Multi-microgrid layered optimization scheduling method and system considering carbon power green certificate market

Abstract

The invention discloses a multi-microgrid layered optimization scheduling method and system considering a carbon power green certificate market, and belongs to the technical field of novel power system scheduling. The method comprises the following steps: constructing a double-layer market coupling architecture comprising an intra-network transaction layer and an inter-network transaction layer, and realizing electric power, carbon quota and green certificate collaborative transaction and price linkage; constructing a lower-layer single-microgrid optimization model and an upper-layer inter-microgrid optimization model based on the architecture; reconstructing an upper-layer model by using an improved state base potential game algorithm so as to process coupling and uncoupling constraints in a distributed manner; and designing a double-layer rolling optimization process iteration solution model to obtain an optimization scheduling result. According to the method, the defects of existing multi-microgrid scheduling in the aspects of market coupling, model architecture, algorithm applicability and mechanism collaboration are overcome, the renewable energy consumption rate and the system economic benefit can be effectively improved, the microgrid privacy is protected, and the method is suitable for low-carbon efficient collaborative scheduling of a multi-microgrid cluster.

Description

A hierarchical optimization scheduling method and system for multi-microgrids considering the carbon electricity green certificate market Technical Field

[0001] This invention belongs to the field of new power system dispatching and energy market operation technology, specifically involving a multi-microgrid hierarchical optimization dispatching method and system that considers the carbon electricity green certificate market. Background Technology

[0002] At present, multi-microgrid dispatching technology mainly revolves around the optimal allocation of resources in a single electricity market environment. Related research has made some progress, but there are still many technical bottlenecks in the coupled scenario of electricity-carbon-green certificate market, which are manifested in the following aspects: (1) Insufficient market coupling adaptability: Existing multi-microgrid dispatching models mostly only consider electricity market transactions and do not fully integrate the coupling effects of carbon market and green certificate market, resulting in the inability to effectively guarantee the income of low-carbon entities and renewable energy entities, making it difficult to incentivize the consumption of distributed renewable energy. Although some studies have attempted to introduce carbon trading or green certificate trading, they have not built a trading architecture that coordinates the three, and the market price linkage mechanism is lacking, which cannot adapt to the characteristics of distributed operation of multi-microgrids.

[0003] (2) Inadequate optimization model architecture: Existing multi-micronet scheduling mostly adopts single-layer centralized or distributed optimization models. The centralized model ignores the distributed characteristics of micronet operators, which can easily lead to the leakage of privacy information; the distributed model has problems such as low computational efficiency and difficulty in handling coupling constraints. The lack of a two-layer architecture that takes into account both "local optimization of a single micronet" and "global coordination between micronets" makes it impossible to balance local economic benefits and overall system benefits.

[0004] (3) Limited applicability of the algorithm: The state-based potential game algorithm has been initially applied to the scheduling of multi-microgrids due to its privacy protection advantage. However, the traditional state-based potential game can only handle coupled constraints and cannot cope with the uncoupled constraints in the electricity-carbon-green certificate market transactions of multi-microgrids, such as the transaction margin limit within a single microgrid and the equipment operation boundary constraints. As a result, it is only suitable for simple scenarios and cannot meet the scheduling needs in complex market coupled environments.

[0005] (4) Poor coordination between market mechanism and dispatch: The existing carbon market and green certificate market mostly adopt fixed price or year-end centralized trading mechanism. They do not combine the time sequence characteristics of multi-microgrid dispatch to design real-time floating price and real-time trading constraints, which can easily lead to trading congestion or drastic price fluctuations. Moreover, the market trading mechanism and multi-microgrid dispatch process lack coordination, resulting in the dispatch results not being effectively implemented.

[0006] In summary, existing technologies cannot achieve efficient coordinated scheduling of multiple microgrids under a coupled electricity-carbon-green certificate market environment, exhibiting systemic defects such as poor market adaptability, unreasonable model architecture, algorithm limitations, and insufficient mechanism coordination. Therefore, developing a two-layer optimization scheduling method for multiple microgrids that can adapt to the carbon-electricity-green certificate market coupling mechanism, consider both local and global optimization, handle complex constraints, and possess high computational efficiency is of significant theoretical and engineering application value for promoting the low-carbon and efficient operation of multi-microgrid systems and contributing to the construction of new power systems. Summary of the Invention

[0007] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a multi-microgrid hierarchical optimization scheduling method and system that considers the carbon electricity and green certificate market. This method is used to solve the technical problems of existing multi-microgrid scheduling methods in the context of multi-market coupling of electricity, carbon, and green certificates, such as poor market mechanism adaptability, failure of optimization model architecture to take into account both local and global optimization, difficulty of distributed solution algorithms to handle complex internal constraints, and insufficient coordination between market trading mechanisms and scheduling processes. These problems result in limited incentives for renewable energy consumption, low overall economic benefits of the system, difficulty in balancing privacy protection and computational efficiency, and weak executability of scheduling schemes.

[0008] This invention adopts the following technical solution: a multi-microgrid hierarchical optimization scheduling method considering the carbon, electricity, and green certificate market, comprising the following steps: S1, constructing a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to multi-microgrid systems, the trading architecture including an intra-grid trading layer and an inter-grid trading layer to realize coordinated trading of electricity, carbon quotas, and green certificates; S2, based on the trading architecture constructed in step S1, constructing two-layer optimization models, the two-layer optimization models including a lower-layer single-microgrid optimization model and an upper-layer inter-microgrid optimization model, respectively realizing local optimization of single microgrids and global coordinated optimization between microgrids; S3, reconstructing the upper-layer inter-microgrid optimization model obtained in step S2 using an improved state-basic potential game algorithm; S4, designing a two-layer rolling optimization process, based on the upper-layer model reconstructed in step S3, solving the two-layer optimization model obtained in step S2 through a lower-upper-lower-upper-higher iteration to obtain the optimized scheduling results of the multi-microgrid system in the carbon, electricity, and green certificate market.

[0009] Preferably, in step S1, the intra-network trading layer is configured with real-time trading constraints and an intraday floating price mechanism, and the carbon quota price and green certificate price are dynamically adjusted by the inter-network trading quota. The inter-network trading layer conducts transactions based on the trading price output by the intra-network trading layer and feeds back the transaction amount to the intra-network trading layer, forming a two-way linkage.

[0010] Preferably, in the intra-grid trading layer, the electricity market adopts a centralized bidding trading model, with the marginal unit bid as the clearing price for unified clearing; the carbon market and green certificate market adopt a P2P trading model; the real-time trading constraints are used to simulate incomplete trading scenarios; and the intraday floating price mechanism is used to adapt to inter-grid trading demand and market stability reserves.

[0011] Preferably, in the inter-grid trading layer, each microgrid operator acts as a trading entity, determining its buying and selling identity based on the trading results of the intra-grid trading layer; the clearing prices of electricity, carbon allowances, and green certificates are determined through matching between buyers and sellers, with the buyer using the seller's lowest bid as the virtual clearing price and the seller using the buyer's highest bid as the virtual clearing price; the inter-grid trading layer also establishes a price linkage mechanism, adjusting the prices of green certificates, carbon allowances, and electricity through changes in renewable energy output.

[0012] Preferably, in step S2, the lower-level microgrid optimization model aims to maximize social welfare within the microgrid, and the objective function covers the revenue and operating costs of user entities, virtual microgrid trading entities, and power generation entities in the electricity market, carbon market, and green certificate market; the upper-level microgrid optimization model aims to maximize the revenue of each microgrid operator in the electricity-carbon-green certificate market, and the decision variables include the inter-microgrid electricity trading volume, carbon quota trading volume, and green certificate trading volume.

[0013] Preferably, the constraints of the lower-level microgrid optimization model include system power balance conditions, upper and lower limits of inter-grid transaction volume constraints, upper and lower limits of unit output constraints, unit ramp-up / slide constraints, hydropower unit capacity constraints, energy storage equipment capacity constraints, and energy storage equipment initial and final value constraints.

[0014] Preferably, the constraints of the upper-level microgrid optimization model include inter-grid power balance constraints, transmission line capacity constraints, carbon quota and green certificate trading volume balance constraints, and upper and lower limits of trading margin constraints.

[0015] Preferably, in step S3, the implementation process of the improved state-based potential game algorithm includes: S301, defining a state space, where each state contains the decision variables of each trading entity and the estimated values ​​of all entities regarding constraints; S302, designing an action space, where the action of each entity is defined as the change in state; S303, constructing a state transition function, enabling each entity to update the next state based on the current state and action; S304, constructing an entity payoff function by combining constraint estimates, realizing dynamic mapping of constraints; S305, proving the existence of the Nash equilibrium point by constructing a potential function, and using a gradient game algorithm to determine the action values, with the convergence condition being that the maximum game residual in the electricity-carbon-green certificate market is less than 10. -4 .

[0016] Preferably, in step S4, the iterative logic of the dual-layer rolling optimization process is as follows: First, run the lower-layer single-microgrid optimization model to obtain the intra-network marginal entity parameters, intra-network net transaction volume, and transaction price; then, run the upper-layer inter-microgrid optimization model based on the intra-network transaction results to determine the microgrid operator's transaction identity, inter-network transaction volume, and transaction price; in the lower-layer single-microgrid optimization model, set a virtual seller entity for the buyer and a virtual buyer entity for the seller, and run the lower-layer single-microgrid optimization model again; finally, run the upper-layer inter-microgrid optimization model again, with the seller using the intra-network net transaction volume as the upper limit of the inter-network transaction volume, and the buyer using the maximum value as the upper limit of the inter-network transaction volume; during the iteration process, the wind and solar curtailment penalty term is initially set to a maximum value and gradually decreased, and the intra-network power balance coefficient is initially set to a small value and gradually increased, until the virtual entity transaction volume is consistent with the inter-network transaction volume, at which point the iteration stops.

[0017] Secondly, embodiments of the present invention provide a multi-microgrid hierarchical optimization scheduling system considering the carbon, electricity, and green certificate market, comprising: an architecture module for constructing a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to multi-microgrid systems, the trading architecture including an intra-grid trading layer and an inter-grid trading layer to realize coordinated trading of electricity, carbon quotas, and green certificates; a construction module for constructing a two-layer optimization model based on the trading architecture, the two-layer optimization model including a lower-layer single-microgrid optimization model for realizing local optimization of a single microgrid and an upper-layer inter-microgrid optimization model for realizing global coordinated optimization between microgrids; a reconstruction module for reconstructing the upper-layer inter-microgrid optimization model using an improved state-potential game algorithm; and a solution module for designing a two-layer rolling optimization process, solving the two-layer optimization model based on the reconstructed upper-layer model through a lower-upper-lower-upper-layer iteration, and outputting the optimized scheduling results of the multi-microgrid system in the carbon, electricity, and green certificate market.

[0018] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market.

[0020] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market described above.

[0021] In a sixth aspect, embodiments of the present invention provide an electronic device including a computer program, which, when executed by the electronic device, implements the steps of the above-described multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market.

[0022] Compared with existing technologies, this invention has at least the following beneficial effects: A multi-microgrid hierarchical optimization scheduling method considering the carbon-electricity-green certificate market incorporates the carbon-electricity-green certificate market coupling into the core of multi-microgrid scheduling. Through a two-layer architecture, it balances the local interests of individual microgrids with global collaboration between microgrids, overcoming the limitations of traditional single-layer models. An improved state-potential game theory algorithm compensates for the inability of traditional algorithms to handle uncoupled constraints, while a rolling iteration process ensures the dynamic adaptability of scheduling results. It achieves an organic unity of multi-market collaboration, local and global optimization balance, complex constraint handling, and dynamic iterative optimization, providing a systematic solution for the coupling scenario of the electricity-carbon-green certificate market and effectively adapting to the distributed operation characteristics of multi-microgrids.

[0023] Furthermore, the intra-network trading layer is configured with real-time trading constraints and intraday floating price mechanisms, enabling carbon quotas and green certificate prices to respond to dynamic adjustments in inter-network trading, avoiding market fluctuations caused by fixed prices. The inter-network trading layer conducts transactions based on intra-network prices and provides feedback on transaction amounts, forming a two-way linkage closed loop. This allows the trading architecture to adapt to the real-time scheduling needs of a single microgrid while also enabling resource sharing between microgrids, effectively reducing the risk of trading congestion and improving market stability. At the same time, it adapts to the distributed operation characteristics of multiple microgrids, providing efficient architectural support for multi-market collaborative trading.

[0024] Furthermore, the electricity market adopts a centralized bidding model, with marginal unit bids used for clearing, ensuring fairness and efficiency in trading; the carbon market and green certificate market adopt a peer-to-peer (P2P) trading model, aligning with the trading needs of distributed entities. Real-time trading constraints simulate incomplete trading scenarios, prompting rational decision-making by entities; the intraday floating price mechanism adapts to inter-grid trading and market stability reserves, avoiding drastic price fluctuations. This achieves precise matching of different market trading models, enhances the flexibility and adaptability of the intra-grid market, and safeguards the trading rights of low-carbon and renewable energy entities.

[0025] Furthermore, the clearing price is determined through a matchmaking process between buyers and sellers. The buyer uses the seller's lowest bid, and the seller uses the buyer's highest bid as the virtual clearing price, ensuring fairness in the transaction. The price linkage mechanism adjusts green certificates, carbon quotas, and electricity prices based on changes in renewable energy output, achieving coordinated fluctuations in prices across multiple markets. This allows inter-grid transactions to reflect the true supply and demand of the market while incentivizing renewable energy consumption, avoiding the impact of single-market price fluctuations on overall transactions, improving the efficiency and stability of inter-grid transactions, and adapting to the distributed operation characteristics of multiple microgrids, promoting the optimal allocation of resources among microgrids.

[0026] Furthermore, the lower-layer microgrid optimization model aims to maximize social welfare, encompassing the benefits and costs of multiple stakeholders and markets, thus ensuring the optimal allocation of resources within the microgrid. The upper-layer inter-microgrid optimization model aims to maximize the operator's multi-market benefits, with decision variables covering three types of transaction volumes, achieving coordinated scheduling of resources between microgrids. This approach balances the local economic interests of individual microgrids with the overall benefits between microgrids, avoiding the privacy risks of centralized models and the inefficiencies of distributed models.

[0027] Furthermore, the constraints cover key dimensions such as system power balance, upper and lower limits of inter-grid transaction volume, unit operation boundaries, and energy storage and hydropower unit capacity, comprehensively covering the core limitations of single microgrid operation. Power balance constraints ensure stable power supply, equipment operation constraints prevent equipment overload or abnormal operation, and energy storage-related constraints ensure efficient utilization of energy storage equipment. Together, these constraints ensure that the optimization results of the single microgrid are technically feasible and economically compatible with external interactions, providing a reliable and realistic boundary foundation for upper-level global optimization.

[0028] Furthermore, inter-grid power balance constraints ensure matching of power supply and demand among microgrids, transmission line capacity constraints prevent line overload, carbon quota and green certificate trading volume balance constraints ensure market trading compliance, and trading margin constraints control trading risks. This constructs a safety boundary for inter-grid trading, avoiding system instability or resource waste caused by unconstrained trading, ensuring efficient microgrid trading under safe and compliant conditions, while also guaranteeing the balance of multi-market trading and providing security for microgrid collaborative scheduling.

[0029] Furthermore, by defining the state space, designing the action space and state transition function, a payoff function with constraint penalties is constructed, achieving effective handling of uncoupled constraints and overcoming the limitation of traditional state-based game theory, which can only handle coupled constraints. The equilibrium verification step ensures the algorithm's convergence, and the convergence condition settings ensure the solution accuracy. The distributed solution mode avoids the privacy leakage problem of centralized solutions, and boasts fast convergence speed, adapting to the distributed operation characteristics of multi-micronet networks, and efficiently handling constraint problems in complex market coupling environments.

[0030] Furthermore, by employing an iterative pattern of lowering the layer before raising it, and then lowering it again before raising it again, combined with dynamic adjustments to virtual entity settings, penalty terms, and balance coefficients, deep collaboration between the two-layer models is ensured. The convergence standard, where virtual entity trading volume aligns with inter-grid trading volume, guarantees the accuracy of scheduling results. The setting of wind and solar curtailment penalty terms incentivizes renewable energy entities to truthfully report power, while adjustments to the intra-grid power balance coefficient ensure initial trading quotas and improve scheduling efficiency. This achieves dynamic adaptation and iterative optimization of the two-layer model, ensuring that scheduling results meet real-time operational requirements.

[0031] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0032] In summary, the method of this invention achieves deep integration and price linkage of electricity, carbon, and green certificates through a two-layer market architecture, thereby incentivizing the consumption of clean energy; it balances local economic efficiency with global synergy through a two-layer optimization model; it protects privacy and improves efficiency by distributing complex constraints through an improved game theory algorithm; and it ensures feasible convergence of the model through two-layer rolling optimization.

[0033] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0034] Figure 1 is a schematic diagram of the principle of the two-layer rolling optimization method of the present invention; Figure 2 is a diagram of the power market transaction results without two-layer model optimization in an embodiment of the present invention; Figure 3 is a diagram of the power market transaction results after two-layer model optimization in an embodiment of the present invention; Figure 4 is a schematic diagram of a computer device provided in an embodiment of the present invention; Figure 5 is a block diagram of a chip provided in an embodiment of the present invention.

[0035] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0038] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0039] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0040] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0041] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0042] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0043] This invention provides a hierarchical optimization scheduling method for multi-microgrid systems considering the carbon-electricity-green certificate market. First, a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to multi-microgrid systems is constructed. Second, single-microgrid optimization models and inter-microgrid optimization models are constructed separately. Then, an improved state-potential game theory approach is used to reconstruct the upper-layer model. Finally, a two-layer rolling optimization process is used to iterate the results of the two-layer model, resulting in a high renewable energy consumption rate and social welfare value. Using this method for economic scheduling of multi-microgrid systems, the scheduling process not only considers decarbonization and economy but also protects the privacy information within the microgrid. This has significant theoretical and engineering application value for supporting the construction of new power systems. It is particularly suitable for the coupled environment of the electricity-carbon-green certificate market, where a two-layer optimization architecture and an improved state-potential game theory algorithm achieve efficient collaborative scheduling of multi-microgrids. This technical solution can be widely applied to distributed renewable energy consumption, multi-microgrid cluster operation, and low-carbon power system construction scenarios.

[0044] Please refer to Figure 1. The present invention provides a multi-microgrid hierarchical optimization scheduling method considering the carbon-electricity-green certificate market, comprising the following steps: S1, constructing a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to multi-microgrid systems; the trading architecture includes two layers: intra-grid and inter-grid. The intra-grid layer sets real-time trading constraints and intraday floating price mechanisms, and the carbon quota price and green certificate price are dynamically adjusted by the inter-grid trading quota amount; the inter-grid layer uses the price obtained from the intra-grid layer to conduct transactions and feeds the transaction amount back to the intra-grid layer.

[0045] (1) Microgrid intra-trading layer: The electricity market adopts a centralized bidding trading model, with the marginal unit bid as the clearing price for unified clearing; the carbon market and green certificate market adopt a P2P trading model, with real-time trading constraints designed to simulate the crisis of incomplete trading, and an intraday floating price mechanism designed to consider inter-grid trading and market stability reserves. Equation (1) shows the real-time trading constraints of the carbon market, and Equation (2) shows the intraday floating price mechanism of the carbon market.

[0046] (1) (2) Among them, for Time period Carbon quota trading volume of each trading entity For the first The carbon trading activity coefficient of each trading entity Set a threshold for carbon trading activity. for Time period The actual output of the equipment for Time period Carbon quota trading quotes from individual trading entities This is the minimum bid threshold for carbon allowances. This is the minimum trading volume threshold for carbon allowances. for Carbon quota trading volume between microgrids during specific time periods For the linear coefficient of the intraday floating price mechanism of carbon allowances, This is a constant term in the intraday floating price mechanism for carbon allowances. for Reference price for inter-network carbon quota trading during specific time periods. This is the threshold for the maximum trading volume of carbon allowances.

[0047] (2) Microgrid Inter-trading Layer: Each microgrid operator acts as the trading entity, and determines the buyer and seller identity based on the transaction results of a single microgrid. The clearing price of electricity, carbon quotas, and green certificates is determined by the matching of the buyer and seller. The buyer uses the seller's lowest bid as the virtual clearing price, and the seller uses the buyer's highest bid as the virtual clearing price. A price linkage mechanism is established to adjust the prices of green certificates, carbon quotas, and electricity through changes in renewable energy output.

[0048] S2. Construct two-layer optimization models respectively; the two-layer optimization model includes the lower-layer single microgrid optimization model and the upper-layer microgrid inter-optimization model: (1) The lower-layer single microgrid optimization model aims to maximize social welfare within the single microgrid, introduces virtual microgrid inter-trading entities, and the objective function covers the electricity market revenue, carbon market revenue, green certificate market revenue and operating costs of user entities, virtual entities, and power generation entities. The expression is as follows: (3) Among them, for The overall benefits for users within the micronet during a given time period. for The overall benefits of power generation entities within the microgrid during a given time period. for The overall revenue of the participants in the virtual micro-network transactions during the time period. for Within the time period micronet Operating costs of the equipment This refers to the total number of power generation entities within the microgrid. This represents the total number of devices within the microgrid. The number of the main power generation entity. For equipment number.

[0049] The constraints include: system power balance conditions, upper and lower limits of inter-grid transaction volume constraints, upper and lower limits of unit output constraints, unit ramp-up and ramp-down constraints, capacity constraints of hydropower units and energy storage equipment, and initial and final value constraints of energy storage equipment.

[0050] (2) The optimization model between upper-level microgrids aims to maximize the revenue of each microgrid operator in the electricity-carbon-green certificate market. The decision variables include electricity between microgrids, carbon quotas, and green certificate trading volume. The objective function is as follows: (4) (5) (6) (7) Among them, , , These represent the electricity market, carbon market, and green certificate market revenues of microgrid operator j, respectively. For the electricity market transaction volume of microgrid operator j, Representing the seller; The carbon market trading volume of microgrid operator j. Representing the seller; For the green certificate market trading volume of microgrid operator J, Representing the seller; The clearing price of the microgrid j in the carbon market and green certificate market during time period t.

[0051] The constraints include: inter-network power balance constraints, transmission line capacity constraints, carbon quota and green certificate trading volume balance constraints, and upper and lower limits of trading margin constraints.

[0052] S3. Reconstruct the upper-level model using an improved state-based potential game algorithm; To address the distributed solution requirements of the upper-level microgrid optimization model, the state-based potential game algorithm is improved to handle uncoupled constraints. The specific steps are as follows: S301. Define the state space: For the state-based potential game system, there exists a state space. Each state in the state space Defined as ,in These are the decision variables for each subject. It represents the estimates of all subjects for all constraints.

[0053] S302. Design Action Space: Each subject i will have an action space based on the state space. Actions on the subject Defined as This refers to the change in state.

[0054] S303, State Transition Mechanism: Design a state transition function so that each subject can update the next state based on the current state and action.

[0055] S304. Design the revenue function: Construct the revenue function of microgrid i by combining the constraint estimates, and realize the dynamic mapping of the constraints: (8) Among them, For micro-network The improved profit function For micro-network The original payoff function, To cooperate with microgrids The number of other microgrids with coupling constraints. The number of types of coupling constraints in multi-micronet transactions. For the first Penalty coefficient for class coupling constraints, For micro-network For the Violation of class coupling constraints For the first Penalty coefficient for class-decoupling constraints, For micro-network For the The violation of class-uncoupled constraints.

[0056] S305. Equilibrium Verification: Prove the existence of the Nash equilibrium point by constructing a potential function. Use a gradient game theory algorithm to determine the action values. The convergence condition is that the maximum game residual in the electricity-carbon-green certificate market is less than 10. -4 .

[0057] S4. Design a two-layer rolling optimization process.

[0058] Through a rolling iteration of "lower layer-upper layer-lower layer-upper layer," the collaborative optimization of the two-layer model is achieved. In terms of the detailed design of this method, the intra-grid model is run first to obtain intra-grid marginal entity parameters, intra-grid net transaction volume, and transaction price. Next, the inter-grid model is run based on the intra-grid results, and the microgrid operator obtains three market identities, inter-grid transaction volume, and transaction price. At this point, the microgrid operator, as the buyer, sets up a virtual seller entity in the intra-grid model, with its transaction upper limit being the inter-grid transaction volume and a lower limit of 0. The microgrid operator, as the seller, sets up a virtual buyer entity in the intra-grid model, with its transaction upper limit being the maximum value and a lower limit of 0. Afterward, the intra-grid model is run again, but the intra-grid net transaction volume is not calculated based on the virtual entity's transaction volume. Finally, the inter-grid model is run again, but the microgrid operator, as the seller, uses the intra-grid net transaction volume as the upper limit for the inter-grid transaction volume, while the microgrid operator, as the buyer, uses a maximum value as the upper limit for the inter-grid transaction volume.

[0059] In theory, by following the steps above, a microgrid containing a large number of renewable energy entities can act as a seller to absorb its renewable energy generation. However, to ensure that renewable energy entities accurately report their predicted power during the first intra-grid optimization, the wind and solar curtailment penalty term pnt needs to be set to a maximum value and gradually decreased during the rolling optimization process. To ensure that each microgrid operator has a certain transaction quota during the first inter-grid transaction, the intra-grid power balance coefficient blc needs to be set to a small value and gradually increased during the rolling optimization process. In this way, by reasonably setting the initial values ​​of pnt and blc, after a few rolling optimizations, the transaction volume of any virtual entity in the intra-grid model of a microgrid will be consistent with its inter-grid transaction volume in the inter-grid model, which can be regarded as the end of the solution process of the two-layer model.

[0060] In another embodiment of the present invention, a multi-microgrid hierarchical optimization scheduling system considering the carbon electricity and green certificate market is provided. This system can be used to implement the above-mentioned multi-microgrid hierarchical optimization scheduling method considering the carbon electricity and green certificate market. Specifically, the multi-microgrid hierarchical optimization scheduling system considering the carbon electricity and green certificate market includes an architecture module, a construction module, a reconstruction module, and a solution module.

[0061] The system comprises the following modules: an architecture module for constructing a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to multi-microgrid systems; an architecture including an intra-grid trading layer and an inter-grid trading layer to achieve coordinated trading of electricity, carbon allowances, and green certificates; a construction module for building a two-layer optimization model based on the trading architecture; a lower-layer single-microgrid optimization model for achieving local optimization of individual microgrids and an upper-layer inter-microgrid optimization model for achieving global coordinated optimization among microgrids; a reconstruction module for reconstructing the upper-layer inter-microgrid optimization model using an improved state-potential game algorithm; and a solution module for designing a two-layer rolling optimization process. Based on the reconstructed upper-layer model, the system solves the two-layer optimization model through iterative processes of lower-upper-lower-upper layers, outputting the optimized scheduling results of the multi-microgrid system in the carbon, electricity, and green certificate markets.

[0062] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used to consider the operation of a multi-microgrid hierarchical optimization scheduling method for the carbon electricity and green certificate market, including: constructing a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to multi-microgrid systems, the trading architecture including an intra-grid trading layer and an inter-grid trading layer. This approach enables coordinated trading of electricity, carbon quotas, and green certificates. Based on the constructed trading architecture, a two-layer optimization model is built, comprising a lower-layer single-microgrid optimization model and an upper-layer inter-microgrid optimization model, respectively achieving local optimization of single microgrids and global coordinated optimization between microgrids. The upper-layer inter-microgrid optimization model is reconstructed using an improved state-potential game algorithm. A two-layer rolling optimization process is designed, based on the reconstructed upper-layer model, to solve the obtained two-layer optimization model through iterative processes of lower-upper-lower-upper layers to obtain the optimized scheduling results of the multi-microgrid system in the carbon, electricity, and green certificate markets.

[0063] Please refer to Figure 4. The terminal device is a computer device. The computer device 60 in this embodiment includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the processor 61 executes the computer program 63, it implements the multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market in this embodiment. To avoid repetition, it will not be described in detail here. Alternatively, when the processor 61 executes the computer program 63, it implements the functions of each model / unit in the multi-microgrid hierarchical optimization scheduling system considering the carbon electricity green certificate market in this embodiment. To avoid repetition, it will not be described in detail here.

[0064] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that Figure 4 is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or use different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0065] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0066] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0067] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0068] Please refer to Figure 5. The terminal device is an electronic device 600, which is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0069] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform the steps shown in FIG1.

[0070] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0071] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0072] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0073] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0074] Example 3: This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor; these instructions can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0075] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0076] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0077] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the multi-microgrid hierarchical optimization scheduling method considering the carbon, electricity and green certificate market in the above embodiments. One or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: Constructing a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to the multi-microgrid system, the trading architecture including an intra-grid trading layer and an inter-grid trading layer to realize the coordinated trading of electricity, carbon quotas and green certificates; Based on the constructed trading architecture, constructing two-layer optimization models, the two-layer optimization models including a lower-layer single-microgrid optimization model and an upper-layer inter-microgrid optimization model to realize local optimization of single microgrids and global coordinated optimization between microgrids respectively; Reconstructing the upper-layer inter-microgrid optimization model using an improved state-potential game algorithm; Designing a two-layer rolling optimization process, based on the reconstructed upper-layer model, solving the obtained two-layer optimization model through a lower-upper-lower-upper-layer iteration to obtain the optimized scheduling results of the multi-microgrid system in the carbon, electricity and green certificate market.

[0078] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the 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.

[0080] Example 4: This example uses a multi-microgrid system with four microgrids as the test object to verify the effectiveness of the multi-microgrid two-layer optimization scheduling method of the present invention that considers the coupling mechanism of the electricity-carbon-green certificate market.

[0081] The test system is connected via a ring transmission line. The distribution network voltage level is 10kV, and the upper limit of the transmission line power is 3MW. The source and load composition of each microgrid is as follows: MG1: Hydro turbine (HD), photovoltaic (PVb), user load (La); MG2: Wind power (WTa), photovoltaic (PVa), energy storage (ESSa), user load (Lb); MG3: Gas turbine (GTb), energy storage (ESSb), user load (Lc); MG4: Gas turbine (GTa), wind power (WTb), user load (Ld).

[0082] The effectiveness of the method was verified by setting up four typical operating scenarios for four microgrids: the load in microgrid 1 operated with a double-peak characteristic, the load in microgrid 2 operated with a single-peak characteristic, the load in microgrid 3 operated with a "peak-valley-peak" characteristic, and the load in microgrid 4 operated with a continuous fluctuation characteristic. Each operating condition was operated for 96 time periods, each time period lasting 15 minutes, and the performance indicators of the method of this invention were compared with those of the traditional method (without inter-grid mutual assistance and constant price mechanism).

[0083] 2) Implementation steps The specific implementation steps of this embodiment are as follows: Step 1: System initialization; build a test system containing 4 microgrids, input source load and equipment parameters, and set market parameters and algorithm parameters.

[0084] Step 2: Update the initial state of the intra-network model; the buyer in the inter-network model sets up a virtual electricity sales entity in the intra-network model, with the transaction limit being the inter-network transaction volume; the seller sets up a virtual electricity purchase entity, with the transaction limit being the maximum allowed value; update the transaction margin constraints.

[0085] Step 3: Solve the optimization model of the lower-level microgrid; each microgrid calls the GUROBI solver to solve the optimization model of the electricity-carbon-green certificate market of the microgrid, and obtain the marginal main parameters, electricity, carbon quota, green certificate trading volume and price of each microgrid.

[0086] The main components addressed by the single microgrid optimization model include gas turbines, gas turbines that have undergone low-carbon retrofitting, water turbines, energy storage units, wind turbine generators, photovoltaic units, adjustable loads, and low-carbon emission adjustable loads. The data for these units includes unit type, output upper and lower limits, ramp and landslide upper and lower limits, and capacity upper and lower limits.

[0087] Step 4: Update the initial state of the inter-grid model; each microgrid operator determines the buyer / seller identity, required inter-grid transaction volume, and transaction price based on the net transaction volume of the three intra-grid markets. Update the buyer's inter-grid transaction limit to its net transaction volume, and the seller's inter-grid transaction limit to the maximum allowed value.

[0088] Step 5: Solve the optimization model between upper-level microgrids; use an improved state-based potential game algorithm to solve the optimization model between microgrids and obtain the inter-grid transaction volume and price.

[0089] Step 6: Convergence Judgment; Calculate the maximum iterative residual of the electricity-carbon-green certificate market. If the residual is less than 10... -4 If the iteration stops, the iteration coefficients are updated and the process returns to step 2. In this embodiment, the convergence condition is met after 5 iterations.

[0090] Step 7: Output and Verification of Results.

[0091] Output the scheduling results under each working condition, and calculate the clean energy consumption index, economic operation index and algorithm performance index.

[0092] 3) Results and Analysis (1) Verification of Renewable Energy Absorption Capacity Under normal operating conditions, the two-layer mechanism in the method of this invention reduces the wind and solar curtailment rate of the multi-microgrid system by 43.07 percentage points compared with the single-layer mechanism, that is, the renewable energy absorption rate increases by 43.07 percentage points, which verifies the promoting effect of the two-layer mechanism on renewable energy absorption; the carbon-electricity-green certificate coupling mechanism introduced in the method of this invention increases the renewable energy absorption rate of the multi-microgrid system by 45.96 percentage points, which verifies the promoting effect of the multi-market coupling mechanism on renewable energy absorption.

[0093] Please refer to Figure 2, which shows the electricity market trading results of a multi-microgrid system without optimization using the dual-layer optimization model and carbon-electricity-green certificate market coupling mechanism of this invention. As can be seen from the figure, the output curve (Poutd) of wind power (WTb) fluctuates drastically, with peak output mismatches with user load (Ld) during multiple periods (such as the 10-20 and 50-60 periods), resulting in a significant amount of wind power not being absorbed locally and a clear phenomenon of wind curtailment. The output of gas turbines (GTa) is frequently adjusted with large amplitudes, requiring frequent start-ups and shutdowns or significant increases and decreases in output to cope with load fluctuations and renewable energy output gaps, resulting in low operating efficiency. The trading load (Loutd) curve is erratic and lacks stable trading guidance, reflecting the inability of intra-grid and inter-grid resources to effectively complement each other under a single electricity market, leading to trading congestion and supply-demand imbalance. In some periods (such as the 30-40 and 70-80 periods), output and load even deviate in opposite directions, highlighting the defects of weak system power balance capability and poor coordination between market mechanisms and dispatching processes under the traditional dispatching model.

[0094] Please refer to Figure 3, which shows the electricity market trading results of the multi-microgrid system after adopting the technical solution of this invention. The matching degree between the wind power (WTb) output curve and the user load (Ld) is significantly improved. During peak output periods (such as the 15-25 and 55-65 periods), the output is fully absorbed through inter-grid power mutual assistance and carbon-green certificate price linkage incentives, and the wind curtailment phenomenon is basically eliminated. The gas turbine (GTa) output tends to be stable, with only small-scale supplementary power generation during peak load periods or periods of low renewable energy output. The operating efficiency is significantly improved, effectively reducing unit energy consumption and maintenance costs. The system reduces costs; the transaction load (Loutd) curve is regular and orderly, with a reasonable distribution of positive and negative values ​​(positive values ​​represent selling and negative values ​​represent buying), reflecting the two-way linkage closed loop between intra-network and inter-network transactions, and the dynamic adaptation of transaction price and transaction volume, which completely solves the transaction congestion problem in the traditional model; the output curve and load curve are closely aligned throughout the entire time period (0-90 time period), and the power balance error is controlled within a very small range, which proves that the double-layer rolling optimization process and the improved state-based potential game algorithm of this invention can efficiently handle coupled and uncoupled constraints and ensure the stable operation of the system.

[0095] (2) Verification of Economic Efficiency: The two-tier mechanism in this invention increases the social welfare value of multi-microgrid systems by 23.15 percentage points compared to the single-tier mechanism, verifying the promoting effect of the two-tier mechanism on the system's economic efficiency. The distributed pricing mechanism in this invention, namely daily floating prices and real-time trading constraints, increases the total revenue of the carbon market and green certificate market by 0.24% to 0.58% compared to the constant price mechanism, verifying the feasibility of the distributed market mechanism for multi-microgrid systems from an economic efficiency perspective. Furthermore, the two-tier market mechanism in this invention, which is coordinated with microgrid dispatch, reduces the volatility of carbon quota prices and green certificate prices by more than 35 percentage points compared to the single-tier mechanism, verifying that the two-tier floating price mechanism is conducive to the long-term stable development of the market.

[0096] (3) The improved state-based potential game algorithm has a 37% faster convergence speed than the ADMM algorithm. In the simulation of 96 time periods, the average number of iterations is only 20, while ADMM has 32. At the same time, the algorithm protects the privacy of micronets through distributed information interaction and is adapted to the distributed operation characteristics of multiple micronets.

[0097] 5) Implementation Conclusion This embodiment, through testing and verification under four typical operating conditions, shows that the method of the present invention can effectively improve the renewable energy absorption rate and market revenue at the same time, and has the advantages of high convergence speed and privacy protection, fully achieving the purpose of the invention; it can be extended to the optimization and scheduling of various multi-microgrid systems, providing technical support for the low-carbon transformation of new power systems.

[0098] In summary, this invention presents a multi-microgrid hierarchical optimization scheduling method and system that considers the carbon, electricity, and green certificate market. It introduces a carbon-electricity-green certificate market coupling mechanism into the multi-microgrid system, enabling coordinated trading and price linkage of electricity, carbon allowances, and green certificates. This effectively improves the revenue of low-carbon and renewable energy entities and increases the renewable energy consumption rate. The two-layer optimization architecture balances the local economic interests of individual microgrids with the overall system optimization benefits, enhancing social welfare. Furthermore, the improved state-potential game algorithm for solving the upper-layer model protects the privacy of microgrid operators. The two-layer real-time floating price mechanism and real-time trading constraints allow the carbon and green certificate market mechanisms to be distributed and integrated into the multi-microgrid system. While promoting the construction of a low-carbon power system, this avoids damage to the revenue of entities within the system and reduces the price volatility of carbon allowances and green certificates, ensuring stable market operation.

[0099] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0100] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0101] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0102] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0103] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0105] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0106] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0109] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A hierarchical optimization scheduling method for multi-microgrids considering the carbon electricity green certificate market, characterized in that, Includes the following steps: S1. Construct a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to multi-microgrid systems. The trading architecture includes an intra-grid trading layer and an inter-grid trading layer to realize coordinated trading of electricity, carbon quotas, and green certificates. S2. Based on the trading architecture constructed in step S1, construct two-layer optimization models. The two-layer optimization models include a lower-layer single-microgrid optimization model and an upper-layer inter-microgrid optimization model, respectively realizing local optimization of single microgrids and global coordinated optimization between microgrids. S3. Reconstruct the upper-layer inter-microgrid optimization model obtained in step S2 using an improved state-potential game algorithm. S4. Design a two-layer rolling optimization process. Based on the upper-layer model reconstructed in step S3, solve the two-layer optimization model obtained in step S2 through a lower-upper-lower-upper-higher iteration to obtain the optimized scheduling results of the multi-microgrid system in the carbon, electricity, and green certificate markets.

2. The multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market as described in claim 1, characterized in that, In step S1, the intra-network trading layer is configured with real-time trading constraints and intraday floating price mechanisms. The carbon quota price and green certificate price are dynamically adjusted based on the inter-network trading quota. The inter-network trading layer conducts transactions based on the trading price output by the intra-network trading layer and feeds back the transaction amount to the intra-network trading layer, forming a two-way linkage.

3. The multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market according to claim 2, characterized in that, In the intra-grid trading layer, the electricity market adopts a centralized bidding trading model, with the marginal unit bid as the clearing price for unified clearing; the carbon market and green certificate market adopt a P2P trading model. The real-time trading constraints are used to simulate incomplete trading scenarios, and the intraday floating price mechanism is used to adapt to inter-grid trading demand and market stability reserves.

4. The multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market according to claim 2, characterized in that, In the inter-grid trading layer, each microgrid operator acts as a trading entity, determining their buying and selling status based on the trading results of the intra-grid trading layer. The clearing prices for electricity, carbon allowances, and green certificates are determined through matching between buyers and sellers, with the buyer using the seller's lowest bid as the virtual clearing price and the seller using the buyer's highest bid as the virtual clearing price. The inter-grid trading layer also establishes a price linkage mechanism, adjusting the prices of green certificates, carbon allowances, and electricity through changes in renewable energy output.

5. The multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market according to claim 1, characterized in that, In step S2, the lower-level microgrid optimization model aims to maximize social welfare within the microgrid, and the objective function covers the revenue and operating costs of user entities, virtual microgrid trading entities, and power generation entities in the electricity market, carbon market, and green certificate market; the upper-level microgrid optimization model aims to maximize the revenue of each microgrid operator in the electricity-carbon-green certificate market, and the decision variables include the inter-microgrid electricity trading volume, carbon quota trading volume, and green certificate trading volume.

6. The multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market according to claim 5, characterized in that, The constraints of the lower-level microgrid optimization model include system power balance conditions, upper and lower limits of inter-grid transaction volume constraints, upper and lower limits of unit output constraints, unit ramp-up and ramp-down constraints, hydropower unit capacity constraints, energy storage equipment capacity constraints, and energy storage equipment initial and final value constraints.

7. The multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market according to claim 5, characterized in that, The constraints of the upper-level microgrid optimization model include inter-grid power balance constraints, transmission line capacity constraints, carbon quota and green certificate trading volume balance constraints, and upper and lower limits of trading margin constraints.

8. The multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market according to claim 1, characterized in that, In step S3, the implementation process of the improved state-potential game algorithm includes: S301, defining a state space, where each state contains the decision variables of each trading entity and the estimated values ​​of all entities regarding constraints; S302, designing an action space, where the action of each entity is defined as the change in state; S303, constructing a state transition function, enabling each entity to update the next state based on the current state and action; S304, constructing an entity payoff function by combining constraint estimates, realizing dynamic mapping of constraints; S305, proving the existence of the Nash equilibrium point by constructing the potential function, and using a gradient game algorithm to determine the action values, with the convergence condition being that the maximum game residual in the electricity-carbon-green certificate market is less than 10. -4 .

9. The multi-microgrid hierarchical optimization scheduling method considering the carbon electricity green certificate market according to claim 1, characterized in that, In step S4, the iterative logic of the two-layer rolling optimization process is as follows: First, run the lower-layer single microgrid optimization model to obtain the intra-network marginal entity parameters, intra-network net transaction volume, and transaction price; then, run the upper-layer inter-microgrid optimization model based on the intra-network transaction results to determine the microgrid operator's transaction identity, inter-network transaction volume, and transaction price; in the lower-layer single microgrid optimization model, set up a virtual seller entity for the buyer and a virtual buyer entity for the seller, and run the lower-layer single microgrid optimization model again; finally, run the upper-layer inter-microgrid optimization model again, with the seller using the intra-network net transaction volume as the upper limit of the inter-network transaction volume, and the buyer using the maximum value as the upper limit of the inter-network transaction volume; during the iteration process, the wind and solar curtailment penalty term is initially set to a maximum value and gradually decreased, and the intra-network power balance coefficient is initially set to a small value and gradually increased, until the virtual entity transaction volume is consistent with the inter-network transaction volume, at which point the iteration stops.

10. A hierarchical optimization scheduling system for multi-microgrids considering the carbon electricity green certificate market, characterized in that, include: The system comprises the following modules: an architecture module for constructing a two-layer carbon-electricity-green certificate market coupling trading architecture adapted to multi-microgrid systems; an architecture comprising an intra-grid trading layer and an inter-grid trading layer to achieve coordinated trading of electricity, carbon allowances, and green certificates; a construction module for building a two-layer optimization model based on the trading architecture; a lower-layer single-microgrid optimization model for achieving local optimization of a single microgrid and an upper-layer inter-microgrid optimization model for achieving global coordinated optimization between microgrids; a reconstruction module for reconstructing the upper-layer inter-microgrid optimization model using an improved state-potential game algorithm; and a solution module for designing a two-layer rolling optimization process, solving the two-layer optimization model based on the reconstructed upper-layer model through a lower-upper-lower-upper-layer iteration, and outputting the optimized scheduling results of the multi-microgrid system in the carbon, electricity, and green certificate markets.

Images