A virtual power plant optimal scheduling method considering electric carbon green certificate coupled transaction

Abstract

This application discloses a virtual power plant optimization scheduling method considering electricity-carbon-green certificate coupled trading, comprising: constructing a multi-data center computing power spatiotemporal collaborative scheduling model; constructing an electricity-carbon-green certificate coupled market trading model; and based on the multi-data center computing power spatiotemporal collaborative scheduling model and the electricity-carbon-green certificate coupled market trading model, constructing a virtual power plant stochastic optimization scheduling model, aiming to minimize the expected total operating cost of the virtual power plant under multiple typical scenarios, and outputting a collaborative optimization scheduling scheme for generalized source-load-storage resources within the virtual power plant. This invention solves the problems of fragmented markets, inaccurate carbon accounting, and underutilization of computing power flexibility in existing technologies by implementing spatiotemporal collaboration of computing power and load, deep coupling of electricity-carbon-green certificates, a hybrid quota and tiered carbon pricing mechanism, and green certificate carbon emission deduction rules. It can significantly reduce overall operating costs, improve the effect of new energy consumption and carbon emission reduction, and enhance carbon asset returns.

Description

Technical Field

[0001] This application relates to the field of power system optimization dispatching technology, and more specifically, to a virtual power plant optimization dispatching method that considers electricity carbon green certificate coupled trading. Background Technology

[0002] As the core infrastructure of computing networks, data centers face increasing energy consumption and carbon emissions, resulting in significant energy efficiency pressures. Data center batch processing loads possess latency tolerance characteristics, enabling peak shaving and valley filling in the time dimension and cross-domain task migration in the spatial dimension. Their spatiotemporal collaborative migration characteristics provide flexible regulation capabilities for the power system.

[0003] Virtual power plants are key carriers that aggregate controllable loads such as distributed power sources, energy storage, and data centers, and represent an important path for the low-carbon transformation of energy. With increasing decarbonization requirements, relying solely on electricity market dispatch is no longer sufficient to meet the demands of deep emission reduction. The synergy of electricity, carbon trading, and green certificate trading markets has become an inevitable direction for virtual power plants to improve their economic and environmental benefits.

[0004] Current technologies for virtual power plant optimization scheduling and low-carbon data center operation have significant shortcomings: (1) The carbon market and the green certificate market are independent of each other and have not established a deep coupling mechanism. They have not accurately modeled the boundary between the transfer of environmental rights and interests of green certificates and the carbon emission accounting, and cannot offset indirect carbon emissions through green certificates, thus losing the space for cross-market arbitrage and reducing carbon compliance costs.

[0005] (2) Carbon emission cost modeling is too simplified, mostly using linear costs or fixed quotas, and does not conform to the hybrid model of free allocation and mandatory quota auctions and the reward and punishment tiered carbon price mechanism. Carbon asset management lacks incentives and it is difficult to achieve the transformation from passive compliance to active value-added.

[0006] (3) The unified framework of the spatiotemporal synergy of data center computing power and the integration of three market signals of electricity, carbon and green certificates has not been constructed. The computing power flexibility only responds to the time-of-use electricity price of a single electricity market and cannot adapt to multi-dimensional market signals. Physical energy conservation and emission reduction are disconnected from financial rights and interests operation, and the comprehensive adjustment value of data center load is difficult to be fully realized.

[0007] Both existing mainstream technologies suffer from the aforementioned shortcomings: One is the multi-market dispatching method for virtual power plants oriented towards traditional resources. Although it constructs a three-tiered market framework of electricity, carbon, and green certificates, the markets operate independently, carbon cost modeling is simplified, and the spatiotemporal characteristics of data center computing power are not integrated, making it impossible to achieve deep coupling and precise carbon asset management. The other is the data center virtual power plant dispatching method based on spatiotemporal collaboration of computing power. This method focuses only on the single electricity market, does not incorporate the carbon and green certificate markets, and does not establish a coupling offset mechanism between green certificates and carbon emissions, making it difficult to support the low-carbon economic operation under the synergy of the three markets.

[0008] In summary, existing technologies cannot achieve deep coupling of electricity, carbon, and green certificates, precise carbon asset management, and the synergistic utilization of computing power's spatiotemporal flexibility, making it difficult to meet the optimized scheduling needs of data center-type virtual power plants for low cost, high flexibility, and deep decarbonization. Summary of the Invention

[0009] To address the aforementioned issues, this application provides a virtual power plant optimization scheduling method that considers electricity-carbon-green certificate coupled trading. This method aims to solve the problems in existing virtual power plant scheduling, such as the fragmentation of the electricity-carbon-green certificate market linkage, neglect of the precise coupling and deduction relationship between green certificates and carbon emissions, and failure to deeply integrate the spatiotemporal translation characteristics of heterogeneous data center computing load with multiple market price signals.

[0010] The first aspect of this invention provides a virtual power plant optimization scheduling method considering electricity-carbon green certificate coupled trading, comprising: Construct a spatiotemporal collaborative model of computing power across multiple data centers, including constraints; determine the flexibility adjustment cost of the virtual power plant based on the spatiotemporal collaborative model of computing power across multiple data centers; Construct an electricity-carbon-certificate coupled market trading model, and determine the transaction costs of the environmental rights market based on the electricity-carbon-certificate coupled trading model; Based on the aforementioned multi-data center computing power spatiotemporal collaborative model and the electricity-carbon-green certificate coupled market trading model, a stochastic optimization scheduling model for virtual power plants is constructed. An objective function is constructed to minimize the expected total operating cost of the virtual power plant in multiple typical scenarios. By solving the objective function under the constraints of the multi-data center computing power spatiotemporal collaborative model, a collaborative optimization scheduling scheme for generalized source-load-storage resources within the virtual power plant can be obtained. objective function F as follows:

[0011] In the formula: s As a typical scenario, N s The number of typical scenarios, Let be the probability of scenario s occurring. The total system operating cost across all typical scenarios;

[0012] In the formula: For electricity market transaction costs, For environmental rights market transaction costs, For the physical operating cost of the system, Adjust costs for flexibility.

[0013] Furthermore, the constraints include device energy consumption constraints based on the nonlinear relationship between server energy consumption and load, service latency constraints based on M / M / 1 queuing theory to ensure task response time, and dynamic balance and task conservation constraints of computing load in time dimension translation and spatial dimension migration.

[0014] Furthermore, the device energy consumption constraint based on the nonlinear relationship between server energy consumption and load... The expression is:

[0015] in, for The number of servers that were active in the data center during that period. This refers to the idle standby power of a single server. This represents the peak power of a single server under full load. This represents the average maximum service rate of a single server. This represents the actual computing power load arrival rate during that period. The service latency constraints based on M / M / 1 queuing theory to guarantee task response time are as follows:

[0016] In the formula: This represents the average service rate of a single server. This is the maximum allowable delay; The dynamic balance constraints of computing power load in the time and space dimensions include computing power flow conservation constraints and user satisfaction constraints based on cumulative deviation: Computing power flow conservation constraint:

[0017] in, The original baseline arrival rate, and They are respectively The amount of batch processing tasks transferred into and out of the data center during a given time period. For data center Migration to data center The amount of computing power required for processing. For data center Migration to data center The amount of computing power required for the task. The number of data centers; User satisfaction constraints based on cumulative deviation:

[0018] in, This is the user satisfaction threshold coefficient. The total scheduling cycle is 24 hours. For the first Data centers in The net change in computing load due to spatial migration during a given time period.

[0019] Furthermore, the flexibility adjustment cost of the virtual power plant is:

[0020] In the formula: This is a set of data center communication links that allow for cross-regional data transfer and computing power migration. Cost per unit of data transmission; , These are the unit compensation prices for time translation and spatial migration, respectively.

[0021] Furthermore, the transaction costs of the environmental rights market

[0022] In the formula: Fixed compliance costs arising from mandatory quota auctions; Settlement costs for tiered carbon trading based on net emissions; The unit price for green certificate transactions. , These represent the number of green certificates that VPP bought and sold in the green certificate market, respectively. This refers to the cost of carbon trading.

[0023] Furthermore, the transaction costs in the environmental rights market are determined according to the following mechanism: The carbon trading mechanism includes a baseline free allocation, a strengthened quota auction hybrid allocation model, and a tiered pricing system with rewards and penalties; a green certificate market trading mechanism that sets differentiated renewable energy consumption responsibility weights for heterogeneous loads; a multi-dimensional market trading time sequence connection architecture and a multi-dimensional market value coupling mechanism for green certificate carbon emission deduction; and a demand response mechanism based on the grid-data center value transmission system, with grid-side peak shaving subsidies and user-side tiered compensation.

[0024] A second aspect of this invention provides a virtual power plant optimized scheduling device considering electricity-carbon green certificate coupled trading, characterized in that the device comprises: The computational power spatiotemporal collaborative model construction module is used to construct constraints and calculate the flexibility adjustment costs of virtual power plants; A module for constructing a coupling model of the electricity-carbon-green certificate market is used to determine the transaction costs of the environmental rights market; The stochastic optimization scheduling model construction module constructs a stochastic optimization scheduling model for virtual power plants based on the multi-data center computing power spatiotemporal collaboration model and the electricity-carbon-green certificate coupled market trading model. The objective function is constructed to minimize the expected total operating cost of the virtual power plant in multiple typical scenarios. The objective function is solved using the constraints of the multi-data center computing power spatiotemporal collaboration model to obtain the collaborative optimization scheduling scheme of generalized source-load-storage resources within the virtual power plant.

[0025] A third aspect of the present invention provides an electronic device, characterized in that it includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements a virtual power plant optimization scheduling method that considers electricity carbon green certificate coupled trading.

[0026] A fourth aspect of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and the computer program, when executed by a processor, is a virtual power plant optimization scheduling method considering the coupled trading of electricity, carbon, and green certificates.

[0027] This application has at least the following advantages or beneficial effects: (1) Significantly reduce overall operating costs: Through refined spatiotemporal collaborative scheduling of computing power load in heterogeneous data centers, system-level peak shaving and valley filling are achieved, fully releasing the economic value of computing power flexibility, which can significantly reduce the overall operating costs of the system compared with traditional solutions.

[0028] (2) Enhance the absorption of new energy and achieve physical emission reduction: Relying on the coordinated operation of generalized source-load-storage, guide the computing load to migrate to green energy nodes and shift to the high output period of wind and solar power, effectively smooth out the fluctuation of new energy output and directly reduce the total physical carbon emissions of the system.

[0029] (3) Deeply reduce net carbon emissions: Construct a deep coupling mechanism of electricity-carbon-green certificates, accurately offset carbon emission responsibilities through green certificate cancellation, and further significantly reduce net carbon emissions on the basis of physical emission reduction, resulting in outstanding low-carbon benefits.

[0030] (4) Significantly enhance carbon asset value-added benefits: realize cross-market arbitrage between carbon market and green certificate market, promote the transformation of virtual power plants from passive carbon compliance to active carbon asset value-added, and effectively enhance carbon market settlement benefits and overall economic benefits. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart of a virtual power plant optimization scheduling method considering electricity carbon green certificate coupled trading, proposed in an embodiment of this application; Figure 2 This is a structural diagram of a virtual power plant optimized scheduling device considering electricity carbon green certificate coupled trading, as proposed in an embodiment of this application; Figure 3 This is a schematic diagram of an electronic device according to this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a virtual power plant optimal scheduling method considering the coupled trading of electricity, carbon, and green certificates, as proposed in an embodiment of this application. Figure 1 As shown, a virtual power plant optimal scheduling method considering electricity carbon green certificate coupled trading includes: S100: Construct a spatiotemporal collaborative scheduling model for computing power in multiple data centers. The constraints of the model include: equipment energy consumption constraints based on the nonlinear relationship between server energy consumption and load, service latency constraints based on queuing theory, and dynamic balance constraints of computing power load in the time and space dimensions. In this embodiment, based on the physical parameters and computing load data of the heterogeneous data center, interactive real-time load and delayed batch processing load are distinguished. A device energy consumption constraint based on the nonlinear relationship between server energy consumption and load is established. The total power expression for a single data center IT device is as follows:

[0035] in, for The number of servers that were active in the data center during that period. This refers to the idle standby power of a single server. This represents the peak power of a single server under full load. This represents the average maximum service rate of a single server. This represents the actual computing load arrival rate during that period.

[0036] Furthermore, based on the M / M / 1 queuing theory, the queuing service process of the computing power network is modeled, and service delay constraints based on queuing theory are established, specifically including: Assume the first Data centers in The time slot has started Taiwan homogeneous servers, and total computing power load Distributed evenly across all running servers. The average service rate per server is [missing information]. The average arrival rate allocated to a single server is... According to the M / M / 1 queuing theory, the average task response time on a single server... (Including queuing time and actual processing time) can be expressed as:

[0037] In the formula, the denominator term It must be strictly greater than 0 to ensure system stability.

[0038] To meet the needs of latency-sensitive services, the system must ensure that the average response time does not exceed the maximum allowable latency. That is, it satisfies the following nonlinear constraints:

[0039] In addition to latency constraints, a maximum CPU utilization limit needs to be set to prevent server overload and crashes caused by sudden traffic surges. This constitutes the steady-state operating boundary of the server cluster:

[0040] Meanwhile, the number of servers that can be activated is limited by the total number of physical servers in the data center. :

[0041] By applying the above constraints, dynamic adjustments can be made while still meeting the service level agreement requirements. Minimize the energy consumption of the data center.

[0042] Finally, for any data center The actual computing load it ultimately processes The dynamic balance constraints of computing power load in the time and space dimensions are jointly determined by the original baseline load, the amount of transfer in / out in the time dimension, and the amount of migration in / out in the spatial dimension. Specifically, these constraints include:

[0043] Expanded into specific component forms:

[0044] In the formula, This represents the original baseline arrival rate; and They are respectively The amount of batch processing tasks transferred into and out of the data center during a given time period; Indicated by data center Migration to data center The amount of computing power required for processing; Indicated by data center Migration to data center The amount of computing power required.

[0045] Frequent or excessive computing power scheduling increases task queuing time and degrades the user experience. To strike a balance between low-carbon economics and user satisfaction, a satisfaction constraint mechanism is introduced. Definition For data centers exist The absolute value of the load deviation caused by time shift during a given period. Satisfaction constraints are constructed based on the cumulative deviation.

[0046] In the formula, This is the user satisfaction threshold coefficient. The total scheduling cycle is 24 hours (i.e.) ), For the first Data centers in The formula represents the net change in computing load due to spatial migration over a given period. It indicates that the deviation in task execution time caused by scheduling strategies throughout the day must not exceed the total number of tasks. Proportion.

[0047] The cost of coordinating computing power in this process is part of the flexibility adjustment cost, including bandwidth leasing fees for cross-regional data transmission, and corresponding compensation for the inconvenience caused to tenants due to task migration or relocation:

[0048] in: Indicates network transmission cost, This indicates that it is inconvenient for the user to receive compensation.

[0049] In the formula: This is a set of data center communication links that allow for cross-regional data transfer and computing power migration. Cost per unit of data transmission; , These are the unit compensation prices for time translation and spatial migration, respectively.

[0050] S200: Construct an electricity-carbon-green certificate coupled market trading model. The model introduces a hybrid allocation mode of "baseline free allocation and mandatory quota auction" and a carbon trading mechanism with tiered pricing based on rewards and penalties; a green certificate market trading mechanism that sets differentiated renewable energy consumption responsibility weights for heterogeneous loads; establishes a multi-dimensional market trading time sequence connection architecture and a multi-dimensional market value coupling mechanism for green certificate deduction of carbon emissions; and constructs a demand response economic model based on grid-side peak shaving subsidies and user-side tiered compensation under the "grid-VPP-data center" value transmission system, and determines the market transaction costs of environmental rights based on the above mechanisms.

[0051] In this embodiment, a carbon trading mechanism based on hybrid quota allocation and tiered carbon pricing with rewards and penalties is first established. The carbon quota consists of two parts: free allocation at the baseline and mandatory quota auction. A tiered carbon pricing mechanism with rewards and penalties is adopted to achieve accurate carbon asset accounting and economic incentives.

[0052] (1) Baseline Free Allocation: The industry baseline method is used to calculate the free quota. Based on the model parameter settings, this study mainly focuses on the quota allocation for the IT load of data centers. Time Period Scene Total free quota Represented as:

[0053] In the formula, The mandatory quota auction ratio coefficient represents... Quotas will no longer be issued free of charge; For the first The operating power of IT equipment in a data center; This is the carbon quota benchmark factor for data center IT equipment.

[0054] (2) Cost of mandatory quota auction: For the remaining The proportional allocation to be distributed by VPPs must be acquired through a paid auction in the primary market. Considering the premium effect of the auction market, auction costs... The calculation is as follows:

[0055] In the formula, This refers to the total quota approved for this scenario; It serves as the benchmark price for carbon trading; This represents the auction price multiplier, reflecting the additional financial cost of acquiring compliant quotas.

[0056] To further incentivize VPPs to reduce net emissions, the model introduces a tiered carbon trading mechanism based on rewards and penalties. This is based on the net carbon emissions of VPPs. The carbon trading cost or benefit is calculated by subtracting allowances and green certificate credits from the actual total emissions and then using a piecewise linear function. Set the length of the stepped interval to be... The basic carbon price is This mechanism includes a reward range and a penalty range, and the specific cost function is as follows:

[0057] In the formula, This is the carbon emission surplus reward factor, which indicates that when net emissions are far below the allowance, VPPs can obtain excess profits above the market benchmark price by selling surplus allowances. This is the carbon emission penalty factor, which indicates that when net emissions significantly exceed the limit, the VPP must pay a punitive carbon purchase cost.

[0058] Secondly, a green certificate market trading mechanism based on differentiated consumption weights should be established. Virtual power plants, as aggregators, participate in the green certificate market. The green certificates they hold must balance the profit from sale with the carbon emission deduction, provided that they meet their own consumption responsibility weight assessment. Therefore, a system-wide green certificate supply and demand balance constraint should be established.

[0059] In the formula, and These represent the number of green certificates that VPP bought and sold in the green certificate market, respectively. This refers to the total legally mandated consumption quota that the system must fulfill according to the RPS policy; The number of green certificates used to offset carbon emissions is considered cancelled once transferred and cannot be resold.

[0060] The model sets differentiated RPS (Resource Power Sales Per Second) assessment indicators for different types of loads within the virtual power plant. Data centers, as a new type of load with high energy consumption and high flexibility, must bear a higher consumption responsibility weight than conventional loads, in order to force them to increase their green electricity consumption ratio.

[0061] Scene Total consumption quota required by the system Calculated based on the weighted average of actual electricity consumption of heterogeneous loads:

[0062] Among them, the electricity consumption of other conventional loads Includes base electricity load and energy storage charging load after deducting reductions:

[0063] In the formula, The weighting coefficient for the responsibility of data center absorption; The responsibility weighting factor for absorbing the VPP's conventional load; This serves as the system's basic power load. Reductions in incentive-based demand response; Power for charging energy storage.

[0064] Furthermore, a multi-dimensional market value coupling constraint is established for electricity, carbon, and green certificates. During the day-ahead optimization phase, the virtual power plant uses a 24-hour scheduling cycle and formulates a spatiotemporal migration plan for unit output and computing power based on the electricity price forecast curve and new energy output scenarios. To achieve temporal consistency, the model assumes that carbon trading prices and green certificate prices remain relatively stable within a single day, and decomposes long-term carbon allowances and RPS (Renewable Power Scheme) absorption targets evenly over time to a single day, thereby achieving dynamic coordination between short-term physical scheduling and long-term environmental compliance.

[0065] Achieving precise quantitative conversion between green certificate environmental rights and carbon emission responsibilities:

[0066] In the formula, The conversion factor for green certificates to carbon emission reductions means that, in physical terms, the electricity that has been "de-greened" can be "re-greened" through financial means. Based on the above mechanisms, the net carbon emissions of virtual power plants ultimately participating in carbon market settlements... It is determined by three factors: total physical emissions, policy-free allowances, and financial green certificate deductions.

[0067] This coupled model establishes an arbitrage equilibrium based on price signals within the virtual power plant: when the price of green certificates is higher than the product of the carbon price and the conversion factor, the system tends to sell green certificates to profit and bear the corresponding carbon emission costs; conversely, it tends to retain green certificates to offset carbon emissions. This allows each spatiotemporal migration of computing resources to simultaneously respond to electricity price fluctuations, carbon price signals, and the supply and demand of green certificates, achieving deep integration of multidimensional markets.

[0068] Based on this, the environmental equity cost of electricity-carbon-green certificates encompasses the compliance costs of virtual power plants in the carbon trading market and the green certificate market. This section integrates the cost of hybrid carbon allowances and the net transaction cost of green certificates.

[0069] in: Indicates the cost of the carbon market. This indicates the green certificate market has matured.

[0070] In the formula, Fixed compliance costs arising from mandatory quota auctions; Settlement costs for tiered carbon trading based on net emissions; This refers to the unit price for green certificate transactions.

[0071] Finally, a tiered compensation model is established, which combines the peak-shaving subsidy revenue of the virtual power plant to the grid side with the tiered compensation cost for the user-side response reduction, thus forming an economic driver for demand response. As a load aggregator, the virtual power plant plays the role of an "intermediary" in the demand response market, and its economic model includes two dimensions: subsidy revenue to the grid side and compensation cost to the user side.

[0072] (1) Grid-side subsidy income: When a virtual power plant successfully reduces load in response to grid dispatch instructions, it can receive government or grid subsidies based on the response amount. Subsidy income during the period The calculation is as follows:

[0073] In the formula, The subsidy price per unit of response quantity; This is the verified effective response quantity.

[0074] (2) User-side tiered compensation cost: To incentivize data center tenants to sacrifice some computing power to participate in the response, the virtual power plant needs to pay compensation to users. Considering the increasing marginal cost of user participation depth, a tiered compensation pricing model is constructed. As the reduction amount increases, the unit compensation price given to users rises in a tiered manner:

[0075] Among them, the step compensation function The grading logic in the middle:

[0076] In the formula: This refers to the compensation unit price for each grade.

[0077] This two-tiered mechanism forms the original driving force for virtual power plants to participate in demand response, while also ensuring that dispatch instructions are only executed when grid subsidies are sufficient to cover user opportunity costs.

[0078] S300: Based on the multi-data center computing power spatiotemporal collaborative scheduling model and the electricity-carbon-green certificate coupled market trading model, a virtual power plant stochastic optimization scheduling model is constructed. With the goal of minimizing the expected total operating cost of the virtual power plant in multiple typical scenarios, a collaborative optimization scheduling scheme for generalized source-load-storage resources within the virtual power plant is output.

[0079] In this embodiment, firstly, a probability distribution model of the uncertainty of wind and solar power output and computing load is established. Monte Carlo simulation and scene reduction technology based on Kantorovich distance are used to generate a typical set of scenarios that can represent the random fluctuations on both sides of the source and load. The random variation of wind speed follows a two-parameter Weibull distribution, and the uncertainty of light intensity is described by a Beta distribution. The actual computing load arrival rate of the data center follows a normal distribution with the predicted baseline value as the mean and the product of the predicted value and the load fluctuation coefficient as the standard deviation, expressed as:

[0080] In the formula: This is the load fluctuation coefficient.

[0081] Based on the above probability density function, a large number of random samples are generated using the Monte Carlo simulation method. There are 1 initial scene, and each scene contains 10 initial scenes. Hourly wind power, solar power, and computing power load curve data.

[0082] The sheer volume of random scenes can lead to excessively large optimization models that are difficult to solve. To address this, a scene reduction technique based on Kantorovich distance is employed to reduce the initial scene set to a smaller set while preserving the statistical characteristics of the original probability distribution. A collection of typical scenarios .

[0083] Each scene after reduction Includes a defined landscape normalization curve , And the computing power load curve, and a corresponding probability of occurrence. And it satisfies the probability normalization constraint.

[0084] Secondly, taking the minimization of the expected total operating cost of the virtual power plant as the objective function, the system comprehensively considers the electricity market transaction costs, the environmental rights costs of electricity-carbon-green certificates, the physical operating costs of the system, and the flexibility adjustment costs under different scenarios with varying probabilities of occurrence.

[0085] Based on multi-scenario stochastic programming theory, the objective function is the probabilistic weighted sum of the total system operating cost under all typical scenarios:

[0086] In the formula, For the scene The probability of occurrence. Total cost in a single scenario. Electricity market transaction costs Environmental rights market transaction costs System physical operating costs and the cost of flexibility adjustment It consists of four parts: .

[0087] Furthermore, a two-tiered physical operation constraint is established for the “virtual power plant system layer – data center node layer”, specifically including: power balance constraints for each node, interconnection capacity and ramp rate constraints, start-stop boundary constraints for micro gas turbines and independent gas turbines, and state of charge constraints and charge-discharge continuity constraints for electrochemical energy storage.

[0088] Finally, binary auxiliary variables and the Big M method are introduced to linearize the nonlinear logical constraints such as tiered carbon trading costs and tiered demand response compensation, transforming the original model into a mixed integer linear programming (MILP) problem for deterministic solution.

[0089] The constructed virtual power plant stochastic optimization scheduling model is a typical mixed-integer nonlinear programming (MINLP) problem. Both the tiered carbon trading cost function and the tiered demand response compensation function exhibit piecewise linear characteristics and contain logical constraints. To improve the model's solution efficiency and ensure the attainment of the global optimum, binary auxiliary variables and the Big M method are introduced to transform the aforementioned nonlinear constraints into mixed-integer linear programming constraints for solution.

[0090] Please refer to Figure 2 , Figure 2 This is a structural diagram of a virtual power plant optimized scheduling device considering the coupled trading of electricity, carbon, and green certificates, as proposed in an embodiment of this application. Figure 2 As shown in the embodiments of this disclosure, a virtual power plant optimization scheduling device considering electricity-carbon-green certificate coupled trading is also provided. The device includes: a computing power spatiotemporal collaborative scheduling model construction module 201, an electricity-carbon-green certificate market coupling model construction module 202, and a stochastic optimization scheduling model construction module 203; wherein, The computing power spatiotemporal collaborative scheduling model construction module 201 is used to construct a multi-data center computing power spatiotemporal collaborative scheduling model. The model includes: equipment energy consumption constraints based on the nonlinear relationship between server energy consumption and load, service latency constraints based on queuing theory, and dynamic balance constraints of computing power load in the time and space dimensions. The electricity-carbon-green certificate market coupling model construction module 202 is used to construct an electricity-carbon-green certificate coupled market trading model. The model includes: carbon trading mechanism constraints based on mixed quota allocation and tiered carbon pricing with rewards and penalties; green certificate market trading mechanism constraints based on differentiated consumption weights; multi-dimensional market value coupling constraints for electricity, carbon, and green certificates; and constraints from a two-tiered incentive-based demand response economic model based on tiered compensation. The stochastic optimization scheduling model construction module 203 is used to construct a stochastic optimization scheduling model for virtual power plants based on the multi-data center computing power spatiotemporal collaborative scheduling model and the electricity-carbon-green certificate coupled market trading model. The model aims to minimize the expected total operating cost of the virtual power plant in multiple typical scenarios and outputs a collaborative optimization scheduling scheme for generalized source-load-storage resources within the virtual power plant.

[0091] This disclosure also provides an electronic device, please refer to... Figure 3 , Figure 3 This is a schematic diagram of an electronic device illustrated in an embodiment of this disclosure. For example... Figure 3 As shown, the electronic device 100 includes a memory 110 and a processor 120. The memory 110 and the processor 120 are connected via a bus communication connection. The memory 110 stores a computer program that can run on the processor 120 to implement the steps in the virtual power plant optimized scheduling method considering the coupling trading of electricity, carbon and green certificates disclosed in this embodiment of the present disclosure.

[0092] The disclosed embodiments also provide a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of a computer device, enables the computer device to perform steps in a virtual power plant optimized scheduling method considering electricity carbon green certificate coupled trading, as described in the embodiments of this disclosure.

[0093] In summary, this invention offers significant advantages over existing technologies: Through refined spatiotemporal collaborative scheduling of computing power load in heterogeneous data centers, the overall system operating cost can be reduced by approximately 74.5%; at the physical emission reduction level, total carbon emissions are reduced from 505.15 tons to 471.56 tons, a reduction of 6.6%; relying on the deep coupling of electricity, carbon, and green certificates and the green certificate deduction mechanism, net carbon emissions are further reduced by approximately 20% on top of physical emission reductions; simultaneously, cross-market value-added of carbon assets is achieved, with carbon market settlement revenue increasing from RMB 26,800 to RMB 31,100, an increase of approximately 16%, significantly improving the economic efficiency and low-carbon benefits of virtual power plants, and realizing a shift from passive compliance to proactive value-added.

[0094] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of this application. It should 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 terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0098] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0099] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0100] The above provides a detailed description of a virtual power plant optimization scheduling method considering the coupling trading of electricity, carbon, and green certificates. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A virtual power plant optimization scheduling method considering electric carbon green certificate coupled transactions, characterized in that, include: Construct a spatiotemporal collaborative model of computing power across multiple data centers, including constraints; determine the flexibility adjustment cost of the virtual power plant based on the spatiotemporal collaborative model of computing power across multiple data centers; Construct an electricity-carbon-certificate coupled market trading model, and determine the transaction costs of the environmental rights market based on the electricity-carbon-certificate coupled trading model; Based on the aforementioned multi-data center computing power spatiotemporal collaborative model and the electricity-carbon-green certificate coupled market trading model, a stochastic optimization scheduling model for virtual power plants is constructed. An objective function is constructed to minimize the expected total operating cost of the virtual power plant in multiple typical scenarios. By solving the objective function under the constraints of the multi-data center computing power spatiotemporal collaborative model, a collaborative optimization scheduling scheme for generalized source-load-storage resources within the virtual power plant can be obtained. Objective function F As follows: wherein: s is the number of typical scenarios, N s is the number of typical scenarios, is the probability of occurrence of scenario s, is the total cost of system operation under all typical scenarios; In the formula: For electricity market transaction costs, For environmental rights market transaction costs, For the physical operating cost of the system, Adjust costs for flexibility.

2. The virtual power plant optimal scheduling method considering the coupling trading of electricity carbon green certificates according to claim 1, characterized in that, The constraints include device energy consumption constraints based on the nonlinear relationship between server energy consumption and load, service latency constraints based on M / M / 1 queuing theory to ensure task response time, and dynamic balance and task conservation constraints of computing load in time dimension translation and spatial dimension migration.

3. The virtual power plant optimization scheduling method considering the coupling trading of electricity carbon green certificates according to claim 2, characterized in that, The device energy consumption constraint based on the nonlinear relationship between server energy consumption and load The expression is: in, for The number of servers that were active in the data center during that period. This refers to the idle standby power of a single server. This represents the peak power of a single server under full load. This represents the average maximum service rate of a single server. This represents the actual computing power load arrival rate during that period. The service latency constraints based on M / M / 1 queuing theory to guarantee task response time are as follows: In the formula: This represents the average service rate of a single server. This is the maximum allowable delay; The dynamic balance constraints of computing power load in the time and space dimensions include computing power flow conservation constraints and user satisfaction constraints based on cumulative deviation: Computing power flow conservation constraint: in, The original baseline arrival rate, and They are respectively The amount of batch processing tasks transferred into and out of the data center during a given time period. For data center Migration to data center The amount of computing power required for processing. For data center Migration to data center The amount of computing power required for the task. The number of data centers; User satisfaction constraints based on cumulative deviation: in, This is the user satisfaction threshold coefficient. The total scheduling cycle is 24 hours. For the first Data centers in The net change in computing load due to spatial migration during a given time period.

4. The virtual power plant optimal scheduling method considering the coupling trading of electricity carbon green certificates according to claim 3, characterized in that, The flexibility adjustment cost of the virtual power plant is: In the formula: This is a set of data center communication links that allow for cross-regional data transfer and computing power migration. Cost per unit of data transmission; , These are the unit compensation prices for time translation and spatial migration, respectively.

5. A virtual power plant optimization scheduling method considering electricity carbon green certificate coupled trading as described in claim 4, characterized in that, The environmental rights market transaction costs In the formula: Fixed compliance costs arising from mandatory quota auctions; Settlement costs for tiered carbon trading based on net emissions; The unit price for green certificate transactions. , These represent the number of green certificates that VPP bought and sold in the green certificate market, respectively. This refers to the cost of carbon trading.

6. The virtual power plant optimization scheduling method considering the coupling trading of electricity, carbon, and green certificates according to claim 5, characterized in that, The transaction costs of the environmental rights market are determined according to the following mechanism: The carbon trading mechanism includes a baseline free allocation, a strengthened quota auction hybrid allocation model, and a tiered pricing system with rewards and penalties; a green certificate market trading mechanism that sets differentiated renewable energy consumption responsibility weights for heterogeneous loads; a multi-dimensional market trading time sequence connection architecture and a multi-dimensional market value coupling mechanism for green certificate carbon emission deduction; and a demand response mechanism based on the grid-data center value transmission system, with grid-side peak shaving subsidies and user-side tiered compensation.

7. A virtual power plant optimized dispatching device considering the coupling trading of electricity carbon green certificates, characterized in that, The device includes: The computational power spatiotemporal collaborative model construction module is used to construct constraints and calculate the flexibility adjustment costs of virtual power plants; A module for constructing a coupling model of the electricity-carbon-green certificate market is used to determine the transaction costs of the environmental rights market; The stochastic optimization scheduling model construction module constructs a stochastic optimization scheduling model for virtual power plants based on the multi-data center computing power spatiotemporal collaboration model and the electricity-carbon-green certificate coupled market trading model. The objective function is constructed to minimize the expected total operating cost of the virtual power plant in multiple typical scenarios. The objective function is solved using the constraints of the multi-data center computing power spatiotemporal collaboration model to obtain the collaborative optimization scheduling scheme of generalized source-load-storage resources within the virtual power plant.

8. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a virtual power plant optimized scheduling method considering electricity carbon green certificate coupled trading as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements a virtual power plant optimization scheduling method considering electricity carbon green certificate coupled trading as described in any one of claims 1 to 6.

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