Resource combination decision-making method considering profit loss risk probability distribution of virtual power plant

Abstract

The invention relates to the technical field of power systems, in particular to a resource combination decision-making method considering virtual power plant income loss risk probability distribution, which comprises the following steps: carrying out risk assessment on the current spot market electricity price, introducing a Markov process to generate different risk probability scenes and corresponding generation probabilities thereof, on the basis, virtual power plant resource initial combination schemes under different risk probability scenes are generated; the resource user cost increase change caused by electricity selling and the probability average cost increase of the resource user for different risk scenes can be calculated; comparing and analyzing the resource user cost increase change and the probability average cost increase to determine a final resource combination decision scheme; and calculating an income loss risk probability distribution of the final resource combination decision scheme to verify the final resource combination decision scheme. Therefore, the problems that a traditional resource combination decision method does not consider a virtual power plant income loss risk caused by uncertainty, and cost loss of resource users is easily caused are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly relates to a resource portfolio decision-making method considering the probability distribution of the revenue loss risk of a virtual power plant. Background Art

[0002] With the continuous increase in the quantity and scale of distributed resources, traditional centralized power systems are facing more and more volatility and uncertainty, posing challenges to their stable operation. Since distributed resources are usually small in scale and dispersed, a single resource does not meet the power market access conditions and has a limited impact on the power grid. However, by combining multiple distributed resources to form a virtual power plant, the collective effect can be better exerted, enhancing the influence of distributed resources on the power grid and their ability to participate in market transactions. The virtual power plant can flexibly adjust the resource portfolio according to market price signals to maximize market revenue.

[0003] However, the power market price is uncertain. Traditional resource portfolio decision-making methods do not consider the revenue loss risk of the virtual power plant brought about by its uncertainty. Resource users fully trust the virtual power plant and completely execute the resource portfolio decisions issued by it. However, the uncertainty of the power market price will lead to differences between the actual settlement scenario and the execution scenario, resulting in cost losses for resource users. Summary of the Invention

[0004] The present invention provides a resource portfolio decision-making method considering the probability distribution of the revenue loss risk of a virtual power plant to solve problems such as that traditional resource portfolio decision-making methods do not consider the revenue loss risk of the virtual power plant brought about by its uncertainty and are likely to cause cost losses for resource users.

[0005] A first aspect embodiment of the present invention provides a resource portfolio decision-making method considering the probability distribution of the revenue loss risk of a virtual power plant, including the following steps: performing a risk assessment on the current spot market electricity price to generate different risk probability scenarios of the current spot market electricity price, their corresponding generation probabilities, and an initial virtual power plant resource portfolio plan; calculating the growth change in the cost of resource users caused by power sales and the average cost growth of resource users facing different risk scenarios according to the generation probability of each risk probability scenario and its initial virtual power plant resource portfolio plan; comparing and analyzing the growth change in the cost of resource users caused by power sales and the average cost growth of resource users facing different risk scenarios to determine the final resource portfolio decision plan; calculating the probability distribution of the revenue loss risk of the final resource portfolio decision plan to verify the final resource portfolio decision plan.

[0006] Optionally, the performing a risk assessment on the current spot market electricity price to generate different risk probability scenarios of the current spot market electricity price, their corresponding generation probabilities, and an initial virtual power plant resource portfolio plan includes:

[0007] By introducing a Markov process, risk assessment is performed on the current spot market electricity price to obtain different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities;

[0008] Taking the minimization of the virtual power plant operation cost as the objective function, functional analysis and variational method are used to process different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities to generate the initial virtual power plant resource combination plan under different risk probability scenarios.

[0009] Optionally, comparing the change in the resource user cost growth caused by selling electricity and the average cost growth of the resource user facing different risk scenarios to obtain the final resource combination decision plan, including:

[0010] Compare the change in the resource user cost growth caused by selling electricity and the average cost growth of the resource user facing different risk scenarios, and select the scenario with the largest current probability in the scenario where the change in the resource user cost growth caused by selling electricity is less than the average cost growth of the resource user facing different risk scenarios to generate the final resource combination decision plan.

[0011] An embodiment of the second aspect of the present invention provides a resource combination decision device considering the risk probability distribution of virtual power plant revenue loss, including: a risk assessment module for performing risk assessment on the current spot market electricity price to generate different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities and the initial virtual power plant resource combination plan; a calculation module for calculating the change in the resource user cost growth caused by selling electricity and the average cost growth of the resource user facing different risk scenarios according to the generation probability of each risk probability scenario and its initial virtual power plant resource combination plan; a determination module for comparing and analyzing the change in the resource user cost growth caused by selling electricity and the average cost growth of the resource user facing different risk scenarios to determine the final resource combination decision plan; a verification module for calculating the revenue loss risk probability distribution of the final resource combination decision plan to verify the final resource combination decision plan.

[0012] Optionally, the risk assessment module includes:

[0013] An evaluation unit for performing risk assessment on the current spot market electricity price by introducing a Markov process to obtain different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities;

[0014] A combined solution generation unit is configured to use functional analysis and variational method to process different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities, with the goal of minimizing the operating cost of the virtual power plant, so as to generate an initial combined solution of virtual power plant resources under different risk probability scenarios.

[0015] Optionally, the determination module includes:

[0016] Compare the increase in resource user costs caused by the power sale with the average cost increase of resource users facing different risk scenarios, and select the scenario with the highest current probability in the scenario where the increase in resource user costs caused by the power sale is less than the average cost increase of resource users facing different risk scenarios to generate the final combined resource decision-making solution.

[0017] An embodiment of the third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the resource combination decision-making method considering the risk probability distribution of virtual power plant revenue and loss as described in the above embodiments.

[0018] An embodiment of the fourth aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the resource combination decision-making method considering the risk probability distribution of virtual power plant revenue and loss as described above.

[0019] The resource combination decision-making method considering the risk probability distribution of virtual power plant revenue and loss proposed in the embodiments of the present invention takes into account the uncertainty of the market operation environment, and feeds the resource combination decision-making back to resource users in the form of a probabilistic solution set. Resource users can calculate the risk probability distribution of revenue and loss based on the above feedback information, so as to execute a resource combination decision with a higher revenue probability and a lower loss probability.

[0020] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. Description of the Drawings

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0022] Figure 1 is a flowchart of a resource combination decision-making method considering the risk probability distribution of virtual power plant revenue and loss provided by an embodiment of the present invention;

[0023] Figure 2A block diagram of a resource portfolio decision-making device considering the probability distribution of virtual power plant revenue loss risk provided by an embodiment of the present invention;

[0024] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0025] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention, and should not be construed as a limitation of the present invention.

[0026] The resource portfolio decision-making method considering the probability distribution of virtual power plant revenue loss risk according to the embodiment of the present invention will be described below with reference to the drawings.

[0027] Figure 1 A flowchart of a resource portfolio decision-making method considering the probability distribution of virtual power plant revenue loss risk provided by an embodiment of the present invention.

[0028] As Figure 1 shown, the resource portfolio decision-making method considering the probability distribution of virtual power plant revenue loss risk includes the following steps:

[0029] In step S101, risk assessment is performed on the current spot market electricity price to generate different risk probability scenarios of the current spot market electricity price, their corresponding generation probabilities, and an initial virtual power plant resource portfolio plan.

[0030] In some embodiments, risk assessment is performed on the current spot market electricity price to generate different risk probability scenarios of the current spot market electricity price, their corresponding generation probabilities, and an initial virtual power plant resource portfolio plan, including:

[0031] By introducing a Markov process, risk assessment is performed on the current spot market electricity price to obtain different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities;

[0032] Taking the minimization of the virtual power plant operating cost as the objective function, functional analysis and variational method are used to process different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities to generate an initial virtual power plant resource portfolio plan under different risk probability scenarios.

[0033] In the actual implementation process, due to the uncertainty of the spot market electricity price, it may bring potential decision-making risks to the dynamic construction of the virtual power plant. Therefore, the embodiments of the present invention will conduct risk assessment on the spot market electricity price through the generation method of the typical scenario set, introduce the Markov process, so as to obtain different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities. On this basis, the initial virtual power plant resource combination scheme under different risk probability scenarios is generated by using functional analysis and variational method.

[0034] Specifically, introduce the Markov process, divide the market electricity price into a discrete state space, each state represents a different interval of the electricity price (for example, low price interval, medium interval, high price interval, etc.), and use the state transition matrix to describe the transition probability from one state to another state, so as to obtain different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities. The specific solution process is as follows:

[0035] Suppose the market electricity price changes in the state space of S = {s1, s2, …, s n}, where si is a certain level of the electricity price. Through historical data and the dynamic characteristics of the electricity market, calculate the transition probability between each pair of states:

[0036] P ij = P(P(t + 1) = s j | P(t) = s i )

[0037] This transition matrix P ij can be estimated by the maximum likelihood estimation or Bayesian inference method according to the actual market data.

[0038] Furthermore, with the goal of minimizing the operation cost of the virtual power plant, construct a resource combination decision model through functional analysis and variational method, and generate the initial virtual power plant resource combination scheme under different risk probability scenarios according to the resource combination decision model.

[0039]

[0040] Among them, represents the expectation operation, indicating that the expected value of the objective function is in different random scenarios, is the functional of scenario , representing the overall cost of different resource combinations, P i (t, ξ i ) represents the joint distribution of factors such as market fluctuations and electricity price changes, and X(t), Y(t), Z(t) are the state variables in the power generation, energy storage and transmission processes respectively.

[0041] In step S102, according to the generation probability of each risk probability scenario and its initial combined virtual power plant resource scheme, calculate the growth change of the resource user cost caused by power sales and the average cost growth of the resource user facing different risk scenarios.

[0042] In the actual execution process, since the probability of each scenario occurring is random during the final settlement, resource users do not fully trust the virtual power plant. For example, the virtual power plant can organize resource users to participate in the market according to the resource combination decision under a certain determined scenario. If the resource users fully trust the virtual power plant, they will execute the resource combination decision issued by the aggregator. However, if this scenario does not actually occur, it will lead to the resource combination decision not being optimal, thus causing cost losses to resource users. Therefore, for resource users who do not trust the virtual power plant, the embodiments of the present invention calculate the growth change of the resource user cost caused by their power sales according to the probability distribution of each scenario and the corresponding resource combination scheme, and then calculate the average cost growth of the resource user facing different risk scenarios, that is:

[0043] Introduce a stochastic process, a jump process, and their corresponding variational methods to accurately describe. The cost growth of a resource user in a certain scenario can be expressed as:

[0044]

[0045] Where is a basis function related to the cost change of resource user k, representing the cost change through the processing of path dependence. The time-varying cost term related to scenario s, including external market conditions or unpredictable external cost factors. is a functional operator used to describe the impact of the change in the power sales volume of the user on the total cost, P sell (t) is the power sales volume of the virtual power plant at time t. represents the standard Wiener process at scenario s and time t, used to model the random fluctuations of each resource user, and can be expressed by the following formula:

[0046]

[0047] Where ε i is the random energy for each time step, and Δt is the time step size.

[0048] The increment of the Wiener process follows a probability distribution: at any two time points, the increment of the Wiener process follows a normal distribution with a mean of 0 and a variance of |t - t'||t - t'||t - t'|, that is:

[0049]

[0050] Further, it is modeled through the high - order probability density function and Laplace transform to simplify the solution process in the transformed form, and calculate the probability - average cost growth of resource users facing different risk scenarios, that is:

[0051]

[0052] Among them, Π t represents the profit and loss of resource users at time t, R M represents the comprehensive integral over all scenarios and external risk states, P(s, ξ) is the high - dimensional density function under scenario s and risk state ξ, L ξ represents the Laplace transform of the function or stochastic process related to the external risk state ξ, L cost (Π t , ξ) represents the Laplace transform of the cost function, and the basic form of the Laplace is as follows:

[0053]

[0054] Then:

[0055]

[0056] Among them, f(t) is the function to be transformed, and e -st is the weighted calculation process.

[0057] In step S103, compare and analyze the change in the cost growth of resource users caused by selling electricity and the probability - average cost growth of resource users facing different risk scenarios to determine the final resource combination decision plan.

[0058] In some embodiments, compare and analyze the change in the cost growth of resource users caused by selling electricity and the probability - average cost growth of resource users facing different risk scenarios to obtain the final resource combination decision plan, including:

[0059] Compare the change in the cost growth of resource users caused by selling electricity and the probability - average cost growth of resource users facing different risk scenarios, and select the scenario with the highest current probability in the scenarios where the change in the cost growth of resource users caused by selling electricity is less than the probability - average cost growth of resource users facing different risk scenarios to generate the final resource combination decision plan.

[0060] In the actual execution process, due to the bounded rationality of resource user individuals, strategic resource combination decisions will be made based on the risk - scenario probability distribution and risk preference. Taking as the reference benchmark value, compare ΔC end_user (s, i) of all possible scenarios with it, and in the case where ΔC end_user (s, i) is less than Select the scenario with the highest occurrence probability in the scenarios, and execute the resource combination decision for this scenario to achieve the avoidance of cost losses with the highest probability. That is:

[0061]

[0062] Among them, RCD is the selected resource combination decision.

[0063] In step S104, calculate the profit and loss risk probability distribution of the final resource combination decision plan to verify the final resource combination decision plan.

[0064] In the actual execution process, since each scenario has the possibility of occurring at the final settlement, the resource user will also obtain various profit and loss results accordingly. To verify the effectiveness of the above method, it is necessary to compare the profit and loss results of the resource combination decision executed according to the original plan and the resource combination decision determined by the method proposed in the embodiment of the present invention. That is, substitute the profit and loss and occurrence probability of the two combination decisions at the settlement of each scenario into the profit and loss risk probability distribution function, calculate the probability average profit and loss of the two schemes, so as to judge whether the method proposed in the present invention can enable the resource user to obtain market benefits with a greater probability and avoid profit losses with a smaller probability, so as to achieve the purpose of balancing market risks. The profit and loss risk probability distribution function is as follows:

[0065]

[0066] Among them, C profit,n and C loss,i are the profit and loss of the resource user in scenario i respectively, and are the probability average profit and loss of the resource user respectively.

[0067] In summary, according to the resource combination decision method considering the profit and loss risk probability distribution of the virtual power plant proposed in the embodiment of the present invention, it considers the uncertainty of the market operation environment, and feeds back the resource combination decision to the resource user in the form of a probabilistic solution set. The resource user can calculate the profit and loss risk probability distribution based on the above feedback information, so as to execute the resource combination decision with a higher profit probability and a lower loss probability.

[0068] Secondly, refer to the accompanying drawings to describe the resource combination decision device considering the profit and loss risk probability distribution of the virtual power plant proposed in the embodiment of the present invention.

[0069] Figure 2 is a block diagram of the resource combination decision device considering the profit and loss risk probability distribution of the virtual power plant in the embodiment of the present invention.

[0070] As Figure 2As shown in the figure, the resource portfolio decision-making device 20 that considers the risk probability distribution of the virtual power plant's revenue loss includes: a risk assessment module 201, a calculation module 202, a determination module 203, and a verification module 204.

[0071] Among them, the risk assessment module 201 is used to conduct a risk assessment on the current spot market electricity price to generate different risk probability scenarios of the current spot market electricity price, their corresponding generation probabilities, and the initial virtual power plant resource portfolio plan. The calculation module 202 is used to calculate the change in the resource user cost growth caused by power sales and the probability-averaged cost growth of resource users facing different risk scenarios according to the generation probability of each risk probability scenario and its initial virtual power plant resource portfolio plan. The determination module 203 is used to compare and analyze the change in the resource user cost growth caused by power sales and the probability-averaged cost growth of resource users facing different risk scenarios to determine the final resource portfolio decision plan. The verification module 204 is used to calculate the risk probability distribution of the revenue loss of the final resource portfolio decision plan to verify the final resource portfolio decision plan.

[0072] In some embodiments, the risk assessment module 201 includes:

[0073] An evaluation unit, which is used to conduct a risk assessment on the current spot market electricity price by introducing a Markov process to obtain different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities;

[0074] A portfolio plan generation unit, which is used to take the minimization of the virtual power plant operation cost as the objective function, and use functional analysis and variational methods to process different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities to generate the initial virtual power plant resource portfolio plan under different risk probability scenarios.

[0075] In some embodiments, the determination module 203 includes:

[0076] Compare the change in the resource user cost growth caused by power sales and the probability-averaged cost growth of resource users facing different risk scenarios, and select the scenario with the largest current probability in the scenario where the change in the resource user cost growth caused by power sales is less than the probability-averaged cost growth of resource users facing different risk scenarios to generate the final resource portfolio decision plan.

[0077] It should be noted that the foregoing explanation of the embodiments of the resource portfolio decision-making method that considers the risk probability distribution of the virtual power plant's revenue loss also applies to the resource portfolio decision-making device that considers the risk probability distribution of the virtual power plant's revenue loss in this embodiment, and will not be elaborated here.

[0078] The resource portfolio decision-making device considering the probability distribution of virtual power plant revenue loss risks proposed according to the embodiments of the present invention takes into account the uncertainty of the market operation environment and feeds the resource portfolio decision to the resource users in the form of a probabilistic solution set. The resource users can calculate the probability distribution of revenue loss risks based on the above feedback information, so as to execute the resource portfolio decision with a higher revenue probability and a lower loss probability.

[0079] Figure 3 The structural schematic diagram of the electronic device provided by the embodiments of the present invention. The electronic device may include:

[0080] A memory 301, a processor 302, and a computer program stored on the memory 301 and executable on the processor 302.

[0081] When the processor 302 executes the program, it implements the resource portfolio decision-making considering the probability distribution of virtual power plant revenue loss risks provided in the above embodiments.

[0082] Further, the electronic device further includes:

[0083] A communication interface 303 for communication between the memory 301 and the processor 302.

[0084] The memory 301 is used to store a computer program executable on the processor 302.

[0085] The memory 301 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0086] If the memory 301, the processor 302, and the communication interface 303 are independently implemented, the communication interface 303, the memory 301, and the processor 302 may be interconnected through a bus and complete communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0087] Optionally, in a specific implementation, if the memory 301, the processor 302, and the communication interface 303 are integrated on a single chip, the memory 301, the processor 302, and the communication interface 303 can communicate with each other through an internal interface.

[0088] The processor 302 may be a central processing unit (CPU for short), or an application specific integrated circuit (ASIC for short), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0089] The embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the resource combination decision considering the risk probability distribution of the virtual power plant's revenue and loss as described above.

[0090] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0091] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0092] Any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of the code of executable instructions including one or N steps for implementing a customized logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art of the embodiments of the present invention.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered a definable sequence list of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in connection with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0094] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), and the like.

[0095] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of the above-described embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0096] In addition, each functional unit in various embodiments of the present invention may be integrated into a processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0097] The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A resource combination decision-making method considering the probability distribution of virtual power plant revenue loss risk, characterized in that: The following steps are involved: Performing risk assessment on the current spot market electricity price to generate different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities and an initial combination plan of virtual power plant resources; According to the generation probability of each risk probability scenario and the initial combination plan of virtual power plant resources, the changes in resource user cost growth caused by electricity sales and the probabilistic average cost growth of resource users facing different risk scenarios are calculated; Compare and analyze the cost growth changes of resource users caused by the electricity sales and the probabilistic average cost growth of the resource users facing different risk scenarios to determine the final resource combination decision plan; The profit and loss risk probability distribution of the final resource combination decision plan is calculated to verify the final resource combination decision plan.

2. The resource combination decision-making method considering the probability distribution of virtual power plant revenue loss risk according to claim 1 is characterized in that: The risk assessment of the current spot market electricity price is performed to generate different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities and an initial combination scheme of virtual power plant resources, including: By introducing a Markov process, a risk assessment is performed on the current spot market electricity price to obtain different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities; Taking minimization of the virtual power plant operating cost as the objective function, functional analysis and variational method are used to process different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities, so as to generate initial combination plans of virtual power plant resources under the different risk probability scenarios.

3. The resource combination decision-making method considering the probability distribution of virtual power plant revenue loss risk according to claim 1 is characterized in that: The comparative analysis of the resource user cost growth changes caused by the electricity sales and the probabilistic average cost growth of the resource users facing different risk scenarios to obtain a final resource combination decision plan includes: The resource user cost growth change caused by the electricity sales is compared with the probabilistic average cost growth of the resource users facing different risk scenarios, and among the scenarios where the resource user cost growth change caused by the electricity sales is less than the probabilistic average cost growth of the resource users facing different risk scenarios, the scenario with the highest probability is selected to generate the final resource combination decision plan.

4. A resource combination decision-making device considering the probability distribution of virtual power plant revenue loss risk, characterized in that: include: A risk assessment module, used to perform risk assessment on the current spot market electricity price to generate different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities and an initial combination plan of virtual power plant resources; A calculation module is used to calculate the cost growth change of resource users caused by electricity sales and the probabilistic average cost growth of resource users facing different risk scenarios based on the generation probability of each risk probability scenario and the initial combination plan of virtual power plant resources; A determination module, used to compare and analyze the cost growth changes of resource users caused by the electricity sales and the probabilistic average cost growth of the resource users facing different risk scenarios, so as to determine the final resource combination decision plan; The verification module is used to calculate the probability distribution of the profit and loss risk of the final resource combination decision plan to verify the final resource combination decision plan.

5. The resource combination decision-making device considering the probability distribution of virtual power plant revenue loss risk according to claim 4 is characterized in that: The risk assessment module includes: An evaluation unit, configured to perform risk evaluation on the current spot market electricity price by introducing a Markov process, so as to obtain different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities; A combination scheme generating unit is used to process different risk probability scenarios of the current spot market electricity price and their corresponding generation probabilities by using functional analysis and variational method with the minimization of the virtual power plant operating cost as the objective function, so as to generate an initial combination scheme of virtual power plant resources under the different risk probability scenarios.

6. The resource combination decision-making device considering the probability distribution of virtual power plant revenue loss risk according to claim 4 is characterized in that: The determination module comprises: The resource user cost growth change caused by the electricity sales is compared with the probabilistic average cost growth of the resource users facing different risk scenarios, and among the scenarios where the resource user cost growth change caused by the electricity sales is less than the probabilistic average cost growth of the resource users facing different risk scenarios, the scenario with the highest probability is selected to generate the final resource combination decision plan.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a resource combination decision method considering the probability distribution of the risk of loss of profit of a virtual power plant as described in any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a resource combination decision-making method considering the probability distribution of the risk of loss of profit of a virtual power plant as described in any one of claims 1 to 3.

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