A micro-balance area responsibility transfer and incentive balance adjustment method, system and medium

Abstract

The present application relates to the technical field of power system operation control technology, and more particularly to a micro-balance area responsibility transfer and incentive adjustment method, system and medium, the method comprising: obtaining historical output data, historical clearing plans and meteorological data of each market subject in the micro-balance area, predicting the output value of each market subject in the future dispatch period and determining the preliminary imbalance responsibility and the system total imbalance power; determining the final target responsibility according to the direction of the system total imbalance power, and transferring the new energy up-regulation responsibility to the thermal power market subject when there is a power shortage; constructing a master-slave game model based on the final target responsibility, determining the incentive price and the response power, solving the optimal response strategy, publishing the incentive price and performing deviation settlement. Through the present application, the core technical problem of the existing technology that the new energy up-regulation responsibility cannot be physically executed and lacks an effective incentive mechanism is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of power system operation control technology, and in particular to a method, system and medium for micro-balance zone responsibility transfer and incentive adjustment. Background Technology

[0002] As the penetration rate of new energy sources in the micro-balance zone continues to increase, wind power, photovoltaic and other main entities are subject to strong volatility and uncertainty due to resource conditions. This can easily lead to deviations between their actual power generation and real-time market clearing plans, thereby causing power imbalance problems in the micro-balance zone. Therefore, how to reasonably allocate the imbalance responsibilities of various market entities in the micro-balance zone and achieve rapid and effective balancing has become an important technical problem in power system operation and control.

[0003] In existing technologies, system balancing typically relies on passive regulation methods, primarily automatic generation control of thermal power units, combined with the "source follows load" approach and an administrative responsibility-sharing mechanism based on deviation assessment, to evaluate or constrain market participants exhibiting deviations. Simultaneously, at the market level, existing mechanisms often lack sensitive price signals reflecting real-time supply and demand tensions. Market participants tend to passively execute dispatch instructions rather than actively participate in balancing regulation based on clear economic incentives. While these existing technologies can maintain system operation to some extent, they have significant shortcomings in micro-balance zones with a high proportion of renewable energy. Firstly, existing responsibility allocation methods usually assume that all types of entities possess corresponding regulation capabilities, failing to fully consider the physical constraint that renewable energy entities generally lack the ability to increase regulation. This leads to the problem that when the system is in a power deficit state, directly assigning the responsibility to renewable energy entities may result in allocation of responsibility but inability to actually execute it. Secondly, existing balancing mechanisms lack market-based incentives commensurate with responsibility assumption, making it difficult to effectively guide different entities, such as thermal power and renewable energy, to actively participate in regulation according to their respective capabilities and profit expectations. This results in low regulation efficiency, high regulation costs, and limited system balancing effects.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method, system, and medium for responsibility transfer and incentive adjustment in a micro-equilibrium zone, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for responsibility transfer and incentive adjustment in a micro-equilibrium zone, the method comprising: Based on the historical output data, historical clearing plans and meteorological data of each market entity in the micro-balance zone, the output value of each market entity in the future scheduling period is predicted, and the preliminary imbalance responsibility and total system imbalance power of each market entity are determined according to the output value and real-time market clearing data. The final target responsibility of each market entity is determined based on the direction of the total unbalanced power of the system, and when the total unbalanced power of the system indicates that the system is in a power deficit state, the upward adjustment responsibility of the new energy market entity is transferred to the thermal power market entity. A master-slave game model is constructed based on the ultimate goal responsibility of each market entity, with the system operator determining the incentive price and each market entity determining the response power. Solve the master-slave game model to obtain the incentive price and the optimal response strategy of each market participant; The incentive price is published, and deviations are settled based on the actual response results of each market participant.

[0007] Furthermore, the output values ​​of each market participant are predicted, and the initial imbalance responsibility and total system imbalance power are determined, including: Collect historical actual output sequences, historical clearing plans, and key meteorological factors for each of the aforementioned market entities; The historical actual output sequence, the historical clearing plan, and the key meteorological factors are used as feature inputs to train a long short-term memory network, and the long short-term memory network is used to predict the output value of each market entity in the future scheduling period. The imbalance responsibility of each market entity is determined based on the output value of each market entity and the real-time market clearing value of the future scheduling period, and the imbalance responsibility of each market entity is aggregated to obtain the total system imbalance power of the micro-balance zone. The initial imbalance responsibility of each market entity is determined by combining the imbalance deviation, installed capacity, and output value of each market entity.

[0008] Furthermore, the ultimate target responsibility of each market entity is determined based on the direction of the total unbalanced power of the system, including: When the total unbalanced power of the system is greater than zero, it is determined that the micro-balanced region is in a power surplus state and resources need to be reduced. When the micro-balance region is in a state of power surplus, the ultimate target responsibility of each market entity is equal to the initial imbalance responsibility of each market entity; When the total unbalanced power of the system is less than zero, it is determined that the micro-balanced region is in a power deficit state and resources need to be increased. When the micro-balance zone is in a power deficit state, the ultimate target responsibility of the new energy market entities is set to zero, and the original preliminary imbalance responsibility of the new energy market entities is determined as the transferred responsibility; The transferred responsibility is allocated to each of the thermal power market entities according to their respective apportionment coefficients, so that each thermal power market entity assumes its own initial imbalance responsibility and the transferred responsibility, wherein the apportionment coefficients are determined according to capacity ratios.

[0009] Furthermore, the incentive price is determined by the system operator, including: An optimization model is constructed based on the ultimate goals and responsibilities of each market entity, with the upper-level system operator as the decision-making body. An objective function is established with the goal of minimizing the total system operating cost of the micro-equilibrium zone within a scheduling cycle, wherein the total system operating cost includes the adjustment fees paid to each of the market entities and the auxiliary service costs or assessment costs corresponding to the remaining imbalance after market adjustment; A system power balance constraint is established based on the total unbalanced power of the system, the actual adjustment power of each market entity, and the remaining unbalanced power. Incentive price range constraints are established based on the upper and lower limits of incentive prices, and total cost control constraints are established based on the operating costs of traditional balancing methods.

[0010] Furthermore, the response power is determined by each of the aforementioned market entities, including: An optimization model is constructed based on the incentive price published by the system operator, with each of the market entities in the lower layer as the responding entities; An objective function is established with the goal of maximizing the net profit increment of each market entity, wherein the net profit increment includes incentive income, spot market opportunity income or opportunity cost, and changes in adjustment costs. The incentive benefits for each market participant are determined based on the incentive price, the actual response power of each market participant, the ultimate target responsibility, and the response accuracy penalty. The spot market opportunity gain or opportunity cost of each market participant is determined based on the real-time spot market clearing price and the actual response power direction of each market participant.

[0011] Furthermore, the determination of adjustment cost changes and constraints includes: For thermal power market participants, the adjustment cost changes are determined based on the secondary fuel cost function and the planned output and actual response power in real time market clearing. For thermal power market participants, output constraints are established based on upper and lower limits of output, and ramping constraints are established based on the maximum allowable upward ramping value and the maximum allowable downward ramping value. For new energy market participants, the adjustment cost will be approximately zero, and the spot electricity revenue lost due to reduced power generation will be counted as the spot market opportunity cost. For new energy market players, only the downward adjustment range constraint is established to indicate that the new energy market players only have the ability to lower prices.

[0012] Furthermore, solving the master-slave game model includes: Construct a Lagrange function based on the convexity of the lower-level optimization model; The lower-level optimization model is transformed into equality constraints and inequality constraints using the Karush-Kuhn-Tucker conditions; The equality constraints and inequality constraints are incorporated into the upper-level optimization model, and the complementary relaxation conditions are linearized using the Big M method and binary variables. The bilinear terms in the master-slave game model are linearized using strong duality theory or McCormick relaxation method, thereby transforming the master-slave game model into a single-level mixed integer linear programming problem.

[0013] Furthermore, the incentive price is published and deviation settlement is performed based on the actual response results of each market participant, including: A business mathematical programming solver is used to solve a single-level mixed integer linear programming problem to obtain the optimal incentive pricing strategy and the optimal response plan of each market participant. The system operator publishes the optimal incentive price and controls each market participant to execute the adjustment response according to the optimal response plan; Deviation settlement will be made based on the actual response results of each of the aforementioned market entities.

[0014] A micro-equilibrium zone responsibility transfer and incentive adjustment system, the system comprising: The predictive responsibility module, based on the historical output data, historical clearing plans and meteorological data of each market entity in the micro-balance zone, predicts the output value of each market entity during the future scheduling period, and determines the preliminary imbalance responsibility of each market entity and the total imbalance power of the system based on the output value and real-time market clearing data. The responsibility transfer module determines the final target responsibility of each market entity based on the direction of the total unbalanced power of the system, and transfers the upward adjustment responsibility of the new energy market entity to the thermal power market entity when the total unbalanced power of the system indicates that the system is in a power deficit state. The game-theoretic pricing module constructs a master-slave game model based on the ultimate goal responsibility of each market participant. The incentive price is determined by the system operator, and the response power is determined by each market participant. The optimization solution module solves the master-slave game model to obtain the incentive price and the optimal response strategy of each market participant; The settlement module is executed to publish incentive prices and settle deviations based on the actual response results of each market participant.

[0015] A computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, can implement the micro-balance zone responsibility transfer and incentive adjustment method.

[0016] The technical solution of this invention can achieve the following technical effects: By predicting the output values ​​of each market participant during future scheduling periods and allocating initial imbalance responsibilities, the responsibility for increasing renewable energy supply is transferred to thermal power market participants when there is a power shortage. Then, a master-slave game model is constructed based on the final responsibility to determine the incentive price and response strategy. After optimization and solution, adjustment and deviation settlement are executed to achieve active balancing in the micro-balance zone. This effectively solves the core technical problems of existing technologies where the responsibility for increasing renewable energy supply cannot be physically executed and there is a lack of effective incentive mechanisms.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for transferring responsibility and adjusting incentives in a micro-equilibrium zone. Figure 2 A logical framework diagram for the transfer of responsibility for imbalances in the micro-equilibrium zone and the market-based incentive mechanism; Figure 3 Comparison chart of the optimization effects of system imbalance; Figure 4 A time-series coupling diagram of the incentive price and the initial imbalance of the system; Figure 5 A chart comparing the risk premium and net income of different types of generating units under the responsibility transfer mechanism; Figure 6 This is a graph showing the nonlinear impact of the penalty coefficient on system cost and the unbalanced reduction rate. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Example 1; like Figure 1 and Figure 2 As shown, this application provides a method for responsibility transfer and incentive adjustment in a micro-equilibrium zone, the method including: S10: Based on the historical output data, historical clearing plans and meteorological data of each market entity in the micro-balance zone, predict the output value of each market entity during the future scheduling period, and determine the preliminary imbalance responsibility and total system imbalance power of each market entity based on the output value and real-time market clearing data. S20: Determine the ultimate target responsibility of each market entity based on the direction of the total unbalanced power of the system, and when the total unbalanced power of the system indicates that the system is in a power deficit state, transfer the upward adjustment responsibility of the new energy market entity to the thermal power market entity; S30: Construct a master-slave game model based on the ultimate goal responsibility of each market participant, with the system operator determining the incentive price and each market participant determining the response power; S40: Solve the master-slave game model to obtain the incentive price and the optimal response strategy of each market participant; S50: Publish incentive prices and settle deviations based on the actual response results of each market participant.

[0023] Specifically, the process begins by constructing a data collection and processing link for each market participant within the micro-balance zone. Market participants include thermal power units, renewable energy plants, and other power generation entities involved in regulation. The collected data includes historical actual output data, historical market clearing plan data, real-time market clearing data, and meteorological data related to renewable energy output. Based on this, a long short-term memory network is trained using the historical actual output sequence, historical clearing plan, and key meteorological factors of each market participant as input. This network predicts the output value of each market participant during future scheduling periods. The imbalance responsibility of each market participant is determined based on the deviation between its output value and the real-time market clearing value during future scheduling periods. Subsequently, the imbalance responsibilities of each market participant are aggregated to obtain the total system imbalance power in the micro-balance zone. Furthermore, the initial imbalance responsibility of each market participant is quantitatively allocated by combining the imbalance deviation, installed capacity, and output value of each market participant. After determining the initial imbalance responsibility, the final target responsibility of each market entity is determined according to the direction of the total imbalance power of the system. When the total imbalance power of the system indicates that the micro-balance zone is in a power surplus state, it means that the system needs to call down-adjustment resources. At this time, thermal power units can participate in regulation through voltage reduction output, and new energy power plants can participate in regulation through wind and solar curtailment. Therefore, the final target responsibility of each market entity can be directly equal to its initial imbalance responsibility. When the total imbalance power of the system indicates that the micro-balance zone is in a power deficit state, it means that the system needs to call up-adjustment resources. Since new energy entities usually operate in maximum power tracking state and lack the ability to adjust upwards, the final target responsibility of new energy market entities is set to zero, and their original upward adjustment responsibility is separated from the initial imbalance responsibility to form a responsibility to be transferred. Then, the responsibility to be transferred is allocated to each thermal power market entity according to the sharing coefficient of each thermal power market entity, so that each thermal power market entity bears its own initial imbalance responsibility and transfer responsibility, thereby ensuring that the responsibility allocation result has physical enforceability. After the final target responsibility allocation is completed, a master-slave game model is constructed based on the final target responsibility of each market entity. In this model, the system operator acts as the upper-level decision-making entity, with the optimization objective being to minimize the total system operating cost within the target scheduling cycle in the micro-equilibrium zone. The decision variable is the incentive price. The total system operating cost includes the adjustment fees paid to each market entity and the auxiliary service costs or assessment costs corresponding to the remaining imbalance after market adjustment. Simultaneously, system power balance constraints, incentive price range constraints, and total cost control constraints are established to ensure that the incentive price is within a reasonable range and that the operating cost of the balancing mechanism is not higher than that of the traditional balancing method. Each market entity acts as the lower-level response entity. After receiving the incentive price issued by the system operator, each entity determines its response power with the objective of maximizing the net profit increment. The net profit increment includes incentive revenue, spot market opportunity revenue or opportunity cost, and changes in adjustment costs. The incentive revenue also considers the response accuracy penalty, and the spot market opportunity revenue or opportunity cost is determined based on the real-time spot market price and the actual response power direction. For different types of market participants, differentiated constraint and cost models are established in the lower-level model. For thermal power market participants, their adjustment costs are described by a quadratic fuel cost function, and upper and lower limits of unit output and ramp-up and ramp-down constraints are introduced in the solution to reflect the actual adjustment capacity of thermal power units. For new energy market participants, since their marginal adjustment costs are low, their adjustment costs can be approximated as zero. Their main economic impact on adjustment is reflected in the opportunity gain or opportunity cost in the spot market. At the same time, considering the physical characteristic that new energy participants only have the ability to adjust downwards, only the downward adjustment range constraint is set to accurately characterize the response behavior boundary of different participants. In the solution phase, for the two-layer optimization structure of the master-slave game model, a Lagrange function is first constructed based on the convexity of the lower-layer model. The Karush-Kuhn-Tucker conditions are then used to transform the lower-layer optimization model into equality and inequality constraints, which are then incorporated into the upper-layer model. For the nonlinear terms in the complementary relaxation conditions, the Big M method is used and binary variables are introduced for linearization. For the bilinear terms in the model, strong duality theory or McCormick relaxation method is used for linearization. Thus, the master-slave game model is transformed into a single-layer mixed integer linear programming problem, which is then solved using a business mathematical programming solver to simultaneously obtain the optimal incentive price and the optimal response strategy of each market participant. During the execution phase, the system operator issues the optimal incentive price to each market participant, and each market participant executes adjustment actions according to the corresponding optimal response strategy. When a thermal power market participant receives a transfer responsibility, it undertakes additional adjustment tasks and adjusts its output according to the allocation results. When a renewable energy market participant receives a reduction instruction, it reduces its output while meeting its own operational constraints. After the adjustment is completed, the actual response results of each market participant are collected and compared with the optimal response strategy and target responsibility, and the deviation is settled. If there is still a residual imbalance after market adjustment, the ancillary service resources further undertake the corresponding balancing needs, thus forming a complete closed loop of "prediction-responsibility allocation-transfer-pricing-response-solution-execution-settlement", realizing proactive balancing control within the micro-balance zone that takes into account both physical feasibility and economic rationality.

[0024] The technical solution of this invention predicts the output value of each market entity during future scheduling periods and allocates the initial imbalance responsibility. When there is a power shortage, the responsibility for adjusting new energy is transferred to the thermal power market entity. Then, based on the final responsibility, a master-slave game model is constructed to determine the incentive price and response strategy. After optimization and solution, adjustment and deviation settlement are performed to achieve active adjustment of the micro-balance zone. This effectively solves the core technical problems of the inability to physically execute the responsibility for adjusting new energy and the lack of an effective incentive mechanism in the prior art.

[0025] Furthermore, predicting the output of each market participant and determining the initial imbalance responsibility and the total system imbalance power includes: Collect historical actual output sequences, historical clearing plans, and key meteorological factors of each market entity; The system uses historical actual output sequences, historical clearing plans, and key meteorological factors as feature inputs to train a long short-term memory network, and then uses the long short-term memory network to predict the output values ​​of each market entity during future scheduling periods. The imbalance responsibility of each market participant is determined based on the output value of each market participant and the real-time market clearing value during the future scheduling period. The imbalance responsibility of each market participant is then aggregated to obtain the total imbalance power of the system in the micro-balance zone. The initial responsibility for imbalance among each market entity is determined by comprehensively considering the imbalance deviation, installed capacity, and output value of each market entity.

[0026] As a preferred embodiment of the above, historical output data, historical clearing plans, and meteorological data of each market entity within the micro-balance zone are collected to predict the output value of each entity during the future scheduling period. Based on the output value and the real-time market clearing value during the future scheduling period, the preliminary imbalance responsibility of each entity is calculated, and the total imbalance power of the system is obtained by aggregation. Within the micro-equilibrium zone, the power deviation between the output values ​​of each entity and the real-time market clearing values ​​during future scheduling periods is a key factor affecting grid security. This embodiment first selects the historical actual output sequences, historical clearing plans, and key meteorological factors of each market entity as feature inputs to train an LSTM network to predict the output values ​​of each entity during future scheduling periods. The formula for the "imbalanced responsibility" of the i-th subject at time t is as follows: ; In the formula, For the "unbalanced responsibility" of the i-th subject, To use long short-term memory networks to predict the output values ​​of each entity during future scheduling periods, This represents the real-time market clearing value for each entity during future scheduling periods; For scheduling periods; Furthermore, the total unbalanced power of the micro-equilibrium region system is obtained by aggregation, and its formula is as follows: ; In the formula, This represents the total number of market entities participating in the calculation of imbalance liability within the micro-equilibrium zone. This represents the total unbalanced power of the system in the micro-equilibrium region, and the total unbalanced power of the system is positive. A value greater than 0 indicates that the sum of the output values ​​of all entities exceeds the sum of their real-time market clearing values, meaning the system is in a power surplus state and resources need to be reduced (such as thermal power units reducing output, renewable energy curtailment, and energy storage charging); the total system imbalance power is negative. < 0 means that the sum of the output values ​​of all entities is lower than the sum of the real-time market clearing values ​​of all entities, that is, the system is in a power shortage state and needs to call up resources (such as thermal power increasing output, energy storage discharging). Based on this, this embodiment comprehensively considers three dimensions: imbalance deviation, installed capacity, and output value, to calculate the preliminary imbalance responsibility of each entity during the future scheduling period. The formula is as follows: ; ; In the formula, Let i be the installed capacity of the i-th entity. To account for the weighting coefficients of unbalanced responsibility, To take into account the weighting factor of installed capacity, To take into account the weighting coefficient of the output value, and + + =1; This step establishes the initial responsibility for imbalances among various entities during future scheduling periods; it achieves the initial quantification of responsibility allocation and embodies the principles of fairness: "whoever deviates, is responsible" and "the greater the ability, the greater the responsibility."

[0027] Furthermore, the ultimate target responsibilities of each market participant are determined based on the direction of the total imbalance power of the system, including: When the total unbalanced power of the system is greater than zero, it is determined that the micro-balanced region is in a power surplus state and resources need to be reduced. When the micro-balance region is in a state of power surplus, the ultimate target responsibility of each market player is equal to the initial imbalance responsibility of each market player; When the total unbalanced power of the system is less than zero, it is determined that the micro-balanced region is in a power deficit state and resources need to be increased. When the micro-balance zone is in a state of power deficit, the ultimate target responsibility of the new energy market entities is set to zero, and the original initial imbalance responsibility of the new energy market entities is determined as the transferred responsibility; The transfer responsibility is allocated to each thermal power market entity according to the sharing coefficient of each thermal power market entity, so that each thermal power market entity can assume its own initial imbalance responsibility and transfer responsibility, wherein the sharing coefficient is determined according to the capacity ratio.

[0028] As a preferred embodiment of the above, an unbalanced responsibility transfer mechanism is implemented to determine the final physical execution target responsibility of each subject based on the direction of the total unbalanced power of the system. In view of the physical lack of upward adjustment capability of new energy units (i.e., they cannot increase output out of thin air), this embodiment designs a physical-level responsibility transfer logic, specifically including the following two scenarios: Scenario 1: When the total unbalanced power of the system When the output is greater than 0, the system needs to reduce its output. At this time, thermal power units can reduce their output by lowering the voltage, and new energy units can reduce their output by curtailing wind and solar power (through converter control). Since all entities have the ability to reduce their output, the final responsibility equals the initial responsibility, and the formula is as follows: ; In the formula, This represents the final target adjustment amount for each entity during the future scheduling period; To establish initial responsibility for imbalances among various entities during future scheduling periods; For future scheduling periods; For the first Individual market entities; Scenario 2: When the total unbalanced power of the system When the output is less than 0, the system needs to increase its output. For renewable energy sources, since they typically operate at the maximum power point and lack the ability to adjust upwards, forcibly assigning an upward adjustment task would lead to physical execution failure. Therefore, their final responsibility is set to zero, as shown in the following formula: ; In the formula, This is the final target adjustment amount for the main renewable energy source during future scheduling periods; It is a collection of new energy entities; At the same time, the initial responsibilities that should have been borne by the main entities of new energy sources are separated, resulting in an unbalanced transfer of responsibilities, as shown in the following formula: ; In the formula, This refers to the amount of unbalanced responsibility transfer. The number of new energy entities participating in the summation; For summation index of new energy entities; This transfer will be forcibly transferred to thermal power units with deep regulation capabilities. The thermal power units will bear their initial responsibility as well as additional transfer responsibility. ; In the formula, For future scheduling periods Thermal power plant main body The ultimate target adjustment amount, For future scheduling periods Thermal power plant main body The initial imbalance of responsibility, The allocation coefficient among thermal power units (usually allocated according to capacity ratio) solves the problem of responsibility allocation failure caused by physical constraints of new energy units, and ensures the physical feasibility of regulation commands.

[0029] Furthermore, the incentive price is determined by the system operator, including: An optimization model is constructed based on the ultimate goals and responsibilities of each market participant, with the upper-level system operator as the decision-making body. An objective function is established with the goal of minimizing the total system operating cost within a scheduling cycle in the micro-equilibrium zone. The total system operating cost includes the adjustment fees paid to each market participant and the ancillary service costs or assessment costs corresponding to the remaining imbalance after market adjustment. Establish system power balance constraints based on the total unbalanced power of the system, the actual adjustment power of each market participant, and the remaining unbalanced power. Incentive price range constraints are established based on the upper and lower limits of incentive prices, and total cost control constraints are established based on the operating costs of traditional balancing methods.

[0030] As a preferred embodiment of the above embodiments, the operation of the micro-equilibrium zone involves a game of interests between system operators and market participants. This embodiment constructs a two-layer optimization model: The upper-level model simulates the system operator, whose goal is to minimize the total operating cost of the micro-scheduling equilibrium zone within a scheduling period T. The total cost includes incentive costs paid to market participants and penalty costs due to insufficient regulation. The objective function formula is as follows: ; In the formula, the first item represents the adjustment fee paid by the system to all market participants (thermal power, new energy, etc.) who participated in the response. This represents the total number of time periods within the scheduling period. This indicates the total number of market entities participating in the response. The incentive price for time period t is (RMB / MWh). The actual regulation power of the i-th subject feedback. This is the penalty coefficient for the unit residual imbalance when a market entity fails to meet the response standards. The final target adjustment amount for each entity in the future scheduling period after the aforementioned transfer calculation. The second item represents the cost of the system calling on backup resources or facing penalties during performance evaluations when the response from market participants is insufficient to completely eliminate the deviation. This refers to the remaining unbalanced power in the system after market adjustment. The market price for balancing ancillary services for the unit of residual imbalance in the system; The upper-level model must satisfy system power balance constraints, incentive price range constraints, and total cost control constraints; The system power balance constraint formula is as follows: ; This constraint describes the power conservation relationship within the micro-scheduling equilibrium region, meaning that the initial imbalance of the system must be shared by the adjustment responses of each agent and the remaining imbalance, where, This represents the total unbalanced power of the system in the micro-balance region. The formula for the incentive price range constraint is as follows: ; In the formula, and These are the lower and upper limits of the incentive price, respectively. This constraint avoids situations where the price is too low, causing the main body to lack the motivation to respond, or the price is too high, causing the system's balance cost to get out of control. The total cost control constraint formula is as follows: ; In the formula, This constraint, representing the operating costs of traditional balancing methods, reflects the economic constraints on technological decisions, ensuring that the mechanism operates within a reasonable range.

[0031] Furthermore, the response power is determined by each market participant, including: An optimization model is constructed based on the incentive prices issued by the system operator, with each market entity at the lower level as the responding entity; An objective function is established with the goal of maximizing the net profit increment of each market participant, where the net profit increment includes incentive income, opportunity income or opportunity cost in the spot market, and changes in adjustment costs. The incentive benefits for each market participant are determined based on the incentive price, the actual response power of each market participant, the ultimate target responsibility, and the penalty for response accuracy. The spot market opportunity gain or opportunity cost of each market participant is determined based on the real-time spot market clearing price and the actual response power direction of each market participant. Furthermore, the determination of adjustment cost changes and constraints includes: For thermal power market participants, the adjustment cost changes are determined based on the secondary fuel cost function and the planned output and actual response power in real time market clearing. For thermal power market participants, output constraints are established based on upper and lower limits of output, and ramping constraints are established based on the maximum allowable upward ramping value and the maximum allowable downward ramping value. For new energy market participants, the adjustment cost will be approximately zero, and the spot electricity revenue lost due to reduced power generation will be counted as the spot market opportunity cost. For new energy market players, only the downward adjustment range is constrained to indicate that new energy market players only have the ability to lower prices.

[0032] As a preferred embodiment of the above embodiments, the operation of the micro-equilibrium zone involves a game of interests between system operators and market participants. This embodiment constructs a two-layer optimization model: The lower-level model simulates the main entities of thermal power and new energy. After receiving the incentive price issued by the upper level, each entity optimizes its actual response power with the goal of maximizing its own net profit. The objective function formula is as follows: ; In the formula, The incentive reward function (including precision penalty). For spot market opportunity gains / costs, To adjust for cost changes, different entities use different cost functions, among which... For scheduling periods, To indicate the first Individual market entities This represents the total number of time periods within the scheduling cycle. The incentive reward function takes into account the response accuracy penalty, and its formula is as follows: ; In the formula, The basic return obtained by providing adjustment to the main body, among which, The incentive price for time period t is (RMB / MWh). This represents the actual regulation power fed back by the i-th subject; As a precision penalty, among which, This is the penalty coefficient for the unit residual imbalance when a market entity fails to meet the response standards. That is, the final target adjustment amount of each entity in the future scheduling period after the aforementioned transfer calculation. ; The formula for the profit / cost of opportunity in the spot market is as follows: ; In the formula, The real-time spot market clearing price for time period t, if the entity provides an upward adjustment service ( >0) is equivalent to generating more electricity and obtaining additional spot electricity revenue; if the entity provides a downward adjustment service ( <0 is equivalent to generating less electricity and losing spot electricity revenue (i.e. opportunity cost). For adjusting cost changes Different subjects have different characteristics: (1) The formula for thermal power units based on the secondary fuel cost function is as follows: ; ; In the formula, This represents the fuel cost function of a thermal power unit. This indicates the output power of the thermal power unit. These are the coefficients of the quadratic term, the linear term, and the constant term of the fuel cost function, respectively. For the first The scheduling period is the first Changes in adjustment costs for individual thermal power plants Contribute to the plan for real-time market clearing. This represents the actual response power. Meanwhile, thermal power units must meet output constraints and ramping constraints, the formulas of which are as follows: ; ; In the formula, , These are the upper and lower limits of the output of thermal power units. , This represents the maximum allowable uphill and downhill climbing values ​​for thermal power units. (2) The main body of new energy power stations has extremely low adjustment costs. Their main economic consideration for participating in adjustment is opportunity cost (considered as opportunity revenue / cost in the spot market). (in Chinese), its formula is as follows: ; In the formula, Indicates the first The scheduling period is the first Changes in adjustment costs for individual new energy entities This refers to the collection of new energy entities. Furthermore, the main entities of new energy power stations only have the ability to lower their prices, satisfying the constraint on the downward adjustment range. The formula is as follows: .

[0033] Furthermore, solving the master-slave game model includes: Construct a Lagrange function based on the convexity of the lower-level optimization model; The lower-level optimization model is transformed into equality constraints and inequality constraints using the Karush-Kuhn-Tucker conditions; Equality constraints and inequality constraints are incorporated into the upper-level optimization model, and the complementary relaxation conditions are linearized using the Big M method and binary variables. The bilinear terms in the master-slave game model are linearized using strong duality theory or McCormick relaxation method, so as to transform the master-slave game model into a single-level mixed integer linear programming problem. Furthermore, the incentive price is published, and deviations are settled based on the actual responses of each market participant, including: A business mathematical programming solver is used to solve a single-level mixed integer linear programming problem to obtain the optimal incentive pricing strategy and the optimal response plan of each market participant. The system operator publishes the optimal incentive price and controls each market participant to implement the adjustment response according to the optimal response plan; Deviation settlement will be made based on the actual response results of each market participant.

[0034] As a preferred embodiment of the above, the two-level game model is transformed into a single-level mixed integer linear programming (MILP) problem by utilizing the Karush-Kuhn-Tucker (KKT) conditions and the Big M method. Since the above model is a bilevel programming problem, the actual adjustment power of the lower-level agent in response to the feedback of the i-th agent is... Incentive price based on upper-level time period t The cost is affected by factors including absolute value terms and nonlinear cost terms, making direct solution difficult. Therefore, this embodiment adopts the following solution strategy: 1) Utilize the convexity of the lower-level model to construct its Lagrange function, and based on the KKT optimality conditions, transform the lower-level optimization problem into a series of equality and inequality constraints (including stationarity conditions). Original feasibility and complementary relaxation conditions 0≤λ⊥g(x)≥0). 2) Add these KKT conditions as constraints to the upper-level model, thereby transforming the two-level model into a single-level nonlinear programming problem; 3) For the nonlinear terms in the complementary relaxation conditions (i.e. constraints of the form 0≤λ⊥g(x)≥0), the Big-M method and binary variables are introduced to linearize them. The complementary relaxation conditions are inherently nonlinear. By introducing a sufficiently large positive number M and a binary variable z, a·b=0 is transformed into a linear constraint form of a≤M·z and b≤M·(1-z). 4) For the bilinear term (price multiplied by power) in the objective function, linearization is performed using strong duality theory or the McCormick relaxation method. Finally, the model is transformed into a standard mixed-integer linear programming (MILP) problem, which can be solved using commercial solvers such as Gurobi or CPLEX, simultaneously yielding the optimal incentive pricing strategy. and the optimal response plan of each entity .

[0035] Example 2; Based on the same inventive concept as the micro-equilibrium zone responsibility transfer and incentive balancing method in the foregoing embodiments, the present invention also provides a micro-equilibrium zone responsibility transfer and incentive balancing system, the system comprising: The predictive responsibility module, based on the historical output data, historical clearing plans and meteorological data of each market entity in the micro-balance zone, predicts the output value of each market entity during the future scheduling period, and determines the preliminary imbalance responsibility of each market entity and the total imbalance power of the system based on the output value and real-time market clearing data. The responsibility transfer module determines the final target responsibility of each market entity based on the direction of the total unbalanced power of the system, and transfers the upward adjustment responsibility of the new energy market entity to the thermal power market entity when the total unbalanced power of the system indicates that the system is in a power deficit state. The game-theoretic pricing module constructs a master-slave game model based on the ultimate goal responsibility of each market participant. The incentive price is determined by the system operator, and the response power is determined by each market participant. The optimization solution module solves the master-slave game model to obtain the incentive price and the optimal response strategy of each market participant; The settlement module is executed to publish incentive prices and settle deviations based on the actual response results of each market participant.

[0036] The system described above in this invention can effectively realize a method for transferring responsibility and adjusting incentives in a micro-equilibrium zone. The technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0037] Example 3; Based on the same inventive concept as the micro-balance region responsibility transfer and incentive balancing method in the foregoing embodiments, the present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, can realize the micro-balance region responsibility transfer and incentive balancing method.

[0038] Example 4; To verify the effectiveness of the proposed method, this embodiment constructs a micro-dispatch balancing zone simulation environment based on a typical local power grid with high new energy penetration in a certain province. This micro-balancing zone has a diverse power structure, comprising 44 generating units. Among them, 11 are thermal power units, serving as the system's regulatory foundation and the main body for responsibility transfer; 26 are wind farms and 7 are photovoltaic power plants, constituting the main source of random fluctuations in the system. The typical dispatch day is June 1, 2025. Considering the sensitivity of the micro-balancing zone to real-time power fluctuations, the simulation time resolution is set to 15 minutes, with a total of 96 dispatch periods throughout the day. The system's unbalanced power data is generated based on the actual operating data of the region on that day. The planned output curves of each entity adopt the clearing results of the rolling market before and during the typical day, while the actual output curves adopt the actual telemetry power. The model uses the historical real-time spot market electricity price for the same period in the region as the benchmark spot price. To simulate the adjustment costs under a market-based environment, the ancillary service market price is set as the sum of the spot price and 1.8 times the base adjustment price (set at 50 yuan / MWh). Meanwhile, to prevent excessive fluctuations in the incentive price and protect the interests of market participants, the model sets upper and lower limits for the incentive price. and Strictly constrained within ±150 yuan / MWh. The coefficients of the quadratic, linear, and constant terms of the coal consumption cost function for thermal power units all reference typical parameters from the IEEE 118-node standard test system. To fairly consider the contribution of each entity to the system deviation and its own adjustment capabilities, the weighting coefficients k1, k2, and k3 used in calculating the initial imbalance responsibility are set to be equal, i.e., all 0.3333. Regarding the responsibility transfer stage, the responsibility transfer allocation coefficient β for thermal power units... i The allocation is strictly determined according to the ratio of the rated capacity of each unit to reflect the principle of "greater capacity, greater responsibility". In addition, the deviation tracking accuracy penalty coefficient α, which balances the weights of "incentive guidance" and "mandatory constraint", is set to 1.0 in the basic example to ensure that the mechanism has sufficient constraint effectiveness in the initial state.

[0039] Through quantitative analysis of the simulation results, this embodiment verifies the effectiveness of the proposed mechanism from the following dimensions: 1) The system's physical equilibrium performance is significantly improved. For example... Figure 3 As shown, the initial total imbalance power of the system accumulated to 22,063.50 MW over 96 time periods throughout the day. After introducing an incentive mechanism based on Stackelberg game theory (taking a penalty coefficient α=1.0 as an example), the final residual imbalance was reduced to 7,570.34 MW, and the overall system imbalance was reduced by 65.69%. Analysis of the deviation fluctuation characteristics showed that the root mean square error (RMSE) of the system imbalance power significantly decreased from 298.74 MW before adjustment to 134.14 MW after adjustment, a fluctuation reduction of 55.10%; the maximum peak deviation throughout the day was also compressed from 829.88 MW to 477.72 MW, a peak reduction rate of 42.44%. The data indicate that this mechanism effectively tapped into the system's regulation resources, significantly reduced the grid's dependence on expensive external ancillary services, and avoided the risk of frequency overruns due to severe local power imbalances. Table 1 shows the physical balance performance statistics, illustrating the effectiveness of the incentive mechanism, as shown below:

[0040] 2) Market-based incentive pricing accurately reflects supply and demand scarcity. For example... Figure 4 As shown, the incentive price trend and the initial imbalance of the system exhibit a significant mirror negative correlation, with a Pearson correlation coefficient as high as -0.8437. During periods of power deficit in the system (such as periods 66-96), the incentive price rapidly climbed into the positive range, reaching a high of 136.09 yuan / MWh, stimulating the peak capacity of thermal power units through a high-premium signal. During periods of power surplus in the system due to large-scale renewable energy generation (such as periods 60-64), the incentive price fell into the negative range, dropping to a low of -125.40 yuan / MWh, playing a crucial role in "congestion relief." Linear regression analysis shows that the system's regulatory supply sensitivity under this mechanism is approximately 1.36 MW / (yuan / MWh), meaning that for every 1 yuan / MWh increase in the incentive price, an average of about 1.36 MW of effective regulatory power can be leveraged on the system side, verifying the effectiveness of the closed-loop transmission mechanism from "physical demand" to "economic signals" and then to "physical response."

[0041] 3) The cross-entity responsibility transfer mechanism achieves economic equity. For example... Figure 5As shown, by introducing the "risk premium" indicator for evaluation, the results show that all 11 thermal power units involved in regulation had positive net benefits during the simulation period. Taking the large main unit (Unit 9) with deep regulation capabilities as an example, by accepting the transferred responsibility, its originally excess regulation capacity was legally utilized, resulting in a cumulative reduction of fines of approximately RMB 189,900 and a net benefit of RMB 630,800. For the medium and small-sized units with relatively limited regulation capabilities (Unit 1), although an additional assessment expenditure of approximately RMB 8,200 (positive risk premium) was incurred due to physical constraints such as ramp rate, the incentive benefit of RMB 71,500 was sufficient to cover this marginal cost, ultimately achieving a net profit of RMB 63,200. This proves that the responsibility transfer mechanism is not a "zero-sum game," but rather achieves Pareto improvement, both avoiding the risk of physical unenforceability for new energy entities and opening up flexible resource monetization channels for thermal power entities.

[0042] 4) Parameter optimization ensures the optimal equilibrium point for system operation. The study selected six typical discrete points α∈{0.0, 0.2, 0.4, 0.6, 0.8, 1.0} for comparative simulation. The evolution trajectory of the total system operating cost and the imbalance reduction rate is shown below. Figure 6As shown, with the increase of the penalty coefficient, the system performance did not show a simple monotonic optimization trend, but rather exhibited significant range oscillations and optimization characteristics. In the low penalty range (α=0.0~0.6), the system exhibited a counterintuitive "perturbation effect." Specifically, when α=0.0, i.e., in the pure incentive mode, the system achieved a low operating cost (3.4174 million yuan) and a high imbalance reduction rate (65.40%) thanks to the highly sensitive electricity price signal; however, when a weak penalty was introduced (e.g., α = 0.2), the system performance significantly degraded, with the cost surging to 3.897 million yuan and the reduction rate dropping to 50.43%. This phenomenon reveals that a weak penalty signal may constitute "decision noise" in the game, causing power generators to tend to adopt a conservative strategy when faced with a mixed signal of both incentives and small penalties, thus interrupting the originally efficient spontaneous adjustment and forcing the system to pay a higher cost to maintain balance. As the penalty intensity crosses the critical value and enters the medium-high range, a strong constraint effect begins to appear and drives the system to reach the optimal equilibrium. Data shows that when α=0.8, all system indicators reach their optimal state across the entire domain: total operating costs drop to a low of 2.9161 million yuan, saving approximately 25% compared to the worst-case scenario of α=0.2; simultaneously, the imbalance reduction rate climbs to a peak of 72.12%. At this point, the penalty is sufficient to force all units with adjustment capabilities to fully pursue their balance responsibilities, without triggering a surge in physical adjustment costs. This represents the model's optimal balance between physical safety and economic benefits. However, when α is further increased to 1.0, system performance declines again, costs rebound to 3.683 million yuan, and the reduction rate drops to 61.46%, indicating that excessively high penalty coefficients lead to market participants excessively avoiding risk, resulting in efficiency losses under over-constraint. Considering all factors, this study concludes that α=0.8 is the optimal parameter setting for operation in the micro-balance zone, at which the system achieves optimal physical balance at the lowest economic cost.

[0043] In summary, this invention constructs a micro-equilibrium zone imbalance responsibility transfer and market-based incentive architecture based on Stackelberg game theory, and designs a unique responsibility transfer mechanism to transfer the responsibility for physical increases that cannot be executed by new energy units to thermal power units. This solves the problem of responsibility allocation failure caused by the physical constraints of new energy, and ensures the physical feasibility and economic rationality of regulation commands. By combining a two-level game model and a linearization solution strategy, it simulates the interaction between system operators and market participants, thereby accurately reflecting the guiding role of price incentives on the main players' regulation strategies. This achieves a significant reduction in real-time power deviation and effective control of operating costs, and thus enables proactive balance control of the micro-equilibrium zone from both physical feasibility and economic incentive dimensions.

[0044] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for responsibility transfer and incentive adjustment in a micro-equilibrium zone, characterized in that, The method includes: Based on the historical output data, historical clearing plans and meteorological data of each market entity in the micro-balance zone, the output value of each market entity during the future scheduling period is predicted, and the preliminary imbalance responsibility and total system imbalance power of each market entity are determined according to the output value and real-time market clearing data. The final target responsibility of each market entity is determined based on the direction of the total unbalanced power of the system, and when the total unbalanced power of the system indicates that the system is in a power deficit state, the upward adjustment responsibility of the new energy market entity is transferred to the thermal power market entity. A master-slave game model is constructed based on the ultimate goal responsibility of each market entity, with the system operator determining the incentive price and each market entity determining the response power. Solve the master-slave game model to obtain the incentive price and the optimal response strategy of each market participant; The incentive price is published, and deviation settlement is performed based on the actual response results of each market participant. The ultimate target responsibility of each market entity is determined based on the direction of the total unbalanced power of the system, including: When the total unbalanced power of the system is greater than zero, it is determined that the micro-balanced region is in a power surplus state and resources need to be reduced. When the micro-balance region is in a state of power surplus, the ultimate target responsibility of each market entity is equal to the initial imbalance responsibility of each market entity; When the total unbalanced power of the system is less than zero, it is determined that the micro-balanced region is in a power deficit state and resources need to be increased. When the micro-balance zone is in a power deficit state, the ultimate target responsibility of the new energy market entities is set to zero, and the original preliminary imbalance responsibility of the new energy market entities is determined as the transferred responsibility; The transferred responsibility is allocated to each of the thermal power market entities according to their respective apportionment coefficients, so that each thermal power market entity assumes its own initial imbalance responsibility and the transferred responsibility, wherein the apportionment coefficients are determined according to capacity ratios.

2. The method for transferring responsibility and adjusting incentives in the micro-equilibrium zone according to claim 1, characterized in that, Predict the output of each market participant and determine the initial imbalance responsibility and the total system imbalance power, including: Collect historical actual output sequences, historical clearing plans, and key meteorological factors for each of the aforementioned market entities; The historical actual output sequence, the historical clearing plan, and the key meteorological factors are used as feature inputs to train a long short-term memory network, and the long short-term memory network is used to predict the output value of each market entity in the future scheduling period. The imbalance responsibility of each market entity is determined based on the output value of each market entity and the real-time market clearing value of the future scheduling period, and the imbalance responsibility of each market entity is aggregated to obtain the total system imbalance power of the micro-balance zone. The initial imbalance responsibility of each market entity is determined by combining the imbalance deviation, installed capacity, and output value of each market entity.

3. The method for transferring responsibility and adjusting incentives in the micro-equilibrium zone according to claim 1, characterized in that, The incentive price is determined by the system operator, including: An optimization model is constructed based on the ultimate goals and responsibilities of each market entity, with the upper-level system operator as the decision-making body. An objective function is established with the goal of minimizing the total system operating cost of the micro-equilibrium zone within a scheduling cycle, wherein the total system operating cost includes the adjustment fees paid to each of the market entities and the auxiliary service costs or assessment costs corresponding to the remaining imbalance after market adjustment; A system power balance constraint is established based on the total unbalanced power of the system, the actual adjustment power of each market entity, and the remaining unbalanced power. Incentive price range constraints are established based on the upper and lower limits of incentive prices, and total cost control constraints are established based on the operating costs of traditional balancing methods.

4. The method for transferring responsibility and adjusting incentives in the micro-equilibrium zone according to claim 3, characterized in that, The response power is determined by each of the aforementioned market entities, including: An optimization model is constructed based on the incentive price published by the system operator, with each of the market entities in the lower layer as the responding entities; An objective function is established with the goal of maximizing the net profit increment of each market entity, wherein the net profit increment includes incentive income, spot market opportunity income or opportunity cost, and changes in adjustment costs. The incentive benefits for each market participant are determined based on the incentive price, the actual response power of each market participant, the ultimate target responsibility, and the response accuracy penalty. The spot market opportunity gain or opportunity cost of each market participant is determined based on the real-time spot market clearing price and the actual response power direction of each market participant.

5. The micro-equilibrium zone responsibility transfer and incentive adjustment method according to claim 4, characterized in that, The determination of adjustment cost changes and constraints includes: For thermal power market participants, the adjustment cost changes are determined based on the secondary fuel cost function and the planned output and actual response power in real time market clearing. For thermal power market participants, output constraints are established based on upper and lower limits of output, and ramping constraints are established based on the maximum allowable upward ramping value and the maximum allowable downward ramping value. For new energy market participants, the adjustment cost will be approximately zero, and the spot electricity revenue lost due to reduced power generation will be counted as the spot market opportunity cost. For new energy market players, only the downward adjustment range constraint is established to indicate that the new energy market players only have the ability to lower prices.

6. The method for transferring responsibility and adjusting incentives in the micro-equilibrium zone according to claim 1, characterized in that, Solving the master-slave game model includes: Construct a Lagrange function based on the convexity of the lower-level optimization model; The lower-level optimization model is transformed into equality constraints and inequality constraints using the Karush-Kuhn-Tucker conditions; The equality constraints and inequality constraints are incorporated into the upper-level optimization model, and the complementary relaxation conditions are linearized using the Big M method and binary variables. The bilinear terms in the master-slave game model are linearized using strong duality theory or McCormick relaxation method, thereby transforming the master-slave game model into a single-level mixed integer linear programming problem.

7. The method for transferring responsibility and adjusting incentives in the micro-equilibrium zone according to claim 1, characterized in that, The incentive price is published, and deviations are settled based on the actual response results of each market participant, including: A business mathematical programming solver is used to solve a single-level mixed integer linear programming problem to obtain the optimal incentive pricing strategy and the optimal response plan of each market participant. The system operator publishes the optimal incentive price and controls each market participant to execute the adjustment response according to the optimal response plan; Deviation settlement will be made based on the actual response results of each of the aforementioned market entities.

8. A micro-equilibrium zone responsibility transfer and incentive adjustment system, characterized in that, The system employing the micro-equilibrium zone responsibility transfer and incentive adjustment method as described in claim 1 includes: The predictive responsibility module, based on the historical output data, historical clearing plans and meteorological data of each market entity in the micro-balance zone, predicts the output value of each market entity during the future scheduling period, and determines the preliminary imbalance responsibility of each market entity and the total imbalance power of the system based on the output value and real-time market clearing data. The responsibility transfer module determines the final target responsibility of each market entity based on the direction of the total unbalanced power of the system, and transfers the upward adjustment responsibility of the new energy market entity to the thermal power market entity when the total unbalanced power of the system indicates that the system is in a power deficit state. The game-theoretic pricing module constructs a master-slave game model based on the ultimate goal responsibility of each market participant. The incentive price is determined by the system operator, and the response power is determined by each market participant. The optimization solution module solves the master-slave game model to obtain the incentive price and the optimal response strategy of each market participant; The settlement module is executed to publish incentive prices and settle deviations based on the actual response results of each market participant.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor, can implement the micro-balance zone responsibility transfer and incentive adjustment method as described in any one of claims 1-7.

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