Power system scheduling method and system considering power-carbon market collaboration

Abstract

The invention relates to the technical field of electric power system scheduling, in particular to an electric power system scheduling method and system considering power-carbon market collaboration, and the method comprises the steps: building a multi-objective optimization function based on a power-carbon market transaction mechanism, the multi-objective optimization function comprises a system operation cost minimization function, a carbon emission minimization function, a renewable energy consumption maximization proportion function and a revenue balance function; based on the load value of the power system, solving the multi-objective optimization function by adopting a distributed optimization algorithm to obtain an output plan of the power system; and scheduling the power system according to the output plan. According to the invention, the electricity and carbon market transaction mechanism is fully considered, and the electricity market and the carbon emission market are organically combined. Through the solution of the multi-objective optimization function, the optimal configuration of power and carbon emission resources can be realized, so that effective interaction and collaboration are formed between the power market and the carbon market.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology, and specifically to a power system dispatching method and system that considers the coordination of the electricity carbon market. Background Technology

[0002] Against the backdrop of global energy transition and climate change response, the power system, as a core component of the energy sector, is facing unprecedented challenges in its dispatching methods. Traditional power system dispatching primarily focuses on ensuring reliable power supply and reducing generation costs, typically relying on conventional energy sources such as thermal and hydropower. However, with rapid economic development and continuously growing energy demand, the limitations of traditional dispatching methods are becoming increasingly apparent.

[0003] To promote efficient energy use and effective carbon emission control, the electricity carbon market has emerged. The electricity market guides power generation companies and users to rationally adjust their electricity production and consumption behavior through pricing mechanisms, improving the efficiency of electricity resource allocation. The carbon emission market, by setting carbon emission quotas and trading mechanisms, incentivizes companies to reduce carbon emissions, thus promoting the achievement of carbon reduction targets across society.

[0004] However, the electricity market and carbon emissions market currently operate with a degree of independence and lack effective coordination mechanisms. This prevents the full realization of the synergistic effect of the electricity and carbon markets during power system dispatch. Summary of the Invention

[0005] (a) Purpose of the invention The purpose of this invention is to provide a power system dispatching method and system that comprehensively optimizes multiple aspects, including power system operating costs, carbon emissions, renewable energy consumption, and revenue balance, while also considering the coordination of the electricity carbon market.

[0006] (II) Technical Solution To address the aforementioned issues, this invention provides a power system dispatching method that considers the coordination of the electricity carbon market, comprising: establishing a multi-objective optimization function based on the electricity carbon market trading mechanism, wherein the multi-objective optimization function includes a function for minimizing system operating costs, a function for minimizing carbon emissions, a function for maximizing the proportion of renewable energy consumption, and a revenue equilibrium function; Based on the power system load value and constraints, a distributed optimization algorithm is used to solve the multi-objective optimization function to obtain the power system output plan; the constraints include power system operation constraints and carbon emission constraints. The power system is dispatched according to the aforementioned output plan.

[0007] In another aspect of the present invention, preferably, the power system operation constraints include power balance constraints and upper and lower limits of power generation unit output constraints; the carbon emission constraints include total carbon emission constraints.

[0008] In another aspect of the present invention, preferably, The carbon market trading mechanism includes a carbon emission quota auction mechanism and a carbon credit trading mechanism. The carbon emission quota auction mechanism is used for the initial allocation of carbon emission quotas, and the carbon credit trading mechanism is used for the secondary trading of carbon emission quotas.

[0009] In another aspect of the present invention, preferably, the step of solving the multi-objective optimization function based on the power system load value using a distributed optimization algorithm to obtain the power system output plan includes: The power system is divided into multiple power generation entities, each of which includes several power generation units, local load values, and boundary nodes. The local load values ​​include the power system load values ​​after being divided according to the number of power generation entities, and the boundary nodes are power network nodes connecting different power generation entities. Within each power generation entity, a local optimization problem is constructed based on local load values ​​and local constraints. The objective function of the local optimization problem is a local expression of a multi-objective optimization function within the power generation entity. The local optimization results of each power generation entity are iteratively updated through a distributed iterative algorithm until the global convergence condition is met, and the global optimal solution is obtained. Based on the global optimal solution, a power output plan for the power system is generated.

[0010] In another aspect of the present invention, preferably, the step of iteratively updating the local optimization results of each power generation entity through a distributed iterative algorithm until the global convergence condition is met to obtain the globally optimal solution includes the following steps: Through a distributed iterative algorithm, each power generation entity exchanges boundary variable information with its neighboring power generation entities. The boundary variable information includes the power and carbon emissions of the boundary nodes. In each iteration, each power generation entity updates the constraints and objective function of the local optimization problem based on the received boundary variable information, and solves the local optimization problem to obtain the updated boundary node power and carbon emissions. The updated boundary node power and carbon emissions are sent to the adjacent power generation entities. The above steps are repeated until the global convergence condition is met and the global optimal solution is obtained.

[0011] In another aspect of the present invention, preferably, The global convergence conditions include: The power difference at the boundary nodes of each power generation entity is less than the preset power threshold; The difference in carbon emissions among the various power generation entities is less than the preset threshold for carbon emissions; The difference between the local optimization objective function value and the global optimization objective function value of each power generation entity is less than the preset tolerance.

[0012] In another aspect of the present invention, preferably, The function for minimizing system operating costs is expressed by the following formula: Where U1 represents minimizing the system operating cost, This represents the output of the i-th traditional power generation unit during time period t; This represents the output of the j-th renewable energy power generation unit in time period t; N represents the number of conventional power generation units; M represents the number of renewable energy power generation units; This represents the power generation cost of traditional power generation unit i. This represents the power generation cost of renewable energy power generation unit j; This represents the auction price of carbon emission allowances for time period t. This represents the carbon credit trading price for time period t. This represents the carbon emission allowance of power generation unit r during time period t. This represents the amount of carbon emission allowance traded by power generation unit i during time period t.

[0013] In another aspect of the present invention, preferably, the function for minimizing carbon emissions and the function for maximizing the proportion of renewable energy consumption are expressed using the following formulas: Where U2 represents minimizing carbon emissions, and U3 represents maximizing the proportion of renewable energy consumption. This represents the output of the i-th traditional power generation unit during time period t; This represents the output of the j-th renewable energy generation unit during time period t; This represents the power system load demand during time period t; N represents the number of traditional power generation units; and M represents the number of renewable energy power generation units.

[0014] In another aspect of the present invention, preferably, the revenue equilibrium function is expressed using the following formula: Where U2 represents the equilibrium return, This represents the revenue of the i-th traditional power generation unit in time period t. This represents the ideal return of a traditional power generation unit. This represents the revenue of the j-th renewable energy generation unit in time period t. This indicates the ideal return on a renewable energy power generation unit.

[0015] In another aspect of the present invention, preferably, a power system dispatching system considering coordination in the electricity carbon market includes: Establishment Module: Based on the electricity carbon market trading mechanism, establish a multi-objective optimization function, which includes a function to minimize system operating costs, a function to minimize carbon emissions, a function to maximize the proportion of renewable energy consumption, and a revenue equilibrium function; Acquisition Module: Based on the power system load value and constraints, a distributed optimization algorithm is used to solve the multi-objective optimization function to obtain the power system output plan; the constraints include power system operation constraints and carbon emission constraints; Dispatch module: Dispatches the power system according to the output plan.

[0016] (III) Beneficial Effects The above-described technical solution of the present invention has the following beneficial technical effects: This invention incorporates system operating costs, carbon emissions, renewable energy consumption ratios, and revenue equilibrium into the objective optimization function. By comprehensively considering the electricity carbon market trading mechanism and power system operational constraints, it optimizes the allocation of power generation resources. Revenue equilibrium, as part of the multi-objective optimization function, helps balance the interests of various participants in the power system. It fully considers the electricity carbon market trading mechanism, organically combining the electricity market and the carbon emissions market. Through solving the multi-objective optimization function, it achieves optimal allocation of electricity and carbon emission resources, enabling effective interaction and synergy between the electricity and carbon markets. Attached Figure Description

[0017] Figure 1 This is an overall flowchart of one embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0019] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] Example 1 A power system dispatching method that considers coordination with the electricity carbon market. Figure 1 An overall flowchart of one embodiment of the present invention is shown, as follows: Figure 1 As shown, it includes: Based on the electricity carbon market trading mechanism, a multi-objective optimization function is established. This function includes minimizing system operating costs, minimizing carbon emissions, maximizing the renewable energy consumption ratio, and achieving revenue equilibrium. The electricity carbon market trading mechanism can include electricity market trading rules, carbon emission rights trading mechanisms, and a linkage mechanism between the electricity and carbon markets. Through a well-designed market mechanism, power generation units can be incentivized to reduce carbon emissions and increase the renewable energy consumption ratio.

[0022] The aforementioned carbon market trading mechanism includes a carbon emission allowance auction mechanism and a carbon credit trading mechanism. The carbon emission allowance auction mechanism is used for the initial allocation of carbon emission allowances, while the carbon credit trading mechanism is used for the secondary trading of these allowances. The carbon emission allowance auction is the method for initial allocation of carbon emission allowances. During the auction process, the regulatory agency acts as the seller, selling carbon emission allowances to buyers such as power generation units. The auction mechanism ensures the fair allocation of carbon emission allowances and guides power generation units to reduce carbon emissions through market price signals. The carbon credit trading mechanism allows power generation units to buy and sell carbon emission allowances among themselves. If a power generation unit's carbon emissions are lower than its allocated allowance, it can sell the excess allowances to other power generation units that exceed their emission limits. The carbon credit trading mechanism helps incentivize power generation units to take emission reduction measures, thereby reducing the overall carbon emission level.

[0023] Based on the aforementioned collaborative optimization model for the electricity carbon market, a multi-objective optimization function is established. This function includes minimizing system operating costs, minimizing carbon emissions, maximizing the renewable energy consumption ratio, and achieving revenue equilibrium. Minimizing system operating costs includes generation costs and grid maintenance costs, which are reduced by optimizing dispatch strategies to lower the overall system operating costs. Minimizing carbon emissions refers to reducing fossil fuel consumption and increasing the utilization rate of clean energy during power generation, thereby reducing carbon emissions. Maximizing the renewable energy consumption ratio involves optimizing dispatch strategies to increase the grid connection ratio and utilization rate of renewable energy sources such as wind and solar power, reducing wind and solar curtailment. Revenue equilibrium, as part of the multi-objective optimization function, helps balance the interests of various participants in the power system. In a collaborative electricity carbon market environment, different power generation companies, users, and market operators have different interests. This dispatch method, by rationally allocating power generation tasks and market trading shares, can ensure that all parties obtain relatively fair returns in the market, promote the healthy and stable development of the electricity carbon market, and enhance the enthusiasm and vitality of market participants.

[0024] The minimized system operating cost is expressed by the following formula: Where U1 represents minimizing the system operating cost, This represents the output of the i-th traditional power generation unit during time period t; This represents the output of the j-th renewable energy power generation unit in time period t; N represents the number of conventional power generation units; M represents the number of renewable energy power generation units; This represents the power generation cost of traditional power generation unit i. This represents the power generation cost of renewable energy power generation unit j; This represents the auction price of carbon emission allowances for time period t. This represents the carbon credit trading price for time period t. This represents the carbon emission allowance of power generation unit r during time period t. This represents the carbon emission allowance trading volume of power generation unit i during time period t. It comprehensively considers the generation costs of traditional and renewable energy power generation units, as well as carbon emission-related costs. By optimizing the output allocation of each power generation unit, lower-cost generation methods can be prioritized. For example, when electricity prices are low and carbon emission allowances are sufficient, the output of traditional power generation units can be reasonably increased; when renewable energy resources are abundant, the utilization of renewable energy power generation units can be increased, thereby reducing overall generation costs. Simultaneously, consideration of carbon emission allowance auction and trading costs prompts power generation companies to optimize production decisions, avoid unnecessary carbon emission cost expenditures, further reduce system operating costs, and improve the economic efficiency and market competitiveness of power companies.

[0025] The minimization of carbon emissions and the maximization of renewable energy utilization are expressed using the following formula: Where U2 represents minimizing carbon emissions, and U3 represents maximizing the proportion of renewable energy consumption. This represents the output of the i-th traditional power generation unit during time period t; This represents the output of the j-th renewable energy generation unit during time period t; The denoting factor is t, where N represents the power system load demand during time period t; N represents the number of traditional power generation units; and M represents the number of renewable energy power generation units. Minimizing carbon emissions by limiting the output of traditional power generation units, especially those with high carbon emission coefficients, and encouraging the use of clean energy generation can effectively reduce the overall carbon emissions of the power system. Maximizing the renewable energy absorption ratio aims to address the intermittency and volatility of renewable energy generation and increase its absorption in the power system. Optimized dispatching allows renewable energy generation to better match the load demand of the power system, reducing wind and solar power curtailment.

[0026] The equilibrium of revenues is expressed using the following formula: Where U2 represents the equilibrium return, This represents the revenue of the i-th traditional power generation unit in time period t. This represents the ideal return of a traditional power generation unit. This represents the revenue of the j-th renewable energy generation unit in time period t. This represents the ideal return for a renewable energy generation unit. The return equilibrium objective function helps balance the returns of traditional and renewable energy generation units. In the electricity market, different types of generation units have different cost structures and market competitiveness. This objective function ensures that all types of generation units receive relatively fair returns in the market by narrowing the gap between actual and ideal returns.

[0027] The method involves using a distributed optimization algorithm to solve the multi-objective optimization function based on the power system load value and constraints to obtain the power system output plan. The constraints include power system operation constraints and carbon emission constraints. Power system operation constraints can include physical constraints such as power balance constraints, upper and lower limits of generator unit output constraints, and line transmission capacity constraints, ensuring the safe and stable operation of the power system during dispatching. Carbon emission constraints involve limiting the carbon emissions of the power system, which can include setting an upper limit on total carbon emissions and limiting the carbon emission intensity of specific generator units, aiming to promote the development of the power system towards a low-carbon and environmentally friendly direction. In this embodiment, the power system operation constraints include power balance constraints and upper and lower limits of generator unit output constraints; the carbon emission constraints include total carbon emission constraints.

[0028] Furthermore, the power balance constraint is expressed using the following formula: in, This represents the output of the i-th traditional power generation unit during time period t; N represents the output of the j-th renewable energy power generation unit in time period t; N represents the number of conventional power generation units; M represents the number of renewable energy power generation units; D t For the load demand in time period t, Let t be the network loss during time period t.

[0029] The upper and lower limits of the power output of the power generation unit are expressed by the following formula: in, , This represents the lower and upper limits of the output of traditional power generation unit i. This represents the maximum available output of renewable energy generation unit j during time period t.

[0030] The total carbon emission limit is expressed using the following formula: in, This represents the carbon emission intensity of traditional power generation unit i. This indicates the upper limit of total carbon emissions from the power system.

[0031] The power output plan includes the power output of each generator unit, the proportion of renewable energy consumption, and carbon emissions; The output acquisition plan includes: The power system is divided into multiple generating entities, each containing several generating units, local load values, and boundary nodes. The local load value includes the power system load value divided according to the number of generating entities. The boundary nodes are power network nodes connecting different generating entities. The power system can be divided into multiple generating entities based on geographical region, grid topology, or the distribution of market participants. Each generating entity includes: several generating units, such as thermal power plants, wind farms, and photovoltaic power plants. The local load value is the power load value of the area served by the generating entity. Boundary nodes are power network nodes connecting different generating entities, used for power and information transmission. The local load value is based on the global load value. The division method may include: The load is allocated proportionally to each power generation entity based on its historical load proportions. It is also allocated regionally based on geographical regions or grid zones. Finally, it is divided according to load characteristics (e.g., industrial load, residential load). Boundary nodes are power network nodes connecting different power generation entities, serving functions including: power transmission (transferring electricity between entities) and information exchange (transmitting power, carbon emissions, and other information during distributed optimization). Each power generation entity only needs to exchange a small amount of boundary variable information with its neighbors to independently perform local optimization solutions, thus reducing computational and communication costs while protecting the data privacy of each entity.

[0032] Within each power generation entity, a local optimization problem is constructed based on local load values ​​and local constraints. The objective function of the local optimization problem is a local expression of a multi-objective optimization function within the power generation entity. The local optimization results of each power generation entity are iteratively updated through a distributed iterative algorithm until the global convergence condition is met, and the global optimal solution is obtained. Based on the global optimal solution, a power output plan for the power system is generated.

[0033] Furthermore, in this embodiment, the step of iteratively updating the local optimization results of each power generation entity using a distributed iterative algorithm until the global convergence condition is met to obtain the globally optimal solution includes the following steps: Through a distributed iterative algorithm, each power generator exchanges boundary variable information with its neighbors. This boundary variable information includes boundary node power and carbon emissions. Boundary node power reflects the power transmission between power generators, affecting the power flow distribution and power balance of the power system. Carbon emissions reflect the environmental impact of the power generator's production process; in the context of a coordinated electricity carbon market, carbon emissions affect the power generator's costs and benefits. Each power generator can exchange boundary variable information with its neighbors via a communication network, which can be real-time or periodic, depending on system requirements and actual conditions. For example, in a smart grid, high-speed communication technology can be used to achieve rapid and accurate transmission of boundary variable information.

[0034] In each iteration, each power generator updates its local optimization problem constraints and objective function based on the received boundary variable information, and solves the local optimization problem to obtain the updated boundary node power and carbon emissions. After receiving boundary variable information from neighboring generators, each generator needs to update its own local optimization problem constraints. For example, changes in boundary node power may affect the power balance constraints of a power generator, and carbon emission information may affect carbon emission constraints. Taking power balance constraints as an example, if the boundary node power of a neighboring generator increases, the power generator may need to adjust its own power generation accordingly to meet the power balance requirements of the entire system. The objective function is also adjusted based on the received boundary variable information. In multi-objective optimization, changes in boundary node power and carbon emissions may affect objectives such as minimizing system operating costs, minimizing carbon emissions, maximizing the proportion of renewable energy consumption, and achieving revenue equilibrium. For example, if the carbon emissions of a neighboring generator are high, the power generator may focus more on reducing its own carbon emissions in the objective function to achieve the overall system's carbon reduction target. Each power generator uses appropriate optimization algorithms, such as linear programming or nonlinear programming, to solve the updated local optimization problem. During the solution process, the power generation entity will determine the optimal power generation plan based on its own equipment characteristics, cost function, and updated constraints, thereby obtaining the updated boundary node power and carbon emissions.

[0035] The updated boundary node power and carbon emissions are sent to neighboring power generators, and the above steps are repeated until the global convergence condition is met, obtaining the global optimum. Each power generator sends the updated boundary node power and carbon emissions to its neighboring power generators. This process allows neighboring entities to obtain the latest information, enabling them to perform the next round of local optimization.

[0036] Furthermore, in this embodiment, the global convergence condition includes: The power difference at the boundary nodes of each power generation entity is less than the preset power threshold. When the power difference at the boundary nodes of each power generation entity is less than the preset power threshold, it means that the power interaction between the power generation entities tends to be stable and the power flow distribution no longer changes significantly.

[0037] The difference in carbon emissions among the various power generation entities is less than a preset threshold. In the context of coordinated electricity carbon market operations, carbon emissions are a crucial indicator for measuring the environmental impact and costs of power generation entities. During the iterative process, each power generation entity adjusts its power generation methods and output, thereby altering its own carbon emissions. When the difference in carbon emissions among the various power generation entities is less than the preset threshold, it indicates that the carbon emissions of each entity are stabilizing, and the overall carbon emission distribution of the system has reached a relatively balanced state.

[0038] The difference between the local optimization objective function value and the global optimization objective function value of each power generation entity is less than the preset tolerance. Each power generation entity has its own local optimization objective function, the purpose of which is to maximize its own interests or minimize its costs while satisfying its own constraints. The global optimization objective function, on the other hand, comprehensively considers multiple objectives of the entire power system, such as minimizing system operating costs and minimizing carbon emissions. When the difference between the local optimization objective function value and the global optimization objective function value of each power generation entity is less than the preset tolerance, it indicates that the local optimization results of each power generation entity are close to the global optimal result, and the entire system has achieved good coordination in terms of multiple objectives.

[0039] This invention incorporates system operating costs, carbon emissions, renewable energy consumption ratios, and revenue equilibrium into the objective optimization function. By comprehensively considering the electricity carbon market trading mechanism and power system operational constraints, it optimizes the allocation of power generation resources. Revenue equilibrium, as part of the multi-objective optimization function, helps balance the interests of various participants in the power system. It fully considers the electricity carbon market trading mechanism, organically combining the electricity market and the carbon emissions market. Through solving the multi-objective optimization function, it achieves optimal allocation of electricity and carbon emission resources, enabling effective interaction and synergy between the electricity and carbon markets.

[0040] Example 2 A power system dispatching system considering coordination with the electricity carbon market includes: Establishment Module: Based on the electricity carbon market trading mechanism, establish a multi-objective optimization function, which includes a function to minimize system operating costs, a function to minimize carbon emissions, a function to maximize the proportion of renewable energy consumption, and a revenue equilibrium function; Acquisition Module: Based on the power system load value and constraints, a distributed optimization algorithm is used to solve the multi-objective optimization function to obtain the power system output plan; the constraints include power system operation constraints and carbon emission constraints; Dispatch module: Dispatches the power system according to the output plan.

[0041] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

[0042] The present invention has been described above with reference to embodiments thereof. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. The scope of the invention is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

[0043] Although embodiments of the present invention have been described in detail, it should be understood that various changes, substitutions, and modifications can be made to the embodiments of the present invention without departing from the spirit and scope of the invention.

[0044] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A power system dispatching method considering coordination with the electricity carbon market, characterized in that, include: Based on the electricity carbon market trading mechanism, a multi-objective optimization function is established, which includes a function to minimize system operating costs, a function to minimize carbon emissions, a function to maximize the proportion of renewable energy consumption, and a revenue equilibrium function. Based on the power system load value and constraints, a distributed optimization algorithm is used to solve the multi-objective optimization function to obtain the power system output plan; the constraints include power system operation constraints and carbon emission constraints. The power system is dispatched according to the aforementioned output plan.

2. The scheduling method according to claim 1, characterized in that, The power system operation constraints include power balance constraints and upper and lower limits of power generation unit output constraints; the carbon emission constraints include total carbon emission constraints.

3. The scheduling method according to claim 2, characterized in that, The carbon market trading mechanism includes a carbon emission quota auction mechanism and a carbon credit trading mechanism. The carbon emission quota auction mechanism is used for the initial allocation of carbon emission quotas, and the carbon credit trading mechanism is used for the secondary trading of carbon emission quotas.

4. The scheduling method according to claim 3, characterized in that, The process of solving the multi-objective optimization function based on the power system load value using a distributed optimization algorithm to obtain the power system output plan includes: The power system is divided into multiple power generation entities, each of which includes several power generation units, local load values, and boundary nodes. The local load values ​​include the power system load values ​​after being divided according to the number of power generation entities, and the boundary nodes are power network nodes connecting different power generation entities. Within each power generation entity, a local optimization problem is constructed based on local load values ​​and local constraints. The objective function of the local optimization problem is a local expression of a multi-objective optimization function within the power generation entity. The local optimization results of each power generation entity are iteratively updated through a distributed iterative algorithm until the global convergence condition is met, and the global optimal solution is obtained. Based on the global optimal solution, a power output plan for the power system is generated.

5. The scheduling method according to claim 4, characterized in that, The process of iteratively updating the local optimization results of each power generation entity using a distributed iterative algorithm until the global convergence condition is met to obtain the globally optimal solution includes the following steps: Through a distributed iterative algorithm, each power generation entity exchanges boundary variable information with its neighboring power generation entities. The boundary variable information includes the power and carbon emissions of the boundary nodes. In each iteration, each power generation entity updates the constraints and objective function of the local optimization problem based on the received boundary variable information, and solves the local optimization problem to obtain the updated boundary node power and carbon emissions. The updated boundary node power and carbon emissions are sent to the adjacent power generation entities. The above steps are repeated until the global convergence condition is met and the global optimal solution is obtained.

6. The scheduling method according to claim 5, characterized in that, The global convergence conditions include: The power difference at the boundary nodes of each power generation entity is less than the preset power threshold. The difference in carbon emissions among the various power generation entities is less than the preset threshold for carbon emissions; The difference between the local optimization objective function value and the global optimization objective function value of each power generation entity is less than the preset tolerance.

7. The scheduling method according to claim 1, characterized in that, The function for minimizing system operating costs is expressed by the following formula: Where U1 represents minimizing the system operating cost, This represents the output of the i-th traditional power generation unit during time period t; This represents the output of the j-th renewable energy power generation unit in time period t; N represents the number of conventional power generation units; M represents the number of renewable energy power generation units; This represents the power generation cost of traditional power generation unit i. This represents the power generation cost of renewable energy power generation unit j; This represents the auction price of carbon emission allowances for time period t. This represents the carbon credit trading price for time period t. This represents the carbon emission allowance of power generation unit r during time period t. This represents the amount of carbon emission allowance traded by power generation unit i during time period t.

8. The scheduling method according to claim 7, characterized in that, The functions for minimizing carbon emissions and maximizing the proportion of renewable energy consumption are expressed using the following formulas: Where U2 represents minimizing carbon emissions, and U3 represents maximizing the proportion of renewable energy consumption. This represents the output of the i-th traditional power generation unit during time period t; This represents the output of the j-th renewable energy generation unit during time period t; This represents the power system load demand during time period t; N represents the number of traditional power generation units; and M represents the number of renewable energy power generation units.

9. The scheduling method according to claim 8, characterized in that, The revenue equilibrium function is expressed by the following formula: Where U2 represents the equilibrium return, This represents the revenue of the i-th traditional power generation unit in time period t. This represents the ideal return of a traditional power generation unit. This represents the revenue of the j-th renewable energy generation unit in time period t. This indicates the ideal return on a renewable energy power generation unit.

10. A power system dispatching system considering coordination in the electricity carbon market, characterized in that, include: Establishment Module: Based on the electricity carbon market trading mechanism, establish a multi-objective optimization function, which includes a function to minimize system operating costs, a function to minimize carbon emissions, a function to maximize the proportion of renewable energy consumption, and a revenue equilibrium function; Acquisition Module: Based on the power system load value and constraints, a distributed optimization algorithm is used to solve the multi-objective optimization function to obtain the power system output plan; the constraints include power system operation constraints and carbon emission constraints; Dispatch module: Dispatches the power system according to the output plan.

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