Multi-agent coordinated dispatching method for power market considering carbon trading and ancillary services

Abstract

The application discloses a kind of multi-agent collaborative scheduling method of electric power market considering carbon trading and auxiliary service, the method first completes the initialization of multi-agent data and multi-market parameter, builds the policy parameterization configuration module of market rule that can be self-adaptively adjusted;After constructing the cross-market double-layer optimization objective function that fuses electric energy, frequency modulation peak shaving auxiliary, carbon trading, set up frequency modulation peak shaving auxiliary service special constraint;The response behavior of multivariate flexible load is modeled, and the contribution degree of each subject adjustment increment is quantified;Through hierarchical non-cooperative game, the unified market clearing of grid side and the optimal balance of multi-agent income are realized;Based on the incremental contribution index, the differentiated income allocation is completed, and the rolling correction scheduling scheme is corrected through intraday model prediction control;The application can be simultaneously adapted to long-term, spot, frequency modulation auxiliary, carbon trading composite electric power market, balances the economic benefits of multi-market subject, improves the level of new energy consumption and power grid operation flexibility.

Description

Technical Field

[0001] This invention belongs to the field of power market optimization dispatching technology, specifically relating to a multi-entity collaborative dispatching method for power markets that takes into account carbon trading and ancillary services. Background Technology

[0002] Currently, a high proportion of renewable energy is connected to the grid, and China is gradually building a multi-level composite power market system that includes medium- and long-term, spot, frequency regulation and peak shaving ancillary services, and carbon trading. A large number of diverse market players, such as microgrids, virtual power plants (VPPs), load aggregators (LAs), and distributed energy storage, are participating in grid dispatch.

[0003] However, existing power market dispatch models have three major flaws: 1. Insufficient ability to coordinate and adapt to multiple markets: Most existing studies only focus on the single spot market of electricity, and fail to achieve cross-market coupling and linkage of electricity market, frequency regulation and peak shaving ancillary service market and carbon trading market. They cannot adapt to the domestic "medium and long term + spot + ancillary service + carbon trading" composite market operation rules, and the dispatch results have problems such as market settlement conflicts and low unit adjustment willingness. 2. The multi-stakeholder interest distribution mechanism is crude: Traditional demand response incentives use fixed compensation coefficients and do not distinguish the differentiated adjustment contributions of distributed photovoltaic, energy storage and flexible loads. The distribution of income among virtual power plants, user-side resources and power generation entities is unbalanced, and the enthusiasm of flexible resources to participate in the market is insufficient. 3. Poor integration with policy constraints: Existing dispatch models only assign simple parameter values ​​to policies such as renewable energy quota system, carbon reward and punishment, and peak-valley electricity price, without a modular adaptation architecture. When market trading rules, carbon pricing mechanism, and ancillary service compensation standards are iterated, the model needs to be reconstructed on a large scale, resulting in poor engineering practicality. Meanwhile, existing technologies mostly focus on virtual power plant two-layer dispatch, carbon capture power plant peak shaving, load aggregator demand response, and microgrid independent optimization. They lack a unified framework to achieve a complete dispatch scheme that simultaneously realizes carbon trading, frequency regulation ancillary service coupling, multi-stakeholder game collaboration, modular policy adaptation, and differentiated revenue distribution. This makes it difficult to directly implement in the market-oriented operation scenario of China's new power system. To address the aforementioned issues, it is essential to develop a multi-stakeholder collaborative dispatch method for the electricity market that takes into account carbon trading and ancillary services. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a multi-entity collaborative scheduling method for the power market that takes into account carbon trading and ancillary services. This method enables collaborative optimization among multiple market entities, including microgrids, virtual power plants, load aggregators, and distributed energy storage. It integrates the calculations of three markets: electricity, frequency regulation and peak shaving ancillary services, and carbon trading. It constructs a differentiated revenue allocation algorithm based on the contribution of incremental resources and designs a policy parameterized configuration module to quickly adapt to market rule updates. This improves the level of renewable energy consumption, reduces the overall operating cost of the system, and balances the economic benefits of various market entities.

[0005] The objective of this invention is achieved as follows: a multi-stakeholder collaborative dispatch method for the electricity market that takes into account carbon trading and ancillary services, comprising the following steps: S1, Initialization of multi-entity basic data and multi-market parameters: Collect basic operational data of multiple market entities such as virtual power plants, microgrids, load aggregators, and distributed energy storage, and import operational parameters of the electricity market, frequency regulation and peak shaving ancillary service market, and carbon trading market; build a policy parameterized configuration module with built-in adjustable parameter groups for carbon rewards and penalties, ancillary service tiered compensation, and renewable energy quota assessment. S2 constructs a cross-market joint optimization objective function based on two time scales: day-ahead and intraday. The objective function simultaneously includes the cost of purchasing and selling electricity, the compensation revenue of frequency regulation ancillary services, the cost of carbon trading compliance, the cost of quota assessment penalties, and multi-dimensional operational constraints such as power balance, frequency regulation and peak shaving ancillary services, carbon emissions, energy storage lifespan, and power flow security. S3, Multi-entity differentiated flexible response behavior modeling: Clustering to extract typical user demand response curves, fitting the mapping function between economic incentives and load adjustment, and quantifying the incremental resource contribution indicators of each market entity in frequency regulation, carbon emission reduction, and new energy consumption. S4. A hierarchical game framework is established to complete the joint clearing of multiple markets: The upper layer is the unified clearing of electricity, frequency regulation and carbon quota by the power grid dispatch center, with the optimization goal of maximizing the social welfare of the system; The lower layer is the non-cooperative game among market participants, which iteratively updates the bidding strategy with the goal of maximizing their own comprehensive benefits, solves the Nash equilibrium, and outputs the optimal bidding plan of each participant. S5, complete cross-market revenue sharing based on incremental contribution: According to the incremental contribution index obtained in step S3, split the ancillary service subsidy, carbon trading dividend, and new energy consumption reward, allocate the revenue according to the adjustment contribution ratio of each entity, and implement tiered penalty deduction for entities whose dispatch response deviation exceeds the threshold. S6, Intraday Rolling Scheduling Correction and Policy Adaptive Update: Based on real-time wind and solar load forecast error correction frequency regulation AGC hierarchical command; Market rule parameters can be directly modified through the policy parameterized configuration module without reconstructing the optimization model, generating a corrected collaborative scheduling scheme; S7 outputs scheduling results and market settlement reports: It outputs multi-entity day-ahead and intraday scheduling output plans, frequency regulation reserve configurations, carbon trading compliance volumes, and market settlement reports for each entity, completing the closed loop of multi-market entity collaborative scheduling.

[0006] Preferably, in step S1, the basic data of multiple market entities includes at least: the output parameters of photovoltaic, wind power, and micro gas turbines within the virtual power plant, the charging and discharging power and cycle life constraints of the battery energy storage, the power limit of the microgrid regional interconnection line, and the adjustable capacity range of interruptible loads and temperature-controlled loads under the jurisdiction of the load aggregator.

[0007] Preferably, in step S2, the frequency modulation and peak shaving auxiliary service constraints include fine-grained frequency modulation capacity demand constraints and AGC hierarchical frequency modulation output constraints that take into account energy storage lifetime protection.

[0008] Preferably, in step S3, a two-stage clustering algorithm is used to classify different types of flexible loads, and a quantitative functional relationship between the incentive electricity price and the load adjustment amount is established through polynomial fitting.

[0009] Preferably, in step S4, the solution process for the hierarchical game joint clearing is as follows: the upper-level scheduling center sends market price signals to each lower-level market participant; after updating their bidding strategies, the lower-level participants send their bids back to the upper level, and the process is iterated until the change in bids between two consecutive bids by each participant is less than a preset convergence threshold. The game is then determined to have reached Nash equilibrium, and the optimal market clearing result is output.

[0010] Preferably, the preset convergence threshold is a precision threshold set before the iterative calculation, used to determine whether the multi-entity non-cooperative game has reached Nash equilibrium; during the iteration process, the changes in the bids and declared outputs of each market entity in two adjacent iterations are compared. When the change is less than or equal to the preset convergence threshold, it is determined that the bidding strategy of each entity tends to be stable, the iteration is terminated and the market equilibrium clearing result is output. If the change is greater than the preset convergence threshold, the bidding strategy of each entity continues to be updated iteratively.

[0011] Preferably, in step S5, the resource increment contribution index is calculated by weighting the frequency regulation capacity contribution coefficient, carbon emission reduction contribution coefficient, and new energy consumption contribution coefficient, and serves as the sole basis for calculating cross-market revenue distribution.

[0012] Preferably, in step S6, a model predictive control algorithm is used to perform rolling feedback correction on the intraday frequency regulation dispatch command based on the real-time output deviation of new energy sources.

[0013] Preferably, the policy parameter configuration module independently encapsulates four types of adjustable parameters: carbon trading price, quota assessment penalty, frequency regulation tiered compensation coefficient, and peak-valley electricity price. When market rules are updated, only the internal parameters of the module need to be modified to adapt to the scheduling model.

[0014] Due to the adoption of the above technical solution, the beneficial effects of the present invention are: (1) This invention deeply couples the three major markets of electrical energy, frequency regulation and peak shaving assistance, and carbon trading, which can be adapted to the domestic composite power market architecture and solves the problems of settlement conflict and insufficient utilization of flexible resources caused by single market dispatch. (2) This invention introduces an incremental contribution benefit distribution algorithm to replace the traditional fixed coefficient compensation, accurately distinguishes the differentiated adjustment value of source, load and storage, and can balance the interests of virtual power plants, microgrids and user load aggregators, and enhance the willingness of flexible resources to participate in the market; (3) The present invention sets up an independent policy parameter configuration module, and the carbon, quota and ancillary service policy parameters are modular and adjustable. When the market rules are iterated, there is no need to reconstruct the scheduling model, which greatly improves the practicality of engineering implementation. (4) The present invention adopts a two-layer hierarchical game optimization architecture, which can take into account the overall social welfare of the power grid and the maximization of the benefits of each market entity, while also taking into account the power grid security constraints and the market-oriented autonomous trading needs. (5) Based on multi-timescale collaborative scheduling (day-ahead + intraday rolling), combined with energy storage life protection and frequency regulation fine modeling, this invention reduces the scheduling deviation caused by new energy fluctuations and improves the system frequency stability and the local consumption capacity of new energy.

[0015] In summary, this invention can effectively reduce the overall operating cost of power systems, increase the absorption rate of new energy sources, balance the benefits of multiple stakeholders in the power market, and has better engineering adaptability. It is suitable for market-oriented dispatching engineering scenarios of microgrids and virtual power plants under new power systems. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of the collaborative scheduling method of the present invention.

[0017] Figure 2 This is a schematic diagram of the multi-market hierarchical collaborative scheduling dual-layer optimization architecture of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0019] like Figure 1 and Figure 2 As shown, this invention provides a multi-stakeholder collaborative dispatch method for the electricity market that takes into account carbon trading and ancillary services, comprising the following steps: S1, Initialization of Multi-Entity Basic Data and Multi-Market Parameters: Collects basic data of all market entities within the scheduling cycle, including operating parameters of photovoltaic, micro gas turbine, energy storage, and flexible load within the virtual power plant, microgrid power constraints, and demand response potential of users under the load aggregator; imports multiple market parameters, including spot electricity price, frequency regulation capacity compensation price, carbon trading price, renewable energy quota indicators, and unit carbon emission factors; builds a policy parameterized configuration module with built-in adjustable parameter groups for carbon reward and punishment coefficients, ancillary service tiered compensation coefficients, and quota assessment penalties.

[0020] S2 constructs a cross-market joint optimization objective function with both day-ahead and intraday time scales: It establishes two-layer optimization objectives for day-ahead scheduling and intraday rolling scheduling, integrating four cost / benefit items: electricity purchase and sale costs, frequency regulation and peak shaving ancillary service compensation benefits, carbon trading compliance costs, and quota assessment penalty costs. It configures multi-dimensional operational constraints, including system power balance constraints, frequency regulation and peak shaving ancillary service constraints, carbon emissions, distributed power output upper and lower limits constraints, energy storage charging and discharging power and cycle life constraints, unit carbon emission upper limit constraints, and distribution network tie-line power flow safety constraints. Specifically, the frequency regulation and peak shaving ancillary service constraints include fine-grained frequency regulation capacity demand constraints and AGC-based hierarchical frequency regulation output constraints that take into account energy storage life protection.

[0021] S3, Multi-entity Differentiated Response Behavior Modeling: For the diverse flexible loads under the load aggregator, a two-stage clustering method is used to extract typical user demand response curves. An economic incentive-load adjustment mapping function is established through polynomial fitting. An incremental contribution index is introduced to quantify the adjustment contribution of different entities in frequency regulation, carbon emission reduction, and new energy consumption, providing a calculation basis for subsequent revenue sharing.

[0022] S4, Multi-Agent Game Layered Joint Clearing Calculation: Upper level: The power grid dispatch center performs cross-market unified clearing with the goal of maximizing the social welfare of the system, and simultaneously clears the trading volume of electricity, frequency regulation reserve capacity and carbon quota trading volume; Lower layer: Each market participant (virtual power plant, microgrid, load aggregator) engages in non-cooperative game theory, updates its bidding strategy to maximize its own comprehensive benefits, iteratively solves the Nash equilibrium, and outputs the optimal declared power output, declared frequency regulation capacity, and carbon trading compliance volume for each participant.

[0023] The specific process is as follows: the upper-level dispatch center sends market price signals to each market participant at the lower level; after updating their bidding strategies, the lower-level participants send their bids back to the upper level, and the process is repeated until the change in bids between two consecutive bids by each participant is less than the preset convergence threshold. At this point, the game is determined to have reached Nash equilibrium and the optimal market clearing result is output.

[0024] The preset convergence threshold is a precision threshold set before the iterative calculation, used to determine whether the multi-entity non-cooperative game has reached Nash equilibrium. During the iteration process, the changes in the bids and declared outputs of each market entity are compared between two adjacent iterations. When the change is less than or equal to the preset convergence threshold, it is determined that the bidding strategies of each entity tend to be stable, the iteration is terminated and the market equilibrium clearing result is output. If the change is greater than the preset convergence threshold, the bidding strategies of each entity continue to be updated iteratively.

[0025] S5, Cross-market revenue sharing based on incremental contribution: Based on the resource incremental contribution indicator calculated in step S3, ancillary service subsidies, carbon trading dividends, and renewable energy consumption rewards are broken down, and revenue is distributed according to the actual adjustment contribution ratio of each entity. A tiered reward and punishment mechanism is adopted to deduct dispatch penalties from entities whose response deviation exceeds the threshold, and these penalties are included in the overall market settlement. Among them, the resource incremental contribution indicator is calculated by weighting the frequency regulation capacity contribution coefficient, carbon emission reduction contribution coefficient, and renewable energy consumption contribution coefficient, and serves as the sole calculation basis for cross-market revenue distribution.

[0026] S6, Intraday Rolling Correction and Policy Adaptive Update: Based on real-time renewable energy output and load forecasting errors, the scheduling boundary is updated, and model predictive control correction (AGC) hierarchical frequency regulation instructions are introduced; the policy parameterized configuration module receives market rule update instructions, quickly modifies independently packaged carbon price, frequency regulation compensation, and quota penalty parameters, without reconstructing the optimization model, and outputs the updated multi-entity collaborative scheduling scheme.

[0027] S7 outputs scheduling results and market settlement reports: outputs the day-ahead / intra-day power output plan, frequency regulation reserve configuration scheme, carbon trading compliance volume, and revenue settlement details of each entity, completing the multi-market joint scheduling closed loop.

[0028] The present invention selects a distribution network system comprising 3 virtual power plants, 2 regional microgrids, and 1 load aggregator as an implementation example. The system includes photovoltaic, wind power, micro gas turbines, lithium battery energy storage, and interruptible flexible loads; it participates in the regional electricity spot market, frequency regulation ancillary service market, and carbon trading market, and implements renewable energy quota assessment.

[0029] S1, Data Initialization: Collect the rated output of each distributed power source, the upper limit of energy storage charging and discharging power, and the adjustable load capacity; enter the day-ahead time-of-use electricity price, frequency regulation capacity compensation of 30 yuan / MW·h, carbon transaction price of 55 yuan / tCO2, annual renewable energy quota of 18%, and the policy module presets carbon excess penalty of 80 yuan / t and response deviation tiered reward and punishment coefficient.

[0030] S2, construct a cross-market comprehensive objective function: Objective function = Electricity purchase and sale cost − Frequency regulation and peak shaving ancillary service revenue + Carbon quota purchase cost + Quota non-compliance penalty; Constraints include system power balance, energy storage cycle life constraints, frequency regulation and peak shaving ancillary service constraints (fine-grained frequency regulation capacity, energy storage AGC hierarchical frequency regulation constraints), unit carbon emission upper limit, and distribution network tie line power flow limit.

[0031] S3, Flexible Load Response Modeling: Two-stage clustering of industrial and residential flexible loads under the load aggregator, fitting a quadratic polynomial function of incentive electricity price and load reduction amount, and calculating the contribution coefficient of frequency regulation and carbon emission reduction increment of each load unit.

[0032] S4, Layered Game Theory and Joint Clearing: The upper-level dispatch center optimizes the total system's electricity, frequency regulation, and carbon trading volume to maximize social welfare; the lower-level three virtual power plants, two microgrids, and load aggregators engage in non-cooperative game theory, iteratively updating bids until Nash equilibrium is reached, obtaining the optimal output and frequency regulation declaration capacity of each entity.

[0033] S5, Differentiated revenue sharing: Based on the frequency regulation capacity, carbon emission reduction, and contribution of renewable energy consumption provided by each entity, the auxiliary subsidy and carbon revenue are separated; for entities with an actual daily adjustment deviation exceeding 5%, tiered penalties are deducted and included in market settlement.

[0034] S6, intraday rolling optimization: real-time wind and solar power output forecasts are updated every 15 minutes. Model predictive control is used to correct the frequency regulation commands of energy storage and gas turbine AGC. The policy module can directly modify carbon price and frequency regulation compensation parameters without modifying the optimization model.

[0035] S7, output scheduling results: output the day-ahead power output plan for 96 points, real-time frequency regulation backup plan, carbon compliance volume of each entity, and settlement table of sub-items of revenue.

[0036] Simulation results show that, compared to the traditional single-market scheduling method, this embodiment reduces the overall system operating cost by 12.7%, the amount of abandoned renewable energy by 18.3%, and the variance of revenue for each market participant by 65.2%, resulting in a more balanced distribution of benefits among multiple participants.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A multi-stakeholder collaborative dispatch method for the electricity market that considers carbon trading and ancillary services, characterized in that, Includes the following steps: S1, Initialization of multi-entity basic data and multi-market parameters: Collect basic operational data of multiple market entities such as virtual power plants, microgrids, load aggregators, and distributed energy storage, and import operational parameters of the electricity market, frequency regulation and peak shaving ancillary service market, and carbon trading market; build a policy parameterized configuration module with built-in adjustable parameter groups for carbon rewards and penalties, ancillary service tiered compensation, and renewable energy quota assessment. S2 constructs a cross-market joint optimization objective function based on two time scales: day-ahead and intraday. The objective function simultaneously includes the cost of purchasing and selling electricity, the compensation revenue of frequency regulation ancillary services, the cost of carbon trading compliance, the cost of quota assessment penalties, and multi-dimensional operational constraints such as power balance, frequency regulation and peak shaving ancillary services, carbon emissions, energy storage lifespan, and power flow security. S3, Multi-entity differentiated flexible response behavior modeling: Clustering to extract typical user demand response curves, fitting the mapping function between economic incentives and load adjustment, and quantifying the incremental resource contribution indicators of each market entity in frequency regulation, carbon emission reduction, and new energy consumption. S4. A hierarchical game framework is established to complete the joint clearing of multiple markets: The upper layer is the unified clearing of electricity, frequency regulation and carbon quota by the power grid dispatch center, with the optimization goal of maximizing the social welfare of the system; The lower layer is the non-cooperative game among market participants, which iteratively updates the bidding strategy with the goal of maximizing their own comprehensive benefits, solves the Nash equilibrium, and outputs the optimal bidding plan of each participant. S5, complete cross-market revenue sharing based on incremental contribution: based on the incremental contribution index obtained in step S3, split the ancillary service subsidy, carbon trading dividend, and new energy consumption reward, allocate the revenue according to the adjustment contribution ratio of each entity, and implement tiered penalty deduction for entities whose dispatch response deviation exceeds the threshold. S6, Intraday Rolling Scheduling Correction and Policy Adaptive Update: Based on real-time wind and solar load forecast error correction frequency regulation AGC hierarchical command; Market rule parameters can be directly modified through the policy parameterized configuration module without reconstructing the optimization model, generating a corrected collaborative scheduling scheme; S7 outputs scheduling results and market settlement reports: It outputs multi-entity day-ahead and intraday scheduling output plans, frequency regulation reserve configurations, carbon trading compliance volumes, and market settlement reports for each entity, completing the closed loop of multi-market entity collaborative scheduling.

2. The multi-stakeholder collaborative dispatch method for the power market, taking into account carbon trading and ancillary services, as described in claim 1, is characterized in that: In step S1, the basic data of multiple market entities includes at least: the output parameters of photovoltaic, wind power, and micro gas turbines within the virtual power plant, the charging and discharging power and cycle life constraints of the battery energy storage, the power limit of the microgrid regional interconnection line, and the adjustable capacity range of interruptible loads and temperature-controlled loads under the load aggregator.

3. The multi-stakeholder collaborative dispatch method for the power market, taking into account carbon trading and ancillary services, as described in claim 1, is characterized in that: In step S2, the frequency regulation and peak shaving auxiliary service constraints include fine-grained frequency regulation capacity demand constraints and AGC hierarchical frequency regulation output constraints that take into account energy storage lifetime protection.

4. The multi-stakeholder collaborative dispatch method for the power market, taking into account carbon trading and ancillary services, as described in claim 1, is characterized in that: In step S3, a two-stage clustering algorithm is used to classify different types of flexible loads, and a quantitative functional relationship between incentive electricity price and load adjustment is established through polynomial fitting.

5. The multi-stakeholder collaborative dispatch method for the power market, taking into account carbon trading and ancillary services, as described in claim 1, is characterized in that: In step S4, the solution process for the joint clearing of the hierarchical game is as follows: the upper-level scheduling center sends market price signals to each market participant in the lower level; after updating their bidding strategies, the lower-level participants send their bids back to the upper level, and the process is repeated until the change in bids between two consecutive bids by each participant is less than the preset convergence threshold. The game is then determined to have reached Nash equilibrium and the optimal market clearing result is output.

6. The multi-stakeholder collaborative dispatch method for the power market, taking into account carbon trading and ancillary services, as described in claim 5, is characterized in that: The preset convergence threshold is a precision threshold set before the iterative calculation, used to determine whether the multi-entity non-cooperative game has reached Nash equilibrium. During the iteration process, the changes in the bids and declared outputs of each market entity are compared between two adjacent iterations. When the change is less than or equal to the preset convergence threshold, it is determined that the bidding strategies of each entity tend to be stable, the iteration is terminated and the market equilibrium clearing result is output. If the change is greater than the preset convergence threshold, the bidding strategies of each entity are updated iteratively.

7. The multi-stakeholder collaborative dispatch method for the power market, taking into account carbon trading and ancillary services, as described in claim 1, is characterized in that: In step S5, the resource increment contribution index is calculated by weighting the frequency regulation capacity contribution coefficient, carbon emission reduction contribution coefficient, and new energy consumption contribution coefficient, and serves as the sole basis for calculating cross-market revenue distribution.

8. The multi-entity collaborative dispatch method for the power market considering carbon trading and ancillary services as described in claim 1, characterized in that: In step S6, a model predictive control algorithm is used to perform rolling feedback correction on the intraday frequency regulation dispatch command based on the real-time output deviation of new energy sources.

9. The multi-stakeholder collaborative dispatch method for the power market, taking into account carbon trading and ancillary services, as described in claim 1, is characterized in that: The policy parameter configuration module independently encapsulates four types of adjustable parameters: carbon trading price, quota assessment penalty, frequency regulation tiered compensation coefficient, and peak-valley electricity price. When market rules are updated, only the internal parameters of the module need to be modified to adapt to the scheduling model.

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