, environmental protection characteristics and other aspects, showing obvious heterogeneity, but the existing dispatching method is mostly limited to the optimization configuration and operation control of energy storage within a single region, without fully considering the complementary characteristics of different types of energy storage in cross-regional power grid, resulting in low overall utilization efficiency of energy storage resources, and it is difficult to fully play the collaborative role of various types of energy storage in fluctuation suppression and power supply guarantee. From the perspective of cross-regional collaborative optimization, although the traditional centralized optimization method can theoretically achieve the global optimization of cross-regional power exchange, it has the problems of low calculation efficiency, high risk of privacy leakage and lack of reasonable incentive mechanism. In summary, the existing technology cannot effectively coordinate the complementary utilization of cross-regional energy storage and the distribution of interests of participants, and cannot fundamentally solve the core problems of power balance, efficient dispatching and fair income distribution of cross-regional power grid under high penetration of new energy. Therefore, a new technical solution is needed to break through the existing bottleneck.

[0044] Based on the above situation, the present application proposes a multi-regional power grid dispatching optimization method and related device, which will be further described in detail below in combination with embodiments and drawings.

[0045] As Figure 1 shown, it is the first flowchart of the multi-regional power grid dispatching optimization method of the present application, which can include:

[0046] S101, obtaining regional power grid operation data.

[0047] In practical applications, the power grid operation data can cover the generation side data, such as unit output, power generation cost, start-stop state, etc.; the transmission side data, such as line transmission capacity, network loss, topological structure, etc.; the load side data, such as regional power consumption load, load prediction curve, etc.; multi-energy coupling data, such as electric-thermal conversion efficiency, gas-electricity linkage parameters, etc.; constraint condition data, such as environmental protection limit, equipment operation limit, etc.

[0048] In S102, according to the regional power grid operation data, a multi-energy collaborative optimization model between regions is solved to obtain the transaction electricity and the transaction price of the multi-regional power grid cooperation as a dispatch optimization scheme. The objective of the multi-energy collaborative optimization model between regions is to minimize the operation cost when the regions cooperate. When the multi-energy collaborative optimization model between regions is solved, an asymmetric Nash bargaining regional cooperation game model is constructed to split the solving problem of the multi-energy collaborative optimization model between regions into subproblem 1 and subproblem 2, and subproblem 1 and subproblem 2 are respectively optimized and solved. Subproblem 1 is a cost minimization problem of a cooperative alliance composed of multi-regional power grids, and subproblem 2 is a cooperative alliance revenue distribution subproblem of the cooperative alliance composed of multi-regional power grids.

[0049] It should be noted that the present application converts the actual demand of multi-regional multi-energy dispatching into a solvable mathematical model through the multi-energy collaborative optimization model between regions, clearly defines the core objective and boundary constraint of minimizing the operation cost when the regions cooperate, and in practical applications, through the description of the multi-energy collaborative constraint, the complementary value of different energy forms is excavated to reduce the overall operation cost of the alliance, thereby providing a clear mathematical framework for subsequent splitting and solving. The traditional single-objective optimization model cannot solve the fairness problem of regional revenue distribution. The introduction of the asymmetric Nash bargaining regional cooperation game model takes into account the differences in resource endowment and negotiation position of each region, splits the original optimization problem into two logically related subproblems, solves the minimum cost and maximum potential revenue of the cooperative alliance as a whole through subproblem 1, and then distributes the cooperative revenue to each region according to the fairness principle through subproblem 2 to ensure that each region benefits from cooperation and improve the stability of the cooperative alliance. The present application balances the overall benefit of the cooperative alliance and the individual benefit of the regional power grid through splitting and solving, improves the stability and sustainability of the cooperative alliance, avoids the breakup of cooperation due to uneven revenue distribution, and provides a fair and efficient decision basis for determining the transaction electricity and the transaction price.

[0050] In addition, in the present application, subproblem 1 is a convex optimization or non-convex optimization problem, which needs to be solved by an efficient algorithm to obtain the optimal operation strategy, and subproblem 2 is based on the result of subproblem 1 and is solved by a formula or a numerical method to obtain a revenue distribution scheme. Finally, the revenue distribution result is converted into executable transaction electricity and transaction price to form a complete dispatch optimization scheme. The real-time performance of the scheme can be ensured, and the output transaction electricity and transaction price have both economy and fairness, which can be directly used as the execution basis for regional power grid dispatching.

[0051] The application fully excavates the generation cost difference of each regional power grid, the load complementary potential and the multi-energy coupling value, reduces the overall operation cost of the cooperation alliance, and compared with the traditional decentralized dispatching mode, can significantly reduce the generation loss, the standby capacity investment and the cross-regional transmission cost, and improve the power grid resource allocation efficiency. With the help of asymmetric Nash bargaining, the fair distribution of income is realized, which not only considers the bargaining power difference of each regional power grid, but also ensures that each regional power grid can obtain higher income from cooperation than independent operation, effectively resolves the interest conflicts between regional power grids, improves the sustainability of cross-regional cooperation, and avoids cooperation breakdown caused by uneven income distribution. Through sub-problem splitting, the complex multi-objective optimization problem is converted into a single-objective problem which can be solved efficiently, reducing the computational complexity and ensuring that the scheme can be generated quickly within the dispatching time window, meeting the needs of real-time dispatching of power grid. The multi-energy collaborative optimization model between regions can fully consider the consumption constraints of renewable energy such as wind power and photovoltaic power, improve the consumption rate of renewable energy through cross-regional dispatching, reduce the consumption of fossil energy and carbon emissions, and provide technical support for the green and low-carbon transformation of power grid.

[0052] As shown in Figure 2 , it is a second flowchart of the multi-regional power grid dispatching optimization method of the application, which can include:

[0053] S201, a regional power grid structure framework considering heterogeneous energy storage difference is proposed.

[0054] In the application, a regional power grid structure framework considering heterogeneous energy storage difference is proposed, which includes two typical regional power grids, which are respectively denoted as regional power grid 1 and regional power grid 2. Specifically:

[0055] The regional power grid 1 is mainly composed of thermal power units, photovoltaic power stations, wind power units, industrial high-load energy loads and electrochemical energy storage systems, which are used to reflect the characteristics of the energy system of the industrial region. The regional power grid 2 is mainly composed of thermal power units, photovoltaic power stations, wind power units, civil loads and pumped storage units, which are used to reflect the characteristics of the energy system of the civil region.

[0056] Among them, the load characteristics of the regional power grid 1 are industrial load smooth and large power consumption, and the electrochemical energy storage system has the characteristics of fast response speed but limited capacity. The load characteristics of the regional power grid 2 are civil load fluctuation, photovoltaic power generation output is sufficient at noon, and pumped storage capacity is large but response speed is slow.

[0057] The regional dispatch center is configured to coordinate the power exchange between the two regional power grids on the basis of ensuring independent operation of each regional power grid. Specifically, when the regional power grid 1 generates surplus power during the peak period of photovoltaic power generation, the regional power grid 2 can transmit power to the regional power grid 1 through the tie line; when the load demand of the regional power grid 1 is high or the energy storage is insufficient, the regional power grid 2 can transmit power to the regional power grid 1 through pumped storage or other power sources to realize cross-regional complementary resources.

[0058] Through the above configuration, the electrochemical energy storage system and pumped storage can be fully utilized to complement each other in capacity scale and response speed, so that the regional power grid 1 transmits surplus power to the regional power grid 2 during the peak period of new energy power generation, and the regional power grid 2 provides power support to the regional power grid 1 during other periods, thereby realizing efficient complementary and coordinated optimization of resources between regions.

[0059] S202, a cross-regional multi-energy collaborative optimization model based on cooperative game is established.

[0060] 1) Objective function of the cross-regional multi-energy collaborative optimization model.

[0061] In the multi-energy collaborative complementary optimization dispatching, each regional power grid can trade power with the upper-level power grid, or complement power with other regional power grids through the DC tie line.

[0062] In the cross-regional multi-energy collaborative optimization model, the upper-level power grid and other regional power grids are both transaction objects, but the cooperation and resource sharing between regions are given priority, and only when the load demand of the region and the interconnected region cannot be met, the power is purchased from the upper-level power grid. In the case of economic optimization, the cross-regional multi-energy collaborative optimization model is shown in formula (1):

[0063]

[0064] wherein, is the operation cost when the regions are cooperatively operated, is the number of thermal power units, is the serial number of the thermal power unit, is the total number of time periods, is the periodic thermal power unit operation cost, is the periodic new energy unit abandoned wind and light cost, is the periodic load side demand response cost, is the periodic electrochemical energy storage system operation cost, is the periodic pumped storage unit operation cost, is Cost of purchasing and selling electricity of the period regional power grid interacting with the superior large power grid, For Cost of power interaction between period regional power grids.

[0065] The above-mentioned costs can be calculated by the following formulas:

[0066] a. Cost of thermal power unit operation.

[0067]

[0068] Wherein, Cost of thermal power unit operation in the period Start-stop state, , , And are the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the coal consumption curve, Output power of the thermal power unit at the moment.

[0069] b. Cost of abandoned wind and light of new energy units.

[0070] (3)

[0071] Wherein, Penalty coefficient of abandoned electricity of new energy units, Abandoned wind power of the period wind farm, Abandoned light power of the period photovoltaic power station.

[0072] c. Demand response cost on the load side.

[0073] (4)

[0074] Wherein, Response compensation income of the period reducible load, Period reducible load, Indicates Unit power reduction compensation unit price in the period. d. Operating cost of electrochemical energy storage system.

[0075]

[0076] (5)

[0077] Wherein, Unit charge-discharge cost of energy storage, , ​​​​​The charging and discharging efficiency of the energy storage device is The charging and discharging efficiency of the energy storage device is The charging and discharging efficiency of the energy storage device is

[0078] e. Pumped storage unit operation cost.

[0079] (6)

[0080] wherein, is The operation cost coefficient of the pumped storage unit in the time period, is The output of the pumped storage unit in the time period.

[0081] e. The cost of purchasing and selling electricity of the regional power grid interacting with the superior large power grid.

[0082] (7)

[0083] wherein, is The purchase price of the regional power grid interacting with the superior large power grid in the time period, is the selling price of the regional power grid interacting with the superior large power grid, is The purchase amount of the regional power grid to the superior large power grid in the time period, is The selling amount of the regional power grid to the superior large power grid in the time period.

[0084] f. The cost of power interaction between regional power grids.

[0085] (8)

[0086] wherein, is The transaction price of the regional power grid and the regional power grid cooperating in the time period, is The transaction electricity amount of the regional power grid and the regional power grid cooperating in the time period, is the number of times of cooperation of the regional power grid.

[0087] 2) Constraint conditions of the multi-energy collaborative optimization model between regions.

[0088] a. Power balance constraint.

[0089] (9)

[0090] wherein, The number of thermal power units. This refers to the serial number of the thermal power unit. For pumped storage units in Power generation during the period For pumped storage units in Pumping power during the period for Time-of-use regional power grid and regional power grid The electricity between them is mutual.

[0091] b. Operating constraints of thermal power units.

[0092] (10)

[0093] (11)

[0094] in, This is the minimum output of the thermal power unit. This is the maximum output of the thermal power unit. for thermal power units The output power, The ramp rate of the thermal power unit. The landslide rate of the thermal power unit.

[0095] c. Constraints of electrochemical energy storage systems.

[0096] (12)

[0097] in, For energy storage Active power during a time period For energy storage devices The amount of charge during a given time period. For energy storage devices Discharge amount during the period For energy storage The discharge efficiency, For energy storage Charging efficiency, For energy storage Maximum discharge power, For energy storage Maximum charging power, For energy storage Maximum active power during the time period For energy storage Minimum active power during a given time period For energy storage State of charge during a period of time For energy storage State of charge during a period of time For the rated capacity of energy storage, For the maximum energy storage capacity, This represents the minimum capacity for energy storage.

[0098] d. Constraints of pumped storage units.

[0099] (13)

[0100] (14)

[0101] in. This is the minimum pumping power of the pumped storage unit. For pumped storage units in Pumping power during the period For pumped storage units in Power generation during the period This is the maximum pumping power of the pumped storage unit. and All are binary variables. This is the maximum power generation capacity of the pumped storage unit. This is the minimum power output of the pumped storage unit. for Water storage capacity of pumped storage units in reservoirs during specific time periods. for Water storage capacity of pumped storage units in reservoirs during specific time periods. This is the average conversion factor during pumping of water by the pumped storage unit. This is the average conversion factor (electricity to water volume) when the pumped storage unit generates electricity. for Water storage capacity of pumped storage units in reservoirs during specific time periods. for The amount of water stored in the reservoir by the pumped storage unit during a given period.

[0102] e. DC tie-line transmission constraints.

[0103] DC tie-line transmission constraints include tie-line planned power constraints and tie-line daily exchange power constraints.

[0104] (15)

[0105] (16)

[0106] in, For connecting lines exist Maximum transmission power during the time period For connecting lines exist the minimum transmission power of the time period, for the tie line in the transmission power of the time period, for the tie line the total value of the planned transaction electric quantity of the whole day.

[0107] f. the load curtailment constraint.

[0108] (17)

[0109] wherein, the output of the load curtailment in the time period, the maximum power of the load curtailment in a single day, the minimum power of the load curtailment in a single day.

[0110] g. the power interaction constraint between regions.

[0111] (18)

[0112] wherein, the upper limit of the power interaction between the regional power grid and the regional power grid , the lower limit of the power interaction between the regional power grid and the regional power grid .

[0113] h. the power interaction constraint between the regional power grid and the superior large power grid.

[0114] (19)

[0115] wherein, the maximum value of the purchased power, the maximum value of the sold power, a state variable for limiting that the regional power grid cannot purchase and sell power at the same time.

[0116] S203, establishing an inter-regional cooperative game model of asymmetric Nash bargaining and solving.

[0117] 1) Establishing an inter-regional cooperative game model of asymmetric Nash bargaining.

[0118] In the inter-regional cooperation game problem, the fairness and rationality of income distribution is the key factor for long-term maintenance of cooperation. The existing Nash bargaining model can balance the interests of multiple parties to a certain extent, but the traditional method is usually based on the symmetric bargaining assumption, that is, it is assumed that the bargaining power of each participating region is the same, so that the distribution result fails to fully reflect the differentiated contributions of each region in terms of power supply, energy storage adjustment and load response. To solve these problems, the present application introduces asymmetric Nash bargaining under the cooperation game framework. The inter-regional cooperation game model based on asymmetric Nash bargaining introduces a bargaining factor reflecting the actual contribution of each region, maximizes the overall cooperation benefit while achieving differentiated income distribution, thus balancing fairness and incentives, and ensuring that each region has the motivation to continue participating in cooperation after weighing the cost and benefit. The inter-regional cooperation game model is specifically expressed as:

[0119]

[0120] Wherein, is the number of participating Nash negotiation subjects, is the running cost of the regional power grid when running independently, that is, the Nash negotiation breaking point, is the running cost of the regional power grid after participating in Nash negotiation, represents the income obtained by the regional power grid participating in Nash negotiation cooperation. The constraint condition indicates that the running cost of the regional power grid after cooperation is less than the running cost before cooperation.

[0121] The inter-regional cooperation game model belongs to a multivariate coupled non-convex nonlinear optimization problem. In order to facilitate solution, the present application equivalently decomposes it into two strictly convex sub-problems, first solves the cost minimization problem of the cooperation alliance as sub-problem 1, and then distributes the income on this basis as sub-problem 2, thereby realizing efficient solution of the inter-regional cooperation game model. Specifically:

[0122] a. Sub-problem 1: Cost minimization problem of cooperation alliance.

[0123]

[0124] By solving equation (21), the optimal amount of power exchange between regional power grids can be obtained.

[0125] b. Sub-problem 2: Cooperation alliance income distribution sub-problem.

[0126] In energy trading, because the purchase price of electricity from the grid is generally higher than the sales price, the economic value of transmitting a unit of electricity to the grid is usually higher than the value of obtaining the same amount of electricity from the grid. This price difference mechanism assigns a higher economic contribution weight to energy supply activities. Asymmetric bargaining models, originating from economics, emphasize that under conditions of information or power asymmetry, different negotiating parties reach a profit-sharing agreement through negotiation based on their own bargaining power.

[0127] First, calculate the total energy provided by each regional power grid in the time domain during the optimization process. and the energy gained :

[0128] (twenty two)

[0129] (twenty three)

[0130] Furthermore, a method based on the natural constant is selected. An exponential function with base is used to construct a nonlinear energy mapping function, which quantifies the bargaining power of each regional power grid based on the contribution of energy sharing. :

[0131] (twenty four)

[0132] in, This represents the maximum electrical energy supplied in each regional power grid. This represents the maximum amount of electrical energy received in each regional power grid.

[0133] Finally, based on Nash negotiation theory, a payment maximization model based on asymmetric Nash bargaining is proposed:

[0134] (25)

[0135] in, For the regional power grid after participation in the cooperation The optimal solution. For the regional power grid after the Nash negotiations Online transaction costs.

[0136] To obtain the optimal solution of the payment revenue maximization model, we take the logarithm of equation (25) and transform the problem of finding the maximum value into the problem of finding the minimum value. The transformation process is as follows:

[0137] (26)

[0138] in, To quantify the regional power grid based on the contribution of participating in energy sharing The extent of their bargaining power.

[0139] 2) Solve the inter-regional cooperative game model based on the Accelerated Alternating Direction Method of Multipliers (AA-ADMM) algorithm.

[0140] This application employs an improved AA-ADMM algorithm to optimize the solution of subproblems 1 and 2. This algorithm can avoid the problem of slow convergence speed caused by improper parameter selection.

[0141] (27)

[0142] in, For the first The penalty factor for the next iteration. For the first The penalty factor for the next iteration. The scaling factor is the ratio of the original residual to the dual residual. , The scaling factor for step size updates is used to increase the penalty factor to balance the residual relationship and accelerate convergence, while the latter is used to decrease the penalty factor to avoid excessive parameters that could cause convergence oscillations. For the first The original residual value of the next iteration. For the first The dual residual value of the next iteration.

[0143] During algorithm optimization, the step size can be dynamically adjusted based on the magnitudes of the original and dual residuals. When the original residual is large, the step size is appropriately increased to balance the variable relationships and accelerate convergence; when the dual residual is large, the step size is appropriately decreased to promote convergence and stabilize the objective function; when the difference between the two is small, the penalty factor is dynamically adjusted to further improve convergence efficiency. Specifically:

[0144] a. Solve the cost minimization problem among regional power grid alliances.

[0145] When equation (28) is satisfied, it indicates that the parties in the regional power grid alliance have reached a consensus on the transaction.

[0146] (28)

[0147] We introduce Lagrange multipliers and penalty factors to construct the augmented Lagrange function for subproblem 1:

[0148] (29)

[0149] in, For regional power grid The augmented Lagrangian function of subproblem 1, For regional power grid and regional power grid exist Lagrange multipliers for the time-phase problem 1 For regional power grid and regional power grid exist The penalty factor for sub-problem 1 of the time period. for Time-of-use regional power grid and regional power grid Electricity traded in cooperation.

[0150] The steps for solving subproblem 1 based on the AA-ADMM algorithm are as follows:

[0151] 1) Initialize the number of iterations Maximum number of iterations for The convergence accuracy is The electricity trading volume of each regional power grid is The initial Lagrange multipliers are The penalty factor is Adaptive parameters .

[0152] 2) During the iterative solution process, each regional power grid interacts and updates information based on the iterative results of other regional power grids. Specifically, after receiving the corresponding iterative information from other regional power grids, each power grid updates its decisions based on its own operating status and constraints. .

[0153] 3) After completing one iteration, update the Lagrange multipliers according to equation (30):

[0154] (30)

[0155] in, For regional power grid and regional power grid exist Time period Lagrange multipliers for subproblem 1 of the next iteration For regional power grid and regional power grid exist Time period Lagrange multipliers for subproblem 1 of the next iteration For regional power grid and regional power grid No. The penalty factor for subproblem 1 in the next iteration. for Periodic regional grid and regional grid first iterative cooperative traded energy, for Periodic regional grid and regional grid first iterative cooperative traded energy.

[0156] 4) Calculate the primal and dual residuals:

[0157] (31)

[0158] (32)

[0159] where, is the regional grid and regional grid first iterative primal residual value, for Periodic regional grid and regional grid first iterative cooperative traded energy, for Periodic regional grid and regional grid first iterative cooperative traded energy. is the regional grid and regional grid first iterative dual residual value, for Periodic regional grid and regional grid first iterative cooperative traded energy, for Periodic regional grid and regional grid first iterative cooperative traded energy.

[0160] 5) Update the penalty factor according to equation (33) and update the iteration number .

[0161] (33)

[0162] where, is the regional grid and regional grid first Penalty factor of sub-problem 1 in the next iteration.

[0163] 6) Judge the convergence of AA-ADMM algorithm.

[0164] (34)

[0165] where, is the iteration convergence criterion, close to a very small number.

[0166] If it is satisfied, the iteration is terminated. Otherwise, go back to step 2) to enter the next round of iteration until the convergence condition is met or the maximum number of iterations is reached.

[0167] b. Solve the maximum benefit problem of regional power grid alliance.

[0168] When formula (35) is satisfied, it means that the subjects of the cooperation alliance reach a transaction consensus on the benefit distribution sub-problem.

[0169] (35)

[0170] where, is the transaction price of the regional power grid and the regional power grid cooperation in the time period.

[0171] Solving obtains the optimal value of the power transaction amount of each regional power grid .

[0172] Introduce the Lagrange multiplier and the penalty factor to construct the augmented Lagrangian function of sub-problem 2:

[0173] (36)

[0174] where, is the augmented Lagrangian function of sub-problem 2 of the regional power grid , is the Lagrange multiplier of sub-problem 2 of the regional power grid and the regional power grid in the time period, is the penalty factor of sub-problem 2 of the regional power grid and the regional power grid in the time period, is the transaction electric quantity of the regional power grid and the regional power grid cooperation in the time period. is the transaction electric quantity of the regional power grid and the regional power grid cooperation in the time period. is the transaction electric quantity of the regional power grid and the regional power grid cooperation in the time period. Cooperative transaction price For Period regional grid And regional grid Cooperative transaction price.

[0175] The solving steps of subproblem 2 based on the AA-ADMM algorithm are as follows:

[0176] 1) Initialize the iteration number , the maximum iteration number is , the convergence precision is , the transaction electricity price of each regional grid is , the initial Lagrange multiplier is , the penalty factor is , and the adaptive parameter .

[0177] 2) In the iterative solving process, each regional grid interacts and updates information according to the iteration results of other regional grids. Specifically, after each regional grid receives the corresponding iteration information from other regional grids, it updates its decision in combination with its own operating conditions and constraint conditions.

[0178] 3) After completing a round of iteration, update the Lagrange multiplier according to formula (37):

[0179] (37)

[0180] Wherein, is the Lagrange multiplier of subproblem 2 of the th iteration in the th period of the regional grid and the regional grid , and is the Lagrange multiplier of subproblem 2 of the th iteration in the th period of the regional grid and the regional grid , and is the penalty factor of subproblem 2 of the th iteration of the regional grid and the regional grid , and is the cooperative transaction price of the th iteration of the regional grid and the regional grid in the th period, is the cooperative transaction price of the th iteration of the regional grid and the regional grid in the The transaction price of the (k-1)th iteration cooperation.

[0181] 4) Calculate the original residual and dual residual:

[0182] (38)

[0183] (39)

[0184] wherein, is Periodic regional grid and regional grid The transaction price of the (k-1)th iteration cooperation, is Periodic regional grid and regional grid The transaction price of the (k-1)th iteration cooperation, is regional grid and regional grid The penalty factor of the (k-1)th iteration subproblem 2, is Periodic regional grid and regional grid The transaction price of the (k-1)th iteration cooperation.

[0185] 5) Update the penalty factor according to formula (40), and update the iteration number .

[0186] (40)

[0187] wherein, is regional grid and regional grid The penalty factor of the (k-1)th iteration subproblem 2.

[0188] 6) Judge the convergence of the AA-ADMM algorithm.

[0189] (41)

[0190] wherein, is the iteration convergence criterion, close to a very small number.

[0191] If it is satisfied, the iteration is terminated. Otherwise, return to step 2) to enter the next round of iteration until the convergence condition is met or the maximum number of iterations is reached.

[0192] The AA-ADMM algorithm has obvious advantages in engineering landing, scheme stability and result interpretability, and can ensure high reliability and high practicality of the application in actual operation scheduling.

[0193] The present application proposes a complete structure modeling and optimization method to solve the problems of high operation complexity, insufficient energy storage utilization and unreasonable cooperation allocation mechanism in multi-regional power grids with high proportion of new energy. First, a heterogeneous energy storage difference regional power grid structure framework is established, which models the regional power grid with high industrial load proportion and the regional power grid with obvious civil load fluctuation, respectively configures electrochemical energy storage and pumped storage, and fully reflects the differences in response speed, capacity scale and operation characteristics of different energy storage. Second, a multi-energy collaborative optimization model based on cooperative game is constructed, which integrates thermal power units, wind power, photovoltaic and various types of energy storage into a unified framework, and realizes the reduction of overall operation cost and the improvement of new energy consumption capacity through cross-regional coordination. On this basis, an asymmetric Nash bargaining mechanism is introduced, which converts the contribution of each region in energy supply, energy storage regulation and load response into bargaining weight, ensures that the income distribution result is fair and has incentive, and effectively solves the problem that the traditional symmetric distribution method cannot reflect the regional differences. In addition, the inter-regional cooperative game model is decomposed into a social cost minimization sub-problem and a revenue distribution maximization sub-problem, and an accelerated adaptive alternating direction multiplier method is used for distributed solution, which realizes fast convergence by using dynamic step and penalty factor correction. Each region only needs to exchange limited energy and price information to complete the optimization process, so as to balance the calculation efficiency and privacy protection. Through the above method, the present application can realize the complementary utilization of heterogeneous energy storage in regional power grid, improve the flexibility and economy of cross-regional power grid, and promote the long-term stability and sustainability of alliance cooperation, providing reliable technical support for high proportion of new energy consumption and regional power grid collaborative operation.

[0194] In summary, the application realizes the economic and efficient operation and fair income distribution of the cross-regional power system by constructing a regional power grid operation model that can characterize the differences between electrochemical energy storage and pumped storage, introducing cooperative game theory, and combining with distributed optimization algorithm. To quantify the complementary value of different energy storage technologies, the application innovatively establishes a multi-regional power grid operation framework, considering the multi-energy complementary characteristics of thermal power units, photovoltaic, wind power, industrial and domestic loads, and heterogeneous energy storage. On this basis, a cooperative game model is constructed through an asymmetric Nash bargaining mechanism, linking the income distribution and the contribution of each region, avoiding the problem of insufficient reflection of individual contribution in the traditional average distribution method, thereby ensuring the fairness and sustainability of the cooperation of the cooperation alliance. In addition, the cooperative game model is divided into two sub-problems: one is the social cost minimization problem, which is used to optimize the inter-regional power exchange and energy storage scheduling; the other is the income distribution maximization problem, which is used to determine the economic incentives of each party in cooperation. To improve the calculation efficiency and protect the privacy of regional data, the application also uses the accelerated adaptive alternating direction multiplier algorithm, which realizes the fast convergence and stable solution of the algorithm through dynamic step adjustment and penalty factor correction.

[0195] As shown in Figure 3 , it is a schematic diagram of a multi-regional power grid dispatching optimization system of the application, which can include:

[0196] A data module for obtaining regional power grid operation data;

[0197] An optimization module for solving the inter-regional multi-energy collaborative optimization model according to the regional power grid operation data, obtaining the transaction electricity quantity and transaction price of the multi-regional power grid cooperation as the dispatching optimization scheme; wherein the objective of the inter-regional multi-energy collaborative optimization model is to minimize the operation cost when the regions cooperate; when solving the inter-regional multi-energy collaborative optimization model, the inter-regional cooperative game model of asymmetric Nash bargaining is constructed to split the solving problem of the inter-regional multi-energy collaborative optimization model into sub-problem 1 and sub-problem 2, and respectively optimize and solve sub-problem 1 and sub-problem 2, sub-problem 1 is the cost minimization problem of the cooperation alliance composed of multi-regional power grids, and sub-problem 2 is the income distribution sub-problem of the cooperation alliance composed of multi-regional power grids.

[0198] It should be noted that in the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the above described system embodiment is merely illustrative, and for example, the division of the blocks is only a logical function division, and there can be another division manner in actual implementation, for example, multiple blocks can be combined or integrated into another apparatus, or some features can be ignored or not executed. The modules illustrated as separated components can be or can not be physically separated, and the components illustrated as modules can be one physical unit or multiple physical units, i.e., can be located in one place or distributed to multiple different places. According to actual needs, some or all of the modules can be selected to implement the purposes of the embodiments of the present application.

[0199] In addition, each module in the various embodiments of the present application can be integrated in one processing unit, or each module can be physically present separately, or two or more modules can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0200] The embodiments of the present application also provide an electronic device, which can include one or more processors, memories and communication interfaces.

[0201] The memory, the communication interface and the processor are coupled together, for example, the memory, the communication interface and the processor can be coupled together through a bus.

[0202] The communication interface is configured to perform data transmission with other devices. The memory stores computer program codes. The computer program codes include computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the multi-region power grid scheduling optimization method.

[0203] The processor can be a processor or a controller, for example, can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a hardware component, or any combination thereof. It can implement or execute the various exemplary logical blocks, modules and circuits described in combination with the present disclosure. The processor can also be a combination of implementing computing functions, such as one or more microprocessors in combination, a combination of DSP and microprocessor, and the like. The processor can be used to support the electronic device to perform the method steps provided in the above embodiments.

[0204] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0205] The computer readable storage medium provided by the embodiments of the present application stores a computer program, and the computer program is executed by the processor to implement the steps of the multi-region power grid dispatching optimization method.

[0206] The computer readable storage medium involved in the present application includes random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CDROM, or any other form of storage medium known in the technical field.

[0207] The above is only the preferred embodiment of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for multi-zone power grid dispatch optimization, the method comprising: The multi-region power grid comprises a region power grid 1 and a region power grid 2; the region power grid 1 comprises a thermal power unit, a photovoltaic power station, a wind power unit, an industrial high-load energy load and an electrochemical energy storage system; the region power grid 2 comprises a thermal power unit, a photovoltaic power station, a wind power unit, a civil load and a pumped storage unit; comprising: acquiring region power grid operation data; solving a multi-energy collaborative optimization model between regions according to the region power grid operation data to obtain transaction power and transaction price of multi-region power grid cooperation as a dispatch optimization scheme; wherein an objective of the multi-energy collaborative optimization model between regions is to minimize operation cost when the regions cooperate; constraints of the multi-energy collaborative optimization model between regions comprise power balance constraints, thermal power unit operation constraints, electrochemical energy storage system constraints, pumped storage unit constraints, direct-current tie-line transmission constraints, reducible load constraints, power interaction constraints between regions, and power interaction constraints between region power grids and a superior large power grid; when solving the multi-energy collaborative optimization model between regions, an asymmetric Nash bargaining inter-region cooperation game model is constructed to split the solving problem of the multi-energy collaborative optimization model between regions into sub-problem 1 and sub-problem 2, and sub-problem 1 and sub-problem 2 are respectively optimized and solved; sub-problem 1 is a cost minimization problem of a cooperative alliance composed of multi-region power grids, and sub-problem 2 is a cooperative alliance revenue distribution sub-problem of the cooperative alliance composed of multi-region power grids.

2. The multi-zone power grid dispatch optimization method of claim 1, wherein, The objective of the multi-energy collaborative optimization model between regions is represented as: wherein, is the operation cost of inter-regional cooperation operation, is the number of thermal power units, is the serial number of thermal power units, is the total number of time periods, is the time period thermal power unit operation cost, is the time period new energy unit curtailment cost, is the time period load side demand response cost, is the time period electrochemical energy storage system operation cost, is the time period pumped storage unit operation cost, is the time period regional power grid and upper-level large power grid interaction purchase and sale electricity cost, is the time period regional power grid interaction cost.

3. The multi-zone grid dispatch optimization method of claim 2, wherein, The inter-region cooperation game model is represented as: wherein, is the number of participants in the Nash negotiation, is the regional grid operating cost when operating independently, i.e. Nash negotiation breakdown point, is the regional grid operating cost after participating in the Nash negotiation.

4. The multi-zone grid dispatch optimization method of claim 3, wherein, Sub-problem 1 is represented as: Sub-problem 2 is represented as: wherein, optimal solution for the regional grid participating in cooperation optimal solution for the regional grid participating in cooperation optimal solution for the regional grid participating in Nash negotiation over-grid transaction cost.

5. The multi-zone grid dispatch optimization method of claim 4, wherein, When optimizing and solving sub-problem 1 and sub-problem 2, an accelerated adaptive alternating direction multiplier algorithm is adopted.

6. A multi-zone power grid dispatch optimization system, characterized by, The multi-region power grid comprises a region power grid 1 and a region power grid 2; the region power grid 1 comprises a thermal power unit, a photovoltaic power station, a wind power unit, an industrial high-load energy load and an electrochemical energy storage system; the region power grid 2 comprises a thermal power unit, a photovoltaic power station, a wind power unit, a civil load and a pumped storage unit; comprising: a data module configured to acquire region power grid operation data; an optimization module configured to solve a multi-energy collaborative optimization model between regions according to the region power grid operation data to obtain transaction power and transaction price of multi-region power grid cooperation as a dispatch optimization scheme; wherein an objective of the multi-energy collaborative optimization model between regions is to minimize operation cost when the regions cooperate; constraints of the multi-energy collaborative optimization model between regions comprise power balance constraints, thermal power unit operation constraints, electrochemical energy storage system constraints, pumped storage unit constraints, direct-current tie-line transmission constraints, reducible load constraints, power interaction constraints between regions, and power interaction constraints between region power grids and a superior large power grid; when solving the multi-energy collaborative optimization model between regions, an asymmetric Nash bargaining inter-region cooperation game model is constructed to split the solving problem of the multi-energy collaborative optimization model between regions into sub-problem 1 and sub-problem 2, and sub-problem 1 and sub-problem 2 are respectively optimized and solved; sub-problem 1 is a cost minimization problem of a cooperative alliance composed of multi-region power grids, and sub-problem 2 is a cooperative alliance revenue distribution sub-problem of the cooperative alliance composed of multi-region power grids.

7. An electronic device, comprising: comprising: The electronic device comprises a memory, one or more processors; the memory is coupled with the processor; wherein the memory has computer program code stored therein, the computer program code comprises computer instructions, when the computer instructions are executed by the processor, the electronic device executes the steps of the multi-region power grid scheduling optimization method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium has a computer program stored therein, when the computer program is executed by the processor, the steps of the multi-region power grid scheduling optimization method in any one of claims 1-5 are realized.

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