Virtual power plant group carbon electricity transaction method, system and device considering wind and light output uncertainty, and medium

Abstract

The invention belongs to the technical field of virtual power plant group carbon electricity transaction, and discloses a virtual power plant group carbon electricity transaction method, system and device considering wind and light output uncertainty, and a medium, so as to solve the problem that in the prior art, the transaction economy is reduced because wind and light output risks are ignored. The method comprises the following steps: acquiring carbon electricity market data, wind and light historical output data and equipment parameters; constructing a VPP polymerization model, and calculating an economic cost model of each polymerization unit participating in the carbon electricity transaction; respectively solving output edge distribution density functions of the wind turbine generator and the photovoltaic unit by using a kernel density estimation method, and establishing a risk measurement model based on conditional value-at-risk; and integrating the virtual power plant group carbon electricity transaction model and the risk measurement model, constructing a virtual power plant group carbon electricity transaction model considering the wind and light output uncertainty, and solving and outputting the purchase and sale electric quantity of the virtual power plant group in the electricity market and the carbon emission permit transaction volume of the carbon market through an optimization algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon electricity trading of virtual power plant clusters, and specifically relates to a carbon electricity trading method, system, equipment and medium for virtual power plant clusters that takes into account the uncertainty of wind and solar power output. Background Art

[0002] As an intelligent energy system, a virtual power plant (VPP) leverages advanced communication, control, and metering technologies to integrate distributed generation resources (such as photovoltaic and wind power), energy storage systems, demand response resources (such as central air conditioning and flexible loads), and fuel cells into a unified system for unified scheduling and management. Carbon-electricity trading within virtual power plant clusters is a key tool for distributed energy management. By aggregating multiple types of adjustable resources to participate in electricity and carbon market transactions, it achieves the coordinated optimization of electricity and carbon emission rights.

[0003] Currently, virtual power plant group trading methods mainly focus on economic scheduling, usually by building deterministic optimization models to minimize costs or maximize benefits. However, these methods have significant limitations: First, traditional models often use expected value or typical scenario methods to deal with fluctuations in wind and solar power output, which makes it difficult to accurately quantify tail risks under extreme weather conditions; second, existing technologies separate and optimize electricity trading from carbon trading, ignoring the impact of the coupling relationship between the two on overall economic efficiency; third, trading strategies lack risk adjustment mechanisms, which can easily lead to large fluctuations in returns when actual output deviates from forecasts. This neglect of the uncertainty of wind and solar power output has caused virtual power plant groups to face problems such as reduced trading economics and weakened market competitiveness in actual operation. Summary of the Invention

[0004] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the objects of the present invention is to at least solve one or more of the above-mentioned problems in the prior art. In other words, one of the objects of the present invention is to provide a carbon electricity trading method, system, equipment and medium for a virtual power plant group that meets one or more of the above-mentioned needs and takes into account the uncertainty of wind and solar output. By introducing the conditional value at risk theory, a correlation model between the probability distribution of wind and solar output and transaction costs is constructed to effectively solve the problem of reduced transaction economics when a virtual power plant group participates in a transaction due to ignoring the risk of wind and solar output.

[0005] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a carbon electricity trading method for a virtual power plant group taking into account the uncertainty of wind and solar power output, comprising the steps of: S1, obtaining carbon electricity market data, historical wind and solar power output data and equipment parameters, wherein the carbon electricity market data includes electricity purchase price, electricity selling price, carbon emission purchase price and carbon emission selling price, the historical wind and solar power output data includes photovoltaic historical output power and wind power historical output power, and the equipment parameters include fuel cell power generation power, fuel cell unit power generation cost, energy storage system charge and discharge capacity and energy storage system charge and discharge dissipation coefficient; S2, based on the carbon electricity market data and the equipment parameters obtained in step S1, constructing a VPP aggregation model and calculating the participation of each aggregation unit in carbon electricity trading Economic cost model, the aggregation units of the VPP aggregation model include photovoltaic units, wind turbines, fuel cells, energy storage systems, central air conditioners and flexible loads; S3, based on the historical wind and solar power output data obtained in step S1, use the kernel density estimation method to solve the output marginal distribution density functions of wind turbines and photovoltaic units respectively, and establish a risk measurement model based on conditional risk value; S4, integrate the virtual power plant group carbon power trading model constructed in step S2 and the risk measurement model established in step S3, construct a virtual power plant group carbon power trading model considering the uncertainty of wind and solar power output, solve it through the optimization algorithm, and output the purchase and sale electricity volume of the virtual power plant group in the electricity market and the carbon emission rights trading volume in the carbon market.

[0006] As a preferred solution, the economic cost model includes electricity market transaction costs, carbon market transaction costs, energy storage costs, central air conditioning call costs, flexible load call costs, fuel cell operating costs and P2P transaction costs; the constraints of the economic cost model include fuel cell output power constraints, fuel cell climbing constraints, energy storage system charging and discharging power constraints, energy storage system charge state constraints, energy storage system power constraints, central air conditioning cooling power constraints, indoor temperature comfort constraints, flexible load constraints, carbon emission constraints and carbon-electricity emission balance constraints.

[0007] As a preferred solution, the minimum total cost is taken as the optimization goal, and the objective function of the VPP aggregation model is: , Where, It is an aggregation unit participating in the transaction in the virtual power plant group. is the total amount of aggregated units participating in the transaction of the virtual power plant group, To contribute to a certain scene, is the total number of wind and solar power output scenes, For a certain trading period, is the total number of trading sessions, Powering the scene The probability of and Aggregation units The transaction costs of participating in the electricity market and carbon market during period t, Aggregate unit Internal energy storage costs, and Aggregation units The discomfort costs of internally regulated central air conditioning and flexible loads, Aggregate unit Internal fuel cell operating costs, Aggregate unit P2P transaction costs.

[0008] As a preferred solution, the kernel density estimation method adopts a Gaussian kernel function and dynamically optimizes bandwidth parameters through a least square error method.

[0009] As a preferred solution, the kernel density estimation method is used to estimate the output probability density function of the photovoltaic unit from the discrete photovoltaic historical output power data. , and then integrate it to obtain the corresponding marginal distribution function ; Probability density function of photovoltaic units and marginal distribution function The expressions are

[0010]

[0011] Where, To estimate the number of data points used, Represents the dimension of the data, is the covariance matrix, is the expectation matrix, Indicates A multivariate Gaussian distribution centered at point The density contribution at The contribution of each sample point is summed and averaged to obtain an estimate of the density function.

[0012] As a preferred solution, the output marginal distribution function of the wind turbine is consistent with the solution method of the marginal distribution function of the photovoltaic unit, so the output marginal distribution function of the wind turbine is The expression is Where, To estimate the number of data points used, and are the covariance matrix and expectation matrix, respectively, derived from the data, Indicates A multivariate Gaussian distribution centered at point The density contribution at The contribution of each sample point is summed and averaged to obtain an estimate of the density function.

[0013] As a preferred solution, the expression of the carbon electricity trading model of the virtual power plant group considering the uncertainty of wind and solar power output is:

[0014] Where, In order to consider the total transaction cost of the virtual power plant group and the comprehensive total cost of the uncertainty of internal wind and solar power output, is the risk preference coefficient, which indicates the investor’s attitude towards risk. The value range of is [0, 1], The larger the value, the more risk-averse the virtual power plant group is. is the total cost of the virtual power plant group participating in carbon power trading, It is an aggregation unit participating in the transaction in the virtual power plant group. is the total amount of aggregated units participating in the transaction of the virtual power plant group, Aggregate unit The output uncertainty risk value.

[0015] In a second aspect, the present invention provides a carbon electricity trading system for a virtual power plant group that takes into account the uncertainty of wind and solar power output, which is used to implement the carbon electricity trading method for a virtual power plant group as described in the first aspect.

[0016] In a third aspect, the present invention provides an electronic device, wherein the computer device includes a memory, a processor, and a computer program, and when the computer program is executed by the processor, the virtual power plant group carbon electricity trading method as described in the first aspect is implemented.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the virtual power plant group carbon electricity trading method as described in the first aspect.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a carbon electricity trading model for virtual power plant groups that takes into account the uncertainty of wind and solar power output, while also considering minimizing total costs. Through this model, it effectively solves the problem of reduced transaction economics when virtual power plant groups participate in transactions due to ignoring the risk of wind and solar power output.

[0019] 2. The present invention uses the kernel density estimation method to solve the marginal distribution density functions of the output of wind turbines and photovoltaic units respectively, and accurately measures the output uncertainty of wind turbines and photovoltaic units within the virtual power plant group, thereby solving the problem of the virtual power plant group's transaction economy being damaged due to inaccurate risk assessment during the transaction process.

[0020] Further or more detailed beneficial effects will be described in conjunction with specific examples in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 It is a flow chart of the carbon electricity trading method of a virtual power plant group described in an embodiment of the present invention.

[0023] Figure 2 is a structural diagram of the electronic device provided by an embodiment of the present invention.

[0024] Figure Number: 200. Electronic equipment; 201. Processor; 202. Communication bus; 203. User interface; 204. Network interface; 205. Memory. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0026] In the following description, multiple embodiments of the present invention are provided. Different embodiments may be replaced or combined, and therefore the present invention may be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments that include one or more of all other possible combinations of A, B, C, and D, even if such embodiments may not be explicitly described in the following text.

[0027] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present invention. Various examples may appropriately omit, replace, or add various processes or components. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0028] In order to facilitate a better understanding of the embodiments of the present invention, before explaining the specific implementation methods of the present invention in detail, its application scenarios are first described.

[0029] The virtual power plant cluster carbon electricity trading method described in the embodiments of this specification is applied to areas with a high proportion of renewable energy access, collaborative trading scenarios of multiple market entities, and linkage scenarios between the electricity market and the carbon market. In these scenarios, the application of the virtual power plant cluster carbon electricity trading method aims to achieve efficient and reasonable operation of the virtual power plant cluster in carbon electricity trading, and achieve optimization goals in multiple aspects such as economy and environmental protection.

[0030] The following is a brief explanation of the virtual power plant, distributed peer-to-peer transactions, and conditional value at risk involved in multiple embodiments of this specification: A virtual power plant (VPP) is an intelligent energy management system that aggregates heterogeneous resources such as distributed photovoltaics, energy storage systems, and controllable loads through advanced communication technologies. In this embodiment, a VPP specifically refers to a multi-VPP collaborative system that simultaneously produces and consumes electricity and manages carbon assets. Its core features include: the inclusion of at least three types of adjustable resources (such as photovoltaics, energy storage, and flexible loads in this embodiment), minute-level dispatch response capabilities, and support for two-way pricing in both the electricity and carbon markets.

[0031] Distributed peer-to-peer (P2P) trading refers to a direct energy trading model that does not rely on traditional power trading centers and is implemented through blockchain or smart contracts. Distributed peer-to-peer trading realizes a decentralized direct purchase and sale of electricity model with higher flexibility and market efficiency.

[0032] Conditional Value at Risk (CVaR) is a probabilistic statistical method used to quantify extreme risks. The method was proposed by Rockafellar and Uryasev in 1997 and is used to measure the average excess loss when the loss exceeds a given VaR level.

[0033] Example 1: like Figure 1As shown, this embodiment provides a carbon electricity trading method for a virtual power plant group taking into account the uncertainty of wind and solar power output, including the following steps: Step S1, obtaining carbon power market data, historical wind and solar power output data and equipment parameters.

[0034] Specifically, the carbon electricity market data includes electricity purchase price , electricity sales price , Carbon emissions purchase price and the selling price of carbon emissions The historical wind and solar output data includes the historical photovoltaic output power and historical wind power output , the equipment parameters include fuel cell power generation , fuel cell unit power generation cost , Energy storage system charging capacity , energy storage system discharge capacity , energy storage system charging dissipation coefficient and the energy storage system discharge dissipation coefficient .

[0035] Step S2: Input the carbon power market data and equipment parameters obtained in step S1, build a VPP aggregation model and calculate the economic cost model of each aggregation unit participating in carbon power trading. Taking the minimum total cost as the optimization goal, the objective function of the VPP aggregation model is: , (1) Where, It is an aggregation unit participating in the transaction in the virtual power plant group. is the total amount of aggregated units participating in the transaction of the virtual power plant group, To contribute to a certain scene, is the total number of wind and solar power output scenes, For a certain trading period, is the total number of trading sessions, Powering the scene The probability of and Aggregation units The transaction costs of participating in the electricity market and carbon market during period t, Aggregate unit Internal energy storage costs, and Aggregation units The discomfort costs of internally regulated central air conditioning and flexible loads, Aggregate unit Internal fuel cell operating costs, Aggregate unit P2P transaction costs.

[0036] Specifically, the aggregation units of the VPP aggregation model include photovoltaic units, wind turbine units, fuel cells, energy storage systems, central air conditioners and flexible loads.

[0037] Specifically, the economic cost model includes electricity market transaction costs, carbon market transaction costs, energy storage costs, central air conditioning call costs, flexible load call costs, fuel cell operating costs and P2P transaction costs.

[0038] More specifically, the expression of the electricity market transaction cost is: , (2) Where, and The prices for purchasing and selling electricity in the electricity market are respectively. The purchasing price of the aggregate unit is generally higher than the selling price to prevent the aggregate unit from arbitrage. and Aggregation units The amount of electricity purchased and sold from the electricity market.

[0039] More specifically, the expression of the carbon market transaction cost is: , (3) Where, and are the purchase and sale prices of carbon emissions in the carbon market, and Aggregation units Carbon emissions bought and sold from carbon markets.

[0040] More specifically, the expression of the energy storage cost is: , (4) Where, and Aggregation units The amount of charge and discharge of internal energy storage, and Aggregation units The charging dissipation coefficient and dissipation coefficient of the internal energy storage.

[0041] More specifically, the expression for the central air conditioning call cost is: , (5) Where, Call the user discomfort coefficient for central air conditioning, Aggregate unit The indoor temperature of the user, Aggregate unit The most comfortable temperature for users.

[0042] More specifically, the flexible load call cost is expressed as: , (6) Where, Call the user discomfort coefficient for central air conditioning, Aggregate unit Flexible load value of internal users, Aggregate unit The load baseline value of internal users.

[0043] More specifically, the fuel cell operating cost is expressed as: , (7) Where, Aggregate unit The unit power generation cost of fuel cells, Aggregate unit The power generated by the fuel cell inside.

[0044] More specifically, this embodiment takes into account the diversity of products in P2P transactions. The P2P transaction cost can be approximately expressed as a linear function of the transaction volume between each VPP. The expression for the P2P transaction cost is: , (8) Where, is the total number of aggregation units participating in the transaction, and is the bilateral transaction coefficient, which reflects product differences, such as differences in transmission distance, carbon emissions, etc. and Aggregation units and aggregation units The amount of electricity and carbon emissions traded between countries.

[0045] Specifically, the constraints of the economic cost model include fuel cell output power constraint, fuel cell ramp constraint, energy storage system charging and discharging power constraint, energy storage system state of charge constraint, energy storage system power constraint, central air conditioning cooling power constraint, indoor temperature comfort constraint, flexible load constraint, carbon emission constraint and carbon-electricity emission balance constraint.

[0046] More specifically, the expression for the fuel cell output power constraint is: (9) More specifically, the fuel cell ramp constraint expression is: , (10) Where, and Aggregation units The ramp-up and ramp-down rates of the internal fuel cell.

[0047] More specifically, the expression for the energy storage system charge and discharge power constraint is: 、(11) (12) More specifically, the expression for the state of charge constraint of the energy storage system is: , (13) Where, is the state of charge of the energy storage, and Aggregation units Charging efficiency and discharging efficiency of internal energy storage.

[0048] More specifically, the energy storage system power constraint expression is: , (14) Where, and Aggregation units The minimum and maximum storage capacity of the internal energy storage.

[0049] More specifically, the expression for the cooling power constraint of the central air conditioner is: , (15) Where, 、 、 Aggregate unit The parameters describing the building's cooling characteristics and weather conditions are related to building characteristics such as walls, windows, floors, and outdoor temperature. Aggregate unit Energy efficiency ratio of central air conditioning refrigeration unit, Aggregate unit The cooling capacity of the central air conditioner.

[0050] More specifically, the indoor temperature comfort constraint is expressed as: (16) More specifically, the expression of the flexible load constraint is: 、(17) (18) More specifically, this embodiment uses the baseline method to calculate the certified emission reduction of wind and solar power, and approximately assumes that the certified emission reduction of wind and solar power is proportional to the power generation. Domestic wind and solar certified emission reductions It can be expressed as: (19) Where, is the baseline emission factor, which is determined by VPP It is obtained by taking the weighted average of the marginal emission factor of electricity and the marginal emission factor of capacity in the region.

[0051] In addition, fossil energy units (such as fuel cells) within the virtual power plant cluster will generate carbon emissions during operation. Carbon emissions from fuel cells It can be expressed as: (20) Where, Aggregate unit The carbon emission intensity per unit output of the fuel cell.

[0052] More specifically, the expression of the carbon-electricity emission balance constraint is: (twenty one) (twenty two) Where, Aggregate unit Carbon emission limits for fuel cells within the vehicle.

[0053] Step S3: Based on the historical wind and solar power output data obtained in step S1, the kernel density estimation method is used to solve the output marginal distribution density functions of the wind turbines and photovoltaic turbines respectively, and a risk measurement model based on conditional value at risk is established.

[0054] Specifically, this embodiment adopts the kernel density estimation method based on Gaussian kernel function, assuming The probability density function is of independent and identically distributed one-dimensional random variables, the kernel density estimation formula can be expressed as: (twenty three) Where, is the sample size, is the bandwidth parameter, which plays the role of smoothing the curve and determines the effect of kernel density estimation.

[0055] Specifically, this embodiment uses the kernel density estimation method to estimate the probability density function of photovoltaic output from discrete historical data. , and then integrate it to get the corresponding marginal distribution function , as shown in equations (24) to (25): (twenty four) (25) Where, To estimate the number of data points used, Represents the dimension of the data, is the covariance matrix, is the expectation matrix, Indicates A multivariate Gaussian distribution centered at point The density contribution at The contribution of each sample point is summed and averaged to obtain an estimate of the density function.

[0056] Based on the aforementioned kernel density estimation method, the probability density function of wind power output data can be estimated, and the marginal distribution function can be obtained by integrating it.

[0057] (26) Where, To estimate the number of data points used, 、 are the covariance matrix and expectation matrix, respectively, derived from the data.

[0058] In the process of kernel density estimation, the selection of bandwidth parameters determines the smoothness and final effect of kernel density estimation. Taking the kernel density estimation of photovoltaic data as an example, the bandwidth parameter in formula (23) is Affected by the covariance matrix To better adapt to the distribution characteristics of massive historical data, this paper uses the interpolation method to obtain the optimal bandwidth and takes an equal bandwidth at each discrete data fitting point. Based on the least squares variance (LSCV) principle, the bandwidth parameter is optimal when the mean integrated squared error (MISE) is minimized.

[0059] (27) (28) (29) (30) (31) The integrated mean square error can be obtained by using equations (27) to (31): Then use the insertion method to insert the estimated formula Inserting the asymptotic formula, we get the asymptotic mean integrated squared error (AMISE), as shown in Equation (32): (32) beg Minimum value, find its first-order derivative and set it to 0, that is, When the value is minimum, it corresponds to the optimal window width, as shown in the formula: (33) By dynamically adjusting the covariance matrix size and shape to obtain the best bandwidth parameters , which can make the kernel density estimation effect reach the best.

[0060] Based on formulas (23)-(33), at a certain confidence level Under these conditions, the uncertainty risk of wind and solar power output can be described by their respective marginal distributions: (34) (35) (36) Where, and Represent the CvaR values ​​of the output uncertainty risk of photovoltaic and wind turbines respectively.

[0061] Step S4: Integrate the virtual power plant group carbon electricity trading model constructed in step S2 and the risk measurement model established in step S3 to construct a virtual power plant group carbon electricity trading model that takes into account the uncertainty of wind and solar power output. Solve it through the optimization algorithm to output the amount of electricity purchased and sold by the virtual power plant group in the electricity market and the carbon emission rights trading volume in the carbon market.

[0062] Specifically, the expression of the carbon electricity trading model of the virtual power plant group considering the uncertainty of wind and solar power output is: (38) In formula (38), In order to consider the total transaction cost of the virtual power plant group and the comprehensive total cost of the uncertainty of internal wind and solar power output, is the risk preference coefficient, which indicates the investor’s attitude towards risk. The value range of is [0, 1], The larger the value, the more risk-averse the virtual power plant group is. is the total cost of the virtual power plant group participating in carbon power trading, It is an aggregation unit participating in the transaction in the virtual power plant group. is the total amount of aggregated units participating in the transaction of the virtual power plant group, Aggregate unit The output uncertainty risk value is determined by the investor's risk preference coefficient, and the two objectives are weighted according to the investor's risk preference coefficient, and transactions are conducted based on the investor's attitude towards risk.

[0063] Example 2: This embodiment provides a carbon electricity trading system for a virtual power plant group that takes into account the uncertainty of wind and solar power output, and is characterized by being used to implement the carbon electricity trading method for a virtual power plant group as described in Example 1.

[0064] Example 3: like Figure 2 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0065] The communication bus can be used to realize the connection and communication among the above components.

[0066] The user interface may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0067] The network interface may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, and the like.

[0068] Among them, the processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire electronic device, and performs various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of DSP, FPGA, PLA. The processor can integrate one or a combination of CPU, GPU and modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but may be implemented separately through a chip.

[0069] The memory may include either RAM or ROM. Optionally, the memory may include non-transitory computer-readable media. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, sound playback function, image playback function, etc.), instructions for implementing each of the aforementioned method embodiments, etc.; the data storage area may store data related to each of the aforementioned method embodiments, etc. The memory may optionally be at least one storage device located remotely from the aforementioned processor. The memory, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a trading application. The processor may be configured to invoke the trading application stored in the memory and execute the steps of carbon power trading in a virtual power plant cluster as described in the aforementioned embodiments.

[0070] Example 4: This embodiment provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer or processor, causes the computer or processor to execute the above-mentioned Figure 1 If the components of the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0071] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0072] Those skilled in the art will appreciate that all or part of the process steps in the method of the first embodiment described above can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. The technical features of this embodiment and the implementation scheme can be combined in any manner unless they conflict.

[0073] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0074] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0075] The foregoing is merely an exemplary embodiment of the present invention and is not intended to limit the scope of the present invention. That is, any equivalent changes and modifications made in accordance with the teachings of the present invention are still within the scope of the present invention. A person skilled in the art will readily come up with the embodiments of the present invention after considering the specification and practicing the disclosure herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art that are not described in the present invention. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present invention are defined by the claims.

Claims

1. A carbon electricity trading method for a virtual power plant group considering the uncertainty of wind and solar power output, characterized by: Including steps: S1. Obtain carbon electricity market data, historical wind and solar power output data, and equipment parameters. The carbon electricity market data includes electricity purchase price, electricity sales price, carbon emission purchase price, and carbon emission sales price. The historical wind and solar power output data includes historical photovoltaic output power and historical wind power output power. The equipment parameters include fuel cell power generation power, fuel cell unit power generation cost, energy storage system charge and discharge capacity, and energy storage system charge and discharge dissipation coefficient. S2. Based on the carbon electricity market data and the equipment parameters obtained in step S1, a VPP aggregation model is constructed and an economic cost model of each aggregation unit participating in carbon electricity trading is calculated. The aggregation units of the VPP aggregation model include photovoltaic units, wind turbines, fuel cells, energy storage systems, central air conditioners, and flexible loads. S3. Based on the historical wind and solar power output data obtained in step S1, the kernel density estimation method is used to solve the output marginal distribution density functions of the wind turbine and photovoltaic units respectively, and a risk measurement model based on conditional value at risk is established; S4. Integrate the virtual power plant group carbon electricity trading model constructed in step S2 and the risk measurement model established in step S3, construct a virtual power plant group carbon electricity trading model that takes into account the uncertainty of wind and solar power output, solve it through the optimization algorithm, and output the virtual power plant group's purchase and sales electricity volume in the electricity market and the carbon emission rights trading volume in the carbon market.

2. The carbon electricity trading method for a virtual power plant group considering the uncertainty of wind and solar power output according to claim 1 is characterized by: The economic cost model includes electricity market transaction costs, carbon market transaction costs, energy storage costs, central air conditioning call costs, flexible load call costs, fuel cell operating costs and P2P transaction costs; The constraints of the economic cost model include fuel cell output power constraint, fuel cell ramp constraint, energy storage system charging and discharging power constraint, energy storage system state of charge constraint, energy storage system power constraint, flexible load constraint, central air conditioning cooling power constraint, indoor temperature comfort constraint, carbon emission constraint and carbon-electricity emission balance constraint.

3. The carbon electricity trading method for a virtual power plant group considering the uncertainty of wind and solar power output according to claim 2 is characterized by: Taking the minimum total cost as the optimization goal, the objective function of the VPP aggregation model is: ， Where, It is an aggregation unit participating in the transaction in the virtual power plant group. is the total amount of aggregated units participating in the transaction of the virtual power plant group, To contribute to a certain scene, is the total number of wind and solar power output scenes, For a certain trading period, is the total number of trading sessions, Powering the scene The probability of and Aggregation units The transaction costs of participating in the electricity market and carbon market during period t, Aggregate unit Internal energy storage costs, and Aggregation units The discomfort costs of internally regulated central air conditioning and flexible loads, Aggregate unit Internal fuel cell operating costs, Aggregate unit P2P transaction costs.

4. The carbon electricity trading method for a virtual power plant group considering the uncertainty of wind and solar power output according to claim 1 is characterized by: The kernel density estimation method adopts a Gaussian kernel function and dynamically optimizes bandwidth parameters through a least square error method.

5. The carbon electricity trading method for a virtual power plant group considering the uncertainty of wind and solar power output according to claim 4 is characterized by: The output probability density function of the photovoltaic unit is estimated from the discrete photovoltaic historical output power data by the kernel density estimation method. , and then integrate it to obtain the corresponding marginal distribution function ; Probability density function of photovoltaic units and marginal distribution function The expressions are Where, To estimate the number of data points used, Represents the dimension of the data, is the covariance matrix, is the expectation matrix, Indicates A multivariate Gaussian distribution centered at point The density contribution at The contribution of each sample point is summed and averaged to obtain an estimate of the density function.

6. A carbon electricity trading method for a virtual power plant group considering the uncertainty of wind and solar power output according to claim 5, characterized in that: The output marginal distribution function of the wind turbine is solved in the same way as the marginal distribution function of the photovoltaic unit. The expression is Where, To estimate the number of data points used, and are the covariance matrix and expectation matrix, respectively, derived from the data, Indicates A multivariate Gaussian distribution centered at point The density contribution at The contribution of each sample point is summed and averaged to obtain an estimate of the density function.

7. The carbon electricity trading method for a virtual power plant group considering the uncertainty of wind and solar power output according to claim 1 is characterized in that: The expression of the carbon electricity trading model of the virtual power plant group considering the uncertainty of wind and solar power output is: Where, In order to consider the total transaction cost of the virtual power plant group and the comprehensive total cost of the uncertainty of internal wind and solar power output, is the risk preference coefficient, which indicates the investor’s attitude towards risk. The value range of is [0, 1], The larger the value, the more risk-averse the virtual power plant group is. is the total cost of the virtual power plant group participating in carbon power trading, It is an aggregation unit participating in the transaction in the virtual power plant group. is the total amount of aggregated units participating in the transaction of the virtual power plant group, Aggregate unit The output uncertainty risk value.

8. A carbon electricity trading system for virtual power plants that takes into account the uncertainty of wind and solar power output, characterized by: Used to implement the virtual power plant group carbon electricity trading method as described in any one of claims 1 to 7.

9. A computer device comprising a memory, a processor, and a computer program, wherein: When the computer program is executed by a processor, the virtual power plant group carbon electricity trading method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the virtual power plant group carbon electricity trading method as described in any one of claims 1 to 7 is implemented.

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